Objectively, stuff has become easier,

but then why am I thinking as hard as I

ever have? None of our competitors are

crushing us either. And we're in the

coding agent space. All our competitors

are super into AI. So, you would think

in our space there would be a huge gap,

but there just isn't. The 24 to

29year-old engineer will soon become the

most valuable asset in technology

because they have prei principles and

post AI speed.

>> Person like me has all the advantages.

Person not like me has all the

disadvantages. Everyone is just saying

mantras to themselves. A defense

mechanism is to confidently assert a

future in which you're a winner. And

that's almost what every single

prediction that you see is happening.

>> That past 10ish years of building dev

tools and understanding dynamics is

really helping. The strategy that we

know how to play really well is to pick

one temporary bad guy and then galvanize

all their competitors, push something

forward against them. There is a world

where the net result of all these AI

coding tools is the same amount of work

gets done, but all the engineers are

happier cuz their job is easier. That's

not good enough for a lot of companies.

So they're just going to say

Doc Rod is a co-founder of Open Code,

the most popular open source coding

harness. He's also a very downsource

when it comes to AI, which can be a bit

surprising when you consider that he

built such a widely used AI tool. Today

we talk about the rapid growth of open

code to closer to 10 million active

users in less than a year. The memo Dak

sent to his team admitting they were

shipping too many features, taking on

too many hacks, and AI usage not helping

them move faster. why inference is one

of the most profitable businesses in

tech right now and why even open code is

bottlenecked by GPU supply and many

more. If you'd like to ignore the hype

on social media around AI and instead

talk about where it helps or hinders

productive engineering teams, this

episode is for you. This episode is

presented by antithesis. Verify your

systems correctness without human review

or traditional integration tests and

avoid bugs or outages. I'd also like to

mention our season sponsor, Turbop

Buffer. You probably heard someone say

rag is dead these days, but it's not. It

just doesn't mean what it meant in 2023.

Back then, retrieval was pretty

straightforward. The human asks a

question, your code embeds it, you hit a

vector database, top K results come

back, those get stuffed into context,

and the LLM answers. Now, look at what's

actually happening inside something like

Open Code or any serious agent product.

In 2026, a human sends one prompt to an

orchestrator agent. That agent fans out

to sub agents. Each sub agent is hitting

separate systems. A vector index, a full

text index, grabbing the file system,

running CLI commands and SQL against

OLTP and OLAP stores, reading and

writing a memory system, reranking

results, looping, and calling more

tools. One human prompt turns into

dozens or even hundreds of searches

across totally different shapes of data.

In practice, this means a lot more

complexity, a lot more cost, and a lot

more potential performance issues,

especially when you need to scale up the

system. This is exactly what Turbo

Buffer is built for. It's a ridiculously

scalable, fast, and cheap search engine.

It's built on top of object storage for

reliability and scale with smart caching

on MVME SSDs, so it's very fast. It's

priced so you can let agents loose with

proper in 2026 without seeing your bill

explode. Check it out at

turbopuffer.com/pragmatic.

Dax, welcome to the podcast.

>> Yeah, thanks for having me and thanks

for coming all the way to Miami.

>> I wanted to jump in something really

interesting about you. You're building

one of the most popular AI engineering

harnesses, open code, which is speeding

up how people write code and and just

like turn out software. And yet you're

claiming that this itself is not enough.

Like this itself will not get us to

better software. I've always said the

easiest products to build are ones that

you can use yourself. And obviously we

built Open Code so that our team can use

it and it's like we are the customers of

the product. Uh so we use it

aggressively as much as anyone else can.

And of course it is like we build it so

we think it's useful and we use it every

day and it's a critical part of our

workflows but all the old problems that

I've always struggled with are still

there. I'm working as hard as I ever

have. I'm struggling as much as as I

ever have. So a lot of the job has

become easier. But yeah, it's it's a

weird feeling because objectively stuff

has become easier. But then why am I

like thinking as hard as I ever have?

You know, it's a a weird feeling to have

both those things be true. And it's

interesting because the like a lot of

the people who are decision makers,

CEOs, often hands-on people like

hands-on CEOs, CTO's, founders at

companies, they kind of think, oh, look,

we've got these tools. Coding used to be

the hard part, right? Like like

objectively, it took us so much time. It

it still takes, you know, to get into

the zone if if you're going back to

coding by hand. So if that's faster, cuz

that's where we used to spend most of

our time. Like it should be faster. Like

everything should be faster. But why do

you think this is not the case? Or or

like what is getting in the way of like

just like shipping high quality software

faster, better, right?

>> Yeah. I mean, there's different there's

different life cycles, different

companies. There's like pre-product

market fit, there's achieve product

market fit, which is kind of where we

are. Uh, and there's companies that have

like have had product market fit for

like a decade. Uh, and I imagine that

things look very different across these

three. For us, pre-product market fit,

it to me it doesn't really help that

much cuz you're trying to figure out

what you should be doing. Um, and yeah,

like maybe it helps you swing a lot. But

I've always thought it's better to think

a lot instead of swinging a lot. I think

you could you can eliminate a lot of

ideas or directions just by, you know,

spending a lot of time in your head and

with your team talking. Um, obviously AI

doesn't speed that part up. We're at the

phase where we're we've achieved product

market fit. Now our task is to kind of

hit the potential that we have. Um, and

the issue for us is there's a million

different directions we can go in.

There's uh all the obvious stuff we can

do. There's all the stuff that our users

are telling us that we have to do.

There's stuff that our competitors are

doing. And it's very easy to just one to

one do each one of those things because

we have a problem, prompt the agent.

Competitor has a feature, prompt the

agent. User has a problem, prompt the

agent. If you add that up, you think,

"Oh, we shipped a thousand features. Now

that adds it to a good product." It

actually adds it to a horrible product.

>> Like a Frankenstein.

>> Yeah. Nothing's cohesive. You look in

there, you're like, "We shouldn't have

shipped this." The moment you ship

something, you're stuck supporting it

forever. And by supporting, it means any

future feature you build is going to

like interact with it. So you still have

to be very conservative with what you

put out there. It's hard to undo

anything. Just cuz we can ship 10 times

more doesn't mean we have 10 times as

many good ideas to ship out there. So,

uh, in a lot of ways, my struggle has

now been how do I like slow everyone

down? Um, and like understanding that

yes, our process can look very

different, but should it look very

different? Like we, you know, we we've

done uh in the past 6 months, we've kind

of operated very differently than we

ever have. A lot of stuff went wrong

because of that. So now we're pulling

back and figuring out, okay, what from

the old world still makes sense. So

yeah, we're like figuring out what we

should be doing. And I I definitely

don't feel like, oh yeah, we're like

killing all our competitors. We're using

AI so much better than everyone else.

Um, and by the way, none of our

competitors are crushing us either. Like

no one out there is using AI so well

that they just like we can't even

compete, right? Um, and we're in the

coding agent space. All our competitors

are super into AI. So you would think in

our space there would be like uh a huge

gap, but there there just isn't. Yeah.

So I want to rewind back to to the very

beginning of how you know before AI

before a lot of these things. How did

you get into tech and software

engineering?

>> Yeah. Uh so I uh I grew up I kind of

cliche story I grew up programming as a

kid. Uh my dad was a software engineer

so a little bit easier for me to get

into than for other people. Then just

started working out of high school. Uh

founded a company. Thought it was cool.

Thought I knew what I was doing. Looking

back in hindsight like wow I didn't know

what I was doing at all. Um that

eventually got aqua hired as a small

aqua hireer. Uh and I ended up in like

the real tech industry. bounced around

as a consultant, founded a few

companies, uh, and then ended up doing

open source pretty much the past six

years full-time.

>> But going back to the very beginning, I

found or saw some references to you

working on Minecraft, Minecraft servers.

>> Yeah. Yeah.

>> Back in the beginning, can can you take

us back a little bit?

>> Yeah. So, uh, so Minecraft obviously

everyone knows what Minecraft is. Um,

and again, this is another cliche story.

You talk to a bunch of people in tech,

they have a similar similar backstory.

There was a modding framework for uh

Minecraft that came out. I ended up

working on the framework itself. I ended

up making a bunch of mods with it. I

found that I liked creating like

interesting sandboxes. I never like play

like I played the game a little bit, but

I didn't play the game that much. I like

had a server that like a 100 people

would play on and I would just use these

mods to create like interesting

situations to try to like understand

like how people would behave under

certain scenarios. And I found that like

fascinating. Um but yeah, that required

a lot of like programming Java stuff.

What's interesting is that community

obviously I was like very new to

programming. Uh it was all done over IRC

or the community existed on IRC but

there were some like very senior very

good programmers in there. The people

there I felt like were people that

weren't particularly career motivated.

They were probably in a comfortable

enough situation where they worked like

two hours a day. Uh and they were

talented but they just didn't have any

like desire to like breer chase at all.

So they funneled it into the Minecraft

stuff and I got to talk to them and

learn from them. So uh I feel like in

like the couple months I was doing that

I learned I learned a lot.

>> And then you you went to the startups

you founded a startup and then after

being the founder at uh Iron Bay I saw

looking back your history you became a

head of engineering at Wright Health.

>> Yeah.

>> Can you tell me a little bit about what

was that like? That was in 2017 or so

like pre-COVID.

>> Yeah. Yeah. So that was a company in the

transportation and healthcare space. Uh

it was just me and the co-founder

originally. Um, and that that company

grew to like 20 people or so. That was

my second swing at doing a startup, I

would say. And it got further than my

previous attempts. Um, but all but like

it kind of ended up in a disaster. Uh, I

did end up meeting my wife there. She

was a head of product. I was a head of

engineering. We got together after a

year. So, better than a startup exit, I

would say. Definitely learned a lot.

But, uh, yeah, it was one of the

situations where everyone was super

young in their 20s. And I think there's

this uh stereotype of startups that oh

yeah, it's a bunch of like super young

people, you know, building stuff. After

that experience, if I ever end up

investing in companies, I'm not going to

invest in companies that with a bunch of

young people cuz we were all just like

our brains weren't fully developed.

We're all insecure about various things

still and that like played out with like

company politics and and dramatics and

stuff. So, uh yeah, that cliche of like,

oh yeah, just a bunch of young people. I

think that's like the exception, I would

say, looking back.

>> So, so like a bunch of young people

succeeding is probably the exception.

>> Yeah. Yeah. And of course we have like

famous stories of that. Um but you know

like on average I like for me I felt

like my brain didn't finish developing

until I was like 26 I think. So prior to

that you know I don't know if I really

should have been uh running a startup.

>> And when you say like stop developing is

it just kind of like getting enough

experience to like understand how like a

business actually works or like

professional relationships. What in what

sense? Yeah, I think for me it's like

startups are so intimate. Uh because

it's just you and a few people and it's

very intense like you are this is the

thing you're doing like you're not

really this is your hobby, it's your

job, it's kind of everything. If you're

not like fully developed as a person, if

you're just trying to prove something to

the world, if you're still insecure

about certain things, all of that shows

up at work. Um especially when such an

intimate situation like that. So

fighting, conflict, all that stuff. uh

the way I perceived the situation even

my own understanding of what was going

on just basic like how to be a person

and live in the world. Uh I think at

least for I think I think for guys like

takes a while for their brain to fully

settle and when it did it felt like day

and night like I was a different person

but uh it definitely took a bit and now

you can look back at that time and just

kind of be like oh gosh like what was I

doing? What was I thinking?

>> Yeah. Everything was just so magnified

and way more intense and way more

emotional than it than it needed to be I

think. Okay. So the people who knew the

DAX back then might be a little bit

surprised.

>> Yeah. Yeah. I think so. But again,

that's when my wife met me. So something

something was right there.

>> Did you in your first few years, so you

you founded a startup, then you went you

kept going at like very early stage

startups either as a founder or an early

founding engineer or so. Did you ever

consider larger companies at that time?

>> Yeah, I think um that was always there

because uh you know this so for me this

era was like the early 2010s or

mid2010s. Um the big tech companies were

very prestigious at that point. Like

there was a lot like everyone you met

was trying to get a job at uh either one

of the big top tech companies or one of

the hot like unicorn startups at the

time. That was like the thing to try to

do. If you weren't doing that, you

basically were going to like fail in

tech. So I felt like some kind of pull

from that point of view. But I also just

couldn't get my together to like do

that. Like I knew what it what it takes

to there's like a thing there's a set of

things you have to do to to kind of be

successful there. I just couldn't even

do that. So, uh, yeah, I could say like,

oh, yeah, I chose not to do it, but

really I I I just don't think I was

>> What do you think the things were back

then?

>> I mean, you know, there was a very

structured interview process for these

things. Uh, you know, you have to do

certain like programming problems,

whatever. And I think I was a good

programmer. And I think I'd done some

interviews that were like that. And I

some of them I even did well in. But the

level of competition and like people

study for it, people focus on it, like

just randomly swinging at it, I don't

think I would have uh landed anything.

So yeah, I I was into building stuff. I

was into like p more practical things.

Um and I just struggled to do anything

outside of that. And then how did you

transition into open source? Well, I

serverless stack was big project of of

yours. was it was a a toolkit for

building a uh building full stack a

applications I think.

>> Yeah, that that was kind of where our

initial focus was. Um yeah, and how we

got into open source. So after ride

health kind of ended up in in disaster,

I took a job at a like a series B stage

startup at that time for me was like the

biggest company I'd ever worked at. Uh

and I eventually got put in like a in a

director role. First time being like a

full-time manager with no other like uh

like no no like active programming

tasks. Uh and I still wanted to program

a lot. So I ended up exploring a bunch

of open source things at the time. You

know I had like three or four hours of

manager meetings every day. But then

outside of that uh you know I had time

to explore open source things like that.

And I started to build my own kind of

crappy stuff and I came across um SST

which had just launched. It was a was

like maybe like a couple months old.

That was Frank and Jay. They both uh had

created this thing originally started

contributing to it. They were raising a

series like a they were raising around.

when they were kind of getting out of YC

raising a round. I invested in the in

that round and then a month later I

ended up joining the team and they gave

me the money back as salary but then now

I had to pay taxes on it. So it's a if

you're going to invest in a company make

sure you're not going to join them. It's

a bad use of money. Yeah.

>> And then you did SST and then you did

some other stuff as well on the side,

right? Open next.

>> So Openext I think was probably a thing

that blew us up initially. Um this

wasn't even a thing that we wanted to

build. we were uh survey serving people

in the AWS space. Every single day

someone would come to us and be like we

like what you guys are doing but we

really need help deploying NexJS AWS and

over and over like for a year people

were just nagging us about this and we

didn't want to do it cuz we weren't

next.js s users very tedious. You

actually has a very complex framework

trying to recreate the right

infrastructure for all that in AWS is a

lot of work. Digging into like

JavaScript bundling like NexJS internals

like not very fun, not very exciting

work. Uh so we made Frank do all of it.

So he basically slogged through through

that and figured out how to uh get it

all working AWS. Our goal from the

beginning was this project shouldn't

exist. It's just a weird gap because uh

the NexJS team is focused more on

Purcell and it wasn't like a malicious

thing or anything. it's just where the

attention would naturally go. Uh so

we're like let's fill this gap. The end

ideal state should be that you know

Versella eventually just manages all

this and there's no need for this. So we

built that. Uh it annoyed Verscell a

bunch. Um uh but yeah it was very useful

for the people that you know were trying

to deploy on in other places. Eventually

uh we kind of rallied some other

providers that had problems with Nex.js

as well. also cloudflare netifi I think

Microsoft got in there eventually too

Google got in there eventually uh and

they kind of built stuff adapters for

open next and eventually the nextjs team

had time to kind of like okay let's make

an official adapters API and and in the

past like year or two it became a much

more like collaborative thing uh and I

think the need for open next is slowly

going away and then how did open code

come about because that that's a pretty

recent story it started sometime in

summer of last year summer 2025

>> in less than a year yeah back in

February very uh our company we were

basically doing a push to hit to hit

profitability. Um so with SST we had a

monetization path for that basically for

like 3 or 4 months we're like running

out of money and then but then our

revenue was going up and then in

February we had like one month of money

left but then we also broke even at the

same at the same time. Weirdly though we

were all like really calm about it. It

just felt like I know it felt like

something would would work out and it

and it did. Uh so that gave us time to

kind of take a step back and think about

okay we can technically do whatever we

want now like what do we want to be

doing and for a while we're like

obviously AI is a thing to work on this

decade especially if you're working in

dev tools we've been through waves

before where like there's just a thing

that happens and whenever a thing that's

happening it seems like it's not making

a lot of sense because whenever

something that is has a lot of potential

it attracts a lot of investment by its

innate nature most that investment

doesn't make sense so it's very easy to

look at anything exciting that's

happening and think none of that makes

sense. I'm not going to participate in

it. But usually what happens is a few

things in there make a lot of sense. And

if you just completely sit out, you just

miss out on that. I think we've been

through a few waves of kind of doing

that. Uh where we saw the AI thing and

we're like, okay, a lot of this stuff is

stupid, but there's definitely a real

value here. We need to be doing

something in it. Uh and we took a few

swings at a few ideas and like a lot of

them we didn't even fully launch. Uh cuz

it did we just discovered they didn't

make sense while we were working on it.

And then eventually we started to use

cloud code as a team. It was the first

AI coding tool that stuck for us. It

like directly solved some of the the

workflow annoyances that we had. Um this

is great obviously uh this should exist.

At some point I asked why weren't we the

ones that built this? Like we should

feel bad about that. And then we

realized okay maybe there is like still

an opportunity to do something given our

experience in open source. Uh we kind of

saw I think so we were saying earlier

like you don't have to just ship a

million products to figure out something

that works. If you sit and think you can

figure something out and we kind of

looked at it from a positioning point of

view like there's a market there's a

bunch of coding agents out there. For

some reason no one has grabbed the open

source territory. There was no coding

agent that was like we are the open

source option and that obviously is a

super valuable territory. every single

dev tool we use whether it's databases,

compilers, whatever eventually the open

source option becomes a default option,

right? That combined with the fact

there's heavy heavy competition uh for

models like sure claude was and was like

popular but there's billions and

billions and billions of dollars

invested like they're not just going to

let anthropic win there's going to be

push from open AI there's going to be

push from the open source side so given

that we saw that chaos we saw that it's

really valuable to have the open source

positioning that tries to work with uh

all the models so the initial push for

us was to kind of ignore whatever was

going in the market and just make sure

that we claimed that open source spot

which we were able to do and then yeah

like our numbers have been pretty crazy

since then.

>> Can you tell me about the the growth

like you launched in June 2025 how the

growth was both for usage but but also

for the team like when you started how

how big was even the team was was it

most of the the existing smelly labs

working on this or just a small

>> Yeah. No, so we were just three people

at the time. So it was just the

co-founders. Yeah. And then um uh we

hired uh one of my friends who was also

interested in working as and he helped

us build like the initial thing. So he

joined uh so that was four and then we

convinced one of our uh like a really

good designer that we had always wanted

to work with to join us as well. Uh but

that was after we launched. So that was

more like in the fall. But yeah, so we

we launched and the growth was really

good like immediately. It was better

than anything that we'd ever done. But

by December we hit uh 650,000 monthly

activives. And at that point we're like

wow this is great. We uh back in the

fall we kind were kind of telling people

our goal was to hit 1 million by uh next

early next year. Everyone thought we

were crazy. Like that was like they oh

okay sure. Then in January we did 2.5

million monthly activives. Uh so like

went from 650 to 2.5. Uh last month

we're at 6.5 I think this month. We're

still halfway into the month so I'm not

sure. Maybe close to eight. Our next

goal milestone was 10. There was a

massive jump. So we went to 650 in

December and 2.5 in January. What was

that jump? Is is that the the winter

break jump where everyone started to

realize that these models are really

great?

>> Yeah. So, having been at DevTools for a

while, we knew that in January there's

always a bump cuz like I think people

take time to learn new things in

December with the time off and they have

time to try new stuff. So, we what we

typically see is we see a dip into the

holidays and like a giant spike in the

first week back. For this, we saw growth

into the holidays which we've never seen

with any other product before. So like

despite the fact that people were off,

it was higher usage than ever. And then

January came around and Anthropic helped

us helped us a lot by uh you know they

they like wanted to ban anthropic

subscriptions in open code. Uh that blew

up into like a huge thing like we barely

even commented on that but like just the

user base was so upset. Anthropic

accidentally put us and them in like the

same sentence which we don't deserve cuz

they're a much you know bigger more

successful company. But that week that

kind of happened by accident and that

like spiked our numbers like crazy.

>> So So I guess in in hindsight cuz what

happened is Entrophic silently banned

being able to use cloth code

subscriptions inside of Open Code which

which was every tool including Open Code

did that cuz it was a bunch of other

third parties but they didn't allow Open

Code to do this and then this led to

this like outrage.

>> The thing with them is I think that

they're kind of new to working with

developers. Um, it's okay to do things

like like of course you need to do what

you need to do to make your business

sustainable. It's totally fine. But

randomly dropping a block at at like

9:00 p.m. at night, that just sets you

up for like people to hate you. Uh,

doing like a phase communicated roll out

over a month, I think they would have

>> giving a heads up.

>> Yeah. Yeah. Yeah. I think people would

have someone upset. Of course,

everyone's going to be upset no matter

what, but it wouldn't have been this

concentrated moment of like everyone

being being really uh really annoyed.

This reminds me what we were talking

about how AI allows you to do things

really quickly and fast. Do you think in

this case An entropic might be you know

stepping in this trap of of they can do

things really fast and they do things

really fast but for example in this case

they can just implement a block like

this you know like it it the PR probably

took like what like a few seconds or a

few minutes whoever did that might have

not thought through the implications I

think it's just a consequence of any

kind of fast growing company uh you

forget the amount of leverage you now

suddenly have like you do one small

action and it ripples through millions

of people like we're dealing with this

ourselves like we've never worked at

this scale before. We put out a bug the

other day where um almost everyone has a

terminal in dark mode and we open code

it opened up in light mode. So we like

flashbanged a bunch of people and I'm

like in the past that would have been

like a 100 people we hit but like I did

this like a million people this week. So

uh yeah but but in inside this block

obviously we know it worked out well in

in hindsight but when it happened I mean

at the time Opus 4.5 or 4.6 six was the

most powerful model out there best for

coding and you saw that entropic just

blocked you know like clos before you

knew that this press would happen like

was the scene's reaction was it just

like stay calm keep going yeah what's

weird is we knew this day would happen

at some point and for some reason when

it happened we all just felt excited

because I think we were kind of like

thinking about this for a while and we

knew this would happen we also knew that

like we live in this crazy bubble like

especially on Twitter where everyone has

a a $200 Cloud Max subscription. At that

point, we were at 650,000 uh monthly

activives. There's no way 650,000 of

them have Cloud Max subscriptions. Like,

it's crazy for the average person to

spend $200 a month on anything. So, we

knew it's it was like a smaller subset.

Um,

>> and also at the time, we had been

working on deals with pretty much every

other company to officially support

their their subscription in Open Code.

So the previous weeks leading up to

that, we got Microsoft agree for uh

GitHub copod to be officially blessed by

Open Code. We got a bunch of different

companies in the works. We had announced

any of them yet. And the big one we

hadn't gotten or we haven't even

approached was OpenAI. So when this

happened that night, forget what it was

Thursday night. I don't remember exactly

when. I got like a hundred tags on on X

being like, "Oh, they they banned it.

They banned it." And I was like, "All

right, it's go time." So, I messaged

OpenAI being like, "Hey, tomorrow, uh,

everyone's going to wake up. Everyone's

going to be really angry at Anthropic.

You guys have a chance to score like a

PR win by taking the opposite stance and

officially supporting Open Code." The

next morning, they confirmed like,

"Yeah, let's do it." So, in the morning,

everybody was like, "Oh, Anthropic

blocked Open Code. They're screwed." I

saw a bunch of people being like,

"They're worth nothing now. Like,

they're they're going to go to zero,

whatever." I was like laughing to

myself. I'm like, "Okay, just wait till

the end of the day." And then we figured

out the integration, we implemented it

and the end of the day we announced like

open AI is officially supported in open

code. We knew what was going to happen

and like I going all the way back to the

open next thing, right? Uh the strategy

that we know how to play really well is

to pick one temporary bad guy and then

galvanize all their competitors to push

something forward against them. Um so

Anthropic was unfortunately in that

position. Uh so we kind of got the rest

of the industry to you know support open

code support like access to these models

in different places because they were

all competing with with Enthropic. So

these are like nice strategies that you

can run in these situations even as a

small company. I'm starting to get a

sense that that past 10ish years of

building dev tools or like at least like

five of open source and understanding

dynamics is really helping you because

even even when you told me about how you

were thinking of open code you're

talking about strategy about how there

was this gap and there's all these

competing model providers and with an

open alternative over time in a lot of

ecosystems the open one wins and the

vendors compete for example with Linux

you know Linux is open source but the

distributions are are for-profit

companies Red Hat had Ubuntu or

Canonicle and so on and they all compete

and and they make it better. So even in

this case it sounds like you you know

like it all goes back to you actually

had a strategy that you expected that if

the players play the vendors play as

they play it will make sense.

>> Yeah, exactly. If you get your

positioning right, the world just keeps

handing you wins that you didn't even

expect. So like we never predicted this

exact scenario but like our fundamental

understanding of the position was right.

like if if there is a neutral party, all

these companies with billions of dollars

will kind of use a neutral party to

advance their own like company's

interests. So, it's beneficial to be

that thing in the middle.

>> Yeah. And then right now, COEX came

forward. They saw it as a way to

increase brand awareness, usage, et

how much is growing. And I'm sure

logically at some point they might turn

around at some point say let's say they

win the market or they become market

leaders, they might do the same saying,

"Okay, we no longer want to support this

thing." But at that point there might be

other uh players as as long as there's

multiple uh interested parties.

>> Yeah, exactly. So at some point maybe

open AI becomes a bad guy that we have

to like kind of galvanize everyone

around. So it's it's uh I mean it's why

competition is good. This is kind of

exactly how competition plays out. I

think the thing that people maybe

underestimate is like we're a small

company. We're not like a huge company

at all. You can apply if you apply

pressure in the right places, you can

actually make things happen. We get a

lot of criticism and and like anger from

people being like, "Oh, you guys are

being so mean to Anthropic." They

criticize like the way we approach

things, but these are billion-dollar,

you know, huge companies, you know, like

us being able to apply any amount of

pressure to get something to happen.

It's very difficult. And this is what it

looks like. People might think that

it's, you know, they're above it or

whatever, but uh having more access to

these things, having more people be able

to use the tools they want is a good

thing that you can love cloud code, be

like, I'm never switching off cloud

code. That's great. you should probably

still be into the fact that people can

use other other tools that they like,

right? And going going back to why open

code is so successful and that there was

a gap using what you've learned about

dev tools in general, like why was there

a gap? What do you think was a

difference that you did outside of just

you know like writing the writing the

tool to start with? The the biggest

advantage to in DevTools is the fact

that everyone working at DevTools is a

programmer and programmers are horrible

at B2C products. They don't realize

DevTools are B2C products. You basically

treat

>> B2C as a business of consumer.

>> Yeah. Exactly. So like you treat them

like the most extreme mindset to have is

like you treat them as though you're

launching Instagram or social media app,

something like that. Yeah. Yeah, you can

do a top down process and there's plenty

of companies that do like an intense

top- down enterprise process and that

works and that's great, but the dev tool

products that are like massively adopted

to the point where they're basically a

standard, they're all bottom up. Like

they're individual developers start to

use them, start to like them and they

kind of creep into companies and they

grow in that direction. And to do bottom

up, you have to basically think like a

consumer company. Um, and programmers

generally are very very bad at thinking

this way. So we very much focused on the

moment you open open code it should feel

very different uh and better than some

of the other options. And to do that we

had to build like a groundup uh terminal

rendering framework. That's not what

cloud code did. That's not what any of

these other coding terminal coding

agents did. They just used ink or

whatever and you know they made it work

for they need. But we invested on it

upfront because from the moment you use

it it feels like something different. It

might not be for you like maybe you

don't like the overwhelming experience.

maybe it feels like too much, but you

still walk away thinking the people that

did this are competent probably. Um, so

immediately getting people a good

feeling and then immediately getting

them prompting without any layers of

friction. So we focus on just reducing

that friction as much as possible and

focusing on things like I'm on a

lockdown enterprise laptop. How do I can

I use open code? Like optimizing for all

of those things. And our harness wasn't

very good for like the first like five

months of open code. But it was good

enough. It was good enough that most

people couldn't really tell a

difference. And once we won enough

share, then we went back and like tried

to make our harness like good and smart

and optimize and all those things. But

uh it was inverted from what everybody

else was doing. Everybody else was being

like you have to build the smartest

harness and that's how you win. We did.

We have like a mid-level harness, but we

are the most used. So uh and now we're

able to like you know creep up and

actually have the best harness and are

also the most used. So I think it was

like an inverted strategy that we did

that uh other people didn't fully

understand

>> and to get into the inverse strategies

the technical level like you you you

launched with a framework called Tari

right?

>> Oh that was for a desktop app. So the

desktop I would say I wouldn't even say

that was that was mostly a mistake on

our part. So I love terminal stuff. I do

all my work on the terminal. So our

numbers are said we're going to hit like

8 million monthly active users. I don't

think 8 million people should be using

the terminal. I think it's a little over

uh we have like too many users for what

the product is. I think a lot of them

would probably have a better experience

on uh on on a guey and we understood

that very early and so we were kind of

experimenting with like a web app with a

desktop app and uh so we're like this is

probably going to be the direction

things go. Unfortunately, we were right

like that that is like where things are

going, but we didn't like treat that

project too like we should have treated

it a lot more seriously and try to get

it like uh out there way faster and

thought a little bit harder on on the

technical stuff. So yeah, we just kind

of picked stuff and we didn't like think

too hard about it and uh it ultimately

was a mistake and we're moving back to

Electron now. But open code is is

growing amazingly fast. Interesting

enough, there's this growth hack that

some harnesses are doing. I would say

growth hack where in the PR on GitHub,

they actually put which harness did it.

Cloth code is is doing it. GitHub

copilot is doing it. Some others are not

doing it. And in open code, you are not

doing it even though this could be like

almost like free marketing. Yeah. So,

initially we had that cuz initially

we're effectively a cloud code clone. If

cloud code did it, we did it. So we had

that like with the commit it would say

you know committed with open code

whatever in GitHub and then we got a

bunch of people being like hey can I

disable can I have an option to disable

this and I thought about it and I was

like this is so l it just felt lame to

me. I'm like you can be like a casino

right casino just tries to trap you in

there with every little trick trick to

like get you to stay. Um that's like the

extreme of doing a consumer product. I

felt like we didn't have to go to that

extreme. It felt like a little bit lame

to like try to it's like too obvious of

a of a growth hack, right? like everyone

sees it, everyone knows why that's

there. Yeah. So, I just wasn't a big fan

and so instead of having the option, we

just turned it off by by default.

>> You know, you're still running a a

company that is a for-profit company in

the end or or you need some you need to

make bankroll. What is the business

model behind open code?

>> So, uh we have two lines of business

basically. Uh so, initially when we

first launched open code, a big problem

that people had was or that we had again

thinking about reducing friction, they

had to connect something. They had to

connect their anthropic account. They

had to connect their OpenAI account at

that time. When you sign up for

Anthropic, you couldn't even get enough

rate limits to even use something like

open code. So, a lot of our users

couldn't even use it. So, we're like,

okay, we have to at least build some

kind of inference service that like you

can sign up for and get access to all

the models with the rate limits you

need. Uh, so we built that and we called

it open code Zen. And we initially just

built it as like an onboarding thing to

smooth out onboarding, but that grew

like a ton. uh and with the amount of

open source models are becoming popular.

We also found there's a ton of

difficulty in hosting open source models

correctly. Uh so Zen has become a place

to aggregate the best model. I mean of

course frontier models are easy but then

the best inference for all the open

source models. All that became really

popular. Um that business is growing a

ton. I think a couple months ago we

announced like that hit 50 million uh

run rate uh within like five or six

months.

>> Wow. Um, and the margins there can be

pretty good because open source models

you can host at a decent margin. That is

growing like crazy. Uh, we didn't really

expect that, but that that's like a big

part of it. The other side of it is

extremely boring. If you are a company

that's using Open Code and you have a

thousand engineers, you can't just tell

them all to go download Open Code and

like add an open API key. You need some

kind of control plane to like set up all

the providers, permissions, budget

controls, rate limits. So, we have a

product there. Uh we're going to make

that publicly available soon, but right

now it's just been like enterprise

deployed. Uh so just if you're a company

that's using open code at scale, you

need some administrative software to to

run it. You can't practically use open

code at scale without something like

that. That's also open source, but you

know, most people just pay for our

hosted version. Uh the other thing is I

think uh it's finally the time has

finally come where people are looking at

how much they're spending on uh on LM

and they're like, what are we doing? Are

we actually getting anything anymore

done? like we so like companies are now

looking at their cost and trying to

figure out how to optimize it a little

little bit. It's great timing because

open source models are now very

competitive. Uh they are 10x cheaper

blending that in and having good

inference for open source models is

becoming a part of our business as well.

So these big companies you know they

need the control plane but then kind we

kind of just give them inference access

as well to the these other models and

they end up just kind of naturally

starting to use it. If that ends up

being a main part of our business, we

might stop charging for uh the control

plane itself and just charge for the

inference.

>> Dax was just talking about the boring

but essential work to get enterprises on

boarded. SSO, permissions, control

planes, all the stuff every serious

company eventually needs, which is where

I need to mention our season sponsor,

Work OS. Sooner rather than later,

you'll need to get around building these

enterprise features. Not just control

planes, but things like O for apps and

agents. Work OS handles it. SSO, SKIM,

fine- grained authorization built for

how agents actually operate. The fastest

growing AI companies, Entropic, OpenAI,

Cursor, Perplexity. They already trust

Work to solve these problems. Check it

out at workwest.com.

I'd also like to talk about our

presenting sponsor, and this is we just

talked about slowing down to speed up,

thinking hard about what to build so

you're not sprinting to the wrong

destination. But quality is part of the

destination, too. You don't want your

product to flashbang a million people

like the team at Open Code did that one

time. Antithesis is a property based

testing platform that enables you to

express the properties your system

should have then verify that those

properties will hold in the chaos of

production. With antithesis

specification and thorough verification

becomes a seamless workflow, giving you

clarity so you can deliver quality.

Check out antithesis.com/pragmatic

to learn more. And with this, let's get

back to DAX and open code. We had a

recent tweet about how inference is

actually really really profitable like

you were quoting someone who was saying

like oh you know like these these AI

model providers are are you know like

having might be having financial

difficulties. Can you explain to those

of us software engineers who you know we

don't we don't do inference I mean we

use these models and we we just assume

that this this must be our our business.

How can it be profitable? Why is it

profitable? What are you seeing?

>> Yeah. So I think this is uh it's kind of

there are different parts of a business.

So if you look at the pure inference

part of a business if you think about

what's the floor on the cost the floor

is the cost of electricity. Um there's a

capital cost to acquire the hardware.

Once you have it to deliver a token the

cheapest it can get is the electricity

to power it. And obviously there's other

infrastructure there's like operations

staff. There's stuff like that. Um, but

we have seen models that we look at the

sticker price of it and we know like the

uh uh cuz because we rent GPUs at scale

to run the models and we still use

middleman by the way. So we're not like

going all the way down to the down to

the floor. Even for us there are some

models the sticker price and the cost to

us there's like an 80% margin in there.

And I think the other thing that people

don't notice is prices have gone up.

It's confusing because it seems like

they haven't. But they've gone up from

the point of view as we used to all use

sonnet as our default because opus was

too expensive. Then they made opus

cheaper so that we started to use opus

as our default but it was still way more

expensive than sonnet. So the prices

have gone up and the cost to host these

models haven't changed. Um so that's one

aspect of it being really profitable.

And anthropic of course they have and

openai crazy scale. They have the

biggest GPU deals that they've done. Um,

so I wouldn't be surprised if they're

looking at like 90% margin at current

prices. I don't think that's like a

defensible margin long term. That's

crazy to be able to make that much with

this amount of money going through as

well. It's interesting cuz when Brian

Canrell was on the podcast, he used to

work at building a cloud service that

would compete with AWS and he said that

back then this was the same thing. AWS

back then hid their financials. Everyone

thought and they told everyone that

cloud is a terrible business and it's

like you red blood everywhere. And then

he started to do it and he said like

actually it's a really freaking

profitable business to run a cloud but

it's kind of a welcome secret because

why would Amazon or any other provider

advertise the business that it's it's

printing money.

>> There's always negative sentiment that

exists for any business that's getting

hyped. They have no incentive to correct

it. Um so again it's complicated because

I know the training costs are a big part

of it. Uh the R&D department is is

hugely expensive but long-term inference

makes sense as a business and I think it

it always will. Yeah.

>> This being said, you also said something

in in public about GPUs about you you I

I'm quoting you. There are just enough

GPUs. It's crazy that even a company our

size is being bottlenecked by us.

>> What does that mean?

>> Yeah. So, across the whole stack of

GPUs, so everything from like producing

the GPU to supporting hardware to like

labor, everything everything is like

super tight right now. The demand for

inference is growing. So, like I don't

think it's linearly growing. I think it

might even be exponentially growing. But

we haven't made our production of GPUs

grow exponentially. That's like kind of

a linear process. So as those lines

intersect, there's going to be uh

tightening. So for us like there's we

have G GPUs that we need to reserve. We

have to pay a lot up front. Um everyone

is now hoarding because everyone's kind

of expecting this crunch to kind of

continue. It's very hard to get capacity

for inference. And uh the other thing

that's crazy I think I posted about this

you know we see things like oh a company

has raised $2 billion or whatever to do

something in AI and that feels like a

crazy amount like wow that's that's a

huge amount like you have to be like a

crazy startup to do that the big tech

companies are spending like tens of

billions in a year like Amazon Meta

whatever like they dwarf anything that's

happening in the startup space so they

are just vacuuming up like all the

demand any company that is in the supply

chain they don't want to talk to you

because they're busy trying to get

something with Amazon or Microsoft or or

Google. So, yeah, it's very tight times.

I I think it'll get resolved. I think

any my whole career, anytime I've seen

any kind of shortage, there was a tense

period and it was met with like crazy

over supply. It might be different this

time, but generally that's how how I see

things go. But right now, it's tight. I

I want to talk about the the hype of

what productivity gains uh these you

know AI tools spec specifically AI

agents are giving to engineering teams

and and the reality and and you wrote a

now very heavily quoted tweet which

which was I'll quote just some parts of

it. Everyone's talking about their teams

like they were at peak of efficiency and

bottleneck by the ability to produce

code but the way things actually look

like is and you listed a few things like

your team your your or has good ideas.

People are now using AI to be 10x more

productive. They're using to turn out

their tasks with less energy to spend

and so on. So like this was a few months

ago I think two or three months ago when

you wrote it.

>> Yeah. I think there there's so many

dimensions to this. So the thing I was

talking about in that post is the

majority again we forget how big the

software engineering industry is. Every

company in the world employs software

engineers to some degree, right? Um, the

majority of these environments aren't

like the most motivating, exciting

environments. Most people there are

trying to do their job, go home to their

kids, like have a have a a reasonable

life. You give them a button that lets

them do their work faster. The natural

place for them to do is hit that button

as much as possible, do the same amount

of work, and just cash in that extra

time, right? Which makes total sense.

It's like if you're not if you have no

reason to be above and beyond motivated,

you're not going to really use that to

like, you know, push your organization

harder. Yeah, these tools maybe make you

more productive, but like be really

realistic about your employees. Like

where are they going to like cash in

cash in those gains? Obviously, some

companies are not like that. They're the

employees are motivated. They have good

reason to be. They're compensated in a

way that that makes sense. But most

places aren't like that. Uh, and the

problem with that is usually in those

environments, it's like there will be a

couple people that are irrationally

motivated because, you know, they love

the work they do, etc. Even though the

rest of the company isn't as motivated,

they're usually the ones that are trying

to make sure everything is good quality,

make kind of push everyone to try

harder. They're all now overwhelmed by

like slot PRs. And we we've had a few

people on our team that have joined and

their previous company was like this,

like they were the person that still

cared. the rest of the organization just

like hits the button and gets their task

done and they're drowning in just

garbage and they're getting burnt out

and they're leaving. So motivation and

team and people and humans and like

emotions all that stuff is still a big

factor in in all this. How do you think

companies like let's say a m midsize

company or even a small company might

have to rethink kind of motivation

compensation thinking about work now

that you know like I was Steve was

talking with Steve Yagi about this on

how an engineer could if they're super

motivated and they put in the task and

energy they can produce a lot more

output and if it's well directed that

could be better business results but it

does come at a a huge cost in energy And

again, if you're if you're just being

paid by the the same paycheck, it

doesn't necessarily make as much sense

to do so. So, I'm I'm I'm wondering like

uh what what kind of early thoughts you

have so far. And of course, for you guys

in a startup, I guess it might be a bit

easier. I mean, obviously for us, you're

right, it's easier for us. Um we are in

a very exciting space. The people that

join our company are very competitive.

Uh they want to get in there and they

want to win. So, that's like a big

driver. everyone has equity that if we

do our jobs right like it'll be pretty

meaningful for them uh at some point I I

even preai I feel like uh again I can

only speak for startups as like that's

where my experience is. I'd always see

startups post jobs where they're like

we're hiring two engineers and they post

two salaries and these are like okay

salaries. I always thought like why just

hire one person and combine the salary

you'll get someone that'll like

meaningfully change the direction of

your whole life. Like people will start

showing up at that at that level. Um, so

we've always been willing to pay pay

quite a lot. We don't need to hire a

thousand people. We need to hire like 20

good people. Um, so generally I think

good salaries still go a long way. But

yeah, at bigger scales like I think it's

just hard like when when your company

hits a certain size like there is just

no good reason for someone to do much

more than what their job strictly ask

them ask them for. So I don't have any

good ideas here. And I I wonder if it's

a a thing where it is hard to retrofit

this this technology and and the way of

working to existing structures which

have been and I guess the question goes

back to like how how much will like the

AI native startup like yours or or ones

that are starting today change like how

will their workflows change the type of

work even the roles right like what is a

software engineer even do in terms of

okay of course they prompt AI to write

code but what about product what about

all these other things.

>> Yeah. Yeah. I mean yeah I think the

bottlenecks are still there. you're

still figuring out what you should be

doing. You can spend a year just

figuring out the right thing to do. And

then once you figure it out, maybe it's

fast to actually build it. But yeah,

like I I think the the joke I made was

pre AI, I would spend 95% of my energy

thinking about what to do and 5% of my

energy doing it. Now I spend 96% of my

time thinking about what to do and 4% of

my time actually doing it. So yeah, it's

like a 20% improvement, but dayto-day it

feels as hard as ever. Another

observation you made is at a lot of

companies and you will I guess see this

secondhand from from open code being

being used at companies that the CFO is

now going what do you mean each engineer

costs extra $2,000 per month. What are

things you're hearing about in that area

cuz this is happening.

>> Yeah. So the with I think with every

technology there's a phase of like

flexing where there's so many uh

companies now that want to be seen as

like we're so future facing we're like

our process is ahead of everyone else's

like you have no chance you're going to

catch up and they're all bragging about

how much they're spending. So there's

like right now there's a push to be like

each engineer is spending like 10,000 a

month and that's like that's so worth

it. like our business is so successful

and and so useful and so efficient that

any amount of money is worth it and and

there's a lot of that narrative. Then

there's like and that that's fake

that'll go away. Uh then there's a real

narrative of like yeah these companies

hire thousands of engineers if they

literally cost $1,000 extra a month that

like breaks your whole budget. Like

that's not a a thing you can kind of

casually do. We're in a temporary period

of like we're experimenting to see

what's possible and and and everyone's

learning. I doubt this is going to be a

sustainable thing. Like these companies

were already pretty tight on what they

could afford, especially if they can't

directly point to clear results of

here's the issue, right? The net result

of this, I'm not saying this is the

case, but there is a world where the net

result of all these AI coding tools is

the same amount of work gets done, but

all the engineers are happier because

their job is easier. That's not good

enough for a lot of companies. So,

they're just going to say go back to

typing out the code. It's also an

interesting dynamic where there's also

the pressure of well when everyone else

is doing it e even if if we just stick

with the happier analogy and again let's

let's assume that the the quality of the

output overall wouldn't change if

everyone else is doing it and you're

like okay well it's not worth it let's

stop it then your best engineers will

leave so I I'm also hearing some uh CTO

saying like well we're giving access to

better better tools because we're one of

the best companies like it would look

silly if we said like oh you can only

use I don't know GitHub copilot anymore

we're not allowing you to get whatever

open code or off code or whatever else

that is cuz we like our best people

would leave.

>> Yeah. No, that that is a real thing.

It's kind of like uh I mean again

depends on if if you have someone that's

really good that has infinite

opportunities using Jura is a risk

because they're going to be like I hate

using Jura every day and they're going

to leave. And I know people that have

literally have done that. So that that's

like always a always a dynamic. So

>> but but that will be at the top of the

market right. Yeah. So it's again I

think at the scale we're at we're just

so exposed to like how big the world

truly is and the world we typically play

in it's so small compared to everything

else that that exists and at most these

companies it is just use copilot just

plug copil into open code and that's all

you get and like your limits your limit

and then and that's what's there um and

there's some narrative being pushed that

these companies are going to die because

they're going to fall behind etc but uh

I don't know I just don't think it's

that straightforward Another

longer uh post that uh that I I paid a

lot of attention to is the one that you

sent out to your team to the open code

team which was on on the topic of saying

all right we should do three changes

I'll quote you basically we have the

following challenges none of them are

new but I think they're turbocharged by

LMS number one is shipping features not

worth shipping number two when iterating

on features sometimes the original

design design is off and it forces you

something hacky but the LM cannot not

deal with the hacking. And number three

is we need to spend more time cleaning

things up. How did you get to these

observations and what's changed since

you and how how did the team respond? So

the first thing right features that we

shouldn't have shipped so easy just to

respond to a problem by shipping a

feature. So having more restraint uh and

like so we we try to like flesh that out

a little bit more like here's the type

of stuff that we want to ship. Here's

the type of stuff that like we'll wait

on um until we have more clarity.

There's having more restraint there. The

thing about shipping hacky stuff, that

one is I think the probably the biggest

challenge because forever this has been

a problem where you have a system that's

doing a bunch of stuff. You need to add

a feature to it. The system doesn't

exactly support the feature. So your

options are rethink the system from

first principles, redesign it so it

supports that feature or just absorb the

hack for temporarily. And you can make a

judgment call on which one you do,

depending on how bad the hack is,

depending on how valuable to the company

uh it is to get this feature out. You

make the judgment call. That judgment,

that ability to have that judgment is so

distorted right now because the agent

will just do the hacky thing for you.

The agent will kind of deal with the

hacky problems that come down the road.

And it's way easier just to be like, "Oh

yeah, it's a temporary fix." So we're

shipping way more hacks in places where

we should have just rethought the whole

system from ground up or like redesigned

it to to and like refactor it to make it

more flexible. So I think our judgment

is just off. Does this have to do that

when I as an engineer, you know, preAI

like I'm making a hack, I know I'm

making a hack, I'm thinking about it, I

feel bad about it, I I feel bad.

>> Yeah, exactly.

>> But I do it anyway. But you know, I I

spend time thinking about it and there's

kind of like a little bit of prickle

there. And then when I do a second hack,

I remember the first one and I I feel

even worse about my and I can still

justify, right? after a while like like

there's feelings that especially

when you're someone who cares and the

reason you care is you have an

experience you've been burnt you know

you're you're you're placing landmines

for someone else maybe even for yourself

and is is it just that the agents a I

mean they just do it they don't have

feelings and they also surprise the

effort suppress the thinking they they

don't even tell you I'm doing hack

doesn't even know it's doing a hack it's

just following whatever the training

data is which is pretty lowquality code

on the internet right

>> yeah Exactly. I I think way is exactly

right. That prickle that feeling that

you get, it's like muted now cuz someone

else it's kind of like you've made

someone else deal with the problem. The

problem is still there and the the

landmines are still going to blow up on

you eventually, but like you're not you

don't feel that bad feeling as much

anymore. So your judgment is skewed.

You're not getting that feedback loop. I

I guess it's like, you know, like

there's always this like very relatable

story of the CEO who just delegates

stuff and then like doesn't understand

why things are terrible on the the the

floor and then like one day goes down

and like you know gets into like doing

the actual work and realize oh my gosh

these conditions are are terrible.

>> Exactly. versus the CEOs who were

hands-on and they tried to stay in touch

and do I don't know simple stuff like or

CTO's doing coding in the environment

like I think stripe CTO did this like

once for a week every few months and

then felt like oh this is painful let me

do that

>> yeah exactly like you need to I mean

just like you need to be using your own

product you need to be also like you you

need to feel the pain that the users are

feeling same thing with your code base

same thing everywhere so yeah I think

all of life is about having the proper

feedback loops in place and it's very

easy for those to go away and then the

third thing that you said in in this

memo was related to this one is we need

to spend more time cleaning things up.

How do you deal with that? And I mean

because you're also a founder, how do

you justify it? Because you know there's

this thing of like especially when

you're a startup, okay, you've just

found product market fit, but there is

this pressure to move move quickly and

cleaning things up. It will not get you

more customer love or revenue or any of

the stuff that you care about as a

business. Yeah, it's really hard because

every single day we wake up, there is a

thousand people yelling at us, telling

us to do XYZ things. There's a thousand

people telling us we're doing everything

wrong. Every single day, uh there is

like a competitive new product that

shows up. Uh very overstimulating. If we

let ourselves just get pulled by those

forces, I don't think it results in

anything good. And I think what's also

cool is cleaning stuff up is easier than

ever. Like you think of a new way. In

the past, I would realize, oh, there's

like a new pattern that we should be

using and we would just start to use

that stuff for stuff going forward, but

it was too much work to clean up all the

old stuff. But now, you can clean up all

the old stuff. It's like you can ask the

agent to go implement the new pattern

everywhere. It's very easy to clean up

tech debt. It's very easy to like find

new patterns and implement them across a

codebase. Very easy to clean up like

dead old patterns. And yeah, you're

right that there's no like direct uh

result of of doing that. I think you can

be a successful company without ever

doing that. The way I think about it is

there's a million ways to be a

successful company. There's all these

different companies that are successful

out there. If you had your pick of any

company you could work for, you probably

wouldn't pick 99% of them. So, I want to

make sure we build a place that we're

happy to work at 5 years from now. Our

day-to-day is hap we feel good. We feel

good working there every single day.

It's not this thing where it becomes

miserable to work there. We can't get

anyone good to join us cuz it's mostly

about slogging through like legacy

stuff. And in in this memo after you

listed all of these things and you know

you're basically saying all right we're

we're shipping a bunch of stuff that we

we don't need. We're not cleaning it up.

We're not thinking you actually said

something really interesting. The and

I'm quoting you again. The worst part

about all of this is I don't think we're

trading this off to move faster. I think

we're moving at a normal pace.

>> Right. Yeah. Yeah. It feels like we're

going fast, but then I look back and I'm

like like I don't know if we actually

are going that fast. And I don't think

we're going any slower than our

competitors. We're certainly not going

any faster than them. So if you're

trying to look at productivity, it's so

easy to trick yourself into thinking

you're being more productive. And when

you sit down and really look at it,

often times like it's not as crazy as

you you expect. I guess what you're

saying is, you know, don't be complacent

and accept but but like just be critical

and look at is this working? Like should

we should we slow down to speed up?

Should we focus on the invisible stuff?

Those kind of things. I I really believe

in just like preparing a lot and like

setting the foundations, right? And then

then when you spring, you spring with

like so much more force than you would

if you just forced it earlier on. One

thing I like about you is you you do

call out BS

when it it's a BS especially when it's

really trending on social media. Again,

you're you're a lot on X. This happens a

lot specifically. And here's a tweet

that I'll I'll read to you that you

you'll remember. It went very viral. Uh

so many people, you know, VC folks all

said like, "Yeah, this is the future."

And this is what it said. The 24 to 29

year old engineer will soon become the

most valuable asset in technology pre

because they have preai principles and

post AI speed and it's an undefeated

combo and people are saying like yeah

this is the future like this generation

who is not too old to have the old

things they they will be killing it

again they have no preconceptions of

what used to be possible and you did

call BS honest can you can you talk tell

me like you're thinking

>> well it's it's funny cuz like I'm just

so tired every single Every single day I

wake up and I open the feed and just

prediction after prediction. The

future's going to be like this. If

you're just going to be like that, if

you're just going to be that, everyone's

just like making stuff up, right? That

one's funny because that that's a very

classic post where it's like person like

me has all the advantages. Person like

not like me has all the disadvantages.

It's like everyone is just saying

mantras to themselves cuz the root thing

that's going on here is we are

experiencing a moment of great change.

Everyone is very nervous about what that

means for their own position in it. a

defense mechanism is to confidently

assert a future in which you're a

winner. And that's almost what every

single prediction that you see is

happening. You almost always trace it

down to companies like mine will be

successful, other companies will fail.

My job won't be replaced by AI, but

everyone else's job will be replaced by

AI. And everyone has some like

rationalizations for this, but the root

thing that's happening here is everyone

is scared and worried and not sure about

where things are going. And we are just

bombarded with predictions and

protecting their own psyche. Um, yeah.

I'm just tired of predictions. Like,

yes, we're going to have time of great

change. I just focus on like the next

day. Like, what can we do today? What

can we do tomorrow? I didn't know I was

going to be working on this a year ago.

I don't know what I'm going to be

working on a year from now. I'm just

trying to do the thing that makes sense

right away. Uh, this thing about like,

oh, young people have that that's that's

like always been a thing. Young people

have a lot of advantages. They have the

classic disadvantage of not having a lot

of experience, but having a lot of

energy. Uh, I have a lot of experience,

but not as much energy as a 20-year-old.

So, it's just the same thing that's

always been like, you know, it's And

it's funny cuz he like that one's

particularly funny cuz he said 24 to 29.

Why not like 18 to 25? You know, it's

just so it's clearly he falls in that

age range, which is why he's saying

that. It's just like Yeah, just it just

made up. And I think everyone's just

like nervous and worried.

>> I I think it's good to just be honest

about it because it's true. Like I I

mean I I'm nervous and worried as well

just because it's

>> the change is faster than before. And

this is talking with like the great of

the industry who have lived through the

Ken Beck, Martin Fowler, Grady Buch, and

they're all saying that they have seen

change like this. Like for example, when

they went from mainframes to to

microprocessors, but it was throughout

more like a decade, not a matter of a

year or so, or maybe we're now at this

point two or three years. And they're

they're all saying the same thing that

the change is faster, but but yeah, like

as as you say, like it's it's very easy

to to rewrite history and say like it

was always obvious, but it's just hard

to tell.

>> No, it's not. Yeah, it's so unobvious.

Everything that ends up happening is

always counterintuitive. there. There's

always people that like see it correctly

and like line themselves up properly for

it, but none of the obvious things ever

happen. It's like it just the end say

we're going to end up at it's going to

be so weird from our point of view now.

It's going to make total sense in

hindsight. So yeah, predicting is a lot

harder than people think it is. So far

with with open code, especially with

open code, you and the team have reached

really good success. What are the things

that comes to building products and your

engineuring principles that have not

really changed from from the early days

or or the ones that you've learned and

you've stuck to them and it it helped

make Open Code successful as well? Yeah,

I think for us on the product side, like

I said, it's very simple. You probably

only have one good idea in your whole

product. Get the user to experience that

as fast as possible. Well, it sounds so

easy, but if you go try every single

product out there, you will see a

million accidental steps they introduced

from the user hearing about your product

to like seeing the value in it. Very

hard to keep that minimal. Very hard to

like not let that creep back in. It's

like a constant thing. So, we do this I

do this thing where um like I have a

command that like runs up a new Docker

Docker container and runs open code

which lets me experience like the first

time experience. I do that like once

every two weeks and I'm always catching

stuff that we've messed up in that

process by accident. The job of product

isn't to just receive a problem and ship

the immediate solution. It's to absorb

all the problems and understand that

there's one solution you can ship to fix

50 different problems. Right? That is

hard. That is difficult. That takes

experience. That takes thinking. That

takes talking to users. That takes

talking to your team. That takes

understanding your codebase. A product

is a way to abstract a solution for like

many different issues, right? That's

always going to be hard and that is a

skill that you can infinitely get get

better at. Um I used to be horrible at

it. I'm okay at it now. I probably got

another few decades of getting better at

it. Yeah, that that's what I'm focused

on like how to build stuff that solve

problems well and AI has not helped me

even a little bit. What has helped you

there? what what has helped you to get

this like product sense better or or

like you know figuring out cuz it it

sounds like we we talked a lot about

thinking about reflecting about having

one elegant solution that can solve

multiple seemingly or unrelated

problems. I mean, it's just uh it's

years of experience in in situations of

right feedback loop, right? When I mess

something up, I get a literal human

yelling at me and like saying horrible

things about me. When I design something

poorly, I have to slog through the

codebase and like do something that

should have been easy that's a lot

harder now. Yeah. So, for us, it's all

about making sure everyone on the team

has those feedback loops. No one is

insulated. Um, when you're in that type

of environment, even for just like a

couple months, you just get so much so

much better. And I think a lot of

organizations,

uh, it's hard to keep that type of

situation as your company grows. I think

it's probably impossible. Um, so if you

mostly worked at larger companies, this

is probably the thing that you've never

fully experienced. Um, I've been at

companies where the product team would

want to like ship something faster, so

they'll cut stuff to get it out sooner.

The pain is not felt by them. It's felt

by their support team. The support team

deals with the angry people, and the

support team fixes things manually for

them. and the engineering product team

are like, you know, hey, we did it. We

shipped the feature and they don't

realize that there's like a a side

effect that they created. So, yeah, like

really being in that feedback loop just

makes you makes you better. Uh, I think

caring a lot about building something

for a lot of people. I think this is

another thing that uh you don't have to

try to build stuff for millions of

people. I'm not saying you have to do

that, but it is very interesting to have