The Pragmatic Engineer - The Pragmatic Engineer AMA

Episode Date: July 8, 2026

Brought to You By:• Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages.—In this special “ask me anything” episode of Pragmatic Engineer podcast, I am in the hot seat facing questions sent in by subscribers that are read out by guest Volodymyr Giginiak, CTO and cofounder of Wordsmith AI, a legal tech startup (note: I’m an investor).I tackle your questions on the software industry, AI, hiring, engineering organizations, career growth, the business model of the Pragmatic Engineer, and more. We also discuss where software engineering is headed, and I offer advice on some specific situations. Thanks to everyone who sent questions!—Timestamps00:00 Intro01:56 From Uber to writing09:22 AI-native SDLC14:00 AI and hiring19:06 Engineers currently thriving22:18 Junior roles24:44 Meta’s war mode27:54 AI at Big Tech vs. startups36:46 Tech debt41:36 Types of engineering managers44:40 Measuring AI productivity48:30 The value of CS degrees50:53 AI at Pragmatic Engineer56:09 Future-proofing your career1:01:36 The EU job market1:03:55 Making money as a creator1:08:20 What’s next for The Pragmatic Engineer1:09:27 Bunq and Pollen1:13:38 Spotting trends1:14:33 Book updates1:15:20 Favorite books & tech products1:17:13 What won’t change in engineering—The Pragmatic Engineer deepdives relevant for this episode:• State of the software engineering job market in 2026• The impact of AI on software engineers in 2026: key trends. • How 10 tech companies choose the next generation of dev tools • The reality of tech interviews—Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [e-mail]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Today's episode is a different one. It's an AMA where I answer questions that you submitted. Asking the new questions is Giggs. That is Voldemir Gigniak, C2 at Wortsmouth. Wirtsmith is a legal AI startup where I'm an investor and know the team well, and Giggs was just in town to help out with this AMA. We've grouped the questions as observations across the industry, opinions on AI, opinions on hiring, questions about myself, advice on specific situations, and the private engineer as a business. Thanks to Anticistis for being our presenting sponsor. With Anticesthetist, you can verify your system's correctness without human review or traditional interrogation tests and avoid bugs or outages. With this, let's jump in. Hey, Gerge, welcome to this reversed podcast AMA. It's really nice to be a guest on my own podcast. This is really cool. And thanks for coming here. For some background. We know each

other from Wordsmith, which is one of the very few startups I still investing, because I stopped investing, but about two years ago I invested with a friend, Ross, who I worked together. It's really nice to have you here. Yeah, and, you know, I very appreciate you putting trust in us investing and let's get started. So first question, what made you switch from a full IC role like at Uber to focus on sharing, reporting, tech content? Yeah, so at Uber, I started as an IC, and I was an IC for about 10 years before Uber. I started as a senior engineer. I became an engineer manager manager pretty quickly. It wasn't an IC role, but I guess a manager role, but it doesn't change the story too much. I was hitting about four years at Uber and two things happened at the same

time. One is Uber in 2020 had layoffs because COVID hit Uber's business really bad. I had access to our internal dashboard where we saw the revenue for rides and it was just going down very close to zero and I was actually sharing it to my team because I figured transparency is a good thing. I'm not sure that was the smartest thing, but I probably still do it again. I was showing people like, this is not looking good and we were all collectively freaking out a little bit. And layoffs came very predictably. It was a 20% layoffs. About a quarter of my team was unfortunately gone.

And the remainder of my team, our mission no longer made sense in this new world where we were building something for drivers when we thought there would not be as many drivers or we would have to compete with them, but because of COVID, drivers actually were flocking to the platform. And so I got a new team to work with,

but it felt to me for the first time in four years that they were going really well. And I just felt demotivated. I knew that the business would be doing poorly. And I also asked myself, like, why, you know, what I wanted to do after Uber. And before, right before I joined Uber, I got this offer, which was an amazing compensation package, which a bunch of stock. And I told myself, well, stock, I mean, who knows if Uber will go public or not. But I said, if Uber does go public and this, this money turns into stock, I had about, I got about $500,000 worth of stock as a grant option.

And I'm like, if I have like 500K in my bank account, well, I can take a risk. And for the next thing, I can actually do a startup. So I remember this and Uber had gone public. And that 500K stock turned into 400K because of the stock price was a bit lower. And then you have to pay taxes on it. So it was less. But I still had a lump sum sitting in my savings account. And I was like, huh, I don't have to work actually for like, I could not work for like two, three years easily.

I was like, well, maybe I should take a risk. And my plan was leave Uber, finish writing the Software Engineers Guidebook, which is something I started writing at Uber, just finish it in six months, and afterwards do what you've done, which is start a startup, join a startup. Because I was a little bit tired of being a middle manager. They tell you your manager, you know, congratulations, you became a manager. They should have said you became a middle manager, because now your job is to keep your team happy to keep management happy, and especially I was in a different region. I was in Europe. So this was easy, but when layoffs came, it was a lot of politics, a lot of explaining regulations that it wasn't what I wanted to do.

It also keep your peers happy in terms of your manager of peers. It was pretty tiring. And I was like, I want to be in charge next time because I have a lot of ideas, but I felt I was like fighting the machine, if you will, in some sense. So that was my plan. It involved nothing with writing except just finish this book. I have a legacy. I can give this book to people.

I can be proud of it. But then what happened is, similar to software engineering, when you started a project and software inject you've never ever done before. You know, you're a junior engineer, you're doing your first migration. You think it'll take two days, and then two months later, you're still stuck there. And it was the same thing with writing this book. I've never written a book. I knew it's a big project, but I was like, yeah, six months should be enough.

Six months later, I'm still, I'm like treading water. I wrote three other short books. So, but my main book was not progressing. And I asked myself, like, okay, like, I gave myself about six to eight months to like, get this book out and then just go and have a real job. In my mind, a real job was either just start a startup,

be a founder, or go back to being an engineering manager or staff engineer or a CTO, a smaller place. And I was like, okay, well, I should be honest with myself. Like,

what am I doing right now? And what will I be doing? And I was like, either I start and I raise funds to start a startup. And my idea of startup was just Uber and site had a lot of platform engineering teams, copy one of the things that they were doing. My idea was actually we had an internal RFC system, request for comments, where we actually had a system that put these Google Docs together,

and we graded and all that. And it was a pretty cool system. I thought maybe I could productionize that. A lot of Uber startups actually came from people looking at internal platform stuff and taking it and either making it open source. Temporal is XUBER, Chronosphere, XUBER, Observability System, and many others. So actually, it's not all that radical. But then I was like, well, if I did that,

I just have to fully focus on that. And on the side, I was doing writing. I was writing a few books, actually. I was blogging. I was doing YouTube videos out of fun. And I was like, well, I need to stop that if I do that. Because if I raise money, I owe that to my investors. I will hire people.

And for about five to 10 years, I'm going to be happy to be just focused 100% on that. I talked with my brother, he was on a second startup. And he said, like, look, if you start a startup, do it because you are ready to spend 10 years of your life on it. Like, you need to believe that right now. Because if you don't, it's not going to work because startups are just really hard. It's not a popular thing to say. And I wasn't sure I was right to spend 10 years on like an RFC system.

I wasn't that excited about it. And then I asked myself like, okay, like what is this drive? Like why do I really want to do this startup or a startup? And I was trying to be honest. I had two answers. One was the money in the sense of like this was 2021. It seemed everywhere I looked, X Uber startups, they were valued a billion. They were unicorns in like a matter of like, you know, a year or two.

it seemed too easy. And I was reasonable. I was like, that will probably not happen to me, but what might happen is I might be able to build a unicorn in like, let's say, 10 years time. And by that time, I will still, you know, if I'm a sole founder, I might have five or 10% stake because I'll count with a lot of high dilution, which is $50 million. And let's say we have an exit and I leave. And then I pay taxes and I still have $25 million, which is like exactly 24 more than I would need, you know, outside of buying house. And then I have the, this, you know, FU money, what would I do? The answer was like, well, I'd probably like share what I know. I'd probably like, you know, write a book. I'd probably like, you know, do some

YouTube videos. I was like, huh, interesting. Like, I could do that right now. And the other reason I wanted to do the startup was the small teams. I always loved working both at Uber and at my previous companies at SkyScanter where I met Ross, co-founder of Wordsmith. We were a small team, us against the world. And I love that feeling like being either an engineer on that team or the manager of that team. I didn't enjoy being a manager of managers, but I know long hair that connection. And that was the other reason. And actually, that was

I guess the more legit reason. But in the end, I didn't have this exciting idea. And I actually, I was like, if this startup was successful, I would just be writing, probably. So I was like, let me try that. I saw SopSak was staking off. Lenny Roshiski shared that he had 2,000 page subscribers for product

management news editor. I thought if Lenny has 2,000 page to describe for product management, there's 10 times of many software engineers as product managers and every single team, and they're not as likely to buy, but there was no paid newsletters for software engineers. So I was like, let me try it out. I gave myself six months, and I figured it might not work, and then it just worked. It took off. That makes sense. Next question. Have you seen engineering teams at Big Tech that adopted AI Native SDLC, and how does they collaborate across engineering product and design? Yeah, so AI Native SDLC, software development life cycle. Even the whole,

you know, like SDLC is an interesting one before we go into AI because like what is SDLC? It used to be you plan, you code, you deploy, you monitor. And some people used to call this waterfall and then there was agile where you just like iterated a lot faster. And interesting thing like outside of big tech or outside of these large tech companies, if you go to a large company that is not like a big tech, not one of the Googles or metas, they often have like pretty rigid processes around scrum specifically they say we're very agile we have scrum or they have the safe system the scaled agile framework which has a bunch of meetings and like a really rigid way to be agile and of course there there's a bunch of like money

and consulting and all that but they think they're very agile and then they're very surprised to see how most of teams inside of the likes of uber or meta or or even google work which is like oh we kind of have this like you know problem we actually plan we sit together we kind of do like i don't know a few days of planning and then we code it and then we deploy it and then we get some feedback and we might iterate and they're like well that's waterfall we're so much more agile and actually like the whole thing about waterfall and and and agile is it doesn't matter anymore waterfall used to be a thing i talked with ken back when in what it literally used to be like a year or two of planning and like having like this much documentation and we don't do that anymore so the software development cycle is an interesting one. And almost every modern company up to AI used to have RFCs or RFDs or

design docs where people would write down because they realize that you should, if you plan things ahead and then you build, you'll have better results. Like plan things into thinking through. Now the whole AI native SDLC, the closest I've seen to a company who is big and successful and making a lot of money and employing, you know, like hundreds or thousands of engineers is entropic. They don't employ thousands of engineers. They employ probably hundreds of engineers right now. But they're a very interesting place. They're not a product company decisively.

They're a research lab. And they just do everything super fluidly. Like you can see it in Claude Code. And I've talked with Boris Churning about this. They don't do design docs. They just do prototypes all the time. They kind of show it to themselves. but I wonder if it's really replicable, and I also wonder when it will break down.

In the sense that Cloud Code is a great product, it's now the leading coding harness. So, like, they did an amazing job and just with prototypes and iteration and using AI and getting feedback and fixing it and responding on social media, they respond to bugs. They fix it immediately. But there's a question to me, like, sometimes, like, how much do they plan? Do they have a strategy? Like, with pricing, they keep changing the tiers back and forth. Entropic is the closest I can think of,

but I did not see any company that managed to really retrofit anything. What I'm seeing almost every company do, they are building AI infrasystems. So, for example, they will build a coding agent that talks with all their internal services that's plugged into. Google is doing this, Ramp is doing this, Uber is doing it. So I think what's happening is they're building a lot better tooling to make this easier, and I think that's where we'll see.

And I still have one last question, which is, If you have a business that is working, it's making money, it has a rhythm, you have customers who are used to certain things. How much do you want to change inside everything versus just changing it slowly to make sure, for example, a case of Uber, people expect that when you press the button, the car arrives, that the drivers are there. There's processes behind this, which are non-software. Like, you need to do outreach campaigns for the drivers. You need to let them know weeks in advance when there will be a big event so that they can prepare for it. The pace of the business has not changed because of AI, even though AI speeds of development. And finally, like, when you just go too fast, you might forget the basics, which I'm seeing a lot.

Spotify is a good example where I've talked with their CTO and their team and they say they do AI very responsibly, which is great to hear. But then again, as a customer and a user, I'm so frustrated because they seem to be down so much. Like I couldn't publish an episode two or three weeks ago because they were down and they don't have a status page. and I don't know if it's AI or not, right? It might not be. But then the other day, like, the whole site just went down. And I'm like, if you're using AI, you're sure not using it for to make better reliability.

Have you seen how AI is impacting what employers look for in candidates? Yeah, well, it's impacting it because it feels to me that they just don't really know what to look for. I mean, I'm going to ask you for this one. I'm going to turn it because you guys are hiring. How did it change how you're hiring for software engineering? and then I'll answer. Yeah. So in our case, we definitely structured interview quite differently.

So the main thing that we're looking for now is the ability to reason through what AI is doing and correct it and do the appropriate research. So actually, it's interesting. Like our interview process, we give away a homework, which is pretty classic, but we expect that this homework will be done with AI. But then we basically have a very long discussion around this homework, and we are checking, okay, you pick this algorithm. Was it AI picking it for you?

Or did you actually do research and you figure it out what is appropriate? Or here is a design decision that you made. How did you made this decision? Again, like is it automatic decision by AI or you understand it deeply and you can course correct? And then we are looking, so we are peeking into different parts of the code and we are seeing how a candidate can react on the spot, whether they can spot an issue, whether they can come up quickly

with a solution to the issue. So basically, the ability, to reason through and of research and not just apply all the solutions that AI generates automatically. So this makes a lot of sense and this is, I've seen a lot of similar things with startups doing it. And when we think of how hiring is changing with AI, before AI, there were two worlds in hiring. There was the Google interview process, which is the lead code interview process. And this is because Google decided early on that they want to hire for raw intelligence. They had puzzles initially, like, you know, like how many golf balls spit in New York or something

like that, but they realized that doesn't really scale that well. And they found coding interviews, algorithmical coding interviews to work really well because it's selected for a few things. It's selected for people who have computer science basics, which Google needed. Specifically, going to universities where they teach computational complexity and some of those things. It also selected to, you know, like apply under pressure, explain your thinking. And it's very scalable, meaning you can train, you know, like a thousand interviewers and give them like a pool of 200 questions and it doesn't matter if a few questions leak the bar will hopefully be the same. And it works right for Google. It really does. Oh, and the bonus is that people, once they know

that this is expected of them, you need to prepare. And if you're unwilling to prepare for this, you're not going to be a good fit at a place like Google where sometimes you need to do stupid stuff. There's performance reviews. You need to do this thing. There's a new project coming up, which makes no sense, but we need to do it, but we need to do it. And, you know, like corporate needs people who put up with BS processes every now and then without too much complaint. So, So it kind of selects for that. So it's kind of wonderful. And this is why most of big tech has just adopted that.

And Google knows that you're not going to do that work. You're not going to use those algorithms. But again, it works good enough for them because they hire people who are adaptable. You learn stuff and you pick up new things anyway. And then startups, you just hire for practicality. So this is where trial weeks have been popular, where a lot of startups used to hire by just giving you real work. For example, take home, fix a real bug in a few hours or a few. days and they could actually see like, oh, you're actually doing the work. And startups who are

doing open source often would just hire the contributors to the repository. What AI has changed is, first of all, the algorithm will interview it. It just whizzes through it. So remotely doing it no longer makes sense. And with the take home where you used to give someone a difficult take home, you can do it in a AI will complete it pretty well. So you don't really get that signal. So my bet is that what will happen is these worlds will stay, except, the in-person part is, well, decision will be made. You'll have a filtering, like have a take-home task that you can do with AI, and you can cheat, if you will.

But when you will talk with them on Google, they will still have you come into the office and you'll have to do this whiteboard interviews. If you didn't prepare, like, no, AI is not going to save you because you don't have access to it. And startups will probably want you to what you did is explain what you did. And a small percent of your startups who can do, they will just have the trial weeks. What ones that linear does? Come work with us for a week.

Like you need to collaborate. You can use AI. Of course you can, but it's not the main thing of it. So I think hiring will be honestly just more, there'll be more, as a candidate, it'll be more friction. You'll need to invest more time. It'll feel more unfair because there will be no clear rules that we have been gotten used to. And it will be messy. It'll be also more subjective.

Just a reality. Yeah, work together weeks, by the way is an amazing way to hire. We did that at the earlier stages. It's just a little bit hard to scale. but it's interesting that linear managed to scale it. And by scaling, you mean that, yes. It's hard to do it so most candidates will say yes because you need to take time off.

The only reason Linear can do it is they are very, very well known in the industry. And even like a lot of people say that I'm so sorry. I cannot do it. I'd love to work there, but I just don't have the time. And so they lose a bunch of those folks. What kind of engineers are thriving and excelling right now? We hear about layoffs and slowdowns,

but surely some are doing better than either. Yeah, so we do hip-read layouts, but I talk with engineers who are very much in demand, just as so or maybe more so than before. And what these people have is they either work at startups or well-known tech companies. They are interested in the business. They're so-called product-minded. You know, they don't stop at borders. And by this time, when AI came around, they just got into it.

They somehow wheezed their way either at their company saying, okay, I'm going to work on this AI project building something on top of AI, often AI infra, like I will help build this part. And now they are actually considered experts in this end. Most companies that are hiring and trying to hire a position, so ones that are hard to fill is I'd like an engineer who has a few years of experience. They've actually built something with AI. Like they're not an absolute noop to this.

They will help me able to decide what architecture should we use. Should we use rack? Should we use fine tuning? Should we use an off-the-shelf model? Should we use our own model? Should we run an on-prem? Should we do it off-prem? What about the inference costs?

What about like should we use grok? Should we use Cerebras? So whatever, you know, like five years ago, this was you hired an engineer who knew about cloud and could help you figure out at a startup. Now you're hiring someone who knows about inference and some of these things. And so engineers who have been doing this are in very high demand. The only problem they have is sometimes if they work at the likes of Google meta or a wealth on the startup, these other companies are surprised at how high of a compensation ask they have.

But these people are very high in demand. The people who are having trouble is either at their current work, they just have no exposure to use any AI. So they don't have this experience with building AI infra. You know, they still build software and they use Claude Code and Codex, but everyone does that. They feel a bit stuck on how to go about this. And they don't have good pedigree,

meaning they don't work at a company that is assumed to be a modern company. And those people are finding it hard to make the jump. So now they're thinking, should I just do some side projects? and my answer will be like, well, at the very least, if you want to make that jump between the tiers of companies, and in my mind, I have the tri-model model, of course, but also I have this model of like the company where you have like consulting companies where you're just like an Accenture or Cabgim and I or one of these where you're giving the client projects, they're really struggling right now. You have the product companies where you work and you build products.

And within the product companies, you have the venture funded product companies where you actually have a bunch of money to build quickly, scale, the compensation one will be higher, you're now competing and hiring from the likes of big tech. And then at the very tough you have. Right now, it's the AI labs, the antropics, the open AI, whatever Google used to be in 2004 and meta, in 2010, that is right, an Uber and for a short time in 2015 or so. Now that's, that's Entropic and Open AI. And it's hard to jump between these tiers. So for example, a lot of people are like, oh, I love to work at Entropic. Well, I mean, dream big, but the reality, is that I know so many people working at Google and meta and Facebook, they want to get into

those places, but these places are extremely selective now. So entry-level web product engineers is saturated. But what's the hiring landscape for juniors in low-level system, hardware, software integration embedded, or defense tech looks like? Same surplus or genuine shortage of system-level thinking? I'm less familiar with lower-level systems programming. I would just just assume that it's not as saturated. When I talk with the Primatic Summit in February, I talk with an engineer who is working on low-level systems,

mostly C++-System assembly. And we talked about who's using AI, cloud code, codex, cursor, etc. And he was the only one in the group. There was about eight of us talking. Everyone's like, yeah, using it almost all those 100% of my code is generated by back then it was Opus 4.5 or 4.6, or I think it was Codex 5.4.

And he was the only one saying, like, we're using it, but maybe like 30% of my code because it's just very low level. These areas have always been, to me, a different world than the general big tech. Like big tech hires these people. They feel a little bit closer to electrical engineering, hardware engineering. Now that area in general, I observe there's just a big demand. There's a lot more startups. There's a lot more money in hardware tech.

So hopefully it will be good. And I also believe that knowing the basics, like knowing, if you can code in C++, plus an assembly, like, I think that's really useful knowledge. And you can build on top of that because most people who know a high-level language, type script, whatever, like most of them will not know how to go down to C++. If you know C++ and you can build high performance, low-ladency systems, you can learn easily the rest of a stack. And if you're in the situation, I would just look for those specific offerings.

In junior positions, either you have pedigree, which makes it easier, which means you're in a good school or you had an internship at a good place, or if you're in school, try to get that pedigree, try to get into an internship program, or build some impressive projects either on the side or contribute to open source, which is still a pretty good way to stand out, especially with AI contributions being rejected.

You will have to work hard if you want to get to a prestigious place and accept a stepping stone as well. Like right now, I think getting as a junior a job is better than getting no job. And once you have a job, try to excel, even if it's a shitty job, to be the best there, you'll build up a good network. And at some point, hopefully, you'll have a stepping stone, a new opportunity to come in to go to the next level. A few questions about big tech. So when a company like meta lays off 10% after a record year and then reassigns

another 10% without consent, how does leadership fail to anticipate the obvious heat to culture and morale when everyone inside and outside can see it? Yeah, this is the question, right? The interesting thing, I talk with meta inside of like some directors and even above, and they see it. So this is not a question of like, does leadership not see it? This is a question of does the founder specifically Mark Zuckerberg not see it and why does he not see it? Or if he sees it, why does he not care? And we're now going to territory of like assuming what a specific person thinks. In the case of meta, like meta is the only one who's done this.

No other company that has a career CEO, I'm looking at Uber, I'm looking at Microsoft, I'm looking at Google, they have not done this because they probably know what would happen. And they don't want a part of their business to go down for no reason in terms of outages, losing some of their best people. Because what's happening right now with meta is some of the best engineers who up to a few months ago thought, you know, I like meta, always treated me well. We're investing in AI. We might or might not be winning, but it's doing good.

Stock is doing good. I have a good work. I've balanced been here for 10 years. Now, some of them have been reassigned to do this work. that they don't want to do, like this data labeling. You can make it interesting. And I talk with people who are in this organization, this AAI, ADO organization, advanced AI, that's as AAI,

and ADO is a data organization. But they joined and they're making the most of it and they're engineers with less experience. But these people realize, like, well, I mean, leadership specifically the CEO no longer cares about engineering as a whole. So we can only speculate. Clearly, it feels like Meta has had in the past some existential times.

One of them was when Google Plus launched somewhere in the 2010s. And it's well documented. There's a book about it. I'm not sure if Chaos Monkeys covers it, but it has been really well documented where meta went full on on wartime mode. It was like, look, Google is coming after us. They want to kill us and everyone worked really hard because everyone understood that the fate of the company was on the line.

And my sense is that Mark Thucumber probably thinks that this is the case right now, for some reason that is not really articulated and others don't necessarily understand. And he probably has his reasons. I don't know why he's not telling people because this is, meta is operating in wartime mode except everyone is like, where's the enemy? Like revenue is record high.

They're doing amazingly well in the ads business. Their products are growing. And for some reason, it seems existential to Mark Zuckerberg to own AI. But again, this is where when you look at the patterns, like the Metaverse also looked existential to some extent. and now AI is looking existential.

I think people are starting to ask a question like, okay, can you just pick a lane? And in all fairness, it might be hard for Meta or Mark Tuckerberg because meta still does not own any platform anywhere. They are an application layer still. And I think he really wants to break out of that. And I think it's just being a bit reactive potentially. This is all speculation. So I think the easiest thing would be just ask him if you can answer.

Among big tech companies, specifically Google, Amazon, Meta, Microsoft and Apple, How do they feel they're doing on AI adoption in engineering, who is accelerating, who isn't, and who is managing transition well? I think Google is trying the most. They have the one where they give a free reign to everyone to build AI tools. It's a bit chaotic, but people are building a lot of things internally. And they're the only big lap who actually have an AI model with Gemini, and they have a Gemini organization.

And there's always talks about how they're doing compared to open AI and traffic, but they're the only ones who have any sort of competition, In fact, Gemini is the only product which is actually eating into a Chad GP's market share. My editor the other day was telling me, I don't use Chad GPT for my queries. I use Gemini because I really like Gemini. And I think he also said that it's free. Okay, I guess there you go. So in this way, they're actually, I think, way ahead of the others.

Meta seems to be bogged down by building and training their own AI and morale is just going down because people don't really see the point. Microsoft is in this weird place where, like, It's still very political. As far as I understand, there's the organizations, there's the co-pilot for there's the core AI organization. GitHub is under core AI now? So is AI their mandate or is source control? They seem to forgetting about that and their reliability does not tell.

Azure is fighting with everyone for capacity. They don't have enough. Microsoft is more focused on politics than AI in my assessment. Apple, I talk with people at Apple, but like Apple is very secretive. And like Amazon is secretive because their engineering culture is pretty good. I'm surprised they're so secretive, but Apple is secretive because their engineering culture is absolute trash from all I gather. It's duct tapes everywhere. I'm not sure much is happening in Apple, but because Apple is not doing too much, I personally hope that they will actually seize local AI, locally running on your hardware because they have a very strong hardware thing.

So one thing I think Apple is doing good is they haven't forgotten about their core business, which is making devices and a software that's decent. It's not great, but it's decent enough that people don't leave. and maybe that will actually be a winning strategy. Amazon, they also, they're an interesting one. So Amazon is the example to me on how difficult it is to retrofit innovation. Compared to Google, they're trying so hard to like have AI everywhere internally. They built Akiro, their internal tool, and they have their own models, but they're all subpar. It's all people dragging their feet.

They rather use clot code. And Amazon is full of smart people. So to me, Amazon, a good example of just how Amazon Microcom, how difficult it is to to bring AI to a large organization. Companies that I think are doing a lot better than all of these companies are, I guess the little tech, not the big tech, but the publicly traded companies

who are smaller. Uber, Ramp, even Intercom, block, say for the layout, but they're the ones, they're building AI infrared because they don't have an identity crisis. All of these Amazon, Microsoft, meta, Google,

they're like, look, we need to own the whole stack. We need to build the AI model. We need to build the application. layer and then, you know, we need to become a platform. And Uber and Ramb is like, no, like, we know our place. We want to use these the very best possible way. We will take Cloud Code Codex. We don't care. We don't want to build one of those. We will integrate it as much as we can inside of us. We will not have a foundational model. We will like buy or use the best one. And so they're just focusing on optimizing it for their business. So I think they're the ones who are kind of

the most ahead in terms of LARC companies right now. Entropic and specifically CloudCode are shipping at extraordinary rate, using agents for implementation, tests, reviews, incident response, and many other things. Is this how AI native development will look like, or is it very extreme environment and others will be wrong to copy that directly? I think it's just very hard to copy Antrofx. So we cannot deny that Anthropic is the best example for AI native development at scale, together with potential the Codex team. And when I say Antrophic, I actually mostly mean clot code and also their model, but it's all interwinded

because in AI lab, their product is, don't forget, Anthropics product is Clod. It's not Clod code. Clot code is a revenue generator until Clod is so good. Their product is the model that they get a new version every few months, and they do a bunch of work with training, pre-training,

post-training, and then the tooling around it. And everything is, it's like a beehive all around this one thing. So the only way you could copy it is you become an AI lab. And the product is just a byproduct, which right now is doing great, even though Anthropic, for example, don't even have an enterprise sales team that a lot of other vendors would have. Maybe they have, but it must be pretty small right now. I always feel that they're a bit

of an anomaly. Where I'm interested, and I'm not seeing all that much yet, is startups on how startups are completely changing how they work. And I suspect the reason I'm not seeing it is when I talk with AI-native startups who are like, okay, you know, we're founders, we'll use AI for everything, and you start a company, you realize the first hurdle is, like, how do you get traction? And at worst, Smith, like, you guys luckily have gotten traction. You kind of pass that point, but a lot of founders, it doesn't matter how AI native you are, if you don't have customers, if you don't have a market segment, if you don't have any of this. And I suspect that, I wonder if that's going to be more important, that like get traction doesn't matter how.

And once you have traction, it's a little bit like even pre-eastern. AI, you could assemble an amazing engineering team and build a first version of a product, or you could just have a really bad engineer, but have a really good idea and launch that product and it takes off. Uber was a good example where when it took off, Travis Kalan just hired some contractors, made an ugly app, but it did something that people wanted. Oh, and he was the right place in San Francisco. So I wonder if like AI Native is overrated and like once you have a business model, of course you can optimize it, but will AI Native? make all the difference? I'm not sure. And another good example is Coinbase. They're really trying

to be AI-native, do all of those things. But in the end, they're a crypto company. If the crypto market goes up, they will do great. And now they did layoffs because crypto market just went down. So, like, you can be as AI-native as you want. And maybe you'll be able to do the same with fewer people, but I'm not as sold on this. Yeah, to me, it feels like artificially trying to become AI-native is a bad strategy, right? Like just saying, Anthropic is doing that. So we'll copy it and try to implement. What I think works really well is when you're seeing the problem and you understand that, oh, actually this problem can be solved really well with AI. For example, you know, incident response, right? So why don't we try AI to do a first pass understanding

what's happening, right? Like it seems like an obvious idea and like if we have problems with incidents and debugging time is taking a lot, we can try and if it sticks, then good. But some other process might not work in the company. So it depends if there is a problem and it feels like it can be solved with AI, then it's like a good idea to adopt the practice. I wonder if instead of AI native, which is just think about, like, companies where, like, AI is an natural tool that you reach for. Like you, for anything, you try it out and it might or might not work, but you're not precious about it. You use it if it makes sense and you throw it away if it doesn't or you'll

revisit it later. Yeah, and just you have another tool that can help you. Next one. Can you share something about today's presenting sponsor? What? Like, is this real as a question that people are asking? No, this was actually not submitted by. by anyone, but I still want to talk about it.

Now, I admit, this was the one question I sneaked in because I really wanted to share something visually interesting about our presenting sponsor, Anticis. It's how different their UI is. Let me show you with three examples. We already know that Antisyseses verifies your system's correctness by running your whole system in a hostile simulation and finding bugs.

Here's the UI for casualty analysis. You can open a report for a bug and see the probability of a bug occurring throughout the timeline of the simulation. In this case, we can see that At Virtual Time 25, something happened that makes this bug close to 100% to occur. So we can jump into this point in the Virtual Timeline simulation to read the logs. This kind of bug probably visualization is one that I've just not seen before.

There's also this neat log explorer. You can filter on error messages and then visualize how common or uncommon the error is over time. For example, here we're looking for failing linearization failures, the purple line. And you can understand how rare or common a specific failure was. Again, I've yet to see this kind of error visualization, and I really like the innovation on the UI here. And finally, the Multiverse Debugger. You can go back in time and replay a debug timeline.

And you can inject bash commands at any time without affecting the playback of the bug. How cool is that? For example, here we're listing files in the current directory, but as you can imagine, you can debug the whole environment much easier. I really like how the team at Anticistis are pushing what's possible with both debugging

and verifying software. Head to nisysd.com slash pragmatic to learn more. Is ignoring code quality for speed with AI worse at long term? Some engineers still review the plan, architecture and code. Others rely on SDD plus harness and these regards the code. Plus or short term, but is AI good enough to make up for worse code? This is a big question, isn't it? I wonder if there's like any answer.

Like I feel as engineers, I think we know what answer we want. We want the answer to be, yes, quality is important. Yes, care and craftsmanship is important. And this hasn't changed. Like, even before AI, like, we wanted this to be true. But when I got inside of Uber, I learned about some horrible hacks that Uber did that looked really painful. For example, the old Uber app before 2016, before we had the rewrite, you would open

the Uber app and you would see the ETA of the cars, you saw the products, and you could like pull the slider and then it would show like how many minutes the next category would be like for example Uber black is like two minutes. Uber van is like six minutes and you pull it and you saw some other information on the screen. And what happened is that app was polling the server every five seconds to give me all the information. It was a package. And so every five seconds you would get an increasingly large data package. But by that time it was a few hundred kilobytes, I believe that was coming back. And the reason that they did this is and this is just terrible like, strategy. It's, it's, it's inaccurate, it's slow, it's, it's really wasteful on resources. It's,

it's also just stupid, honestly. And this was in 2016, but by that time, we should have just pushed this information. But the reason this happened is the back in that team was small and the front and the mobile and the web teams were larger. And they were getting frustrated that whenever they wanted to change in the back and to get some information back, it will take, you know, like days, weeks, months. And so they asked the backend team like, hey, can we do something about it? And they're like, well, there's this really hack a solution where we just send this like big blob together and you can go on the back end, you can add whatever you want into this blob, and they're like, perfect, perfect. And it actually unblocked Uber for a long time to like grow independently, but

it was a terrible architecture. And so this is an example where like, this is clearly tech depth, but tech debt can speed you up. And I wonder if with AI, this is also true that should we not look at tech depth in the stages of a product or a company? Early stage, you're looking for an idea, just like go with tech depth. We don't know if. it'll work. You'll probably toss it out. There's companies at this stage where we just try prototypes and it doesn't matter if it's beautiful or not. Once you found product market fit, there's this, Ken Beck has the three X's, the, I think, explore, expand, extend. And there's other other ways to say this. But in the expand phase, you found product market fit, you want to

scale up. You want to quickly reach a bunch more users and you're kind of okay with hacks at this point to grow faster. And the last phase is when you're mature, you want to, make things good. And what I've seen of likes Uber, again, pre-AI, is when you find product market fit, you have a bunch of customers, you have a bunch of demand, you will now have enough revenue and money that you can hire people who can help you fix these hacks. So I wonder if it's the same with AI. Maybe we're overthinking that if you're in early stages, you're just doing a prototype, just go all and don't worry about the cold quality, which might hurt you. If you're at a stage where you're now scaling up, I mean, pay more attention. And if you're a

stage where it's a mature product, it's actually making money, we don't want to mess it up. I'm looking at Instagram's product, for example, which is a mature one, but meta still messed up. That is probably where you want to be very careful and pay attention, understand it. Oh, and final thing is AI doesn't only let us build faster. It allows to refactor faster. So we have no excuse not to do that every now and then. Yeah, I completely agree. I think it's basically false decodomy that it can be only speed or quality. Like it's more about segmenting in time or in code base, right? So infrastructure, maybe more attention to quality, product may be more attention to

speed. Their hand-pin repeated shifts in AI tooling and best practices. I.I. makes it easier to find exploits and create them, an AI jungle. What would it take for the industry to seriously create standards rather than hoping they emerge? Yeah, well, first, AI is so new. It keeps changing. Like, I think any standards would make no sense. And I think standards just naturally emerge. Like, I haven't seen any patterns. So MCP, Anthropic, when they're still a small lab, they're not a leading lab. They're very small. They created this thing called MCP and everyone thought, it's kind of, it makes sense and it comes from a non-threatening place. It's a small lab, which we don't really know. They're kind of cool, but they're not, Google was bigger, open air was bigger.

And then all these large companies adopted it because there was a lot of politics in it. So I think it's accidental. Entropic today, if they try to do an MCP, people would like, no, like they are, we don't want to be locked in. So I think they'll just emerge. I'm sorry, like I don't have a, I I don't say anything like planned happening here. Companies like Entropic have engineering managers coding a lot. And at Meta, and I presume at Uber as well, the philosophy was actually the other way around that EM should mostly focus on people, but the right approach for engineering managers in AI era. I mean, this is a philosophical question, and like people have strong opinions about that. For example, we know like from the, when at Twitter, Elamast took over Twitter and

renamed his ex, he fired a bunch of people and he mandated that engineering manager should code while having 20 plus reports, which sounded like pretty insane to do both. I'm not sure there's a right or wrong model. I've seen all sorts of models work out. There's pros to both. There's like when an injury manager does not code, they will care far more about people. They will pay more attention to what is frustrating people

at the personal level, at the organization level, and they will try to fix those systems, and they'll try to take really good care of people. Injury managers who code, they will be more in the details. They will be able to give more technical guidance. They will have better technical discussions, and they will care a lot less about this first category of things.

And they also probably will not have bandwidth, to like make systems of all changes or go to like meetings to for example like you know like work with HR to like actually like change some policy that makes no sense and like upsets a few people or work with a bunch of other other teams to like have this new system instead of everyone just duplicating the work so right now the industry is definitely going very strong in the direction that managers should be technical let's forget about this people management stuff so I think people need to unfortunately expect less guidance and support from managers managers who love doing this part and are very good at the people part will feel probably underappreciated for a while.

And I think there's a pendulum. I think it'll swing back. And I think we've been at the side where we have been very focused on people and has been very rewarded as a manager. And it was great to be an engineer at the companies like this. It's now going back where it would be less so. And I wonder if it'll come back again. At some large companies that you reported on not using AI aggressively is a career risk. How should leaders prevent adoption from becoming a theater? Talking leader boards, mandatory usage, cold volume targets, rather than real outcomes. So I wonder if this is like almost over because there was a part where I talk with CTOs and engineering leaders at all sorts of companies and they were really frustrated saying,

oh, my injures are not using AI. But this was before Opus 4.5, this was before, well, mostly before Opus 4.5 and GPT 5.4 and before cloud code was used by many people. This was at the age of auto-complete with like, you know, GPT4. point O or even worse models and like our engineers aren't for using it or one cursor was all was just the tab you know they have the golden tab key I think this is almost like a non-issue like everyone in most places I know uses it and also that's when token leaderboards made a lot of sense Shopify the token leaderboards in that era no one knows about this about them but they did it back then and now they kind of deprecated it so I think it's kind of moot point especially with these strong

models I assume everyone will use it and I think it's almost like meaningless to look at it a bit like lines of code made no real sense to look at it for most engineers. What evidence would persuade you that organization achieved an actual AI productivity gain rather than just more code, more PRs, or more humans to review? That's a good one, right? Before I answered, just like taking a step back. Like, when I worked at Uber, it was the first company where I joined where it kind of like people told me, like, don't worry about the revenue.

We just care about growth. Like, as long as we grow, we're good. Like, we're just raised more money and then we hire more people. and then we grow faster and we raise our money and we hire more people. And even I remember my manager was telling me like headcount when I became a manager. I was like, how does headcount allocation work? It's like, you know, do you need to make business plan or something. It's like, oh, no, no, no.

Like it's, it's kind of a black box here. Like it's, it's this weird thing where you get a head count allocation. And if you fill it quickly, you get some more. And I was like, how does that work? And turns out that because like in Amsterdam at the time we could hire quickly, the head counts were reset at the end of the year. And if you didn't use it, they reallocated with. the or it was a really weird time and it felt off to me. I'm like surely like if I hire a person

for and it costs they cost they cost X like they should generate at least as much value right but there's not right now like we don't live in an age like that like oh this is like different I always felt the wrong and and so there were opportunities where I could have worked in a team or led a team which was a purely platformed team with no direct business value and it was kind of I was unsure like it was a cool technology there was a team who was building something similar to React Native just internally because React Native did not fit our needs. And I was like, I'm not sure I see the business use case. So I always stayed on teams where I was very comfortable that we are actually making money.

Like I knew how I was making money. And I always had this in my mind that if someone asked like, what would you do if you hire two more people? I would have an answer. Here's how much more revenue would generate. And if someone asked, what would happen if I took away two of your people or half your team or your whole team? I'd be like, no problem.

Here is the business impact. Here is how much revenue we would make. And so when it comes to AI productivity, can we really distinguish from business productivity? I mean, there's only two ways that a business revenue-wise can make a difference. And this is just a very capitalist way of thinking about things, of course. But one is either you make increments or revenue, meaning money that you would have not made before. If you would have made that money before, it doesn't matter. Like if you're a crypto exchange and, oh, we're making more money because there's more crypto volume,

that's not AI, it's the market. But if we launch this new product and it's now making money that we didn't do and AI is helping with that, That's, I guess, the value for AI or cost savings. And I wonder if AI's biggest use case is just cost savings, which is kind of depressing to me. But the AI native companies that are making money, I do see the ones which are selling an AI product. You know, the AI labs are obvious ones. There are startups, let's say AI incident review, who are making money because of that

product. So I think that's a use case. But otherwise, it's pretty iffy, pretty finicky. And I still have this private thought of like, will AI be a bit more like cloud in the sense that cloud is everywhere now, and including in banks, just that we will never go on cloud and now they're in AWS. But as a customer, no one cares if you have cloud or not. It used to be as a cost saver, a more flexible way to control costs. And I think AI, maybe it's a more flexible way to control your own costs or like what people do work. It's a weird thing, but to me,

it feels closer to cloud than like technology like mobile, which created a whole new market of everything. What is a popular current belief about AI and engineering that you think is incorrect? I think it's incorrect as things. It just makes things easier. If you're using AI and your life is getting a lot easier, like, are you trying hard enough or are you like telling your stuff? And because to me, like, I use some of it for my business. And it actually like makes me think just as hard if not harder or work as harder. So I think like believing that AI makes work easier or our jobs easier. It's just wrong. How important our degree and university prestige in hiring today? Is computer science becoming a prestige field like law or architecture leading to fewer self-taught professionals? Unfortunately, I believe it is. And this is less to do with the degree and what they're teaching, but more about the market. There was a time around like 2015 to 2020 where you could get hired at a company for a well-paying job

by doing a boot camp, which is like three months, a six months, sometimes 12 months, versus a four-year-year. or five-year degree in computer science. And the reason was there was just a huge shortage. Like all of the people graduating from the university were swapped up. That has ended. Majority of companies do not hire from boot camps. Very, very few in pockets, maybe in the UK or else we do apprenticeships, but they're very small.

And the top universities are still getting, those graduates are getting hunted down at the likes of MIT, Caltech, Harvard, many others, Waterloo in Canada, Imperial College in the UK and so on, but they're not getting as many competing offers as before. And even at the mid-level of schools, it's just harder. So when it was hard to hire someone with a computer science degree, people went for like lower self-taught and those things.

But now they do it less. I even had someone tell me who is self-taught, worked in the industry for five years, lost their job about two, I think, a year and a half ago, that for a year she couldn't find a position even though she was doing like SRE work and infrastructure work. And I think in the end, she said that she's either considering changing fields or just doing her own thing.

I think that I think it's easier than ever to do your own thing, but companies, I think, will be more picky. And the value of the degree, it's a bit underrated. If you're living in your current country and you don't plan to leave, like it might matter a bit less. But first of all, large employers often like have this requirement just for filtering, saying we need a degree. It just filters out a bunch of non-qualified people, saying we need a computer science

degree just filters out the art majors and they don't have to look through as many resumes because they already have too much, even if they have this one thing. But the degree is very important for visas. If you're, for example, in a country and you'd like to move to another country, typically more towards the West, and they like, without a degree, it will be very difficult with the immigration system. So like, that's something that's worth keeping in mind. That thing can pay dividends even decades later when you're not thinking too much about it. So a few questions about yourself now, do you still spend time programming yourself or testing large language models? And if so, what percentage of the time?

I spend most of my time researching and writing, but increasingly now for my business, the Primatic Engineer, I have a backend that manages group subscriptions, some customer support functionality that I'm building. I'm building it myself. And now I might have like some folks help me on my team as well. But when I could get a SaaS now, I'm like, I don't want to get a SaaS. I just want to build it myself. So it's simpler stuff. honestly it's like CRUD database. It runs on infrastructure like render. I use the tools. I use codex. I really like codex and GPT5.5. I also use

cloud code as well. I play with cursor. I sometimes try factory. So I try to rotate these tools and it just makes it so much easier for me to get back into it. But I don't spend most of my time on it. And in your own workflow as a creator, writing, podcasting, researching, have you seen productivity gains from AI? So this is the interesting thing where I think I should have. So I don't use any AI for my own writing. Like I, I did a few of these experiments more for curiosity saying, hey, here's some notes. Generate an article in the voice tone of the pragmatic

engineer. First of all, it is an actress's job on it. I don't think it sounds like me. Second of all, like it just has those, I don't know, it just feels artificial like the links. And then most importantly, I really, really enjoy like, like I love writing. I don't like, it's not the thing of writing. It's the thinking. Like when I write, I keep thinking. And a lot of times on social media, when I will post something and it gets a bunch of likes or views, it's often, I'm just writing. And I have this idea when I'm like, revisiting the, you know, this topic for the third time. And I'm like, that's an interesting idea. So I just post that idea out there and I just go back to writing. And then later I see, like, you know, people respond to it. Because I guess what people see is just an original idea that

comes like, most of my social media is my byproduct of writing and researching. Like, Most people don't know this. Like, there are so many people who are optimizing social media for likes or things or all of this thing. But for myself and a bunch of people that I know and respect, it's kind of like their side thing. One good example, I read someone on Hacker News wrote about is that their favorite YouTube creators in photography. This person was a hobby photographer's. They favorite photography creators are not professional YouTube creators about photography. They're photographers who have a business and they actually do shots

and then they have a YouTube channel where they share every now and that it's infrequent, it's not there. And I also think of myself as my main thing is I research what's happening at a tech industry. I talk with engineers. I try to keep an ear on the ground as much as I can because I talk and I do this by just being in touch with a bunch of software engineering folks I know, some friends. And when I see interesting things, I dig into it. That's, for example, how I noticed that something was really off at meta.

I've only ever since things being slightly off at meta for so long time. But now I have 10 or 15 people who I know there for years. And now, like, most of them were like sounding the alarm bell. I'm like, that's new. I haven't heard that before. And it turns out I was right about just how bad things have gotten there. But in my workflow, I use it for research when I'm like, here's a topic. Like, I'm going to research Rams engineering culture.

All right, deep research on all the platforms, like, give me all the stuff. And I would have thought that this would have freed up time, and I guess it frees up some of that time, but I would have never spent that much time researching. So I don't feel that I'm working less interesting enough. And what capability do you worry I might weaken in your personally? For example, coding fluency or technical recall or writing from blank page. I don't think like the writing will suffer because I just don't use it there. I don't even have spell checks on. I just don't like it.

Or I know I turned grammarly off as well because I hate when it like wants to reorganize. it I think it's whenever you overrely on something, it could make it less efficient. Like, for example, one thing I now over rely on is, like, just deep research. Like, I want to find all the things on the web. So my ability to, like, find things on the web might be worse, but I'm not too worried about that because first of all, it was just grudgy task. Second of all, I don't really trust the internet that much. Like in deep research, I still check where it gets references from.

When it's too much Reddit, I'm like, I'm not sure this is going to be 100% checked out. But with coding, I now just prompt and write the code. And my ability to write code by hand will probably be degrading. But I don't personally mind that part all that much. So I think it goes back to like, look, whenever you're using AI for a bunch of soldiers, just know that that skill will go down. And are you okay with that? And I'm kind of okay with it.

Has AI ever tempted you to go back to building software? It's now so much easier to build software. like it probably would have tempted me but right now I just love what I do and I actually love the human connection of actually talking to people and getting a window into what other people are doing but it is making me build more software and be more ambitious so there's this project that I've been putting off for a while which is a self-service sign up flow for for companies for the pragmatic engineer so like the whole company domain and I'm actually just building it because it's so much easier to get started with it's less intimidating. Vladimir is K-engineer in banking

early sorters and he's worried about staying relevant. So he's tempted to quit for full CS education, but it's quite scary to give up good paycheck. Feel stretched. How should he think about future-proofing his options? What I see in terms of future-proofing is the single best ways if you should-proof it is work at a company which is doing stuff that is very relevant. You know, this is building products, building modern products, building products that incorporate some level of of AI where it's okay to experiment a banking when it's a rigid place, it might be the opposite. But my first

advice would be inside the company and you start a project where you are just doing some experiments with AI. This is why Google is such a great place right now. I know it might not be too popular to say, but they encourage doing this. Like, oh, you're on your team, you're building a product, cool. And you have a suggestion to like build this new experiment with AI. Yeah, go ahead and do it.

And I have a feeling that a lot of companies will be receptive to this because right now there's a bit of like every leader thinks like we should use AI more and if someone comes to says like I have an idea and I'll do it on part time it's a win win worst case is you know you learn about rag or you learn how to implement this thing it can be an internal tool and that's why there's an explosion even at larger companies like Uber with internal AI tools just to start doing that I think that's the best way to stay relevant because if you take a computer science degree or do it full time it's it will still be it could be behind the industry right now. Also, like, you can do a degree part-time, but because it's such a big technology

shift, like the best way is to be hands-on. So my advice would try to do that as part of your job. That's the easiest. Everything else is harder, leaving for a new place, interviewing for a new place all harder. Of course, you can try to do side projects, but I find that unless it's something that truly motivates you, like unless you have this thing that you really want to build, like this health app that you really want and it doesn't exist and do it. But other than that, it could be easier to do it at work. My two cents. How can you surround yourself with highly motivated top-notch programmers when your classmates aren't at that level and it feels like too much to catch up to? I mean, if your classes are not that motivated and you are, try to find a different group of friends.

Well, it depends on where you are if it's high school, then you're stuck with them, which is fine that even while I was a high school, there was only two of us who are coding. And luckily, there was another person. Maybe you can find someone from a different class, maybe on an online community. I've heard some Alice Reel on my podcast. When she was in high school, she joined online communities and started to build. She actually started to contribute to some software there. So like that's one way to find. If this would be at work, try to either change teams, if you can internally to move there

or outside of your project, like take projects where you can work with other people, like seek out and try to follow those people or get towards them. because a lot of people will be motivated. And also, this is the thing where when you're in that situation, you can change companies, it makes a difference. When I worked at in banking, one of my first jobs, my colleagues were super nice. They were such nice people, but they were not in love with technology.

None of them were. And then when I moved to Skype, everyone was. And it was just such a big difference. So Akash is saying that his son is heading for an IT Focus High School, dreaming of becoming a gaming developer. What does a pass and the job market looks like in five years from now? and what should he do to prepare himself?

Everyone's asking this question, right? If only we knew. I mean, I personally believe that I try to draw parallels from other industries because we don't know what's going to happen exactly with AI, this tool that we know that coding is so much easier. It will probably make some of the other parts of a job's easier. But I like to think of a parallel, for example, a construction where like if you want to build a house today or at least,

okay, renovate your house significantly, you could walk into the DIY store, or you can go online and you can order a bunch of equipment, including professional equipment. You can get the same equipment as professionals. On YouTube, you have professionals making videos of how to build a wall, renovate a wall, tear down a wall, do that. You could do all of that. You have the information and you have the tools and you have the materials.

You can buy the top-notch materials. It just takes a bit of work. So why do people in construction have a job? Well, I guess most people don't want to do all that and they'd rather hire a professional. So I think what will happen in the tech industry is exactly this, where, and of course more people are, fewer people are calling out an electrician to like change a light bulb or, or even some of the more advanced work. A lot of people are using YouTube and DIY shops are probably getting way more business. But I think there will be professional.

So if you want to be a professional in a field, there will be a path to that. And to get into that, it will go to universities and education. I'm fairly certain that the game that will be released in 10 years, which Acquish's son, will hopefully be working on, it will be built by a studio that's either a startup or a AAA studio. And if it's a AAA studio, they will hire graduates from some of the top universities, from people who have been building games on the side. And for Akrisha Stun specifically, I have an episode with Jonas Tyroler, who builds games and one of his games got a million sales with two of them building it.

I would suggest that to watch that episode, but also Jonas, he shared a video of all the games he built over like 10 years or 15 or 20 years. And he has been building games on the side. So if his son wants to become just encourage him to start building games on the side right now. In this hard market, what do you recommend for engineers in the EU? Keep aiming for tier one companies or stick with tier two job? Yeah. So this is the tri-model structure.

I have a tier one. I put it as the local companies, like the local supermarkets, the ones that are really competing for local talent, tier two as regional and tier three as global. That's the big tech. And like in the job market, well, first of all, like when the job market is really like volatile on certain like staying put can be a good strategy. At the same point, like I would not stop looking for opportunities because on one end, like the job market feels a bit different than in 2023. 2023 was a brutal market. It was layoffs everywhere and no one was hiring. Right now there's some layoffs, but so many

companies are hiring. So now it could be a great opportunity to jump a tier up, to a startup, to building products, to having more autonomy, to using more of these AI tools. And if you stick at a company that is just really moving slowly, you might not have that opportunity. I talked about the engineers who are really in demand. They have a few years of hands-on experience with these tools. They will be in demand in a few years' time as well. And if you will still have zero years of that, well, you're kind of sitting in one place. So I would be opportunistic in looking out, maybe looking at job openings, talking with your network, not ignoring fully recruiters, seeing what's out there, look, if you get a job offer, you can always say no.

If you have no job offers, I mean, you're going to stay at your current place probably anyway. How can engineers and students use AI to learn and explore new technologies and concept better? I think you can do deep research a lot better. You can ask it to explain stuff. But the way I see it, like, it, AI only ever helped me learn about stuff when I wanted to learn about something. what you want to learn. It's a tool. It'll help you. But I wouldn't also fully, like, throw away things like, like, like, books, other resources, like, maybe like videos, uh, tutorials.

And also just building your own thing. Like that, that's what I mean. Like, biggest makes it, it's not make it easier. It's not going to make it easier to learn, especially when you're not motivated. So, like, decide what you want to learn. And yeah, it can help you, but, like, just, just learn it in that case. Like, just have no, you, you have one fewer excuse when you want to do it. And if you don't want to do it, just don't do it. So not IRS is asking a question, so I guess it's very safe to share all the information. How much do you earn from this and why start this instead of the tech job? The last time I shared specific numbers was I think I think in the first year of the publication where I shared that I had like 2,700 paying customers

and it's gone a lot beyond that. It's now more than 10,000 paying customers of the news letter. I also now have some sponsors in the podcast. And the reason I don't like to talk about the specific money, you know, there's people like, here's exactly how much I make, is every time I do that, I get so many questions coming in from people like, oh, I also want to make this much. Can you advise me? Can you have a call with me? Can you coach me?

Can you mentor me? I want to quit my job. I want to do this thing. And first of all, I'm very grateful that it's amazing business, but it's just not what I'm good at. I don't want to give financial advice to people. And I didn't even think this was possible. But to actually not, like, be that, like, vague.

when I left Uber, my compensation was going down a little bit because of the four-year vesting. But in my best year at Uber in the Netherlands, I made, I think it was like something like 288,000 euros back then. It was like 320, 330,000 or something like that. And 120 of that was base salary. I think it was like about 26 or 27k bonus or maybe 30K bonus. It was a big cash bonus and the rest was in equity. And like when I started this, I, I didn't think it would go too far. I thought I'd give it a job. But most of why I didn't think it would go to go for just being realistic. Like Lenny shared his numbers of 2,000 page subscribers and you do the math, that's $300,000 roughly, give or take. And he was going up. And I thought, well, I mean, maybe I, could I get

there? Maybe yes, maybe no, but we'll see when we get there. But I, in the first week of starting publication, I had 100 paying customers, which is like that was $10,000. So that's paid up front, which is, okay, that's very nice. In six weeks, I got to 1,000 pay subscribers. There was still $100 before I raised. And I started to raise the prices back then, but it was like around $100,000. And then I kept going up. And I started to be on a higher annual run rate in about like, I think, four or five months,

then my old Uber best total compensation. And it was still going up. And I was like, okay, what's going on? So I just kind of stopped looking at or thinking too much about the money or these things. I started to focus on just writing that one really good article. I did this for a year and a half, two years actually. And then I looked up and I was like, well, actually really loved doing this. It actually, I didn't know that you could make more than working at the big tech by doing this thing, your own business.

And this is also something that you can realize if you're like with your own business, you have the potential to make more. And also, you know, one of the reasons you probably left meta as well, where you were probably very highly paid is you have the opportunity with a startup with your own business. I'm very lucky that this has happened. But also one thing, like, I love my days. I find it very, very exciting every day what I'm doing. And that is what keeps me doing this. And I honestly, I just love being in charge. Like right now I'm sitting here because I'd like to sit here and I'm having a great time with you.

But if I didn't want to, I didn't have to do this. And I do well when I create my own structure. But it really helped me. I don't think I could have done any of this without going through that like 15-ish years as being a developer, like just doing the work. I always tried to do the best work that I could. I had a lot of structure. I have a lot of, I made a lot of connections who actually helped so much with this business. Like a lot of times my guests are people that I know or I reach out to them to advice.

So luckily, I feel almost like, like, wow, like was this possible? and I didn't think this was possible, but now I'm just kind of rolling with it and I'm like, yeah, it's great. I love it, I enjoy it. I'm also not too attached to it in the sense that like, look, if the business wouldn't do that well or people for some reason,

you know, they stop being interested, it's like, well, I can live with it as long as I help some people, I give value to some people. And also, this is the interesting thing. Like, I could make more revenue by like juicing it more. Like I could put more things behind paywall.

I've gotten feedback from people saying, why did you put so much of this outside of the paywall? Whenever I think something is important and more people should get access to it, I try to not put it behind the paywall even if it hurts the business. Because again, it's kind of nice to be able to do that. What's next, Gary Gaye? Any expansion plans for the pragmatic engineer? Yes, the interesting thing is if this was a VC-funded company and I took VC funding,

I would have to expand. But I don't. The only plan I have is I would like to make the Pragmatic Summit more regular. There was one in February in San Francisco. there will be one in the beginning of the year also in San Francisco and I'd like to get to a point where I can have one in Europe as well and I'd like to be able to do this on a more regular basis so ideally my dream but like this is more down to logistics

and energy on some of those things is to have one in the US or Pragmatic Summit and one in Europe in London or somewhere else and getting to that point I would be very happy and also I'm growing my team very slowly we know how a small team So I'm just figuring out ways that I can have folks involved and help with even more ambitious research. I'd love to do even going deeper. I have so many ideas of companies to research, industries to research, sometimes some boring industries. Like at some point I'd love to go into the utilities company and go through like how they build software.

It sounds pretty boring, but it's very darn important. Have you ever gotten in trouble over an article? Has everyone tried to sue you? Yes. once. Two articles actually. One, I never published because I decided not to publish. This was the beginning of the publication. For some reason, I really got upset at Neobang Bunk in the Netherlands. Because I read about their hiring practices. They do intelligence tests, rush to our tests before doing a technical interview. And I thought that's kind of messed up. And I tweeted about this. And a bunch of people who were unhappy at the company wrote to me like, oh, here's some juicy stories about how terrible this company is.

and here's all the things that they do and here's, I have, and they had evidence and all that. And it was like, some of it was like, oh, wow, this is like, oh, crazy. And so I started to write an article about that. This was in the first year of the paramedic injury. This was December. So I started in August and this was in December. And I had an article ready that was pretty damning.

It probably read like a hit piece. Like, I didn't have any agenda, but it was just like, negative, negative, negative, and this. And can you imagine this and that? I was about to publish it. I even sent it over to the company to bunk and sync and do you, because my editor was like, you should probably send this over to them. But then I slept on it.

And I was thinking, what am I going to achieve with this? Like, at the company, inside a bunk, I'm not helping anyone because they'll be defensive and it's actually a business. It employs people and it's growing and it's playing more and more people. And then I also got a message from someone

who said that they had a bad experience there, but it was also very helpful because this person came from, I think, Egypt and no company would hire him in the visa on the Netherlands, but bunk did. and they were pushing him really hard and some things felt unfair, but it was a stepping stone and that person now works at Facebook and said it could have never happened without bunk,

and they took a chance on me. I was thinking like, well, I'm not going to help the company. The article has zero positives. It just says, don't do this, don't do that. And also despite this, they actually have a business. And I was like, I'm probably missing something here. And I decided to not publish it because I decided, that's when I decided I want to publish things where I actually share things at work.

Like, and I wasn't sharing any of the things that made bunk work. And actually, they're now even more successful company. So they, well, and I think this is the thing. Like, every company has its ups and down down. So that was the thing that I did not publish and I didn't get in trouble for that. A bunch of journalists reach out to me later to like get all the juicy details because they want to read, but I just deleted the whole thing. The thing that I almost got in trouble for I was really stressed about is the deep dive on Poland. Poland, the events company, who really pissed me off because I was just covering layoffs across the industry.

mentioned at Poland was one of the many who did layoffs and I knew people there who left Twitter and Deliveroo and some good companies to work at Poland because it was a good, good company, good salary, flexible perks. And I just briefly mentioned them in my article saying they did it layoffs, it was poorly handled. On an all hands, someone brought up saying the pragmatic engineer, I was the only one who mentioned. The pragmatic engineer mentioned that we did layoffs and it was poorly handled. What do you think of it as a CEO and a CEO said like, ah, this is This is not like it's like a BBC or Panorama. It's like some small publication of an agenda against us.

Don't worry about it. It's incorrect anyway. And I was like, and they shared this back with me. And I was like, what? And so the company did not pay employees. They lied about them. They canceled health insurance. It was like loss of lies and unpaid salaries.

And I just decided like this thing with me. Like the guy said, I'm not a panorama. So I did a proper investigative article where I collected a lot of stuff on how it went on, including a double charging of a payment. that was a little bit double charge. This guy's an outage. There's now reporting out about it from the BBC. I might or might have not helped

with some of that reporting for the BBC, not for my... I couldn't put it in my article. Because when I sent it over to Poland, they said that this is libelous, this is libelous, this is libelous, meaning they could sue me. And I had to think about, like,

do I really want to do that? So I actually self-censored. And I put so much effort into that article, so much stress. And I realized that investigative journalism is just not for me. And it's a good read. The BBC later made it.

I made a documentary. I also helped them with that, but I realize this world is not for me. Other than the book, a newsletter, what's something surprising you have found through your writing? Usually just find ideas as they go,

because they fester. I also have a long list of things that I collect. Like, I'm not sure if I have any specific things. Trends sometimes pop out a bit more, as I'm seeing, multiple people talk about them at the same time. For example, there was this, and sometimes it just

reinforces the things that I, I'm kind of thinking could happen. In January, when I started using over the Christmas break, clock code a lot more and I was really impressed with it. I was like, wow, this is really good, but is it just me? And I started to read around, I did some research and I saw a lot of people saying the same thing. And that actually

encouraged me to write the article saying, like, I think coding by hand is over. And this was very early on. And I actually got some flogers. from it from some people like, how can you say this? You're an AI shill. But I was like, actually like, I felt this is where it's going based on my experience. And then I got a bunch of evidence and I talked with a few more people. So it either reinforces some opinions I have or it also gives you new ideas. Do you plan a new edition of the guidebook updated for AI era? And what would you change to better reflect the LLM era? Right now this book stayed surprisingly

durable for AI because it doesn't, it didn't contain too much about coding to start with. But the non-technical parts, things like understand the business, think about software architecture, those are more relevant, but at the lower levels, at some point I'll probably be updated, but I think I want to wait until we figure out, like, how, like, what our practices actually work, like one will have like so-called best practices for certain companies. I think it'll take a while, but I'll probably revisit at that point, yeah. What's your favorite technical book? So I'll give you two.

One is the philosophy of software design. I just love this book. It's still, to this day, the only book that actually compares architecture approaches between groups of students and what we can learn from that. I wonder with AI if we could now replicate this, like have agents, like build different software, but it still wouldn't be the same. But it's just a really nice written book. I really like the idea of modules, shallow modules, deep modules, and so on.

And then I also enjoyed Kent Beck's tidy first book. really thin book, but I just like how crisp every single idea is, even though like that book might be a bit less relevant when you're writing a bit less code, but I just like the thinking that's behind it. Besides Kraft, what are some of your favorite software tech products? I really like granola for meetings. It not only takes auto notes, it fills out your notes, and it's just such a delightful example of what like an AI added product could be. Like I'm happy to pay for that because I get more value and it's easy. note-taking, less issues with it not having to think about that.

I wish actually that I could see like more products that are like that. And I also, I still really enjoy perplexity's search functionality, especially the deep research. Every product has rolled out deep research, but perplexity is still the one that seems to be the fastest. It, like, it's, I wish it was what Google would do for, for search. and again, it's something that I pay for and I have like no affiliation for it and this is specifically the search. I don't like their new push for like computer

or any of that stuff. But like again, like from the beginning, like I feel there's some things where like AI can really add just this a new experience. I'm like, oh, I didn't know this could exist. Forget what changes. What's one thing about software engineering that you bet will be the same in five years?

I think there will be just as a big, big demand. I hope it's a bigger demand for professionals who care about the craft and who are true professionals. And in the sense, true professionals that you know where the industry is at. You know what the tools are. You've used them. You've used most of them. You know what their tradeoffs are.

You have no ego. And you just choose the right one for the right job. And right now today, this will involve like, okay, what kind of tools I use to write code with? How do I test it? How do I deploy it? How do I verify the correctness of the system? And as a professional, you care about the things that the,

average person would not. Like if I'm a building architect, I'm not one, but I would imagine that when I look at a building, I see all the things that as a pedestrian, I don't really care about. I'm like, oh, it's beautiful glass windows and the architect is probably thinking how it holds up,

what kind of characteristics, what about earthquakes, what about this, what about that? And I think that having us software professionals who can look at that, what software, work with it, and change it, be unafraid of changing it with high confidence because we have the tool set, the tools, you know, sometimes, again, with building,

sometimes you put a scaffolding to make some changes. Sometimes you don't need to, you just like do a quick job. I think that will be a lot more in demand. And I hope that we'll have more people who care about this and an AI is not going to scare them away. Or maybe AI just scared away the people who never really cared about the software. They just always cared about, you know, like making a quick buck end, like just it, but it was never about the industry. Yeah. So these are all the questions. Gary for the very interesting conversations. I really appreciate it.

Thank you. It's a bit weird to sit there because usually that's my line that you just said. But gigs, this was awesome. Thanks so much. Thank you. And thanks to everyone, of course, who submitted questions. Well, this was a different format. And finally, it was nice to not be the one asking the questions for once.

Leave you comment and let me know how you like this one. Thanks and see you in the next one where we're going to return to usual setup.