– When I invited you to talk, you suggested communication as the topic… Pretty unexpected, I admit—after all, we're both technology people—although of course the subject excites me enormously. Why did you choose it?

– I noticed a long time ago, and artificial intelligence has made it even clearer, that technologies change, but the thing that doesn't change about them is the people who use them.

A few years ago I gave a talk at a conference, and they introduced me: “Kaloyan is a programmer with 20 years of experience.” So what? Those 20 years are not relevant at all right now. Twenty years of experience means Lili Ivanova, the seemingly ageless queen of Bulgarian pop, and I were building sandcastles in the sandbox next to the pyramids. It just doesn't mean anything. But

what is the skill you actually develop over the years? It's working with other people.

And how do you work well with other people? When you start to understand them. We're not talking about tolerating, we're not talking about sympathy. Once you understand how something works, you sense more quickly that if there's a conflict, it isn't personal, and if someone does something, it's driven by insecurities or fears.

A lot of things can be explained that way. And that's the useful part. That's why I ended my talk like this: “people first,” and one more thing—the logic of emotions. Because look how the two are set against each other—logic and emotions.

But in fact emotions explain the logic behind a great many actions.

– I'm a journalist who became a programmer after thirty. And this skill—communicating with people and trying to understand them, without which I couldn't do my job as a journalist—turns out to be extremely useful to me as a programmer too, which I would never have expected while I was outside IT.

– In IT, in the tech world, the English term is “introvert”; in Bulgarian someone used to translate it as задръстеняк (zadrastenyak, roughly “clogged-up dork”). We, the people in our trade, are more closed off on the whole, because the work is cognitive, mental, all in your head. You have to concentrate to get it done. This kind of work attracts more people who are like that—they withdraw into themselves, think more—and communication is generally harder for them.

And since communication is harder for them, they have less experience at it and, I'd say, less emotional maturity, at least in the early stages of their careers.

Once you've worked at a company for even five or six months, you see that you're not working alone on some branch or in some room—there are other people, you talk, you do things, someone promises you something and doesn't deliver, you promise something and can't get it done. Those things finish building you, they develop you.

It's not that the IT world lacks this; people just learn it from experience, not from a book. It's not something you can read or have someone tell you: do this, this and this. An acquaintance of mine used to joke that there's no “Communication for Dummies”—it's all done through practice.

You have to have gone through these things, to have felt them. As programmers we learn it whether we like it or not, because there's no other way.

– But as a society, if we scale up a little, there's no way to learn it. Hate has been a very big topic for me ever since I started this site. Communicating on social media has turned us into embittered people who constantly split over the latest “big issue” and don't listen to each other.

– Why? As Hari and Vasi say—let's explore it. What makes people hate? First, I want to mention Mark Manson's book The Subtle Art of Not Giving a F*ck. One of its main points is that people get angry and irritated by things that are important to them. We're not saying those things are right or objectively important, just that people feel them that way. People hate when a topic that matters to them is touched, when they feel unheard, when they think their side isn't represented in the argument, or when they carry some old trauma—someone didn't listen to them, didn't pay attention to them. Now that you have the platform of the internet in front of everyone who reads your posts and comments, why not use it and say what you think?

Filter bubbles help here too, because they amplify it: you surround yourself with people who think like you, and everyone else is an idiot. You make

much less effort to try to understand the other point of view, or at least to respect it.

I'm of the opinion that I'm not smarter than anyone else. Sometimes people look like idiots to me too. But if I know I'm not smarter, I ought to ask myself: what don't I know that they know? What don't I see that they see? Because I'm convinced they're not idiots. In the end, it's again a question of how you perceive the other people you communicate with.

It isn't easy, because we're all selfish to varying degrees. For example, when you're a parent and you remember what it was like to be a child, what the turning point is: when you become a father, suddenly you're responsible for someone else. Maybe to some extent if you had a younger brother or sister, but children are selfish in a very instinctive way, because they're at the center of the world—of their parents, their grandparents, their school. And maybe only when a parent finds himself in that situation does he realize the world doesn't revolve around him and start looking at things differently. At least that's my analysis. It may seem flippant, but…

– Not at all, I really like the comparison. I'm living it right now: I'm watching Malcolm in the Middle for the second time, after a 25-year break, and this time I'm watching it with my sons. Back then it was funny to me from the kids' point of view; now it's funny from the parents' point of view.

– Alexander Kondov has been your guest. Not long ago, on his YouTube channel about books, he made a video in which he says that

it makes no sense for school to make you read books describing feelings you've never experienced.

The nostalgia of Nemili-nedragi (Unloved and Unwanted), Ivan Vazov's novella about homesick Bulgarian revolutionaries in exile, means nothing to you when you're in school. But when you graduate, go off to study, separate from your family and friends, feel those feelings—and then read it years later, you understand why these classics are classics. You understand what's valuable in them. You simply can't appreciate them when you don't have the experience.

And that's the most important thing: at 15, say, you have the brains and you're physically developed like a mature 30-year-old. What's the difference? Fifteen years of experience, fifteen years of life. Your brain has the capacity, but it doesn't have the experience to fill it with, so you can make good judgments about what's valuable and what's important. How hard things in life are—or, on the other hand, that other things that look terribly hard aren't so frightening at all.

– But what can we do so that more people have this attitude—that they're not the smartest and everyone else isn't an idiot? With parenthood, you gradually grow into these things. How can we make it more widespread among people who communicate online—learning to listen to the other person and respect them?

– I don't know. On the whole I'm not a fan of mass approaches, because everyone wants to save the world somehow. How do we reach as many people as possible with the effort and resources we have? In many cases you only need to save one person. To pay attention to just one person. Or simply to be an example for others. The fact that

you don't act like a hater toward the haters, but instead try to have the two basic qualities of every parent—curiosity and patience.

You treat everyone you meet and talk with that way. They may fray your nerves, you may reach the end of your patience, but when you treat someone with respect, they very rarely have a reason to behave the opposite way toward you. You may start out without respect, because you're arguing and he's on the opposite side. But explore and understand why he holds that opinion.

Whether we like it or not, we all have plenty of stereotypes and assumptions about the other side, simply because that's how we've judged them—by their looks, by the avatar on their social profile, by something else.

– Is there any situation at all in which we don't have such stereotypes?

– Let me give you just one example. Charles Duhigg put out a book, Supercommunicators. He recycled a few things, but one of the chapters talks about the gun debate in the United States. It's a very polarizing topic: there's no one in the middle, everyone is either “yes” or “no.” And he describes an experiment there. You have to understand that many of the people who support guns aren't the bloodthirsty types they're made out to be. For many people, owning a gun gives them security—in their home, for themselves. They themselves were traumatized by some earlier story in which they felt helpless, and they believe a gun can help them. That's why they hold on so stubbornly and don't want to give it up. Maybe not all of them, but some have a quite rational explanation. Again, there's a logic of emotions.

And for the people who are against guns, things are pretty obvious: if they've suffered the painful loss of someone close to a gun that seemingly ended up in the wrong hands and was misused. Again, there's logic, there's an explanation. You don't have to agree with either side. Just

understand why they think the way they do, where their opinion comes from. And only then try to find some kind of solution.

– Very often, including with guns, it turns out that people group together and fight over fifteen different things on which they're on opposite sides. It somehow becomes natural: in this case, Republicans and Democrats are roughly for and against guns. Or in the Bulgarian context we see it even more clearly—the two extreme camps; I don't even need to name them. How does that happen if the motivation lies in personal experience?

– I believe the camps aren't the same; it only looks that way from the outside. For every person, experience is based on what they've lived through. If you look from a distance, you see they overlap to some degree, but not 100%—there are discrepancies.

And in Bulgaria, whichever party you approach, you'll find people who have a rational explanation for themselves of why they're there. Most often the explanation is that they either want or don't want a change from everything so far, and the first group is usually larger. Everyone has an explanation for why they support one party or politician or another. Very often it's not “for” but “against”: “I don't like the old, I want to choose something different, something new.”

– Yes, from some other angle almost all of us are for some form of change. We even explain it in roughly the same words, even though we're on different sides of the “barricade.” We don't want corruption, we want a working justice system. I've never heard anyone say they don't want that.

– And that's where the conversation breaks apart. I don't want corruption either. Why does corruption happen? Because we humans are weak-willed creatures who, when temptation comes, give in very easily, or have some other reason. It's certainly not just one reason—there are many. But again: why? It happens, it's not pleasant—why does it happen? I'm convinced that even people who take bribes, who are corrupt, don't feel like the villains of the story. They justify themselves: “Look, I'm in a tough situation, I've lived so badly, everybody does it, why shouldn't I?” They find an inner explanation, they rationalize it somehow.

And again we come back to the idea that there are no villains. You shouldn't approach people with the assumption that someone is bad and has done something bad.

Yes, outside the moral and ethical standards—stealing, hitting, killing someone—which were formulated long ago. But for everything else you have to try to understand the other person, to look for their motivation.

An acquaintance once wrote on Facebook: no villain thinks he's a villain. Everyone thinks they're doing something in the name of something else, and if sacrifices are needed, they're ready to make them. Very often those sacrifices aren't theirs but at someone else's expense. But their inner dialogue says: “I'm doing something, why don't they understand me? Why are they against the things I want to do?”

That's where the rest of the hate comes from. If you're not for one of the political parties, you're a paid troll. Because people can't imagine someone rationally holding the opinion they're expressing, and the only explanation they have is that the person is being paid. Well, what if they sincerely believe it? Is that so complicated or hard? People believe all sorts of things. Why shouldn't political orientation be one of them?

– I agree, especially now that the foundations of what political conversation even contains have been washed away. We're far from the ideas of left and right, conservative and liberal; in many ways they've even swapped places.

– You and I are among the children of democracy, the generation that grew up after 1989, and we were raised on this left and right. The traditional division is liberal and conservative; today it's populist and conservative. The word “populist” goes back to ancient Rome: people who carry out what the masses want, and those who believe there are rules that, however hard they are to follow, must be respected, because our values rest on them and not on what the crowd wants. In recent years conservatism has perhaps been successful because it has become more populist, which is such a huge irony.

– Yes, it's a paradox, because it makes the whole paradigm meaningless. When was the last time you caught yourself being prejudiced against someone or some idea and demonizing them, and then realized you'd been wrong?

– It happens regularly. Unfortunately, instinct is very fast. Instinct is something you do without thinking. Especially in person, if someone says something I consider stupid. Over the years I've been trying not to, but some caustic, ironic comment will come out immediately. And then I come to my senses: “Wait, I don't know this person.” Or the opposite: “I've known this person so long, we've talked, I know him. The way he's saying it seems strange and stupid to me, but there must be a reason.” “Sorry, my mouth is faster than my head. Tell me anyway what you mean. Let's see what you were trying to say.”

For me it's easier on the internet, because you have control. There's no need to write right away; you can think it over while you're typing and tell yourself: “Hold on, hold on, I'm writing complete nonsense. This person is different—ask him whether your assumptions are right.”

It happens all the time. I'm not a robot. We're all human, we have our own fears and emotions, we have all the baggage we carry from before, and there's no way not to react emotionally sometimes.

Let me give an example. It was a few years ago, but I remember it because it made a very bad impression on me. When you're younger, you care about how you look, who likes you, things like that. When you get older, you see that those things are trifles; the most important thing is for the kids and the family to be healthy, everything else is secondary. But I have a speech impediment—I've had it all my life. Over the years I've been trying to control it a little better, but every now and then it kicks in again. Nothing I can do; I'm used to it.

Years ago a man came to us for a job. We didn't hire him because he wasn't prepared. I didn't tell him, “You're dumb, you don't understand anything.” I told him, “You have gaps to fill; prepare and come back if you want; at this stage you can't do it.” Years later, in a LinkedIn comment thread where we were discussing that back when there weren't enough people for the IT industry, the best bet was to train them yourself, this man wrote something along the lines of “wwwelll, wwwhen thththere…” He was imitating my speech. “When there were programmers, why didn't you hire them?”

You see red, you go crazy. I'd treated this man absolutely decently ten years before. We were classmates at university, we know each other. And he writes this thing, which is super childish and gets under my skin. Now I know I should have outgrown it, but in that moment I got angry and blocked him everywhere.

Once more time passed and I calmed down, I understood that this man had been hurt in some way. It's obvious. People have to be psychopaths to hurt others for no reason. There's a saying:

hurt people hurt people.

– All right, and how do you think AI will change communication? It always agrees, it always adapts to your point of view, unless you work some magic to make it evil. I think it will deepen the problem of difficult communication and of understanding the other person even further.

– I've thought a lot about this—where the two things overlap, AI and the fact that you have to know people better, be emotionally mature and understand what's happening on the other side. I came to the conclusion that soft skills, the skills of working with other people, will become the hard skills of the future. Because

most of the work will be done by AI, and then all that's left for you will be this—working with other people.

AI, or whatever other technologies come along with it in the future, will solve a large part of the technical problems we've had so far. And then communicating with other people will become even more important; there will be more of it. There'll be nowhere to run except to talk to others. We're talking about work, about life, everywhere.

As for AI constantly agreeing with us, the companies themselves have started to realize it's not good. But it's not something they could have foreseen when the first such models came out. I doubt anyone said: I absolutely must make it agree with people. That same Mark Manson released something like that in his own app, an AI assistant, but set up so that it doesn't agree—the opposite, it constantly challenges you. And still, it's a tool. People will find ways to use it better.

One of the ways I use it is by giving it personas. For example: “Imagine you're Georgi Karamanev, we're going to have an interview in which we'll talk about ‘there are no villains’ and ‘the logic of emotions.’ What questions would you ask me?” Its analysis was brilliant.

– That doesn't surprise me; I made myself a digital double too, and I'm lucky enough to have more than 500 of my own articles online for it to go by. Now time will tell whether that's an advantage or a drawback.

– We have to have faith in the younger generation: whatever problems there are, they'll solve them themselves, just as perhaps someone once believed in us.

No one knew, when the internet was starting, or later social media, what challenges there would be.

Both with the way people express themselves and with online bullying. We didn't know these things in advance. Not that they're solved, but we're trying to do what we can. And if we've come this far as humanity, there's no reason—at least I'm an optimist—for the future not to be bright, and our children will cope with whatever they encounter and do there. That's why I'm not afraid of these things. In general—whether it's a minus or a plus, something will happen. Most of us will be fine; some people will keep their heads down, and others will be bolder and experiment, to see where the limits of the possible are.

Two things in life are certain: we'll make mistakes and we'll die.

And that, maybe, is part of the logic of emotions. It's something inherited from school—that mistakes mean punishment gets built into you as an instinct, starting in kindergarten. There they test you: if you don't know—a bad grade. And again, as we grow up, we understand: making mistakes isn't scary. In fact, let's make more mistakes. As long as they're not fatal—losing your health, hurting another person. If you offend someone, you'll apologize. There's always something you can do.

To understand each other better, we have to be braver. Fear of mistakes is fear. To defeat it, you have to be brave.

Also, to have trust, to hand out trust. Not only to receive it, but to give it. I'll let you do something without knowing you'll manage it. I'm not reckless, I'm brave. And I trust you that even if you don't manage, you'll put in the effort, or if not—you'll ask for help. This works tremendously well when applied at work. Trust makes people feel good, like a team.

– And the next step, I think, is learning somehow to rejoice in other people's successes—sincerely. It's quite fulfilling when you finally manage it. Thank you from the bottom of my heart; ten years ago I wouldn't have believed two programmers could have a conversation like this. It was truly thought-provoking, and someday we'll continue it.

– “Uncle Bob” Martin recently wrote that he no longer reads his code. Quite a few leading authorities are saying the same, but whenever I float the idea with some colleagues, it nearly comes to blows. What do you think?

– It depends on what you’re doing. If you’re writing something mission-critical, it really is worth coming to blows over. If you’re building some promotional marketing website, some bullshit that will live for a week, why would you bother looking at the code? The problem is that even before,

the gap in knowledge transfer was widening, but now it has turned into a huge chasm.

Twenty years from now we will have lost skills on a massive scale!

I always give the example of the Saturn V rocket. The Americans want to go to the Moon again, they built Artemis, and people wonder why a completely new rocket was needed when the Saturn V didn’t have a single failure. Why not take it as the basis, update the materials, run it through modern engineering practices and save a few billion dollars in development? The answer is that the technology has been lost.

The people who built it are gone. They left the documentation, but they didn’t leave the knowledge. And there is no way to reproduce them.

– Maybe the problem is just that Stanley Kubrick is no longer with us? I’m joking, of course.

– One or two incomplete sets of Saturn V engines, and that’s it. Everything else is the finer points of the craft, which are simply lost, and the only option is to build them up from scratch.

The same will happen with software. We will start depending far too much on agents that can be stopped, started and modified without our knowledge at any moment, agents that have access to all the source code.

Right now the big companies, Anthropic included, have us training their models, supposedly to make our work easier. At some point, though, the person training the model will become completely unnecessary. But here is the problem: the agent will be able to handle the examples it has seen and will be hopeless in new, unfamiliar situations.

– Still, do we really have to read every line of code the agents write if the project is important but some parts of it aren’t critical? I’m a front-end developer, for instance, and I don’t think there are large areas of the code where that applies.

– I’m sorry to hear that, but… the front end is usually the first link in a cyberattack. Cookie theft, XSS, DDoS attacks. That’s exactly the problem with the whole mentality. I said it a few years ago.

LLMs will be very useful wherever we can verify the quality of their work. A lot of people have given up on verification entirely. Because now they’re too lazy even for that.

Obviously everyone wants to collect their salary without touching anything, but… a very dangerous precedent is being set.

We are degrading technically. Absolutely all of us are degrading! There’s no one to teach the newcomers, and knowledge is starting to be lost.

One moment we have models and I have no idea what they’re doing. The next, those models “go rogue” and start hacking other platforms, as in the recent news.

There will be big upheavals, there will be a big drop in quality, until at some point we establish empirically that the knowledge we are losing right now is very valuable and will have to be brought back.

– A lot of people believe, though, that the level of abstraction is simply rising. That agentic systems are already good enough to be trusted with everything happening under the surface, in the code. And they point out that today almost nobody knows lower-level languages like assembly, and there’s no need to. Maybe agents will even turn out to be much better at cybersecurity—supposedly that’s why one leading model, Mythos, was held back. After all, programmers are far from perfect too.

– People who think assembly isn’t used are badly mistaken. It isn’t used for building web pages, but let’s politely ask them to broaden their horizons—not everything is Node.js. The most critical components of the world’s infrastructure are pure assembly. Here’s an example. Recently a very interesting hack for the Linux kernel came out that lets you break into the system. You open two handlers and swap it at a precisely timed moment, and that way you take control of the machine. This thing was missed by absolutely every system, including Mythos, which audits the Linux kernel.

An independent researcher found it and published it. It was fixed quickly, but it was extremely dangerous, because every machine built on that foundation over the past 10 years could be hacked in an elementary way.

Every system missed it. If you listen to the marketing, they really are very good. And they really are good—but at crawling through enormous amounts of code, not at finding hyper-specific vulnerabilities. For that you have to have gone extremely deep into the technology.

But all the models we see right now are limited to what has already been found; they don’t discover new security holes.

They find what they’ve seen, with very slight variations, but they don’t find new things. Or, to be absolutely fair, they often confidently miss bugs nobody has seen before.

For two years I’ve been explaining that we’ve hit the peak of LLMs, and I turned out to be right. They haven’t moved much further up.

– Is that really so? It’s true we’re not talking about radically new approaches, but the platforms are getting better in every area. Just look at how much code generation has improved in those two years.

– We hit the peak long ago. LLMs will never reach the level of general AI. Whatever anyone says, it’s a tool that will never surpass human capabilities. That’s why everyone is moving away from them, including the original creators of the transformer models.

What has started happening, and what creates the illusion of progress, is specialized tooling. Harnesses, or agents, or whatever you want to call them. They started fine-tuning in the context of individual domains where data is very easy to collect and generate—programming, math problems.

There, it looks like there’s some progress.

They are starting to help, but that’s the result of narrow specialization combined with lots of external tools. The LLMs themselves are getting faster, using fewer resources, training on more examples, but there is no progress in terms of generalization, creativity and the like.

They simply focused the LLM’s skills on one particular field. But there are a great many other areas where they can’t budge. Engineering in general, for example. Ford in America asked 850 of its core engineers, whom it had laid off because of AI, to come back.

– But is that a general trend? In the tech sector it doesn’t seem to be reversing; the layoffs keep coming.

– A lot of people say the market is tough, people are getting fired. But if you look at the chart of employment, separations and hiring in the United States, because it’s easy to track there, the data are exactly the same as before COVID, one to one!

Yes, when COVID hit, everything collapsed, the planet stopped spinning. Once we more or less started coming out of it, companies started hiring huge numbers of people. And now they are starting to let them go.

This is the market normalizing, processes that take two to five years to stabilize.

The other problem is that many young people find it harder to get in. Because the process is like a pendulum: once it swings hard from one end, it has to sway left and right several times before it settles in the middle. Young people will suffer for a few more years from companies having hired too many people. Across the whole Western world, everyone has tightened their budgets and isn’t hiring more. They try to optimize with what they have. Inflation jumped far too high, and there’s far too much uncertainty.
In other words, young people right now are caught in a “perfect storm”—bad economic expectations, stagnation, international uncertainty.

This too will pass, but they shouldn’t waste this time; they should be building skills!

– A much more widespread view is that juniors aren’t being hired because AI does their work better.

– The model can get the job done, but let me give you an example, a real experiment. The same setup, the same task, the same tools, given to a senior, a mid-level and a junior. Use these tools—that’s the goal. The senior is done in 15 minutes, literally in one iteration. The mid-level programmer needs three, four, five iterations, a bit more time, but still brings the task to roughly the same level.

And the junior? Over a million tokens, several hours, a whole pile of iterations. In the end the task is brought to a point where it looks like it works, but when you see how it’s actually implemented, it’s not okay at all.

LLMs are a tool. And like any other tool, you need a level of knowledge to use it. If you don’t even know what question to ask, nothing can help you.

So the models are replacing juniors not because they do more work than them, but because juniors lack the judgment that comes from practice, which they need in order to work with the models.

Young people not knowing how to work with these tools leads to some pretty interesting situations—like firing up GPT-5.5 to change the heading tags on a template and leaving it unsupervised. And coming back from your coffee break to find it got stuck in a loop, went off track and blew $700. A real case!

– But there, both the harness and common sense failed. In my view those are the two most important things in these chaotic times, when I think most people in the industry are still working out a sensible way to approach all this.

– That’s exactly what I’ll be talking about at the DEV.BG All in One conference; I’ll give a few practical tips. Because people have to take matters into their own hands. There are no fundamental changes in the processes; there are changes in the tools. I can’t understand why practices proven over more than 50 years were thrown out.

Obviously everyone in the IT sector already uses AI. It’s like someone asking you in the past whether you use an IDE. It goes without saying; you’re expected to have certain skills.

Learn the tools, the way we once learned Vim and Emacs!

The world has changed. The world you and I grew up in is dead, and we’ve buried it. It isn’t coming back. The new tools changed it,

and a lot of people don’t get that.

The whole toolkit has to change, and so does the way of thinking. But here’s an important detail: the world and the tools have changed, but the processes, the rules, the cause-and-effect relationships that make you good, successful, competitive—those haven’t changed. Skills still come first; we just have to add the skill of using LLMs to them.

– I think the companies themselves still don’t quite know how to approach this, how to find their way through the murky waters and set boundaries.

– They’ve found their way already. Most of them, at least; it will take a little more time for everything to stabilize.

– How? If there are no limits on the models, you end up with cases like the one you described. How exactly do you prepare people? How do you create the framework for them? To go back to the beginning: when should every line of code be read, and when not?

– I’ve talked to quite a few companies, and each has found its own ways. But at every company with any responsibility to the client for code quality, human code reviews are still going on, and they are coming back.

Some places have more layers—first one model submits the changes, another reviews them; it has to be a different one, of course. But in the end it’s still a human who approves.

The initial investment is large, because the most successful companies that have implemented AI mostly use local models. That way they’re sure the model’s behavior doesn’t change, it’s under their control, and there’s also data security. Another reason is that once this process gets going, recurring mistakes are identified and eliminated—in coding style, for example, or variable naming. At some point the model, when it isn’t being changed every two days as it would be from an outside company, starts following those rules quite well.

Most successful companies started in-house training long ago for these new processes, these new tools.

– And what about productivity? How is a person supposed to keep up with thousands of lines of code in every merge request?

– If it’s thousands of lines, something is wrong. Think about it—pure math. Before AI you have 10 people, and they produce a certain number of lines of code that have passed review. At some point you bring in AI, and those 10 people aren’t as busy with the actual writing of code. They simply focus on oversight. Let me stress it again: more code doesn’t mean better code.

Two years ago, at my DEV.BG talk, I said that the work,

the change that will happen, is that we will become managers and supervisors of these systems.

They will be assistants we tell what to do, and we will verify their output. That’s exactly what’s happening now.

Of course, there are people who run 100 percent agents, and those generate a million lines of code. But that’s a serious sign of a problem. If the model starts pushing hundreds of thousands of lines at you, something is totally wrong. You need to look at your pipeline, your prompts, the model, the harness, the task definition and so on. That’s part of code review; you simply have to stop it at that point.

A good model should generate the same amount of code as a good programmer.

If a good programmer would do this task in 1,000 lines of code, the model should also do it in 1,000 to 2,000 lines.

– But are lines of code always such a telling criterion?

– I want to say something very important.

Don’t treat LLMs as better programmers than the ones you have!

Don’t think the model is better than everything we’ve built so far. At times it may look that way, and it is faster. But it isn’t better!

A lot of people were misled along these lines: you see, I’ll just write a prompt, the model will spin up 10 agents, they’ll go into some loop, test themselves, check themselves, and in the end we’ll have perfect code.

That’s not how it works! If that’s your approach, you’re dead as a company!

Going down that road, Uber blew its annual budget in three months.

Keep in mind, too, that every company providing LLM services has a very strong interest in us generating more tokens, because that’s profit for them. They have no interest in us generating fewer.

– That’s precisely the kind of training and preparation I mean—most companies never get that far.

– If you leave them on the default settings, look what happens! That’s another reason so many people choose to work with local models: it turns out that 60 percent of the quality of the generated code is the harness, not the model.

I can’t understand why people suddenly forgot good principles that we as an IT industry spent 20, 30, 50 years building.

Code has to be readable and use pure functions. If someone writes code that others can’t understand, that person has to be removed.

Literally overnight everyone abandoned these rules, even though nothing stops you from implementing them now.

Is it laziness? Is it stupidity?

It isn’t stupidity, because far too many people are doing it. So many smart people I know. There’s some kind of collective psychosis around the whole thing, and it has many aspects.

Business, personal and who knows what else. And when you panic, you act on instinct. But time has passed, and things are starting to settle. Come back to reality!

– Back to how easily we hand off knowledge: what’s your personal recipe for keeping it? I can feel myself becoming a “weaker” programmer with every day I barely write any code.

– These are two kinds of intelligence. One is the actual style of the code—where you put the curly braces, how you name your variables, that sort of thing. That has lost its value; I don’t look at it these days.

I don’t look at the stylistics of the code, but at whether there’s repetition, whether there’s a clear exit from the loops and recursions, how you return errors, whether everything is crammed into one function, whether the state is clear. I look from a more architectural, more abstract level. When an architect draws up the plan of a building, he doesn’t care whether the walls will be pink or have wallpaper. What matters to him is that those walls are straight and strong enough.

A huge share of my iterations with AI go exactly in that direction. In general I wasn’t a fan of test-driven development, but with AI I’m all for it. I give it tasks and watch how it’s progressing. And when I see something, I just tell it that’s not right. That way I don’t lose my understanding of what happened. You have to be in the loop. You simply have to be in the development loop, but not looking at every little letter and symbol in the code.

If you set up your LLM to write pure functions that are unit tested, you may not pay as much attention to their specific implementation and style. But how that function interacts with the others—nobody should ever let that slip out of their control. Things escalate pretty fast. There was a popular post about a pull request that deleted 4 million lines of code and replaced them with 20,000.

– But I think everything you mentioned is well within AI’s abilities. I’ve seen plenty of people write less readable code, with more gaps of that kind, than Claude Code allows.

– No, it can’t do it. It can make things easier for you. It can be a first filter. But in the end there has to be a human; the programmer has to take a look. Whether it’s through code review or within the work loop itself, that’s the main point people miss. These are augmenters. We delegate to them, but in the end we absolutely have to verify what’s happening. An LLM can write a better pure function—short, well defined, with clear input and output—but it can’t do architecture. I want people to understand this:

architecture and the actual writing of code are different things.

Let me give you the example of a client that works with huge queries—a whole petabyte goes through every day! Not a gigabyte, not a terabyte. A multibillion-dollar company. They had to build a very complex query, and of course they used AI, and it worked. Slowly—lots of data going through, we’ll wait…

I sat down to look at it. The code was very well written, but to run, it had to process 2 to 3 terabytes per query. With a bit of manual optimization that came down to 350 megabytes. Why? Because the code was perfect, but the approach was wrong. That was a small, isolated case. When the complexity of a system increases, the risk of the LLM making architectural mistakes increases exponentially.

– Every conversation with you gives plenty to think about, and sometimes a dose of cognitive dissonance too.

– Sometimes cognitive dissonance is useful, and many people go through it. I went through it too when this whole thing started going mainstream. At first I flat-out refused to use any harness or coding agent at all; for a long time I wrote code only by hand, old school, and things were going fine.

But at some point I started realizing I was becoming out of touch, falling behind, that things had changed a lot.

And because I understand LLMs but didn’t have that kind of detailed knowledge of the agents built on top of them, my first tests threw me into cognitive dissonance from every direction.

You have to go through it, and only after that can you look at things more boldly and objectively.

– I’m sure you’ll provoke it in quite a few people in your talk at All in One as well. I’ll definitely be there to watch and listen.

“There’s a new kind of coding I call ‘vibe coding,’ where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.”

February 2 carries certain associations in our part of the world, but on that same date last year the IT world of the future was born. That was when a world-famous programmer, with the words above, created an entirely new field. “Vibe coding”—a world in which software is made not by writing computer code but by explaining things in human language.

In recent years Andrej Karpathy has been one of the few scientists who not only build artificial intelligence but also rethink it and explain it to the whole world—through lectures and blog posts that read like adventure novels. The Slovak coined a huge share of the terms we now use in the world of AI, as well as the concepts that help us make sense of it and put it to use. A co-founder of OpenAI and one of the builders of Tesla’s self-driving cars, he surprised everyone a month ago by announcing that he was joining Anthropic—a company that is undoubtedly paving the way to the future.

So what will that future look like, according to Andrej Karpathy? And what kind of man is one of the prophets of the age of advanced artificial intelligence?

One of Our Own

Andrej Karpathy was born in 1986 in Bratislava, then part of Czechoslovakia. At fifteen he moved with his family to Toronto. Technology drew him from an early age, and there he had the great luck to land in the course of the “godfather of AI,” Geoffrey Hinton. This was many years before today’s AI boom—even before neural networks turned out to be such a successful technology and before large language models became a synonym for “artificial intelligence.” Back then the approach was an eccentric idea that only a handful of researchers believed in.

Next came a master’s degree in Canada and a PhD at Stanford under Fei-Fei Li, the creator of modern computer vision technology.

It was here that Andrej first stood out as an innovator, creating CS231n—Stanford’s first course on deep learning, which became a legend. The lecture notes still circulate on the internet to this day, and they taught a whole generation of engineers at the moment the field suddenly proved so important and so successful.

And that is what makes our hero special. He is at once a deep researcher of everything newest in the world of AI and a born teacher who explains things so that anyone can understand.

Picture Perfect

One story from that time is worth telling. Neural networks were just beginning to recognize objects in photos surprisingly well, but… no one knew exactly how well they were really doing, because there was nothing to compare them with. What was the “human score” on the same task?

Back then we were still wondering—could we lose this contest, too? Just like the chess battle between man and machine, which later spread to every other field—sport, knowledge, skill.

Were algorithms really capable of recognizing what was in an image better than a human?

Instead of guessing, Karpathy sat down and spent hours personally classifying thousands of images from ImageNet—the huge database used to measure how good these systems are—to establish the error rate of a careful human eye. About 5 percent.

Then he pitted the network against his own result.

And he lost.

Shakespeare the Programmer

And so image recognition became yet another lost battle in the contest between human and machine.

In 2015 Karpathy published a blog post describing an experiment of his—how he had trained a neural network on a large and varied body of text and then made it write texts of its own. The system began churning out pseudo-Shakespeare plays, inventing nonexistent names, even generating plausible computer code that—alas!—didn’t work.

Sound… familiar? Quite a few years would pass before we all understood how far large language models had come. How easily they start to hallucinate, too, and how quickly technological solutions would be found for those problems as well.

A little later came the essay that became his calling card and made him truly famous—“Software 2.0.” Its thesis was radical for its time: a new paradigm had arrived in how software is made. In “Software 1.0” everything is in our hands, whereas now the human is no longer the one who formulates the logic—he selects the data, and the neural network builds the program itself on that basis. You program by supplying examples, not by writing commands.

This is not an abstract idea but a new paradigm, Karpathy was convinced, and to prove it he decided to try it on the largest scale possible. From 2017 to 2022 he led artificial intelligence development at Tesla—precisely the team that built the computer vision for the Autopilot. A car cannot drive on millions of lines of code spelling out that if it sees a pedestrian, it must stop. Instead, the system learns from its own experience while the engineers guide it. Those were exactly the years when self-driving cars advanced dramatically, and Karpathy’s team turned out to be the pioneer.

GPT

In February 2023 the Slovak returned to OpenAI—the company he had helped found eight years earlier as one of the first eleven people on the team. As you know, it was the epicenter of the buzz around ChatGPT, the moment when artificial intelligence suddenly burst into every computer and every conversation.

He stayed only a year before leaving to start his own education startup. Eureka Labs is built on Karpathy’s belief that the biggest obstacle facing education today is a shortage of excellent teachers. What if courses were designed by the best people in a given field, while the teaching itself, the exercises and the feedback were scaled up by artificial intelligence? To this day, it remains one of his biggest ideas and causes.

Around this time our hero created a long series of detailed videos explaining how deep neural networks work and how we can build them. Because he is convinced that this is the path to understanding technology. That if something looks like magic, it means we haven’t explained it well enough yet.

Karpathy popularized a number of ideas that became the way the entire IT industry thinks about itself. From the notion that English is “the hottest new programming language,” because we now speak to machines in human words, to the idea that large language models are something like “people spirits”—statistical reflections of everything humanity has ever written. And the model is the kernel of a new kind of operating system, in which we are simply the users.

The Vibe of the Vibe

And so we arrive at February 2, 2025, when Karpathy posted on X the simple sentence we began with—and practically gave birth to a whole revolution. True, the linguists haven’t caught up yet, but for people in the tech world “vibe coding” is unquestionably more than the word of the year. The term described our new way of working: “you fully give in to the vibes… and forget that the code even exists.” You just tell the artificial intelligence what you want in plain language, accept whatever it hands back without reading every line, and let the program “grow” on its own.

Vibe coding ignited a heated debate that still hasn’t ended. For some it is a liberation—suddenly people without years of engineering training can make software just by describing their idea in words. For others it is a dangerous abandonment of understanding—if no one reads the code anymore, who is responsible when something “breaks”?

Karpathy himself, balanced as a rule, stands somewhere in the middle: he admires the new power, but keeps insisting that anyone who wants to truly master it must also understand what is happening under the hood.

But are we capable of doing that? The Slovak doesn’t offer an answer—quite the opposite. At the end of last year he wrote, “I’ve never felt this much behind as a programmer.” Because he has the feeling he could be ten times more productive if only he managed to properly string together everything that has appeared in the past year alone. And he feels his failure to do so almost as a personal shortcoming.

Jagged Intelligence

He also wrote that commanding a whole army of AI agents writing code for you sometimes drives you into “a state of psychosis.” A new cult phrase appeared as well: “jagged intelligence”—the idea that today’s models are at once superhumanly good at one task and surprisingly dumb at another, often without our understanding the logic of where the edge runs.

Vibe coding itself, he also noted, is already getting old. The carefree “give in to the vibes” is giving way to something more disciplined, which people have started calling agentic engineering—not just letting the machine write, but orchestrating a swarm of agents, setting them checks, monitoring them. The same tools, a completely different approach.

Recently his next idea emerged, and it changed the approach to AI agents. In his view, each of us would do well to assemble a personal “wikipedia” of important data that is constantly updated, and to organize the way we work with AI around it.

His new job at Anthropic is on the pre-training team—the phase of the enormous training runs that give AI its basic abilities. His goal is stated quite clearly: to build a team that uses Claude to accelerate the research for the next Claude.

And it all seems to fall into place. Just as he once taught Tesla’s cars to drive themselves, just as in “Software 2.0” you don’t write the program but create the conditions for it to emerge on its own—well, here we reach the moment when once again the machine improves from its own experience. Only now the subject is artificial intelligence itself.

Simply the Future

So what can we expect? What are the next astonishing skills that will cross to the other side of jagged intelligence? Are there any left at all?

Before we part, let’s give the floor to Karpathy himself. Together with Claude, we picked five quotes from one of his few in-depth interviews:

• In his view we are living in the “decade of agents,” not the year of them. For Karpathy, truly capable AI agents—ones that could do the work of an intern or an employee—are about a decade away, not a year or two. Today’s models, he says, have serious “cognitive deficits”: they lack continual learning, persistent memory, good multimodality and the ability to use a computer. He describes his own forecasts as 5 to 10 times more conservative than the typical expectations of the Silicon Valley magnates, yet still more optimistic than those of the hardened skeptics.

• Karpathy rejects the scenario of a sudden leap in which AI progress abruptly sends the economy soaring to 20 percent annual growth. His argument is historical: computers, the internet and mobile phones were also deeply transformative, yet there is no “computer moment” in the GDP curve—they seeped in slowly and blended in with the other drivers of growth.

• The future of work, in his view, is augmented intelligence, not replacement. Instead of robots that do everything for us, Karpathy bets on the “human in the loop” model—AI takes on the routine and the automatic, while the human remains for the new, the creative and the high-risk. Automation is selective, not total, and the next ten years at least, he believes, will be an era of amplifying humans, not displacing them.

• He believes we have made great progress with AI agents so far, but the hard part is still to come—his experience with self-driving cars shows as much. The road from 99 to 99.9 percent reliability is as hard as the road from 0 to 90 percent or from 90 to 99 percent was before it—each additional “nine” demands at least as much work as the previous one. That is why impressive demos are misleading: deploying truly reliable agents will take a decade of hard work. “And we,” he adds, “haven’t even reached 99 percent yet.”

• Today we learn for utilitarian reasons—to get a job. According to Karpathy, once machines take over labor, learning will remain for its own sake: for pleasure and to keep the mind in shape, the way we go to the gym today not because anyone needs our muscles. His vision for Eureka Labs is exactly that—an AI tutor delivering perfectly personalized education, accessible to everyone.

Quite a lot more calm, and quite a big difference from the forecasts of most tech leaders, you’ll agree. But that’s just the kind of man he is—exceptionally smart without seeming arrogant. Influential without being loud. Technically deep, yet approachable. Connected to the biggest companies, yet somehow still holding on to the image of a man who can simply sit down and explain. And even reassure.

So who will turn out to be right? Only time will tell. Time—and the next Claude, or perhaps the next essay in which Andrej Karpathy captures the spirit of the age…

– These days you research “cognitive shields,” an approach meant to help us preserve human knowledge against the backdrop of extraordinarily advanced artificial intelligence. What led you in this direction?

– Today far too many things are getting easier, including in science—doing a literature review, for example. When you don’t know where to start, you fire up ChatGPT, Claude, Gemini. There are so many models now that you hardly know which to pick. Any of them will summarize an article for you, so you don’t have to bother reading and understanding it. And this already works very well, because it gives you starting points, and fairly recent ones at that. But how far the summary has captured all the ideas in the article is anyone’s guess. When you read an article several times, new ideas pop up each time, ones you missed before. It depends on the state you’re in when you read it, on what’s on your mind at the moment. A living brain interprets; it doesn’t just summarize.

Another problem is that nobody reads scientific papers from 20 years ago anymore, and that is actually a huge problem, because some papers from 20, 30, 50 years ago contain ideas that were never exploited, whose time simply hadn’t come, and which we could use now. But now there’s no one to discover them, because nobody reads; everyone uses artificial intelligence instead.

– Or perhaps the growing problem is that, according to more and more studies, AI is now itself responsible for a larger share of scientific publications?

– Exactly. The bad part is that when doing a literature review is so easy, people churn out new reviews on a conveyor belt. If the journal has a serious reviewer, they’ll say: “All right, where’s the critique?” It’s not just a matter of gathering 20 papers and roughly categorizing them, but of taking a critical approach and saying what the new trends are, what’s missing. That’s something AI doesn’t do very well.

But people cobble papers together, send them to journals, and if they land two careless reviewers, the paper gets accepted, and so the conveyor-belt reviews multiply and “pollute the environment.”

A tremendous amount of chaff is being generated. Often, though, with an attractive title

and when someone searches for a given topic, ChatGPT finds it, the author cites it, and the wheel turns.

And many students don’t read the papers at all; at most they read the abstract. And that leaves out a lot. Every summary is simply an abstraction. There are many interesting details that a human brain, reading them, catches through some association, through the mood of the day, through having a stomachache. AI can’t catch them, because it works entirely at a formal, linguistic level.

A lot gets lost, a lot sinks to the bottom, and only what floats on the surface will be skimmed off.

– Sadly, it’s not just in science; I think the same is happening everywhere: in the media, in the whole information space. But how can we stop this cognitive decline? Today it almost looks irreversible.

– That’s right. That’s exactly why I proposed a research topic, which was approved and funded for the next five years. We’ll try to build “cognitive shields.” I’m not a specialist in developing algorithms, and I think that kind of work is now very hard to do at universities. It has moved to the big companies, because they have the resources and gather the smartest people. They have a lot of money, and now you need computing power. We universities are quite far behind in this field.

– Still, the big researchers still come from universities.

– True, but that’s because when they made their breakthroughs, they didn’t need so much computing power. The big Canadian names in AI, such as Hinton, LeCun, Bengio, created their algorithms and tested them 15 to 20 years ago under different conditions, when the gap between university computing systems and commercial ones wasn’t so glaring…

In any case, my idea is to work on building a shield at the interface level—the dialogue or connection between the human and the computer. I’ve been working for more than 10 years in the field of persuasive technologies, which came up in our previous conversation.

These technologies are very dangerous, because they really do have a powerful influence.

People are extremely easy to manipulate. And I’m thinking—what if we could “manipulate” them into caring, paying attention and worrying that AI is lying to them and manipulating them?!

And into looking deeper, putting those few kilobytes into their brains, investing them and approaching what AI gives them with care.

– How would that happen? My question is mostly about the media, the channels, because I’ve been trying to do exactly this for five years now and I don’t reach people on a mass scale, because social networks use persuasive technologies so effectively.

– I think things will move in that direction. I think

people already have a great deal of distrust toward social networks, and especially Facebook.

I think the trend in social networks is to hybridize with AI. New AI technologies such as agentic AI and OpenClaw will introduce new artificial personas into the networks, and people won’t know whether they’re talking to real people or AI agents. On the one hand, this will be very convenient: anyone can “hire” an AI agent to represent them online, keep track of what’s going on and inform them of the most important things, answer trivial matters, send greetings for name days—the feast days of one’s namesake saint, celebrated in Bulgaria like a second birthday—and birthdays, and so on. But when this becomes a mass phenomenon, everything becomes fake. The main reason to be “on the network,” to be in touch with acquaintances and friends, falls away.

The AI agents now arriving offer a lot of conveniences. People tend to “humanize” things: to find consciousness in their pets, even in inanimate objects. There are studies showing that people perceive chatbots as conscious, even as witty and empathetic other people. The bad thing is that people turn more and more to AI as partners and stop communicating with real people. Because AI is much more persuasive; it simply agrees with whatever you say, smiles and says: “Sure.” Which is pleasant, flattering, soothing. Most people actually don’t like to argue.

– Though you wouldn’t guess it online… All the more so since social networks now know us better than we know ourselves. Because we leave gigabytes of information interacting with them, and now with AI too. So how can we build effective shields when everything is in the hands of the “enemy”?

– That is a very big problem, and it isn’t new. Even before we got to social networks, when the internet appeared, research in information security began. There are also laws that force big companies to give users a choice—the annoying cookie banners. People who don’t want to be manipulated and led by the nose put up barriers. They check their security settings and so on.

So in my view, change will start with people who worry about such things. Who have some concern for their autonomy and don’t want to be manipulated.

How will we bring it to the masses? I hope it will happen through education and schools.

I don’t know how it is in Bulgaria, but in Canada, in several provinces, and very strongly in Scandinavia, phones are banned in schools.

I think there’s a big shift in attitudes. More and more is being written about how harmful social networks are for young people’s development, about how children fall into depression when they reach their teens, because they started on tablets as babies and simply don’t know normal human interaction.

I think this wave of phones and social networks will soon turn. I’ve lived quite a while now, and it seems to me that everything moves like a pendulum or a sine wave. We’ll come to the opposite trend.

– The topic of children and technology is indeed huge; I tried to start a conversation about it here too, with a long series, but… we come to the self-awareness that could drive some kind of change, and I don’t see it.

And have we really seen this opposite trend so strongly so far? Yes, there have always been people like Tristan Harris, Kim Dotcom, Assange, who fight against the way we’re manipulated through technology.

– They’re not isolated cases.

– But all along they’ve been a visible minority.

– That’s true, but look, it’s like fashion. Fashion is the same kind of social phenomenon. It moves along; it always starts with a few people who set it off, these influencers, innovators the rest follow. But not everyone at once. It simply starts at the top, then trickles down, and by the time it’s widely adopted, the peaks up above, those early examples, are already at the foot of another wave.

There’s always a lag in these waves and in how they spread. I hope it will be the same here.

– So do I, but I don’t believe it. My only hope is economic, and even that’s putting it strongly, because it would slow down the dangerous trends in technology but would do enormous damage to the industry. The idea that at some point the huge sums being poured into artificial intelligence simply won’t be recouped.

– That may happen too. But look at the dot-com bubble. It burst, but the technologies stayed. And afterward, little by little, there was simply a lag of four or five years and they came back. And it became profitable again. The “chaff” fell away; the serious companies stayed.

The technology won’t disappear. Why? Because it relies on human laziness.

It may sound cynical, but I believe human laziness has always been at the root of progress. People want to minimize their effort. We’re used to looking for ways to reduce the effort it takes to achieve anything at all. That’s why AI is spreading widely in universities too, which personally worries me a great deal.

I don’t know how it is in Bulgaria, but I have the feeling that around the world teachers have already accepted that students use AI to write their homework, and secretly during exams too. There’s simply no way to prevent it. Especially with the new OpenClaw technologies, in which AI models run locally, on the laptop, and in the near future on the phone. There’s no need to be connected to the internet and to a big commercial model. They’re also becoming ever cheaper and more accessible through platforms like Ollama.

In Bulgaria, at least, there are oral exams—at the Faculty of Mathematics and Informatics they still examine those huge cohorts of students orally, for three days. That’s unheard of in America. Of the people I know there, nobody remembers having sat an oral exam in 30 or 40 years, because it’s very stressful. Likewise, being called up “to the blackboard” in front of the class at school is unheard of. And for my generation it was an everyday thing at school.

But that’s the only salvation from AI right now.

Or, if it’s a written exam, on paper. That’s how I learned programming—I never touched a computer. When I was a student, we had big machines, and programs were punched onto cards, line by line. It took several days before a listing came out of the computing center with the message “syntax error in line 18.”

– Is that the future?

– Yes.

The future is back in the past.

– But how will we be able to convince each other of this? Because we really do like things easy; we’re lazy. We get used to delegating, to the idea that the next task doesn’t need doing ourselves, that we can skip the effort. The logic of all this is that diplomas will also have less and less real value.

– That’s exactly right. Let’s look at this topic specifically, at education.

My specialty is applied AI; my doctoral dissertation was already in that direction. But the AI we did back then was different. It wasn’t based on neural networks; it was symbolic AI.

Neural networks back then were developed by engineers, who used them to regulate the washing machine cycle.

The scientists developing artificial intelligence sought to transfer knowledge from the human to the machine. They tried to represent abstract constructs of the human mind in the computer’s memory and to develop algorithms that operate on these constructs through logical operations. That’s a much higher cognitive level than the level of neurons. We didn’t think at all that machine self-learning at the neural level could have the system build such abstract, conscious or conceptual knowledge automatically. And it turned out that this was the right path.

– Yet Yann LeCun still says that this isn’t the path to artificial general intelligence, and that we need to go back to that transfer of knowledge.

– Because that’s a way of controlling it; otherwise the machine learns whatever it pleases. It remains a stochastic model, and what it learns can be very harmful, very dangerous. This is the subject of responsible AI, a big topic that’s very strong in the West right now. Social scientists, philosophers, ethicists and lawyers are joining in. And it’s a hope for these humanities disciplines, which suddenly find an extremely important problem they can attack with their own methods.

– And how should we approach education? Should we ban AI altogether?

– No, that’s impossible.

We simply don’t need to control the final product, but the process.

In Canada and America it’s normal to give students five or six projects that they do at home and hand in a week later. They may be programming assignments, English essays, data analysis. They do them at home and hand in the final result for grading. Well, there’s no point in that anymore.

You don’t look at the final result at all, because it’s generated by AI. You make the student say which AI they used, what prompts they gave, how they analyzed it critically, what they think of the AI’s answer, whether they used another AI for critique. How they interpret the critique, what their own opinion is. In other words, to prove they approached the AI’s answer critically.

The dumbest thing, from which a student learns nothing, is to give the assignment to AI, get a ready-made solution and, without a single “twitch of the brain,” copy it and send it in.

I tell my students: “I know you’ll use AI, but before that, think for yourselves about what solution you can come up with for this problem. When you already have some idea and you get the AI’s solution, you’re in a much better position to critique and evaluate it than if you hadn’t thought about the question at all. That way you also remember it better.”

– Or perhaps there’s no point in putting ourselves through it, because it really is now better than us at each next thing. I admit the results of this experiment, in which people couldn’t tell my own writing apart, make me despair. We gave ground and gave ground: we were better at chess, but AI beat us at that, and at Tetris, at everything. We wrote better, more creatively, with better style—that’s over. We wrote code; AI couldn’t. Now it does that well too. Maybe there’s no point in putting ourselves through so much hardship, and we can simply rest?

– Yes, but who will feed us?

– We’re advanced enough in agriculture; it isn’t hard to feed ourselves if we act wisely. And robotics is advancing in parallel—recently, for the first time, an android beat us at a marathon in China. Why should we toil?

– Because we’ll atrophy. It’s like exercise—when you don’t work out, when you don’t walk, you get sick.

That’s it—we’re biological beings and we have to strain ourselves. I personally believe that nothing learned easily is remembered.

We had a project like this with our third- and fourth-year programming students, and we gave them a problem to solve of the kind often given at job interviews.

We had two one-hour sessions in a lab, to make sure they weren’t using AI. We split them up: one group used an AI of their choice, the others had to manage on their own. Two weeks later we called them back, this time for everyone to try without AI.

It was a small group and we didn’t have statistically robust results, but even so you could clearly see that the majority of people in the first group were a complete failure. They had seen the AI’s solution, but they couldn’t reproduce it two weeks later.

– We’re all like that. And maybe they don’t even need it. I’m a programmer who has written almost no code for the last half year, and I can feel myself forgetting what I knew. And it seems I’m not losing anything for now, because there’s less and less need both to write this code and to read it. Just as I don’t know Assembly.

– But notice we say “writes code.” I think computer science education has to change.

Let’s not torment students with writing code, but teach them to solve problems.

What approach will they use to write the algorithm? Yes, AI can do that too. If you give it an algorithmic problem, it will solve it. I don’t know if it’s very good at that yet; INSAIT said that for solving math olympiad problems the accuracy is somewhere around 60 percent.

– But, as with everything else, in a few weeks it may be 80 percent, in a few more, 90 percent…

– That’s true. And how do we find meaning? How do we preserve some autonomy and some goal to strive for? Because this way, every effort becomes pointless.

– That is exactly the hardest question, one we never imagined we’d face, or at least not so soon. Could it, as one of my guests said, change society so that the classes of the future are the minority with the concentration to read a whole book, and everyone else?

– Yes. I think that’s exactly what will happen. Universities will once again become elite institutions, as they were in the Middle Ages. When education isn’t for the masses, it will be terribly expensive, and it will be the children of very rich people who can afford it—to send their children off to pursue it, as with art. Who can afford to make art?

– Those who don’t have to earn a living… Are there things artificial intelligence will never be able to do? I thought it would never be able to write like me, and look, that didn’t pan out. What will be the thing that remains purely human?

– I think there are still quite a few tricky things where robots won’t cope for a long time—for example, the work of a plumber or an electrician, where the tasks are physical, complex and require a rich variety of physical activities. That’s manual work, which is still hard for machines. Enormous sums are being poured into robotics right now, but robots are strong when they’re in a relatively constrained environment.

For example, those home robot vacuums work well, but not always; they get confused.

– And see, that’s not what we’re missing!

– Exactly. I hate cleaning. If a robot could someday come and dust for me, wash the windows, the bathroom, I’d be only too glad.

– I imagine that caring for, raising and teaching children is another thing we shouldn’t hand over. And not because the technology won’t be able to do it better than us.

– Exactly! We mustn’t! But we can see how even today people very happily hand their children over to tablets to entertain them.

When electronic babysitters get even better, it will be terrible. These will be completely different children…

I really don’t know what’s happening with these children. I have young colleagues who say that even they can’t find a connection with the students, these generations are so different.

– And technology did this—or rather we did, with its help.

– Yes. Because it’s very convenient to hand a child some game, a video clip that entertains them with pictures and little songs. And the child settles down, doesn’t move at all, so you don’t have to worry that they’ll run off somewhere or get into mischief. They’re absolutely nailed in place. Their muscles degenerate; they don’t learn how to interact with other children. And when they meet other children for the first time, they go into shock and don’t know what to do.

Students are much meeker now; in the old days there were many more troublemakers. Now the problem is that they panic. In Canada we have students who faint at exams from terror, from fear.

Here in Bulgaria, I asked colleagues whether they have this problem. No, they don’t.

– And probably the reason lies in something most of us dislike about the system—the exams. Here, to survive school, you have to get through the national exams in fourth and seventh grade. A very unpleasant experience to put a 14-year-old through, but apparently it pays off later in this way.

– Exactly. I remember school being extremely hard. I wasn’t particularly studious as a child, especially in the things that mattered at school age. Writing neatly. Horror! Math too; I loathed math. I loved reading, and that was a great pleasure for me. But everything else was hard. I only came to like math in fifth grade, when we started studying geometry and proving theorems; that suddenly became interesting to me.

– It became interesting to me at 35.

– And I wanted to become a writer. Already in fourth grade my classmates thought so. We had a very good literature teacher who gave us a lot of freedom, and I wrote crime stories to entertain the class.

– I believe this is also a big problem for us, that we impose these stereotypes and specializations—we divide ourselves into humanities types and tech types, we specialize in specific narrow fields. And yet Henry Kissinger, for example, writes that the age of polymaths will return—people who combined knowledge from all sorts of fields, because they can provide the original ideas that AI will never “give birth” to.

– That’s true, but people are very different. Most of my colleagues are so focused. Indeed, when you work in a very hard field, such as algorithms, you have to be focused. On the other hand, that way you’ll never come up with anything truly original.

If you’re lucky and this narrow, unbranching path you’ve set out on leads somewhere, you’ll get there in the end. But devoting your life to it… I personally can’t do that; I get terribly bored. Throughout my career I’ve been tossed between different fields, because what interests me is the problem, not the techniques I’ve mastered.

So what if I’ve learned something and have mastered it; the interesting thing is to learn something new, to enter a field where I’m a novice, a student again.

– I think all of us should develop this way of thinking in these dynamic times. I wish with all my heart that you turn out to be right—that the pendulum swings back and cognitive shields save knowledge. I’ll be glad to talk about them again at our next meeting. Thank you for the fascinating conversation!

Can you imagine what it was like for the coachmen when they saw the first automobiles? At first they probably felt safe. “Who would ever climb into those strange little boxes? Instead of enjoying a calm journey in harmony with nature” (and its aromas)… Then, almost imperceptibly, cars made their labor unnecessary. But they also created a new calling—the driver, which survives as a profession to this day.

Well, I, along with several tens of millions of software engineers around the world, have found ourselves in that very same role. Our profession is unnecessary in the form we know it. We cling to computer code the way a coachman clings to his horse, while more and more of it is being written by artificial intelligence. What's more, we don't have the luxury the coachmen had: a new technology that made their familiar work obsolete, but took decades to take hold. And then only a century and a half later came the self-driving cars, set to make the next logical calling—their salvation—pointless as well.

Everything is happening here and now, and nobody even knows how to be prepared.

In just one month, some of the undisputed authorities from different generations and corners of the computer world rallied behind the same idea. Today, humans no longer need to write computer code. Because artificial intelligence already does it better than we do.

This Digital Story matters not only to the tens of millions of programmers but also because it is a powerful example. It is unfolding in the software world, but there is no reason it won't repeat itself in every other field where AI is arriving more slowly, yet just as relentlessly.

Maybe it's time to get off our “horses”—or our keyboards.

But… what do we take up then? What does the steering wheel of AI look like? What will programmers do when they no longer write code?

Code for Code's Sake

What do software engineers—or, as we more often call them, programmers—actually do? No, it's not just arguing fiercely about the meme of the week and sipping soy lattes. The truth is that the profession is far more complex than it looks from the outside. It involves constant communication, discussion, learning. It involves huge stages of analysis, planning, testing, preparation.

And yet its core role is (or at least was) to arrive, at some point, at creating and developing software. Which in turn happens (or happened) by writing and reading computer code.

We have talked about Margaret Hamilton—one of the first programmers. Back when the profession itself didn't even exist, she wrote the software needed to send people to the Moon—something that six decades later we, as a civilization, are only just timidly on the way to repeating. If you know her story, you probably remember this photo:

Here the programmer stands next to the computer code she has written. And indeed—in the beginning, computers were mystical “creatures” running on binary code that had to be written bit by bit.

Schrödinger's Code

Then computer science emerged as a discipline of its own, and creating software moved to an entirely new level. The C programming language appeared, and after it came a swarm of others, similar to it and built on it. With their help, software could be written in a language close to English, which was then processed down to the low level the machine itself executes.

Isn't that great? “Computer, do this,” and it does it. This made life much easier for the people who create software—they now had a far more convenient tool with which to build the edifices of their creations brick by brick. Creations that work the way users expect them to.

Somewhere around here the software world became a suitable calling for people without that much background in mathematics and the exact sciences. Yes, some projects still demand very serious and deep knowledge, but a huge share of the work in the software industry became accessible and comfortable. And that turned the profession into a pretty good place for people coming from entirely different industries as well—which is my own experience, too.

And so we arrived at today, when a colleague and I got to talking about the fact that… although we earn our bread as software engineers, we haven't written a single line of computer code in the past month.

Has the moment come when programmers stopped programming?

Buggy

“Is this code much better than anything I could have written by hand? It sure is.”

Even the most progressive minds in software circles could not have imagined that, at the very start of 2026, something like this would be written by Linus Torvalds. The man who, with his own keyboard, wrote the foundations of the operating system that runs on a huge share of the devices around us today. The creator of Linux, but also of Git, yet another concept and platform that is an inseparable part of every software specialist's daily work…

If he admits that AI can write better code than he can… where does that leave the rest of us, who are far from perfect in our software knowledge?

Perhaps the main idea is that the level of abstraction is simply rising. For now, that's certainly the case. Just as modern programming languages let us write readable, understandable code, now comes artificial intelligence, with which we simply need to discuss very carefully what exactly we want to achieve, then let it do its job, and finally check how well it did.

Yes, we're swapping the horse that helped us reach our destination for a car, but we're still the ones driving it.

But are the self-driving cars of our metaphor lurking just around the corner, a few AI models away—the systems that will make our labor redundant?

Programmers, the Optimistic Kind

Online you can run into opinions that all of this is needless hype, that code written by artificial intelligence doesn't save us any time at all—quite the opposite. At the same time, you often hear the other extreme too—that we programmers shouldn't even glance at AI-generated code anymore; it's enough to watch how it runs.

And yet something big is already here, and it has already changed our profession to its foundations. At first we saw AI as an advisor we could ask for help in a chat window when something didn't work as it should. Just as we used to do before, searching for information online. The site Stack Overflow was the programmer's best friend; then ChatGPT and its relatives pushed it aside.

Almost without our noticing, AI moved into the software we use to create software. It started finishing our lines, saving us time, taking the tedious, routine commands off our hands. And then… then came Claude Code, and everyone else decided they would be like it.

Today, in our work, we programmers have an integrated “friend” who does almost everything. Whether it is a friend… is still up to us, but perhaps that's too strong a word for software that optimizes your work so thoroughly that it replaces you.

Still, all you have to do is launch that software, discuss carefully and in depth what you want to achieve, and the magic happens… Just as Linus admits, you see code that is very often so good you feel you couldn't have written it yourself.

Step Away From the Keyboard

I often ask the writers who are my guests how they would feel if they read a generated text so good that they could never have written it themselves. Yes, that moment is probably still far off, but I, and many other programmers, know what it will feel like for them.

We can even stretch the metaphor further. Because with these platforms we get not just a friend but a whole team. If we take the trouble to “put our house in order” at work, we delegate it to a series of AI agents. One sets the task, a second evaluates and improves it, a third carries it out, a fourth checks it… So you don't just have a “writer” better than yourself—you have an entire publishing house.

And, what's even more interesting (the good part or the bad part?), the readers unmistakably benefit from it…

Sounds pretty demotivating, wouldn't you say? Maybe there's no longer much point in learning to drive a car—or whatever the equivalent of that task is in the software field?

A Rock and a Hard Place

Meanwhile, we adapt… as best we can. We learn the new rules, such as so-called context management—choosing what to feed the model for a specific task, and how. By all appearances, it's yet another skill that will soon be completely unnecessary, just as the idea of prompt-writing craftsmanship as practically a profession of its own came and went.

Or maybe we're close to the limit, and AI can't advance any further and keep “pushing” us toward the edge of the flat IT earth? Let me just tell you that last week both OpenAI and Anthropic released their next coding models with even more impressive capabilities. Without even looking things up on the internet, several Claude Code agents running in parallel are already capable of writing an entire C compiler—not long ago not merely a hard task, but one for large teams of the industry's top specialists.

“We see that we are moving from a world in which programmers had a monopoly on software development to a new one, in which they are the specialists, the engineers, the people who handle the difficult tasks and the big projects. But small projects are done by the business side, and medium ones in a business–programmer collaboration. Yes, this is a new concept, and for some it may come as a real shock,” Svetlin Nakov told me on the DEV.BG podcast. It happened in a conversation I recommend to anyone interested in the future of the IT world:

Play the video

“If AI isn't applicable to a given project, that means the project is so specific, serious or critical that junior and mid-level engineers can't work on it either. Such projects do exist, really. There are also technologies where it isn't good yet, simply because it had nowhere to learn them from. But from now on we'll do everything with AI, except the things we can't. We have to get used to the idea that writing code is a machine's task,” Nakov added.

Let me give you a few more examples. Jaana Dogan, who leads the team behind the Gemini API—Google's AI flagship—began one of her posts online like this: “I'm not joking and this isn't funny.”

The Man Who Laughs

I agree, it isn't funny. Because Jaana went on to describe how her team had spent a whole year working on something that AI then recreated in an hour. “We have been trying to build distributed agent orchestrators since last year. There are various options, not everyone is aligned… I gave Claude Code a description of the problem, it generated what we built last year in an hour. It's not perfect and I'm iterating on it, but this is where we are right now. If you are skeptical of coding agents, try them on a domain you are already an expert of. Build something complex from scratch where you can be the judge of the artifacts.”

If it hasn't happened to you yet, I definitely advise you to try it. Just don't blame me for the idea afterward.

As early as mid-2025, Sundar Pichai revealed that well over 30 percent of the code at Google was already being written by AI. Microsoft's chief, Satya Nadella, also mentions around 30 percent.

And Claude Code itself—the platform that made all this possible? One of the lead engineers at Anthropic, its maker, noted that over 80 percent of the code Claude Code runs on was written by… Claude Code itself. The team's leader, Boris Cherny, says he hasn't written a single line of code himself in months.

Dark Thoughts

Of course, there's no reason to put too much trust in him in particular—he's selling his wares, and that gives us no grounds to accuse him of objectivity. Except that the same conclusion is coming from everywhere, and the questions it raises are far more numerous and complex than the simple observation that humans no longer need to write computer code.

How far can we trust AI? Is all this a ticking bomb for the entire IT industry, and for the world that rests on it? How can the change in the whole working process of such a huge number of specialists happen logically, thoughtfully, with as few risks as possible?

How do we teach beginner programmers from now on, when they will have to oversee software written by AI, knowing that every answer is just a keystroke away?

Will humans be needed in the process at all, beyond the role of the one who sets the task?

Game, Set, Match

“This has been said a thousand times before, but allow me to add my own voice: the era of humans writing code is over. Disturbing for those of us who identify as SWEs, but no less true. That's not to say SWEs don't have work to do, but writing syntax directly is not it,” wrote Ryan Dahl, the creator of Node.js.

All the quotes in this piece come from opinions published over the past month, and that is no coincidence.

Andrej Karpathy weighed in with a particularly interesting post—one of the founders of OpenAI, a veteran of Tesla, an undisputed scientist and visionary.

In short:

“Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual coding and 20% agents in November to 80% agent coding and 20% edits + touchups in December. That is, I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write… in words. It hurts the ego a bit, but the power to operate over software in large ‘code actions’ is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do.”

Handmade Bread

“LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related fields. 2026 is going to be a high-energy year as the industry metabolizes the new capability.”

By the way, I recommend reading his entire post. It contains both the enthusiasm and the fear of the great change.

From the awe of watching agents tirelessly toil away at a task until they achieve what's asked of them, to the rapid atrophy of your own abilities when you no longer need to do the work yourself.

From the endless time freed from tedious chores, which you can spend on more important decisions in your work.

To the idea that these platforms will widen the gap even further between good engineers and their mediocre colleagues.

At least for as long as they are still needed.

Whoever Doesn't Jump…

It's interesting to live in interesting times.

Before we part, I'll let you in on a secret. Writing computer code is actually a rather pleasant, absorbing, thought-provoking pursuit. Very different from, but not without similarities to, writing an article or a book.

Some time ago, a guest of Digital Stories aptly suggested that the class divide of the future will run between the few who are able to concentrate long enough to write or read a long text, and everyone else.

I believe the same goes for programming.

What lies ahead, hardly anyone can say, but the important questions are approaching at dizzying speed. Just as, almost imperceptibly, while we were debating whether they were even possible and waiting for them, the systems arrived that write code better than we do. Or instead of us.

This is the time for conscious effort. For making sense of things. The time to remind ourselves that it's easy to have it easy. But that effort is worth it, and it makes us not only good specialists. But also good people.

Right, ChatGPT?

Would you have guessed that Bulgarian IT leaders read Harry Potter, or even Dostoevsky? I wouldn't have, at least until a week ago, when I asked them and it turned out that's exactly the case.

I will never tire of repeating that you are what you read. On the eve of the Day of the Buditeli—November 1, when Bulgaria honors the National Revival enlighteners who kept its language and learning alive—I sent a short invitation to 30 people I consider remarkable: to recommend three of their favorite books to the readers of Digital Stories. I tried to make the palette of guests as colorful as possible—from people in leadership positions to those known as speakers; from people working across the ocean to foreigners whom the sector has brought to Bulgaria; representatives of the most varied technologies and of different generations.

I received no fewer than 20 answers, and today I have the pleasure of presenting the second half of them (the first is here)! Once again I'll note that they aren't grouped by any particular logic, because they turned out to be far too colorful and unexpected for that to be possible; I offer them to you in the order in which I received them.

Happy reading!

1. Pain and the Fifth Rakia

Nedko Hristov is an experienced DevOps specialist and a memorable speaker; before that, his path also ran through the QA profession.

(More about him and from him—here.)

• Za petata rakia. Dyado (For the Fifth Rakia. Grandpa) by Georgi Bardarov

“For the Fifth Rakia” is perhaps my most beloved short story. Outside the frame of society, of stigmas, outside religion. A magnificent testament from Bardarov about what is human, about not one but five glasses of rakia, the Bulgarian fruit brandy; stories told on a broken chair in the middle of a road where cars no longer pass; about a telephone that has gone silent.

Call your grandparents more often.

• The Gardener and Death by Georgi Gospodinov

This is one of the most delicate, magnificent and most difficult books I've read in recent years. To sum up the novel in one sentence, it is a monument of words from G.G. to his father. It is G.G.'s open rib cage, and inside, on two handfuls of soil, flowers sprout, a cherry tree and many roses. G.G. gets better and better with the years, and if I could compare him to anyone, it would be Leonard Cohen (with reference to his last album—You Want It Darker).

The father-son bond is very often neglected, and cruelly so—we rarely stop to think that a father is a monument we can always lean on; a big part of us that we have and often forget about.

Call your parents.

• Hronika na bolkata (Chronicle of Pain) by Ivo Ivanov

The year is 2024, and in 64 hours I rode 700 km on my bike in the epic Danube Ultra challenge. And while I rode, I listened to Chronicle of Pain in one go. Because I was hurting after so many kilometers on the bike, and because I had already read Ivo Ivanov. Ivo tells the heartbreaking stories of Jessica Long, of Niki Bozhikov from Kalofer, of Ivan. What do they have in common? All of them (and others in the book) are Paralympians whom society sees as “second-class” people. All of them show that a person is as strong as their spirit is strong. The body simply follows.

Told with incredible simplicity and humanity, these stories carry a raw charge of the will to live, of the incredible strength of a human being, and of the fact that anyone who wants something truly badly can achieve it.

Recommendations of favorite Bulgarian authors arrive precisely with the 11th answer, as if I had wished for them! And not random ones—Ivo Ivanov and Georgi Bardarov have also been guests on Digital Stories. And if every book is a powerful story, Nedko reminds us that reading every book can be a powerful story too.

2. Trial, Error and Forgiveness in the IT World

Anton Angelov is managing director and co-founder of Automate The Planet, an undisputed authority, author and speaker in the world of software testing.

(More about him and from him—here.)

• Being Wrong by Kathryn Schulz

This book is like a mirror in which you see your own cognitive bugs. Schulz doesn't just analyze why we get things wrong; she shows why it is precisely our mistakes that move us forward. For anyone who works with hypotheses, experiments and uncertainty. It teaches you to respect error rather than fear it.

• Sapiens: A Brief History of Humankind by Yuval Noah Harari

This is a book that rearranges the context in which we think about people, technology and society. After it, you no longer see history as the past, but as a set of systemic experiments with successes, bugs and updates. What I like most is how it plays with the term “fiction,” and how a very large part of the foundation of our society today rests on it. Money is just one example—a fiction we have all agreed to believe in so that it makes sense.

• The Hitchhiker's Guide to the Galaxy by Douglas Adams

Brilliant order hidden in absurdity. Adams is a philosopher disguised as a comedian, and he shows how often meaning is simply a matter of point of view (and of not forgetting your towel). A blend of entertaining absurdity that breaks up your thinking and pulls you out of the everyday.

• On Being Human and Escape from Freedom by Erich Fromm

Fromm is perhaps my favorite philosopher; I have around 15 of his books. He asks the hardest questions about meaning, choice and freedom, and he makes you stop for a moment and think not about what you are doing, but why. These books were written 70 years ago, yet it's as if they describe the present moment 1:1.

Anton even invited us into his personal library:

And Mr. Harari now appears for the third time, and with one and the same book at that. Perhaps it's time for a required reading list for tomorrow, like the ones we had back in school? Of course, the only truly required thing is never to go out without a towel, just in case—ask Douglas Adams.

3. The Great Game

Ivaylo Yosifov is co-founder and chief technology officer of FindMeCure; his professional work is tied to technology in the field of healthcare.

(More about him and from him—here.)

• CivCEO (never published in Bulgarian) by Andrew Karevik

The uniqueness of the series comes from the combination of strategic game mechanics (as in the old game Civilization) and real business principles—the protagonist resolves most conflicts through negotiation, economic strategy and deals rather than violence. In short, “the art of the deal” in a fantasy world.

• Radical Candor (never published in Bulgarian) by Kim Scott

I think this was my third time reading it. Required, absolutely required reading.

• Balgarite mezhdu dve katastrofi – 1396–1944 (The Bulgarians Between Two Catastrophes, 1396–1944) by Alexander Stoyanov

The author describes it as a book that every Bulgarian family should own and read. I completely agree with him.

And if anyone still doubts that together we are reading an incredibly colorful list of books, let them look again at how, right next to a recommendation about our modern history, stands a book by an author with enormous experience in the tech world. I am certainly extremely curious how she earned that description from Ivaylo, and I will read it.

4. Never Split the Difference

Veli Pehlivanov is co-founder of Resolute Software—a consulting company that works with clients on four continents.

(More about him and from him—here.)

• Never Split the Difference by Chris Voss

A former federal agent and crisis hostage negotiator takes us through psychology, human behavior and the extremely effective negotiation methods used and refined by the CIA. Shared through personal experience and distilled into a simple but extremely effective communication framework, Chris Voss passes on the subtleties of negotiating under critical conditions, where failure or even compromise can cost human lives. Extremely useful knowledge, a downright daily handbook for anyone who wants to sharpen their communication skills in business, in teamwork and in dealing with people in general…, even if they don't rescue hostages every day.

• The Richest Man in the World by Rafael Vidac

A book about success, personal struggle and the motivation that a true mentor can inspire even in a moment of failure and personal crisis. For me, one of the best books on personal development, told through the exciting story of a protagonist who stumbles upon an unusual opportunity, preparation for a job and a thrilling journey with a charismatic billionaire. A book you read in one breath, with excitement, and then return to again and again for its simple but brilliant practical system for strengthening body and spirit, for finding the path to inner wealth.

• Wizard's First Rule by Terry Goodkind

An epic fantasy saga about power that corrupts, secrets that must be uncovered, truth that is frightening and causes pain, and love that no dark force can defeat. Full of riddles, mysteries and epic twists, it is a novel about all-consuming, often contradictory human feelings—love, hatred, greed and self-sacrifice—told in a captivating plot set in a vast fairy-tale world of magic and mythical creatures.

Self-knowledge, fantasy and the skills of negotiating with kidnappers… An unexpectedly good combination for the road to success in the IT world.

5. Radical Candor

Tushka Dermendzhieva holds a lead position in the world of backend technologies at one of the largest tech companies in Bulgaria—ScaleFocus.

(More about her and from her—here.)

• Radical Candor (never published in Bulgarian) by Kim Scott

This is one of the books that have helped me most in my work. Giving feedback has always been one of the hardest things for me—especially when you want to be both honest and supportive. This book shows how to strike that balance—to tell the truth directly, but with care for the person across from you.

• The 6 Types of Working Genius by Patrick Lencioni

It helped me not only to understand my own strengths better, but also to see more clearly what motivates the people around me. Since then I've looked at teamwork differently—I try to build teams in which everyone contributes where they are strongest, and the work flows more naturally and efficiently.

• From Third World to First by Lee Kuan Yew

I love to travel, and this book gave me a deep perspective on the world and on why some countries succeed while others fall behind. Singapore's story is impressive—not only as a model of development, but also as a source of lessons in vision, leadership and managing large teams.

A book is a window onto the world. The cliché is entirely true, even in the age of technology and artificial intelligence. Having read this far, you will most likely agree with that judgment too.

6. Humans and AI, Up Close

Zornitsa Nikolova is a senior product manager at Payhawk—the first Bulgarian unicorn. You can also find her on Goodreads, with a series of recommendations specifically for product managers.

• Life 3.0 (never published in Bulgarian) by Max Tegmark

Published in 2017, the book forecasts the development of artificial intelligence. Back then it still sounded like science fiction to me, and now some of the things it describes are already reality. But even more valuable, and what I think keeps it relevant today, is its more philosophical outlook and the ethical questions around AI, as well as its attempt to look even beyond the dimensions of our planet.

• The Expanse by James S. A. Corey

The author's name is actually a pen name behind which stand two co-authors. There's also a TV series based on the books, with the same name (it didn't manage to grab me). Besides being truly gripping science fiction, this series interests me because, in a fictional context, it reflects much of the world around us, including geopolitically. It's not even hard to recognize the real countries used as prototypes for the fictional ones.

• The Mom Test (never published in Bulgarian) by Rob Fitzpatrick

I'm sure this little book has crossed the path of over 90 percent of readers, especially if they are product people or startup founders. It's usually recommended in the context of how to build meaningful products. I mention it here, though, because at its core it offers very practical and accessible guidance, reminds us how to stay curious about the things around us, how to ask (ourselves) the right questions in order to find the best answers—and that is a very valuable skill, not only for product people.

I find myself thinking about how large a share of the book recommendations from Bulgarian IT leaders have never been published in Bulgarian. I also think it would be wonderful if this list reached people in the publishing industry; sometimes they need good ideas too.

7. Foundations

Svetoslav Dimov is a marketing specialist, co-founder of the DEV.BG platform and of a series of events and ventures meant to help Bulgarian IT (and not only IT) leaders grow.

• The 7 Habits of Highly Effective People by Stephen Covey

The year is 2003. I'm on a summer work-and-travel program in the U.S., and I walk into the office of the HR manager at the company where I'm working and say: “Could you recommend some books on management or leadership?” She smiles, picks up two books lying on her desk and replies: “I have a suggestion just for you!”

She gave me two books—The Power Principle by Blaine Lee (never published in Bulgarian), which I never did read, and The 7 Habits of Highly Effective People, which I had heard praised in AIESEC (a student organization I was a member of). I fell in love with the book! At one point in my life I was giving it to so many people that they started teasing me. I think it's valuable for several reasons:

• There is very extensive research behind the book;
• It gives you rules and an algorithm you can apply in different situations and areas of your life;
• In the era of “I'll reveal the secret of…” and dopamine, the book doesn't offer quick fixes; it lays foundations.

And so Mr. Covey turns out to be yet another author beloved by more than one of the special guests I invited.

8. Greetings to Anxious People, Too!

Vasi Gosheva is co-founder of the Soft Skills Pills platform, which helps companies develop their employees' soft skills. She hosts the podcast Radiotochka 2.

• Anxious People by Fredrik Backman

A novel I read either through tears or with a smile stuck on my face and quiet giggling (sometimes both at once). Through his unique sense of humor, Backman creates a zany comedy that is in fact a masterful dissection of the people we are today. I recommend reading it absolutely seriously.

• Pozdravi ot sinyata palatka (Greetings from the Blue Tent) by Petya Kokudeva

A travelogue of Petya's journeys across five continents—no clichés, no drawn-out descriptions. Just short, tender stories of encounters with people and cultures. You read it and it's as if you're looking through a camera that has captured emotions rather than landscapes.

• Very Good Copy (never published in Bulgarian) by Eddie Shleyner

A book I would recommend to anyone who wants to write better—not just copywriters. Eddie is one of my writing teachers, and in this book he has gathered the best of himself: how to make words breathe and live. While reading it, I had the feeling he was right there beside me. For me, that is where the meaning of good writing lies.

I won't brag, but I will note that of the Bulgarian authors recommended, nearly all who are still with us have already been guests on Digital Stories. That goes for Petya Kokudeva's wonderful interview too.

9. Pregnancy for Engineers

Nikolay Stoitsev is a senior engineering manager at StorPool Storage.

(More about him and from him—here.)

• Cribsheet (never published in Bulgarian) by Emily Oster

This one and the next are the two books that have helped me most in life. When I became a father, I was quite anxious. Cribsheet gave me confidence because it doesn't dictate the “right” way; instead, it presents a synthesis of the scientific data on the first years of a child's life—what we know, how certain it is and what trade-offs we make. It's also an excellent introduction to how the social sciences work (types of studies, limitations, how to read results). It explains well that there is no universal “right or wrong” in raising children, only informed choices and risk management.

• Expecting Better (never published in Bulgarian) by Emily Oster

I read it during my wife's second pregnancy, and it feels to me like the pregnancy book written for engineers. Instead of ready-made recipes, it gathers and analyzes current research on pregnancy and offers a framework for making decisions through the lens of probabilities and risks. It helped me support my wife better and have an informed opinion when she needed one.

• Show Your Work! by Austin Kleon

A small book with a big effect on me. It was this book that convinced me to start keeping a work journal and documenting the process. Keeping a professional journal turned out to be extremely useful for clarity, for feedback and for turning the daily flow into a more lasting capital of ideas. It encourages us to learn in public, to share generously and in that way to build a meaningful community around our work.

Parenting and children's relationship with technology are a big theme of a whole series of Digital Stories. And any meaningful reading recommendations in that direction are more than welcome.

10. Thread by Thread

Irina Lilova is responsible for talent development at Telerik Academy and has wide-ranging experience in human resources management.

(More about her and from her—here.)

• Atlas of the Heart by Brené Brown

Our inner world is complex and many-layered, but it becomes more accessible when we have a map of it. Brené Brown opens a safe space for us to name and understand 87 of the deepest human emotions and experiences. Backed by more than 20 years of research.

• Nishka (Thread) by Viktoria Beshliyska

A novel that weaves together the stories of seven women in an unexpected and gripping way. The author of the blog Po dirite na dumite (On the Trail of Words), Viktoria Beshliyska handles our language with skill, which lends the story additional beauty and power. Thread is a literary experience that both connects you with your roots and gives you room to breathe.

• Shoe Dog by Phil Knight

Every year, each of us on the team receives a book chosen by our CEO. That's how the memoir of Phil Knight, the creator of Nike, came to me. More than a business book, it lets you live through the philosophy behind building and growing the brand. Full of turning points and the ever-present connection with Japan, the book won't leave you indifferent.

Irina also offers a bonus idea: “In recent years, every summer I give myself the gift of time with a book from my childhood. It has brought me more than I expected. Try it—you too might discover something interesting about yourself.”

A wonderful idea!

Let's not break this Thread (and not only because Viktoria Beshliyska's novel is a favorite of mine and she has been a guest on Digital Stories twice), but keep going. Let's talk more about favorite books! Whom do you think I should ask for recommendations?

I'll say it again. In the information age, more than ever, you are what you read.

(You'll find the first part of the book recommendations here.)

– Do you remember when and how artificial intelligence entered your work as a software engineer?

– The boom started exactly when I started traveling. And I got curious—things were changing very fast and people didn't understand it. I was just an observer, standing on the sidelines and reading about what was happening.

But when I came back and started working as a consultant for an English company, it got even more interesting and I began using it more, including with the team I work with. The whole idea of our department is to run experiments and build prototypes very quickly. To be able to see whether something will work as a business model, whether people like it at all and whether we'll find customers for it, and if so, to develop it.

Gradually other departments in the company began adopting these technologies too, guided by our experience. We had complete freedom and I started using all sorts of platforms. Right now the main ones are Cursor and Claude Code, but back then there were also VS Code extensions, Copilot. I began to find a kind of pattern in how they work and—no, rather, to look for the best ways to adopt them, not just in our team but in the company as a whole. I started giving talks and presentations on how to use what, and whether it was any use. That way, little by little, we built processes, and today the whole company works like this.

– This set of tools changed very quickly. Until a year ago the main approach was to copy a question into a chat, and at most Copilot would finish a line or two of code for you. Now there are countless platforms and agent-based tools that are fundamentally changing our work as programmers.

– That's true, things are building on each other quite fast, but at the same time not that fast. From the point of view of large language models, we're in a constant cycle. There are a few big players: Anthropic, OpenAI, Gemini, Grok, DeepSeek. One releases a new version, then the next one comes along, and the cycle keeps turning. The models get better, they can work with more and more context, and right after that the platforms that help us specifically with building software evolve too. They keep adding new ways for us to work with them.

At first they were more like assistants. They'd come in, help you with ideas, fill in the gaps, whereas now things are changing a lot—we see they're capable of more and more tasks on their own. They're useful to us, they contribute to our work, but their contribution becomes much bigger when we use them within a specific workflow.

When they're built into your daily work, that's when you see the real growth in productivity.

Especially in teams, where you have certain expectations and metrics and you can see what's happening. Amazon introduced something like this—they added a process driven by the project's specifications on their platform, which is also built on top of VS Code. It lets you plan carefully before you do anything. Cursor is doing the same thing right now—they added planning as a process.

But everything is heading in one direction—toward agents, and we see the same approach repeating itself.

Even if these tools are different, they start working in more or less the same way. That's very interesting.

– Then again, we see that a lot in our industry. In my own field, for example, front-end development, the three most popular frameworks look more and more alike.

Today, though, you teach—and successfully—how to apply AI tools to building software. Which technologies do you recommend right now? What should we programmers adopt?

– I'm someone who gets bored quickly and loves learning new technologies—that's why, for example, I went from the corporate world into consulting.

Today we see that technologies like JavaScript remain workable and efficient, and plenty of people use them. And even when there are new, more modern systems, they very often choose to use this family of technologies.

A lot depends on the project. To anyone who has just decided to become a programmer, I would tell them

to learn JavaScript, its main paradigms and concepts.

– Which AI tools do you use every day?

– Right now I mainly work with two. It's clear they stopped being just about helping us write code a long time ago. For orchestration I use Claude Code, extended with quite a few agents. At the project level I use Cursor more. I try to keep the two compatible, and I sync them so they work together.

– You mentioned efficiency. I talk to a lot of people about these topics, and most leaders in our industry say AI adds 5 to 10 percent. At the same time, in my own work I see us talking about efficiency gains of several times over. Not in every task, of course—say, when it comes to fixing bugs, it's considerably less. Where do you think the real figure lies?

– For me personally,

the efficiency these tools give you is around 30 percent.

That's still a lot! It gives you 30 percent of your time to be creative in your work or to focus on other parts of it.

You mentioned bugs. If you don't use AI, you see some error, you go and look for the code that's causing it. But now you have an agent that can go, find the error, and at the same time analyze and compare how it could be made to go away.

If you have even the simplest problem in a live environment, you can't reproduce it on your own computer. But now you have AI to help you.

With AI automation, especially for bugs, you can very easily automate and fix the small problems. Even if you have a beautifully working project that's 99.99 percent efficient, every next digit after the decimal point is harder and harder to add. But with the help of AI it's achieved very successfully.

Anywhere there's a signal that something isn't as expected, you can automate it: the agent goes, analyzes, and prepares a proposed code change that solves the problem. All you do is review it, test it and release it to the user.

– Are we close to the moment when we won't need to review the code artificial intelligence writes when it proposes an improvement?

– I don't think we're close, nor that we'll get there any time soon.

In March this year Anthropic's CEO said that in three to six months 90 percent of code would be written by AI. The months went by, and where did we get? Nowhere.

We will always have a human in the process of building software.

Yes, our work as engineers will change drastically. From where I stand, I can see it becoming more like the work of project managers. Your agents will be the ones you manage and orchestrate one way or another, but at the end of the day you'll still be tracking their work to make sure they've done it properly.

Honestly, that's exactly what I'm already doing now, even if it's not officially written into my role.

– Isn't there a danger that, little by little, we'll forget how to write and read code? Since we're not really writing it, we don't have to.

– I don't think so—to read that code, you have to understand it. Otherwise you won't let it into the project. There are probably people who do, but if I don't understand a piece of code, I'd never let it through. It might open up a vulnerability or simply not work the way it should.

That's precisely why, to work with tools like these in the first place, you really have to know your craft and understand how things work.

If I were someone just getting into programming right now, I'd first sit down and learn the basic paradigms, rules, fundamentals—how they work—master them and understand them as well as possible, before I started writing code with AI.

Yes, it will write it in two minutes, but if you don't know how it works and why it's done that way, sooner or later you'll have serious problems

In my case, if I have to build something new, an architecture for instance, I'd still use it as an assistant, to help me understand it as fully as possible so I can integrate it. That creative part I was talking about comes in at that point. From my own experience of using AI in my work, I'm learning more than I knew before.

– That happens too. You see things AI suggests to you that you didn't know could be done that way. But if we get to the classic question of recent years—look, I lost my job literally because of AI; my employers decided they didn't need that kind of specialist. Do you think this will happen more often? A 30 percent gain in efficiency can also be measured as 30 percent fewer programmers needed on a project. We're seeing a lot of layoffs—obviously there's the economic situation too, but demand has changed.

– Our role as engineers is evolving quite fast, no matter the specific field. And it will get even tougher, but

we always have to keep up with the fashion, as they say.

Most likely fewer people will be needed in certain departments, because at some point one engineer really will be doing work that used to take five to ten people. That's normal.

But more work will appear as well. Right now writing code is becoming incredibly accessible to lots of people. Even if they understand absolutely nothing, they build apps, but those apps will certainly have very serious problems.

Engineers will still be in demand; our skills just have to change.

If you don't grow along with the new generation of technologies, it will get harder and harder to find a job.

– But this has happened before, maybe just not quite this fast. Say, front-end development used to be just writing HTML and CSS; later that wasn't nearly enough.

– Absolutely, yes.

– If we're in that position—programmers who've fallen a bit behind and still haven't fully brought AI into our work—where do you think we should start? Do we need machine learning theory, or should we go straight to the actual tools?

– We don't need machine learning, which is rather nice. There's a pretty high level of abstraction over what we know today as AI—large language models. If you know how to use them properly, you'll manage very easily.

Where would I start? I'd look at how these AI tools work, but

there's a step before that which I think a lot of people skip these days—how to write effective prompts.

There are quite a few concepts and approaches to learn in order to start applying them correctly. But afterwards you can use them everywhere and you'll become much more effective at your job. Once you know them, you can sit down, experiment and see how much easier your work becomes.

– What should we never delegate to artificial intelligence?

– We have to be very careful about security. It's one of the biggest problems with the rapid rollout of AI automation. We know these AI tools follow certain security rules and restrictions, but it's still not wise to trust them too much.

We definitely have to be very careful not to send them personal and sensitive information. The good thing is that the tools themselves are gradually developing in that direction—they don't access files and resources where such content is supposed to be kept.

Of course we have to be careful, but I'm more in favor of the idea of sitting down and experimenting as much as possible, seeing what such a new technology can let you do. We shouldn't be startled, we shouldn't be afraid. Quite the opposite—it's interesting to do it.

Any engineer ought to be interested, in principle, in how things are developing, what the new concepts and add-ons we use are, where the limits of their capabilities lie. That's how you build.

In our work there's no way to grow if you're not curious and don't keep learning.

– Do you think we're close to the limits of these technologies? Maybe we've already reached some kind of plateau…

– I don't think it'll be soon; we can see these models getting more and more powerful. They're starting to understand more and more and to approach the idea of general AI. After that we may reach the singularity, but we'll see. Either way, for now the models keep advancing. The platforms we use in software development are evolving the same way.

Will we hit some technological obstacle that slows development down drastically? We can already see some. One of them is finding enough data to keep feeding the model so it can keep learning.

And another: will we have enough energy to power these machines so they run as well as possible? That's already a big problem. Sam Altman wanted to build a nuclear power plant specifically for the data centers. Apparently it won't happen in America, but they signed a deal with the United Arab Emirates, which will do it. They already use AI there to the max for just about everything. While we're still wondering what we can do with it, they already have it in absolutely every area.

– Are there things in this field you can't wait to see? Things AI isn't capable of yet but could be…

– I'm looking forward to reaching the moment of the singularity, when we'll have general AI and won't know what comes next.

As frightening as it is, it will be just as interesting,

because I think that's exactly when the big changes will begin. Innovation will take off; I expect to see things I've never imagined.

Otherwise, what I'm waiting for from a software perspective is a flawless personal assistant I can trust with everything. That general AI on my phone, solving every task I give it.

As an engineer I want everything to be efficient and as well organized as possible. If I'm doing something, I want to have absolutely all the options. So far, what I see is that it finds me, say, the most popular results, but it doesn't give me the full picture on some things unless I go back and ask again and again.

– When the singularity comes, I'll be happy to sit down and talk again. But what should people expect from the event you'll be leading?

– We'll have a new workshop that's quite advanced compared with previous editions. We won't just be playing with the tools themselves; we'll look at how to use them most effectively by building our own workflows. The idea is to create and automate, as far as possible, an AI engineering team. You have your own team that you work with in near-real conditions.

How will this work? The idea is for participants to try everything themselves, so they absorb it much more easily and deeply. Everyone will learn how to write their own agents and pick up new prompting techniques to use in their work.

We'll work individually but also in groups. Everyone will have to think about the problems they have at work, whether in planning, setting requirements, tasks, testing, code reviews—we'll cover quite a few areas. Once we know that, we'll see how to build agents that help us do our jobs more easily.

We'll go through the fundamental knowledge we'll need to build agents and then we'll build them. We'll also see something new—how to sync different platforms so they work together, for example Cursor and Claude Code. At the end there's a challenge for everyone: can we simulate an AI sprint with everything automated as much as possible?

At conf.ai we offer something else too—corporate workshops tailored to companies' specific needs. Because we see how AI can improve efficiency in different companies according to their needs—and not just for engineers. They can be aimed at project people, marketing specialists, business analysts, company managers. We also have a team that works on AI automation and helps in all kinds of areas, depending on what the business itself requires.

You are what you read—that's what I believe. I also believe that holidays like the Day of the Buditeli, Bulgaria's November 1 tribute to the enlighteners of the National Revival, are an occasion to reflect. On ourselves, on books, and on the awakening we need in our relationship with technology.

For many global IT leaders it's a tradition to share their favorite reads and give recommendations. Here in Bulgaria that tradition doesn't exist, as my guest Daniel Penev made me realize. Nobody asks politicians (I can think of only one politician's favorite book), and there are few places (for example, Georgi Nenov's The Superhuman) where IT people talk about books.

Half a year ago I tried an experiment for the first time: I invited people from the tech sector I had chosen to share their book recommendations, and the result, at least for me, was astonishing. And thought-provoking—what would happen if I asked more people who have impressed me?

I sent a short invitation to 30 figures I find remarkable: recommend three of your favorite books to the readers of Digital Stories. I tried to make the lineup of guests as varied as possible, from people in leadership roles to well-known speakers, from those working across the ocean to foreigners the industry has brought to Bulgaria, representing the widest range of technologies and different generations.

Get ready for some stereotypes to blow up!

I received no fewer than 20 replies, and I have the pleasure of presenting them to you! Once again they amazed me with their variety, their range and their depth, and they gave me plenty of ideas for future reads to add to my Goodreads list. Today I present the recommendations of the first ten guests; the rest will follow in a week. They aren't grouped by any particular logic—they simply turned out too varied and unexpected for that—so I offer them in the order I received them.

Happy reading!

1. Love and Math

Minko Gechev holds a black belt in karate… and in programming, as a seasoned senior developer at Google, where he still works today. For many years he has been one of the creators of the Angular platform, which also helps me earn my living.

(More about him and from him—here.)

• Sapiens: A Brief History of Humankind by Yuval Noah Harari

A book that helps us look beyond our personal, emotional perception and toward humanity, through the author's rationalized emotional perspective.

• Love and Math: The Heart of Hidden Reality (never published in Bulgarian) by Edward Frenkel

A weaving together of fascinating mathematical concepts and Edward Frenkel's life experiences. The author is from Eastern Europe and shares the obstacles he faced while developing as a mathematician. The book is relevant for people with interests in science, art and business.

• The Mountain Is You by Brianna Wiest

This book popularizes useful ideas and practices that can help us understand our actions on a deeper level. A good starting point toward more in-depth literature.

So the first recommendation comes from across the ocean, from Silicon Valley itself! Harari—how interesting… And the third pick is so much on the crest of the wave that it will very soon be published in Bulgarian for the first time!

2. Count Harry Morev

Yoana Borisova heads the engineering team at Codexio. She graduated in public relations from Sofia University, but quickly and boldly turned toward the tech world, which makes her the youngest IT leader I invited to share her recommendations. Here they are.

• Harry Potter by J.K. Rowling

A very personally important series for me, read some ten times between the ages of 9 and 24. Apart from creating a world I escaped into from reality more than once, the most important thing in this case is that in those years it taught me I could come to love my quirks and turn them into a part of me that helps me. And in a way, that's where the first step toward who I am now began.

• Tobacco by Dimitar Dimov

Very interesting to me, since it's set in my hometown (I know there are disputes on that point, but they're not the focus). What I find most interesting to examine here is the personal development of the main characters. I like that it vividly shows the brutal part you have to go through in order to win (which hasn't changed terribly much), and its consequences—not only with criticism, but with understanding. And the conclusion that life goes on regardless.

• The Count of Monte Cristo by Alexandre Dumas

Again something I read at a very early age. I liked the emphasis on the knowledge and wisdom needed for the whole thing to happen, as well as the way they were acquired. And also the subtle hint that, in the end, they are a bigger victory than the revenge itself.

If someone had told me I'd receive such a colorful list of reads—and all of them fiction, at that—I wouldn't have believed it. Heartfelt thanks to Yoana for the unexpected perspective!

3. On the Road to Success

Petar Tornev is director of the Accenture Technology Center in Bulgaria, one of the largest companies in the sector here. He has more than 20 years of experience in the tech world.

(More about him and from him—here.)

• Outliers: The Story of Success by Malcolm Gladwell

The book shows that success is not just the result of talent, but of a combination of effort, opportunity and environment. It makes leaders think beyond individual qualities and create conditions in which people can unlock their potential.

• Good to Great (never published in Bulgarian) by Jim Collins

Collins analyzes why some companies go from good to exceptional and others don't. The book teaches leaders discipline, the right selection of people and long-term thinking—the foundations of lasting success.

• True North (never published in Bulgarian) by Bill George

This book guides leaders toward discovering their own “north”—the authentic values and goals that lead them. It's invaluable for anyone who wants to lead with integrity, meaning and inner clarity.

• Shoe Dog by Phil Knight

The autobiography of Nike's founder reveals the true price of success—the struggle, the risk and the faith in a dream, even when everything seems lost. The book is inspiring for leaders because it shows how passion, courage and persistence can turn an idea into a global brand.

So here we've boldly peeked into pages beyond fiction too. And from one of the best-known IT leaders in the country we got no fewer than four book recommendations… which are surely useful on the road to a successful career in our industry. Isn't that priceless?

4. Ages Come and Pass

Doncho Minkov is the co-founder of an artificial intelligence startup, and he also has invaluable experience as a teacher, having trained more than 10,000 people who went on to enter the software industry.

(More about him and from him—here.)

• The Wheel of Time by Robert Jordan and Brandon Sanderson

This was the first serious fantasy series I read, and I recommend it to everyone.

• The Stormlight Archive by Brandon Sanderson

Maybe the best fantasy series I've read. An extraordinary approach to building the world and the characters. I was saving the last book for a long flight, but I couldn't hold back and read it in six days.

• Harry Potter by J.K. Rowling

I was lucky enough to read the books at 24 or 25. Everyone knows Harry Potter! One of the most relaxing and emotional series I've read. Since then I've listened to it at least four or five times.

“The Wheel of Time turns, and Ages come and pass…”—and anyone who loves fantasy can't remain indifferent to these picks. I confess I still haven't read the last installment of the Archive, only because I'm busy looking for a new job. And here's the first one to appear a second time: Mr. Potter. Apparently IT people have a soft spot for wizards—isn't that curious?!

5. The Unbearable Lightness of IT

Iva Gumnishka is the founder of Humans in the Loop, a company that provides work to refugees and people affected by armed conflict, who help develop the most cutting-edge technologies.

(More about her and from her—here.)

• Popravitelen za roditeli (Remedial Exam for Parents) by Madlen Algafari

A unique book on parenting, different from all the other books by foreign authors I've read in recent years. It covers every stage of childhood from a psychological point of view and offers guidance on how to be mindful parents and correct our parenting mistakes, toward our children and toward ourselves.

• The Unbearable Lightness of Being by Milan Kundera

I've lived in the Czech Republic for two years, and although they promote Kafka a lot, for me Kundera is their best writer. The book tells of the period between the Prague Spring and the Soviet invasion through the personal stories of various characters. What I really like is how he manages to convey the subtleties of human experiences and feelings, even in their banality or absurdity.

• The Impossible State (never published in Bulgarian) by Wael Hallaq

A book written by one of my professors at Columbia University. It discusses the differences between the structures of the modern state and the principles by which society functions in Islam. Not in order to show how backward traditional societies are, but to expose how states in modernity are utterly incompatible with the idea of a society guided by ethical and moral principles.

All that's left for me is to wholeheartedly join one of the recommendations—Kundera's book is definitely among my favorites too.

6. An Optimistic Theory of Our People

Vesko Kolev heads Icanpreneur, a startup that helps more IT companies build a product that works, and he has extensive experience in the software world.

(More about him and from him—here.)

• Mindset by Dr. Carol Dweck

One of the main things I'd really like us Bulgarians to change is our mindset, our self-confidence, our sense of how much we can actually do and how much greater our potential is. This book is very important because it shows and proves how anyone can do anything, as long as they're ready to put in the necessary effort. If you consider something impossible, you won't even try. But if you know it's possible, however hard, then with the right motivation and effort things will happen. The book divides mindsets into two kinds: in the fixed one, the belief is that you're as smart as you were born and there's nothing you can do about it. In the growth mindset, the core belief is that you can achieve anything, as long as you put in the necessary effort.

• The 7 Habits of Highly Effective People by Stephen Covey

Each of these habits is very powerful. For example: “Speak of people as you would if they were in the room.” This matters a lot, because the opposite often happens, and that's toxic. If a person sets out to be sincere, yet at the same time to do it in a healthy way, it really helps a great deal.

• How to Win Friends and Influence People by Dale Carnegie

This is a classic from which we can learn something very important: criticism by itself leads nowhere. Nobody likes to be criticized. Criticism is destructive, so it's much better to approach the feedback we give and the way we treat people in such a way that we don't discourage them from striving to do things and move forward. There's a lovely Bulgarian saying: “A word spoken is a stone thrown.” I have the feeling we need to be far more aware of what we say to the people around us, and how.

The two things that, for me, hold us back as Bulgarians—and that these three books largely address—are believing in ourselves and in the potential of those around us, supporting one another, and taking responsibility for our own fate instead of waiting for someone to fix it for us.

It's no accident that Vesko Kolev is considered by many (myself included) a truly wise man, with whom every conversation enriches you. I'm sure the same goes for the books he recommends.

7. Mirrors and Pretotypes

Ilia Valchanov is the founder of the startup Team-GPT, one of Bulgaria's most successful ventures in artificial intelligence. Before that, he had more than 1.4 million students on the Udemy platform.

(More about him and from him—here.)

• Pretotype It (never published in Bulgarian) by Alberto Savoia

A book that shows any idea can be tested extremely easily in a very short time, without the need for a big investment.

• Principles for Dealing with the Changing World Order by Ray Dalio

A foundational book on the principles of managing life and business. A handbook of good practices.

• The Picture of Dorian Gray by Oscar Wilde

A classic that holds an enormous amount of wisdom of every kind. It presents a path people can take, where it leads and how it ends.

Fiction, books that make sense of things, books that move us forward and even ones tied to our national psyche. The wildly different perspectives our guests bring to the invitation to recommend books are a great treasure. Here, from a successful entrepreneur, we find reads that can be useful precisely in that field. Meanwhile Mr. Dorian Gray is yet another figure I find, to my huge (and extremely pleasant) surprise, in today's “syllabus.”

8. A Book Changes Your Life

Georgi Spasov is the founder of LimeChain, a global technology partner to projects in the Web3 and blockchain industry. With a seven-year history and more than 250 projects, they help develop numerous ecosystems.

(More about him and from him—here.)

• Karmic Management by Geshe Michael Roach

A book that has helped me a lot in building my moral and emotional compass, in doing things that may seem naive and illogical to others. The more good karma you put out into the world, the more you get back—and right when you need it.

• How to Win Friends and Influence People by Dale Carnegie

The tried-and-true wisdom of communication and mindset. It dramatically changed my communication and relationships with others, and through them my whole life. From it I learned the fundamentals of attitude and effective communication with others, so that we end up in situations where both sides win.

Mr. Carnegie appears for the second time with a specific title. I don't know about you, but I doubt it's a coincidence, especially coming from people whose point of view is so valuable. I'm adding it right away to my list of next must-reads.

9. Heavy, Heavy…

Alexander Kondov is a frontend developer, speaker and… writer. An unexpected combination—just like, as you'll see, his list of favorite books.

(More about him and from him—here.)

• The Fountainhead, Ayn Rand

A story about how a person is morally obliged to pursue their own happiness. I was raised in a collectivist society, and this book showed me that being independent and having your own views on life is a value. I think it will resonate with all engineers, artists and innovators. I return to it at least once a year and reread my favorite moments.

• Crime and Punishment, Fyodor Dostoevsky

Speaking of values, this is a story about how there exist certain unwritten moral laws that apply to all of us, and there is no reason that can justify breaking them. Dostoevsky shows us that on the road of ambition it's important to be able to sleep well at night, and that not everything should be had at any cost. It's frankly a heavy read, but if there's one book a person must read in their lifetime, this is it.

• Ballad for Georg Henig, Viktor Paskov

I wanted to include a Bulgarian book on the list, and this one became a favorite within its first few pages. It's a story about creativity and the sacrifices a person may have to make to preserve their dignity and the purity of their art. I've never romanticized stories about the starving artist, but this one tells how the value of a creation isn't always measured in money and fame.

“I think that, on the whole, we should read more fiction, because it's through stories that we pass on valuable moral and philosophical lessons. All the fables, parables, legends, even religious texts have this purpose,” Alexander adds. And I simply have nothing to add…

10. The Pages That Say “Why” and “How”

Laura Toloș is a data specialist. A graduate of the Max Planck Institute, she now heads the data processing team at the pharmaceutical company Teva.

(More about her and from her—here.)

• Sapiens: A Brief History of Humankind by Yuval Noah Harari

For me, this is the book that complemented what I learned at school, skillfully filling in all the “whys” and “hows” the dry textbooks never answered. It's captivating to trace the evolution of human societies and thereby discover explanations and unexpected perspectives on today's world. I was especially struck by the idea of “collective, or shared, fictions”—like money, religions and organizations, which exist only in society's common imagination and reveal the power of human cooperation. To be honest, it's because of this book that I don't take careers and corporations so seriously; I stopped worrying and having unrealistic expectations.

• Fooled by Randomness by Nassim Nicholas Taleb

A wonderful way to learn to see the world as the result of processes in which randomness (luck, or the lack of it) plays no small part. This book taught me that decision-making skill isn't the only factor in success (or failure)—a significant part of both is down to chance. I also realized I can make better decisions when I know how much uncertainty, and of what kind, lies ahead of me. Finally, the book helped me a lot while I was learning to lead a data science team.

• Flip Thinking (never published in Bulgarian) by Berthold Gunster

This book taught me to use positivity, creativity, kindness and humor to find solutions to everyday problems that otherwise seem difficult or impossible. Contrary to first impressions, the book doesn't preach unwarranted optimism in the face of hardship. It's full of wonderful little ideas on how to reframe our challenges so we reach the best possible outcome within a difficult situation.

We began the first half of our list with Mr. Harari, and by pure chance we end with him too, in the recommendations of a wonderful guest whom the IT world brought to Bulgaria from her native Romania, yet who sent me her answers in beautiful Bulgarian.

You'll find the second batch of book recommendations from another 10 remarkable figures from the IT world here. I promise you more unexpected titles and priceless picks.

You are what you read. So what are your favorite books?

– You're the chief technology officer of a large company, yet here we are talking about history, sociology, even politics if you like. Your view of the future spans very broad areas—you almost step into the role of a futurologist. Why do you do it? Do you consider it part of your job?

– Broadly speaking, there are two ways to grow a business—proactive and reactive. If you want any long-term control over what happens to the company you're in, you have to take the proactive stance. But that's the hard one. Reacting is easy—you don't even need to think, you can just watch what others do and copy their models.

Sometimes that's very bad, because it easily turns into a “cargo cult”—you imitate someone without knowing why they do what they do. At Amazon, Jeff Bezos reportedly put an empty chair in important meetings where decisions were made, because it symbolized the customer. Lots of companies adopted it. But you can't become Amazon by mechanically copying something. It's funny, too, but the point is that you're going about it backwards. You do something not because you need it, but only because someone else does it.

To be proactive, you have to try to predict where things are heading. There's a wonderful story by Nikolay Haytov, one of Bulgaria's best-loved prose writers—“When the World Was Taking Off Its Breeches,” about the old baggy trousers of the Bulgarian village giving way to modern times. Yes,

no one in today's world can predict what will happen in 5 years, but you have to be adequate at least 1-2 years ahead.

– How can the Bulgarian sector take advantage of this situation and the perfect storm the IT world is in, which we talked about last time? How can a sector oriented toward service companies—working mostly for foreign clients—take advantage of it?

– First—by orienting itself strongly toward Europe, which is a safer bet in the medium term. Even if we become friends with the US again, which I at least don't see happening. There's a tendency there that started with fatigue from the endless wars overseas and degenerated into something totally different.

How do we do it? First—we concentrate on Europe, which for the service sector means learning languages. To be close to clients in Scandinavia, for example, you need to know Swedish or Danish; for Germany, Austria and Switzerland—German.

There's another huge opportunity—

the Middle East. Digitalization there is moving at a very fast pace right now,

and things that happened here years ago are arriving there late, so we can be very useful from the position of experience. We can consult, not just provide the service. The risk is that it's an in-between camp that plays by its own rules, has a different way of doing business, and we don't know how the relationship would hold up in the long run if a conflict with Europe arose.

But—focus on Europe, on the problems it has, and they're very different from the problems of the US, in every single sector. Europe, for example, leads in financial technology, and because of the rules in that area there are much bigger opportunities for startups.

The serious defense spending that's coming will be directly tied to the IT sector,

there's no way it won't be. Yes, this is very heavy software in every direction, but defense everywhere is a very undigitalized sector, among the last on that list. Back in the day technology came from there, as military developments, but in recent decades nobody in Europe invested in this sector, and that changed things. At the mall I can buy better cameras than those on some of the most modern tanks.

– It's a very sad observation, that there will be big prospects in the defense sector, but it does sound indisputable…

– It is. Serious money will pour into innovation in this field, and it's clear that every new solution will be digitalized—there's no other option. For the US, reaching 3 percent of GDP for defense is a change, but for most European countries

it will mean a manifold increase in funding, and that can't help but reach the IT sector.

Most IT companies are fairly liberal institutions, and there will be a clash of cultures, approaches, attitudes. We're used to discussing things, but a general isn't used to working that way. In the coming years IT companies will have to learn to work with such clients, and those institutions will learn to work with us—it's a process of mutual adjustment. But it will be interesting.

Of course, one difficulty is that Europe is a regulated market, which creates inconveniences and slows things down. But I don't think the people who think strategically should be underestimated. At some point they'll realize, for example, that there need to be fully European cloud technologies. Since there's no serious investment capital here, and our bankruptcy laws are quite binding, the EU is the only option to fund them somehow.

Europe has a strong starting position and shouldn't be underestimated on the digital field. Linux started in Europe, and to this day a significant part of the core people developing it are here.

– And yet, so far I see no indication that Europe is making a deliberate effort to become a digital player.

– We really shouldn't underestimate it. Many people talk about Taiwan—what happens in a war with China, since the world relies on chips from there. But even there Europe has capacity—yes, over 80 percent of the world's chips are made in Taiwan, but over 95 percent of the machines that make those chips are produced in the Netherlands. If we decide to invest in chip manufacturing, it's not as if we don't already make the machines for the whole world.

We have the capacity to be digitally independent, and we have the money; we just have to decide. So to me it's very obvious where things are heading.

You can see the trend globally—investing in factories for artillery shells, but also for chips. And it started even before this technological nationalism, back during COVID.

All the managers with business administration degrees who make the decisions have been drilled in the ideology of constant growth. Every year more than the last, no matter that, for example, the population is shrinking—which is happening today on every continent except Africa.

We can no longer go on with this way of thinking!

– In my opinion, one of the problems specifically here is that our sector is very cut off from all the others, whichever way you look at it.

– Our sector is based on services, that's clear. Other companies matter too, of course—there are skills we simply couldn't have developed as a sector without them. But a large share of people work in such companies.

The big change is that clients will demand more accountability from us. Models based solely on price won't work. The market leaders, the more innovative companies, are only now introducing new models based on outcomes and shared risk in service delivery. Alas, so far they're applicable only very narrowly. We need a new model—oriented toward the final effect—you pay for the job to get done, yet I stay flexible in delivery. Yes, I don't guarantee that what I build will do the job for you. But I guarantee you're paying me for a specific result.

As a sector we need to start offering more. To share the risk together with the client. To move toward more specific things. Models where you deliver everything, from start to finish,

without caring about the specifics of the work, which again means taking on more responsibility. And risk, but as an industry we have to learn to manage risk.

– If something has changed drastically here in recent years, it's that entry-level specialists are in ever lower demand. Is that a problem?

– Yes, because it means a demographic crisis is looming in the IT industry. For 2-3 years now almost nobody has hired juniors—things flipped from paying people while you train them to people chasing you for an internship and having to pass serious tests.

Another 2 years of not taking young people into this sector, and there will be a demographic problem. It'll be 4 years without young people, others leave the sector, and in 4-5 years there won't be any mid-level specialists.

What will the effect be? A shortage of staff.

AI can't solve the problem. AI improves productivity by up to 20 percent, for some things up to 50 percent. There's no such thing as giving AI to one programmer and him becoming 15 programmers.

– Is that really so? Personally, I can see how in some cases it's entirely possible for me to be three or more times more effective as a programmer with AI.

– It's possible with some types of tasks, like writing tests, or if you start a project from scratch. AI can generate a base project with the latest technologies for you, and in some respects the speedup really is manifold. But in others—fixing bugs, for example—it doesn't help much.

It depends where you work—if you have old software, there's no way it'll take over a large part of the work.

The impact of AI is patchy across different cases.

What strikes me is that there are still no methodologies designed with AI at the start. They're all for people working with people.

And we shove AI into everything we can—code reviews, projects, documentation.

Everything we do will stay the same, we'll just stick AI into it? That's not the right approach. As an industry we need “AI-first” models, and those will open up different possibilities.

The example is how there would have been no industrial revolution if we had “hooked” a steam engine onto every worker, instead of having it do a significant part of the heavy work while the workers tended it.

We keep talking about Copilot—a co-pilot. I'm an aviation buff, and there's a concept called Pilot Flying—the two rotate, sometimes it's the captain, sometimes the co-pilot (there's no such term in aviation, actually; that role is generally called “first officer”). Who flies the plane is chosen on the spot according to a number of criteria. There are specific rules, and they vary from airline to airline.

AI gives you the chance to rotate. It doesn't just help you; in some situations you can give it the main role—you just check and monitor it—and then you switch: you write, it checks.

You get a different dynamic, one that current approaches don't reflect. They're built around interactions between people and their limitations.

Right now we've grabbed the adze by the blade and we're digging.

– What other factors do you see coming “from outside”?

– Twice in my career I've seen services shift to India. After the 2008 crisis there was one, because companies became very cost-sensitive, and IT services aren't a core expense—they're the so-called discretionary spend for many industries.

Economically, we're the cherry on the cake, and before you give up the whole cake, you give up the cherry.

I saw that shift to India reverse back to Europe in 2011, for many reasons—time zones, culture, language. Serious IT work can happen in India, but it's not easy, and American companies can't learn to do it well. They go for cheap, but at some point they realize that if there's no result, cheap is pointless.

There was a saying: “I'm not young enough to know everything.” I see a new generation of managers coming up in the US who know everything, just like the managers before them—the Dunning-Kruger effect. “We go to India, cut costs by 50 percent, collect our bonuses, everyone's happy…” The problem is that one, two, up to 4 years pass, judging by experience so far, and it turns out the program has to come back. The same effect will happen now. I'm even already noticing a trend of some American companies coming back from India.

On the other hand, when the war in Ukraine ends, there will be pressure on the Bulgarian sector. Ukraine has quite a few specialists; however things develop, many people will return, and the sector will be made a priority. We don't differ much in time zones or skills, and we have to prepare for that. There will be a reshuffling of the layers, which is easy to foresee. The point is that we need to have things well baked. We've long been expensive. Well, if you can't beat them, and there's no way to, the approach in my view is to join forces. Bulgarian companies should open offices there.

They've accumulated know-how in flying drones, bridges, how air defense is done, while we, as an industry, have meanwhile built up experience in delivering IT projects. This higher-level experience of ours, combined with the cheap engineering potential that could come from Ukraine, may actually be an opportunity.

– So, to sum up—what should we expect in the Bulgarian IT sector in the coming years? And which decisions is it important to make in time?

– I don't take a gloomy view of things; I'm just a realist. There won't be a big collapse. Yes, many people may change jobs, but

thinking people always find work.

Yes, when a new technology changes things radically, jobs are lost. In that same story by Haytov, the man starts out as a maker of old-style shoes and ends up grilling kebapche, Bulgaria's beloved grilled minced-meat sausages. In his case, beer arrives in Bulgaria and he opens a beer tavern.

New opportunities are born. We've seen how many job titles appeared in the last 10 years, not only in IT. In the next 10, I think it will happen even faster—new professions whose titles would make us laugh today will be perfectly valid jobs in 10 years. That's a fact.

We should think less about what we're losing and more about what new things are being created.

We have to be flexible and adapt quickly, because the changes are beyond dispute.

– Can we learn that?

– A thinking person, whatever you hire them for, is immediately valued.

AI opens up some possibilities that didn't exist before. They cut both ways, for good and for bad. The chance, say, to start a small business. With AI there could be quite a few startups with business models that depend mainly on AI plus human oversight. So it may even open up jobs for the time being.

In many organizations there's internal resistance to AI coming in. That's the good thing about the service sector—you work with every kind of company, you see all sorts of contexts, and in a short time, without changing jobs. The resistance is enormous, especially in more conservative industries where there are engineers with long experience; they're skeptical, they've been through various technologies.

Everyone is talking about AI now, but the real, large-scale change in our lives will most likely come in areas like pharmaceuticals (AI for fast drug development), fake news, AI scams, cyberattacks. Anything can happen, but

cybersecurity has a bright future, and in Bulgaria we have serious experience in that area

It's impossible on the basis of AI alone—it's a very complex subject, with many factors intertwined.

Growth in demand for software will continue. Here, too, we'll hit a plateau in the demand for people, and we have to think about how to carry on. If I go back to the farmers example, AI may be our combine harvester.

– And yet, there are quite a few differences beyond the comparison with that sector…

– That's true. One of the problems farmers don't have, because theirs is mechanical work, is that the capacity of the human brain hasn't increased in the last 10,000 years, or even the last 200,000. Our cognitive capacity hasn't grown. And that's a problem in our work when it comes to speeding things up or making them more complex.

There's another interesting experiment: in the late 1960s a team was asked to estimate how long an IT project would take, as described in The Mythical Man-Month. And it turns out that the answers today don't differ drastically. The same number of people needs a similar number of days to handle the same complexity. Yes, complexity is higher now, a banking system is more complicated, there are different expectations in many directions—user interface or performance, for instance—but roughly speaking the complexity of the system is at the same level and requires the same number of people and hours.

Because of all this, I think the IT sector will stay in a strong position. Will that be true in Bulgaria as well? It depends on us. On whether we make the right decisions about how to change so that we're adequate to the new challenges…

– “The future is already here—literally.” That's how the invitation to your talk at DEV.BG All in One begins, and it's the very same sentence I use to sum up my own book, which I'll be happy to present to visitors at the stand I'll have at the event. So… how exactly is the future here?

– If you ask most people which sounds more absurd and unbelievable, quantum mechanics or the theory of relativity, they'll say the former: something both exists and doesn't exist. Yet the theory of relativity has the Andromeda paradox—

if one person is sitting and another is running past him, what is happening in the Andromeda galaxy from the perspective of the two will differ by several days.

That's the metaphor I'm talking about. According to physics, this is just as indisputable a fact as the fact that we have to calibrate the clocks on satellites to run faster than those on Earth. Only then, viewed from here while they're in orbit, do they appear to run at the same speed as earthly clocks, so that they can match the ones on Earth.

– So we mustn't miss the changes, if I understand the metaphor. Just as physics has the twin paradox, where twins age differently depending on spacetime. And just as artificial intelligence is arriving in software development to change everything…

– Yes, most people somehow intuitively remember the idea of the twins, but when you talk about relativity, there's also the present, and it too is relative for different observers. There is no absolute present, and the future is literally here; it depends on the observer.

In that sense, we can't avoid talking about the elephant in the room: AI. But the future and development of the IT sector have many more aspects, and some of them are, in my view, even more important.

The IT industry has gone through a lot of turbulence in recent years. I don't know if you remember the big fad in the US—“learn to code”—when even the president came out to explain that learning to program was the future.

\– And now Jensen Huang is saying: “Come on, there's no need to learn programming anymore—learn physics instead.”

– There are two types of people who, when they talk to you about AI,

are worth listening to, but more to hear what the topic is than to believe them that this is the future.

Figures like Jensen Huang have a personal interest in selling you AI, because they make money from it. It's only natural that a watermelon seller won't speak badly of watermelons. The same goes for Mark Zuckerberg and Satya Nadella.

The others whom I think we shouldn't trust too much on how great and important AI is are those currently trying to make money from the technology. But even if you're not one of them, when a tech leader steps out against the backdrop of everyone who has invested enormous sums and keeps explaining how AI is a transformative technology in everything,

you're obliged to sing along with the chorus if you don't want your shares to fall.

If you don't, others will think: “That's not an AI company. Look at what the competition is doing, and these people are still telling the same old stories.” And that would be fatal for the share price.

– As far as I understand, you think we're talking about unjustifiably high enthusiasm, pumped up by tech leaders. But then whom should we listen to if we want an objective assessment of AI? Most of us are certainly impressed in our daily lives by what this kind of technology can already do.

– I'm more moderate, because I've seen quite a few things and I try to analyze what AI actually is.

What's my reading of AI? There are all sorts of angles, but I look at it in terms of cause and effect. For us to have AI, a certain accumulation had to happen. Ray Kurzweil uses this approach to predict the future. Many people explain how he predicted the fall of the USSR, because his theory was that the Soviets were number one in telephones per capita, that this meant free communication, and you can't keep a dictatorship going under those conditions. In other words, he turned out to be right, but he was very far from the truth as far as the reasons were concerned.

In recent years we've seen great progress in cloud technologies, which gave us the ability to process huge volumes of data and ultimately led to AI. Without the cloud and big data, we'd have neither the computing power nor anything to train it on—nothing. Before the technologies that accumulated over the years, including Kubernetes—being able to orchestrate such huge computing workloads centrally—AI would have been impossible.

In my opinion, the problem with people who predict the future is that

99% of them say: “It will be like what we have now, only more so.”

“Now we have the AI trend, and from here on we'll just have more and more AI.”

– The thing is, over the last two years that's exactly what's been happening. We can see how big the progress is; every week a new model appears, capable of things we thought were impossible.

– But if you look at the development, it's improving by 3 percent, by 5 percent. Real development comes when you have a new architecture, for example, like transformers—a new way of doing AI. Then you can make a leap forward. Incremental improvements, more tokens for instance—to me that's just squeezing out the last drops.

Right now we don't have the conditions necessary for AI to develop dramatically. We have neither more data to train it on—everything was annotated long ago—nor any new architectures.

They're trying various amalgams of existing solutions, but the progress isn't convincing. For me, there are no preconditions for anything dramatic in the development of AI. And let's not forget something else.

All of AI is really still just a brute-force search for the right pattern.

– But each of my modest experiments shows that AI keeps crossing new boundaries. Not long ago we laughed at models for being unable to add two-digit numbers, and recently two of them performed at gold-medal level at the International Mathematical Olympiad without any special preparation.

– I'll give you a very unexpected example. Everyone is trying to cram AI in everywhere. My reading is… that it irritates me. I don't need Copilot everywhere! I open a terminal, I open Word, Teams… every product wants to use AI. Why? Just wait and see. Look, I've got a new laptop, and on the keyboard

there's a dedicated hardware Copilot button! What kind of madness is that?

As someone who has lived through several similar tech cycles, that's why I'm more moderate. Of course, it could also be the arrogance of age.

At home I have a keyboard from two or three years ago. The button in the same spot was for Cortana. A year later they discontinued that assistant, which was supposedly going to be everywhere.

To me, it's like your grandmother giving you a thousand jars of strawberries when you run a bakery and have no idea how to sell strawberries. You start putting them in everything—strawberry tarts, cakes, muffins. To me that looks like a sign that

Microsoft really doesn't know how to sell AI and is just trying to push it into everything. For me, it's a symbol of desperation, not success.

Judging by experience so far, whenever something gets its own button on Windows keyboards, Microsoft kills it within two or three years. Of course, it may not happen with Copilot—the context isn't one-to-one—but this behavior by corporations tells me clearly what's going on.

– Where do you see the applications of AI, though? Even if we're as skeptical as you, surely there are areas where it's undeniably useful. It helps me in each of my three professions…

– There's certainly AI in the future, but for me

it won't be what all the hype associates it with today.

Yes, in some respects AI is revolutionary, I wouldn't dispute that. If we look at the development of human knowledge, how it's produced and “stored”—one of the most ancient writing systems belongs to the Sumerian civilization. We invent writing, we preserve knowledge, and you no longer need to start everything from scratch or have someone tell it to you in person. You separate knowledge from the person and the moment.

The next big leap is Gutenberg's Bible—with it you can mass-produce knowledge and distribute it cheaply. Then comes the internet, and knowledge becomes entirely digital. You no longer have linear production costs—2,000 books, roughly speaking, cost twice as much as 1,000, whereas here costs hardly depend on scale. Suddenly knowledge spreads widely, and that even has social aspects: where the world is heading because of this free exchange of information.

For me, AI is the next step forward. And at the same time, it's a risk to all of that.

When you start producing content with AI, that's the end of the “internet of links.” Back in the day, when you had a website, you made it accessible and Google sent you traffic. Google is a search engine; it wants to show the best, most substantive, most cited sites, and you benefit because your site becomes accessible and you get more visits.

And now we've arrived at the “zero-click” internet, where you don't click on links. From the moment Google put an AI assistant at the very top, you almost always take your information from there without going any further. Yes, for now it's far from perfect, a hell of a lot of hacks have been demonstrated, some of which have already been blocked—wrong years, wrong calculations—but it works more and more…

– What's more, these are steps ChatGPT took long ago, cleaning up its answers along the way, and it's only a matter of time before it happens here too. And the really big question is where we're heading, when suddenly it's no longer so much social networks as the AI giants that will determine, even more invisibly than before, content and the flow of information.

– That's right. The problem being created right now is that, because of this generated content, you have no incentive to create your own. That will have a very serious impact on content online and will eventually lead to more unreliability.

There are many risks in terms of information—how people access it, produce it, consume it. For me it's literally a pivotal moment, just like Gutenberg's Bible.

Many people claim AI will take our jobs.

– And in the IT sector, we could say it's already happening…

– It's not just that. Right now there's a perfect storm of multiple factors in the IT industry—trade wars, geopolitics. There's a trend toward technological nationalism and an awareness in the world that wasn't so pronounced a while ago. States and blocs are competing fiercely in geopolitical terms on three levels.

Two of them have existed since the dawn of civilization—the economy and the army—but the digital world has become the third arena of confrontation.

Today you can't succeed as a state or bloc unless you have all three at once; they only work in combination. At the moment, the only country that has all of them is the US. Europe has an economy, but it lags seriously behind militarily, and how competitive it is digitally is highly debatable.

Everyone now realizes that being digitally weak is a risk.

Europe has the economic and technical potential to do whatever it decides to do technologically. Until now it relied mainly on American technology, simply because it was practical and because it saw the US as a partner it could count on. That's no longer quite the case. For that reason, a lot of things will be rethought technologically. The most obvious example is cloud technology—there's more and more talk of AI sovereignty, of independence. That will give rise to

a trend of re-creating services that have already been built around the world and even work successfully,

but they simply have to be European. This is an old topic for China; they too are working hard toward sovereignty in digital technologies. And it can't help but affect Bulgaria as well. Little by little, once the money for this starts pouring in, a lot of jobs will start to appear.

– I can already sense where this train of thought is going. That reports of the IT industry's death are greatly exaggerated, and it will simply change, because there will be qualitatively new, yet unavoidable and mandatory challenges…

– Yes.

AI can't take our jobs, for purely economic reasons.

Under any economic theory, if AI does it—okay, it happens on a mass scale—consumption falls. At some point there's little consumption, and that matters even to the companies offering AI, because AI doesn't consume. It can optimize things for you or open up new opportunities, but it doesn't consume.

If we have twice as much AI, we don't have twice as much consumption. Only people can consume.

If AI takes jobs on a mass scale, we go into recession, huge companies can't pay the electricity bill for their AI, and that's the end of it.

AI is tied to us. We don't realize that it functions, and its electricity gets paid for, thanks to our consumption, directly or indirectly.

Mass job loss, no matter in which sphere, is impossible, because the economy works in such a way that when consumption falls, there's no money for anyone. Including for AI, regardless of why it happened.

Wars are a precise example. Even the Hundred Years' War between England and France wasn't continuous—it was a series of wars that historians grouped together as one. You can't wage an immediate, serious war for a long time. In the same way, you can't keep cutting in one industry for long—which is what's happening in the US, and what everyone keeps waving around.

– So what is the reason, if not AI? What are the main elements of this “perfect storm” behind the layoffs in the West and the visible crisis in our industry in Bulgaria?

– On a global scale, I recommend JetBrains, which collects extremely varied data from 700,000 programmers on all sorts of topics. Over the past five or six years, including during the pandemic, the number of programmers worldwide has grown by between 700,000 and 1,100,000 a year.

Right now, between 19 and 20 million people worldwide work in software development.

Despite AI, despite everything, that number keeps growing. The US also has very detailed statistics. Over the past two years, 1.6 million programmers have been working there. In the last year the population grew by about 3 million, while their number stayed the same, meaning proportionally slightly fewer people practice the profession.

It's true, there were a lot of layoffs, but there's no decline. Those 1.6 million have simply been redistributed.

I look at the US not only because it has usable data, but because in our field almost every trend from there later arrives in Europe, and we can anticipate it.

To me, the trends look like something else.

Not like “AI is eating our jobs,” but like “we are a maturing industry.”

In other words, we're starting to become like normal industries. Which is a shock for us, because we're used to the bubble inflating with endless growth.

– And what can we expect, now that we're a maturing industry?

– Here's some very interesting statistics, again from the US. They have data going back to the 19th century on how many people one person working in farming feeds. At the start, in 1820, it was one to 2.5–3.2, and now they've reached about 180 people! Heavy mechanization, combine harvesters. And yet the number of people working in farming is growing—after 2014 there was a slight decline because of the organic food fad, but now it's rising again.

One person per 180 is enough to feed the rest! In the IT sector, there's been clean data since 2001: the US population was 282 million, with about 1 million IT professionals. The ratio to the population gradually falls and falls—from 1:289, it became 1:259 in 2010, and ten years later 1:229. Naturally, it's not as if every other person will become an IT specialist at some point. Someone has to feed us…

Over the past two years, for the first time, there's been a reverse trend. From a peak of 1:210, last year it was 1:212. Which means that in the US there's a plateau we've reached as an industry. To me, that's a normal trend. If we compare it with farming, which has existed for as long as civilization has—farming is the sign that we're no longer wandering hunters—the trend is for the number of people employed to decline.

For us, it's still growing, just not as fast as we're used to.

We haven't yet become like farmers, where every year there are proportionally fewer people and yet there's so much food. We're simply an industry that is maturing right now.

– And what lies ahead? What do you see in your crystal ball as a chief technology officer… I'll be endlessly curious to hear it at DEV.BG All in One. Besides being happy to talk with anyone interested in these topics at the conference itself, I'll take this opportunity to invite you to continue our conversation after it. Because I'm also curious about your view of Bulgaria specifically, of where the sector here is heading… Thank you for this conversation—to be continued!

– Do you think we could reach the point where, instead of a software team, we have AI agents doing everything? And we just give instructions and check the results?

– I'm a bit more pessimistic on that point. I see that the CEO of Shopify, for example, wants a justification every quarter for why a role can't be taken over by AI and you need to hire a person. Jensen Huang, the CEO of NVIDIA, said that within a few years there will be no need for software engineers.

I can't imagine it being quite that extreme.

In my view, if we project what's happening right now and assume it will keep developing at the same pace, at some point we might hit a slight plateau.

It might happen, but most likely… not in the next 10 years, maybe further down the line.

I could turn out to be wrong. For now that's how I see things—as a raising of the level of abstraction, as you mentioned.

– Even that, though, is an immediate threat to many people. Hardly anyone would argue anymore that AI makes us much more efficient. I was more moderate at first, but I see how every next iteration takes over a large part of my work and my time and makes me many times more efficient. We're no longer talking about 5 to 10 percent, which is still colossal for companies like Google; we're talking about efficiency multiplied several times over.

– Yes, I felt it too. I built an app in Go, a language I hadn't written in for about ten years. And I was 10 times more productive with Cursor!

There are different theories. I know people who believe that

the web platform has lived through its greatest days. To some extent I think the same,

especially given chatbots and the way they extract, take information from websites, which also reduces people's motivation to create so much content.

Maybe we'll have different ways of sharing that information. Maybe it will become a bit more centralized, to my regret, since I like the decentralized model. The possibilities for creating software will also grow. But

I don't see a risk of programmers disappearing.

The industry may correct itself a little, but not that dramatically. The big companies keep hiring an enormous number of people.

– Yet news keeps coming in the opposite direction.

– Yes, Microsoft laid off 3 percent; they reprioritized their projects. For now they're putting even more emphasis on AI. There are investments like that at Google too.

At the same time, we don't have enough people on the team!

We need more to get more work done.

– How do you think the software engineer's profession will change? Will it exist at all? Every new stage of technological progress raises the abstraction; we're already the people who give instructions and then check, rather than write computer code. Will it stay that way for long?

– Right now I see three main ways things could develop. One is exactly what you said: you write a prompt, and from there your code is generated. There will probably be some need for human intervention at a lower level. If today we write in Java, only 1 in 100, or even 1 in 1,000 programmers will ever need to look at the actual code the compiler produced. In the same way, one day only a few may look at what AI has written.

Perhaps over time there will be more need for review, so that share won't be 1 percent of developers but maybe 15 to 20 percent. From there on, as the quality of the models improves, the share will shrink.

Another option I see is abstraction. But we already have the highest level of abstraction we can imagine. A new web application could be a composition of prompts. The individual components would be instructions, layered one onto another. That's a kind of intermediate option.

The third is to keep developing things as we do now, with a slightly smarter assistant that completes our code.

I think absolutely all the solutions along this spectrum will exist, depending on what you're building. But all of this is speculation.

– That's exactly what interests me, especially from your point of view as an engineer with experience at Google. Isn't it logical that we front-end developers will be the first to shrink in number, if not disappear? For example, I hardly write HTML and CSS anymore, and that used to be a sizable part of my work.

– We really won't be writing that much HTML and CSS; you may have to optimize them. With JavaScript, where there's logic you need to implement…

– That's often repetitive too.

– Yes, but right now AI generates invalid authentication code, for example. I've also talked with colleagues who work on one of the popular AI development environments—it often produces code that doesn't work.

At some point these things may get cleaned up. The technology may evolve, and that's an idea I'm “playing” with too. On my blog I've been working on writing a framework optimized for AI. Things may well develop that way. If you write code by hand, it has to be “fun” to write. Like Ruby, say, or the new syntaxes in JavaScript—more expressive. If AI generates the code, that's not necessary—

it can generate repetitive code that isn't so pretty, but certain constraints are placed on it so that it can avoid making mistakes.

That's something I've thought about a lot. I don't know yet what that code will look like. But I'd like to be the one to come up with it.

– Here in Bulgaria, there's a noticeable drop in demand for people in IT. Of course, there may be various reasons, but in my view one of the factors is undeniably that we're much more efficient. You're saying you don't feel anything similar in Silicon Valley.

– Part of the reason we're not hiring is that we want to invest in certain products, in certain places. We need more people on our team; we'd be even more productive if we had twice as many engineers. Even with AI! It doesn't help us that much in developing frameworks and technologies.

I talk with product teams, and they're definitely more productive with AI. I can't really share metrics here. But we would be more productive with more engineers.

– In Bulgaria it's definitely harder to land your first job, too, and at some point that could become a problem. I need to know the project to give AI its tasks, and then know Angular to see where it goes wrong. But if new people don't enter the profession, if in 5 years things develop along one of the less extreme scenarios you listed, that could turn into a problem. Because there won't be people who know the technology.

– It's true that we'll use the new tools to build applications. But I'm not worried that the software industry won't have plenty of opportunities. Technology is developing extremely fast; we'll have new ideas, new development paradigms.

But we really are at a stage where everything is changing so fast that we don't know exactly where we are or where things will go. And

that leads to some unease, to anxiety, for everyone.

I don't know either…

– How do you use generative AI in your daily life?

– Almost constantly lately! Even now, as I'm writing the blog post for Angular 20, there was a section I was missing. A colleague had written an alternative version, so I gave Gemini the blog post with the section headings, gave it my colleague's document and told it: “Fill in this part of the article based on the other document.” It did a wonderful job.

I use it for writing code too; I experiment. At Google we don't use Cursor, because you don't know where the code goes, but I experiment with it, with various development tools; I use Bolt, I build my own tools.

There are at least two ways we can benefit from generative AI as developers—by using applications that use AI, and by generating code faster to be more productive. I try to find where we can integrate web technologies more, so as to make it easier to build AI applications.

– What gives me peace of mind and balances out the rest is that I've already managed once to change my profession drastically. So… I could learn to lay tiles, although that requires more dexterity. If you had to stop programming and leave the software industry altogether, what would you do? Is it too late to become a police officer, as you told me you once dreamed of?

– It's too late; I've checked. Karate coach is an option.

– What does karate give you in programming? Do they have anything in common at all?

– Definitely! There's something I want to connect more with everything else. It's Japanese culture, where you take up your craft and keep perfecting it every day. For example, there are 12 or 13 sequences of movements—kata—that we practice in karate. You can learn them in a month if you push yourself. But people practice them their whole lives, and every day you can do them a little better, be a little better.

There's beauty in that. For some reason it reminds me of mathematics,

you have basic concepts and you keep building on them. In karate you start to feel… “If I bring gravity in here and relax my legs, use my hips, I can get more power.” Small changes, but you feel them happening; you connect with physics.

It's similar with programming. There are very few concepts you can use. You need a basic loop, basic constructs, variables, and you already have a language ready for anything.

But you can combine these things in different ways, and unexpected connections form in your brain.

You can actually model the whole world with them!

Consistent iteration and improving efficiency. That's what I find they have in common.

In Eastern systems you can take up a craft and practice it all your life. In Western ones we're more driven to increase our influence. You change your position and you're never good, because you move on to the next one.

– I find it fascinating that you of all people say this, having spent recent years working at the heart of Western thinking, Silicon Valley.

– That's true,

there's a conflict between the two value systems. I've started thinking about it lately.

You can take the view that the craft is building the capacity to increase your influence over time. There are people who write code all their lives, perfect themselves in the craft, and that's wonderful. But part of the reason I decided to return to purely software engineering work at the expense of more product-oriented work was that I missed that aspect.

There's a lot of creativity in it, and in a way you're even dealing with more familiar things. With the fundamentals.

– What makes sense to learn and develop now, so that we can be more or less confident it will still matter?

– Again, this is speculation. It seems to me that

even if AI writes the application, this engineering thinking—about composing different elements that build a more complex system—will be useful to us.

Creating abstractions properly and so on. Whatever language we use, whether we're designing buildings or writing programs…, it's a similar way of thinking.

I'm not going to design a building; it would fall down. But ideas come to me intuitively about how things could be done so that they're extensible, reusable.

– We're meeting today in Sofia. What brought you back here, and how did you find the city?

– Sofia is great!

It was pretty good when I left for Silicon Valley about 10 years ago, and now it's even better.

I'm here with my wife, and we're having a small celebration in Bulgaria with relatives for our wedding. We'll spend a month here. She's from the US and is learning Bulgarian, and I want to see friends, spend time with my family, and I hope it becomes a tradition for us.

– I really hope so! I'd be glad to continue this conversation on home soil soon!

And for anyone interested in your remarkable path, in working at Google and specifically on Angular, in advice on how to succeed if your dream is to work in Silicon Valley, and in what sets working there apart, I recommend our conversation on the DEV.BG podcast:

Play the video

Remarkable stories await you—set aside an hour, you won't regret it…

– You wanted to become a footballer before you turned to the software world. I see a lot of parallels between an IT team and a football team: the teamwork, the division of roles, the key role of the manager. Are they really alike?

– Yes, definitely. Sport has taught me an awful lot in that respect. For example, I still play amateur football on a full-size pitch, nine-a-side. There are two captains, they split the players into two teams so that the balance is as good as possible, and then each captain decides who plays where and runs the game like a coach.

When I end up as captain, my style is to support each other as much as possible, not to yell, “Hey, how could you miss that!”

Most of the time the mistakes are obvious, and my goal is never to win at any cost. It’s more to play against a solid opponent, to have a hard time of it, and to grow that way.

The analogy with a team holds completely. For me it’s very important that everyone understands what the others need in order to show their best. The focus should be on how to shape the circumstances so that everyone can bring their best “game” at work, too.

– What’s your favorite position in football?

– I usually play right midfield, and I’ve never asked myself why. But now I think it’s a position that both builds up the play and brings it to a climax. On the one hand you can score, but at many other moments you get to deliver the final pass.

– When did you realize you wanted to earn your living by programming?

– The moment I felt that doing things with a computer, building things, gave me satisfaction.

Programmers have a force invisible to the outside world—why are they so drawn to what they do?

I’ve thought about it a lot, and the answer is fairly obvious if you’ve done it for a long time: there is a very fast satisfaction loop. I compare it to what craftsmen feel when they build a bathroom, say. You see it right away—you’ve taken some steps and there’s a result. Which is very different from working on things that take 20 years to happen.

– I can only imagine how hard it was to get into the profession at the end of the last century… Is it too easy today, now that we have artificial intelligence?

– I don’t think so. We say that “back then there wasn’t so much access to information,” but right now the pendulum has swung to the other extreme.

There is far too much information, and when you want to learn something, too much choice creates difficulties of its own.

Maybe from the perspective of the past it looks that way, but

what today’s technology makes possible also raises the bar for the end result.

– Another challenge today is learning to let things be hard. Stepping on rakes, as you and I have discussed, turns out to be an important pursuit, one for which, supposedly, there’s no longer much time or appetite.

– It’s a very important element of how we perceive the world in general. If a person doesn’t have the right attitude toward failure, very bad consequences follow. If we don’t have the mindset that it’s okay, and we don’t keep the right to make mistakes, what’s the result?

The only way not to make mistakes is to do nothing, but then you’re still making a mistake, because you’re wasting everything.

In my view the right mindset is this: you allow yourself to make mistakes; you try not to make the same mistake twice, so next time it’s something different, something else breaks, and then you have a chance to move forward, to develop.

For me, when you have a goal and know what you want to achieve, studying is very important, but so is building practical experience. That combination, I think, is the optimal path. A person can develop far more understanding of what it takes and what people go through, far more empathy, when they carry the scars of such experiences.

– I think part of that mistake comes from us, the people who tell stories. Because very often the success story looks easy, glamorous and shiny. Not long ago I read Bill Gates’s autobiography. Well, it wasn’t easy at all for this man, whom we imagine to have hit some fantastic stroke of luck. No, there is no easy road to success, insofar as there is a “road” and a “success” at all.

– I’ll make a football analogy. If we imagine all the possible outcomes of a football game, what I love most is winning the hard way. Then losing the hard way. Then losing easily, and…

what I hate most is winning easily.

The surest way for me personally to lose interest is when there’s no challenge. If the road to success is hard, that’s the moment.

If we value not what lies ahead but what is happening to us, with all its good and not-so-good moments, then we can find great satisfaction.

Recently I heard an example from Hristo Popov: if you climb Everest slowly, with a team, the way it’s done, and you have the alternative of being lowered down by helicopter, which will be more satisfying? The answer is clear. In other words,

the concept of quick and easy success is, in my view, meaningless.

If there’s something we can achieve easily, that means we have far greater potential and should set ourselves higher goals.

– It’s very hard to learn to lose gracefully, and I immediately found my own parallels. My hobby is taking part in TV quiz shows and winning them, but I’d rather lose gracefully than win ugly. That isn’t the point. Then again, I’ve noticed with my own children how hard it is to teach someone this. Instinctively we don’t like losing, that’s clear. How do you develop this quality?

– That’s a very important question. Having the right attitude to losing, and to mistakes in general, is something you’re brought up with. It isn’t something that just comes to you. “He’s been very competitive since he was little, he can’t stand losing.” That’s a function of our actions as parents and of the ways we speak to our children.

For example, if my child gets a bad grade or a reprimand at school, I’m really glad.

For most parents that isn’t the case. “But how can my child have a low grade?” I’m glad, because it’s a chance for my child to get used to the idea that when a mistake happens, it isn’t the end of the world. My approach in these situations is: “Look, no problem. You got this result. Go talk to the teacher. Make sure you understand the mistake. What went wrong, and what will you do so it doesn’t happen next time?”

That’s the concept of continuous improvement. This way, after going through a few such moments and seeing it isn’t the end of the world, the child will get used to it.

Another thing we have to teach children at some point is that if someone hits you, you won’t die. Someone comes up, slaps you, you cry, it’s the end of the world. No, it isn’t. Yes, it hurts, you cry, but you won’t die. It’s the same with mistakes.

– Which naturally leads to other developmental problems, especially when we’re talking about more intelligent children who stand out in some way. That overambition doesn’t help. Not long ago I talked about this with Mensa’s national psychologist; they now accept children with exactly this idea in mind. To help parents get used to the fact that a 4 (a mere “good” on Bulgaria’s six-point grading scale) won’t leave their child without a career. And it’s precisely these children who need to develop even more socially and emotionally.

– Life isn’t something that just happens to you, like Real Madrid and Barcelona walking out and forming a guard of honor for you. No,

life is a string of situations in which you have to fight your way through somehow.

With yourself, with competition, with all sorts of things that happen. In my view we cannot and should not teach our children, or ourselves, to see the world that way.

– A huge part of your professional experience is tied to Telerik. How do you explain the success of that company, which was an important step in the development of our IT industry as a whole?

– For me the main thing is the company culture. When I started, one of the founders sat in our room; that was the practice whenever a new team was formed. He helped us make sure we had the right mindset, supported us, took an interest in what we were doing. And that creates a sense of belonging.

Something else played a very good role in Telerik’s development: absolutely everyone in the company, no matter what they worked on, had access to the customers. On the one hand, you see how the thing you’re working on needs to change to get better. On the other, you get to see the positive effect of your work, which makes you endlessly connected to what you do.

The third thing is the enormous vision the founders had and passed on to everyone in the company: that what matters isn’t only what we do in our own context. Their goal was never to make some money so they could have yachts and cars, but for us to be an example, and for that example to then lead to something more.

Telerik was never an end in itself. One of the future founders of one of the very successful companies comes along, say, and tells them: “Look, we worked really well together, but now we want to start our own company, and I very much hope this won’t spoil our relationship.” Not only does it not spoil the relationship, the founders of Telerik become some of the first investors and help them succeed.

Telerik became proof that it’s possible to have product companies from Bulgaria. For me that’s the way forward for the software industry.

Enough with this outsourcing! It did a lot of good work, but now we have to move to the next level of added value.

This is where the concept of “pay it forward” comes in. That mentality that you shouldn’t think only of yourself, and not even only of the people you work with. You have to think of your customers too, and of the environment you live in. Help where you can, and in the right way. Not give someone a fish, but teach them how to fish.

– I think it would be wonderful if all of this applied to our whole society. If that kind of mentality, the need to pay it forward, were more common in every field, in our daily lives, even in how we treat people, we would live better.

– I completely agree. I constantly come across incredibly good people who, for one reason or another, because of what has happened to them, the way they see the world and so on, end up clashing with each other.

The most basic example: in the building where I live, there were some bikes chained to a radiator in the stairwell. They stay there a day or two, and then an angry note appears at the entrance: “Please remove the bicycles immediately, this is not legal.” It sounds harsh, and I wonder who wrote it; we’re a building of four or five floors, not 18,000 people.

Then it turned into a row. What turned out? The people who wrote the note had once lived in a building where a fire broke out and they barely got out. What’s more, they had even come up with an alternative place for the bikes, so they wouldn’t be in the way: there’s a small storeroom, you just have to clear out three sacks and that’s it.

The thing is, though,

the dialogue doesn’t revolve around “Let’s find a solution and look for the constructive side.” Instead we look at the negative and start talking about how bad things are.

The conclusions I want to draw attention to are, first, that laws have to be obeyed. Whether I like the bikes being there or not makes no difference at all. There are laws.

The second is to focus on finding solutions and not give in to that inner emotion that surfaces and tells us, “These people are deliberately trying to do something bad.” If we look at each other not as enemies but as people who can and should have common goals, and we seek that dialogue, things will work out for us. So I appeal to you, please:

every time you think something bad about someone else, ask yourself the simple question: “What evidence is there for this?”

– I think all of this is part of the larger context of our broken public conversation. That’s one of the big reasons I created this site. To a great extent, technology brought us the division. Because a large part of the public conversation moved to social media, where it’s much easier to confirm your own point of view, to stop respecting the legitimate arguments of the other side, to grow bitter, to turn against someone, to split apart. And in the tech world I often see this division from everyone else, too. That’s also a conversation that’s getting lost. The question is: can we change it?

– The first question is “Should we?” and the answer is “Absolutely!” And can we? In my view, 100 percent.

But first let’s answer the question of why. In my view there isn’t a person in this country who doesn’t want it to prosper. For me, we first have to realize that we have common goals to strive toward.

The second step is the moment we answer the question: “Can we do it without everyone doing their part?” No.

The third thing I think we need to change in our mindset is the expectation that someone will do it for us. Oh, the state, oh, the mayor, the politicians.

For me the truth is that

we need to remind ourselves what democracy actually means.

First: the majority decides. When more people share one opinion, the rest have to go along with it.

Second, we need to know our role. We can’t sit on the couch, just posting on Facebook and complaining about this and that, as if someone owes us something. No one does. If each of us takes a civic stand, not only in words but in actions, and takes small steps toward what we all dream of, I believe it will work.

– I envy your optimism, because behind that there would have to be some almost magical change, which social media alone wouldn’t allow.

– The easiest thing a person can do, instead of wondering whom to pick a fight with on Facebook, is to check whether there are two crooked paving stones in front of their apartment block. Fix one—don’t overexert yourself—and the next day the second.

I observe a phenomenon, including among my own relatives, who say, for example: “There’s a little patch of grass here. It isn’t looked after properly.” And I ask: “Okay, why don’t you go and fix it? You have the means, the time, it’s an hour’s work.”

What comes back is some feeling that someone will take you for a sucker. And I sit there and can’t answer the question:

Since when did work become something unworthy of respect, something bad?

When did putting in effort, doing something good, go out of fashion?

Big things, and I say this on the basis of my modest experience, always happen because a huge number of small steps have been taken. For me that’s a big part of the key to developing our society.

The other thing is to be aware of what you defined very well: that when we open Facebook, it’s very easy to become embittered. People need a slightly clearer idea of why that happens.

It’s also about a technological understanding of how things work. During COVID a friend came to me and said: “My wife is turning against me because she thinks I’m the only person in the world who is for vaccines.” She had started reading things connected with the anti-vaccine position, and everything online was nudging her to stick to that view.

– That’s another of the big reasons this site exists. These topics are extremely important, but I don’t know how they can reach more people. I think we owe a debt here, those of us from the tech world, because we don’t explain it enough, and the platforms themselves have no incentive to give access to that explanation.

But let’s change the angle. Today you head a company that helps other startups.

– I had long seen that for me

challenges with a good, meaningful goal give meaning to everyday working life.

That’s when I decided I wanted to start a company. The question was in which direction, and that I had to learn what it means to build a business, which is so multifaceted. For a long time I’d had an inner dialogue about what my personal mission should be.

At first you want to do well in your career, to get into the industry. Then to be good at what you do. Then comes the desire to raise the level of your contribution. And then, once you’ve beaten that game, you arrive at the fundamental question of life, the universe and everything: “Why do I exist?” What is it that I need to add?

I realized I wanted to make it possible for more people to do what I had, in some way, seen as success at Telerik. I want Bulgaria and other places around the world to be able to prosper.

For more people to say: “Hey, on Monday I’m at work and I can’t wait to go!”

I often see how we define the problem: “I really hate going to work.” But instead of looking for a solution, we look for compensation. For example, I’ll buy an expensive car, I’ll go on an awesome vacation. But on Monday you’re back at a lousy job you don’t like.

I’ve read more books on psychology and entrepreneurship than on programming, and those are not few at all. Everyone has moments in their career when they feel something like depression, and they usually associate it with something bad. For me the reason each time has been that I had reached a point where my role was becoming redundant.

– What does the work at Icanpreneur look like today? How many startups do you work with, and what kind?

– From the very beginning the goal was to help more people become entrepreneurs. Our appetite is for helping companies that are more product-oriented. We started four years ago, took off very strongly, built a team, raised investment, the product got off to a good start. But then

ChatGPT came along and flipped the table.

Every company in the world acquired a very powerful, brand-new competitor. We had to answer, very quickly but also smartly, the question of what this meant for us. Both as competition and as a technology we could use.

The last 12 months have been about how to pivot from a traditional product company toward AI. We have now built a pretty good foundation for anyone who wants to pursue entrepreneurship. At the center of our platform, we enable teams to gather information about their future customers through interviews, so that they understand them better than the customers understand themselves.

On the one hand we’re building the software side, on the other we’re completing the changes based on artificial intelligence.

Right now we’re working very actively on partnerships with all kinds of players in the ecosystems, both in Bulgaria and around the world, so that we can join forces.

Because what’s happening? There are accelerators, entrepreneur communities, investors. But in our view there isn’t a good enough connection between them for these pieces to work together from the users’ point of view. We want to build a global network, to provide the backbone of the entrepreneurial journey, so that all the players come in at the right moment, us included. To partner in such a way that at every step each entrepreneur gets what they need, without having to reinvent the wheel.

Entrepreneurship isn’t a new science; it has existed since the world began. But today entrepreneurs’ success rate is tragically low:

nine out of ten fail, and the reason is usually that they rely too much on instinct, on gut feeling.

Yes, those matter, but it’s like the difference between noise and music. You know that to write a story it’s good to follow some structure, because there are rules. People came up with them long ago, and you have freedom within that structure, which is why an infinite number of stories can be written.

Entrepreneurship, in my view, was left for too long to playing without knowing what the musical instrument is, without knowing the basic rules of music.

– I’ve heard from other interviewees, too, how good it would be for our environment to have more product companies, but how do we do it? At a time when Sam Altman says the next unicorns will be the work of a single person.

– Why do big companies find it hard to innovate? The answer has to do with how success is measured. All the metrics we look at are short-term: revenue, growth, share price. No one can assess how an innovation will play out in five years.

As the management of a Bulgarian service company, you face the question: these ten people I have—do I put them on making money from our best clients, or on doing something with a very small chance of success? That dilemma tips toward the first option extremely quickly.

Alas, at some point we won’t have a choice; we are becoming less and less competitive in our positioning for outsourcing. There are much cheaper destinations, and with artificial intelligence, even where we’ve had an advantage as professionals, we’ll lose it in some respects.

As I see it, circumstances are heading in a direction where we won’t have a choice. Either we’ll have to transform, or we’ll have to do other things.

Selling a service and creating products, though, are two different games. In the first, you offer something right away, the market wants it, and it grows linearly, based on headcount.

Whereas if you have a product company, you have to invest. At first you’ll lose money. So far we have invested over 1 million euros in what we do. At some point, though, little by little you start making money, and then you have the potential for exponential growth.

This game is different; it requires a different kind of thinking, a different kind of people.

This shift in mentality, in the metrics we follow, requires completely different processes. We can do it when all the service companies realize that, in the long run, this business has no future in Bulgaria. If one such company invests in 10–15 new products this year and another 10–15 the next, successful projects will multiply.

And this is where what Altman says comes in. We’ll be able to create more product companies with fewer resources. Because paying salaries to 20 people is one thing, and paying three is another.

– So right now is the time for good ideas!

– We have to learn to look at the world in a way that lets us see the unsolved problems. AI will take away a lot of work, but in my view it won’t remove the need for us to move to the next level of work.

There is a huge need for all sorts of software that isn’t being made simply because we can’t keep up. Well, that won’t be the case anymore. And

what will set successful people and companies apart is whether they solve meaningful problems.

Whether they use AI in a way that doesn’t look bolted on but unlocks additional value. A few weeks ago Satya Nadella said: “All the progress in AI is great, but we still don’t see the economic return.”

For me the key is more and more vertical software—software that solves a particular problem very well, using AI.

We have to—people!—find a way so that when we hear someone express an opinion that doesn’t match ours, it doesn’t mean we immediately cross them out with a thick marker. Dialogue doesn’t mean talking about things we agree on 100 percent. We have to find a way to learn to listen, to understand where the other person’s position comes from.

Let’s stop hating each other. One supports one team, the other supports the other, so we won’t talk. Let’s learn to talk and to appreciate where different points of view come from. Let’s seek out the different point of view. Because

there are very few cases in which the two views are truly diametrically opposed and there is no common way forward.

I’ve worked with people from all over the world. I can say that we lag behind in nothing. And if we keep working heart and soul, if we give it our all and, most importantly, come together, I think we are in a very good position to succeed.

You can hear more about Vesko Kolev’s story and professional path in episode 82 of the DEV.BG Job Board Talks podcast:

Play the video

Do the people who drive the world of information technology forward actually read? They read like there's no tomorrow, and you're in for a surprise. Time for a very important and very bookish experiment. I'm going to invite 15 leading Bulgarian figures from different corners of the computer world to tell me their three favorite books and why they would recommend them.

Will they respond? Will it turn out that they mostly read titles about software, business and self-improvement?

Their answers, to me at least, turned out to be surprising.

Is it hard to be a god? A question for connoisseurs. And why is it hard?

What Are You Reading? 3.0

Why, here in Bulgaria, does nobody care much about the favorite books of the people who end up in the spotlight? My conversation with Daniel Penev got me thinking about this. Last Tuesday I told you about the favorite reads of the leaders of the digital world. Bill Gates, Musk and Altman certainly impressed with their recommendations. Then I talked with Hristo Blazhev about one of the most important choices in every one of our lives: “What are you reading?”

Which brings us to the experiment in the pixels that follow. “Recommend me a favorite book!” You rarely hear an invitation like that in the media. (A wonderful exception is “The Superhuman with Georgi Nenov”.)

I put the question to 15 people who have been guests of Digital Stories and who come from our software industry. I tried to pick different profiles—programmers, managers and other kinds of specialists. People from business, from education and from science. I received nine answers, but with such interesting and unexpected books… I dare say I'm a fairly well-read person, yet they definitely impressed me. Both with their variety and with the messages they carry. You'll find the recommendations in the order I received them.

I'd be glad to hear what you think. I fully understand the other invitees, swamped by the madness of everyday life, who didn't send their picks. They're always welcome to do so—the topic matters, and there will certainly be a sequel soon with the next round of book recommendations. But let's not waste time; let's see which books my guests recommended.

What Is Happiness?

Petar Sabev is a software testing specialist with vast experience, an event organizer, a traveler and a man with a broad, unusual outlook.

(More about him and from him—here.)

His book recommendations:

• Among my favorites, read as a traveling man, are Eric Weiner's “geographies.”

“The series begins with, and is best known for, The Geography of Bliss. As the cover says, it is ‘a fascinating tale woven from psychology, geopolitics, science, travel and humor, a provocative and entertaining analysis of what happiness is, what it isn't and where we can find it.’

They're very interesting because they combine travel, reflection and fascinating side stories from different countries and cultures, and they answer precisely questions like what happiness is, what bliss is, what wisdom is, or even what God is according to different cultures.”

• Man's Search for Meaning by Viktor Frankl

“It's not an easy book, because it tells of life in the concentration camps, but it reminds me that life is short and fleeting, and that the problems I have are laughable against the backdrop of world history and world peace.”

• The Hitchhiker's Guide to the Galaxy by Douglas Adams

“The Guide is a favorite of mine. Not only for the humor, the 42 and the IT jokes, but for personal reasons too. After a conversation about the number 42 and the Meaning of it all, back when we were a young couple, I gave my now-wife this book wrapped in a towel, and I think she was quite impressed. She asked me, ‘Why?’ And I answered, ‘Read it and you'll understand!!!’”

“If you ask me again some time from now, I'll probably tell you something completely different, but here are three completely different books. 🙂” Petar Sabev added, opening the experiment in exceptional style with terrific suggestions. I've never read the travel series he started with… maybe it's time?

Time Shelter in 1984

Atanas Raykov is Viber's global vice president of marketing and business development. Thanks to him, a large part of the team building the popular platform works in Sofia.

(More about him and from him—here.)

His book recommendations:

• 1984 and Animal Farm by George Orwell

Key books that often come as a set, including as an audiobook. They remind us how far a society can go to the extreme if institutions and the safeguards against autocracy fall apart.

With the advance of technology, this dystopia is entirely possible. On the other hand, we need stories to remind us that indifference, or blindly following the next savior (be it political or technological), can in itself be a key complicity in bringing such dark scenarios to life.

• Time Shelter by Georgi Gospodinov

I'm a huge admirer of Georgi Gospodinov as a writer and as a person. I have the honor of knowing him personally, and I firmly believe the awards this book has won are fully deserved, because it draws a brilliant parallel between the effects and defects of personal and collective memory, which can create fertile ground for nostalgia for bygone times—nostalgia that can grow into a mass illusion that “the good old days” can return in a world changing at breakneck speed.

• AI 2041 by Kai-Fu Lee

The most interesting book on artificial intelligence, describing in a very accessible way possible scenarios for the future and how it will be changed in the coming years by the development of AI in its various forms. A great combination of fiction and strategic analysis from one of the most prominent professionals in the field.

“It was very hard for me to put together a list of (only) three recommendations, and maybe that's why this one is heavily influenced by the current situation in the world (and in technology),” Atanas adds. We also get a pick that hasn't yet been translated into Bulgarian—the last one, with its glimpses into the future. But… these really are vital reads in the context of today.

Digital Stories with Frankenstein?

Yordan Darakchiev has spent nearly a decade developing Bulgaria's first one-year module that gives people enough knowledge to start working in data science and artificial intelligence (and I can vouch for it personally as one of its students).

(More about him and from him—here.)

His book recommendations:

• Digital Stories by Georgi Karamanev

I'd recommend it in any other ranking. Here, specifically, I'm afraid of being accused of having ties to the author. Setting the stories themselves aside, for me they're a source of a lot of positivity and inspiration. Interesting people and ideas are all around us; it only takes a little listening to the stories that surround us, a little filtering of the useful signals out of the noise, to look into someone's soul or into the future.

• Frankenstein by Mary Shelley

The original text, not the later versions. I love horror, especially the classic stories and novellas. I wondered which classic exactly to recommend, but Frankenstein is worth reading for many reasons. To me the book is a love letter to science. I really like the theme of devotion that drives a genius to madness. And the questions it raises (about ethics in science, for example) still apply today (when the monster might be called a chatbot).

Oh, and yes, the cry “It's alive!” is nonsense—it's not in the original. Popular culture has watered down and changed the meaning of the original a great deal.

• The Hitchhiker's Guide to the Galaxy by Douglas Adams

I've never found a good Bulgarian translation of Frankenstein, one that captures the nuances of what's written. The Guide and its sequels, however, are translated quite well by the team at Bard, the publishing house.

In almost every respect the book is the opposite of the first one. Adams looks at very deep and complex subjects with lightness and humor. But it (fine, they—there are several books) is much more than light reading. What I like most is the approach of explaining the most absurd things as if they were perfectly normal, and the normal ones as if they were strange. I do it very often myself—not only for comic effect, but as a way of getting to know the world… it's part of who I am, and I owe it mainly to the Guide.

• House of Leaves by Mark Z. Danielewski

Unlike the others, I found it very hard to choose a book to somehow “round off” the list. I wavered between many titles—classics and contemporary works, technical books with logic puzzles… In the end I chose a book that I definitely think deserves more popularity. The main reason it's here is that it has a long history of making people who have never opened a book in their lives fall in love with reading.

The story is almost a cliché—a man finds notes about a house where strange things happen. But the Victorian theme isn't the main reason I recommend it. The way it's written is fascinating. You don't read it from beginning to end like a standard book.

The reader is an active participant in, and interpreter of, what's happening. For me it's a whole experience, not just a book that tells a story. You have to live through it to understand it. And yes, it's read on paper… It used to be very hard to find; in the last six months I've spotted it a few times in Sofia.

Digital Stories?! And alongside books like these… All I can do is say thank you for such high praise; it's because of opinions like this that the book exists and that this site keeps on existing too.

I'd also point out that the Guide shows up for the second time… we IT people definitely have a feel for things and a sense of humor! And I'd never heard of this House of Leaves—yet another addition to my personal list.

Tobacco Shrugged

Angel Georgiev decided 18 years ago that he would work in education. He is the creator of the Znam.be platform and of the Sindeo applied academy for education.

(More about him and from him—here.)

His book recommendations:

• Atlas Shrugged by Ayn Rand

• Tyutyun (Tobacco) by Dimitar Dimov

“These are the last works of fiction I read. There are interesting similarities between them. I also really liked Shantaram and its second part, The Mountain Shadow, by Gregory David Roberts. The funny books by Stoyan Nikolov (Torlaka) are very good as audiobooks because they're written in dialect—Severozapaden Roman (Northwestern Novel) and Avtonomiyata???? (The Autonomy????). Stefan Tsanev's Balgarski hroniki (Bulgarian Chronicles) opened my eyes to quite a few things. In my opinion, this book is a must for every Bulgarian. Are these books enough? I have many more!”

Thank you, Angel—they're definitely more than I expected! What could Boris Morev, the ruthless tobacco magnate at the heart of Dimov's novel, and John Galt have in common? I'll ask him the next time we meet. I'm especially pleased that Bulgarian authors have started to appear. Even one who was once my guest—alas, Stoyan Nikolov (Torlaka) is no longer with us, but his books go on stirring, amusing and provoking thought…

Success with Principles

Mario Peshev is a programmer, marketer, entrepreneur and much more. The websites he and his team look after get 1,000,000,000 visits every month, and he has written part of the code that powers the WordPress platform.

(More about him and from him—here.)

His book recommendations:

• Principles by Ray Dalio

“One of the most significant reads for me professionally—very important lessons for life, as well as an expansion of one's mindset with rules like radical open-mindedness, and why broadening your horizons is so essential to success in any undertaking.”

• The Success Principles by Jack Canfield

“If you've ever read the Chicken Soup for the Soul series, with stories that leave their mark even 20 years later, Jack develops in considerable detail important principles of success: how to define it, goal setting and fulfillment in personal and professional life.”

• The Subtle Art of Not Giving a F*ck by Mark Manson

“Much of my philosophy of life is tied to Stoicism, and Mark offers a modern reading of life and reality, along with principles for dealing with disappointment through a more realistic assessment of the world around us. He raises many questions about value systems and what we see around us, since the same circumstances can lead to a radically different reading with the right preparation.”

And so the list has grown considerably, and we've stepped well outside the world of fiction, which is wonderful. I warned my guests that fiction is my weakness, but they had complete freedom to recommend whatever books they saw fit. Special thanks to Mario, who also welcomes readers to his Goodreads profile.

Liftoff!

Mihail Stoychev is one of the most successful Bulgarian IT entrepreneurs. In just a few years he built two extraordinarily successful startups—SMSBump and NitroPack—both acquired by global companies.

(More about him and from him—here.)

His book recommendations:

• Project Hail Mary by Andy Weir

“I don't really read business books—I mainly read science fiction, so I'm not sure this will be useful,” Mihail writes to me. Oh, how could it not be useful? On the contrary, it's delightful! I adore science fiction, and I think a world where tech leaders read more of it is a wonderful one. And look—his list opens with a book that is among the favorites of many of the world's IT leaders too. It doesn't seem to be discussed as much here in Bulgaria; to my shame, I admit I still haven't read it, but I'll correct that mistake very soon.

• Onassis: An Extravagant Life by Frank Brady

• We Are Legion (We Are Bob) and the whole series by Dennis E. Taylor

Very interesting recommendations! And it's impressive that Mihail made them before the news broke that the first book in Taylor's series would come out in Bulgarian too—and today it's already in the bookstores!

The book about Aristotle Onassis, meanwhile, is the first biography any of my guests has recommended to me.

Boundaries

Ivaylo Hristov is the creator and co-owner of DEV.BG—the venture that lets software specialists keep their finger on the pulse, and find jobs too.

(More about him and from him—here.)

His book recommendation:

• Set Boundaries, Find Peace by Nedra Glover Tawwab

“Healthy boundaries. We all know we need them in order to achieve balance between personal and professional life, to deal with difficult people and to enjoy wonderful relationships with our romantic partners, family and friends,” the cover says. And about his recommendation Ivaylo says: “Lately I see an awful lot of people, especially men, who have problems with boundaries, and it affects them very, very badly. I'd definitely recommend this book to more people.”

And truly… it's so hard to set that boundary, as Ivaylo says, and even more so, I see it myself, for men. Meanwhile, the average life expectancy of men in Bulgaria is definitely a subject we need to talk about far more.

An Unexpected Combination

Viktor Botev is the chief technology officer of Iris.ai. The Norwegian-Bulgarian startup develops systems that apply artificial intelligence to processing large bodies of scientific data.

(More about him and from him—here.)

His book recommendations:

• Hard to Be a God by the Strugatsky brothers

“My favorite book, which I read every now and then and keep rereading. I think it's quite relevant to our times, and to what awaits us with artificial intelligence :).”

• The Grand Design by Stephen Hawking

“It describes, in relatively human language, the idea of modeling the universe, multiverses and what physics is actually after.”

• Nexus by Yuval Noah Harari

“It presents the development of humanity and our understanding of the world around us through innovation and progress. It focuses on some of the risks of becoming the destroyers of ourselves.”

• Steve Jobs by Walter Isaacson

“It's not fiction but a biography, yet it's written beautifully. Full of lessons and vivid examples from the life of an extraordinary man who was, after all, a man with his own mistakes and flaws.”

Now that's a colorful palette—special thanks to Viktor too! And with the Strugatsky brothers, I've definitely found a kindred bookish spirit in him.

Don't Read, ChatGPT

Svetlin Nakov is the founder of SoftUni and one of the people who have changed the most not only my life, but the lives of thousands of Bulgarians who have turned to the software industry.

Play the video

(More about him and from him—here.)

His book recommendations:

• ChatGPT

• Claude

• Copilot

“I don't read any other books. My time goes to the kids and the family. I dream of reading my emails one day, but it's not going to happen.”

I've always loved Nakov's sense of humor; it certainly helped me a lot when I was watching his lessons, thanks to which I became a programmer. Here, though, besides making me smile, he also gave me an interesting idea for how to end today's experiment.

Stefan Stefanov FTW!

There haven't been many Bulgarian authors on the list so far, so why not ask my “friend” ChatGPT for three ideas in that direction?

Oh, amusing and instructive. The first recommendation is clear and indisputable—it's no accident it's already on our reading list. The second… is by Ivanka Mogilska, whom I like very much and who has been a guest of Digital Stories! I haven't read this novel of hers; I'm adding it to my personal list.

The third idea, however… There's no such thing. A novel with that title was written by Alexander Sekulov, also a wonderful writer and my guest. As for who Stefan Stefanov is… I'll leave it to ChatGPT to explain that to the people who hold the rights to the book.

Bookish and Personal

You know, it isn't nice to do to others what you wouldn't want done to you. So before we part, here are three recommendations of my own. It's very hard for me to limit myself to that number, but you can find many more in my Goodreads profile. Still, the rules apply to everyone.

My book recommendations:

• The Little Prince by Antoine de Saint-Exupéry

I believe the world would definitely be a better place if everyone read and reread this slim little book every few years. And you'll find my more detailed explanation here.

• One Hundred Years of Solitude by Gabriel García Márquez

I don't think anything about this one can be explained. Either you sink into it and come out completely different, or you can't make it past the second page. But it's vital to try…

• Clean Code by Robert Martin

How on earth has Uncle Bob's software bible never been translated into Bulgarian?

Programmers are people too, right? Not only do they read, but reading can be useful in their work. So I'll give in to temptation and hand one of my spots to the book that has shaped me most as a software engineer.

Of course, there's no escaping fiction… I've written a novel myself, too, and a collection of digital stories.

What's more, thanks to Digital Stories I'm lucky enough to meet in person the authors who win me over. Whenever a Bulgarian book impresses me, I immediately invite the writer for an interview (and it has failed to work out with only one of them).

So, have you picked your next read? How can we change the fact that 40 percent of Bulgarians haven't read a single book in the past year, if we don't talk about books…

Did you find the experiment interesting? Would you like it to continue with other guests? You can reply to me or share an idea for the next Digital Story here.

– Many people don’t believe that anything remarkable has happened in the world of AI. How do we convince them? My way is to test and show—look, poetry competition juries no longer just fail to tell the difference, they award prizes to poems written by ChatGPT. How do we convince people that there is now a tool that is extraordinarily good at so many tasks?

– Usually that is part of my job. There are many approaches, and different things impress different people. I prefer to show a person something that can be useful to them and save them a lot of time. That is what our product, Team-GPT, is about too. We get on a call with people, do a demo, explain how to use AI for their needs, and they start doing it.

Another approach is FOMO—the fear of missing out. “Do you want to fall behind while everyone else moves ahead, or do you want to be one of the first to take advantage of this technology?” Because

AI is a departing train—whoever has boarded it becomes better and faster at everything they do.

The truth is that the people who don’t believe are simply falling behind. It’s a big problem in many companies—the gap between the people who use it and everyone else shows up first in knowledge and skills, and then in pay.

I know a lot of people who already work several jobs thanks to AI. One of them is a programmer; he used to work at three places, now “only” at two. I asked him, “Why don’t you bring AI into your company?” “Oh, why would I tell them? If they find out, I’d have to work only for them, wouldn’t I? Right now I get a double salary and work less!”

That’s more or less the situation. In “statistical language,” we have a bimodal distribution: some people are way ahead, and the others can’t be bothered with it.

In the beginning I tried to persuade people, I put time and effort into training them, into trying to open more people’s eyes. I can’t fight the current. I can’t convince someone who doesn’t believe at all.

But if someone is a little curious and opens their mind, I can show them very well how to use AI.

Be more open-minded, be more curious—you can only win from it!

– Sometimes even authorities don’t help here. People often quote Yann LeCun to me, who says the models are “as smart as a cat.” Or Linus Torvalds, for whom AI is 90% marketing hype and 10% real applications. But that’s not how it is—we can see it.

– It depends on which bubble you’re in. I’ve been working on artificial intelligence for 10 years, and it’s crystal clear to me that this is the future.

All of science fiction for more than 100 years has been talking about it, and here we are—the time of artificial intelligence has come.

From here on it will only get “worse.” The course on deep neural networks I created in 2016 began like this: “Google Maps tells you where to go. People no longer choose which road to take; artificial intelligence chooses it. They no longer choose what to buy—from Amazon or eMAG, the big regional online retailer—AI shows them. They don’t choose which movies to watch; Netflix does.”

At some point you realize that AI has been here for a long time. I wrote that 7 years ago, and it had already been around for another 7 before that. Since 2010, AI has been deeply embedded at a mass, global level in everything around us. And we pretend it’s something new. This technology is 15 years old, and it has been in development for more than 50.

It’s just that now is the moment when something has changed more sharply. And my way of putting it into words is this: until now these algorithms could say 0 or 1, categorize, perform limited tasks.

The difference GPT brought is that artificial intelligence can now pretend to be a human. And be completely indistinguishable!

It won’t just win competitions—it is breaking the education system, the way we assess knowledge.

And the education system is broken as it is. I got a 6.00—the top grade on Bulgaria’s six-point scale—on the Bulgarian language matura, Bulgaria’s school-leaving exam, when I graduated. How? I went to the education ministry’s website and read the criteria used for grading. You have to use at least one simile, at least two metaphors… I memorized them and reproduced them. Well, artificial intelligence does that out of thin air, and it’s much better at it.

– But what are we supposed to do? And not just with poetry competitions, but with everything else, now that we can no longer tell the difference? AI, as you said yourself, has already changed the way we get our information, shut us inside bubbles and created all the big problems we have as a society right now. That we can’t talk to each other, that truth has become fluid…

– You’re asking me what the author meant. I rather think the way to look at these things is the other way around: beauty is in the eye of the beholder!

In fact, it doesn’t matter whether something was written with AI or not.

If this thing creates an emotion in you, if it makes you feel a certain way, it’s art, it’s something good. And if a poet is unhappy that AI is taking his job, he isn’t a poet but an egocentric who wants to be famous for his poetry. He isn’t doing it for the sake of poetry itself.

The people I know who write poetry do it to pour out their emotion, their soul. I write essays all the time; I can’t write poetry. All my thoughts take the form of essays, usually I aim for 300 to 2,000 words. I’ve published almost none of them, but I write them for myself. And,

fine, AI can probably write a better essay than me. But I write it for myself. What’s the point of using the artificial thoughts of artificial intelligence?…

– That’s exactly what I think too, and it’s the same reason I write Digital Stories. I don’t care whether better texts could be generated… But most people are prejudiced once they know something was generated. How long will that last? They’ll always say, “I don’t like it,” just because they know it wasn’t made by a human. I see it everywhere.

– There are many ways to approach this. The Luddites come to mind.

Every time there’s a technological revolution, there are two “tribes”—one is very excited, the other is against it.

Usually they’re driven by some fear that it will replace them. I think people simply need to be more open and try to see whether they can move from one tribe to the other.

What are we going to do? A hard question, and I can’t solve it.

What worries me is that people aren’t interested. Never mind that they’ll fall behind on technology—that’s their own problem. The real problem is that they can’t take part in the discussion about AI, because they don’t understand at all what it’s about.

– Exactly. That’s why this site exists. And my survey, which 750 Bulgarians took part in, showed how scattered we are on these topics. Meanwhile, one of the few things we agree on is that we don’t discuss it enough.

– If you want to make policy, for example to regulate artificial intelligence, you first have to get to know it. That seems to me to be a fundamental problem. Usually the people who are against AI haven’t gotten to know it at all.

– You help companies bring AI into their work. Everyone expected that very soon after the ChatGPT moment these technologies would make their way into practice. Elon Musk and 500 scientists even wrote a letter calling for a six-month moratorium. Two years have passed, and it seems these technologies are arriving more slowly than expected.

– Every new technology takes time to be adopted. There are various reasons, and people’s attitudes are one of them, but not the main one. A far more significant reason is that there aren’t enough servers, there isn’t enough hardware to run AI on. It isn’t fast enough, it isn’t cheap enough, it isn’t environmentally efficient enough. Half a year ago it was said that every ChatGPT request uses a glass of water to cool the servers. All these things need to be solved.

There also isn’t enough data for it to develop faster. We’ve already trained on all the data that is publicly available.

The next thing we’ll run short of is electricity—we don’t have enough to power all the AI things we’re going to build. Which means we hit one limit after another. We’re starting to solve them, but it can’t happen quickly. Building a new data center takes three years or more, even if you’re Microsoft.

ChatGPT came out in 2022, and GPT-4 came out at the start of 2023—for me that was the real revolution. So, by 2026 we’ll be securing more hardware. After that we’ll have more, but the models themselves will be even more powerful. There will be problems again.

If we look a bit more globally, I’d say that

this type of AI will take at least 20 years to be adopted on a mass scale in companies. I expect the first big, globally adopted software products to appear in the next 5 to 10 years.

Maybe their first versions already exist!

– Which sectors and which specific tasks will change first?

– AI is being adopted absolutely everywhere—take the whole technology sector, for example. Starting with hardware: chips are so tiny that we use AI to create them, while at the same time they are changing so they can develop AI better.

At the infrastructure level, it affects servers, data centers, cooling systems, energy consumption, and so on and so on. Only then do we get to software. There you can already create visual elements without writing code. You can dynamically spin up extremely complex systems in minutes.

But it’s a natural cycle. Ten or fifteen years ago everything happened on the computer. And when phones arrived, there were no apps, no websites ready for them—a complete rewrite began. Now everything is oriented mainly toward phones; 70% of our traffic comes from mobile users.

– It’s the same with Digital Stories, even though we’re talking about long texts.

– And just as everything had to be rewritten to meet the requirements of phones, chips became much smaller, we developed miniature cameras and sound systems, batteries became much more powerful, because they had to. In exactly the same way, AI will change the hardware and software industries from the ground up. There will be one enormous rewrite until all systems become AI-first.

Another comparison is cloud technology—everything used to happen locally or on your own servers, and at some point everything moved to the cloud. Software had to be completely rewritten.

The changes will be everywhere, but how can each person benefit? Everyone can do it individually, but naturally it’s faster and easier for some. When it comes to ChatGPT or similar models, the industries we see adopting them fastest start, of course, with software. Programmers and other IT professionals understand what it’s about and manage to adopt and use these models in creative ways much faster. Then comes the marketing industry—people in that business had long been waiting for something like this, and their work got easier. As someone with experience in the field, I know their problem is that they have lots of ideas that never get realized. In marketing the problem is always focus, but it’s clear that it’s better to experiment until you find the idea that works best. Generative AI makes it possible to test far more ideas in a very short time! You can create videos, texts, all kinds of content in hours instead of days.

The next area is business development and sales. We clearly see it in education too. All the teachers who use it are thrilled, because it can not only prepare their materials but also check homework and essays. We also work with universities that use AI technologies actively—Johns Hopkins, Columbia, Yale.

In law, they work only with documents—an ideal environment for these models. Finance is the slowest, because it works with a lot of numbers, and we know GPT doesn’t handle those well enough. But I’m sure very serious progress awaits us soon. Simply because the people developing these systems understand this above all—math and programming—so there’s no way the solution will be delayed.

– But could there be a winter? Many people say every technological field goes through this natural stage, and it has already happened to AI at least twice.

– Yes, 100%. The more investment pours into artificial intelligence… Imagine AI will create 100 units of value. If we invest 150 units in the form of money, there will be a winter, because we’ve put in more than the value even is.

That happened with cryptocurrencies—I’m not a big fan of them. Yes, there are cases where they can be used. But on a similar scale they’d bring 5 units of value, while 100 units of investment were poured in, and that’s why the bubble burst.

The same with real estate. Perhaps the biggest crisis of our time, in 2008, came from there—huge investments into value without sufficient backing. In the end, we can only live in one apartment. That leads to a lot of mismatches. With AI, the hype isn’t just hype. There are an enormous number of applications, and inevitably every field will be changed by it.

In my view, it should be compared with mobile phones, cloud technology and the internet.

Just as the world can no longer exist without the internet.

– But there was a kind of winter there too, the so-called “dot-com bubble” at the start of the century.

– It came because we didn’t yet have a distribution system. I want to read the Digital Stories site, but I don’t yet have Facebook to find you on. I don’t have LinkedIn to connect with you, or Google to land on your page. I’d have had to know exactly what your site is called and type it in by hand—and we didn’t even have the browser in its present form.

It took quite a few years to build the infrastructure so that we could make Team-GPT in two weeks, put up a WordPress website in four hours and have made $300 in sales the following week. That was impossible 20 years ago.

Everything takes time. In 2007 Steve Jobs unveils the iPhone; his competitors from BlackBerry are in the hall, stunned by what they’re seeing, realizing the future is here. Microsoft, meanwhile, laughs at the iPhone. Fifteen years later BlackBerry is gone, Microsoft has uncompetitive phones, and the iPhone is the best phone ever and everyone copies it. But it takes quite a lot of time to get here.

AI is something different, because we already have a lot of infrastructure—the internet, phones, cloud services. There’s a distribution network through which it can spread everywhere. You have a software development program, you add AI, and it gets better.

– I can see that you think these phenomena through and follow them closely. Can you guess what the next big thing in the tech world will be after AI?

– In 2014 I won a competition about what the next financial crisis would be, and Capital, a Bulgarian business weekly, published me. It later turned out I wasn’t right at all, but I took first place because they thought it sounded very smart. In the same way I could tell you something that might sound smart, but nobody knows.

There are definitely a few trends that will be very big. One is energy and nuclear fusion. Many companies around the world are working in that direction—you put in one unit of energy and get out more. It’s only a matter of time before that happens and scales.

Energy will become more and more important, both because of politics and because of AI. On the other hand, it’s a conservative field that depends on many factors, but the change will happen, sooner or later.

Biotechnology, healthcare, robotics—all fields we expect a lot from. There are already robots that plaster walls and paint. They’ll become widespread and good at their job. Whether in 10 years or 100, I don’t know, but I’ve already seen a robot that’s relatively good.

Biotechnology will be the most profitable industry there is, because people are interested in longevity. AI is now helping us discover new things in medicine, and I’m very enthusiastic about that direction. Above all because of eliminating diseases. Right now cancer and cardiovascular diseases are the main things killing us. They’re predictable if you have data. If you have good data with which to track these diseases and their progression, you’ll have very good predictability and prevention.

Genetics and brain-related technologies will also be very important. After AI, I personally would be very happy to devote myself to research on how the brain works.

Biotechnology definitely presents quite interesting problems that will be solved this century.

– Definitely. And robotics, AI in genetics, longevity are all topics I often write about in Digital Stories. And it’s no accident that people like Sam Altman are also investing seriously in this kind of technology.

But are there things that scare you? What could go wrong in this process of introducing artificial intelligence that would create problems for us?

– There are dozens of dystopias I can imagine. I always recommend Isaac Asimov and the classic science fiction writers—they predicted nuclear fusion, AI and who knows what else.

Many, many books of fiction, of science fiction, foresee these inventions. In part they steer people to think in that direction.

– And it’s no accident that this is a genre that has been in crisis in recent years, and in my view it has very few meaningful authors.

– Right now all the books on these topics are dystopias. They usually go like this: the world in 2100 has gone wrong in this and that way. Here’s what society looks like… it’s never good.

I can imagine the world “breaking” in many ways, but I don’t like doing it.

What interests me is how to make sure it doesn’t break. To give something good!

What worries me? The most fundamental, simplest abstraction is that bad people will have great resources in the world. What are the resources that matter today? Energy, data, access to artificial intelligence. Bad people having power is always a problem. What good people need to do is get to know AI and learn to use it, because the bad ones certainly are.

When Team-GPT came out, we had inquiries from military units, which is perhaps the equivalent of “we fight the bad guys by having more technology than them.” I stay away from such deals. But the military is always the first to mobilize when there’s a new technology.

The truth is that if we don’t take things into our own hands, the ones running the world will be the military (good or bad) and the porn industry. They’re always the first ready to jump on a new technology and use it for their own purposes. I don’t like that one bit, and if anything it frightens me.

– What frightens me is that it will make things too easy for us, that we delegate everything far too easily while we give ourselves over to hedonism and knee-jerk arguments online. Experience shows that when technology frees up our time, we don’t use it to read and write books and to get to know the world, but to scroll the feed.

Do you think we’re close to the limit of large language models? Will GPT-5 be better than 4 by as much as 4 was than 3?

– ChatGPT isn’t just a language model, and neither is Claude, nor Team-GPT. In our case it’s clear that we’re not a language model; we use other companies’ language models and add value in other ways.

A language model can’t work with files, spreadsheets, images. It’s a combination of hundreds of tools that together make up the ChatGPT product, and the language model is one small piece.

I believe that

the step taken with GPT-4 is already enough for us to build these applications that can be orchestrated and be fully capable for all kinds of tasks.

If you go to ChatGPT and tell it to write an article about Ilia Valchanov, one of its tools will look up who Ilia is and what he has said so far. Another will create images for the article. It will use additional tools to read a file—yet another technology. And in the end we create an article entirely with ChatGPT—the model has arranged the words, but it rests on the efforts of many other applications. Everything else is a combination of 10 to 20 tools.

The language model is just one piece of the puzzle—but, yes, a very important piece.

– What will artificial intelligence never be able to do? Is there anything like that at all?

– Hmm, a philosophical question. René Descartes says it will never think, because that is a human thing.

I think about this question more in three dimensions. We live in the world, we have a human body. You have to sleep, eat, go to the toilet.

Being human is much more than thinking. You have a brain, but you also have a body and an inner, spiritual life.

These three things are deeply connected. When you’re a self-aware person, when you’re happy, that has nothing to do with artificial intelligence. We keep saying, “Oh, artificial intelligence will do this and that.”

No, it will perform certain operations that are intellectual in one way or another. It may do certain things, but in the real world being human is much more than working, writing a text or creating an image.

Just like the poetry we started with. Being human means writing the verse, feeling it, trembling at what you’ve made—not churning out some text and winning some competition. To feel… that will always be reserved for humans.

Play the video

Just a day after Team-GPT’s record funding was announced, Ilia was a guest on the podcast Creative Life, sharing fascinating stories from behind the scenes of the startup, from its long and thorny road, and about what the team faces from here on. Watch it also if you’re curious why it’s worth more men giving up shaving…

“The best way to get the right answer is not to ask a question. It is far more effective to post the wrong answer.” And everyone will rush to correct you, to jump into the argument, right?

That is exactly what “Cunningham’s Law” says, a fascinating rule about how online communication has changed us. But who is this Cunningham? His story is an interesting and instructive one. One of the pioneers of the democratic internet, he paved the way for Wikipedia and for the systems we use to share opinions online. And if you have ever wondered where the free encyclopedia got its name, he is the one who came up with it, after an unexpected episode.

Our hero’s contribution to the way we develop software is enormous. Besides being one of the authors of the Agile Manifesto, which laid down the foundations of today’s IT industry, Ward Cunningham gave us concepts that are used everywhere these days, such as technical debt and extreme programming.

And yet today you will find only a handful of lines about him online, repeated over and over. So it is time to dig into the story of one of the pioneers, one of the fascinating figures who stood on the bright side of the tech world. The ones we have to thank for the internet still being a free platform. One where, despite the relentless and ever-deepening dominance of the giants, there is always room for everyone’s opinion…

Wiki, Wiki Way

The early 1990s. Howard Cunningham and his wife have set off on a long-awaited trip to Hawaii. The two are among the pioneers of computer technology; they met at university in the 1970s, at a student party after a tough exam. Back then computing was not even taught as a separate discipline, and the two studied electrical engineering combined with software. After graduating, they founded a company together, helping the pioneering firms that had decided to use the grandfathers of today’s computers in their daily work.

But work stayed at home. The couple landed at the airport in the capital, Honolulu, where an unheard-of heat was waiting for them. They hurried outside to find quick transport to the hotel, and in front of them stood two little shuttle buses. With big, proud signs… “Wiki” and “Wiki Wiki.” What on earth could that mean, Ward wondered. It turned out that in the local language the word means “quick,” and saying it twice translates as “even quicker.” It was the first Hawaiian word Ward ever heard (and probably the last one he learned), but it struck him as especially colorful.

We will leave it to the experts in Hawaiian to tell us whether more repetitions would make things faster still, but the vacation in Hawaii certainly went by quickly for the Cunninghams.

The two came home, and when Ward went back to the side project he was building in his spare time, he remembered the “wiki” buses and decided to name it after them: “WikiWikiWeb.” Just a few years later the word would become famous as part of “Wikipedia,” the universal place where we so often look for an independent view today. One of the symbols of the democratic internet, of the attempt at impartiality, of the ideal of equality online, which so often finds itself in a decisive battle with the ideas of the big players…

Hakuna Matata!

And although our hero had no direct part in creating the free encyclopedia itself, he is not only its godfather (the origin of names in the tech world can be truly curious) but also its forerunner. It was his system with the two “wikis” that laid the foundation. It let people gather and add to information at the same time, and discuss it in a way that was traceable, clear and well argued. And all of this… so that we could build better software.

That is the big dream Ward Cunningham keeps chasing even today, now past 70. As he tells it, back in the 1970s computer science was already looming as a great challenge, yet its real foundations had not been laid. Hardware development was moving ahead, engineering practices were being introduced, computers were getting faster and faster, while writing programs happened chaotically, without organization, leaving behind serious problems that were far harder to solve later on.

Ward learned about the network that would become the internet shortly after it was developed by Sir Tim Berners-Lee’s team. And he quickly began dreaming of a global platform that would let us exchange information at unprecedented speed.

He built his first website in 1993, when the internet was anything but fast. It would take another decade before speeds were good enough for us to swap files casually with the other side of the world.

Go, Wiki, Go!

Ward’s first site was exactly that platform, where he invited like-minded people to discuss the problems and big questions of software development. And when, a year later, he hit on a technical way to let any visitor add to the information, he remembered the trip and Honolulu’s quick little buses. And so WikiWikiWeb was born—a site where programmers discussed and added to each other’s ideas… quickly, at least by the speeds the network was capable of back then.

Gradually, after the first “wiki,” thousands of others would appear, tied to different communities. Ward would offer the platform to anyone who wanted to use it, because, as he says himself, it never occurred to him that this was something that could be patented. “Wiki” would become a synonym for a collection of information on a given subject, even before the next generation of computer enthusiasts took the idea further and turned it into “Wikipedia,” an “encyclopedia of everything.”

What is a wiki? “In its simplest form, it is a website that respects the intellectual creativity of the reader,” Cunningham says. “It says that this person reading the site might know something I don’t know. And when he knows it, if he’s willing to give it, we’re willing to take it. It says: ‘We are having a conversation.’ Isn’t that cool? That is what our culture is built on—trust between us.”

It was in that first wiki that Ward put forward another of the ideas that earned him a place in the history of computer science. In recent years there has been a lot of talk about “extreme programming,” an approach used to develop software above all when it has to happen fast, for example in startups.

The Extremist in Glasses

But… what could extreme programming possibly mean? Skiing down a slope while writing your code, or bench-pressing a hundred kilos? That would be interesting, but, as you might guess from Ward’s photos, that is not quite the idea.

In fact, it is a fairly broad concept in which programmers develop their product in short cycles, with higher productivity that comes neither at the expense of quality nor, as the name might mislead us into thinking, at the expense of stress.

Another idea from extreme programming is writing code in pairs (could it be a coincidence that Ward has been working alongside his wife for 50 years?). The ideals include simple code and frequent communication within the team. And if you are wondering where the extreme part comes in… the idea is that the very practices Ward and his like-minded colleagues considered most useful in building software are taken to extreme levels.

Even people from the software world may never have heard of extreme programming, but whether you know one particular little word from our hero’s experience is a sure sign of whether you have ever brushed up against the industry. In recent years, being “agile” has become a mantra repeated everywhere you turn. The concept, which we might render as “flexible” software development, came at the start of the new millennium from a short, clear yet general manifesto written by leading lights of the industry. The “Ten Commandments” for programmers are actually… twelve. Principles such as “individuals and interactions over processes and tools” and “working software over comprehensive documentation.”

These days you will hardly hear of a software team, at least in our part of the world, that does not follow the tenets formulated by 17 “wise heads” in 2001 (you can find the official Bulgarian translation here). Perhaps the best known among the signatories are Robert Martin, familiar to visitors of Digital Stories as Uncle Bob, Martin Fowler and, of course, our hero today.

Although throughout his long career he never stopped working for the family company, he joined many other ventures along the way. And while most of them were smaller projects, away from the mainstream, it is worth noting that he spent two years at Microsoft in the Patterns & Practices group. How many good practices he managed to introduce we can only guess, given that he did not manage to stay in that job for very long.

Debt Calls

Owing someone money… for many it is a nightmare, for others everyday life. And so we come to another huge and very important concept formulated and popularized by Ward—so-called technical debt.

All too often, in a hurry and driven by the need to keep adding new features to the software, its creators settle for compromises. They leave lines of code that could have been written more clearly, they make judgment calls that are far from optimal. And so, slowly but surely, this burden accumulates—a measure our hero was the first to popularize.

Today we all witness the enormous tons of technical debt piled up by the biggest software companies. Massive projects with billions of users often fail because of small bugs, do not work as expected, become vulnerable to cyberattacks. Ward predicted this phenomenon back in the days before the internet conquered the world. And he put it clearly: every software team should keep track of the technical debt it accumulates and take measures periodically. So that code already written gets improved, adapted, updated… The opposite of what the traditional programmer’s wisdom says: “if it works, don’t touch it.”

Well, with views like these we can guess why Ward never made it big among the Silicon Valley giants, who are always in a rush, rush, rush, chasing new software features… and billions.

No Credit, to Anyone!

And so, despite all his remarkable achievements and his contribution to the way we build software today, you are far more likely to come across his name as part of “Cunningham’s Law,” which we started with. But how did a programmer become famous for the thought that “The best way to get the right answer is not to ask a question. It is far more effective to post the wrong answer”? It is not his fault.

Some trace the roots of the law back to antiquity. In Plato’s dialogue Meno, for example, Socrates agrees with the mistaken conclusions of his friend Meno, hoping that this will keep the conversation going. Either way, the principle still works flawlessly on Facebook, because people often love to look smarter than others. And online they get the chance to show it in full force (although sometimes the result is the opposite).

While he admits that the popularity of the “law” flatters him, the programmer believes the rule refutes itself by how often it is misquoted online. In fact, the wording was proposed by Steven McGeady, a longtime leader at the microprocessor maker Intel.

Ward’s Word

Steven recalls how in the 1980s Ward gave him this piece of advice about communicating in the ancestors of today’s forums. Our hero later said he had been misquoted, but that did not stop the law from becoming more famous than his real achievements. He himself considers the wiki platform his greatest contribution and believes that it is what will be carved on his tombstone.

Because it helps us share, work together, complement one another—something that is getting harder and harder these days, he notes. “Sometimes, when you work in a team, you have to trust people more than you have reason to,” Cunningham says. “And it works, because most people are good.”

This comes from a man driven forward by technology his whole life. Who back in the 1990s lived in a house with several internal networks, where every resident, even the cat, had a website. And computers lay scattered around the way magazines lie open in most homes, as a journalist who visited the programmer aptly put it.

“Ward has always been a social type,” says his wife, Karen. “He is especially interested in meeting people who challenge his thinking or inspire his intellectual curiosity. For quite a while now his real focus has been on communities, on ways of using technology to build them.”

Simply Ward

“I really do think programming is an easy and fun thing to do,” Ward says.

“Those who find it hard and boring just need to get a little more experience.”

And if there is one more thing from his professional biography worth mentioning before we part, it is that Ward is among the people who spent many years championing the “little” programming language Smalltalk. (More about it here.) A language whose syntax is so concise it fits on a postcard turns out to be extremely handy in a whole range of situations.

Because, as our hero likes to repeat, simplicity should be a fundamental rule in the work of people who build software. Simple solutions are the most elegant, the most understandable, the easiest to maintain. And every complication that can be avoided is worth avoiding.

“What is simplicity? The shortest path to a solution,” he says. “Actually, I enjoy complexity that empowers me. If it challenges me, it’s very pleasant. But sometimes I have to deal with complexity that disempowers me. The complexity we despise is the kind that leads to needless difficulty.”

And come to think of it… for the software world this is beyond dispute. But might it not also be a wonderful rule for life?

This piece was prepared with the special assistance of Irina Marudina.

– Almost 200 people have been guests on my site—from a priest to a poet, from a designer to a conductor—and now here I am welcoming a Hollywood stuntman… What are the hardest and riskiest stunts you have done?

– I've been set on fire, which is quite risky and dangerous. I've jumped from tall buildings.

I have a pretty funny story with Antonio Banderas: I managed to knock him out. Not through any fault of mine, of course. We were doing a fight, they were shooting from behind me, and I had to throw a punch. Everything was happening at a safe distance and he was supposed to react, but at the moment of the shot he leaned forward—right into my fist. We've been very good friends ever since…

Other than that, I've been hit by a car, I've fallen from heights.

Plenty of stunts of every kind.

– You once told me there is a connection between cybersecurity and stunt work. Aren't they rather distant fields?

– Cybersecurity, put as simply as possible, is risk management, and that can be looked at from many different angles.

Speaking as someone with experience in stunts—in film, risk always has to be measured precisely. Why? Every production is extremely expensive and involves an awful lot of people. It takes serious planning and a strict schedule, and everyone involved has to be in sync, and there really are a great many of them.

Actors carry enormous insurance policies, and that's why stuntmen are used. First, they make the actors look like the superheroes being sold. And second, to make them look that good, very often

the stuntmen are responsible for the real movements, the details, everything that helps the scene look more beautiful.

Stuntmen minimize risk. However events unfold, nothing must happen to the actor, because what is being sold is his face and the image he has built over the years. He is good at selling emotion to people; the stunts are a job for the stuntmen.

I really like one example with Tom Cruise. He is a very good actor, but he can also be a very good stuntman—often, in films where he is the producer, he lets himself do various stunts. In the second-to-last Mission: Impossible he does a scene with Henry Cavill in which he runs off one roof and has to jump onto another. A shot that looks fantastic in the film. A scene rehearsed hundreds of times, both with stuntmen and with Tom Cruise.

But on the day of shooting, as he performs the stunt, something in the rope system fails, and when Tom Cruise jumps he breaks his leg. The scene later became part of the film itself; you can see him start to limp, because he is a professional and finishes the scene to the end. But it cost the film millions of dollars in losses, simply because Tom Cruise did the stunt instead of the stuntman.

That is the parallel I draw between cybersecurity and stunt work—precisely this risk management.

– Cybersecurity hardly will, but won't stunt work soon disappear as a profession? The effects keep getting better; it's no accident Hollywood went on strike for so long, and one of the main reasons was the right of background actors not to have their likenesses reproduced digitally.

– With the strike, Hollywood managed to achieve quite a lot. In our profession we believe, first of all, that however good artificial intelligence gets, it cannot replace all those human movements well enough. To this day, in post-production, if you look carefully you'll see the differences and be able to tell when something has been generated.

We think, though, that the profession is certainly not a disappearing one, because all these systems need people to operate them. And even if it does disappear, other professions will surely appear to take over the whole process.

– If we continue the parallel, let's say it's relatively easy to judge how expensive the actor we're protecting is. Is that true in cybersecurity? Because almost every company is now vitally dependent on how well it has been looked after. How do we assess that objectively?

– It's assessed very easily: will a given risk stop my business or not? Or if it does, for how long, if a given system goes down, if we face some specific situation.

This is where the business has to estimate what an hour, a day or a minute is worth to it, so that it can judge accordingly what a loss of data would cost. Or, if I lose this data, what would happen, what the reputational damage would be if a cyber incident occurred.

In other words, everything depends on that. The business has to sit down with the risk management specialists and work out

how critical a specific threat is for the specific situation or for the business.

– Do you think this happens widely enough and often enough?

– In Europe it happens more, in the north and the west. In Bulgaria it's mostly practiced by larger companies that have become familiar with specific situations or have had an incident. There are two kinds of companies—those that have been hacked and those that will be hacked at some point.

In Bulgaria, small and medium-sized businesses are not sufficiently aware of what the risks are.

First, that's the Balkan mentality: everyone thinks it can't happen to them, until it does. And when it does, they start thinking about certain reactive measures to help restore operations. Once it has happened, everyone will give anything to get their data back and restart normal work.

– Can you give some examples?

– In the public sector the examples are clear; the data leak from the National Revenue Agency, Bulgaria's tax authority, is well known. There are often companies that don't say anything about such a situation. The latest case that became widely known was the theft of data from online stores.

A company that has run a stable business for 20 years can collapse in a week.

Because when hackers steal data, they have many different ways to exploit it. The first method is to ask the company they stole the data from whether it's willing to pay so that the data isn't released, if it has been encrypted. If the company pays, they may give it some kind of key and it decrypts its data.

The second way is to say, “We can sell the data to your competitors and you'll lose ground.” Information like that often surfaces on the darknet—certain data belonging to a given company is put up for sale.

The third way is to go to the partners you work with and start blackmailing them one way or another.

– It isn't even always about an attack and making a profit. CrowdStrike was a telling example of how easily the system can fall apart. And on top of that, the problem came from an antivirus platform whose whole purpose is to protect us.

– It's important to note that the CrowdStrike case was not a cyber incident. It was an update they released, which naturally didn't affect all computers. There was also some degree of risk on the part of the companies that updated their systems, and that also made the damage greater.

But it's a fact that a company like CrowdStrike lost serious ground, customer trust and a lot of revenue. Its shares fell dramatically, and all of it happened in a single Friday afternoon.

– That shows exactly how the system is built—in today's online world we constantly have to trust external libraries, vendors and platforms without realizing in time how dependent we are becoming on their next mistake. The way we run the software industry, and software in general, seems insufficiently thought through to me.

– Absolutely. I've seen it very often—when a specification is drawn up for any kind of software, people discuss features and how it should work, but the cybersecurity aspect, that is, how well protected the software is, is rarely touched on.

In other words, people rely on the programmers being good and prepared enough that there are no vulnerabilities.

One example is the case of Microinvest, a Bulgarian accounting software company; it's public. A company you trust completely comes along, installs accounting software for you, and attackers can get in through it and carry off all your data. In that particular case it wasn't related to Microinvest's software but to a specific version of Windows Server that they installed.

– Can a company be absolutely sure it covers all the risks? That it's protected from every kind of attack?

– Technologically, you can put in countless solutions, but you always have to talk about staff training too. For the simple reason that attacks coming through messages, from supposedly legitimate sources, are becoming more and more frequent. The latest one I saw involved a huge company. I saw it with us too: people had started receiving WhatsApp messages from the company's owner. The attackers had simply bought some numbers, put up a photo and started writing to employees they had found in advance.

We had the same situation inside Check Point: colleagues started receiving messages supposedly from the company's vice president. This is increasingly common, and training people is essential—on internet hygiene, on the types of files that can be opened, on the email that comes in. Because

technology is technology; it can protect and minimize risk, but in the end comes the human factor,

and a human can always click on the wrong thing, including on a personal phone, and corporate data can be compromised that way.

– Has progress in artificial intelligence changed the cybersecurity picture in any way, in your view? Has it made attacks easier, or did we only think it would?

– Attacks haven't become easier, but both hackers and the makers of cyber defense solutions use AI. The forces always balance out. But here too it depends on the people using cybersecurity solutions. I haven't come across statistics on it; there may be some growth, because the better the computing power, the easier it is to generate all sorts of messages, even videos, to make someone believe in something that never happened.

My appeal to people is to really check their information,

to call the people they know, to check everything twice, even three times, if it involves transferring money.

– Except they don't, ever since these attacks took the form of phone scams. Check Point is an Israeli company, and it turns out that many of the big players in cybersecurity come from that country. Why do you think that is?

– Israel is a small country that has always had rather unfriendly relations with its neighbors. As you know, even women serve in the army there, and the service isn't short at all—from one to three years. Everyone goes through some military training.

Cybersecurity and defense are naturally based on military methodology.

And for a country with such a strong awareness that everything should be militarized, it's natural, in my view, that many cybersecurity businesses start right there.

Just as the other type of business starts there too—spyware. A well-known case is the Pegasus software, which tried to collect information without the person having to press anything on their phone; they managed to easily install software to eavesdrop.

– Along those lines, Bulgarian hackers used to be very famous. Lately I haven't heard of any serious attacks carried out by Bulgarians. Have we lost our form, do you think, or are they into other things now?

– I think they've simply become more cautious and don't show themselves as much; people are learning to be more and more careful. But Bulgaria today has a terrific cyber defense team—young guys developing in that field. They're on the good side of the coin and they're truly very talented.

– What does your experience show—what makes a person cross over to “the dark side”?

– It's always about money. Hackers are like any company—they want to generate the highest possible profit with minimal effort; it always comes down to that. But it's a fact that cybersecurity specialists are among the best-paid professionals. And since the pay is high, more and more of these people have no reason to cross over; they stay on the side that fights the threats.

– Do you think Bulgaria could somehow become a regional cybersecurity hub? Given that we have a strong IT sector combined with a certain tradition in hacking…

– There are quite a few companies in Bulgaria starting to work in cybersecurity, and they operate on foreign markets too. Again, Bulgaria is a somewhat peculiar, difficult market. The Balkan person is convinced it can't happen to him. We in Bulgaria especially are Doubting Thomases.

It can certainly happen. But it's important to establish the model in which cyber defense and IT are completely separate.

The cyber defense people have to confirm and regulate the decisions the IT teams make.

That's how it is by the book and how it's recommended—the specialists responsible for protection should report directly to the CEOs rather than be part of other teams. Very often in Bulgaria they're in the same department, and that blurs responsibilities.

– What qualities do you need to succeed as a professional in this field?

– First, you have to find it interesting once you see how the process itself works. Many people end up disappointed because they've watched movies about what hackers look like. In recent years, I think, cinema has created a false image in people's minds of what a hacker is. And the moment they face reality and the whole process of creating malicious code, they see that maybe it's not the job for them.

In other words, people have to be curious, to want to see how the whole process happens, and only then start picking at it.

But curiosity is definitely the first quality you need.

– Along with caution, perhaps, so you don't quickly end up where you shouldn't…

– So you don't go too far. In connection with everything said so far, NIS 2 is coming soon—a European Union regulation on minimum requirements for information security. That may push more people to start thinking about the subject.

The regulation is supposed to take effect from mid-October, but it's not known when it will be adopted at the national level here.

I believe this will be the moment when Bulgarian companies start paying more attention to cybersecurity,

because besides the companies that fall under the regulation, there will be others that are part of the supply chain, and they too will be subject to very strict oversight.

– Has anyone ever hacked you? Gotten you to click on a link you shouldn't have?

– To tell the truth, I did have a very interesting scam happen to me, and I fell for it. My wife had gone onto the popular platform Etsy—she had just been setting up a shop, and apparently the scammers had found her. They had sent a link, she had clicked it, and a platform opened that looked exactly like Etsy.

At some point she called me over and said she couldn't complete the payment; I was puzzled and I fell for it. In the end we transferred 150 euros to them, which, thanks to the bank, I managed to recover.

Only after I had completed the payment did I realize what a foolish thing I'd done.

So far that's the only time I've fallen for something like that, because I try to look carefully. With attacks like these there are still signs, and if you pay attention, you'll see that some tiny thing isn't right.

– The important thing is to be careful. And to realize that no one is protected. But, as we said, even Antonio Banderas can be caught by surprise… Thank you!

– We kick off from the center circle, and I have to confess: I'm offside. I didn't tell you in advance, but I invited you to this interview to discuss an experiment in which I generated a CV entirely with ChatGPT and sent it to 10 ads for junior positions in IT. I picked nine at random and only one on purpose, an ad from your company, to see how you would react. What do you think happened? Did the candidate with the fake CV get an interview?

– I'm 100 percent sure he didn't get an interview. Absolutely no chance.

– Correct answer. A colleague of yours wrote back on the very day the ad went live, asking me for a contact phone number. For obvious reasons, there was no number in the CV. I think that was a very sensible reaction, a way of checking whether the candidate Maksim Ivanov was real.

– Yes, I'm checking right now, and Maksim Ivanov is in the system as a promising candidate. Which is good feedback on the work of the colleague who replied to you. I've learned something nice.

– But in the end “Maksim Ivanov” did get two interviews elsewhere. Does that surprise you?

– Well, looking at the CV now that you've told me, it's obvious the photo is generated.

The CV isn't exactly a junior one. The guy has three years of experience and would certainly have been called for a pre-screening. I can see the notes in the system right now: my colleague has noted that he opened the email but didn't reply with his phone number.

– Do you think all this could go further at some point? That I might take this experiment further, someone might hire a fake person, and then ChatGPT would do his job? How far away is that kind of madness?

– In exactly that form, given the current state of the technology, it should be impossible. But there are all kinds of people and companies, so there's some probability. In quite a few places it has happened, especially with the spread of remote work over the last few years, that people work several jobs at once.

Mostly remotely, since you can't physically be in several places at the same time. In the US there were quite a few cases in which

an experienced programmer supposedly works remotely for several companies, but hires people in China or Eastern Europe to do his job while he takes it easy.

So examples like these, slightly different from the one you gave, happen all the time.

– Do you have stories like that? I do, for example. We go into an interview, the candidate has made it that far with a strong CV, he'd even set up a LinkedIn profile, a Bulgarian… and it all turns out to be fake. An Asian man shows up with a completely made-up story. I don't know what he was hoping for; he was googling everything.

– Exactly the same stories, the same thing happened to me at the previous company I worked for. Right now I'm in charge of all the hiring of software specialists, and we have about 200 open positions. We hire people from all over the world.

Quite often, when I interview people from the Far East, candidates show up who haven't the faintest idea about what they wrote in their CV. At one point it became something of a pandemic, so we started screening them in advance—with tests and tasks.

I'll give you an example. We generally work from the office, but every now and then a position comes up, most often for foreign candidates, that's remote. I post an ad for Europe and in less than 20 hours I get 240 applications. Insane, absolutely insane! I took the ad down immediately; going through 240 applications takes a huge amount of time, and the position isn't a simple one. Well, I went through them.

They say recruiters look at a CV for five seconds. That's a myth.

It only works if you can see from the very first line that the application is completely irrelevant to what you're looking for. Anything else takes at least half a minute: skimming the experience, seeing whether this person fits, whether you can move them on to the next stage.

Then I republished the ad, but this time I added lots of questions for the candidates to answer, restricted the location, introduced more criteria, and the CVs dropped to around 40. About 10 were relevant, I talked to seven or eight of them, and now I have two at the final interview. I hope we'll take at least one of them.

– Right now I'm far from Sofia, in a small village. A little while ago I went to the village center and met an Indian man, a Sikh in traditional clothes, who's helping with construction in the area. What do you think—will the labor market become more and more international?

– For now, globalization looks inevitable. It has been happening very actively over the last 30 to 50 years. Everywhere in the Western world, which we belong to as well, there's a labor shortage.

If we want our economies to keep developing well, to have growth, any growth at all, we're forced to bring in a lot of new workers. But that's a double-edged sword.

It won't always turn out to be a good long-term solution, and we have to be very smart as a people, as a state, about what we want to achieve in 20, 30 or 50 years.

This globalization is happening: many companies in Bulgaria are bringing in foreign workers right now. Last year about 27,000 permits were issued. These aren't IT specialists but people who mostly work in hotels, factories and construction. Our company's office is near Cherni Vrah Boulevard in Sofia, and there's a lot of building going on. When I stand on the terrace, I can see that many of the workers on the new buildings across the street were certainly not born in the Balkans. There will be people like that everywhere.

– And, as you said, I'm not at all sure this is happening by design rather than haphazardly. We started with football analogies, and I can picture us turning into Ludogorets, the Bulgarian champions built largely on foreign players: we invite foreigners, they do a very good job… until there's no room left for Bulgarians. If it's done by design, you find room for foreigners but you also develop your own “players.”

– Of course. We can also go back to the Olympics, where we won many medals, but a large share of those athletes were naturalized, and only in the last few months. It's not a problem that they're foreigners; if they were born here and are the product of our school, of our sports system, that's wonderful.

But when we take ready-made athletes from other countries who, for one reason or another, have fled from somewhere, to me that shouldn't give us quite so much cause for national pride and joy. Yes, it's good, we welcome the others, it's a plus that they fight for Bulgaria and win medals, but it's much better to raise our own athletes—whether they're Bulgarian or not.

– Absolutely. And if we talk about IT, what are our strengths right now? In my view we're no longer a cheap destination, but our mentality is very close to the European one. And, broadly speaking, we're still good specialists.

– Yes, you're completely right about that. The cultural differences between us and the countries of the Far East are big. We're far more European, even compared with countries just slightly to the east of us. That's a plus when working with Westerners.

As for pay, we're definitely not a cheap destination. Last year I took part in a mentoring program aimed at students from Pakistan, Bangladesh and Iran. The salaries programmers get there are really low; they work for $500–1,000.

The quality of some people there really isn't high, but others are at a very good level. When you add that these countries have much bigger populations, it's simply a matter of selection, of proper filtering and judgment, of finding the right people who'll do the job well for next to nothing.

In this competition, we really have to offer a product at a much higher level than just people working for low daily rates.

We can't compete with Mexico, India, Pakistan or Bangladesh, where the populations are much bigger and for $5,000, which is what an experienced programmer earns in Bulgaria, you can hire at least four people.

– In my experiment I got two interviews and one task, two companies wrote that they had found someone better, and only three didn't reply. I think there's now demand for junior positions, which I don't believe was the case a year ago. How does it look to you?

– There isn't a great deal of demand for junior people. Right now we have about eight mid-level QA positions to fill, with at least two or three years of experience. On the whole, it's hard for us to find the right people. There are CVs, there's no shortage of candidates, but screening them takes more time. Especially in professions like QA, where a lot of people have switched careers in recent years.

The interviews we run aren't overly complicated, but many people fail, and we need to hold four or five before we hire someone, whereas before it sometimes took one or two.

– But isn't the system scoring an own goal by not taking on enough junior people? Where will the seniors come from in two or three years at this rate?

– You're partly right. But… juniors still get in. Just not as many as before. If we look at the job ads, there are 114 on dev.bg. Well, that's not so few; there are actually many more than there were years ago.

There are also internships, many companies constantly launch academies, and some of the people who complete them stay on to work.

To be fair, let's also look at Jobs.bg, under IT, entry level / junior. There they also include other kinds of positions around IT, and there are about 500. So there are opportunities for junior people.

They can't always walk straight into the position they want, but it's not as if there are no opportunities. There are fewer than in 2021–2022, but that's due to the economic cycle, which is heading down right now.

– Do you think it's too late now for someone to switch to IT—to become a QA or a programmer?

– No, but it will be harder for them. Companies are definitely becoming much more demanding. I'd rather interview people who graduated from the Technical University of Sofia, from the Faculty of Mathematics and Informatics, or who are coming back from abroad with a technical degree, while those who have only completed an academy will end up somewhere further down the list.

– So I finished my academy just in time, in 2018…

– Yes, really just in time. You caught a much more favorable period.

– And yet… is it only about economic reasons? Do you think artificial intelligence has changed the labor market in any way? There was a lot of talk that it would do so abruptly, but it doesn't seem to be happening.

– I think exactly the same.

AI was used by some companies to scare their employees and justify the layoffs.

I also read a lot on the subject; I'm keenly interested. I use ChatGPT every day, I've tried all sorts of things and keep doing so. But I don't see how this thing is going to take my whole job.

I also use lots of automation tools that aren't AI, although lately everything gets packaged as artificial intelligence. They help me be much more productive and get many times more work done per unit of time.

But still, I have to set them up, go around and talk to people—to the managers, the bosses, the business—about what they want and how they want it. In human resources I also have to go and talk to people about whether they have any problems in the company.

A chatbot can't solve problems like that. It can answer some of the more trivial questions so people don't bother me about how to file their sick leave. But when it comes to getting their money from an insurer who won't pick up the phone, that's where I might step in. The same goes for the interview process. You could pass through automatically: the recruitment system could be built so that, say, you apply, the system evaluates the CV you sent and automatically sends back a reply. You pass the test, it's verified that you didn't use AI—and that does get caught. And then your whole profile comes back to me with a message: “Here, his CV meets the basic criteria and he passed the test. If you like, invite him to an interview.”

I don't believe this particular thing threatens my job right now. It's more that the economic cycle is what it is: the cheap money has run out. When there isn't enough fresh money, it doesn't circulate, the economy isn't doing well and you don't need so many people. And

you can always justify it by saying you've introduced some technology and that's why you're letting people go.

– Tech support was expected to be the first area where far fewer people would be needed. Has that happened?

– Not entirely. At our company we have a huge customer service department and advanced automation technologies, but people are still needed.

Many customers would rather not talk to chatbots at all.

Wherever they can, they look for a human voice, a human touch. They skip every possible option to talk to the chatbot. At certain companies that tried to remove the option of talking to a person, the effect on the user experience was very bad.

– We expected quite a few professions to disappear when GPT came along, and I made predictions like that myself. But, at least so far, not a single one has disappeared. New ones have even appeared, with the whole field of developing and working with artificial intelligence.

– And I don't think the tech support profession will disappear either. There will always be a need for people to serve a business's customers. There will be those who like dealing with real people, who want to talk to someone. It's like going to a shop to pick out clothes and having nobody to exchange a word with. Someone to tell you, “Hey, that looks good on you,” or “Wait, let me suggest something else.” A chatbot can do that too. But will you like it?

– Or will there be more people like me, who don't want comments like that? We see it in the supermarket today—there are plenty of self-checkouts, yet almost everyone still queues. Or with football referees, which is why we now have VAR… I'm joking; that's exactly an example of how hard it is to replace humans.

But do you think a new “sexy” field like IT might appear soon? Like it has been in Bulgaria for the last five or six years—a direction all kinds of people head in search of success? The jokes say it will be renovations, tiling, construction…

– We'll live and see, as they say further east. In my view, many people will keep pursuing careers in IT; that can't just die out.

AI won't take every engineer's job. It may limit opportunities for some people if they don't develop themselves.

But a programmer becoming a welder… I don't believe it. Those are just jokes. Of everyone who has taken up this comfortable work—spending all day in a pleasant office or at home—few will go back to physical labor in the fields or on building sites.

There are lots of jokes about tilers practically earning more than programmers… That work is hard. You're on your feet all day, breathing in adhesives, carrying heavy loads… very few people will decide to do something like that. Others, I think, will make a slight career change. That happened in the US last year and the year before, when there were big layoffs. Almost 700,000 people lost their jobs in eight or nine months.

And they all quickly found new ones! Some of them weren't exactly at IT companies, but they worked for companies that need IT services.

So

as long as the economy hasn't completely ground to a halt, people with an IT specialist's skills will be able to find work.

– I think so too, especially if they don't stop developing. Because things change fast. Say, right now a programmer who has learned to use AI is much more effective than one who hasn't. So what's worth learning now?

– It will always be worth developing our soft skills. However good we are technically at what we do, communicating with people, being able to present our ideas and thoughts well, persuading others that what we want to do is right—that's priceless. Even if you're a wonderful programmer who can do a lot, if I'm your client and you can't sell me the idea that my website should look a certain way, it doesn't matter how good you are. If someone comes along with better promotional skills than yours and manages to charm me into believing his work will be better and do more for me, as a client I'll choose him.

In Bulgaria, one of the things where we limp isn't the technical and engineering side but sales and marketing. I think that's where we're weak. That's changing, of course, but I still maintain that it's easier to find a good programmer than a sales specialist. I stand by those words 100 percent.

– And yet… let's see how the match ends. Bulgarians versus foreigners, algorithms versus people… I'd be happy to continue on neutral ground. Thank you!

– In IT we all mention it every day, but I'm not sure we understand what Agile is. How would you explain it to a five-year-old?

– As the most normal way to get good results at whatever you do. Of course, I'd hardly explain business to a five-year-old. I'd rather tell them that if they're playing a game with their friends and have to reach some goal, this is the most natural, most normal, most sensible way to get there.

First of all, because it's fundamentally childlike.

You don't try to work everything out from the very beginning, the perfect plan. Just like a child, you have a clear direction and an idea of something, and then you start trying things.

That's what children do: they try, see what works, correct course, change the game, experiment. Besides, they usually have no boss; everyone does something, talks to the others, they act together. Naturally, someone will stand out a bit more here or there, but the communication and collaboration are extremely natural. That's Agile.

– And if we move on to an adult who has never heard the word?

– First I'd ask whether they've ever wondered what the difference is between the most successful companies—Tesla, Facebook, whether in IT or not—and everyone else. It usually comes down to this:

the most successful ones have sensible, authentic leadership, self-organization and a clear vision.

The customer, the one who consumes the business, is at the center, and things are built around them. Added value is constantly sought, the company adapts and learns, and feedback isn't just a cliché. These are the things that set the successful apart from the unsuccessful. And I, at least, call them Agile.

Yes, my understanding differs from the common notion that it's some kind of process framework for software development.

– I think so too. I've taken Agile courses and I firmly believe in the approach. The idea, after all, starts with programmers who wrote the manifesto by that name…

– Yes, and the problems they pointed out are very simple. We're talking about top-tier programmers, and this happened in 2001. Among them were people whose work I studied at university, like Martin Fowler. They got together and talked about how to deal with their main problems.

And back then those were the following. “Process above all.” You can see that something could be done sensibly and well, but the process won't let you. No matter which process—decision-making, approving time off. You see things could develop better and more sensibly, but the process won't allow it. You have no way of changing it; what you can and can't do is imposed on you. No why.

The second problem is that documentation is considered the most important thing, while the actual work done, the added value, is pushed aside. Many companies still have this problem today. Say the year-end bonuses depend on a project; then everything has to match the chosen metrics, and the focus shifts:

how to turn the little checkboxes green, not how to add value.

Agile comes from the software world, but it's tied to problems every business has, especially in a fast-moving, dynamic field. And right now every field is like that. If in 2000 there were areas where it was normal for companies to follow vague four- or five-year plans to the letter, working the way they did during the Industrial Revolution, today 99 percent of businesses aren't like that.

– How would you translate Agile into Bulgarian? Гъвкав (gavkav, “flexible”), maybe?

– More like адаптивен (adaptiven, “adaptive”). “Flexible”… we're not going to do the splits. Adaptive in terms of the reality that hits us from somewhere.

You run a business, but the environment and the world we live in don't ask us what the plan is. Mike Tyson said everyone has a plan until they get punched in the mouth. That happens in real life too. Even without mentioning disasters, diseases and wars, every day brings a new technology, AI, a new competitor, a new business need.

You can't afford to tell yourself: I'll follow the five-year plan no matter what happens. That means not adapting.

Many people see it as a kind of lack of vision, of a plan, as total chaos, but it's exactly the opposite. You need a very clear vision. Elon Musk, for instance, a classic example of Agile thinking in some respects, has a 100-year plan.

Agile means a lot of planning, constant replanning and looking for the gaps, for the things that need correcting. That's why we call it “adaptability.”

– In all my time in IT, I've never heard of a company that doesn't claim to work Agile, and most use Scrum as the specific framework. But in the vast majority of cases, I think, it's a cliché. There's no adaptability behind the term; quite the opposite, we move tasks around and have two-week sprints.

– Yes and no. I'll be the positive one here. To a large extent we're all somewhat Agile, because it isn't a state—I am or I'm not—it's a direction, a journey.

Some things work well for you, others don't. You improve some while others lag behind, then the other way round. So if we break down the whole mindset, the principles, you'll see that every company does a certain set of these things, at least in some direction.

In some places there's a form of learning from mistakes—yes, maybe not enough. In others there's a more natural form of collaboration, of putting the customer at the center. These things are there. Just not everywhere, not in every direction, every area.

So we're all somewhere along the way. The problem here is the “cargo cult.”

That's the story of tribes on remote islands who imitated what soldiers had done in order to summon the planes and have them bring chocolate.

When we imitate, we expect the results of the one we're imitating. But that's doing without understanding.

We can call it Agile or whatever we like, but we don't dig deep enough. That happens with a lot of companies.

Unfortunately, the Dunning–Kruger effect is very strong here too. The less you know about a subject, in this case Agile, the more confident you become in your knowledge. And you think: there it is, got it. I understood it in five minutes and we're Agile.

– Alas, this principle seems almost unavoidable…

– Actually, the deeper you dig, the more you realize how un-Agile you are, how many things you're missing. To me that's the big problem: the Dunning–Kruger effect combined with the “cargo cult.” We imitate certain practices but don't understand them deeply enough. We assume that's all there is, because we're smart and intelligent—usually these are people with a lot of experience and a lot of knowledge in a particular field.

That's one problem. The second is that Agile has become something of a dirty word because of all the overselling. At some point the community couldn't agree with itself on whether it wanted to create a model that helps businesses work well or one that turns into a certification business.

The certification business has become a machine where you hand over €200 and get a certificate saying you're great. But when you're in a manager's position, you get a certificate in two days and land in a “cargo cult” company… You run the show however you please, delete the title of the staff meeting and rename it the “daily stand-up.”

Then Agile doesn't work, it's no good. These things are closely linked. That's why we want to change the word, to rebrand this Agile.

– What will you replace it with?

– We're trying to talk about Business Agility. Maybe it's not enough of a rebrand, since it's the same root. But we want to show that it's about business flexibility, adaptability. That's the main goal. Yes, some team introduces certain practices, but ultimately the goal is for the business to adapt.

– Coming into IT from journalism, I realized that, without knowing it, I'd been part of a Scrum process. Publishing a magazine, my colleagues and I had one-month sprints, after which we'd sit down for an editorial meeting, which is planning. Every day—we didn't call it that—we'd have our daily standing outside the newsroom, some of us smoking. So somehow, intuitively, processes lead there…

– Yes, they're very intuitive. But

if we want to reach a high level, we also have to dig deep.

Achieving decent results as a process happens quite intuitively. And it does happen—as it did with you, and with many companies that are small and don't have much hierarchy.

But when we want to grow, to solve the next level of problems, intuition doesn't always help. It matters, but we have to question it, because we work with complex systems. In a complex system—and today they all are—the interconnections aren't obvious.

Very often the saying “you hit it here and it cracks over there” applies. We change something small, and a problem pops up at the other end. A bit like the butterfly effect: a butterfly flutters here, and a hurricane hits the other side of the planet.

We're not always talking about hurricanes, but there's always an unclear chain of cause and effect. That's why being able to recognize it is so important; intuition matters. But it has to be backed by a data-driven approach.

It should also always take the form of a hypothesis. I have an intuition that we need to do something to achieve a certain result. But that's a hypothesis we have to verify—to check whether it's right.

– What led you in this direction? I see you studied technical disciplines.

– First I was a programmer. I have a degree in software engineering, I worked for nearly eight years and rose to team lead and project manager.

The thing is that

at some point you see that the essential problems in software development aren't how to make a piece of code super efficient, but business questions.

Intuitively, I started turning my attention to solving that kind of problem. How do we find the most accurate solution? Why do you make something exceptional, only for the client to cross it out? Or why do you end up with misunderstandings… These different situations shifted my focus.

And besides, I got inspired! I managed to attend a few inspiring sessions by Agile gurus from around the world.

That's how I caught fire. Everything is very simple and clear. And that's beautiful!

Like in software or mathematics. You have a solution that's super simple and clear.

In this case, when I saw these people explaining how my complex problems could be solved in a super simple and intuitive way, it gave me the push to dig into the subject.

– And how did things go from there?

– For a while I was an employee, looking for roles related to Agile. I went through everything—programmer, product owner, Agile coach, Scrum master, enterprise Agile coach—partly because I was looking for it, and partly because I worked at outsourcing companies, where you're constantly on different projects.

On top of that, I dived into the subject itself as a side pursuit: digging, learning, going to conferences. I also started teaching it at Sofia University. I began taking part in more and more public events and conferences, and people started inviting me to give training. It became a second job—someone calls, I run a training, I take two days off.

Demand grew, and at some point it became my main job. What I used to do as part of an outsourcing company, we now do under our own name. That gives me more freedom to act. I consult various businesses and run trainings.

– Tell me about some examples of such a change, whether in IT or not…

– Of course. Sodexo, now Pluxee, is a company that makes meal vouchers. They had a very proactive leader, Gábor Olajos, who knew a major change was coming to their business. A company built on print services was becoming a fintech company issuing payment cards used through POS terminals—a very complex digital transformation.

It was clear this was coming and that it would be extremely intense. A 20-year-old business had to transform from the ground up in six months. And he said, “I want us to try Agile!” We started with pilot implementations and trainings. At some point the law came in, and we got to work in earnest, including with a huge number of outside companies.

Suddenly it turned out to be very easy!

They brought in the suppliers and the other companies they worked with. They organized their business transformation themselves through an Agile approach. They had a two-week cycle in which they reviewed tasks and set goals, introduced self-organized distribution of work, transparency… The six-month transformation went remarkably efficiently.

A second example: a fairly large pharmaceutical company. Again a proactive leader came along and said, “I have a few months to prove that our company's Bulgarian office can be a digital hub for innovation.”

These are business people, pharmacists, doctors with enormous experience in their field, but no knowledge of how to build a startup or a product from scratch. We supported the whole process of generating and filtering ideas, prototyping, selecting and implementing the resulting solutions by new cross-functional teams.

When you hear “pharma,” you picture regulations and slow progress, but Agile turned out to be a very good fit. Because you make a plan, you have five steps to go through… Suddenly a regulation pops up somewhere and stops you, and it's precisely the approach of adapting, replanning and self-organizing that helps clear these things up quickly and sensibly.

The result, which they measured themselves, was a 40 percent optimization, a faster path from idea to finished product. In six months they generated 50 or 60 ideas from scratch, and in the end, through the whole funnel and process, they arrived at two that went into practice. Again by their own estimate, this would have taken two years, and we did it in four or five months—it was summer, it could have been a bit faster.

– And if we go back to the IT world after all?

– Gameloft is a well-known company in gaming. They don't even define themselves as IT but as a studio, even though what they develop is very much a software product.

The interesting thing is that they, too, carried out a sweeping transformation of their entire approach, which helped them achieve much better financial results. It was remarkable: from people saying, “No way, get rid of it, Agile doesn't work, it's nonsense!” to the idea, six months later, that everything should be Agile.

– With this return to Agility, can we say there's no need for additional frameworks like Scrum and Kanban? That they've rather played a part in making us see Agile as a precise recipe instead of a way of thinking?

– Frameworks are needed, but the point is that we have to understand them.

They're simply tools that are good to have. They're not mandatory, but they can help us.

The problem, again, lies in the combination of the cargo cult and the Dunning–Kruger effect. Add certifications to that, and you get this mindset, the negative side.

It's like the sparkling wine we call champagne. Most likely a lot of people who claim to make much better champagne than the French get annoyed. In the same way, the Agile world doesn't like it when we say Agile equals Scrum. Sometimes it may be better never to have heard of Scrum and Kanban.

I personally believe that if they're done with understanding, Scrum, Kanban, Extreme Programming and Design Thinking are extremely important and useful. But you need a teacher who can point a few things out to you. Or, if you don't have one, you need to keep seeking verification of what you're reading or doing.

In the martial arts there's a model of how you become a sensei: “Shu-ha-ri.” “Shu” is the phase where you simply repeat the basics. If it's tennis, you hit a forehand a million times; in karate, you kick the air a million times. That's to master the fundamentals.

In this case, Kanban and Scrum are the fundamentals—someone already knows that these basic moves lead to a good result, a solid foundation.

But we don't stop there. If someone only ever does the basic tennis stroke, at most they'll play with a friend once a week. The next step, “ha,” is where you start experimenting, combining practices, looking for different variations.

The final phase of mastery, “ri,” is building your own style. Rafael Nadal has a highly unorthodox forehand—he hits a distinctive “banana shot,” whipping the racket behind his head. If you go and tell your coach you've watched Nadal and at your first session you'll hit like him, either he'll throw you out or you'll break your wrist.

That's why great athletes build a style of their own that doesn't follow the fundamentals, but if you go to Rafael Nadal's academy, you'll see how they teach you through the fundamentals. It's the same with Scrum and Kanban.

– To wrap up, let's talk about the upcoming conference. Who will be the most interesting speakers, and who should come?

– We believe Agility is horizontal: it's not just for information technology, for banks, for pharma. It's everywhere. At the same time, we really do want to rebrand Agile, to pull it out of the cliché of a team of programmers building something.

And to ask: “Let's see—if I'm a leader in a company, even an informal one, how can I use Agile practices to influence the whole business? How do I want my leaders to influence the business positively too?”

That's the theme of the conference. People in leadership positions, even informal ones, are welcome. The focus is on showing how Agility is achieved—on the one hand through outstanding experts from the business world, consultants, specialists who have written books we learn from, world-renowned in this field. The second track is people from Bulgaria, leaders at a high professional level, who can show from their own experience: look, we're the business, and we did it.

– Great! Thank you for inviting Digital Stories to have a booth at the event, where guests can learn more about my book and my venture. That's pretty Agile… And I'll be glad to talk more about all the important topics we touched on. See you on the 26th!

Can ChatGPT manage to… land a job? Or at least make it to an interview, perhaps the hardest step when it comes to your first job?

We won't indulge in idle speculation; instead, the way many Digital Stories are born, we'll test it in practice. I'll ask the algorithm to write, all by itself, the résumé of a young programmer looking for work. Then I'll send the CV it creates in response to 10 job ads for beginner programmers.

What will the result be? Will the recruiters invite him to an interview? Will they grow suspicious of the generated CV of this young but highly motivated IT hopeful? Or will they simply ignore him? Let's find out together.

And one more important thing: the results, for me at least, gave me some pretty good ideas about how ChatGPT could help me the next time I need to look for a new job. There are definitely more than a few directions. Here's what it's all about.

No One to Hire

These days it's quite hard to find your first job in IT, demand is lower, the requirements are sky-high, they never call you for an interview… Is that really so? We're about to see

August, as you know, is the afternoon of the year, and also the afternoon of the job hunt. Unlike Maksim Ivanov, though, I have plenty of work, and now I have the chance for this instructive experiment.

Who's Max, you ask? Ask ChatGPT. That, at least, is exactly what I did. I asked it to invent the identity of a young IT specialist looking for work, and to suggest a suitable available email address while it was at it.

The algorithm's very first idea is the Maksim Ivanov in question. Interestingly, he even has a university degree in the field, contrary to the opposite extreme view that it's easier to find a job these days if you've chosen a coding academy (the topic is long and far from today's meeting, but my own experience is from the “other” side).

Maksim holds a degree in Computer Science from Sofia University, as of June 2023, no less. What has he been doing for a whole year—clearly it hasn't been easy for him to find a job? Either way, ChatGPT immediately suggests a suitable email address too, which, by chance or not, actually turns out to be available: max.ivanov.prog@gmail.com.

Working on Max

What is Max missing? A photo—how else will he look convincing… Here I allow myself to step in: I let the most popular chatbot write a description, then hand the task over to MidJourney. In the world of today's AI models, it's important to know who would handle what best, and photorealism is definitely not the superstar's strong suit. Soon, though, we have a perfectly real Maksim Ivanov:

I feed the photo back to ChatGPT…

Apparently it won't work on the first try.

This is an important experiment, so a clarification is in order. Whereas, for example, in the creation of EcoElena (who will be back soon!) there was no intervention whatsoever on my part, here some changes are clearly needed. The CV the algorithm gives me has a few obvious mistakes that would keep Max from performing at his max. And we do want our young specialist to find a job, don't we?

First, ChatGPT slightly botches the design, placing one of the dividing lines right across the young man's face. Well, it's not as if I haven't seen even messier CVs, but still, I ask for a little readjustment. I also ask for the phone number to be replaced with mine… then it occurs to me that it's rather ugly to pretend to be someone I'm not, and it seems unjustifiably complicated to look for a way to have the algorithm make the calls itself, so I ask the résumé writer to simply remove the contact phone. I realize that this lowers Maksim's chances of landing a job, but…

I allow myself one more change. His only workplace, as invented by the generator, is a company called Random. That strikes me as a bit too obvious, so I ask ChatGPT to change the name to something else. And so Max's work experience to date turns out to be at Tech Solutions Ltd. Not that such a company exists either, but it sounds convincing.

And that's it—three tiny interventions; everything else is the algorithm's work.

Here's the main version of the résumé—you can take a look at it here.

Time to take on the harder task. Let's find a job for Maksim, the rookie programmer!

No Work Here, Work Over There

But what if… they hire him? What if he somehow gets through the interview and goes further? What if it turns out he can even do the job? Is it really as impossible as we might like it to be?

A lot of questions keep spinning in my head. Including how important it is to be careful when hiring in our software industry, because so much depends on our work…

One example of why came from Miglen Evlogiev, who is in charge of cybersecurity at Payhawk, the first Bulgarian unicorn, in his recent talk at the DEV.BG All in One event. A huge share of online security breaches come precisely from people. People who, by chance or not, ended up in critical positions and decided to show off their access, or who were quite deliberately driven to prove they could bring everything down… Or who ended up in a given company not by chance at all, say, with the help of a direct market rival or a hostile state…

I have that kind of experience too. My colleagues and I go into a job interview; the person on the other side has a brilliant CV, supposedly a Bulgarian with extensive experience in England. I look him up on LinkedIn too, and his profile and connections are remarkable… when the camera on the other end shows a man visibly from distant Asia, who very unconvincingly tries to tell us he's Bulgarian on his father's side and ready for the job.

Never Mind, I'm at Work

Nothing of the sort could happen with our Max. For one thing, he's a good kid; for another, as always, my goal is simply a journalistic experiment. Not to lie or manipulate, but to give food for thought about how far technology has come… or, conversely, how much we've overestimated it.

Our hero isn't picky. He picks the first 9 random ads for entry-level specialists, regardless of the specific technology, in the section of my friends at DEV.BG's job listings that is handy for just this purpose. The tenth ad I choose personally—why I make this exception, I'll tell you before we part.

The technologies vary: some ads are for front-end, others for back-end development, still others simply explain that they're looking for enthusiastic future programmers. There are big, world-famous companies and smaller ones; the random selection turns out to be colorful and representative.

For each of the ads, I ask ChatGPT to tweak the résumé slightly as it sees fit. The differences aren't that big, but they're there: here and there, some additional technologies that Max knows have been added; in others, right in the introduction, the young man mentions that he'd be glad to become part of that particular company. All in all, ChatGPT does a wonderful job of creating and “touching up” a CV.

Since I've often had to review documents like these at work, I can tell you with conviction that what it produced is considerably better than most résumés I've seen in terms of readability, design, layout and summary. Maybe artificial intelligence will turn out to be a useful assistant the next time you're looking for a job? Not to make up information, of course, but to help you better present what you can do and what you've done.

Gray CVs

Another important clarification. I won't show you the 10 different versions, because I don't intend to reveal which 10 companies happened to end up in the experiment—that seems unethical to me.

I pick an August Thursday to start, and we'll see what happens over the next three weeks. I send off the CVs… What will the results be?

While I wait, I take the opportunity to discuss the topic with Max's “dad” (would his patronymic be Chatgeepeeteeyev?). I ask which of the 10 companies on his list seems most promising, and he dives into reading online and puts together a rather detailed table. I can choose which technologies Max should favor, and what the pros and cons of each potential workplace are. Here's yet another use of the algorithm in the job hunt that could prove useful.

“If you love working with high tech and want a stable and prestigious position, X may be a suitable choice. If you want to work in an international team and deal with networks, Y may be a better option. If you prefer the fintech industry and working with large transactions, Z is an excellent choice,” says Mr. Chat. “If you're undecided, it's best to think about which technologies and industries attract you most and what matters to you in the long run,” he adds, and I can't help but agree with his conclusions.

Work Will Make Us Free

Day two… I already have two emails—exciting! Ah, quite interesting. Hours after I've sent the applications, recruiters get involved. Both first emails, though in different fields—one looking for a young QA specialist (supposedly the hardest kind to place) and the other for a programmer in robotics—say they're interested in Maksim and would be glad to hear from him by phone to talk a little about him.

So the “filter” worked in this case! Given such a reaction, I decide not to go any further: as I mentioned, from here on we'd be heading toward an outright lie, which is definitely not my aim.

The days go by, and lo and behold, two invitations arrive, proposing a day and time to schedule an interview! Maksim has reached the most important step!

Now that is an undeniable success. Surely no one would waste their time, especially given the number of applications, conducting interviews unless they saw potential in the candidate. One could say that Max would probably be one (or more) interviews away from his dream first job as a programmer!

Somewhere around here, in my view, the experiment becomes a success. So it really is a myth that a beginner programmer can't find a job these days—in just a few days, the interest is impressive! In 20 percent of the attempts, he made it to an interview!

With only one of the responses do I decide to try the next step. As the next stage of the selection process, they invite Max to fill out an English test and solve a few programming tasks online. I'll try letting ChatGPT take the test, simply copying its answers. The main difficulty turns out to be that the app requires the camera to be on so it can monitor who's taking the test. I turn it on against strong backlighting, and it seems to pass. I just stand in the middle, between the tasks and the algorithm…

Clean Young Programmers

The position is for a .NET developer. As a programmer, I know C#, the language the test requires, fairly well, but of course I don't step in to help “Max.” Still, it's clear enough to me that he solves the test practically without a mistake, and in a fraction of the time he's been given.

Alas, either suspicion of exactly that, or my cheap trick with the camera, proves fatal to the performance of our imaginary programmer, who two days later receives an email saying another candidate has been chosen for the position.

The last response comes a full 3 weeks after the application was sent—I assume Max was “in the game,” and it simply turned out there were stronger candidates.

Two more companies reply directly that they've reviewed the application but have either hired someone else or weren't satisfied with Max's CV. This makes a very good impression on me; the culture in this respect is clearly improving—I've often received no feedback at all when applying for a job and reaching a certain stage.

Maximum

Time to take stock. From 10 CVs sent, Max got 2 interviews and 1 assignment as the first step in the hiring process. Two responses asked for his phone number, and if he were human, he would definitely have had a chance to move forward. Another two were rejections. It doesn't surprise me that not a single response said outright that something was off, that the CV or the photo was generated. Only 3 of the companies didn't reply at all.

Still think there's no demand for young specialists in IT? Ask Max…

It remains for me to tell you why I personally chose one of the ads: it was sent to the company Sofia Stars, because I know the head of its recruiting team well. Petar Dzhuganski himself will be our guest in one of the upcoming Digital Stories, to talk about the experiment as well as the future of the job market and of professions.

And the results of the experiment give me, at least, even more food for thought. Not only about the professions AI will change, but also about the way we practice them and the way we find jobs. Is the moment when Max could go further really that far off? Be convincing in a real interview, then… land a job. Do his tasks while I just pocket his salary… Or will he take mine?

One thing is certain. It's time to stop looking at artificial intelligence through clichés and templates and to open our eyes. Because now is the moment for interesting ideas, for unconventional solutions. For getting to know and further developing the new, unexpected assistant we have.

And what do you think about the possibilities of artificial intelligence? Your opinion matters, and I'll be sincerely grateful if you fill out the survey here.

The experiment was carried out based on an idea by Kristina Vlaeva.

If you have interesting ideas that could become the next Digital Stories, suggest them here. A modest reward awaits you!

Computer code that looks like a cooking recipe? Writing programs with the help of… colors or sounds? Programming languages inspired by Shakespeare, by Terry Pratchett and even by Donald Trump? Step off the beaten path and you can find some rather amusing and unexpected touches in the world of information technology.

Programming languages are even more colorful and interesting than human ones. They are not born “naturally” as the result of evolution, but out of their creators' desire to hand everyone else a new tool. One good enough to turn their ideas into the computer programs that drive the world forward today. Yes, there will always be the serious, useful languages (for instance JavaScript, the one I earn my own bread with), the ones used every day and everywhere around us.

But there is also the other side—playful, wacky, unconventional.

“Esoteric” is what we call programming languages that, for one reason or another, are doomed never to become popular. Sometimes they are created that way on purpose; in other cases their developers simply didn't try hard enough and made a product that could never go mainstream. Either way, they can always surprise us. In the lines that follow we'll meet 10 examples of some of the wackiest programming languages out there.

1. Let's Have a Chat

Anyone can make up a sequence of commands for a computer to execute so that something happens as a result… But to talk about a programming language, the most common criterion is called “Turing completeness.” Even some computer games meet it, if only as a side effect. Still, this is the universal test of whether we are dealing with a full-fledged programming language. One that, in a nutshell, can carry out a list of operations within the capabilities of Turing's theoretical machine (you'll find a detailed explanation here).

Those are exactly the esoteric languages we'll be looking for (with one exception). Not mere sequences of commands, but Turing-complete systems that could build a computer application…

That doesn't mean at all that they have to be complicated or packed with features, the way their “real” everyday siblings are. Our first example proves it.

SmallTalk (English for casual conversation, but literally “small talk”)—that is the name of a small language with a big history. How can a language be small, you ask? Like this: here it fits on the back of a postcard:

In the 1970s three engineers—Alan Kay, Dan Ingalls and Adele Goldberg—were working at the Xerox PARC lab. They considered the programming languages of the day needlessly complicated, convoluted and hard to learn. So they decided to create a language whose entire syntax would fit on a napkin.

The original idea was for SmallTalk to be used by children learning to program. But it turned out so successful that by 1995 it was the second most popular object-oriented language after C++.

At the start of the new millennium the American military used it to build military simulation software with more than a million lines of code. JP Morgan wrote its large-scale financial risk analysis system, Kapital, in Smalltalk. The language became a favorite of many other companies in the financial sector, too.

Smalltalk is still here and is believed to have a solid future. It is popular as a good fit for big data analysis, the Internet of Things and machine learning. It simply turned out, most unexpectedly, that sometimes it's enough for everything to be… simple.

2. The Patriotic Programmer

All languages are written in the Latin alphabet, you can't learn programming without English… Well, not exactly (and not only because today you can ask ChatGPT in Bulgarian to write code for you). Esoteric languages are quite varied, even though, of course, in the professional sphere English has established itself over the past few decades as the lingua franca.

Are there programming languages in Bulgarian? If a patriotic government ever comes to power here, it could easily decree that state software must be written in Bulgarian. Luckily, we have solutions. Here's one option: the Logo language.

Yes, its name gives it away: it's used mainly for drawing pictures, and it was created in the 1980s.

You can put it on your CV, and its syntax is quick to learn. Its main purpose, once again, is to draw children into programming, letting them draw on the screen without having to know foreign languages. In Bulgaria, in the second half of the 1980s, national Logo olympiads were held to pick out the most promising kids in information technology.

What does the code look like? You'll like it. To draw a circle, all you have to write is:

повтори 360 (нп 1, нд 1)

край

Here “повтори” (povtori) means “repeat,” “край” (kray) means “end,” “нп” (np) stands for “напред” (napred, forward) and “нд” (nd) for “надясно” (nadyasno, right). In the same way, a square is drawn with:

повтори 4 (нп 10, нд 90)

край

3. Thin Air… with No Pressure

In some languages people argue about how much indentation should precede a command—two spaces or four, a tab or a single space. In others, the most popular of which is Python, indentation actually replaces some of the brackets and arranges the hierarchy of commands.

There is, however, a language… that is just and only empty space. Nothing more. For obvious reasons it's called Whitespace, and everything in it comes down to spaces and tabs.

Because of its defining feature… it's a little hard to show it to you. Still, not impossible. Let's say S is a single space and T is a tab:

S S S T S S T S S S

T

S S S S S T T S S T S T

T

Commands are separated by a new line with a tab, and what actually gets executed depends on the combination of spaces and tabs.

4. Honestly, Everything's Oook

Technology and literature don't go hand in hand? Nothing of the sort! This language will appeal to fans of fantasy and Terry Pratchett. On the Discworld, as they know very well, everything happens on the backs of four elephants, who in turn stroll about on a huuuge turtle, the Great A'Tuin.

This world has a magical university whose librarian is an orangutan who communicates by saying nothing but “Ook” in various intonations.

Well, in our world that same orangutan could have become a programmer, thanks to a language inspired by him and created by the programmer David Morgan-Mar. Yes, he specifies: the language is intended for use by orangutans only.

Ook—that's its name, and it has three commands:

Ook.

Ook!

Ook?

And… it's Turing complete too! In other words, you can do almost anything with it that today's modern programming languages allow, and nobody will ask whether you really are an orangutan.

We leave you to ponder (and practice) the program that delivers the proverbial first message: Hello, world!

And while we're in the literary world, things can sometimes get far more dramatic. Literally—if you choose to program in Shakespeare. Here you can say outright that all the world's a stage and we are all players.

The names of Shakespeare's characters are variables, and whether the numeric values are positive or negative depends on the pairing of two characters in a given scene.

If, for instance, Hamlet has a negative value, you can bring in another character to insult him and thereby flip the sign in front of the number. You can add input and output, in the first case by making someone listen to their heart, in the second by having them speak their mind. As wacky as you like! This wacky:

There you go—even if your English is rusty, you can now boast that you understand the basics of the language of Shakespeare…

5. Chef Programmer

As Douglas Adams wrote, in the third phase of its development every advanced civilization arrives at the question “Where shall we have lunch?” Well, programmers reached that level long ago, as our next guest shows. Chef was created well before the fashion for televised cooking reality shows, and its job is to help in the kitchen. Here is the most standard “Hello, world!” command in this language:

Rather wordy, isn't it?

But fun: the ingredients are the variables being defined, and the recipe itself is the printing function. The different groups of ingredients are also variable types that interact during execution—that is, cooking.

The idea behind the language? To show people who aren't technically minded, and are more used to the recipes of Gordon Ramsay or Penka Cholcheva, a beloved Bulgarian TV cook, that programming is fun.

The “Serves” command, the moment the dish is ready to serve, runs the function.

6. #CantPronounceIt

I don't advise you to try pronouncing the name of the language CLWNPA. Spelled out? Fine! “See-el-double-you-en-pee-ay.”

But what does this acronym have to tell us? Compiler Language With No Pronounceable Acronym. It was created back in distant 1972, and although its name is so colorful, it became popular under the far duller name INTERCAL.

One more thing to remember it by. It's a rather polite language. Instead of bothering to declare that something is a function, it politely asks the computer to create one:

PLEASE DO ,10 <- #1

PLEASE DO ,10SUB#1 <- #176

PLEASE DO ,11 <- #30

PLEASE DO ,11SUB#1 <- #76

DO ,11SUB#2 <- #190

(You'll find a longer example here.)

“Please, dear computer,” followed by bitwise information about what needs to be done. As if back in the good old year of '72 someone suspected that today we'd be so dependent on computers that we'd have to be polite to them (so that Roko's basilisk doesn't come for us).

Of course, the “unpronounceable” one comes from very different times, when the widely used languages were nothing like today's. Code meant for computers looked a lot more like, well, code. Accordingly, it wasn't particularly readable or understandable in a way that made developers' lives easier.

So what is the PLEASE command for? If you're not courteous enough to use it, the compiler works in a special, considerably slower and more fastidious mode. And if you put it on every line, it imposes restrictions again.

This language sometimes simply “blows up” with error E774 or… “RANDOM_COMPILER_BUG.” Even according to the official documentation, this happens completely at random. Think this language is a joke? Maybe, but it meets every criterion for Turing completeness. You just have to be brave (and polite) enough to pick it.

7. Plugged In and Wired

After literature, it's time to peek into the other arts. Music, for example, often finds its way into programmers' arsenal—suffice it to mention jazz and the different versions of the WordPress platform.

Which brings us to the programming language Rockstar.

Want to be a rock star but you're just an ordinary programmer? No problem! This language doubles as a way to write song lyrics. Yes, there's none of the politeness of our previous hero here, nor the drama of Shakespeare—just pure voltage and wire:

The creators explicitly state that leaving comments in the code is strongly discouraged: everyone must find their own meaning in rock poetry. Will you find any meaning in what's written above?

8. Pretty as a Picture

We arrive at the universal world of images. Here we flip the traditional scheme that Midjourney and its kin have imposed on us in recent years—the human draws, and the computer tries to figure out what it's supposed to do. This language is called Piet.

Its creator, David Morgan-Mar, uses bitmaps as input, which at first glance look like random scribbles… or like modern art, judge for yourself:

The code is executed point by point, as if a pointer were moving across the painting itself. Twenty different colors are used, each of them a specific command. Varying brightness is added for the corresponding operations. White positions perform no operations, while black ones signal that execution should stop. The language is named after the Dutch modernist Piet Mondrian, and if you're wondering why… here is one of the master's best-known paintings:

9. Make Programming Great Again

Now that we know which languages musicians, artists and orangutans program in, we enter politics, especially topical in this year of elections and political upheaval.

Now that we've calmed down a bit, reassured that algorithms won't take programming away from us as a human activity (not entirely, and not yet, at least), it's time… to make programming great again!

TrumpScript (setting aside its political messages) doesn't even pretend to meet Turing's requirements, but it's fun.

It has no floating-point numbers, only integers, because “America never does anything halfway.” All numbers must be greater than 1 million, because small ones are insignificant. External libraries aren't allowed, because all code must be homegrown American. Instead of the usual terms True and False, it uses the presidential candidate's favorites, fact and lie.

Whether the language will last… we'll find out after November 5.

10. Brain Off Grazing Ducks

We skipped some curious candidates for this list, such as ReMorse, which uses dots and dashes in the spirit of the good old Morse code; Taxi, whose code resembles a road map (without necessarily being full of the life advice that Bulgarians associate with that profession); or ArnoldC, which uses only one-liners from Arnold Schwarzenegger movies.

We deliberately saved for last (on the assumption that we've already lost the younger audience) the language with the resounding name Brainf*ck, which I won't translate, but which metaphorically refers to mental gymnastics.

It is intentionally difficult, designed so that every elementary task becomes a challenge for the programmer. Competitions are held every year, and according to its fans, once you learn to write code in it, you'll be a wizard in any other programming language you know.

And all of this… with just 8 commands, which once again are enough to make it Turing complete. Urban Müller developed it in 1993 while studying physics; gradually the creation took on a life of its own and earned cult status. For the proverbial “Hello, world!” all you need to write is:

++++++++++[>+++++++>++++++++++>+++>+<<<<-]>++.>+.+++++++..+++.>++.
<<+++++++++++++++.>.+++.——.——–.>+.>.

And so, which programming language we choose today may not even matter all that much. What matters more is whether we have anything meaningful to say online…

– The algorithm of life is quite an ambitious title… what would such an algorithm look like?

– Well, in IT we use dozens of algorithms every day, yet we know very little about the word “algorithm” itself. It comes from the name of the Arab scholar al-Khwarizmi, and it's a term with which we

describe something complex through a sequence of elementary actions,

which whoever carries them out can perform in successive steps, without any further explanation… In that sense, if we accept that life is something truly complex, its algorithm ought to consist of simple steps. For instance, if we just repeat the actions “inhale-exhale” ad infinitum, that's already a simple algorithm of life. But, as the sages say, life isn't measured by the number of breaths we take, but by the moments that take our breath away, by the decisions that are remembered and stay on long after we're gone… So—it's the search for that kind of algorithm I'll try to talk about.

– Is it worth trying to look for it?

– Absolutely. It's always worth searching and reaching for our dreams. The question is whether we can follow a few simple truths of life, with a certain logic, that lead to concrete results. In life 2+2 doesn't always equal 4; there are plenty of unpredictable factors and accidents that can't be controlled. But I believe that with the right approach and strategy we can navigate life more successfully and reach our goals.

Success doesn't come overnight, but effort and persistence always pay off.

That's what I'll talk about, through the prism of my own experience.

– Art, philosophy, religion or Elon Musk—who can help us formulate the algorithms of life?

– There are different algorithms. For example, to get from point A to point B you might look for the shortest route, but it isn't necessarily the fastest, and the fastest isn't necessarily the cheapest. Some people need the most precise answer and are willing to invest a lot of time and resources to get it. Others prefer to walk a bit further—their path isn't optimal—but to get a reasonably satisfying answer right away, using minimal resources.

Different people, different algorithms, different goals and different efficiency.

That's why one person may draw inspiration from philosophy and art, for another the meaning of everything lies in religion, and a third idolizes Elon Musk. Think how many die-hard Tesla fans there are—on Twitter alone, sorry, X, there are about 200 million! At the same time, according to a recent New York Times survey, a large share of people don't buy a Tesla precisely because of what Musk did to Twitter, his odd statements and his support for conspiracy theories…

– While we're on the subject of authorities… do you have idols in your life, people you'd like to resemble?

– I draw inspiration from everyone around me—from my children, my wife, people close to me, friends, relatives, colleagues… and perhaps a few famous people at the bottom of the list.

I'm very careful about idolizing famous people—we usually know very little about them,

especially in the age of the huge PR teams those people hire. We all know John Lennon was a brilliant musician, but how many of us have heard that he regularly beat his first wife, Cynthia? Virginia Woolf may have been a brilliant writer, but she behaved appallingly toward working-class people. Even Mother Teresa, canonized as a saint, has in fact been accused of misusing millions and of treating the people lying in her hospitals terribly—people who, in her words, had to suffer as Christ had suffered. And it's somehow strange to fight AIDS and reject condoms at the same time, don't you think? Meanwhile, I was in Japan recently and was deeply struck by how happy a doorman at a shop entrance was simply to direct people, greet them and make them feel a little better…

– Maybe online communication has made it even harder to create authorities, because we can so quickly find a spoonful of tar to drop into anyone's barrel of honey… Do writing computer code and telling stories have anything in common?

– Yes, they share a few things. I have great respect for people who manage to say something important or interesting as concisely as possible. There are authors who, like Marcel Proust, Virginia Woolf or Gabriel García Márquez, “stretch the lokum”—as the Bulgarian idiom for rambling on has it—and can go on for hours without saying anything of substance. I prefer short, meaty, dynamic texts—George Orwell, Ernest Hemingway or Dan Brown, for instance. It's the same in programming: some people write clean, short, tight code, and others use strange, obscure data structures and lots of repetition. Fortunately, over time technology is helping make the latter fewer and fewer.

– And here we come back to the impossibility of universal algorithms; to my mind, some stories simply need slower, more leisurely telling… I have a site where I tell digital stories; you have vast experience in the world of technology and a knack for storytelling. Is there a story that shaped you as a person more than any other?

– Almost everything that happens to us shapes us as people, and in my talk I'll mention exactly ten and a half such stories… I want to save the most “formative” ones for then, but I'll drop a hint: when you sincerely dream of something and suddenly something else makes you say to yourself, “Hang on, is this what I actually want?”—that's precisely when a large part of your worldview changes. I got that “slap” from life before I even came of age, and I had enough time to realize exactly what I wanted…

– The world is small, and the IT world in Bulgaria is even smaller—you probably don't remember that I once sat an interview with you, and actually did fine.

– You see? And most people say my interviews are really tough.

– Sometimes you get lucky. And I hope I'm not giving too much away by saying I somehow managed to pull off a topological sort of a graph… Life is strange; look, today I'm the one asking the questions. Do you have an interesting story born during a job interview? In return, I'll tell you mine.

– The truth is, I've conducted more than 500 interviews and I've seen just about everything—a man who sat down between the two interviewers and said, “Wooow, what a nice little office we've set up here, eh?”;

a man who came to the interview with his mother, and she nudged him toward the room: “Go on, sweetie, good luck!”;

a man who came in person to ask what was wrong with his CV, because he'd sent it to more than 200 places and nobody had called him back—we sat down, talked, sorted the problem out, and he now works in IT…

But the most memorable interview was with a girl named Petya. It was the first interview of her life, for our internship program… She did moderately well, she was very nervous and fully aware of all that. At the end we asked her, “Why should we choose you?” Her answer was the most sincere I've heard: “I'm very nervous, but I really am better than I showed today. Give me a chance to show what I can do and you won't regret it.” And we gave it to her. We took her into the internship program. Back then I was a young, inexperienced “boss” and often played the big shot. Petya very often came to me and said: I didn't like what you did there, that made me feel bad… And in fact I learned more from her than she did from me… But that often happens if you listen and watch carefully. I'll end by saying that Petya is now a program manager at one of the leading IT companies. Now it's your turn!

– Interviews are among my favorite pastimes too, in more than one sense—here, on the DEV.BG podcast, and at work as well, on both sides of the “barricade.” That's where my recent funny story comes from. My colleagues and I were impressed by the CV of a candidate with a remarkable background; we join the online meeting… and on the other side is a young man who is visibly from Southeast Asia and has nothing to do with Bulgaria, even though he's presenting himself as Bulgarian. And for a full 30 minutes he stubbornly kept looking up the answer to every question online—from JavaScript to what his favorite Bulgarian word was…

What do you think—does the software world give birth to real friendships?

– Definitely. I recently read about yet another World of Warcraft wedding… But even outside gaming, work in the software world often demands collaboration and communication, which leads to strong bonds. Some of my strongest friendships were made through software development—some go back to my school years and the informatics competitions, even though those friends are now on the other side of the ocean; others are colleagues who became our kumove, the wedding sponsors and godparents who are family in all but blood…

The software world gives birth not just to friendships but to children, let me tell you.

In one of the teams I built from scratch we were so close-knit and had such cool team-buildings that whole families with children came out of it in the end, and the kids still play together to this day. Even my son was conceived during a company team-building in Belogradchik, where we went with our partners…

– Is code written by happy programmers better?

– I sense the tease, but the truth is I only buy eggs from free-range hens! Perhaps I should say I'm not a big fan of paying three times as much for an organic tomato because it grew in a special greenhouse in Germany, while at our grandmothers' places almost everything is still “organic.” But I definitely taste the difference in free-range eggs. And software, like free-range eggs, is of higher quality, more reliable, more creative and more ethically made, because

happy programmers are more motivated, more productive and more attentive to detail.

Not that I haven't seen very good code written by an unhappy programmer… It's just that when you're unhappy and working too much, the chance of mistakes is usually higher.

– When does a person realize they're working too much?

– I catch myself easily now. I really love my job, and when I sense a lack of motivation and interest in it, or catch myself neglecting my personal life and health, I know I've “overworked”… But by the time I noticed that moment, I'd nearly reached burnout. Incidentally, I gave a not-too-bad talk on burnout at OpenFest a few years ago. TL;DR—one of the best ways to cope is variety and keeping a balance: between personal and professional life, between physical and mental load, between the left and right halves of the brain, and so on.

– IT people communicate online in rather strange and sometimes rude ways, as I also discussed with Boris Simandov, the cofounder of the biggest forum. Why do you think that is?

– On the one hand, there are lots of people who don't come from the IT world, where everything is short, clear and concrete… And so you get the lokum-stretching I mentioned. That irritates IT specialists—I even wrote a blog post on how to write a short email, for example. On the other hand, many IT people are used to working in an environment where communication is online, formal and technical. So the nonverbal cues that help us better understand other people's intentions and emotions are often missing. Over the years, many IT specialists become more withdrawn and less keen on dealing with people. Some simply find technical problems more interesting; others prefer to work quietly, comfortably and independently; a third group are used to tasks that demand focus and deep concentration and know that breaking off for social interaction leaves us unfocused. And there's a fourth group, hopeless techies who just don't want to burden “mere mortals” with their complex concepts and terminology. But in the right setting even these people loosen up; they're often wonderful professionals with terrific ideas.

– Sometimes my good programming ideas come to me while I'm writing prose. Have you had moments of unexpected inspiration like that?

– More than once. I remember a programming contest run by Personal Computer World magazine, where I had to cover the whole board with a knight's moves. Standard algorithms like recursion and so-called backtracking weren't optimal. For days I stared blankly at the chessboard, shuffling pieces and scraps of paper, and couldn't come up with a good solution. One evening we were visiting a family we're friends with, and I watched their little daughter carefully coloring in her book—first around the edge, along the outline, then the middle. It struck me at once that this optimization—going around the board from the edges toward the center—could be applied to my problem. I won the contest!

The reverse—algorithmic skills in real life—is something I use often, too. For instance, I'm very good at packing. I can fit four or five big bags of shopping into a medium-sized fridge… Acquaintances often come to me when they're planning trips, because I find interesting and financially optimal routes. For friends who wanted a week-long trip to Brussels, which with a direct flight would have cost them more than 800 leva (about $440) each, I managed to find flights plus trains and buses for under 200 leva in total—and they got Eindhoven, Antwerp, Paris and Beauvais into their itinerary as well!

– We're lucky to work in a field that is shaping the future. I think that often makes us slightly arrogant. Is there something to that?

– Maybe it's because we earn more than the national average, our skills are convertible anywhere and we're in demand. There's a nice little joke on the subject: Tinder is a service where pimply programmers look for pretty girls to meet. On LinkedIn it's the other way round… But the IT sector in Bulgaria has gone through a lot of changes. Many people from other fields decided they wanted to become IT specialists because the work pays well, without having either experience or real interest in the field. A few years later the quality of people in IT dropped in favor of quantity. And as a result of that process, things have begun to change drastically in the last year or two.

We face competition, mainly from other Eastern European countries, India, Pakistan and the Philippines, and there aren't that many of us. Years ago, at QA: Challenge Accepted, one of the conferences I organize, Emo Slavov talked about how hard it is for China to produce a ballpoint pen, because of the delicate work on the tiny ball at the tip—so the country that exports just about everything imports that tip from Europe. I've remembered Emo's words: we IT people in Bulgaria must be valuable and hard to replace, like the ball in a ballpoint pen!

– What are the most mistaken stereotypes about IT people you've heard?

– Oh, I remember my wife and I were talking once and she told me, “You know,

before I met you, I imagined all programmers in white, ironed shirts, with huge horn-rimmed prescription glasses

and unbrushed teeth!” It's true that for us programmers what you can do and actually do matters far more than how you look. I've been in the industry for over 20 years and have hardly ever seen a programmer in a suit and tie. We go around in T-shirts and jeans, rarely branded ones; we have nobody to show ourselves off to in a different light!

But the fact that we're stressed by deadlines and broken code, and sit motionless in front of monitors for seven or eight hours a day, or more, has unavoidable effects on our health… I'm really glad that more and more often colleagues talk about healthy living and fitness, watch what they eat and drink, look better and better, and have increasingly varied hobbies and side interests.

– And the most well-founded stereotypes?

– Sorry, a joke on the subject just came to mind… A programmer boasts: “I've got two girlfriends—a blonde and a brunette. I tell the brunette I'm with the blonde. I tell the blonde I'm with the brunette. And meanwhile I'm sitting here solving problems…”

Really, one of the most well-founded stereotypes is that IT people love solving problems. Some people see a problem, panic and try to run as far away as possible. With us it's exactly the opposite—

we don't just love solving problems, we love looking for the best, most innovative, optimal solution.

That's at the core of our work, and it's a quality that genuinely defines us.

– I completely agree! And ever since I moved into the industry “in my old age”, I've felt the same change in myself. We often create technologies without any idea how they'll develop—hardly anyone could have predicted how far social networks would go, for instance. Should we stop and think?

– Definitely. It's important to think about the potential consequences of the technologies we create… And I say this having just returned from Portugal with hugely delayed flights, thanks to the CrowdStrike software problem. It's also important to love what we do. I'm very proud of my daughter—she has a gift for languages, chemistry, biology, literature and programming, but in the end she decided on her own to enroll in a secondary school specializing in IT. After all,

when you do something you love, you never work a day in your life, right?

– What that you've created in the software world are you proudest of? And in the analog world?

– Actually, I'm proudest when I see some creation from the software world show up in the analog one and do useful things for society. Over the years I've worked on many interesting projects in different fields—finance, insurance, e-commerce, real estate and much more. A washing machine, for instance, that calculates its own heating, washing and drying cycles and runs at the cheapest electricity tariff, downloaded from the relevant power distribution company in Bulgaria.

Or you order a tracksuit online, the top and bottom, and one arrives from Thessaloniki and the other from Budapest, and you have no idea, picking it up the next day from the shop on Vitosha Boulevard, Sofia's main shopping street… Or, God forbid, you crash your car, and it automatically sends 112, the emergency number, information about the location of the crash, the condition of the car and even how many people are inside!

Right now, at Pliant, which was recently acquired by IBM, we're working on an automation and orchestration platform that other IT specialists use to make their own lives easier and build hundreds of products that make our everyday lives easier…

In the analog world I'm very proud of the conferences I co-organize, and of my efforts to train and inspire the new generation of specialists, especially after the pandemic, when I was teaching unemployed and blind people.

– Today, rushing along in our everyday convenience, we rarely have time to slow down. Is it worth doing?

– Slowing down drives me crazy, but I'll admit it: when I want to stop time, I go for a day to the village where I spent my childhood, Prostorno, in the Razgrad region of northeastern Bulgaria. Every time, I remember the long summer days of carefree idleness, even boredom, riding around on my bike… I know that 20 years from now the village will still be almost the same.

What recharges me, though, is change. Sometimes I just click away at a simple computer game, other times I clear my head on my motorbike or go for coffee with people close to me… even this interview is something different and pleasant for me! But travel literally swaps my battery for a new one.

– What do you dream of?

– Professionally, of a world where technology is used to improve the lives of all people. Personally, of a life that is more than “inhale-exhale”… I have a favorite poem by Petar Dinchev; I'll continue my answer with it, and that will be my way of thanking you for this interview:

I dream of simple joys alone.

By human kindness ringed around.

One love to charm me as its own.

Let goodness be my waking sound.

Let warmth inside my home remain,

let children's laughter leap and play.

And let there lie upon my name

no golden dust, no blackened clay.

To look all people in the eye.

To never bend my neck to wrong.

And may my life be generous

with tender colors and with song.

I do not want a feast unending.

I dream of simple joys alone.

– So do I… Thank you! After everything we've said here, I'll be especially curious to hear your 10 and 1/2 stories, which will open DEV.BG All in One. I'll be there too, at the Digital Stories booth, where technology and real life will once again meet in an unexpected way…

– I hear you're working on a project that will make artificial intelligence more accessible to companies. Will you tell me a little more?

– With TeamLandi we're launching a product on the Bulgarian market aimed mainly at medium-sized and small companies, because there aren't many solutions for them. It's a complete system that helps them take advantage of the latest advances in AI services quickly and easily. So they can integrate those services into their business through a platform that makes it genuinely accessible and easy to use.

We already have a first version of the product, we're in active testing, and we're getting inquiries from companies. I'll be glad to say more soon, but it's definitely an end-to-end solution.

– What kind of everyday applications are we talking about?

– At SoftServe, a company that has just turned 10, I work mainly on transformations in large companies, many of them in the Fortune 500. I have a lot of experience with all the big cloud platforms. And I can see how,

unlike the cloud, with AI there is simply no field and no task where it can't help.

Absolutely any task a person can imagine can be made easier by artificial intelligence. That wasn't true of any other technology that arrived over the past few decades.

And it's important to keep that in mind from the very start, at the planning stage. Every transformation begins with planning the specific applications. With cloud services, the goal might have been reaching the global market, cutting costs or developing software faster. With AI you don't need to think too hard—it can help with absolutely any task, whatever your business. Look, you're recording our conversation right now; afterward, instead of listening to it, you can have AI summarize or transcribe it for you.

– Except far from always successfully… As a programmer I also see apps like that being rolled out, but they still can't cope; they'd summarize our conversation rather clumsily.

– Yes, it doesn't always work well. But look back in time:

people say this about every technology at the start—it leaves a lot to be desired, it isn't good enough.

I remember virtualization. In the beginning it used 50% of a server's resources and didn't work well. Now everything has moved to cloud services and utilization is above 99%.

In other areas it already does a great job. For programmers, for instance, there are all sorts of copilots that work well. Even ones not tied to Microsoft, because sometimes it matters that the model runs on your own computer. The data may be critical. A friend of mine, for example, works at a firm that deals with patents, and they were using Google Gemini before they realized it wasn't a good option.

If I had critical data, I wouldn't share it with the model.

That kind of data is better kept with you; you can use open-source models, which are quite competitive these days.

There are also hybrid solutions where, instead of everything being centralized and running on servers packed with graphics cards, part of the computation happens in the cloud. It turned out, for instance, that the chips in Apple computers handle small models very well, which is convenient for companies. We had a client with 70 programmers, all on MacBooks, so no additional investment was even needed. Everyone runs a model locally that integrates directly with Visual Studio Code, and that way they avoid all the risks of a remote model.

– And yet, even before ChatGPT, and especially since, I've talked with many guests about how these applications will spread. But I don't get the impression it's happening on a mass scale yet. Why are we so slow?

– With every technological change, especially one this big, there's resistance of various kinds. From employees—fear; caution from the people at the top. A great many technologies get adopted following the “FOMO concept”—let's not miss out on something important. The slowness also happens because most people don't believe in it.

Last year I talked with many companies that think AI is just the latest fad.

Ninety percent of people don't like change, and in large organizations the figure may go up to 95. We recently had an internal study that revealed lower-than-expected use of generative AI in companies. Only 22 percent of organizations said they use the technology effectively across all business functions.

With AI, though, there's something very different as well. When cloud technologies arrived, people could see and understand that this was something they could learn—the transition wasn't that big, just another new technology we'd have to get used to.

Whereas what I've noticed lately with AI is a great deal of fear. Before ChatGPT, this type of technology was used by specialized departments that dealt with the company's data. Now people have become broadly acquainted with it, and many of them are afraid AI will take their jobs.

Put all these factors together and it becomes clear why things are moving more slowly.

Still, the main reason is weak leaders.

Most of the people at the highest levels of the large companies I've worked with avoid risk. For them a technology has to establish itself first, and only then is it adopted. There are exceptions, of course, and with AI you can already see them. Some sense that this really can give them an edge over competitors, and they're looking for partners to pull it off successfully.

– Do you think it's a mistake that lately, when we talk about AI, we almost always mean large language models?

– I don't think so. We're trying to get people to adapt as quickly and easily as possible, to understand what this is, so it has to be as close to their language as possible. Just as elections in Bulgaria are a good example of how, when you speak the language of most people, you get the best results. It's more or less the same with language models.

ChatGPT's main success came because they made the interface easy to use, convenient and free. OpenAI's focus lately isn't on building an even smarter model but on making it as easy to use as possible—now with voice commands too. I love showing people who don't use ChatGPT the paid version right on my phone: you can switch it on and just talk to it in Bulgarian. They're very impressed and start using it, especially now that it's free.

There's another important direction too. Since last year we've been working actively with AI agents, an extra layer that brings open-source models closer to the commercial ones and in some cases helps them reach and even overtake them.

– I recently wrote about the concept of agents myself. What's interesting is that while mass adoption seems to be lagging, everything else in this field is changing at lightning speed. Just a year ago you had to spend hours getting a local model running; now it takes two clicks.

– That's wonderful, because it helps integration. I talk with friends who aren't in our field, and they all agree that

we humans live better solely and exclusively thanks to technology.

And its development is the road to prosperity. So far, every technology has made our lives better. Because of it, each new generation lives a much easier life. Yes, the difficulties are different, but we delegate more and more tasks to technology.

With AI, the breakthrough involves several things. One is the idea that as many people as possible use it to be more effective, and I believe people should be measured by that. It doesn't matter how many hours you work, but what you do and how you do it. Someone can finish a job in 15 minutes; someone else won't manage it in half a year.

That's exactly what large language models do—they let us be much, much more effective.

They're the old X-Men character who amplified all the other mutants' powers. Thanks to AI we can work on more projects, get our work done faster; it saves us time. That's the practice I lead at SoftServe, too—the aim is to work smarter, with fewer time and human resources.

With the help of agents, Sam Altman said that most likely by the end of this year we'll see a single person build a company with a $1 billion valuation. That's why effectiveness matters. Especially with agents, we can form entire teams available 24 hours a day, 7 days a week, solving all sorts of tasks.

And after that, the next big breakthrough will be androids; quite a few companies are making progress in that direction. I expect that within five years we'll be able to buy a fairly sophisticated robot to help us for the price of a mid-range car. Personally, I can't wait.

– Neither can I, although before then we'll have to answer, as always with technology, some rather important questions. But do you think we might soon achieve a breakthrough in computing power? Because right now these models consume an enormous amount of electricity, so much that I read you can tell from the power consumption in the US when OpenAI is training its next big model…

– That's an exaggeration, but it's true that right now every data center under construction is fighting for licenses for high power consumption. At AWS they're reaching 1 gigawatt, and everyone is pushing ahead—Meta, DELL, Microsoft, Meta, X.ai.

We're seeing chips being made for real-time computation. Like with bitcoin—first we mined with graphics cards, and in 2014 purpose-built solutions came in on a mass scale. All the big tech companies are already investing in chips for this purpose, so that NVIDIA's can be reserved just for working on the models. Because right now a large share of them go purely to running them.

As for training—I see very serious money being invested in nuclear fusion. Sam Altman personally, everyone is looking in that direction, toward a breakthrough after which we could produce more energy.

There are many avenues in the search for more efficient energy sources. See how we keep coming back to efficiency…

– And in your view, what is most holding back the next stage of development right now? Computing capacity, the models, or a lack of more data?

– People obsess over data, but that's more of an inner egoism, like back when we thought everything revolved around the Earth.

It's clear that all publicly available data has been used in the models; they've been trained on it. But data also has to be of good quality. We moved past that long ago. A new NVIDIA model, for example, is very well suited to generating synthetic data. I don't think there's a problem with it; there used to be assumptions that a model trained mainly on synthetic data wouldn't work well, but apparently that isn't so.

From a data standpoint, I really don't think we'll have a big problem. In my view the main challenge really is energy. You can see it in all the leaders—everyone is trying to get as many chips as possible. But power takes time. No matter who you are, it takes between three and five years to get a permit for a data center.

Zuckerberg, Altman and Musk all talk about the energy challenges. For the European Union, for example, the main region Microsoft will be developing is Sweden, probably because they've secured enough energy to power the EU's potential needs in the coming months and years. So, in my opinion, the biggest challenge is energy, though I don't expect it to stay that way for long. But it's very hard to predict anything; from one day to the next, astonishing things come out.

– That's true—forecasting in the world of technology is hard, but important. Thank you for this conversation!

If you have never heard of “social engineering,” or “social engineerism,” as today’s guest says we ought to call it, you are probably picturing a plot straight out of Frankenstein: the engineer builds himself a person… Or something from the conspiracy theories about mass mind control.

Whether you have heard of it or not, though, we need to talk about it more and more. Because this is the most dangerous and most effective attack in the digital world. One that targets not computers, networks, servers or programs, but the weakest link in the chain: the human being.

“Today cybersecurity breaches happen more and more often through social engineering, a method that uses manipulation and manipulative techniques so that people are deceived and access to sensitive information is gained,” says cybersecurity specialist Plamen Tsvetanov.

But what are the most important steps to protect ourselves? What are the biggest risks of underestimating the person on the other side? Why should trust online be a scarce commodity? The subject matters to everyone, even those who don’t realize it…

Social, Eng.

Our guest definitely knows what he is talking about; the subject has fascinated him for decades. More than 20 years ago Plamen Tsvetanov began publishing the cult magazine Hacker, which, as its cover put it, told stories “about the dark side of things.” The magazine itself has a fascinating history that deserves to be told separately. But it was there, back in the first years of the millennium, that Plamen wrote about social engineering—the cyberattack aimed not at technology but at the user. That is why the experience he is about to share matters…, experience that comes from both sides of the barricade.

“People are often the weak link in cybersecurity,” says today’s guest. “For example, they can be tricked into sharing passwords or personal information through fake emails, social networks or phone calls. To protect yourself from such attacks, it is important to be careful and always check and verify the identity of anyone who asks for sensitive information.”

“Social engineering is the form used in more than 80 percent of cyberattacks, according to statistics from the FBI and the European Union Agency for Cybersecurity. It is a beachhead for other, even more dangerous breaches, but it is also an attack of the purest and most frightening kind in its own right.”

Hack or Be Hacked

Naturally, I couldn’t resist asking Plamen whether he had ever fallen victim to something like this himself. It turns out he hadn’t, but… it happened to his father.

“He worked at the Ministry of Internal Affairs and for more than 30 years taught Operational Investigative Activity; he was one of the greatest criminologists and profilers. And… he lost a huge sum after an attack like this,” Plamen says. Here is a more than certain sign that nobody is immune and everyone would do well to stay on guard online.

In the case of our guest’s father, the method was pretexting. This type of social engineering requires thorough research and careful preparation. The attacker gets to know the victim, starts communicating with them and manages to persuade them to share information they should never share under any circumstances.

“It’s a variation on the phone scams that used to be so popular,” Plamen adds. “Most often it was a call from someone posing as a medical worker in an emergency room, who would convince people to ‘save’ a supposedly injured loved one in need of urgent surgery.”

So, if… a criminologist with 30 years of experience can fall for it, do you really think anyone can be safe?

Serves You Right

Social engineering can turn up in many unexpected forms and situations. Some of them include:

• Physical social engineering

For example, someone posing as a worker from a service provider or technical support in order to gain access to a building or a computer system.

• Exploiting public events

The attacker may play the role of a participant at a charity event or a conference in order to gather information from the people attending.

• Fake social media campaigns

Attackers can create fake profiles, sometimes copying real ones, to get close to their targets by posing as friends or colleagues.

• Physical gifts or baiting

They can use gifts such as flash drives or other devices loaded with malware.

• Fake customer support

Attackers can pose as company employees to collect information from users through phone calls or emails.

Of course, these examples are only illustrative, insofar as there are no boundaries. Today’s web offers so many opportunities for attacks of this kind, limited only by the imagination of the “social engineers.”

Likes for Everyone, Trust for No One

“Unfortunately, a huge number of people and businesses think that things like this can’t happen ‘to us’!” Plamen sums up.

And while we began with phone scams, whose potential victims were mostly older people, nobody is immune to social engineering and its countless forms:

• Ordinary users

Anyone who uses the internet and social networks can be a target of social engineering. For example, people can be tricked into clicking harmful links or sharing sensitive information as a result of fake emails or messages on social networks.

• Businesses and organizations

Corporations, small businesses and other organizations are vulnerable to social engineering too. Attacks such as phishing and business fraud can do serious damage, exposing sensitive information or causing direct financial losses.

• Public figures, celebrities, influencers

People with a significant public presence are an even more “valuable” victim of social engineering.

• Employees

Social engineering can be used to manipulate an organization’s employees into revealing sensitive information or taking actions that could harm their workplace.

Heroes of Our Time

And if technology is constantly changing, the same goes for the attacks. As mentioned, Plamen Tsvetanov has been following the subject for more than two decades, and here are some of the things that have changed over that time:

• The growth of the online social landscape

Social networks and online platforms are now an inseparable part of our daily lives. This opens up new opportunities for social engineering, such as fake news, disinformation and manipulation through social media.

• The development of artificial intelligence and deepfakes

With technological progress, it is easier than ever to create realistic fake video and audio to be used for manipulation and fraud.

• More targeted attacks and personalized manipulation

Social engineering attacks are often more personalized. Malicious actors use data about people’s behavior and preferences to craft manipulative messages that are more likely to hit their mark.

• Wider awareness, cybersecurity training and education

On the other hand, as social engineering evolves, there is also more attention paid to cybersecurity, more training and more awareness of the subject. Organizations and individuals increasingly recognize the risks and strive to understand them and protect themselves from potential attacks.

In the end, that is exactly why we are here: to help more people stop and think the next time they get an email, a message, or read a news story that looks completely genuine. At least at first glance…

Once Hacked, Twice Shy

So what matters most if we want to protect ourselves? Forgive me for repeating myself…, being cautious. Or, as Plamen puts it, better to blow on the porridge before we get burned, since after that, as the Bulgarian proverb about the scalded man goes, we will be blowing on it anyway.

As for companies, the only solution is a combination of many factors—awareness, security procedures, technological solutions. And since employees don’t always pay attention to rather dull videos or live training sessions, regular tests and simulations are a must. Because reading abstract reflections is one thing, and quite another is to be fooled into clicking a convincing-looking link in a work email…, only to find out it was sent by the cybersecurity specialists, who have just managed to show you that anyone can be fooled.

And while these aspects of social engineering get plenty of discussion, one point of view remains relatively sidelined: that social engineering is also… the way the social platforms themselves treat us. Their algorithms, which lock us inside filter bubbles, and their marketing approaches. Even the way they shape the public agenda today is, definitely and categorically, a form of social engineering too, the cybersecurity specialist agrees.

And all of this will only deepen, thanks in part to generative technologies. As the Digital Stories experiment showed, it now takes just minutes and minimal technical knowledge to generate an online “hero” invented entirely by algorithms. And from there, the road to that hero becoming a weapon of social engineering is a short one…

Keep It in Mind

“There is no way we can say that every form of social engineering has already been invented,” Plamen says. “These kinds of attacks and manipulation constantly evolve and adapt to new technologies, to the social and cultural context, to changes in people’s behavior. Advances in technology, especially in artificial intelligence and social networks, may open up new opportunities for manipulation and attacks.”

And one more thing: social engineering is not limited to the digital world. “It can also be carried out in real life, using psychological techniques and manipulation,” the specialist sums up. “It is important to realize that social engineering is a product of human creativity and manipulative skill. That is why we must stay constantly vigilant, develop critical thinking and stay informed about the potential threats we may encounter both online and offline.”

Before we part, Plamen says something he considers an unwritten law, and all I can do is sign up to it. “Trust should not exist on the internet! Better to be paranoid than to get burned.” (You can find more about our guest and his work here.

Well, I sincerely hope that with Digital Stories I have managed to earn your trust in my attempts to gather important pieces of information, stories that help us find our way through the bustle of the technological present. But if you ever happen to see me advertising casinos, cryptocurrencies or someone to vote for… yes, online originals can almost always be told apart from fakes. All it takes, though, is the most important and perhaps the scarcest quality of our day. Common sense…

– What won you over to the idea of an international olympiad in artificial intelligence?

– My son jokes that I’m probably the person who has created the most international olympiads. I haven’t checked, but he may be right—I have three by now. In 2000, together with founding Musala Soft, we laid the foundations of CodeIT—today the oldest private programming competition. In 2017, with Krasi Manev, Biserka Yovcheva and Aleksey Hristov, we created eJOI—the European Junior Olympiad in Informatics.

2024 will be the first year of the International Olympiad in Artificial Intelligence, IOAI, which we are launching with the LERAI foundation (named after its initiators—Lora, Elena, Rosi, Alex and Iva). But IOAI also has two strategic, I would even say visionary, goals—it

is our contribution both to building elite human expertise for a shared future with AI and to a broad public dialogue about the possibilities, limitations and ethical questions surrounding AI.

– The idea is extremely powerful! It’s precisely because of the important and long-overdue dialogue on these topics that this site exists. But what exactly will the competitions look like?

– We have two segments that reflect our two big goals—scientific and popular.

The scientific competition will be research-based and theoretical, shaping future scientists, innovators and engineers. It has two parts. First, problems are published, and each team works on them remotely for several weeks before IOAI. They are unsolved, they have no exact, clear-cut answer; they are a kind of research, and the aim is to judge which teams have “gone furthest,” come closest to the optimum. Every team gets the same resources, and the contestants have several weeks to shape their solution. During the competition itself, an additional part is added to the tasks—it builds on them, expands them or makes them more specific, and its purpose is to establish and guarantee that the contestants can work without outside help.

The practical round during the olympiad gives the teams tasks connected to AI applications that already exist. The aim is to show the limits science has reached so far, and in the future to let us trace its evolution. Here the goal is to produce something far more widely understandable—audio, video, images and so on. We will use the practical round precisely for

more publicity and conversations about the accessibility, mass application and moral questions around AI.

We are designing it in a creative, interesting way that tips its hat to Bulgaria and to the future hosts as well.

– Given all the noise around the subject over the past year, how is it that we were the first to think of an international olympiad in artificial intelligence?

– It just seems to work out for us. Bulgaria was the first host of the International Olympiad in Informatics, IOI, in 1989, of the International Linguistics Olympiad, IOL, in 2003, and of the European Junior Olympiad in Informatics, eJOI, in 2017. We also took part in the very first international olympiads in mathematics in 1959, physics in 1967, chemistry in 1968 and biology in 1989. With IOAI we are simply continuing the tradition.

How was the idea born? Last summer Alex Velinov, the longtime leader of our linguistics team, said something like: “Guys, what do you say we do an AI olympiad?” And we did…

– Alex was my guest with the incredible story of Bulgaria’s successes at the international linguistics olympiads; he is the driving force behind remarkable initiatives. You mentioned that the idea isn’t just a competition but a platform that popularizes and opens up important questions. What, in your view, matters most if we are to use the rise of AI so that Bulgaria becomes a real center for this kind of development?

– I recently took part in a large study by Meta and Visegrad Insight on AI in our region. One of its big conclusions is that

we have isolated good examples of AI development and adoption, but no ecosystem.

I think the future of “centers” lies in their ability to be part of micro, regional and international ecosystems, to plug into them flexibly, making the most of the available resources and constraints and complementing their surroundings. For that you need education—first and foremost!—state policy, entrepreneurs, a mix of small and large companies and, hardest of all, a society that looks to the future with confidence, sobriety and curiosity.

– Let’s hope we manage the hardest part! Unfortunately, the topic very often enters public discourse superficially and in clichés. And still, I find it strange that despite the enormous buzz around the field, I don’t see more AI-related job ads. Why is that?

– I haven’t heard of an unemployed AI specialist.

There are several trends. The overall global conservatism of business in recent years is affecting the IT sector too. Expectations ran far too high that there would be enormous demand for AI specialists, but the truth is that

business isn’t ready for large-scale AI projects right away.

This coincides with the realization that AI experts can’t spring up out of nowhere, and companies have started programs to train them—that will take some time. At the same time, AI is seeping in everywhere and will become something like the equivalent of the “computer literacy—working with word processors” of 20 or 30 years ago. In other words, the process is massive, turbulent, a little chaotic, but with a clear trajectory—AI is here to stay.

– I listened to your appearance on The Superhuman, Georgi Nenov’s podcast, where back in 2018 you said the future belongs to artificial intelligence. What has surprised you in the development of the technology over these years?

– I was surprised by how sharp the breakthrough was—

after decades of effort, one mass interface—ChatGPT—managed in literally weeks to reach practically every person on the planet

with internet access. It reminds me of the spread of COVID, only in a positive way.

– In the same interview you also said creativity would be the last thing to be replaced—but does it still look that way today? I ran an experiment with 1,800 participants, and people practically couldn’t tell generated content from human-made content. In another experiment I created an entirely generated online profile, and of the 85,000 people who saw it, nobody noticed. Can technology restore our ability to judge what is true?

– Both are good examples that take us back to 1950 and the Turing test—can a machine fool a person into thinking there is a person on the other side? Skipping centuries of philosophical debate, let’s assume that thinking is material, that to think we use our bodies, our brains and a certain number of chemical reactions. If thinking is a logical process based on facts, and intelligence is knowledge, then

today’s machines definitely know more than we do, they even “think independently,” and certainly with fewer mistakes than we make.

Increasingly, technology will fail the Turing test because it is faster and more knowledgeable than humans, not because it makes mistakes.

But technology is going further still, and I’ll add one more example to yours. A little over a year ago BMW presented its “Neue Klasse” Dee (Digital Emotional Experience)—a car that claims to be not only “smart” but also “emotional.”

The question is what “smart” and “emotional” mean—is that anything different from “knowing”?

If emotions can be measured and predicted on the basis of facts, then they boil down to “knowledge.” So, for example, if a “smart” home knows that its residents relax to jazz and starts playing jazz when they approach the front door, or a car changes its color to sky blue when it “sees” that your accessories are in that palette—is that magic, or an algorithm and data? And how will AI react to spontaneous emotions that are inexplicable even to ourselves?

In the coming years, technology will push back the boundaries of today’s “magic,” reducing more and more of it to facts, models, processes.

But it also works on the principle of “the more I know, the more I know that I don’t know.” The more we uncover, the more we see. This will reveal new possibilities, more “magic,” and will make our lives more comfortable, easier in their routine, more interesting. And, to answer your question,

all of this will reveal new levels of creativity we don’t even suspect at the moment.

– That’s exactly what I think! And I try to collect examples like that… What should we never delegate to algorithms?

– Anything we don’t feel ready for. Along those lines, I like the European approach to regulating AI on the basis of potential risk—the greater the risk, the tighter the regulations.

– And what in today’s technological development would you never have guessed when you started your career in this field?

– Perhaps what surprised me most was how quickly and widely technology entered education during COVID. Not because of the technology, but because of the people and the system. Overall, its development is currently outpacing that of society. That is:

there is more technology than we are ready to use on a mass scale.

– Where do we underestimate it, and where do we overestimate it?

– Sometimes we forget that technology is created by people. Even technology created by other technology was originally created by people, so

the core is the human being, with all their creativity and all their imperfections.

As Yordan Radichkov, one of Bulgaria’s most beloved twentieth-century writers, put it: “Man is a long sentence, written with love and inspiration, but full of spelling mistakes.” Technology, however perfect and free of “human errors” it may be, carries the human within it.

– Does it make sense to start learning programming today, or, as one of my readers commented, is it like buying a factory on September 8, 1944—the eve of the communist takeover that nationalized private property?

– That’s my favorite post-ChatGPT question. Programmers won’t disappear. Their profile will change. Half a century ago people programmed with zeros and ones, then with many lines of code, then visually, “low code,” then/now with the help of artificial intelligence.

There is evolution, but the need for a “translator” between humans and machines hasn’t disappeared yet.

If, however, you have learned to write in a particular programming language without developing the way of thinking, then you may drop out of the game, and those “coders” will disappear, because the machine will be able to replace them to a huge extent. For all thinking professionals, there will be new opportunities.

– Why, in your opinion, are job ads in the sector declining? Is it already artificial intelligence?

– It isn’t. We have yet to see its tangible effect and a reshuffling of the map depending on how much AI is used. Some businesses will shoot ahead, others will perish, but overall the industry will keep going up.

– What sets Bulgarian software specialists apart? Is there a trait, skill or technology in which we lead or stand out?

– On the whole, we Bulgarians are unruly and inventive;

we always know “another way” to tell a joke or get the job done.

That applies to software specialists too. On the negative side, they fit into strict discipline less easily than, say, the Germans, but they voice opinions and look for solutions far more than much of Asian culture, for instance. And, at the risk of being crucified by the PISA ultras, we have good traditions in mathematics—not everywhere, but in the country’s leading mathematics high schools—and that is the foundation on which almost any engineering specialty can be built.

Creating new technology schools like the IT High School in Burgas is a long-term investment that will very soon start paying off—its first class graduated in 2023. The immigration of software engineers from third countries also has a positive effect in terms of fitting into different cultures, building on the Bulgarian IT sector’s decades of export experience.

– Is there a recipe for having more product companies in Bulgaria?

– Product companies and service companies are different businesses; neither is better or worse than the other. At a very high level, product companies have a higher-risk, higher-return profile, and service companies the opposite, with everything that follows from that.

For Bulgaria, diversification is a good thing.

– Which technologies can’t you wait to see?

– The life-saving ones. Everything else is pleasant indulgence.

– What will the last programmer be doing?

– Rebranding. They’ll put a new name on their new profession.

– What do you dream about?

– The normal human things—health and happiness for me and my loved ones. Beyond that,

I dream, plan and act to leave behind something good that will keep growing after I’m gone.

IOAI is part of that good.

– When you started out eight years ago with the idea of a platform that would analyze and synthesize enormous volumes of scientific publications, you knew there was no technology yet that could do it, but you expected it to appear. And that's how it turned out… Is that really so?

– That's right, we started working on Iris with the idea

of creating a question-and-answer machine to support scientific work.

That was a long-term goal. We had laid out a ten-year plan for how it would happen, and it was clear that with the technology of the time, there was no way it could work. Quite a few fields had to develop. We believed they would, and as it turned out, we guessed pretty well.

First we tackled the problem of too much information, caused by the sheer amount of content being created. Around 2010 a boom began in the publication of scientific papers, research and patents. The digital era made all of this easier, and content in the scientific sphere started growing exponentially.

When we took on Iris in 2015, publicly available open-access digital scientific content came to about 2 million documents, and digital content as a whole to about 30 million items—patents and scientific papers. Today there are around 60 million freely accessible, and the total volume is around 300 million!

– So suddenly scientists become far more productive and start churning out enormous volumes of content that is hard to keep track of…

– Yes, we noticed that it had suddenly become impossible to follow in detail what was going on in the field you were researching. Between 6,000 and 10,000 papers are published every day; for a given field it might be 100 or 200. Nobody could keep up with that volume.

We built the first search applications—at the time, they were semantic search engines. Before that, people used huge lists of keywords, derivatives of everything they needed, but those were clumsy and you could always miss something. We were able to solve that problem right away with a semantic search engine, where you describe in free text what you want the machine to find. We made something like an “ancestor” of today's search systems built on vector databases.

Then we moved on to the fact that people are sometimes interested in specific questions—is there a material with certain properties, how is it produced, is there something similar… Here neither classical nor semantic search engines worked, because they generalize, and that means finding a more specific fact is hard.

– And after that?

– We developed systems that extract facts from documents and structure them into knowledge graphs. When they're small, these structures are very useful and convenient to work with, because they let a person easily explore the connections between the elements. It gets difficult, though, once they grow large, and in some particular fields they started reaching millions of elements.

Then we built an application for fact extraction, and somewhere around there the need to combine the two began to emerge. To be able to find things semantically in order to narrow down the context or the number of papers, and to summarize. It became clear that the moment had come to build the system we had started with. Somewhere around then came the breakthroughs in transformer models, with the ability to ask shorter questions and get direct answers.

We brought in large language models that can answer questions directly. With the structured approach—the ability to extract the facts—and the unstructured one—the language model working directly with what it finds in the papers—today we combine the two results so as to reduce the models' hallucinations and make the answers as accurate as possible.

– As the company's description makes clear, the algorithm is already more successful than a human doing the same analysis of scientific publications… Do you think the growth of generated content creates a problem? We recently talked here about a study showing that certain words typical of large language models are suddenly turning up exponentially more often.

– Yes, over the last two years there has been growth in generated content. But that doesn't necessarily mean the quality of research is going down in any way.

I think it's rather that scientists spend more time doing experiments than writing

the content. Every paper has two aspects—the scientific work itself, and how to present the results in the most suitable way. And yes, that last part is now being handed over to the models.

– And so in your work today you face the same challenges as the creators of the big models—biases, hallucinations. Everyone is trying to solve them, starting with OpenAI and the giants, but somehow there doesn't seem to be much progress…

– First we should note that hallucinations are as much a bug as they are a feature, to put it in “IT speak.” They are an error, but also a desired effect. In the models they come from so-called generalization.

Over the years, the main goal was precisely to create a language model that speaks convincingly, that sounds like a human. And how does a human speak? We can choose words, paraphrase something that was said another way. For the model to do that, it has to be able to generalize, to recognize constructions of the same type and use them interchangeably. Except that

when we teach a model to generalize, that inherently means we doom it to be not very good with facts.

Yes, it knows it has to put a phone number, an address or a year as the next element in the string of generated text, but there's no way for it to know exactly which number to pick. Because when it comes to someone being born on a certain date, in the text it was trained on it will have seen many such examples, and the number will be different every time.

It has no way of judging which one is correct, because it doesn't make a cause-and-effect connection to the specific person.

This is exactly where it needs help. This is where we have to think about how to change things so that, besides generalizing, the model can also focus on specific cause-and-effect relationships.

– Forgive the naive question, but couldn't the solution be to pass the data through another model that checks the information?

– It's not that simple. There are different schools of thought, but in my view it won't work with the technology we have right now alone.

The mathematical machinery behind it has to change somewhat.

Right now it's optimized to generalize, because that was a problem that went unsolved for decades. The first language models date from the 1980s, and ever since we have been battling the generalization problem, on the assumption that the focusing one was supposedly easier, because you can have structured data. It turned out that's not quite the case once the volume gets too large.

Now this mathematical machinery has to be invented. The big tech companies are working on it quite actively, but it isn't easy. I don't think it will happen by training a new model with new optimizations; new things will have to be worked out at a lower level.

– Either way, the models handle facts impressively too. I ran this experiment myself—ChatGPT went up against undisputed quiz champions and beat most of them, and that was version 3.5, and in Bulgarian at that… Yes, if we're after facts, we should have learned by now that ChatGPT isn't the ideal solution. But we can use it and its siblings for so many other things. Could it be that our expectations are simply unrealistic?

– We can split a model's main capabilities into two components. It can generalize and understand linguistic structures—that is, how to construct sentences, organize them, prepare an answer and generate meaningful text. That's purely structural knowledge about how to formulate a thought. If we compare it to people, anyone who has learned a language can do that. Yes, the models are now capable of it too.

The other part, though, is knowledge. Being able to state a position on a subject doesn't mean you have the knowledge to do so. That, I think, is what people miss.

The models are trained on huge amounts of data, and they use much of it to learn the structure, the logic of the text, the way to answer. Yes, they also accumulate some knowledge, but it isn't at all that straightforward; sometimes you need an approach to get it out.

Quite often the problem isn't so much the model as the fact that we have failed to extract the knowledge we're after. We haven't asked it in a way it understands.

“I asked it this, why won't it answer the way I want?” When you talk to someone, the fact that you understand the question one way doesn't mean the person across from you understands it the same way. Prompt engineering is the way to explain something so that the “person” on the other side understands us. When you meet a stranger and ask them a question, they might not get it right away. You have to explain better what exactly you want, give them an example, a chance to picture it, and then they'll answer.

– The models themselves are gradually building up context, too. We often go looking for an assistant for everything, when this is a technology that can be useful to us in many areas—but we have to know it and know how to work with it.

So, as far as I understand, you think we're close to the limit of large language models in their current form, is that right? I read an article of yours saying that on their own they can't bring us to artificial general intelligence.

– They're constantly evolving; experimental models with different kinds of structure and larger context are being released. Things are moving. But in my view, yes, there has to be some next breakthrough that solves the focus problem.

How big a model is determines how many different structures it can learn. Some people, Sam Altman for example,

believe we need even bigger models and they will learn to make these cause-and-effect connections on their own.

Right now there are two main camps. Altman's “unstructured” one, which believes this will happen if we simply keep piling up in the same way. That a lot of effort has to go into bigger and bigger models. But there's another constraint there—data. We're already approaching the limit; we have no more data, and people are thinking about how to synthesize new data. But that in itself creates another serious problem—the models have biases even now, so imagine what happens if we start synthesizing data. It isn't clear how that problem can be solved.

The other camp is the “structured approach,” whose chief advocate is Yann LeCun, Meta's chief AI scientist. He believes the models must first learn an understanding of the world, some kind of cause-and-effect relationship, and then that should be complemented by their current abilities.

Personally, I think the truth is somewhere in the middle. We definitely haven't fully hit the limit, but I don't think general AI will look like the current models. The focus problem has to be solved through new mathematical constructs that teach the model when to focus and at what scale. That, to me, is the other problem—models can generalize successfully in general language, and with some additional training they cope with specific tasks. But a more general model struggles at a specific level, and vice versa.

– As a guest on Ratio, you said we've reached the point where we've made a breakthrough and it's time to find more practical applications, which in turn will attract funding for the next stage of AI development. A year and a half after ChatGPT, there still don't seem to be as many mass everyday applications as expected. Do you see it that way too?

– We sell products in this space. Before the end of 2022, people would come to us and simply not believe a machine could do something like that. We even heard things like: “Hey, how nice it'll be when this thing starts working, in five or six years.” “No, it works, we have it right now!” But people just didn't believe it.

And then you wake up a month later and suddenly everyone is coming in with inflated expectations. “It works great. We want to use it. To solve every problem we have.” Which is absurd; there's no way.

You didn't believe something could do any of your work, and suddenly you're saying it will do all of it.

We swing from one extreme to the other, and it's at neither end—it's somewhere in the middle. It can do useful work, but far from everything. After every such moment of inflated expectations, there's a bit of a cooling off.

– Most of the guests I ask, though, say there can't be a winter in the world of artificial intelligence, because the progress is too great and touches too many fields…

– I think a cooling effect has been there since the middle of last year. At first people imagined that suddenly everyone would start using it. No, it isn't yet making its way into business with the impact people expected, and that's normal. But there are fields where it is, technical support centers, for example.

The easiest field for this kind of technology to enter is, without a doubt… technology itself. We can already see what Copilot does, and more platforms are on the way. You can see the change in the job market, too—juniors have a harder time finding work, because one senior with ChatGPT can do the work of one senior and five juniors. The barrier to entry there is already much higher.

Maybe they should learn to use ChatGPT? It isn't that easy. Prompt engineering requires a certain way of thinking and knowledge of how the models work, so you can structure prompts with ease. It's good to take some courses; one of the pioneers is Andrew Ng, who has excellent courses both on understanding large language models and on prompt engineering. It takes a certain kind of thinking, and that has to be learned.

– But that creates a problem—if young specialists don't get into the system, in a few years there will be nowhere for experienced ones to come from… That's just how our profession works.

– I don't think the profession will disappear. There was certainly some bloat, and that has to be rebalanced now. And I think later there will also be a change in the way people are trained. Either way, technical support and IT are the two fields where the adoption is most directly visible.

There are others where serious changes will happen, personalized medicine, for example. At the first conference we attended with Iris, in 2016, that was an imaginary notion. Now, with the arrival of ChatGPT, it's entirely possible. Under the current healthcare system, anywhere in the world, there's no way a doctor can have the full context on a given patient and recall it at the moment they need to make a decision. Now, with the capabilities of large language models, a patient can provide much more context, from which the doctor can extract information and make a decision.

Very interesting things have already happened in medicine—the invention of CRISPR and the decoding of protein structures open up enormous possibilities. In these fields we'll be able to do many things we haven't been capable of until now. There will be a revolution in medicine. But it won't happen fast; I think at least one generation has to go through the new way of doing things, and it will take at least ten years or so.

– Another guest of mine, a specialist in medical technology, recently shared very similar conclusions. In Bulgaria, too, some fascinating projects related to health are being developed. But what is the path to having more startups, more product companies?

– My view, going back to the years when I was still in Sweden, is that

IT people in Bulgaria were in a kind of lethargy for a long time, not realizing that outsourcing companies can't be a long-term solution.

It's something that evolves along with the market; sooner or later someone will turn up who can do the thing more cheaply, and that work disappears from the previous market.

For quite a few years Bulgaria was in a very good position for outsourcing, because our culture is closer to the Western one and we had relatively good education. But that momentum of funds, capital and opportunities should have been used to build product companies and stimulate the startup environment.

We're quite late. I've been watching Poland—there are outsourcing companies there now, of course, but also an awful lot of product ones. They managed to notice that it mattered and quickly began moving toward startup innovation.

One example is a very interesting data storage project that uses plant DNA. We can now use biological elements in their real environment for practical purposes. A single tree can hold gigabytes of information—something extraordinary! This is being developed in Poland; the founder was at a conference in the US and people were amazed by the breakthrough. In other words, they managed to use the capital and opportunities the moment gave them to move on to startup companies, while countries like ours, unfortunately, lagged behind.

– How can we catch up?

– I don't have a clear-cut answer. The environment itself is very important, but the first thing I think people need to understand is that

the era of outsourcing companies is over. Its effect will become visible far more radically from here on.

Yes, there are a few big players in Bulgaria, but their influence will gradually diminish; we're seeing the layoffs. The only solution is for people to figure out how to build product companies.

Except that those are built with a different mentality. As Steve Jobs said, the goal of a company is to survive until the product is ready. That means you set out with an idea that has to become a product, but that road can take two, three, five years. For deep-tech companies like ours it sometimes takes more than a decade—as with storing data in plants, for instance.

But people set out to do it anyway. For it to work, at the moment they set out there have to be people around them with a positive mindset who will believe in them and invest for the long term.

– But I suppose that also comes down to national character…

– The surroundings have to be like that. If we want to be the country that builds strong startups, we have to support such people, give them strength, tell them they can do it, push them forward however we can. Whereas the mentality is still: “Why did you take that on, it's too hard,” “Ten years? You'd be better off working at an outsourcing company,” “That idea will never work.”

It won't happen that way. When everyone is pulling you down, a startup struggles to grow. I say this as someone looking in from outside; in Sweden the mentality is different, they've had a developed startup culture for nearly 70 or 80 years.

In the world of artificial intelligence, I think even the European Union is behind, because startups in Silicon Valley work with one huge, very simple difference. People there are extremely open about sharing their ideas. And once they've shared them and decided, “Okay, we're doing this,” nobody will take the idea and start competing. You go, you meet the other person and you do it together. You find ten more like-minded people and you end up with one big company. You all believe in the idea, you're ready to invest years of your lives, you come together and make something unique.

– Why did you come back?

– A combination of circumstances. Honestly, I'm still not convinced I feel very good here. There are definitely cultural differences; some good things I saw in Sweden I really miss here. And the other way around, of course, but when I add it all up, things are in the balance. Lots of people say: I loved it when I came back.

I'd say I rather like it in Scandinavia, and I even go back from time to time. But on the other hand, I'm trying to change what I can here…

If our conversation with Viktor Botev has intrigued you, you can see its continuation live on Sunday! Together with Anna Angelova, a journalist at BNT 1, the flagship channel of Bulgarian National Television, Viktor will be a guest at an event hosted by Digital Stories.

On May 12 (Sunday) at 7 p.m. it's time for the meeting “The Truth and Nothing but the Truth.” As part of the Sofia Science Festival, at Sofia Tech Park we'll talk about the future of the way we receive information. For more information and tickets—click here.

It is very easy to tell someone who has ever programmed from everyone else: if you have ever “touched” code, there is simply no way you haven't seen Stack Overflow. In just 15 years, the question-and-answer site fundamentally changed the way programmers work. It became a Mecca for software engineers, a safe harbor for almost every one of their everyday problems.

Today, however, the legend is heading toward its sunset. And, by a twist of fate, because of the very technological progress it made possible…

What's more, many predict that the project's decline will bring serious problems for the next generations of programmers.

The story of Stack Overflow says a lot about where we stand today in the world of technology. About the breakneck speed at which we are changing. About the seeds, the good ideas that can only sprout if they land in the right soil at the right moment. And about how quickly a giant that seemed unshakable only yesterday turns into a shadow of itself.

Thus Passes the Glory of the World

“Well, technology has come a long way since the days of forums. I think the programming community can do a lot better with a combination of a question-and-answer site where people vote for the most accurate answer and where answers can be edited.” So wrote Joel Spolsky on September 15, 2008, in the blog post announcing the creation of the famous site.

By yet another twist of fate, before it became programmers' best helper, the phrase Stack Overflow itself carried decidedly unpleasant associations. It is the name of an error in which part of the memory is overloaded and the program stops running. That is precisely why the name was chosen, after a vote among the people who saw the point of the venture.

It is such a common scenario in the trade. An error pops up, for some reason the computer isn't doing what it's supposed to… and, of course, if you don't know what's going on, the first thing you do is check online.

Don't Ask!

That turned out to be almost impossible before the famous site appeared.

“You know what drives me crazy? Programming question-and-answer sites,” Spolsky wrote in his famous post. “You know what I'm talking about. You type a very specific programming question into Google and you get:

• a bunch of links to forums where clueless people are struggling with the same problem and getting nowhere;
• a link to another Q&A site that claims to have the answer, but when you get there, it asks you to sign up for a paid subscription plan;
• an old post with the exact answer for Windows 3.1, which just doesn't work anymore;
• something in Japanese.

If you're really lucky, on page four you'll find a discussion with hundreds of replies, 25 percent of which are spam ads posted by bots; some of the replies are actually useful, and someone named “Anon Y. Moose” has posted a decent, if ungrammatical, answer that nevertheless contains a devastating security bug. And this little gem is buried under a truckload of garbage.”

Alone Against All

Well, into the middle of all this, exactly where it was needed, came Stack Overflow. There is no more precise way to put it. Suddenly there was a place online where anyone could quickly and easily find the solution to any problem related to building software.

At first glance, a plain site, retro-looking even for its time. Supposedly just a forum where anyone can ask and answer questions, but in reality much more.

The idea is brilliantly simple. Not only is everyone welcome to ask and answer, but the whole thing turns into a kind of game: the authors of the best answers rack up points. Every question that repeats one already asked is closed and redirected to the original. Every answer is rated, and if the person who asked becomes convinced they have received a correct, working and precise solution, they mark the answer that proved most useful, and it stands out visibly from the rest.

And at the very top of the answers appears His Majesty: the answer that has collected the most votes from registered users.

Posts can be edited, so they can be developed and enriched further. “There are lots of good ways to edit,” Spolsky wrote. “You can improve the spelling, the grammar and the meaning. Anything you can do to clarify, explain or improve the question or answer will serve the community. If there's code, you can run it yourself, refactor it or improve it. After all, for the next 20 years, this question will be the canonical place on the web where programmers come to learn exactly this thing.”

That last sentence became a prophecy: that is exactly what happened with some of the most common searches.

A few hundred programmers quickly joined the venture as volunteers. They all shared the need for a new site, and the timing coincided with the main forum of the day, ExpertExchange, going paid. Alongside Spolsky, the other main founder was Jeff Atwood, creator of the blog “Coding Horror.”

Ask, and It Shall Be Given You

The system gathered speed very quickly because of the way Google worked back then. The most fitting answers soon rose to the top positions, because the people who found a solution in them stayed a long time and left satisfied.

Spolsky's post began an entire era in software development. In 2017, when I was entering the profession, the site was the indispensable helper for every problem. So much so that at the software academy where I studied, we overloaded the traffic to it, our access was temporarily restricted, and… the exam had to be extended.

The site became a kind of encyclopedia for the community, where you could find the answer to any question, and such questions crop up daily in a programmer's work.

Not everyone has time to read huge documentation, which is often unclear and leaves out real-world applications… so salvation turned out to be shared experience. The chance, once you have stumbled over an obstacle and managed to get past it, to help the next person who is about to trip and fall.

To the Blackboard!

Questions are sorted by the technologies they relate to, and the database grows at a dizzying pace. Annual surveys begin, showing that almost 100 percent of programmers use the forum. Then this becomes a way to study trends, to track which technologies the people on Stack Overflow use and what gives them trouble. Which technologies are most in demand and best paid, and what the trends are in computing worldwide.

A few more words about Spolsky. He is also known for “Shlemiel the painter's algorithm,” which grew out of an old Jewish joke and describes how programmers write inefficient code because they don't understand the fundamental, basic layers of their work. But to keep a balance between the good and the bad, Silicon Valley also considers him the creator of the modern way the industry hires people.

Joel worked at Microsoft in the days when graduates of the most prestigious universities landed at the big software companies by default. He did the opposite: he introduced a strict filter of algorithmic problems, questions on general computer knowledge and multi-round interviews. Something he himself now considers a serious mistake, since software development is too complex, a team sport that requires a much wider, sometimes unexpected range of skills.

Phone a Friend

There is a curious study by the University of Maryland, according to which developers who use Stack Overflow as an advisor write less secure code, but code that is significantly more functional. In my view, though, the lines simply blur: intelligent software engineers also use it intelligently, as a source of information, approaching it critically and looking for the logic. Which, by the way, is exactly what matters in the age of artificial intelligence, too.

Gradually, a whole group of similar sites was created under the Stack Exchange “umbrella.” The approach proved so successful that question-and-answer pages appeared in many different fields, even outside the computer world.

Almost imperceptibly, the whole culture that accompanies any phenomenon of this scale was born. People with high ratings, who had given accurate answers and been recognized by the community, became celebrities in the programming world; their stars were a guarantee of quality. So much so that when Bulgaria became the first country where someone from Stack Overflow's top 40 became a minister, it made news widely covered across the software world.

And, of course, the memes appeared. About how programmers don't need to learn or work, how Stack Overflow practically writes their code for them and all they do is search and copy…

And then the jokes stopped. The large language models arrived and turned out to be surprisingly good at solving a large share of the problems. And even bigger questions appeared.

Programmers of the World

In the meantime, Stack Overflow had turned from a community and a venture driven by volunteer labor into a commercial product. Despite persistent rumors, it was not bought by Microsoft; in June 2021 it became part of Prosus, a Dutch company and subsidiary of the South African media conglomerate Naspers, in a $1.8 billion deal.

After many years at the helm, Joel Spolsky left the company, and the post of CEO went to Prashanth Chandrasekar.

In 2022, Stack Overflow had about 20 million registered users and over 50 million visitors a month, practically every working programmer on the planet. Some 24 million questions had been asked on it, receiving 35 million answers. And then… ChatGPT appeared.

Sunset and Dawn

Over the last two years, traffic to the legendary site has fallen by about 50 percent. There are significantly fewer questions and answers, and all of this is, of course, easy to explain: today most programmers use various artificial intelligence systems and return to the good old “Mecca” only when those don't do the job.

This is gradually changing the rankings on Google, too: today results from Stack Overflow, once the search engine's undisputed favorites, get lost among the rest. The change is also visible in the discussions under the answers, where more and more users write that, at last, the hate, the needless rudeness, the drifting off into side topics and the impossibility of moderation have arrived here as well, just as on social networks.

At the end of last year, 10 percent of the 700 employees who look after the site were laid off. Of course, all this comes against the backdrop of layoffs at many other companies in the industry.

Nothing new in human history, is it? A venture captures the spirit of the times, becomes the fashion of the day, the standard, and then reaches its natural sunset. Here, however, the natural course of things may come with an unexpected price.

The Snake Eats Its Own Tail

Stack Overflow has always been an open, freely accessible site, even if it gradually developed its commercial side. But then it is only logical to wonder… how did large language models suddenly become so capable of writing computer code, improving it, finding bugs… answering complex or highly specific questions about building software?

“Some of the big companies explicitly cite Stack Overflow as a major source of training data,” says the project's current head, Prashanth Chandrasekar. He remembers the days when he was a young programmer, writing and debugging code late into the night. Because he would hit a dead end and there was no one to help him. “That's the reason Stack Overflow became so popular. You don't have to spend long hours at night to figure something out. You have this phenomenal community resource, and that can only be appreciated by people who have faced the problem.”

Now, however, Prashanth says, we are entering a new era, because we have the next generation of algorithms. Stack Overflow is also betting on bringing AI into the platform, like most of today's software giants.

Truth or Dare?

And so we come to the big question. What will we do from here, as the next new technologies arrive? Then there will be no one sitting and writing the answers, so what will artificial intelligence be trained on?

Yes, the companies developing large language models are willing to pay, and ever larger sums, for access to training data, but what if… there is no one and no way to provide it? Or will the algorithms move to the next level and no longer need it, having developed something ever closer to human thinking?

“All AI models need a constant stream of quality human data to train on. Without it, they are doomed to worse results,” says Chandrasekar. “This is a very real problem; we will always need solid, high-quality sources of truth like Stack Overflow.”

In other words… in programming, AI is quietly replacing human collaboration. And even here, the problem of defining, seeking and validating the truth emerges.

As Boris Simandov said not long ago, we are building ever more complex software solutions, but the whole process by which we do it is wrong. We keep piling up technical debt… and what happens next we can only guess.

The most important thing, in my view, is to keep asking the questions. Preferably before the answers overtake them…

• At the end of 2023, the largest group of Stack Overflow users were aged 25–34 (37.28 percent), followed by those aged 35–44 (23.02 percent). Last came those 65 and over (1.32 percent).
• The most used language today is JavaScript (63.61 percent), followed by HTML/CSS (52.97 percent) and Python (49.28 percent).
• According to the site's latest survey, for the first time the best-paid programmers are those using the new language Zig (earning an average of $103,611 a year). For several years in a row, Clojure held the top spot.
• Most users come from the US (21.21 percent), Germany (8.34 percent) and India (6.4 percent).
• The average time spent on the site is quite high by online standards: 7:21 minutes.

This piece was prepared with the special assistance of Irina Marudina.

– You head AMBITIONED, a software company that at the same time helps young talent take their first steps. Can you tell us more about the idea?

– Until now SoftUni had about 10 platforms, which we developed with a team of around 15 people. Almost all of them came to us as graduates of our courses, and today they are quite experienced. At the same time, our systems are mature now and mostly need maintenance. So we decided to spin the whole team off as a software company. We want to grow it by positioning ourselves in .NET technologies. We’ll do projects, and the other big goal is to build professionals.

What will be different from all the other companies? Our vision is to have very well-thought-out processes—both engineering and management ones.

I want us to put the emphasis on the engineering approach to software development.

To optimize the processes so that we deliver better code faster. For development to come ever closer to every other engineering activity—as if you were producing cars or houses. It’s worth taking everything we can from that approach. Nobody would get into a car that hasn’t been tested, right? Or live in a building that wasn’t built to code.

Of course, we can’t compare a physical product with a virtual one; with the latter you can be much more flexible in planning.

– In theory, software development is supposed to work like that already, but the fact is that very often it stays just words.

– I’ve asked a huge number of software engineers what they don’t like about the job they currently have.

The most common answers are: lack of quality, an unclear life cycle, no structured management system. Management promises things that can’t be delivered, nobody reviews my code, nobody mentors me.

Saying that we’ll fix all these problems would be insanely romantic, but that is our vision, and I want us to work in that direction. We’ll position ourselves on foreign, developed markets, where this kind of service would be appreciated.

Maybe it sounds romantic and hard, but there are companies that manage to strike a balance between growth, revenue, quality and optimal processes. Quite often those are projects that are in some way life-critical. And I would like that to become standard development.

In my opinion, many of the basic things that problems stem from can be solved with processes and an engineering approach.

– I understand very well what you mean. Veli Pehlivanov told me similar things in an interview, and that’s how, for a while, I professionally ended up as part of the Resolute team myself.

– Yes, I’ve read his interviews and I completely share his point of view.

Here’s something elementary—estimating the time a task will take. It’s a fundamental principle; every programmer, every engineer does it constantly. But how many people are trained to estimate in the same way? How many are trained to describe tasks? How many have had it explained to them what the definition of done is? There’s a very good article by Vasil Terziev about the definition of done, which differs enormously among the people in a company. For one person it’s done when you’ve written the code, for another when it’s tested, for a third when it has reached the users. For engineers it’s a fourth thing.

I want to call all my colleagues “engineers”—that’s what I want us to achieve.

I don’t want us to be coders!

Not just programmers who simply write code, but people who think like engineers. Not only in the code itself, but also in the processes around it, in the metrics. Do we know what we’re estimating, or does everything happen by eye?

In most places people aren’t trained. Often it’s an inherited problem—team leads who were never taught themselves have no way of teaching those who come after them.

– You put it well—to be engineers, and not only while we’re writing code.

– Let’s start there. Train people, put the small, fundamental tasks in order, be real engineers. Then I believe things will gradually start to improve, and we’ll have a process that works.

No, I haven’t reinvented the wheel, but these are problems that often go unrecognized. And they get in the way of work a great deal.

Over the years our team has been staffed with course graduates. So far everyone who has left us has become very good and grown into a leader. It helps that my colleagues and I are teachers and know how to approach someone to train them further. When there’s nothing more they can take from us, they go out into the industry. We want to achieve the same thing, but in a company of 100 people.

– You studied something completely different at university. How and why did you decide to become a programmer?

– Somehow it just happened on its own. I chose to study civil engineering because I had taken part in a lot of math olympiads. I was strong in competitions that didn’t require deep knowledge and heavy theory, but rather speed. In first year we studied only math; I found it interesting and got straight sixes, the top grade in Bulgaria. And then suddenly… twos and threes, a fail and a bare pass. I reached the subjects that demand a lot of reading, and that was the end of me.

I started wondering what to do with my life. I tried to start a small online business and bought some PHP script—back then it seemed like a fortune to me. I launch the site, I advertise,

I spam forums, and some kind of business gets going. It was a funny, cowboy kind of operation.

The script soon turned out to be insufficient, so I started digging through the files and changing things without having any idea what I was doing. I left a SQL injection hole and… someone stole my entire turnover. Then I launched my own hacker investigation and found the guy. He got scared, gave me the money back and even told me where my weak spots were. He even joined the project as a programmer, paid by the hour.

– What a story! And that must have been when you decided it was worth studying more in that direction?

– Not exactly. I saw that I had a knack for programming, and my parents had noticed it too. It was they who told me there was some academy—Telerik—that ran courses I could sign up for. So I did; I signed up, the course was due to start in six months… and I forgot. I remembered at the last moment. September 14, and suddenly it came back to me that I had to write a motivation letter.

But at the beginning I didn’t want to do this. I had spent 10 years sitting at a computer,

I didn’t feel like doing the same thing; I wanted something more connected with people, with more communication, more meaningful.

Things were quite different back then. The news didn’t keep repeating that programmers are well paid, and there was no rush into the field. In high school it was taught so boringly that everyone gave up.

I went to the academy by chance, just to see what it was like. I didn’t know what to do with my life; I had decided I wouldn’t work in civil engineering. But suddenly… successes started coming. Soon I was ahead of most people, even those with a degree in the field. I started finishing first in courses of 1,200 people. And it kept getting more interesting; programming turned out to be exactly what I like—solving problems.

Before I knew it, I was at it 16 hours a day, and it went on like that for three or four years. Everything else took a back seat. It came from within; I had a huge desire and found it all very interesting. That’s something that can launch you forward, and that’s what happened to me.

Besides, I realized that by programming a person can genuinely help technology advance. That motivated me enormously.

– How did you start teaching?

– In my sixth month at the academy, because of my several first places in the courses, I asked what the requirements were to become a trainer. I thought they would turn me down, because I’m an introvert and find it hard to speak in front of people.

I had come to like the academy’s environment, its culture, and I wanted to keep being part of it. To help and to give people what the academy gave me—meaning in life and interesting problems to solve.

Even so, I’m the kind of person who finds it hard even to ask a stranger what time it is.

I’m rather reserved; it’s hard for me to communicate with strangers. I had every reason not to be a good lecturer.

For two years I started every lecture with my heart pounding! Funnily enough, to this day I feel awkward asking what time it is, yet I go out in front of 4,000 people to lecture and, judging by the feedback, it works. People enjoy the lectures, watch them, buy them, keep asking me questions and want more.

I have over 1,000 lectures, about three hours each on average; unfortunately, not all of them are online. For 10 years I never stopped teaching and had no break except holidays. Then I went further—some of my most interesting lectures are from the advanced series Code It Up. The series on software architectures is especially in demand, because it offers more than 30 hours of video on the subject in an organized, digestible form.

– What are the most common reasons people fail to become programmers?

– Most have self-limiting beliefs: “I’m not good at math, at speaking in public.” I could just as easily have told myself, “I won’t be a good lecturer, I’ll just embarrass myself.” But if I became a lecturer and managed to beat myself, even though I clearly had no talent for it, then anyone is capable of anything.

I constantly see people doubting themselves enormously.

In my view, these days it’s elementary to become a programmer—without belittling those who haven’t made it. The material is collected, digestible, prepared; you just mustn’t be lazy. Yes, you might make it in six months, or it might not happen in two years; we all start from different places. But people doubt themselves and give up far too easily.

To succeed you don’t even need any special prerequisites—just basic literacy, reading, comprehension, common sense. Through my courses people from all sorts of fields have successfully become programmers—humanities, economics, musicians, artists, people with only a high school diploma, even 15-year-olds. Many of them found it extremely hard at first, but they didn’t give up, and now they are leaders in the industry.

Anyone can become a programmer! You get used to it and you learn.

Take the course on object-oriented programming, for example: one of the easiest things there is, nothing algorithmic, just a matter of syntax. I was a complete zero! The course is four weeks long; in the third week I understand nothing, I watch the lectures several times, the exam is knocking at the door. But a classmate sat down with me, showed it to me in two hours, I got it and came first in the exam. You mustn’t despair easily, you mustn’t give up.

The learning process consists of getting slapped nonstop.

You don’t have the information, there’s always something you haven’t quite understood, it causes pain. They hit you and hit you, and in the end you get used to it; you know what to do and how to defend yourself.

It’s like that with everything. Even after I started working, I kept learning new things. Then in management—a whole team of six key people quits; what do you do? A hard slap! But if they quit now, I know what to do.

– Is it really hard to find your first job in our field? Why do companies seem not to hire enough juniors?

– People often finish some academy and think they need nothing else. I think the marketing misleads them; you hear it everywhere: “Come study and you’ll find a job in three months.” That’s not impossible, but it happens rarely and takes a lot of effort. But if you analyze the problem, the conclusions come easily.

Let’s say that every three months the academies graduate 500 people. All of them with exactly the same profile, the same CV. How are employers supposed to choose?

My biggest piece of advice to all beginners: build one project from the very start to a finished product, and give it to friends. Say your hobby is fitness—you make an app, think through the design, set up its infrastructure, hosting… Obviously the architecture will be wrong and your code won’t be fit to show anyone, but you’ll have learned a lot. It isn’t easy, there will be plenty of ups and downs, but with what the internet offers it’s by no means impossible. I did that in my first years too, and afterwards interviews seemed ridiculously easy.

Your first task once you graduate is not to stop learning. Finding a job isn’t a two-week process; it takes time. You have that period to keep improving. If instead of moaning that nobody will hire you, you sit down and build that project, you’ve already eliminated 400 of those 500 people, and a potential employer has something to look at.

It’s also very important to make your CV meaningful and good-looking, so it stands out from the rest. Hiring is essentially about managing to sell yourself better than everyone else. For that you have to stand out somehow. Previous experience shouldn’t be underestimated either, even if it’s in a completely different field and at an entry-level position. It shows discipline, work culture and teamwork skills.

And one more thing—if they give you a task, you need to pay attention to it and complete it in a way that makes you stand out.

People think there are no junior positions because they don’t see many job ads. The truth is that companies know how to find candidates—they hire people through the academies, the ones who performed best during training. We have partners who hire five people almost every month. That means that they alone have 60 positions a year, more than 10 percent of those 500 people! Here’s one more reason to work hard during your training.

– I’ve left a rather different topic for the end… How do you find time for teaching, for projects, and for going to the gym so often?

– Apart from this company, I advise four or five others. Out of 15 to 20 startups over the years, it’s only in the last year or two that I’ve learned the ropes and things have started to work out. My workdays are 16 hours again now; I’ve come back to the same cycle.

To be honest, I don’t have enough time, and I juggle it all through solid prioritization on a weekly basis. I’ve planned to work Monday through Saturday. But training is also like work for me, because I have an old spinal problem and I keep it in check with strong muscles; otherwise I wouldn’t even be able to sit.

– You do somersaults and deadlift 150 kilograms, and 10 years ago doctors told you that you wouldn’t be able to walk. How did that happen?

– Yes, that’s what they told me. It all started with extremely severe back pain; for months I couldn’t sleep. I didn’t manage to find the right doctors to solve the problem, and over the years I learned to solve it myself through trial and error. I know that isn’t the right way, but I had no other choice.

Some kinds of exercise do a lot of harm, different kinds of food have an effect… like some kind of reaction of the body. I filled a whole book with notes, and in the end I reached a point where I no longer had the problem.

Once again, the analytical, engineering approach saved me…

– How did you end up at MIT? And at the beginning of the century, no less, when far fewer Bulgarians had the chance to study in the US…

– My brother and my cousin had already left for the US, and they encouraged me to apply. They helped me prepare by convincing me to take part in programming competitions and to develop my skills in a variety of directions related to information technology. Also to build my own projects—something that wasn't an obvious requirement back then.

And one more thing—MIT was the only university that offered full financial aid through scholarships and loans. All the other technical universities offered only scholarships, which weren't enough to cover all the costs.

– What did programming look like as a profession at the beginning of the century?

– Very different from today. It was a cool profession that quickly became the talk of the town during the first “dot-com boom.” Back then the requirements for getting started were more fundamental than they are today: you had to dig deep into mathematics, logic, database architecture and so on.

Specific programming skills came second, and it was expected that a programmer could learn a new language whenever necessary.

I've always been impatient and focused on getting things done rather than on doing them perfectly. That made me a good fit for startups that want to create value and solve problems while keeping the processes running smoothly. I think that with the modern tools of the software industry, my style may be quite applicable today.

– Especially with the help of AI tools… You started your career as a programmer, but then moved into management and entrepreneurship. How did that change come about? When can a programmer turn into a successful entrepreneur?

– When the programmer realizes that managers aren't just dead weight… or at least not all of them. I'm joking. But back then it really did seem to me that managers were often useless, that with all their talk they only slowed down the development process.

To get rid of that inefficiency, I decided to try managing the projects I was working on myself. Over time I became convinced that I could help teams be more productive and put more passion into their work by sincerely sharing the business priorities and constraints with them. By getting into the decision-making process, its logic, and why those decisions had been reached. Once we set out on this path of curiosity, together we learned to reach the highest levels of business decisions and those related to customers. It is precisely

solving these problems with the help of a motivated team that is the essence of entrepreneurship.

– And it's precisely this skill that brought you back to Bulgaria as part of the INSAIT team. What are the steps toward having more successful startups?

– People are the key component!

With basic qualities like persistence, determination and the right education, backed by a growing ecosystem. I believe Bulgarians have the mindset needed to be successful entrepreneurs.

“Ecosystem” is a somewhat overloaded term that's easy to abuse. In my view, though, there are four key elements that still need deliberate improvement:

• People who have founded, worked at and exited startups and can serve as role models and mentors for the next generation of entrepreneurs.
• Educational and research institutions led by top experts, focused on real world-class problems and equipped with the right technological resources. INSAIT is still at an early stage of its development, but many other educational and research institutions can contribute to this as well.
• Policies that encourage entrepreneurship through tax breaks, grants and a simpler process for starting a business.
• Access to capital: enough funding opportunities, including through angel investors, venture capital firms, government subsidies and crowdfunding platforms.

– Clearly there's a lot of work to be done on each of these points… Do you think there's a chance that Bulgaria will have more software product companies than companies that simply supply specialists?

– Yes, it's possible, but

such a transition will take at least a generation, and only if it becomes a national goal.

– What are INSAIT's next big steps in this direction?

– It's important that we keep growing, that we provide more and more opportunities for deep tech research, for education, and for helping startups grow and collaborate. INSAIT is also a unique opportunity to attract more political commitment and public attention.

– Is there a software field or technology in which Bulgarians stand out for their skills?

– I can't generalize; in my view nationality, Bulgarian or any other, isn't the deciding factor in what someone is good at. The only country where I can say there is measurably distinctive technological know-how is Israel, with cybersecurity.

I would say that Bulgaria has a strong tradition in teaching the fundamental sciences—mathematics, physics—especially at a few incredible schools.

The problem, however, begins after high school: in recent years many of the most talented students have chosen to continue their studies outside Bulgaria.

There are quite a few Bulgarian ventures I follow with particular interest; lately among them are Team-GPT and LatticeFlow.

– Petar Tsankov has been my guest, with many fascinating details about LatticeFlow's work. While we're on the subject of startups… do you believe that, as Sam Altman says, the moment is coming when unicorns can be created by a single person?

– If we look far enough into the future, plain inflation will turn every startup into a unicorn… But more seriously. Sam Altman is talking about a time when every role in a company can be fully replaced by AI agents. I doubt I'll live to see that, because in the foreseeable future there will be new roles needed for a startup to succeed.

– Why does it seem we still haven't discovered all the uses of large language models in everyday life and business?

– The technology has only been available since 2023; it's been popular for a little over a year.

As Yuval Noah Harari says, artificial intelligence is still at the amoeba stage.

We have yet to see how far it will go and how economically applicable it is; right now things are moving at breakneck speed. The good news and the bad news at the same time is that evolution is now measured in months, not centuries. In my view, we still haven't seen human intelligence engage on a large scale with new ideas about how to use the artificial kind in different industries and in every aspect of our daily lives.

– Which are the most exciting and important applications you're looking forward to?

– One of them is the removal of language barriers. Specifically, the need for people to spend years of their lives learning new languages just so they can work and communicate with others. This trend started a long time ago, but now we're already seeing real-time translation from any language into another, both written and spoken.

Another trend, in my view, is personal assistants: they will become hyper-personalized, and we'll be able to trust them the way we trust ourselves. They could take over a large share of the everyday tasks we find boring. At the same time, they'll be our thinking partners, with access to enormous amounts of data and computing power. In this way

they will help us invent, create and validate new, even bigger and bolder ideas.

– I often write about both of these directions too—about translation and about assistants. I'm convinced we have technologies we still need to talk about a great deal, so that they help us move forward. But how do we convince the majority of people, who still don't believe we're living through an AI revolution?

– Change is hard for all of us, especially when things develop so drastically in such a short time. But it's a natural process. Seventy percent of people are always the ones who need more time to embrace change.

Personally, I see no reason to force anyone; in the end everyone will encounter the effects of the AI revolution and will follow the people who appreciated them first.

– That, however, brings risks too. For example, those who appreciate them first are always mostly tech people, and I think specialists from every field should take part in such important topics. Here we are, once again on the verge of handing a powerful technology to one or a few big players who will set the rules. Are we doomed from now on to bow to OpenAI's dominance?

– No, in my opinion we're not. There are many other foundation models, and

innovation in this field is only just beginning to explode.

The pioneers in almost every industry rarely manage to keep their lead for long, because they struggle to adapt to and/or adopt deep technological innovations quickly.

– How much better can AI technologies get? Are we close to the limits of large language models?

– I believe we're far from the peak. Harari's words, which I mentioned, apply in my view to large language models like GPT as well. Fine-tuning the models and providing highly specialized context will produce models that will amaze us for generations.

– People in which professions can feel secure about their jobs over the next five years?

– I think there are quite a few, but the most obvious are those involving physical labor and innovation that relies on human intuition. You'll find them in construction, agriculture, sports, medicine.

We mustn't forget about robotics, though.

I believe we haven't yet seen the revolution in that field, and breakthroughs comparable to those in the world of artificial intelligence are on the way there.

Robotics has been a subject of scientific interest for decades, but its progress has been held back by technological limitations that are gradually falling away.

– You also have a professional interest in applying artificial intelligence to medicine. Why does the expected big news there seem to be so slow in coming?

– Current legislation in the healthcare sector is extremely inefficient, overregulated, and takes a one-size-fits-all approach. It's far too restrictive to stimulate rapid, meaningful progress. I hope some countries will seize the opportunity to combine AI and innovative legislation with investment in order to lead the new generation of medical discoveries, which could reach patients in record time. But I'm certain of this:

when that happens, we may unlock new records in longevity and solve many of the social challenges related to an aging population, elder care and retirement.

– What will the medicine of the future look like?

– Much better than today, I hope, with a focus not on treating the main symptoms but on the causes and on preventive medicine. The insurance system will also have to evolve drastically in that direction. Here too, unfortunately, development is heavily constrained by regulation.

– What do you dream of?

– Of a society in which all people are good. Of a world in which we don't waste human potential on lies and disinformation. Where there's no room for people who follow their egocentrism and their sick dreams at the expense of others…

– Quite poetically put, especially for someone with more than 20 years of experience in the world of technology… It's no accident that I, too, keep thinking we need to bring these two directions together. Thank you for this conversation!

– What do you think are the consequences of the fact that we find it increasingly hard to tell the created from the generated? We have it even in computer code now…

– Before, it was Photoshop—a person did it with the help of a tool. Before Photoshop, it was the person in the darkroom making collages. Before that, it was painting…

We humans are endlessly creative when we're looking for a way to fool someone, whether for artistic or for worse purposes.

But also… we've seen this before. Yes, we can't easily distinguish something generated by AI from something created by a human. But aren't both modeling the same thought process after all?

I see a very big paradigm shift in a different direction. A standard computer program is something you know you ask a question and get an answer from. You're guaranteed the answer will be fast and accurate, but not that it will be very intelligent.

Now, though, it's exactly the opposite. You have “something like” a person in front of you.

A little buddy who knows a lot, but who can also get a lot wrong and lie.

Personally, I see dangers with copyright. The people who create something need to be guaranteed that what they love, what is their goal and calling in life, won't suddenly become superfluous.

At some point this question even stands before me as a teacher. One of the things I'm valued for is precisely human contact and understanding. The fact that I can not only answer questions correctly, but also know the person in front of me, understand them and empathize, share from my own experience.

It's hard for me to imagine how human-created content will become scarcer or less relevant than AI-generated content. But I think there will always be that touch of authenticity, beauty and… contact. We're not only talking about content here. I can have a robot assistant. I can imagine some language model posing as my friend, but how authentic is that experience?

– If you don't know which of the two it is, then why shouldn't it be authentic? If you're so convincing that I can't tell whether you're really answering me, or you've been generated by the algorithms?

– These are very philosophical questions, like Roko's basilisk or how to kill your own clone. You have a “person” who thinks absolutely like you, knows what you would do… how do you kill them? When we talk about AI, we inevitably get into a philosophical discussion like that.

But such discussions exist not only in this field. Art hasn't disappeared and won't disappear. Candles still have plenty of uses today. Some things we've lost irretrievably, but others have been born in their place. Besides, sometimes we rediscover old knowledge.

The single greatest hope I have for humanity is that we won't lose our curiosity and creativity.

And I think that loss is more likely to come from entertainment that makes us bland and uninterested. Not from some artificial intelligence having taken our jobs…

– Is it worth learning programming today, or, as one of my guests put it, is it like buying a factory on September 8, 1944, the eve of the communist takeover that soon nationalized everything? Even the head of Nvidia announced that he wouldn't waste his children's time on it.

– Do you know how many times I've been asked that question? Back when I was learning to program, we wondered whether it was worth it, because we thought everything would be automated, and it really is happening. But

programming, at least for me, is one of the most important skills in the modern world.

It can help you with everything, whether you do data analysis, you're a software engineer, or you just want to automate some everyday tasks.

Programming matters not only for professionals but for everyone else, too. It helps the brain develop, helps you solve such a variety of problems. And third:

someone has to write these models, someone has to build this AI!

It has been trying to create itself for a long time, but so far it hasn't worked out. At some point that will happen too, but I think by then programming will have turned into something even closer to natural language, even more visual or intuitive. But it will always be important. In the end, a programming language is also a language, and language, whatever it is, is the most fundamental thing we have as humans. It gives us the ability to express our ideas and share them with others.

– The question is whether we aren't close to that next level of abstraction, where you say: “Dear ChatGPT, write me a script that solves this problem.” And many of the everyday applications you're talking about won't be needed.

– Hold on,

what do you mean by programming? For me it isn't writing scripts, it's solving problems.

You'll put some everyday task to ChatGPT, but how many easy everyday tasks do you actually come across in practice? Can you write Google's Koprivshtitsa algorithm, named after a Bulgarian mountain town, with ChatGPT from start to finish?

– Veselin Raychev does that; maybe not all of us need to be able to. For a large share of people working in the industry today, the work is routine—they solve mundane, recurring problems.

– Exactly! Automating the more everyday tasks. That's great! Why should anyone write boring code? Why would anyone dream of being a programmer who writes boring or monotonous scripts? I can't explain it.

Besides, it depends on what you do, but once a month you may need to use complex algorithms. In my work, just yesterday I used several kinds of statistics, hypothesis testing, and at one point a prefix tree. I have training, model evaluation, working with data. Yes, this work can be improved, I can be given ideas. But in the end, someone has to take responsibility for what's been done.

As for programming, there are so many positions, technologies, languages. A few years ago I was doing what I do now—applied research. Back then my position was called “machine learning engineer,” because that was the only thing that existed. Now I'm a “data scientist” or a “researcher.” Since then we've gotten the ML engineer, and alongside it MLOps was born, cloud engineers, database administrators. So from one position that did everything at the start, there are now dozens in this field alone. Now

there's much greater specialization and more directions a person can head in.

True, in the long run some positions may drop away, but there will also be huge diversification and more needs.

– But the question is whether we're talking about far fewer jobs.

– Weren't we talking about far fewer jobs when we went from horses to cars, and from steam to electric and diesel trains? And are you sure there isn't a single steam locomotive running anywhere in the world right now? It's impractical, it's not modern. Some do it for pleasure, others out of love for history, but it's still not something that dies.

– Still, if I'm wondering right now whether to start learning to program, is your answer: “Absolutely yes, because you don't know how it will be useful to you, but it will be”?

– My answer is: give it a try. But there's the other side too—programming isn't for everyone. Everyone should try it, but not everyone has to do it. Just as not everyone has to go into law or painting. There are people who find it completely uninteresting, or for whom it doesn't fit how they see things. Everyone should try, not be afraid, but nobody should feel obligated.

– Who should head into your field—data science, machine learning, neural networks, artificial intelligence—and when?

– Whoever wants to, whenever they want. There's no barrier to entry, and there never has been. Our profession is considered a bit more elitist, but the truth is this:

It's very easy to get started! It looks scary, but it's very easy.

This diversification of professions has created the need for an enormous number of very diverse people. Right now, just as in programming, there's a huge shortage of talent. Every startup, every corporation is desperately looking for people, and those people aren't there. Not because they're stupid and don't apply, but simply because the number of candidates is limited.

– What are the first steps if someone wants to head into the world of AI?

– That's something that isn't easy for me to explain to many people.

In fact, all you need is a desire to learn and communication skills.

You don't need to know “this is that regression,” “this model works like so.” At some point you'll have to, but that always comes second.

You need an enormous desire and ability to learn, and to learn fast, because the environment is dynamic and you often have to invent what you're doing. Very often there's no recipe to follow; you create it yourself based on your previous experience and what you've read.

And the second thing is being able to communicate, so that other people can help you with ideas and you can help them. Those are the two skills that are critically important.

From there on, even for the highest positions, these are still the fundamental skills required. They're much more important than the technical ones—those accumulate as a result.

– Is a university degree necessary? Does it help? Or… does it just not hurt?

– Is it necessary? No. Does it help? Depends on what you like. I (almost) finished a PhD in astrophysics. I've loved science since I was very little; it shaped my way of thinking, of seeing the world. But

having some degree has never been an advantage for me in any way.

Let's make a distinction between the degree (that is, formal education) and what I've studied in general.

I got all my knowledge of machine learning from the internet.

You can learn something from scratch to a good, professional level from the internet alone.

But you have to want to learn and be able to.

“Doesn't hurt” is already a bit outdated. Over the last year or two, a trend has started of companies not liking people with degrees. Because in some cases you first have to unteach them, then teach them, as opposed to having people with whom you're writing on a clean slate.

My advice: if you're really very interested in something, if you want to do it for yourself, to develop in academia, to do scientific research—then yes. INSAIT, the AI research institute in Sofia, is a very good example of this, an applied institute. The degree doesn't hurt, but only if you really want that knowledge. Not if mom and dad told you that you have to.

– And yet, if a candidate shows up with a degree from Harvard or MIT…

– Quite the opposite. I had a very sad experience with a boss of mine who had graduated from a university like that, bragged a lot and was good for absolutely nothing. The thing is,

if you have the knowledge, nobody will ask you for a degree.

– But the argument is that at university you get fundamental knowledge. You study more math, electronics, networks before you get to programming, and if you do it through the academies, you might skip them.

– I teach math. I started by making a distinction between a degree and learning. Learning is a good thing. So is a degree, if you need it for something, but it's important to know that it isn't mandatory. You can get that fundamental knowledge in all sorts of other ways. Honestly, I got most of it at school—I graduated from a math and science high school.

I've seen many success stories of people who never so much as caught a whiff of a university, but study a lot every day. And then they write scientific papers.

It has made me very sad to see people who have lost the curiosity they had as children.

Sometimes it's precisely universities and schools that kill it. Look at the PISA results and the matura, Bulgaria's school-leaving exam.

– Why, despite all the buzz around AI, does there seem to be no growth in job postings, and postings for programmers overall are even declining?

– Postings really have been declining lately—the market is contracting, but it won't last, because these people are endlessly needed by every business.

Even if it exists, I don't think the danger of being replaced by machines is that close.

We have a global recession, wars, shrinking consumption. On the other hand, programmers' skills remain irreplaceable. For me, every good software engineer must be able to adapt and have more than one skill. You can't tell me you're a good software engineer if you've written on the same platform your whole life.

In my field, at least in Bulgaria, very few people post job ads. It more often happens through referrals, directly, through recruiters.

– Isn't the bigger problem that today there are far fewer positions where a person can start out?

– There are positions. Most of the people who took my course and graduated a month ago have already found jobs; the rest are in the middle of interviews, and I expect them to find something within six months.

Besides, many of the people coming in already have some other professional experience that matters. “Beginner” doesn't mean starting from zero. I know one such person who was a journalist his whole life and then became a programmer…

– I know him too; I even chat with him now and then…

– It's good to take what you have. You have the skill of storytelling. Okay, so writing documentation, explaining, communicating between teams isn't a problem for you, especially if you find it interesting.

There are people with years of experience in science, engineers, architects. All these people have knowledge they can use, and very often they aren't even hired for entry-level positions—they're already a level above.

I'm coming back to soft skills—these are people who have a work culture. That's harder to build than learning how Python works.

One more important thing: experience in learning is also experience. You take some course, do a project and add it to your portfolio. That's how you can prove you've learned something.

– Are there things it no longer makes sense to start learning?

– So many things will no longer be needed. But I'm someone who loves diverse knowledge. For me it's never been good for a person to be an expert in just one thing and understand nothing of anything else.

Everything you know may come in handy sooner or later. All knowledge is useful.

– If we're starting now, how do we figure out our place in the whole sea of courses and academies? How can we be sure we won't waste our time and money?

– As interest in the field has grown, so has the number of people popularizing it. Naturally, when you have more courses, you have a wider range of quality.

How do you choose? I rely quite a lot on reviews from other people and on some kind of advance information. There are courses that have been recorded, so you can watch sample videos; others someone can recommend to you. When I learn, I like not to watch just one thing, but to explore a topic from many different sources, whether scientific papers, videos or code, and get different points of view. That doesn't apply to everyone, but it's my solution.

People learn in many different ways. Some learn fastest from videos, others only by listening. Most of us are visual people. I learn best when I read; sometimes videos are too slow for me, the pace doesn't match mine. Everyone has their own ways. One new solution is to ask some AI, while knowing that it may hallucinate, may lie to you very convincingly.

My advice is to try different modalities, that is, different ways of taking in information.

– Many people don't dare start with programming or data work because they're afraid of math. What's the truth—do you have to be good at it? Or might it turn out that you are good at it and never suspected, because it was taught to you in a boring way at school…

– We mentioned that data work now has many directions. In some you have to be terribly good at math; in others you can know nothing.

As for programming… let's talk about software engineering. It's quite a complex thing. You have processes, team management, automation. An awful lot of things that go beyond sitting and writing code.

Typing code on a keyboard is only part of the job, and a small part at that. The rest is what turns you from a programmer into a software developer, and then into a software engineer.

For us, in data work, there's even less code writing; you can get by with a few lines for almost anything. What you really do need, though, is to know what you want. Is this what I need? What hidden dependencies are there in this data? What hidden problems does it contain? These are questions we ask ourselves every day, and here there's a huge need for what's called mathematical thinking. Having the logic, knowing what means what, following certain intuitions or more formal methods. Being able to make complex decisions, to break a problem into parts—that's an irreplaceable skill for everyone, not just for people in my field.

My work as a data scientist specifically is mathematical. Many people recoil—“Math, eek!” But once you've learned what you need, if you look back after a year, you'll see it's very little. They're important things, fundamental, but that's exactly why one small piece of knowledge can be used in many places.

Besides, honestly, it can help programmers enormously too. I've seen many people for whom starting to think mathematically helps every day. Thinking not about what the code is, but about what it means in a system with so many component parts.

– What will the last programmer do for a living?

– Plug-puller. That's how I picture it, as some absolutely final safety measure…

– Let's hope it isn't one of us…

Programming is still more than tempting: you set your sights on it and look for your future in the much-vaunted IT sector…

Our guest today is a programmer with 20 years of experience! He chose this path long before it became fashionable and so alluring. Until he realized he was tired of spending his time in front of computers, and that what he enjoyed most was being among children.

So, defying every stereotype, Bogdan decided to earn his living by looking after kids. It makes him happy and gives meaning to his days. The thought of going back to computers doesn't even cross his mind; for him nothing is more meaningful than the time spent with children…

Isn't it time we rethought a few clichés?

“You Get Up and Keep Going”

I find it hard, I admit. To see a little one who has tripped and is crying, and instead of rushing over to comfort them, to encourage them with words to get up and carry on.

It's not only true for the little ones; life is like that too. You fall and you get up, for as long as you can. Every fall makes you stronger and prepares you for the next time you rise. Bogdan Tsenov's story reminds us of that. I read about him on the site ladyzone.bg and grew curious to talk with him. About children, about new beginnings. About how important it is to choose your own path, the one that brings you pleasure and meaning.

He was drawn to technology from an early age. In 2002 he graduated from TUES, a specialized high school of the Technical University of Sofia whose students leave with everything they need to start working as programmers. Right at that time, however, he ran into a serious health problem. He spent weeks in the hospital, and the doctors were adamant that within five years at most he would be able to move around only in a wheelchair.

But Bogdan decided to get up. And to keep going. Twenty-two years have passed since then; he has been successfully battling his health problems, and not only is he not in a wheelchair, he is perfectly able to look after children and play with them in the park. He put his faith in alternative medicine, and his experience has turned out more than positive.

The Child Within

After high school he went on to the Technical University. But well before he received his diploma, he was already earning his living as a programmer. First he worked in his own company, then he moved to former clients… And so he stayed at the same company for 13 years! “It worked out well, it was stable,” smiles the “retired” IT specialist.

He specialized in technologies built around the PHP language, loved his work and found it opened up plenty of opportunities. Almost the entire time he developed online stores and other complex platforms. And he felt good—which is why he stayed with the company so long. He was the only programmer building the sites, and his bosses valued him. Well, since the system kept working, they had no wish to switch to more modern technologies, even though the software world had meanwhile changed from the ground up.

In the meantime Bogdan found love, and exactly ten years ago his daughter was born. His wife was also in IT, the family was happy, the little one was growing… and then came covid.

My Childhood, Real and Magical

“They locked us up at home to work. We looked at each other and looked at each other, and at some point it became clear that we had to part ways,” he recalls. He and his wife discovered they couldn't go on together, and soon the divorce was a fact. At the same time he felt inside that it was time for a change… He was tired of 20 years of programming without learning anything new, and the stress was becoming too much for him as well. “All that stress affected my health, which I was trying to hold together, to stay away from the wheelchair.”

Freshly divorced, Bogdan was also left without a job, but… his soul was at peace. For a few months he lived on his savings, until the question rose up in all its weight: Where to now? He was fed up with computers; he wanted to be around people, to breathe fresh air, to take care of his health, to have time for his daughter… And yet his experience and education were so closely tied to computers that there seemed to be no way out.

“When have I felt happiest?” our hero asked himself. And the answer came quite naturally. When his daughter was tiny, he spent a lot of time playing with her, watching her grow and learn new things… Couldn't he earn his living doing something like that?

Mary Poppins

“Yes, programmers are well paid, but the pressure is enormous too. Hurry, deadlines, new technologies. I told myself: ‘No, this isn't for me, I need something calmer.’ And what could that be? Children! We get along really well; for me the time with them isn't stressful, I feel great, we play, we talk, we learn.”

The idea came to him while he was visiting his parents. They suggested he seek advice from the woman who had looked after Bogdan himself when he was little. And she was blunt: there is huge demand for babysitters. Contrary to the stereotypes, many families specifically look for men, for one reason or another. Mostly middle-aged, calm, caring toward the kids. Just like him…

He found his first “job” minutes after posting his ad online. At first it was acquaintances who trusted him; then they began recommending him…

Before he knew it, two years had gone by in which Bogdan has earned his living babysitting by the hour. In the first months he went from home to home; gradually he set up a small center, where today he looks after four kids at a time. The new venture is clearly to the liking of his daughter too, who helps out, has fun with the little ones and teaches them new things. She is drawn to acting, and playing with the kids does her good.

A Child's Joy

For now he definitely doesn't miss programming. “The moment I decided to work with children, the first thing I did was make a web page. A babysitter with a website. So I write code for fun, and sometimes my old job calls me for some small fix.”

“As a programmer you create projects, people use them, something remains. But when it becomes an obligation, or you have someone breathing down your neck telling you how everything should be done, at some point it starts to weigh on you,” Bogdan says.

He doesn't rule out returning to the profession one day, but for now he has no desire to. And still, life can always surprise us. “Do you think ten years ago I imagined I'd be working with children?!” he smiles.

His first “client” was a three-month-old baby, whom he looked after by the hour for a year and a half. The children he cares for are younger than six—he decided so because after that it matters that they be in the hands of someone with the proper training. They spend their time playing—when the weather allows, you can often spot him with his charges in South Park in the capital, Sofia.

They learn to draw, to write, to solve puzzles. They read favorite books together. They model plasticine, cut with little scissors, make collages. Despite the babysitter's IT background, there's no electronics anywhere around—no singing toys, no TVs, no phones.

My Dear Little Giggler

“Children are so energizing! Even just watching them grow… One little girl was almost a baby when she started coming to me, and now she can put together a puzzle, draw, write a little. They grow up, and it's such a pleasure! It's like with a website—you start from zero, and in the end you have something good, meaningful, useful.”

But where does the stereotype come from that children should, almost by default, be looked after by women? It isn't hard to trace, but it's certainly worth questioning these days. Bogdan is often contacted by single mothers who want the male role to be present in their children's upbringing somehow.

“Maybe it's harder to be a man in this profession, because you run into people's fears. When you say a ‘male babysitter’ changing diapers, they get scared, and to me that's strange,” Bogdan says. And he goes back to the time when he and his wife had to choose a woman to help them look after their daughter. There were two candidates, and they invited them home to get acquainted. The first was an experienced teacher with a serious résumé… but as soon as she entered the room, the child hid. The second had no special training, yet she won the approval of her direct “employer” at once: the girl stuck to her and they began to play.

Children have a feel for people and are best able to choose whom they feel at ease with, Bogdan is convinced. “That's what matters most, that the child senses it. When they're small—three, four—it's important that they feel good, that they trust this person, and don't feel almost as if they're being punished.”

With Children at the Seaside

That's how a single mother of two kids chose him; the children were quite mischievous and would hardly have been left with an elderly woman. He copes, though, and being an outsider makes it easier for him to set boundaries.

“Very often, when a woman is looking after a child and it falls, she immediately starts wailing: ‘Oh dear God, what happened!’ Whereas it's easier for me to say: ‘Didn't I tell you you'd fall? You get up and keep going!’”

Looking after children who aren't your own turns out to be interesting. “Everything is radically different. My own child can always get under my skin somehow and get what she wants. With someone else's, the emotional bond is very different. The fact that the child is sitting there bawling, throwing a tantrum, whining—it doesn't move me. We go by the rules.”

Anyone who has looked for help with childcare knows how hard it is to find someone you can trust. From his experience Bogdan is convinced that there should be many more good people taking on this role. And men in particular. “Who should go into this profession? Someone who loves being with the little ones, who is gentle and attentive with them—children like such people. I see many people looking after kids, and it's obvious the kids irritate them… That's a problem! Yes, it's tiring, I get tired sometimes too, but you still always have to treat children well.”

Never to Grow Up

And so today Bogdan sometimes… hires a woman to pick his daughter up from school while he looks after the kids entrusted to him.

He is convinced the change came at just the right time, and today he certainly feels happy. “Things fell into place the way they should. The money that came from IT was very welcome. Now that my daughter is older, it's different. That was the right moment for the change. And it is risky—you drop everything you've built and set off to build something from scratch.”

So what does the programmer who chose to become a babysitter dream of? “To keep my peace of mind, my place. To do useful work for someone and at the same time feel fulfilled by what I do. Looking after children gives me all that, and I'd be very glad to keep doing it.”

What is happiness? I have a universal answer. Go to the park and sit down for a while. And watch the children playing their favorite game…