What made you switch from a full IC role

like at Uber to focus on tech content?

>> 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.

>> Have you seen how AI is impacting what

employers look for in candidates?

>> 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.

>> What's one thing about software

engineering that will be the same in 5

years?

>> 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.

>> Have you ever gotten in trouble over an

article? Has everyone tried to sue you?

>> Yes, once. Two articles actually.

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.

>> Hey Ger, welcome to this reversed

podcast. AMA,

>> 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.

>> 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? 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

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.

>> 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?

>> 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 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.

>> Have you seen how AI is impacting what

employers look for in candidates?

>> 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.

>> Yeah. So in our case, we definitely

structure the interview quite

differently. So 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. 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. 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.

>> 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

>> 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.

>> What kind of engineers are thriving and

excelling right now? We hear about

layoffs and slowdowns but surely some

are doing better than other.

>> 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. 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 the company where you have like

consulting companies where you're just

like an Accenture or Capgeemini 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 uh product companies

where you actually have a bunch of money

to 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 entropics the open

AI whatever Google used to be in 2004

and meta in 2010 that is right and Uber

and for a short time in in 2015 or so

now that's that's entropic and and open

AI and it's hard to jump between these

tiers so for example the pe a lot of

people are like oh I'd love to work at

entropic well I mean dream big but the

reality is that I know so many people

working at Google and meta and and

Facebook they want to get into those

places but these places are extremely

selective now.

>> So entry- level web product engineers is

saturated. Uh but what's the hiring

landscape for juniors in low-level

system hardware software integration

embedded or defense stack looks like? Uh

same surplus or genuine shortage of

system level thinking.

>> I'm less familiar with with with lower

level systems programming. I I would

just assume that it's not as saturated.

uh 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 uh 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 or I think it was

Codex 5.4." and he was dealing with

saying like we're using it but uh maybe

like 30% of my code cuz it's just very

low level. Uh 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 and you can build

on top of that because most people who

know a high level language TypeScript

whatever like mo 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 the rest of a stack and if you're in

this situation I would just look for

those specific specific offerings. is in

junior positions. Uh either you have

pedigree uh 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 uh

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 if it's a shitty

job, try to be the the best there.

You'll you'll build up a good network

and at some point hopefully you'll

you'll have a stepping stone, a new

opportunity to come in to go to the next

level.

>> A few questions about big tech. Uh so

when a company like Meta lays off 10%

after a record year and then reassigns

another 10 10% without consent, how does

leadership fail to anticipate the

obvious heat to culture and morale when

everyone inside and outside can see it?

>> Yeah, this this is the question, right?

The interesting thing I talk with meta

inside of like some directors and and

even above and they see it. [laughter]

So so this is not a question of like

does leadership not see it. This is a

question of does the founder

specifically Mark Zuckerberg not see it

and why does he not see it or if he sees

it why does he not care and we're now

going to territory of like assuming what

a specific person thinks in the case of

Meta like Meta is the only one who's

done this no other company that has a

career CEO I'm looking at Uber I'm

looking at Microsoft I'm looking at

Google they have not done this because

they probably know what would happen and

they don't want they 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 it's 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 a AI ADO

organization advanc AI that's as AI and

ADO is is a data organization but they

jo 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

co 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 plus launched somewhere in in the

2010s and it's well documented there's a

book about a chaos uh I'm not sure if

Chaos Monkeys covers it, but 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 they want to kill us and

everyone worked really hard because

everyone understood that the the fate of

the company was on the line. And my

sense is that Mark Tuckber 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 not he's not

telling people because this is Meta is

operating in wartime mode except

everyone's like where's the enemy like

revenue is is is record high. They're

doing amazingly well in in the ads

business. Their products are growing and

for some reason it seems existential to

Mark Zuckerberg to to own AI. But again

this 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 it might be hard for for meta or more

tucker because meta still does not own

any platform anywhere they they are an

application layer still and I think he

really wants to break out of that and

and I think it's just being a bit

reactive potentially this is all

speculation so I think the easiest thing

would be just ask him if you can answer

>> among uh big tech companies specifically

Google Amazon Meta Microsoft and Apple

how do they feel they're uh doing on AI

adoption in engineering who is

accelerating who isn't and who is

managing transition well

>> I think Google is trying the the most uh

they they have the one where they they

give a free reign to like everyone to

build AI tools is a bit chaotic but

people are building a lot of things

internally and they are the only big lab

who actually have an AI model with with

Gemini and they have a Gemini

organization and there's always talks

about how they're doing compared to open

AI and traffic but they're the only ones

who have any sort of competition in fact

Gemini is the only product which is

actually eating to Chad GP's market

share. My editor the other day was

telling me I don't use Chad GPT for my

queries. I use Gemini because I really

like Gemini and I think he also said

that it's free. So, okay, I guess there

you go. So, in in in this way, they're

actually, I 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.

>> 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?

>> 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.

>> 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.

>> 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.

>> 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?

>> No, this was actually not submitted by

by anyone, but I still want to talk

about it.

>> Now, I admit this was the one question I

sneaked in because I really wanted to

share something visually interesting

about our presenting sponsor, 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 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. 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.

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. 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. And and the last phase is is

when you're 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.

>> 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?

>> 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.

>> 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?

>> 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

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. 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.

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.

>> 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?

>> 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 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.

>> What is a popular current belief about

AI and engineering that you think is

incorrect?

>> 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.

>> 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?

>> 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

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.

>> So a few questions about yourself now.

Do you still spend time programming

yourself or testing large language

models? And if so, what percentage of

the time? I spend most of my time

researching and 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.

>> And in your own workflow as a creator,

writing, podcasting, researching, have

you seen productivity gains from AI?

>> So this is the interesting thing where I

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. 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. 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. And

what capability do you worry I might

weaken in your personally? For example,

coding fluency or technical recall or

writing from blank page.

>> I 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. 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.

>> Has AI ever tempted you to go back to

building software?

>> 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. 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? 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 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. 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?

>> 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.

>> 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?

>> 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

>> 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.

>> 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.

>> How can engineers and students use AI to

learn and explore new technologies and

concept better? I

>> 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.

>> So not IRS is asking a question so I

guess it's very safe to share all the

information. How much do you earn from

this and why start this instead of the

tech job? the the last time I shared

specific numbers was I think I think in

the first year of the publication where

I shared that I had like 2,700 paying

customers and it it's gone a lot beyond

that. It's now more than 10,000 paying

customers of the the newsletter. I also

now have some sponsors in the podcast.

And the reason I I don't like to talk

about the specific money, you know,

there there's people like here's exactly

how much I make is every time I do that,

I get so many questions coming in from

people like, "Oh, I also want to make

this much. Can you advise me? Can you

have a call with me? Can you coach me?

Can you mentor me? I want to quit my

job. I want to do this thing. And first

of all, I'm very grateful that it's

amazing business, but it's just not what

I'm good at. Like, I don't want to give

financial advice to people. And I didn't

even think this was possible, but to