Giggs: What made you switch from a full IC role like at Uber to focus on tech content?

Gergely: My plan was leave Uber, finish writing the software engineers guide book in 6 months and afterwards start a startup, join a startup. I was a little bit tired of being a middle manager. They tell you congratulations, you become a manager. They should have said you became a middle manager.

Giggs: Have you seen how AI is impacting what employers look for in candidates?

Gergely: Hiring will honestly just be more friction. It'll feel more unfair because there will be no clear rules we have about used to and it'll be messy.

Giggs: What's one thing about software engineering that will be the same in 5 years?

Gergely: There will be just as a big demand for [music] professionals who care about the craft. You have no ego and you just choose the right one for the right job.

Giggs: Have you ever gotten in trouble over an article? Has everyone tried to sue you?

Gergely: Yes, once. Two articles actually.

Gergely: Today's episode is a different one. It's an AMA where I answer questions that you submitted. Asking the questions is Giggs. [music] That is Voldemir Gignak C2 at Wartsmith. Wordsmith 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 [music] on specific situations, and the pragmatic engineer as a business. Thanks to antithesis for being our presenting sponsor. With antithesis, you can verify your systems correctness without human review or traditional interrogation tests and avoid bugs or outages. With this, let's jump in.

Giggs: Hey Ger, welcome to this reversed podcast. AMA,

Gergely: it's really nice to be a guest on my own podcast. This this is really cool and and thanks for for coming here for for some background. We know each other from Wartsmith which is one of the very few startups I still invest in because I stopped investing but about two years ago I invested with with a friend Ross who I I worked together. It's really nice to have you here.

Giggs: Yeah and you know I'm very appreciate you putting trust in us in 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?

Gergely: 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 I became an engineering manager pretty quickly. It wasn't an IC role but I guess a manager role but the it doesn't change the story too much. I was hitting about four years at Uber and 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 revenue for rides and it was just going down very close to zero and I was actually sharing it to my team because I I figured transparency is is a good thing. I'm not sure that was the smartest thing but I probably still do it again. I was people like this is not 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 stuff some something for drivers when we thought there would not be as many drivers or we had to compete with them but because of co drivers actually were flocking to the platform and and so I I got a new team to to work with but it felt to me for the first time in 4 years that they were going really well and I I just felt demotivated. I 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 I had about I got about $500,000 worth of stock as a grant option. And I'm like if if I have like 500k in my bank account, well I can take a risk and I for the next thing I can actually do a startup. So I remembered this and Uber had gone public and that 500k stock turned into 400k because uh of um the stock price was a bit lower and then you have to pay taxes on it. So it it was 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 a I could not work for like two three years easily. So I like, well, maybe I should take a risk. And my plan was leave Uber, finish writing the software engineers guide book, 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 cuz I was a little bit tired of being a middle manager. They tell you you're a 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 but when layoffs came it was a lot of politics a lot of explaining regulations that I it wasn't what I wanted to do also keep your peers happy in terms of your manager peers it was pretty tiring and I I was like I I want to be in charge next time cuz I I have a lot of ideas but I I felt I was like fighting the machine if you will in some sense. So that was my plan. I 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 start a project in software engineer 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 know 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 uh but my main book was not progressing. And I asked myself like okay like I gave myself about six to eight months to like all right get this book out and then just go and have a real job. In my 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 some a smaller place. And I was like okay well I should be honest with myself like what am I doing right now and what 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 we graded and all that and it was 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 is is exuber chronosphere exuber observability system and many others. So actually it's not all all that radical but then I was like well if I if I did that I just have to fully focus on that and on the side I was doing writing I was 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 5 to 10 years I'm going to be happy to be just focus 100% on that. I talked with my brother. He was on his 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 he's like it's not going to work cuz startups are

Speaker A: just really hard. It's not a popular thing to say. And I 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 two answers. One was the money in in the sense of like this was 2021. It seemed everywhere I looked ex Uber startups they were valued a billion. They were unicorns in like a matter of like you know a year or two. It 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 if I'm a sle 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 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 Skyscanner where I met Ross, co-founder of of Wartsmith. 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 no longer had connection. And that was the other reason. And actually that that was I guess the more legit reason. But in the end, I I didn't have this like 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 Substack was taking off. Lenny Rashiski shared that uh he had 2,000 page subscribers for product management newsletter. I thought if Lenny has 2,000 page subscriber for product management, there's 10 times as many software engineers as product managers in every single team and they're not as likely to 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 uh and I figured it it might not work and then it just worked. It took off.

Speaker B: Yeah, makes sense. Um next question. Uh have you seen engineering teams uh at big tech that adopted AI native SDLC and how do they collaborate across engineering product and design?

Speaker A: Yeah. So AI native SDLC software development life cycle e even the whole uh you know like SDLC is an interesting one before we go get into AI because like what what is SDLC? It used to be you plan, you code, you deploy, you you monitor and some people used to call this waterfall and then there was agile where you just like iterate 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 not one of the 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 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 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 or even Google work, which is like, oh, we kind of have this like, you know, problem. We 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 Beck when it 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 life cycle is is an interesting one and almost every modern company up up to AI used to have RFC's or or or RF RFDs or 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 thing in terms of thinking through. Now, the whole AI native uh SDLC, the closest I've seen to a company who is big and successful and making a lot of money and 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 on on you can see it in cloud code and I've talked with Boris Churnney about this. They don't do design docs. They they just do prototypes all the time. They kind of show it to to themselves. But I I wonder if it's really replic replicable and I also wonder when it will break down in the sense that cloud code is a great product. It's it's now the leading coding harness. So like they did an amazing job and and just with prototypes and iteration and using AI and and getting feedback and fixing it and responding on social media they respond to bugs 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 and tropic is the closest I can think of but I I did not see any company that managed to really retrofit anything what I'm seeing almost every company do they are building AI infra systems so for example they will build according agent that talks with all their internal services that's plugged into Google is doing this. Uh RAMP is doing this. Uber is doing it. So I think what's happening is they're betting building a lot better tooling to make this easier. And I think that's where we'll see and and I I still have one last question which is if you have a business that is working, it's making money. It 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 in case of Uber people expect that when you press the button the car arrives that the drivers are there's there's processes behind this which are non 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 like the pace of the business has not changed because of AI even though AI speeds up development and and and finally like when when you just go too fast, you might forget the basics, which I'm I'm seeing a lot. Spotify is a good example where I've talked with their CTO on their team and they say they're they do AI very responsibly, which which is great to hear. But then again, as a as a as a customer and a user, I'm so frustrated cuz they seem to be down so much. Like I I couldn't publish an episode two or 3 weeks ago cuz 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 they 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.

Speaker B: Have you seen how AI is impacting what employers look for in candidates?

Speaker A: Yeah. [laughter] Well it's it's impacting it because it 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 you guys are are hiring. How how did it change how you're hiring for software engineering? And then I'll answer.

Speaker B: Yeah. So in our case, we definitely structure the interview quite differently. So

Speaker A: the main thing that we're looking for now is uh 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 you know 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 picked this algorithm was it AI picking it for you or did you actually do research and you figured out what is appropriate or here is a design decision that you made how did you make 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 peing into different parts of the code and we are seeing how 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.

Speaker A: So, 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 they want to hire for raw intelligence. They had puzzles initially like you know like how many golf balls fit 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 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 uh going to universities where they teach uh computational complexity and 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 um, you know, like a thousand interviewers and and give them like a a pool of 200 questions, and it doesn't matter if a few questions leak, uh, the bar will be the same. And it works great for Google. It it it really does. Oh, and a 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, we 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 it kind of selects for that. So 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 those algorithms. But again, it works good enough for them because they hire people who are adaptable. You learn stuff and and you pick up new things anyway.

Speaker A: 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. Uh for example, take-home fixed a real bug in in a 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 algorithmic will interview it. 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 in a AI will complete it pretty well. So you don't really get that signal. So my my bet is that what will happen is these worlds will stay except the imperson 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 and Google, they will still have you come into the office and you'll have to do those whiteboard interviews and 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 percentage of of startups who can do they will just have the trial weeks what ones at 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 it's not the the main thing of it so I I think hiring will be honestly just more there will 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 no clear rules that we have been gotten used to and it'll be messy. It'll be also more subjective. Just a reality.

Speaker B: >> Yeah, work together by the way is 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. That's

Speaker A: >> well and by scaling you you mean that yes, you know, it's 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 have they are very very well known in the industry and even like a lot of people say that I'm sorry so I 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 bunch of those folks.

Speaker B: >> What kind of engineers are thriving and excelling right now? We hear about layoffs and slowdowns but surely some are doing better than other.

Speaker A: >> Yeah. So we do hide layoffs, but I I I talk with engineers who are very much in demand just as so or maybe more so than before. And what what these 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 whenever when AI came around, they they just got into it. They somehow whiz weas their way either at their company uh saying, "Okay, I'm going to work on this this AI project building something on top of AI." often AI infra like I I will help build build this part and now they actually considered experts in in in in this and most companies that are hiring and trying to hire positions. 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 they're not an absolute noob to this. They they 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 offtheshelf model? Should we use our own model? Should we write an on-prem? Should we do it off-rem? What about the inference costs? What about like should we use Grock? Should we use Cerrus? So whatever it you know like 5 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 and some of these things and so engineers who have been doing this are in very high demand.

Speaker A: The only problem they have is sometimes if they work at the likes of Google Meta or or or a wellunded startup these other companies are are surprised at how high of a compensation ask they have. But these people are very in 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 this experience with ba building AI infra you know they still build software and they use cloud code and codec but everyone does that they feel a bit stuck on 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 jumps and now they're thinking should I just do some side projects and my 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 there's I have the tri model of course but also I have this model of like

Speaker A: consulting companies where you're just like an Accenture or Capgemini or one of these where you're given 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 compensation won't be higher you're now competing and hiring from the likes of big tech and then at the very top you have right now it's the AI labs the Anthropic the OpenAI whatever Google used to be in 2004 and Meta in 2010 that is right and Uber and for a short time in 2015 or so now that's Anthropic and OpenAI and it's hard to jump between these tiers so for example a lot of people are like oh I'd love to work at Anthropic 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.

Speaker B: 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 stack looks like? Same surplus or genuine shortage of system level thinking.

Speaker A: I'm less familiar with lower level systems programming. I would just assume that it's not as saturated. When I talked with the pragmatic summit in February, I talked with an engineer who was working on low-level systems, mostly C++, some assembly and we talked about who's using AI cloud code, codecs, 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 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 dealing with saying like we're using it but maybe like 30% of my code cuz 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++ and 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 TypeScript 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 latency systems you can learn easily the rest of a stack and if you're in this 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 a 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 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, try 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.

Speaker B: 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 hit to culture and morale when everyone inside and outside can see it?

Speaker A: 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. [laughter] So this is not a question of 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 assuming what a specific person thinks. In the case of Meta, 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 life balance, 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 AI ADO organization (Advanced AI that's AI 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 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+ launched somewhere in the 2010s and it's well documented. There's a book about chaos... 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 Zuckerberg 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 Meta is operating in wartime mode except everyone's 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 Zuckerberg 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.

Speaker B: 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?

Speaker A: I think Google is trying the most. They have the one where they give a free reign to like everyone to build AI tools. It is a bit chaotic but people are building a lot of things internally and they are they only big lab 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 OpenAI and Anthropic but they're the only ones who have any sort of competition. In fact Gemini is the only product which is actually eating into ChatGPT's market share. My editor the other day was telling me "I don't use ChatGPT for my queries. I use Gemini because I really like Gemini." And I think he also said that it's free. So, okay, I guess there you go. So, in this way, they're actually, I...

Speaker A: think, way ahead of of uh 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 the point. Microsoft is in this weird place where like it's it's still very political as far 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 sell Azure is fighting with everyone for capacity they don't have enough Microsoft is focus more focused on politics than AI in my assessment Apple uh I talk with people at Apple but like Apple is very secretive And like Amazon is secretive cuz their engine culture is is pretty good. So I'm surprised they're so secretive, but Apple is secretive because their engine culture is absolute trash from from all I gather is duct tapes everywhere. I I'm not sure much is happening at Apple, but because Apple is not doing too much, I personally hope that they will actually see 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 Kira, their internal tool, and they have their own models, but they're all subpar. It's it's all people are dragging their feet. They rather use clot code. And and Amazon is full of smart people. So to me, Amazon a good example of just how Amazon, Microsoft, how difficult it is to like 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 in front 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 and Uber and Ram is like, "No, like we we know our place. We want to use these the very best possible way. We will take clock codeex. We don't care. We don't want to build a 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 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 lar companies right now.

Speaker B: Entropic and specifically cloud code are shipping at extraordinary rate uh 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 would be wrong to copy that directly?

Speaker A: I think it's just very hard to copy on traffic. So we cannot deny that entropic is the best example for AI native development at scale together with potentially the codeex team. And when I say entropic I actually mostly mean cla code and and also their model but it's all interwinded because in AI lab their product is don't forget entropic's product is claude. It's not cla code. Cloud code is is a revenue generator until claude is so good. their product is the model that they they get a new version every few months and they do a bunch of work with with with training, pre-training, post-training and then the the tooling around it and everything is it's 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 Entropic for example don't even have an enterprise sales team that a lot of other ventures would have. Maybe they have but it's it's it must be pretty small right now. I always feel that they're a bit of a anomaly. Where I'm interested and I I'm not seeing all that much yet is 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 will use AI for everything and you start a company you realize the first hurdle is like how do you get traction and at wormmith like you guys luckily have have gotten traction you kind of pass that point but a lot of founders it doesn't matter how how AI native you are if 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 preAI you could assemble an amazing engineering team and build a first version of a product or you could just like have like 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 when it took off Travis Ken just hired some contractors made an ugly app but but it it did something that was that people wanted. Oh and here it was at the right place in San Francisco. So I I wonder if like AI native is overrated and 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. You know, they're really trying to be AI native, do all those things, but they're 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 like fewer people. But I'm I'm not as sold on this.

Speaker B: Yeah. To me it feels like artificial artificially trying to become a native is a bad strategy right like just saying entropic 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 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.

Speaker A: I I I wonder if instead of AI native which is just think about like companies where like AI is a natural tool that you reach for like you for any 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 you throw it away if it doesn't or you'll revisit it later.

Speaker B: Yeah. And just you have another tool that can help you. Next one. Uh, can you share something about today's presenting sponsor? Was like, is this really is a question that people are asking?

Speaker A: No, this was actually not submitted by by anyone, but I still want to talk about it.

Speaker B: Now, I admit this was the one question I sneaked in because I really wanted to share something visually interesting about our presenting sponsor, Anticys. It's how different their UI is. Let me show you with three examples. We already know that Anticys verifies your systems correctness by running your whole system in 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

Speaker A: 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 atysis are pushing what's possible with both debugging and verifying software. Head to anticysis.com/pragmatic to learn more.

Speaker A: Is ignoring code quality for speed with AI worse it longterm? Some engineers still review the plan. on architecture and code. Others rely on SDDD plus harness uh and disregard the code plus are shortterm but is AI good enough to make up for worse code. This is a big question isn't it like and I I wonder if there there's like any answer like I I I feel as engineers I think we we know what want what answer we want. We we 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 we wanted this to be true. But when I got inside of Uber, I I learned about some horrible hack that hacks that Uber did that was look really painful.

Speaker A: For example, the old Uber app before 2016, before we had the rewrite, you would open the Uber app and and you would see the the ETA of of the the cars. you sell 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 2 minutes, Uber van is like 6 minutes and and you pull it and you saw some other information on the screen and what what what happened is that app was pulling the server every 5 seconds to give me all the information. It was a package and so every 5 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.

Speaker A: And the reason that they did this is is the and this is just terrible like strategy. It's it's it's inaccurate. It's slow. Uh it it's it's really wasteful on resources. It 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 end team was small and the the front end the mobile and the web teams were larger and they were getting frustrated that whenever they wanted to change on the back end to get some information back it would take you know like days, weeks, months and so they asked the back end team like hey can we do something about it and they're like well there's this really hacky solution where we just send this like big blob together and you can go in the back and you can add whatever you want into this blob and they're like perfect and it actually unblocked Uber for a long time to like grow independently but it was a terrible architecture. ure and so this is an example where like this is clearly tech depth but techdep 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 techdup we don't know if it'll work you'll probably toss it out there's companies at this stage where we just try out prototypes and it doesn't matter if it's beautiful or not once you found product market fit there's this Kenbeck has the the three X's the uh I explore, expand, extend. And there's other other ways to to say this, but in 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.

Speaker A: And and the last phase is is when youre mature, you want to make things good. And what I've seen at the likes of Uber again pre AAI 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 the early stages, you're just doing a prototype, just go all in. Don't worry about the code 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. You know, I'm looking at Instagram's product for example, which is a mature one, but Meta still messed it up. That is probably where you want to be very careful and and pay attention, understand it. Oh, and final thing is AI doesn't only let us build faster. It allow us to refactor faster. So, we have no excuse not to do that every now and then.

Speaker B: >> Yeah, I completely agree. I think it's basically a false dichotomy that it can be only speed or quality. Like it's more about segmenting in time or in codebase, right? So infrastructure maybe more attention to quality product maybe more attention to speed. There haven't been repeated shifts in AI tooling and best practices. 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?

Speaker A: >> Yeah, first AI is so new it keeps changing. Like I think like any standards would would make no sense and I think standards just naturally emerge. Like I I I haven't seen any patterns to it. MCP entropic 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 was bigger and then like all these large companies adopted it cuz there was a lot of politics in it. So I think it's accidental. Entropic today if they try to do an MCP people will be 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 see anything like planned happening here.

Speaker B: >> Companies like Entropic have engineering managers coding a lot and at Meta and I presume at Uber as well uh the philosophy was actually the other way around that EM should mostly focus on people. What's the right approach for engineering managers in AI era?

Speaker A: >> I mean this is a philosophical question and like people have strong opinions about that. I for example like we we we know like from the when at at Twitter when El Mus took over Twitter and and then renamed it to X he fired a bunch of people and he mandated that engine managers should code while having 20 plus reports which which sounded like pretty insane to do both. I'm not sure there's a right or wrong model. I' I've seen all sorts of models work out there there's pros to both. There's like when an engine 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 to give more technical guidance. They will have better technical discussions and they will care a lot less about this first category of things. Uh, and they also probably will not have bandwidth to like make systems level changes or go to like meetings to 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

Speaker B: industry is definitely going very strong in a 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 I think we've been at the side where we have been very focused on on people and has been very rewarded as a manager and it was great to be an engineer at companies like this. It's now going back where it will be less so and I I wonder if it'll come back again.

Speaker A: 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? Uh talking leaderboards, mandatory usage, code volume targets rather than real outcomes.

Speaker B: So I I wonder if this is like almost over because there was a part where I talked with CTOs and engineering leaders at all sorts of companies and they were really frustrated saying, "Oh, my engineers are not using AI." But this was before Opus 4.5. This was before uh well mostly before open 4.5 and GPT 5.4 and before cloud code was used by by many people. This was at the age of autocomplete with like you know GPT 4.0 or or even worse models and like our engineers aren't using it or or when cursor was al was just the the tab you know they have the golden tab key. I think this is almost like a non-issue like every everyone in most places I know uses it and also that's when token leaderboards made a lot of sense. Shopify the token leader boards in that era. No one knows about this about them, but they 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.

Speaker A: 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?

Speaker B: It's a good one. right before I entered 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 just raise more money and then we hire more people and then we grow faster and we raise more money and we hire more people and even I remember my my manager was telling me that headcount when I became manager I was like how does headcount allocation work is like you know do you need to make business plan or something it's like oh 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 headcount 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 headcounts were reset at the end of the year and if you didn't use it they they reallocated within 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 cost they cost X like they should generate at least as much value right but they're like no not right now like we don't live in an age like that like oh this is like different I always felt it wrong and and so there were opportunities where I could have worked on a team or led a team which was a purely platform 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 uh something similar to React Native uh 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 hired two more people I would have an answer here's how much more revenue you 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's 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 incremental 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. Well, that's not AI, is it? 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 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 uh 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 it's pretty iffy pretty finicky. And I I still have this 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 said we will never go on cloud and now they're in AWS but like as a customer no one cares if you have cloud or not. It used to be as a cost saver more a more flexible way to to control cost and I think AI it maybe it's a more flexible way to control your own cost or 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.

Speaker A: What is a popular current belief about AI and engineering that you think is incorrect?

Speaker B: I think it's incorrect to think that it just makes things easier. if you're using AI and your life is getting a lot easier, like you're are you trying hard enough or are you like delegating to stuff? And because to me like I I I use some of it for for my business and it actually like makes me think just as hard if if not harder work is harder. So I I think like believing that AI makes work easier, our our jobs easier, it's just it's just wrong.

Speaker A: How important are degree and university prestige in hiring today? Is computer science becoming a prestige field like law or architecture leading to fewer self-taught professionals?

Speaker B: Unfortunately, I believe it is. And and this is less to do with the with the degree and what they're teaching, but more about the market. There was a time around like 2015 to 2020 where you you could get hired at a company for a well-paying job by doing a boot camp, which is like three months to 6 months, sometimes 12 months versus a four year or or five year degree in computer science. And the reason was there was just a huge shortage like the all all of the people graduating from from university were 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 elsewhere do apprenticeships but they're very small. And the top universities are still getting those graduates are getting hunted down at the likes of of MIT, Caltech, Harvard, many others, Waterlue in Canada, Imperial College in in the UK and so on. But they're not getting as many competing offers as as before and and even at the mid-level of schools, it's it's just harder. So when it was hard to hire someone with a computer science degree, people went for like lower selftaught and and those things. But now they they do it less. I even had someone tell me who is selftaught, worked in the industry for 5 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

Speaker B: doing like SR work and infrastructure work. And I think in the end she said that she's either considering changing fields or or just doing her own thing. And that's the other thing 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 in living in your current country and you don't plan to leave, like it 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 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 a degree is very important for visas. If you're for example in 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.

Speaker A: >> 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?

Speaker B: I spend most of my time researching and 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 I'm building. I'm building it myself. And now uh I might have like some folks help me on my team as well. But when I could get a SAS now, I'm like I don't want to get a sauce. I I just want to build it myself. So it's it's simpler stuff. Honestly, it's like crud database that it runs on on infrastructure like render. I I use the tools. I I I use uh Codeex. I I really like Codex and and GPD 5.5. I also use uh clot code as well. I I play with cursor. I sometimes try factory. So I 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.

Speaker A: >> And in your own workflow as a creator, writing, podcasting, researching, have you seen productivity gains from AI?

Speaker B: >> So this is the interesting thing where I I think I should have. So I don't use any AI for my own writing. Like I I I did a few of these experiments more for curiosity saying, "Hey, here's here's some notes. Generate an article in the voice tone of the pragmatic engineer. First of all, it isn't addresses job on it. I don't think it sounds like me. Second of all, like it it just has those I don't know, it just feels artificial like like the links. And then most importantly, I really really enjoy like like I love writing. I don't like it's not the the thing of writing, it's the thinking. Like when I write I I keep thinking and a lot of times on social media when I will post something and 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 to writing and then later I see like you know people respond to it because I guess what people see is 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 there are so many people who are optimizing social media for likes or or things or or all of this thing but for myself and a bunch of people that I I know and and respect it it's kind of like their side thing.

Speaker B: One good example I I read someone on on on Hacker News wrote about this that their favorite YouTube creators in photography. This person was a hobby photographist. Their favorite photography creators are not professional YouTube creators about photography. They're photographers who have a business and they actually like do shots and then they have a YouTube channel where they share every now and then. It's infrequent. It's not there. And I also think of myself as my my main thing is I I research what's happening in a tech industry. I talk with engineers. I try to keep an ear on on the ground as much as I can because I talk with 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. You know that's for example how I noticed that something was really off at Meta. I I've only ever sensed things being like slightly off at Meta for so a long time but now I've I I have 10 or 15 people who I know there and I 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 you know, turns out I I was right about how just how bad things have gotten there.

Speaker B: But in in in my workflow, uh I I use it for research when I'm like here's a topic like all right, 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 like freed up time and I guess it frees up some of that time, but I I would have never spent that much time researching. So I don't feel that I'm working less interesting enough.

Speaker A: And what capability do you worry I might weaken in your personally? For example, coding fluency or technical recall or writing from blank page.

Speaker B: >> I I don't think like the the writing will will suffer cuz I I just don't use it there. I don't even have spell checks on like I just don't like it or I know I turn Grammarly off off as well cuz I I hate when it like wants to reorganize it. I think it's whenever you over rely on something it it it could make it less efficient. Like for example, one thing I now overrely on is like just deep research. like I 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 cuz first of all, it was it was just grudg. 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, [laughter] I'm not sure this is going to be 100% checked out.

Speaker B: But with coding, u I I now just prompt and and write the code. and my ability to 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 like whenever using AI for a bunch of so just know that that skill will go down and are you okay with that? And I'm kind of okay with it.

Speaker A: >> Has AI ever tempted you to go back to building software?

Speaker B: >> It's now so much easier to build software. like it probably would would have tempted me, but right now I just love what I do and I I actually love the human connection of actually talking to people and getting getting a window into what other people are doing. But it is making me build more software and being more ambitious. So there's this project that I've been putting off for a while, which is a self-service signup 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 it's less intimidating.

Speaker A: Vladimir is QA engineer in banking early sorties and he's worried about staying relevant. So he's tempted to quit for full CS education. Uh but it's quite scary to give up good paycheck. Uh feel stretched. How should he think about f future proofing his options?

Speaker B: What I see in terms of future proofing is the single best ways to future 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 where it's a rigid place it might be the opposite but my first advice would be inside a company can you start a project uh where you are just doing some experience with AI this is why Google is is such a great place right now I I I know it might not be

Guest: too popular to say but they encourage doing this like oh you're you're on your team you're building a product cool and you have a 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 cuz right now there's a bit of like every leader thinks like we should use AI more and if someone comes and says like I have an idea and I'll do it on on part-time it's a win-win worst case is you know you learned about rag or you learned 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 just start doing that I think that's the best way to stay relevant because if you take a a computer science degree or 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 my advice would be 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, then do it. But other than that, it it could be easier to do it at work. My two cents.

Interviewer: How can you surround yourself with highly motivated top-notch programmers when your classmates aren't that at that level and it feels like too much to catch up to?

Guest: I mean, if if if your your classmates are not that motivated and you are, try to find a different group of friends and well, it depends on where if it's high school, then you're stuck with them. Uh, which is find even when I was at high school, there was only two of us who were coding and luckily there was another person. Maybe we can find someone from a different class, maybe on an online community. I I've heard some Alis real on my podcast when she was in high school she joined online communities and started to build uh she actually started to contribute to some software there. So like that's one one way to find if this would be at work try to either change teams if you can internally to to move there or outside of your project like take projects where you can work with other people like like seek out and and and try to follow those people or get towards them because a lot of people will be motiv and 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 uh 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.

Interviewer: So Akos is saying that his son is heading for an IT focused high school dreaming of becoming a game developer. What does a pass and the job market looks like in 5 years from now? And what should he do to prepare himself?

Guest: Everyone's asking this question, right? If if only we knew. I mean, I I personally believe that I try to draw parallels from other industries because we we don't know what's going to happen exactly with AI. you know this tool that we know that coding is so much easier. It will probably make some of the other parts of the jobs easier. But I like to think of of a parallel for example construction where like like if if you wanted to build a house today or at least okay renovate your house significantly. You could walk into the the DIY store or you can go online and you can order a bunch of equipment in 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 you have the information and you have the 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 are calling out electrician to like change a light bulb or or or even some of the more advanced work you a lot of people are using YouTube and DIY shops are probably getting way more business but I think there will be professionals so if you want to be a professional in a field there will be a path to that and to to get into that it will will go to university's education I'm fairly certain that the game that will be released in 10 years which Aquas's will hopefully be working on. It will be built by a studio that's either a startup or 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 Acro Stan specifically uh I have a episode with Jonas Tyroller who uh 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 or 15 or 20 years and he has been building games on the side. So if if his son wants to become just encourage him to start building games on the side right now

Interviewer: in this hard market, what do you recommend for engineers in the EU? Uh keep aiming for tier one companies or stick with tier two job.

Guest: Yeah. So so this is the in the try model structure. I I I have a tier one. I I I put it as as the the local companies, like the local supermarkets, the ones that are really competing for local talent. Tier two is regional and tier three is as is global. That's the big tech. And like in in this job market, well, first of all, like when the job market is is really like volatile and uncertain, 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 are some layoffs but so many companies are hiring. So now could be a great opportunity to jump a tier up to a startup to to to some to 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 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 I would be opportunistic in looking out, maybe looking at at job 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 you're going to stay at your current place probably anyway.

Interviewer: How can engineers and students use AI to learn and explore new technologies and concept better? I

Guest: I think you can use deep research a lot better. You can ask it to explain stuff, but the way I see it like it it AI only ever helped me learn about stuff when I wanted to learn about something. So, start with 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 like like maybe like videos, uh, tutorials and also just building your own thing like that. That's what I mean like biggest miscon. It's not going to make it easier to 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 have one fewer excuse when you want to do it and if you don't want to do it just just don't do it.

Interviewer: So

Host: 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?

Gergely: The last time I shared specific numbers was, 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 newsletter. 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 an amazing business, but it's just not what I'm good at. Like, I don't want to give financial advice to people. And I didn't even think this was possible, but to actually not be that vague.

Gergely: 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. Back then it was like $320,000 or $330,000 or something like that. And 120 of that was base salary. I think it was like a 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 didn't think it would go too far. I thought I'd give it a shot. But most of why I didn't think it would go so far, just being realistic. Like Lenny shared his numbers of 2,000 paying subscribers, and you do the math, it's $300,000 roughly, give or take. And he was going up. And I thought, well, I mean, maybe I could get there? Maybe yes, maybe no. But we'll see when we get there.

Gergely: But 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 6 weeks, I got to 1,000 paying subscribers. It 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, than 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, I actually really love doing this. I didn't know that you could make more than working at a big tech by doing this thing, your own business.

Gergely: And this is also something that you can realize if you're 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... I always tried to do the best work that I could. I had a lot of structure. 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 for advice.

Gergely: So luckily I feel almost like, wow, like this was 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 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 an 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?" And 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.

Host: What's next, Gary? Any expansion plans for the Pragmatic Engineer?

Gergely: Yes. So 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 and some of those things, is to have one in the US for Pragmatic Summit and one in Europe in London or somewhere else. And getting to that point I will be very happy. And also I'm growing my team very slowly. We now have 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 a utilities company and like go through how they build software. It sounds pretty boring, but it's pretty darn important.

Host: Have you ever gotten in trouble over an article? Has everyone tried to sue you?

Gergely: Uh yes, once. Two articles actually. One I never published because I decided not to publish. This was at the beginning of the publication. For some reason, I really got upset at Bunq in the Netherlands because I read about their hiring practices. They do intelligence tests, Rorschach 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, "Whoa, wow. This is like crazy." And so I started to write an article about that. This was in the first year of the Pragmatic Engineer. This was December. So I started in August, and this was in December. And I had an article ready that was pretty, 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 Bunq, and saying... 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 Bunq, 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 paying 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...

Speaker A: helpful because this person came from I think Egypt and no company would hire him in a 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. And 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 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 I want to publish things where I actually like share things that work like and I wasn't sharing any of the things that made Bunk work. And actually they're now even more successful companies. So they well and I think this is the thing like every every company has it ups and downs. So that was a 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 wanted to read but I I just deleted the whole thing.

Speaker A: 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 uh I I was just covering layoffs across the industry. I mentioned Poland was one of the many who did layoffs and I knew people there who left Twitter and 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 uh like updated layoffs, it was poorly handled. On an all hands someone brought up saying the pragmatic I was the only one who mentioned it. the pragmatic engineer mentioned that we did layoffs and it was poorly handled. what do you think of it as a co and the co said like ah this is this is not like it's like a BBC or panorama it's like some some small publication they have an agenda against us don't worry about it it's incorrect anyway and I was like and and they shared this back with me and I was like what and so uh the company did not pay employees they lied about them they canceled health insurance it was like lots of lies and and unpaid salaries and I just decided like this this thing was me like the guy said I'm I'm not a panorama so I did a proper investigative article where I collected a lot of stuff on how it went wrong, including a double charging of a payment that was a a deliberate double charge. This guy's at an outage. There's now reporting out about it from the BBC. I might or might have not helped uh with some of that reporting for the BBC, not for my I I couldn't put it in my article because when I sent it over to Poland, they said that this is lielist, this is lielist, this is lielist, meaning they could sue me. And I had to think about like, do I really want to do that? So I so I actually self-censored and I put so much effort into the article, so much stress and I I realized that investigative journalism is just not for me and it's a it's a good read. The BBC later made it made a a documentary. Uh I also helped them with that but I realized this this this world is not for me.

Speaker B: Other than the book and newsletter, uh what's something surprising you have found through your writing?

Speaker A: I usually just find find ideas as as as they go because they fester. I I I I also have a long list of of things that I I collect. Like I'm not sure if I have any specific things. Trends some sometimes pop out a bit more as as I'm seeing multiple people talk about them at the same time. For example, there there was this this and sometimes it just reinforces the things that I I'm kind of thinking could happen. In January when I started using o over the Christmas break clock a lot more and I was really impressed with it and I was like, "Wow, this is really but is it just me? And I started to read around and I did some research and I saw a lot of people saying the same thing and that actually encouraged me to like write the article saying like I think coding by hand is over. And this was very early on and I actually got some flack from it from some people like how can you say this? You're an AI shill. But I was like 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 it also gives me new ideas.

Speaker B: Do you plan a new edition of the guide book updated for AI era and what would you change to better reflect the LLM era?

Speaker A: Right now this book stayed surprisingly durable for AI because it it doesn't it didn't contain too much about coding to start with. Uh 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 I think I want to like wait until we figure out like how like what are practices that actually work like when we'll have like so-called best practices for certain companies. I I think it'll take a while but I'll probably revisit it at that point. Yeah.

Speaker B: What's your favorite technical book?

Speaker A: So uh I'll give you two. It's one is the philosophy of software design. I I I I just love uh this book. I I it's it's still to this day the only book that actually compares architecture approaches between like groups of students and 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 it still wouldn't be the same. But it's it's just a really nicely written book. I I I really like the idea of of modules, shallow modules, deep modules and and so on. And then uh I also enjoyed Kent Beck's Tidy First book. book. It's a 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 the thinking that's behind it.

Speaker B: Besides Craft, what are some of your favorite software tech products?

Speaker A: I really like Granola uh for for meetings. It it it not only takes notes, it fills out your notes and it's just like such a delightful example of what like an AI added product could be. like I'm happy to pay for that cuz I get more value and it's it's easier note takingaking less issues with it not having to think about that. I wish actually that I could see like more products are that that are are like that and and I also I still really enjoy Perplexity's search functionality especially the deep research every uh product has ruled out deep research but Perplexi is still the one that seems to be the fastest. it like it's I I wish it was what Google would do for for for search and again it's something that I I pay for and I have like no affiliation for it and this is specifically a 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 some things where like AI can really add just a new experience I'm like oh I didn't know this could exist

Speaker B: forget what changes what's one thing about software engineering that you bet will be the same in 5 years

Speaker A: I think there will be a just has a big big demand. I hope a bigger demand for professionals who care about the craft and who are true professionals and in the sense true professionals that you you know where the industry is at. You know what the tools are. You've used them. You use most of them. You know what their trade-offs are. You have no ego and and and you just choose the right one for the for the right job. And right now today this this will involve like okay what kind of tool do 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 if I'm a building architect, I'm I'm not one, but I I would imagine that when I look at a building, I see all the things that as a pedestrian, I don't

Gerge: ...really care about. I'm like, "Oh, it's beautiful glass windows." And you're 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 that having us software professionals who can look at that with 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 buildings you 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 AI is not going to scare them away or maybe AI just scares away the people who never really cared about the software. They just always cared about, you know, like making a quick buck and like just it but it was never about the industry.

Giggs: Yeah. So these are all the questions. Uh thanks Gerge for the very interesting conversations. Really appreciate it.

Gerge: Thank you. It's a bit weird to sit there because usually that's my line that you just said, but Giggs, this was awesome. Thanks so much.

Giggs: Thank you.

Gerge: 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 a comment to 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.