# Replit's Strategy for Self-Driving Companies

**Podcast:** Y Combinator Startup Podcast
**Published:** 2026-11-07

## Transcript

I think with AI, everyone will have to go through this period of denial and then depression and then eventually acceptance.
Replit's like this iceberg where there's this visible bit above the water that's like, look, you just tell it in English what to do.
And it writes your code and then underneath there's all this stuff for deployment and sandbox.
I don't think we describe ourselves as vibe coding now.
We think Replit is the platform for the transformation for it to self-driving companies.
More than other companies, though.
I think you guys have been living in the future.
If you had to say, what's the real DNA of Replit?
It is living in the future, for sure.
So you guys basically invented Vibe Coding, right?
And when you did it, in 2024 this was, which sounds like only two years ago, but it seems so long ago.
Technologically.
How did you even know this was going to work?
Because it's obvious now that it did work.
But at the time, it seemed crazy.
It seemed crazy to me.
Well, for us, it was incremental.
It wasn't like for the rest of the world, I felt like a big departure from how people did things.
But we've always- You were already writing more and more of the code.
Well, no, it was sort of like the YC kind of ethos of looking at what your users are doing.
And just that will give you ideas on what to build.
Because you were already doing autocomplete.
We're doing autocomplete.
And so how much of the code were you writing before you went to like full vibe coding where you tell the thing in English what to do?
Yeah.
Where you're just generating code with autocomplete.
How much of the code were you already generating then?
I mean, you could probably say like 50, 60%.
Really?
So that's interesting.
Yeah.
Even before vibe coding, you were already 60% vibe coding.
Yeah.
And that was how you knew it would work.
But the user interface.
Because you thought we can make the 60%.
Why not make the rest?
Well, kind of.
I think that the thing that was very enlightening is watching some of our users code.
And so some users were doing 100% of their code using AI.
But the way they did it.
No, you're talking about once you released Vibe coding.
No, no, before that, before that, I'll tell you.
Now we're finished.
Okay.
So they would open Chachipati in one window.
They would open Repl.it in another window.
This is someone who's never coded before.
How could you tell that they were pasting code out of ChatGPT?
I invited some users to the office.
You would literally watch them?
We would watch them.
Okay.
How do you do this?
And basically, they would copy the code, put it into Repl.it.
Repl.it would produce some error.
They would copy the error, put it in the chat.
Yeah.
And then copy the fix, put it in.
And it was such a manual process.
And I looked at it.
All we have to do is make an API.
Well, we have to automate the entire process.
It wasn't obvious.
It wasn't easy because back then, AI models would do a couple of tool calls.
Oh, you know what's different?
The difference is it's not just you have to automate the back and forth.
You have to understand the errors.
Right?
Exactly.
Yeah, that's hard.
Yeah.
And you have to loop.
And the terminal condition should be that the program is working as opposed to.
Yeah.
That's still true.
Right.
As opposed to kind of like, we'll do one turn, right?
Because that's how ChatGPT sort of worked.
That's how most AI tools worked.
And so can you.
So this is loop indefinitely.
Go to completion.
Yes.
Right.
Which is, that's the new thing.
That's what Claude does.
Yes.
Yeah.
So that's what we demonstrated.
That's what Repl.it first demonstrated is, so we demonstrated a couple of things.
One is that you can run LLMs to completion.
Which I bet was news to the model companies.
They did.
They actually reached out to us and they were like, we're so surprised that Repl.it Asia works.
How did you make it work?
And the crazy thing, Paul, is that The harness circa 24 is 10 times more complex than the harness today because the models were less capable.
Yeah.
So as models get more capable, like what the kind of the engineering you have to do to make them become agents is less and less so.
Does that mean it's easier for people to compete with you?
Yes.
But we have to do, well, that's the thing about the AI business today is that you have to just accept that everything that you're doing is like, getting commoditized constantly.
And you have to like- You're doing the next thing.
Doing the next thing, right.
But the other big thing that we demonstrated is that AI can provision infrastructure.
So up until that point- And you actually have infrastructure to provision.
Yes.
That's the thing that's different between you and all these competitors, right?
Exactly.
So we had built like databases- You'd spent all these years suffering doing system software.
Right.
Right.
So it was a little like tool call, spin up a database.
You know, spin up a Redis, like host this, you know?
And so what people found so surprising when we launched, I mean, you saw the product July 2024.
And when did you launch it?
September 24.
Okay.
And so when people saw it, like Andrew Karpathy said, this was like an AGI moment, like a field AGI moment, because everyone was like, holy crap, like LLMs could do that.
It freaked me out.
Yeah.
I didn't like it at all.
How do you feel about it right now?
Because aesthetically, it was like very offensive for you.
Yeah, I know.
I know.
Because the way I program is the exact opposite of this.
I like, I program, you know, I spend three years writing a thousand lines of code.
And if I can get it down to 980 in the next month, so much the better.
But you give me a principle for all of this, which is when you first invest in a Repli, when you first backdropped it, you said there's a super linear relationship between how, easy it is to program and how many programmers you get.
Yes, that's not just true for programming.
That's true for anything consumers use.
If you make it 10% easier, you don't just get 10% more people, you get twice as many.
Yes.
Yeah, I remember that.
I didn't mean make it this easy.
It's like, it went too far.
I mean, just talk in English and it spits out all this code.
Well, so the realization I had is for Repl.it to be truly successful, we needed to go the whole way.
Because you did know you were one of the early people predicting AI.
Yes.
And like, were you predicting, you were essentially predicting vibe coding in something like 2015 or 17 or something.
Yeah.
2015, 16, in our original deck, we talk about how if you build a platform where there's like millions of people coding, you can like train a machine learning model that predicts.
I mean, that's crazy ahead of its time.
Yeah.
Right.
This was before the Transformers paper.
That was before Transformers.
I'm starting to think that's not the visionary.
That's just your nuts.
If you're that early.
Well, I think I've been fairly good at like predicting where the features.
I'm just joking.
You're not really.
Well, you know, even when I was a kid, like I remember when I used my first Nokia phone, I was like, oh my God, people are just going to be, this is like the new computer.
And it was like literally like a flip phone essentially.
And some of That experience also came to Replit, which is Replit was the first programming platform that had a mobile app.
And everyone laughed at us.
The idea that programming on mobile, programming on your phone, but I had an RFC for it.
Yes.
Not an RFC, an RFS.
Well, you actually, you told me you would like fix bugs using the like Hacker News, like Lisprop from your phone.
That's right.
Yeah.
Not voluntarily.
It was just the only way we get internet access.
But yes, since this like podcast is about like building the future, I think that people underestimate their ability to potentially predict where the future is headed and like build towards it.
You just have to like think.
Like sit down and think?
I don't think many people sit down and think.
No, this is what experts think.
Experts think, all I do is I just sit down and think and I have the ideas, right?
And everybody else tries sitting down and thinking and they don't have the ideas.
What's the secret?
It's because you've been doing this idea for like 20 years.
When did you first start thinking about making something like Replit where you could sort of program online?
Well, it's like when I was like, I had my Symbian smartphone and I was in class and I was like, you know, I wish I could program.
So how long ago was this?
This was like 20, 2008.
Okay, so almost 20 years ago.
This is why when you just sit down and think you come up with the future, right?
Because your whole brain has been thinking about this for 20 years.
Right.
Right?
Obsessively.
Yes.
You've been trying to, you were trying to build Replit before it was called Replit for like, you know, years and years and years, right?
This has always been the big thing.
Right.
So of course you thought of stuff before everybody else.
So you knew back to that famous demo.
right in the summer of 2024 when you called me a bond villain well you seem like a bond villain you know um that's what i felt like that i was being shown this frightening thing because i remember i still remember what you said don't look at that don't look at the code that's object code yeah right and i'm like oh no the stuff i the whole stuff that i make the whole stuff i've spent decades making is just object code and now everyone's realizing that uh like i think uh yeah i've been seeing like like engineers that were also repulsed by that.
I remember your tweet was very controversial when you tweeted that.
That guy who generated vast amounts of code using AI.
Yeah.
Well, even that tweet about me being the one villain and object code and all of that was also controversial.
But now all engineers are, you know, I think with AI, everyone will have to go through this period of you know, denial and then sort of later on, you know, depression and then, you know, eventually acceptance.
Yeah.
I've had it like in like 2020, you know?
Really?
And so it just makes it easier for me.
Like I had the grief of the craft that I love is gone.
I've already grieved.
You felt that too?
Yeah.
Okay.
So you knew what I was feeling.
I knew what you were feeling.
When you showed me that demo.
But you had to feel it so that you could let go.
I think everybody got convinced at the end of last year.
At the end of 2025 is when everybody realized, okay, AI is going to write all the code.
It's always surprising to me what is the trigger for these kind of things.
Because, again, it seemed obvious to me.
And for Replit users, I think they already knew.
For Replit users.
The thing that convinced the rest of the world might have been Cloud Code, right?
Don't you think?
I think OpenClaw.
OK.
I think Cloud Code partly, but OpenClaw, I think, was the first time people experienced this idea of LMs running a completion where you give it a task.
and it's able to go all the way until it's like...
Which you had been doing for a year and a half at that point.
Yes.
But I think the form factor of Open Claw as well being like, you could text it and all of that.
I think I sort of underestimate the power of a form factor, especially for everyday people.
Did it influence what you've done with Replit?
Did you learn anything from it?
Not yet.
I've just learned that lesson.
Yeah, I think it will influence things in the future, which is like, I just assume that I go out and like put out a vision and like build a product and everyone will be like, oh yeah, that's obvious.
But I think in reality, I think you need to tinker a lot with how people experience it.
Do you still watch users using Replit and sort of see what they're doing and are surprised by it?
Yes, I actually didn't like to do that.
But Haya kind of forced me to do it early on.
And I would like be kicking and screaming because I would like feel like intense pain when like users are confused by the software or hit a bug or whatever.
I would be like hesitant.
Like I would actually be, you know, physically.
It's mortifying to watch users use your stuff.
It really is.
But you know why it's mortifying?
Because till now you were in denial about the horrors that were being perpetrated daily out there.
Right.
But our company culture became a lot about being very close to our users, primarily thanks to Haya.
Kind of having that mentality of you just need to constantly talk to them.
She also made you very cool looking.
That was one of the really unusual things about Replit early on.
Amazing, like mind-bending graphic design.
Way better than some nerdy program online kind of company really needs to have.
And it helped a lot with our initial sort of audience, which a lot of them were like teenage hackers.
I thought it was really cool.
It made Replit different.
Yes.
You still have, Replit still, there's something very distinctive about it.
You know, it has a very, Replit, like a lot of very successful companies, it has a very strong sort of character of itself, which sounds meaningless, you know?
You know what I mean, though?
It's like a very sort of a strong solution.
It's not a weak, like in the chemical sense.
Yeah.
It's not a weak solution.
Replit is a strong solution.
Our conceptual...
understanding of our what we build and our product is we always think about like engines and like you know hard mechanical things right it is not that's a lot of what that graphic design was about yeah wasn't it yeah yeah it's like there's something that's sort of like hardcore about it but it's also paradoxical and that you know we we slave away so much to make the UX so simple for users, but on the back end, just like really ugly engineering is happening.
Systems programming.
Yes.
Like real systems programming.
Right.
Right.
That's part of your DNA.
That's part of our DNA.
But it's been always sort of challenging because.
a lot of programmers will look at Replit and dismiss it because it looks very simple.
But then even people who end up coming to work at Replit was like, oh my god.
There's so much stuff behind the scenes.
You do system administration for people's sins.
Yeah, I mean, we do it so they don't have to do it.
Do you think that will be the thing that helps you prevail over competitors?
Because that is one difference.
The depth.
of stuff that you've built and the depth of understanding you have of how to deploy programs online.
Yeah, so that is helping us win enterprise in our category because they are a lot more sensitive to reliability, security, all of that stuff.
It's really hard to get consumers to care about security, for example.
A lot of our competitors do really reckless things.
with regards to building apps that are on secure and so on.
Because if you have agents writing all this code, who knows what it's doing?
Yes.
Right.
Yeah.
And so we put a lot of guardrails, which makes our products sometimes slower, sometimes more expensive.
But enterprises...
really like that.
And so these newly arrived opportunists don't have the same sort of depth of infrastructure.
It's like replets like this iceberg, right?
Where there's this visible bit above the water that's like, look, you just tell it in English what to do, right?
And it writes your code and then underneath there's all this stuff for deployment and sandboxing and stuff.
Yeah, like we have kernel operating system engineers, we have...
Like we built our own like file system from scratch to be like transactional and fully reversible.
I remember hearing all these stories back in the days before Vibe Coding.
That's what all the updates from you would be up about.
We made our whole new file system.
I hope somebody appreciates it.
Yeah.
I mean, when you want to make like coding collaborative, like you had to invent a lot of core primitives that that.
So that's an interesting question.
Did the stuff that you did.
in version one of Replit, where it's just online programming and it's collaborative.
Did the stuff that you did to make it collaborative also help with Vibe Coding?
100%.
I mean, it's surreal how plug and play it was.
So when you put an agent into a Repl, as we call it.
It's like you're collaborating with the agent.
Yes.
So the agent is another user.
So the initial version was basically the agent was just another user on the same system.
And you had everything else built, just ready for it.
Yeah, and even you and the agents could work on the same project and it will just work.
So it's no wonder you discovered Vibe Coding first because you were perfectly positioned.
You had all the other ingredients, right?
I mean, this surreal thing, even the way the company was organized was so perfect.
So we had the infrastructure team that handles code execution team, right?
This is like sandboxing team, whatever.
And then we had the sort of the deployment and cloud services team, just like, you know, you can spin up databases and deploy VMs and whatever.
And then we had the team that was building the glue between the sort of infrastructure and the IDE.
And then we had the IDE team.
And so.
where does AI fit into all of that?
So we ended up creating a new agent team and we put it in the middle.
And all these people are building now for the agent.
So the infrastructure team is exposing tools for the agents.
The IDE team, instead of like building IDE for humans, now they're building edit tools for the agents.
Really?
What sort of IDE do agents even need?
I thought they just generated code.
Well, initially in...
In 24, editing code was actually a hard problem.
Even having it go back and fix mistakes.
Yeah.
So there was no native edit tool.
Now there's the native edit tool every foundation model give you.
But back then, we had to create our own edit tool when you give it a chunk of text.
how, where to replace.
You couldn't rely on line numbers because they would hallucinate line numbers.
There's all of these different things that- You had to make it ridiculously robust in the early days because the LLMs were so bad.
Yeah, they would also be lazy.
So they would write like a function and be like, get lazy in the middle.
It's like, to do, finish it later.
Which is not executable.
Yeah, that was- Although it is now.
That was insane.
And that was both OpenAI and Anthropic had the same bug.
And I think it's because of the chat model.
I think they always wanted the chat to kind of like halt early, like not like produce mountains of text.
So that was like an artifact of that.
And so we had to kind of detect that and like loop over.
So even editing text was...
was a lot of work and now there's like a native edit tool.
Is there anything like an IDE for agents though?
So in the initial days there was a lot of research on.
No, I mean now the way agents work now.
No, no, no.
Okay, now the agents just like generate the code and that stuff.
Now you can just give them a shell.
Okay.
You just give them a shell and they'll just write shell command.
They can do everything with like cat and basic shell commands.
Hmm.
They're very good at shout.
But you needed to have the IDE early on because the agents were bad.
Yes.
Right?
Yes.
And so that was one of the things that enabled you to be so early.
Yes.
Could you have done it any earlier?
No.
Because you got it just at the point where it barely worked at all.
Yeah.
And it barely worked.
And like when we launched it, it was in beta.
And I would say 50% of- I remember I would go over to your house and you would show me things and they would not work.
Yeah.
Like 50, maybe 50% is too much.
you know, too harsh.
I would say like, yeah, 30% of the time it would not work.
Okay.
So why would users tolerate that?
Because it was such a magical experience.
Oh, and also, presumably when they were cutting and pasting stuff back and forth between chat GPT, that also wouldn't work either.
But if they couldn't program at all, then programming 70% of the time was a huge improvement.
Well, the initial set of customers that started paying us a lot of money were people who would otherwise go on.
outsourcing sites and go like get outsourced programmers.
So now you either like pay like $100,000 to get an app done for like an engineer somewhere or you pay Replit like $1,000.
So it was like this two order magnitude difference still is today between like getting an engineer and like having Replit do it.
Yeah.
Arguably the whole existence of the outsourcing shops was evidence of a market there.
Yes.
Right.
Because I thought of them as just terrible.
Like who would ever want this, right?
Who would pay money to these people to make, pay vast amounts of money to make this terrible software?
Yes.
But instead I should have thought, wow, that's a sign of demand.
Yes.
Yeah.
I never thought, I looked at it like a programmer.
We try to satisfy this demand in a few ways.
Initially when we had all these like, you know, teenage hackers on the platform, we built something called Replit Bounties.
I don't know if you remember that.
I remember, I remember Replit Bounties.
And it was trying to.
I was like, okay, AI is not there yet.
They were going to become the new outsourcing shops, just like piecemeal as a bunch of teenagers.
Yeah, but like the insight there was that they're powered by the rudimentary AI we had on Replit back then.
But that in itself was enough of an edge that they have.
The people doing the Replit bounties were already using AI to write the stuff.
Yeah, exactly.
Oh, man.
Exactly.
And it grew really fast, but then I looked at it and I was like, okay.
If this is where the future is headed, that's like a dead business.
So like we shut it down.
Why would it be dead?
Because ultimately people were just asking agents for the entire thing.
And not even use, not even go through the teenage kids asking the agents on their behalf.
Yeah.
Okay.
I mean, that's the case right now.
Like we, you know, we go into big multinational corporations, banks, you know, things like that.
And they have a lot of like.
SaaS software they pay $10 million for or like old software.
And they find like a few Vibe coders inside the company that have adopted Replit and they're going and like replacing all these different systems.
And it starts with internal stuff.
It starts with internal stuff, yes.
And, you know, previously they would tell us that, you know, we've actually scoped this project out and it would have been $10 million.
We spent $250,000 on Replit.
And so it's happening all over the place.
And I think...
Really, the outsourcing industry is sort of dead at this point.
So is it mostly not very technical companies that you're talking to?
Like companies that don't specialize in technology?
They wouldn't call themselves tech companies, right?
We have a lot of tech companies as customers.
I think that that's not where we have the biggest impact.
Because I think they can fish for their own.
But the sort of the banks, the, you know, the...
Ordinary companies.
They need something like Replit, otherwise they'll shoot themselves in the foot.
And really it's driven mostly by people internally who are sort of ambitious.
Like some guy who's in charge of sales and wants to write some tool for his sales department.
Yes.
So this created a bit of an identity question inside Replit.
It's like, well, the enterprise, that's not part of our mission.
But what I tell my team is that those are like entrepreneurial people.
Those are like ambitious people, like, you know, ambitious people building businesses.
This feels like very core to what we do.
Those are scrappy people within companies too.
Exactly.
Even if the companies aren't scrappy, the people are.
The people are, and they tend to get promoted.
A lot of people who bring grappling to their organization.
will get promoted and because they are having great impact.
And also because especially now, all these companies are thinking, we got to use AI.
I keep reading how AI has taken over the world.
We ought to be using AI within our, who within our company understands anything about AI?
Who's using it?
And here's the person using Replit to make stuff.
They're going to be the ones that they trust.
Exactly.
So it's a continuation of that, but it is, there's a bit of like.
What do you call it?
Like schlep?
There's a schlep blindness?
Yeah.
Or just schleps?
Just like there's a lot of like boring work to like be enterprise ready.
Oh, that you have to do.
Yeah.
Well, you have practice doing that kind of stuff.
Yeah.
It's just like systems programming.
Right.
Is it like systems programming?
Yeah, a little bit.
I mean, a lot of it is also just like giving admins and IT a lot of like controls.
over what people could do and like setting budgets and you know.
Oh, they want to have rules.
Yeah.
Yeah, rules.
Yeah.
Well, it's sort of like sandboxing for humans.
Yeah, it's like the principal actor problem, right?
Like, you know, the end users are not actually decision makers for the buyer, like for who buys software, which is why I think most of history of, you know, tech industry, enterprise software has sucked.
But now, I think people have a lot more say on what they want to use and therefore products that are really good are winning in the enterprise, but that problem still exists.
Where sometimes IT and security will force you to make the product worse for the users.
Do you think the answer is give them what they want?
Like they want to do stupid things, let them do the stupid things.
Or do you think the answer is secretly So you're working on behalf of the ambitious, scrappy people in the company and you secretly want to give them a way to get around the bureaucrats.
Yeah, that's a tough one.
So, for example, you take a real thing.
I disagree with like rationing tokens.
So the way companies are kind of approaching this is saying everyone gets, you know, $100 of tokens a month.
What they should care about is ROI.
ROI, exactly.
And what we see from our customers is there's a...
power law of ROI inside companies.
So one person could drive 100x the next person.
I bet there is.
Absolutely.
There always isn't software.
And especially with agents, it becomes, it used to be 10x, now it's 100x, it's 1,000x.
Yeah.
You want to control token spend.
So what you need is to help the companies see where good stuff is happening.
Yes.
And I think there is some invention.
we could do there to help with that.
So in the short term, I think you kind of have to do what they're asking for.
Because that's how you get in.
That's how you get in.
Right.
And then you want to invent something better and then show them the way.
So there's more stuff to build.
There's more stuff to build.
This could be very valuable stuff.
Wouldn't this be valuable stuff?
Because when you think about what you're doing when you're trying to figure out which of the projects that people are using tokens for are the most valuable.
you're fixing the biggest problem inside big companies.
This is why big companies are the way they are.
They can't figure out the value of stuff done by individual people.
Right.
So if you build something that makes them even 10% better at that, you're fixing like the whole fundamental problem with big companies.
Right.
Yeah.
And I think that's a lot more than just vibe coding.
Yeah.
Yeah.
I don't think we're.
I don't think we describe ourselves as vibe coding now.
We wrote this blog post called The Self-Driving Company.
And so the idea behind it is a replete where early adopters of technology broadly, but definitely AI.
we're seeing that we're working differently.
Like there's fundamental shift in how we work.
You mean Replit as a company?
Replit as a company, which is customer zero of Replit.
Yeah, yeah, yeah.
So maybe you should figure this out yourself first.
How do you identify people doing valuable things?
Yes.
And so Replit as a customer zero is the lab we're trying to figure out.
So the self-driving company is sort of this idea of the future of work, the future of how firms are organized.
And we think Replit is the platform for the transformation for self-driving companies.
And so that's sort of the expanded vision of where Replit has had it.
Because ultimately, the thing that I didn't really appreciate and realize is, I've always known that making software creation more accessible would have transformational impact.
I didn't really appreciate that it would fundamentally change how people work.
Like because it seemed we were programmers.
It seems like what programmers write programs, right?
It's not like this the core of the way companies work doesn't consist of programming.
Yes, but maybe now it does it does.
Yeah You know, by the way, incidentally, this is an instance of what happens with the very best startups.
The very best startups, you know, you come up with something and you think you're coming up with some idea you suggested to them, they've already done it, right?
You've already got this big new transformation plan.
Because that's also, that's another thing about the best startups.
The best startups, they're already big.
And then they think of this new thing as if they were a brand new company, right?
And like they get 100x bigger, you know?
Well, I think it's now actually essential to do that because AI, like we talked about earlier, is a massive commoditization force.
So I think where it used to be optional to have to reinvent yourself, it's now mandatory almost.
And you're the way AI flows into companies.
Yes.
gigantic.
Yeah.
Yeah.
You just have to make that be not vague because anybody can say that, you know, anybody listening to this, there's probably someone at IBM thinking, yeah, that sounds good.
We'll say we're the way AI flows into companies.
Everything, everything we've kind of popularized ended up being copied.
Like, you know, we probably have this idea of like idea to app fast.
And it's like, yeah, only every startup that does like.
Five-putting is like that's their technique.
But they're copying the old version of you.
Yes.
You're a movie and they're copying this frame from like six months ago.
Yeah.
Yeah.
That's how it needs to, like you need to be constantly doing that.
But this means replets in every company.
Every company that has competitors.
Right.
That can't afford to just sit.
Yes.
That's the goal.
That's astounding.
Yeah.
So how do you do this?
I suppose gradually is the answer.
Yeah, I think there's always this temptations of like for founders to kind of like come up with this big strategy, make a big news about it.
And I'm just like, oh, this is what we're doing now.
It's like, no, let the customers tell the story.
Right.
It's more like you have this general goal of where you want to end up.
Yeah.
But then you do it incrementally.
Yes.
And what are the increments?
individual things that come up with customers, but you know what you're looking for.
You know you're looking for a customer doing a certain sort of thing.
Okay, let's help them out and then everybody will be able to do this.
And you're in.
That's the great thing.
This is what having done all that systems programming buys you.
It gets you into the customers so you can see the increments of the new things you're doing.
And similarly, like how Repl.Agent for the outside world was this this continuous moment for us internally was incremental in many ways.
I think a lot of things that we're doing at some point, we were like, there's this continuous moment in how companies operate.
It's so interesting.
If you're the experts, if you're the insiders, things that seem like step functions for the rest of the world, you saw before the step and you see after.
So it's smoother for you.
It's smoother.
And I think that's...
It's not all invention, but I think the history of invention, if you, you know, zoom in and kind of read the stories, like, you know.
Steve Jobs tried to do an iPad in the 80s, you know?
Yeah.
Because he knew that something like that would be good.
Yeah.
The technology just wasn't there yet.
Yeah.
And so when you read the story of Apple, it was like, oh, the iPhone was totally obvious, especially after it like the iPhone.
Yeah.
Yeah.
If it was so obvious, how come nobody else did it?
Right.
Right.
But like it was sort of incremental inside Apple.
But for everyone in the world, that was like a...
Totally transformational moment.
But also, if you know the story of how the iPad happened, or the iPhone, originally they were making this tablet, and he already knew what he wanted.
He'd been thinking about this.
And so as soon as he saw it, like, okay, this is it.
Let's make this, right?
And you too.
Whenever something happens, whenever something happens within a company, it's going to be something that you've been thinking about for 10 years.
And you're like, okay, this is the thing to build.
More than other companies though, I think you guys have been living in the future.
Don't you think that's true?
I think that's true.
That's almost, if you had to say, what's the real DNA of Replit?
It is living in the future, for sure.
More even than AI specifically.
It's often frustrating.
I think that...
Because stuff doesn't work.
Well, stuff doesn't work and people change so slowly.
So for example, cloud developments.
Are people convinced about that now?
Not yet.
Still, how are they going to use AI?
It's crazy.
Exactly.
So our prediction internally is that you go from everyone using one agent to 10 agents, to 100 agents, to 10,000 agents at some point.
You can't run 10,000 agents on your MacBook.
It's like there's no way you can do it.
Yeah, Robert proved that back in the 1980s.
Yes.
So eventually we're going to get away from local work in general.
But there's like the people still predominantly do work on their desktop.
Maybe this is what will finally convince them.
Yes.
Right.
That stuff has to live in the cloud.
Yeah.
Yeah.
But it's also fun.
I think it is, you know, working at Replot just ends up being sort of a...
Weird set of existence like you go into this company or like oh my god.
I'm the weird thing is you know what everyone's future is gonna be like because that was why you seemed like a bond villain When you were demonstrating what later and you plus that laugh You were demonstrating what later came to be called vibe coding and here I was sitting there looking at it with dismay and you're like yes, mr.
Bond This is this is the future I think you might even have been wearing a black turtleneck.
I was.
Not turtleneck.
It was a black shirt.
Okay.
Yeah.
The black shirt, the bald head, the beard.
Meet me sitting there like, oh, no.
What's happened to my beloved programming?
And you're like, I already knew.
I've been thinking about this for years, Mr.
Bo.
You know, I do think I have an ability to, like, kill the sacred cow in a way.
Like, I think I've always had this.
When computers got really good at something, I'm like, ah, all right, it's done.
Let's move to the next thing.
I'm not so tied to what I do.
I'm fine with throwing away things and starting over.
In my career, I've started over many times.
I remember you were always rewriting things within Reveille.
Yeah, rewriting things.
But I think most people, they're just kind of rigid.
about this is my identity, this is what I do.
I mean, they should read Keep Your Identity Small, one of your essays.
But I think this moment requires a ton of adaptability.
A lot of people ask me, oh, how do you raise children?
And what do you teach them?
And it feels like the main thing- What do you teach them about AI?
It's like flexibility and adaptability and like being a lifelong learner.
What I tell them- I mean, is that if you have this tidal wave approaching the beach, the safest place to be is surfing on it.
Not standing on the beach holding onto a palm tree.
It'll just get washed away.
And so that's what your users are doing.
Your users, to the extent anybody can see the future and be...
protected against bad things happening, your users are it.
Because they're all using AI.
Like if you want to use AI, Replit is the way to use it.
And using it is the best way not to be squashed by it.
Right.
And this is why we feel our mission is there's like a moral angle to it almost where you want to help these companies transform.
Get those people off the beach and get them all onto surfboards.
Right, exactly.
Exactly.
We did this hackathon with a very large you know, telecommunication company.
And they did this thing where they did a survey before they used Replit.
And the survey after they used Replit, and the survey asked them like, how anxious are you about AI?
You know, how worried and so on.
And so pre using Replit just for like an hour, like 80% were anxious and were worried about their jobs or whatever.
And after they used Replit, it flipped like 80% were optimistic and 20% were something.
For one hour.
One hour, yeah.
And I think people in Silicon Valley do not constantly underestimate how the rest of the world have not experienced what we've experienced just yet.
And it's just going to take a long time.
Because things are moving so fast, right?
things are moving so fast here.
Well, no, that's what I'm saying.
If they're just two years behind, that's forever behind.
Yeah.
Because two years ago, Vibe coding didn't exist.
Yeah.
I mean, most banks are using like ChatGPT like 2023, basically.
Like the thing that is rolled out at most banks, like other than engineering, I think they're like ChatGPT3 or something.
Yeah, it's just an LLM.
It's like barely connected to anything.
And so it's not that useful.
And so like there's like a real lag of what people use.
And Silicon Valley already went to these agents that are doing things for you.
But most companies are still trying to figure out what to do with all the legacy software that they have that they want to rewrite and so on.
And they have to.
They have to.
They can't just ignore it.
That's the landscape.
That's the software landscape within the company.
Maybe all this COBOL code will finally get rewritten.
It is starting to get rid of it.
Really?
Yes.
The COBOL is finally going away.
Yeah, yeah, yeah.
COBOL would eat up a lot of tokens.
Yeah.
That is not very powerful.
Early on in our AI journey, a lot of companies would be like, oh, this REPL thing is cool, but why don't you use it and come rewrite our COBOL?
I was like, we're not going to write your code for you.
We'll teach you how to use this thing.
But now there's a bunch of AI companies that do that.
That will come and rewrite your COBOL.
Yeah.
So there is one last question I want to ask you.
This whole REPLIT.
like for its entire history, the reason it has worked is because you're living in the future to such an extent.
Like you personally, right, are living in the future.
How do you do that?
Why are you living in the future?
Yeah, I mean, I sort of started answering some version of that early on in our conversation.
And I was saying, and you had a good point that it's not just about sitting and thinking about it, but I think a component of it at least is that I actively try to predict things and I actively revise my predictions and look at them.
And I think it's a skill you can get good at.
And there are some certain principles that you can follow.
Early on, I read Ray Kurzweil.
Ray Kurzweil's whole thing is if you look at, if you chart like a log linear chart of like all compute flops in the world, it's this like freaking straight line.
And actually, AI didn't bend it, which is crazy.
It's still a straight line.
It needed AI to happen.
It needed AI to happen.
It's almost a religious sort of thing.
It's like it'll happen anyways.
Yeah.
I saw someone tweet this.
He said, it's not that AI brought on the compute.
The compute brought on AI.
That's true.
Which is a fascinating thing.
Like deep learning would not have happened without the compute.
Everybody thought neural networks didn't work.
Back in the 80s, that was the thing about neural networks.
They didn't work.
But it turns out if you give them 10 billion times as much resources, then they start to work.
Yeah.
Who knew?
So that line, there's a lot of predictions you can make by truly understanding a line.
More broadly.
Okay, so the reason you're so good at living in the future is because you have normalized that line.
You assume that you're not living in a static world.
In fact, that you're living in an exponential world.
Yes, exponential world.
And a lot of what you talk about in your essays is understanding the exponential.
And I think once you truly understand the exponential, like you can look at an LLM.
I can look at GPT-2 and extrapolate.
a lot better than most people.
I imagine chats, I imagine agents, I imagine personal assistants, all of that back in 2019 when I first saw GPT-2.
So I think, but you just have to like believe and you have to, because most people have like this, almost like, you know, there's a suspension of disbelief, like suspension of, like you have to actually believe, you have to.
truly embrace that and go against your instinct to because like I think there's a human instinct.
Oh, there's all these reasons why this would not work and all people implicitly treat things as static.
Yeah.
And the difference with you is that your default assumption is that things will change exponentially.
Yes.
That's what's unusual about you.
Yes.
So having embraced that as a kid, like reading sci-fi and reading Kurt Ray Kurzweil and all of that, I think just you created two things in me.
One is an ability to accept change and like be okay with change.
Computers are going to do more and more of the things of my skills.
Not just accept, expect.
Expect and then expect and build towards that and escape where the puck is going.
And the puck is moving faster and faster.
It is moving faster and faster.
That's what it needs.
But I feel more like in my element right now.
Because...
Well, because nobody else can keep up with you.
That's why someone who's like assumes dramatic change.
Imagine who would be perfect for this world where everything is changing under our feet every six months.
The person who's assumes dramatic change.
Right?
You're just like perfectly suited to this moment.
That's why Replit's doing so well.