A Better End State than AGI? Replit CEO Amjad Masad on 1 Billion Developers

You know, what is special about humans and what's replicable in the machines. And at least in the, in the near term, my view is that AI is going to get really good at sort of two things, things that are highly represented in the data and things that you can construct a very good RL environment for. So what can you construct a great RL environment for? Like obviously with Alphazero games, right, games are famous for, you can have these self play sort of algorithms that develop over time. Now with reasoning models, you know, math is an environment that, especially with Lean, it's like a code, almost like an expression of math that can be executed. That's like a great RL environment, I think code execution as well. So running the code and then doing reinforcement learning on that and things that are already represented on GitHub and things like that. But there's a lot of other domains where we actually still don't know how we're going to make them better, like fundamentally new ideas, new knowledge. It's not entirely clear how we're going to get there. Can you use RL for these like more software things? Perhaps you create a word model, you can approximate these things. But I feel like the sort of the ideas and the creativity in the sense of like coming up with really novel things and understanding the world in very complicated, intractable way and coming up with an idea that could fundamentally change how things work or change the world, I think will still be the domain of the human.

Hello and welcome to Training Data. I'm David Khan and I'll be the guest host on today's episode. Interviewing Amdrad Massage, the founder and CEO of Replit. Amjad's vision of the future is a world in which a billion people on the Internet become developers. And in today's episode we imagine how these billion developers will reshape the economy, society, culture and more. A lot of people talk about AGI as a utopian vision of the future where people aren't working and there's universal basic income. But what if there's a different vision of the future? What if people are working, they're working as developers and they radically change segments of the economy that we never thought we could revolutionize. Things like healthcare, education, industrials and more. That's the topic of today's episode. Enjoy. I'm Jad. Welcome. Thanks for coming on the podcast.

Thank you. My pleasure.

You and I have known each other, I think five years now and I had the chance to invest in you four years ago at CO2, so I've been Able to see the journey. One thing that's been true for you since the first day I met you, and I think for maybe a decade, even before I met you, is you've been talking about this idea of a billion developers coming on the Internet, which is a bold idea. Maybe it's gotten more consensus as AI has come around. Maybe tell us a little bit about that idea. When did that intuition first hit you and how has that journey evolved?

Before we get to that, I remember, I actually remember the first time we met. It was in our Bryant office, which is actually kind of a home, a loft. We're in the basement, sitting downstairs. And so we worked, slash, lived in this, like, really small place in San Francisco. And in terms of the billion developers, it's just like, you know, ever since I, I was a kid, I started programming really early on. Like, my first experience with computers was when I was six years old. And like by seven, I was trying to make things with it. The first program I made was for my younger brother to learn math. And I've done such a good job at it that he works at replit today. So it just always felt like making software is the natural thing to do on a computer. And I was actually surprised that this is the domain of the expert as opposed to this, I think that anyone can do. And, you know, through thinking about why is that the case, it just felt like a lot of tools were complicated. Actually, they were getting more complicated over time. So if you think when I was a teenager kind of building a business, it was Visual Basic and I can like, you know, make an app and Visual Basic with database and everything, kind of shrink wrap everything into an exe and sell it. And then the web web came along and it just felt like a lot more complicated. It was more powerful. You can deliver things over the Internet and then the complexity didn't stop. Like, if you think about a JavaScript application today, but know there's, there's a lot of things you need to do. You need to spend perhaps hours. If you're new, you might spend days kind of setting up the development environment and learning all these esoteric things like, you know, what is webpack and what is transpilation, compilation and all that. And it just felt kind of worse than when I, when I started. And there was kind of no reason, I didn't feel like there was an intrinsic reason for that. There was all these perhaps social phenomena that made it so that programming is a lot more complex. And one is this decentralized nature of open source. When Open source took over and it was in Microsoft product managers kind of designing how programming should look like. But I felt like even then you can have the open source ecosystem being this decentralized innovation machine, but you can create experiences on top of that by mixing the best of the open source to create amazing experiences in the old language of open source hackers. There's the cathedral and the bazaar. And the idea of the bazaar is this complicated mess and this is where, you know, open source software lives. And the cathedral is like something like Apple or Microsoft where you're designing it top down. But I was like, well, that's kind of a false dichotomy. We can build cathedrals from bazaars, right? And that's been kind of the driving motivation for replit. And I felt like, okay, if you make programming something that more people can do by removing this complexity, a lot more people would want to use it. And the nature of computing changes because no longer is there's this big divide between what it means to be a developer and what it means to, to be a user of applications, which was the original vision for computing. So this was the drive behind that. And it just, you know, also the opportunities presented by being a programmer is kind of amazing. And I'm sure we'll get into my story about like the fact that I was able to make all this money when I was a kid, was able to get an O1 visa, get to the US and I felt that opportunity could be a lot more accessible to people.

Maybe take us just to start, like take us 10 years into the future or I don't know, you tell me how many years from now it is when there are a billion developers. What does the world look like, how does the economy look? I mean, I think you have this sort of imagination about all the ways that we're going to build software and the ways that businesses are going to be built are different. And to me it is somewhat of a utopian vision of the future. I know, Tell us a little bit about what that looks like.

Yeah, I try not to be too utopian, but a few things on that. One is the nature of essentially anyone can program. And most knowledge workers would want to develop or make applications or solve problems using software and AI. The nature of what it means to be a company kind of changes because if you think about companies today, we have these roles and we have these silos and kind of the way companies are structured are based on the sort of factory pipeline from the sort of industrial revolution. Actually if you look at society today, A lot of it hasn't really been updated since, since the Industrial Revolution. So the main innovate, the main collaboration slash, sort of work innovation from that era is the pipeline is the idea. You know, you do this one thing and then you pass, you know, whatever you're making to the next person. Eventually there's a car or a toy, whatever at the end of the, at the end of factory chain. And you know, society is sort of designed, but that's like the main design principle that we have. And so you look at the school, for example, you start at like, you know, kindergarten, you go to first grade, second grade, and it's like everything is like kind of created like that. And even in companies it's like, oh, you have the, you know, the, you know, the product manager, you know, creating a PRD and then goes to the designer and then goes to an engineer and then it goes to a release engineer and then the product is out there, goes to marketers, goes to UX researchers and then, and so always the silos and the, and this pipeline model, if you have generalists that can make, that can make increasingly more complicated things and can use computing to its true core and to solve problems, I think think you're going to have people in the organization that can solve problem across the board. So if you're someone in sales and you have an idea to drive more sales, you might spin up sort of like an SDR agent that does this one specific thing that's based on an idea that you have. There's no this separation. Oh, well, I need to go to my boss to go kind of hire someone to do the sdr. You can actually spin up an agent that does that exact thing that you want to do. Perhaps maybe you're on a customer call and a customer's asking, how can I do this X, Y and Z with your, with your product and perhaps your API or SDK. Now traditionally you have to go back to engineering and you have to ask them how to do that. And you have to kind of. But in this world you can spin up something like replit agent and you can say, well, yeah, prototype this thing for me and show the customer in real time. By the way, all the stories that I'm saying are real things from our customers. And so you could think about it as just a company is a set of generalist problem solvers. Solvers. And you see that in startups, right? We see that in startups, but I think that's going to be the case at scale. The other thing is the Way we construct software I think will change. So if you think about how we construct software, again, when you look within one company you see this like sort of factory pipeline. But also when you look at the economy in general, you also see the same sort of separation between different companies and how the products get made. For example, the supply chain, right? In software we have some sort of supply chain where you have like a, you know, database company and then you have a sort of infrastructure hosting company and then you have, you know, whatever front end company that's delivering actually goods and services. And I think the going from the industrial age to the network era, I think that the way we construct things will become more like a network. And so, you know, you can imagine the way software is constructed is if we have something like crypto, Bitcoin, stablecoins, things that are able to, lets people to transact without having to know each other, trust each other. You can imagine software being constructed where I am sitting in front of software agents and I'm going to say, okay, well I need to create this product. And the agents is going to be like, oh well, I'm going to go grab this database from this area, this you know, thing that sends SMS or email from this area. And by the way, they're going to cost this much. And as an agent, I actually have a wallet, I'm going to be able to pay for them. And when I'm going to publish my software, my software is monetized. And whenever there's like a dollar that comes in, it kind of flows through the entire network. And so there's this ambient services that my software agent is able to compose without necessarily having any sort of centralized system. And all these services are operated by hackers and people that are making money on the Internet. And so I think the nature of software itself and the nature of companies and perhaps the nature of economy would change when everyone can be a generalist software and AI agent creator.

Yeah, maybe taking that last point on the economy to its larger extreme, macroeconomics itself changes in it. A world where everyone's a developer. You have all these stories. A lot of your users are abroad, they're in other countries. You have users in how many countries do you guys have users in now?

Every country China is, is, is, is the, you know, is harder, but we have some, some users there, but basically every other country.

So how do you think like the global economy changes the way the economy works today? The people you're empowering, how does this change how these economies work?

So I, I think one Concrete thing. I think Peter Thiel talked about this paradox of the Internet where the Internet was supposed to be the great equalizer, yet it centralized all the wealth in Silicon Valley and this, this one, one area. And so why is the case like this thing that is purely virtual could have been and perhaps should have been much more decentralized. And there are all these things you can say about network effects and things like that. But ultimately I think that part of the problem is yes, we think about this technology as accessible, but it's not as accessible as we think it is. For example, there's a university student in India that was living in some rural area, but he was going to school to study computer science and that didn't have a computer or laptop. He had an Android phone at home and he would go on replit and start programming and learning how to code on his phone because we have a mobile app. And then he started picking up tasks from our bounties platform. So bounties is the ability for people on our platform to provide services to others on our platform. Thinking behind it is like we have a lot of people with time and then we have a lot of people with money and no time. So a lot of people with time and money and it's like obvious trade. So a lot of people learn the coin platform and they want to earn, so we want to present opportunities for them. And so we had an entrepreneur here in the US build actually a technical recruiter building a recruiting app. But he was running into problems and he was able to go on our bounties platform, hire this kid and, and for this kid to kind of fix some of those problems and for the entrepreneur to be able to ship his applications. So. And that kid made money, made more money than his entire family would make for an entire year. And so yes, the opportunities will get distributed. Obviously this is more providing labor services, but you can imagine it actually creating a company and creating something that could, that could be scalable and that way perhaps that this massive wealth generation machine that we call the Internet will be accessible to more people in the world.

Yeah, that's amazing. When I think about your vision, I think it's fundamentally this empowering economic vision. I think it's a vision of wealth, it's a vision of prosperity, it's a vision of giving opportunity to people like. And we'll talk about your story, but people like you and your kid, I'm.

Curious to question to you, like, what do you think is the macro? Because you know, you're more than a finance brain like, what do you think the macro implications of, of everything that we just talked about?

I think that if we go down this route of having a billion developers, the question is, what are all the sectors of the economy today that are not techified, if you will? They're going to get techified. And so you think about healthcare, you think about education, you think about these enormous percentages of GDP that fundamentally have not been touched by Silicon Valley and touched by Silicon Valley technology. And the thing that I think about is, well, the barrier to entry was high for these industries. And so in order to build some of these industries, you had to go sell to them. It was difficult, it was tricky. And so what were the lowest barriers to entry? Consumer, you and I can go download Facebook on our phone and E commerce, we want to buy things and it's pretty easy to make money buying things. And so you think about social and E commerce as sort of the initial engines that kind of get going as the economy was techified. And this is where the, you think about the wage rate for a software engineer as being a proxy for how valuable software engineers are. And so the wage rate has continued to go up. It's sort of one of these surprising things. You would think that as more supply of software engineers came into the market, the wage rate would go down. And what that tells you is the value that software engineers drive is actually higher than the wage rate. And so I think companies like Repl Dot, and as you see this next wave of developers come, you're going to have this incredible prosperity from the value that they're going to go create. And so the thing that excites me is what are all the sectors of the economy where we can go create this value such that the kid in India who's using replit is going to extract some of that value and create prosperity for his family. But also such that the consumer at the end of that experience, call it a healthcare experience, call it a government experience, whatever it might be, is also accruing value. And so we've seen that value creation. But to the extent that there's 100 million people, I think, who've created GitHub accounts today, and there's going to be a billion. And so you have this incredible increase. Um, I think there's a lot more to come.

There's also like a cultural impact, right? Like where Silicon Valley, you know, as much as we have this global view of the, of the world and we can, because we have so many immigrants, we can actually relate to a lot of those people. But there's limits to that. You know, I remember when I was working at Facebook we were redesigning the photos experience on desktop and we designed this amazing kind of vertical scrolling experience where everything was scrolling after the know iPhone came out and everyone was excited about it. And then we went and did an A B test and all the metrics tanked. I was like, what is this? Did we create some crappy product that didn't, didn't work? And then the UX researchers did some tests and then nothing came back as not working. Everyone was happy about the product. Then one product manager actually looked and dug into the metric and found that most like, most like, you know, the majority of Facebook desktop users use it on the, on the netbooks or like those, those laptops where it's like wider screen and they don't have a lot of vertical space. And that was mind blowing to me where everyone in Silicon Valley have these like amazing sort of MacBooks and, and so, so they're, they're limited to how much we can relate to and I think they, the products that we build is often not as suitable to these culture and creates this flattening of the world. Whereas when you have sort of innovation decentralized I think they're going to be able to create applications that are, that are more local, that can like benefit their, their communities and I think there's less of that today.

You've architected your product this way. Maybe you could. I think this is like one of the secrets of replit that people don't fully understand is how do you make a product? How do you make a coding product.

That'S actually good on mobile?

Right. Is sort of people think of this landscape and there's a lot of hard work that goes into that. How do you make it work on all these devices? Right. Maybe talk to us a little bit how you, how you've done that.

Yeah, well initially, I mean the main breakthrough of the, you know, open source project that become repl at the company was that you know, I was the first to compile a bunch of Programming languages to JavaScript to run in the browser. So this technology becomes wasm later on. But you know, I was on the ground floor. It was like a research project by Mozilla, it was called emscripten. And me and my friend were able to Compile CPython to JavaScript using this technology and we contributed a lot to it. We had to create sort of a Unix emulation layer in the browser. It was like very complicated project but it captures people's imagination. We put it up on Hacker News and went super viral and people got really excited about it. I remember one highlight at the time was Brendan Icke, the inventor of JavaScript, like tweeting about it. I was like, wow, we're like kids in Jordan building this thing and people, very important people got excited about it. And at the time I thought that was the best thing. The problem is when I tried to load it on phones they would crash, especially low end like Android phones. And uh, I even wanted to work on like these Nokia Symbian phones at the time. And it's because we were downloading tens of megabytes of, of JavaScript. Um, so it worked for a lot of people. People are excited about it. But the problem with client side execution is that a lot of people's machines are just not very good. Um, and so when we, when we went to, when I went to work at, at Code Academy and maybe I'm skipping a little bit in our story again, we wanted more and more people in the world to learn how to code and we started having users in Africa and other places like that and their computers would not handle tens of megabytes of JavaScript to download. And then so I started building the sort of backend execution environment such that when you're, what you're using on your phone is really a thin client and any code you're typing is just going to the server, executing and like coming back to the client and then the, you know, make it so that you hyper optimize the JavaScript application such that it's very small, it does incremental loading. All that stuff with React right now is really easy. But back in 2011 it was actually quite hard to do. We had to invent a lot of kind of web technologies to kind of make it, make it work. And you know, still to this day, like if you go to the App Store and download the Rat Flut app, it's actually one of the smaller apps on the App Store. It's less than 100 megabytes I think, whereas most apps is on the order of gigabytes.

Why do you think that is? Like the IDE market is one of the, I think the last markets to, to move to the cloud. And if you talk to a lot of Silicon Valley developers today, they still want, you know, the local thing on their laptop. You know, you cannot make me run this thing in the cloud. Whereas every other piece of software we've used, people have eventually given in. And you know, Google Sheets and Figma and all These amazing cloud native companies. Why do you think developers have been kind of the last, last market to move to the cloud?

What is the saying where like the shoemaker is walking around barefoot or something like that? There's a. At least in Arabic, there's the saying where, you know, people who make things for other people are often not satisfying their own needs. I think there's some of that and I think there's some cultural aspect like you, you know, you can't really, you know, I can't really pinpoint a real sort of technical problem that can't be solved or can't be mitigated that prevents people from like coding in the cloud. I think a lot of it is cultural. There's this sense of control over the stuff on my machine. And, you know, there's this old kind of joke in programming where progress in programming happens one generation at a time. So you need the old generations to sort of retire so that for the new generation, by the way, all the folks that are learned to code on repl and Codecademy and things like that, I think they're coming into the market now. They have no problem with coding on cloud products.

I love that. Another question back to the origin of the company. Did you first see that we were going to have LLMs and Sonnet and all these amazing models to actually transform coding back when you started the company? Originally.

So when I was working on at Codecademy and then I went to work at Facebook and before that on the open source roughlet, what I was doing is wrangling code. So I was writing compilers, transpilers, parsers, and those things are very fiddly and very complicated programs. And it just felt like, it just felt like this laborious thing that machines should be better at. It was like parsing this character and putting it in a node and trying to understand the structure of the program. And I read this paper in 2012, which I think sort of is an underrated paper that should be a legendary sort of on the order. Maybe not on the order, but kind of similar to attention is all you need. It's called on the naturalness of software. And this group, they're making the argument that NLP can be applied to code and they make a statistical argument about how code kind of is statistically like natural language. There isn't a lot of daylight between them. And so they build an NGRAM model and they use the NGRAM model to do completion. And by the way, you can scale N gram models like N gram models with the Original language models. And famously Google did this like trillion parameter n gram model that was like pretty good at translating the very, very simple things that's trying to predict the next character based on frequency. So they constructed that thing and then it was performing well. It wasn't performing as well as IDE completions, but then they used static methods like IDE completions to rank the completions from the language model and the result was superior and programmers rated as superior. And it actually did more completions than the static methods on their own or the classical kind of algorithmic methods. So at that point it just felt like, okay, we're, you know, that's the future. Like, you know, you know, deep learning and things like that were coming on the scene and neural networks and if this very simple thing is able to have such results, I'm sure you were going to have better things. And so try to do something like that. But it didn't work really well at the time. But actually when I pitched, you know, I think the CDAG is out there on the Internet, but one, one, one slide in it. I try to do like the Elon Musk master plan, which everyone does these days, but basically it's like, you know, the Elon Musk master plan, which really amazing. It's like, oh, we're going to build the roadster. It's going to be expensive. We're going to use that money to fund the next generation and that will create economies of scale and then so on. And they did it. Basically it was like, we're gonna create this website for hobbyist learners and teachers and we're gonna grow it that way, that way we're going to get a lot of data about how people use and learn programming. Then we're able to train machine learning models and it's going to be this like AI powered, you know, tool. And then that would be a tool that's like more powerful than traditional software, you know, tools, and therefore would let people come to the side, learn how to code, make things and deploy them. And that was like in 2015. Um, and so every year I would try to do something with machine learning. And the first time that I got a glimpse of something working was GPT2. Um, and there was a lot of experiments at the time. You know, a lot of the early community around GPT kind of had the feeling that, okay, this is working because you saw GPT1, you saw GPT2, it scaled even GPT2, there was like two sets of weight, one more parameters. You could tell that scaling is working. And so when GPT3 came, it was amazing. It was like, you know, groundbreaking event. For a lot of us, it was the ChatGPT moment because ChatGPT was not. It was like a UI innovation, perhaps some fine tuning, but it was really clear that it was here and it was coming. And I started orienting the company to take advantage of that.

Do you want to talk to us a bit about Replit Agent? I mean, that feels like a transformative moment in this company and almost like the whole company, you got these millions of users, you had this community and almost like the whole company was built for this moment. Talk to us a bit about Replit Agent.

Yeah, so every year, so we implemented Copilot like features. We're actually the first startup to train code models outside of Microsoft. And OpenAI reopened source them. And for a small time there were soda models, there were 3 billion parameters. And that was really exciting. But the thing that I thought would be transformative is agents. I actually had a thread that went viral at the time in 2022 about how software agents will change programming and perhaps the economy. And so every year we tried it. I remember we tried it with GPT3. The context window wrangling that we had to do was insane. We had to, every time we generate like a class or a function or whatever, we'd have to like summarize it like into one sentence and put it back into context to be able to kind of iterate. So that didn't work. 2023, you had auto GPT and things like that come out and you could tell like, oh, the models are getting better at like, you know, like being coherent a little longer, but it wasn't working very well. October 2023, I gave a talk, a TED talk, and I talked about how in the future people make software. And in it I talked about agents. I kind of predicted sort of test time scaling as well, where I was saying there's going to be a trade off between cost, speed and accuracy. And sometimes you want to pour more compute or time into a task and you're going to have these plans and the agent will iterate on these plans and build a software and you're going to be a human in the loop where you're the sort of the creative engine. It was the plan, it was the roadmap for what we're going to build. And 24, I think around the time 4.0 came out, it felt like we're almost there. And so I essentially put the entire company on that. And in fact we Actually did a layoff in May 2024, partly to kind of cut, burn and focus. But the other part is like I knew in my bones that that was the way to go. And we were doing a lot of other random things that were unimportant. So between sort of March, the first time I got a demo from someone on the AI team, Zen on our team, he showed me something and I could immediately feel like we're there. It was like almost like a baby, a baby kind of agent. So we spent seven, eight months working on it and then it just felt like we were on this treadmill fixing one bug after the other, you know, trying to make it coherent. The transformative thing that happened is like 6-24-4, when Sonnet 3.5 came out. And Sonnet 3.5 had two important properties. Actually the main property that people don't really talk about all that much is Sonnet was able to generate a, like thousands of tokens, like I think up to 32,000 tokens of code coherence code. Like so you can, it can one shot a repo almost. Whereas with GPT4O, when you're doing agents, you're writing one function at a time and then testing. If you remember the cognition demo, it's like, oh, they write one thing, they go to the browser, they write one thing, they go to a browser. And that was like, that was intractable, that was very, very expensive. And we thought that's not going to work. And the thing that's on it, bro, at the time is like writing high quality code and not being lazy about it. Because the problem with OpenAI is products were lazy. There's a real problem laziness where it's a, it, it like you asked it to do something and, and, and at one point it adds a comment and says the rest of the code here, bro, I asked you to make that program. Why are you asking me? Slack.

It is like a human.

Yeah. And so, so Sonnet was this huge unlock and we felt like we were going to be able to ship soon. There's always this tension, the quality and when do you own a ship? And so we set a deadline for my birthday, September 5th. And it was so contentious, actually at least one person in the team kind of quit because they thought we're not ready. And he was partially right. And we launched Agent and there was a lot of excitement because really it's the first software agent that works on the market. The very first. And people got really excited because, oh, you can try this thing and you can get a glimpse of the future. Oh, this thing can bring it in a database and like provision it for you. It can run a migration, it can deploy my app. But then as it escaped the kind of early adopters, a lot of people were disappointed because it was actually kind of crappy. And so we sprinted between September and then December, we had something we're really proud of and we exited at the beta. And although we're, we're growing well, then we, we grew really fast from there and people got a lot of value out of it. Now we have version two coming and it's rolled out to, to some beta users and we've done AB tests and it is, it is anywhere. Depending on the metric, we're looking at between 50 and 500% better. Wow. So it created 50% better deal retention, 50% conversion, 50% better engagement. But the metric that was really great was new users are twice more likely to deploy an app that they made with agents and power users are, you know, five, six times more likely to deploy an app that they made. And deployment of replit is very, very important because that's one of what makes replit great. It's that you can go from an idea to a deploy thing. And we see people who deploy are 10 times more likely to retain on Replit. So that metric we track very well. So now I look at V1, I'm like this crappy thing like we gotta, we gotta ship V2 as soon as possible and like on the team. And so we're starting to roll it out now and it's really, really good.

Can you give us some of the intuition for how you've gotten it so much better? Cause I imagine it's still Sonnet 3.5 that still seems like the sort of model that everyone likes right now under the hood. Like how have you maybe what's the behind the scenes for how the agent actually works and how you've gotten it to be so much better when the base models are very similar.

Yeah. So at the 3.5 sort of era, we had to build this like a fairly complicated multi agent system. We were talking about Langgraph earlier where you know, there was a state machine component to it and there was a lot of non AI state transitions that we're having to make so that the more you put into the eye, the worse decisions it often makes and less coherent. So context window was still very important because there are actually studies showing that although these models are kind of advertising a million tokens, actually after something like, you know, 32,000 tokens reasoning and a lot of benchmarks just like tank like crazy, right. And so there's a lot of context window wrangling. And so we had a manager and it had different context than the editor than the debugger because we had to kind of make sure they're isolated and have their different memories and things like that. Um, and you know, the tooling between all these things had to, had to be different. We built our own sort of protocol for the agent to call tools because actually tool calling was not very reliable at the time. It would like hallucinate tools or hallucinate arguments or. So there was a lot of engineering behind it to, to make it work. When computer use from anthropic came out. So that was 3.5 V2. The moment we laid our eyes on it, we felt that there's something important that wasn't getting marketed. That was, that was start of a, of a, of a transition which is they actually, it looks like they actually fine tuned it for long, for long horizon reasoning. Because if you want something to do computer use, you're going to have all these images in contacts and it's going to have to kind of continuously like click on things reason, think about what to do next, click on the other thing. And so you can actually roll out a long chain of tool calls without that big of a degradation and reasoning. And so we started rearchitecting the model of the agent to be, let's call it less, less multi agent and more single threaded because the, because the models are getting good at it and that's like a much bigger simplification. It added other challenges, but it's a much bigger simplification over the existing model. And so kind of the lesson to learn is that you have to constantly rewrite the systems because you want to make use of the next version of the. And you want to be able to predict what's coming down the line. When 3.7 came out, you know, Cursor had a big problem with integrating it. Everyone hated it on Cursor. I think they just recently fixed it. And the reason is because again 3.7 is more agentic. And so when you try to use it and that composer style request response, it is not very good at that. It's actually worse at that because it's trying to kind of make decisions that, but you're not giving it the space to, to kind of do this looping and, and so, and so you know, there's a really kind of tough lesson for engineering and product teams. If you over optimize for the present capabilities, you're going to enter a local maxima that is quite hard to move out of this like an innovator's dilemma problem on the order of months. You know, innovation dilemma used to be like on the order of decades. Right. Where you know, a company has an innovation, reaches a certain height of success based on a product that they made that people really love. But then there's a disruptive technology and they don't make use of it because it might be destructive for their current business where it cannibalizes their business and so they tend to not pay attention to it and fight it. The classic example is like Kodak had a digital camera product but they didn't actually launch it because I was going to cannibalize their film business, which was the thing that's making the money actually. And this kind of thing is happening on the order of months, which is really hard to wrap your head around. And so the move fast, break things is actually very, very important today right. More than ever. And you have to have, you have to be okay with your, with sometimes like delivering crappy experiences.

You mentioned Cursor. What do you make of the entire landscape? It feels like the landscape of coding tools has just exploded in the last six months and you know, developers are reaching for new IDEs. You know, many more people who didn't consider themselves developers before entering the market. What's your view of the market landscape and how do you think of as, you know, the ideal person that chooses replits versus one of these other tools?

Yeah, so I think there are incremental innovations and there are sort of more disruptive clean slate innovations. And I would put replit in the latter category. So you take VS code and you build an amazing AI experience on top of it. Really Cursor is like a fantastic experience. A lot of people on our team use it. Um, the, the thing is it is by definition incremental. You took a piece of software that, you know, Microsoft has been building for over a decade and you added this, you know, much better experience on top of it. But you're still, you're still kind of, it is still literally this additional layer with, with Route Agent. We actually took a clean slate approach to that and I was like, okay, what do we need to make it that people aren't coding at all? Like, you know, we want to kind of drive towards this vision of no coding. Not only no coding, no DevOps, no it like we don't want you to set up a database we don't want you to write migrations like writing migration is the worst thing ever. Like, you know, if people don't talk about this. But one of the worst things about building software is being able to keep your version control, your database schema and your deployment settings and configuration in concert in lockstep. And most, actually most outages. When you read the postmortems from Google Cloud or aws, it's like a configuration error and usually it's outdated configuration. The software changes, but they forget an environment variable configuration. And so when you have an agent actually do these things, this is actually much better at, at doing that. And so the system that we built for the agent is this sort of transactional locksep system that does, you know, these transformation one at a time. When you hit revert and Replit, it actually reverts the code, but also reverts the database changes and other environmental thing, environment variables, things like that. So there's all these things that we thought about where you as a, as a developer coming to replit, the thing you have to worry about the most is your ideas. You know, you don't have to worry about where do I get object storage and how do I configure my buckets. Right. Whereas when you're sitting in cursor, there's either someone in your company, a backend engineer worrying about these things and you're basically hooking into these APIs, or you're having to build them from scratch and like it would help you hit those APIs, but you still have to architect the larger system. So I think there's like a fundamental category difference between these things. These products are hooking into existing systems and require a lot of existing support around these systems, whereas Replit is trying to be the final tool you have to adopt in order to build a piece of software.

One thing I've heard from users, when I talk to them about relet, Agent and deploying on Replit, one of the things that people love is that you can go straight to deployment and that Replit does the whole thing. You start from nothing and you end up with a fully deployed system. Can you talk a little about that? How do you think the deployment space plays into this? You guys in some ways are not comparable to a lot of these other companies in the sense that you can go all the way on replit. How do you think about that last mile? Who cares about that and how does that play into the long term vision?

Yeah, so in the same way that I can say that cursor helps you with coding doesn't help you with the other stuff. Vercel helps you with hosting doesn't help you with the other stuff. Right. And again, it's like this back to the cathedral and the bazaar, sort of like a bazaar. And you need to kind of find all these tools and configure them and figure out whereas sort of Replit. I mean, another sushi analogy, there's Ala Card and there's Al Makase. Replit is all Makase. We're going to make. We think we have great taste and we're going to make the design choices for you. And it's not for a lot of people. If you want to make all these choices, don't come to replace it. I mean, there's a lot of other products out there. So in terms of like the deployment system, if you want to reach a billion people eventually, like making software a priori, most of them will not know how to set up a deployment environment. And therefore the replit product needs to have a deployment environment. Basically, that's how we approach product in general.

You know, in the early days of replit, there was always this narrative of it's students, it's young people, it's people learning how to code. It feels like in recent years that's really changed and maybe AI has enabled that. I was talking to a Netflix engineer who's telling me about how he's using replit. Paul Graham has obviously always been a big advocate of replit and one of the early believers in the company. How do you think the user Persona has changed and how will it continue to evolve over time?

How amazing of a visionary is Paul? Like, how can he look at a crappy toy like replit and just see that, oh, this one day could be really big? Or the idea that people are sleeping on mattresses and other people's homes, that's going to be like $100 billion company.

It's incredible.

Yeah, it is fascinating. He's also incredibly good human and he's been very supportive during very difficult times in the company when all those weren't. It was frustrating for a long time that, like, oh, Replit as a toy, hobby toy, students, kids, teenagers. On the one hand, I was excited by it because we had a group of users that are willing to experiment, that are willing to try to find the future of software. The future, what we just talked about, of, you know, being able to have an economy built into the software infrastructure. And we tried a lot of that and I think it's going to work. We still have like the main missing thing was the agent. But all these things are going to work and there's this co evolution that happened with these users that was very important. The other thing that that perception was important because sort of competitors weren't paying attention. They looked at replants like oh, that's a toy thing. Why would I pay attention to it? A very close friend of mine and someone who's looked up to in the industry visited us the other day and visit a lot of our friends and he was telling him he was using Replit to kind of prototype ideas for a new company and everyone was like well isn't that like the toy thing that I'm just working on? I was like well no, it's very useful for me and that's how I'm able to iterate on these ideas very quickly. So now yeah, the perception is changing and partly look, I mean we took explicit decisions to make the product more premium. Actually what's fascinating now is Replit is the most expensive product on the market. With Cursor composer I think one request is 4 cents. With Replit one request is 25 cents. And the reason we did that is like we just really want to build an agent, we don't want to build Codegen tool and if you want to build an agent it's going to be expensive, it's going to iterate and it's going to call a bunch of tools with every request. And so it did alienate some users and that partly also shifted the perception and kind of more professionals got on board. But if we're going to reach a billion people, we need to go down market again. But I think, you know, I think we started in one end and now we're starting another end and we'll meet in the middle I guess at some point. But I guess that Replit is the sort of the more roadster now and as we drive costs down by, you know, perhaps using more open source models and things like that, it's going to become more and more accessible to people.

What do you think of Vibes coding?

I don't really like this term. I think you shouldn't fight it because I actually didn't like Genai either. It's just, I don't know.

Same.

Yeah, I don't know, it just like cheapens the possibilities. It's like oh, this thing that generates things. Well actually you can build agents and not just generate things. We can actually reason and Vibe coding is. Makes sense if you're sort of starting from a position of Coder and you're Andrej Karpathy and you don't want to kind of worry too much about the code and you keep hitting enter, whatever. But if you're, if you're starting with RockLive, you're actually not starting from a position of code, you're starting from an idea. You're starting from an idea that you're iterating on and, and then you go in and the agent is unfolding this code in front of you. Actually, when you're using Replit Agent, you don't have the luxury to look at the code. We have another product called Assistant and Assistant is for more advanced people and that you can do more bytecoding there because it's like request response and you can kind of review the code and do all of that. But if I were to explain Replit, it's just Vibe, like don't quote vibe, not vibe coding, just Vibe.

You've always been a bit of a contrarian. One idea that's very popular now in Silicon Valley is AGI. There's this idea there's going to be no software engineers and you sort of have the opposite view. There's going to be a billion software engineers. Talk to how do you think about AGI? What is AGI in your view? And how do you, what do you, what would you say to all the people who say, I talk to a lot of software engineers and they're like, well, I'm not going to have a job. I'm worried about not having a job. I'm worried about no one's going to have a job. We're all just going to be on universal basic income and all this stuff like Replit is in some ways the opposite vision, right, of like, we're actually all going to have jobs, they're just going to be very powerful and we're going to be able to do all these amazing things.

I think it's like a fundamental, it's philosophical difference, like what, you know, what is special about humans and what's replicable in the machines. And at least in the, in the near term, my view is that AI is going to get really good at sort of two things, things that are highly represented in the data and things that you can construct a very good RL environment for. So what, what can you construct a great RL environment for? Like obviously with AlphaZero games, right? Games are famous for, you can have these self play sort of algorithms that develop over time. Now with reasoning models, math is an environment that, especially with Lean, it's like a code, almost like an expression of math that can be executed. That's like a great RL environment, I think code execution as well. So running the code and then doing reinforcement learning on that and things that are already represented on GitHub and things like that. But there's a lot of other domains where we actually still don't know how we're going to make them better, like new, fundamentally new ideas, new knowledge. It's not entirely clear how we're going to get there. Can you use RL for these more software things? Perhaps you create a word model, you can approximate these things. But I feel like the sort of the ideas and the creativity in the sense of like coming up with really novel things and understanding the world in very complicated, intractable way and, you know, coming up with an idea that could fundamentally change how things work or change the world, I think will still be the domain of the, of the human. And AGI will have AGI, but it would be a functional AGI, meaning it would do the jobs that a lot of humans are doing today by virtue of the training data being available and by virtue of some of these jobs having like ground truth that you can train on. And the reason I call it functional AGI is because it's fundamentally not general in that you can throw it in a supernova environment and for it to efficiently learn things, especially when they're, you know, when there's not explicit feedback and be able to, to be successful in that environment, which, which, which, you know, the definition of the universal AI, which it doesn't feel like we're trending that way, but I do think that you can reach something. So the definition of AGI at a lot of these companies is doing economically useful activities in front of a computer. Right? I mean, they, it's like a remote worker is what AGI. I feel like we're going to get there. But if I have a remote worker, I'm going to like create a hundred remote workers and I'm going to implement all my ideas and it's a tool, it's useful for me. Is it going to replace me? Well, if I am like a code monkey, it's going to replace me. But if I see my place in the world as someone who can generate ideas and create products and services because I understand what people want and how the economy works and all of that, I think that's still irreplaceable.

I want to talk to you a little bit about your life story. You touched on coming to the US the O1 visa, growing up in Jordan. Maybe start at the beginning for people who don't know your story, because it's a really inspiring story. And in some ways, your whole life has built up to repl. It. It's not just a company. I mean, your wife works at the company, your cousin works at the. Like, this is like your whole life and your whole mission all in. Bring us to the beginning. Like, how did. How did this all come together? How did you get to the U.S. how did you get into Silicon Valley?

Yeah, yeah. My. One of my first memories, just as a child. I don't know if you remember your first memory, but I remember very vividly that my father kind of getting this. This machine and, like, opening this. This box and, like, putting it together. And I was fascinated by it even before I knew what it does. And I walk over him and I kind of look over his shoulder, him sitting at the keyboard and sort of finger. Sort of typing. He had like, a big manual. Like, you know, you throw it at someone who could really hurt them, and he would. He was reading into it, but one by one, and he was like, finger typing. Cd mkdir, you know, these DOS commands. And. And I remember feeling that it was just, wow, this is like. This is a machine that you can talk to, like, you know, what is the dos? It is like a raffle. You know, this is where the Rappler name comes from. Read, eval, print, loop. Like, you can have essentially a conversation with this machine. So my father would go and attend classes to learn computers, and he would, like, you know, every night he would come in with all these notebooks. He's like, such a big nerd.

The apple doesn't fall far from the tree.

I. I'm different than my father. So my father's a Palestinian refugee. Grew up with, like, this intense focus on education. Like, if we're gonna make it, we're gonna. We're gonna have to be, like, the best educated. We're gonna have to be better than anyone else. We're gonna. So it's like a. A student and working really hard. My mom is sort of the. The opposite. My mom is like a sort of a spiritual. She was into poetry. She taught me a lot of poetry when I was a kid. And I was actually able to. It was like, there's an art to telling Arabic poetry. And so I was this mix of two things because I can be very intuitive. I can go on purely an intuition and take a lot of risk based on some kind of idea or vision or something that I that I feel, feel good about. And I also have this, like, very analytical mind. And I can sit and be, be such a, you know, pain in the butt about, about being so rigorous about certain things. And that's, that's really my father's inspiration. And, and so, you know, you know, one day my father comes home and I, I'm sitting in front of the computer and he was, he was mad. He put all his savings into this machine and, and, and I was like, you know, open the computer apart, took the parts out, put them back in. And I was like, no, don't get angry. I know exactly how to use it. I know exactly how it works. And I, I showed him how to use it. I showed him essentially how to create things, how to open applications that he's been studying all this time. And he was like, okay, this is fascinating, you know, all right, it's yours. And the first program that I wrote, I think I mentioned it earlier, was to teach my younger brother math. And I thought, okay, I can use this thing, this conversational machine. I still think about it that way. Obviously, with AI, that really happened and this idea that you can sit in front of it, learn something, play games, do something fun. And so that was the first program that I built. And then when I was a teenager, I was really obsessed in Counter Strike. So I would go to these Internet and land gaming cafes and I would like, you know, whatever money, scraps of money I have, I would like put it into that and just play, play a lot of Counter Strike and strategy games and things like that. I got very good at them to the point that it was like a source of income. I was actually winning tournaments and things like that, you know, so got into esports early on. So that's like another bench of my life. But one of those, one of the things I noticed about, about those businesses is they were running on pen and paper. I'm like, you have all these computers, you can just write software for it. So I did write this, like client server software that did accounting that gave people, you know, accounts and user, username and password and manage their time on the system and also did security so that people can't format the computers or do, you know, install malware and start selling that. And I made a lot of money on that. I mean, sold it to a lot of businesses in Jordan. By the time I got to college, I had this idea that AI is going to get so good, we're not going to have to write, write software. It just felt like software is this like Pedestrian thing that you do. It's like, not that interesting. It's just like you just do it to make things. And at the time, these wizards of code generations were coming out from Microsoft. So when I went to school, I actually studied more on the electrical engineering side, especially because my father thought that computer science was not a real field, because the Engineering association of Jordan would not admit you into the as an engineer if you don't have, like, you know, electrical engineering. And by the way, my father's the vice president of this organization. I didn't know that.

I learned something new.

He really loves that engineering organization. So. But. But throughout my college experience, I got. I got really into programming languages. I started reading programs, started reading Hacker News Program, wrote a lot on. On Lisp. He actually has a book called On Lisp. And Pulgram's view of programming languages is more of an art rather than science. Like, these artifacts are aesthetic artifacts and not just functional artifacts. And that also played into the reason to create replit, because I wanted to try all these programming languages, and there wasn't a place on the Internet to be able to try all these programming languages. And, you know, after I created REPLANT and had that breakthrough that I talked about earlier, it went viral in the US A bunch of ETECH companies started adopting it. If you remember 2010, 11, there was the MOOC kind of hype. You know, we have the AI hype now. There's a, like, blip period where there's like, the MOOC massive online courses, right? Udacity came out at that period. Coursera came out Code Academy, and I got a bunch of, um. They all started using replit, by the way. And I got a bunch of offers. I eventually decided, come to the US got an O1 visa, because a lot of my work was published in the news and everywhere else. And so it was. It was. It was possible to get an O1 visa. Landed in New York early 2011. The only money that I had was taken from me at the airport. And the reason is in the airport in Amman, I had like, perhaps $700. Those. Those are the money that I'm going to the US with. And the folks at the airport did not know what an O1 visa is. Apparently, like, no one in Jordan had ever gotten no one visa to get to the US and they didn't think it was like a. A resident visa. They thought it was like a visitor visa and that I needed a ticket back. And I was like, no, it's like, I can go there. I can Work, it's a work visa. And they didn't believe me. I was like, go look it up on the Internet. They didn't believe me and they made me buy a ticket back. And there was something like 500, 600.

Oh my God.

So I arrived there with like about a hundred bucks and My salary was $80,000. 70 or $80,000 in New York City, that's pretty vague. Yeah, well, not in New York City when your rent is like $2,000.

I think at some point you had someone offered to buy the company for.

A lot of money.

How did you decide to turn that down? That seems like that was a pretty big decision coming from this background. You grew up, you've made it to the U.S. you got this job, then you have an offer to buy your company for life changing amount of money.

Insane amount of money. We were like six people at the time. And you know, the numbers that were thrown around is between 500 million to a billion dollars. We're six people. And so it would have made me insanely rich. Right. So, and it was a tough time. I, I wasn't really happy about the culture then. Like we grew to six, seven people or something like that from, from the three that are our family essentially. And we hired people that I didn't like very much and, and the culture was changing and, and I kind of wanted to do a reset. And at the same time, my mom was diagnosed with cancer back in Jordan and I had to go back. The entire family had to go back, which is half the team.

Wow.

To spend time with my mother. And it was very stressful time. And the thing that made me, which is like the absolute rational thing to do is take the money, go home and, and live, I guess, happily ever after or something. But I felt two things. One is my dreams. I've had the dream of being in Silicon Valley for so long. The first time I knew about Silicon Valley is through a low budget movie called the Pirates of Silicon Valley where it's the dramatized fight between Steve Jobs and, and Bill Gates. And I was like, wow, this is a really valid place. It's like there must be like flying cars and like really like mass advanced technology. Of course you come here and it's like, it's like the suburbs, but, but I always wanted to be here. I thought the innovation I was really, I was reading about all these entrepreneurs. I, I, I felt like this is the most important thing and those people are heroes. And I felt like the weight of Silicon Valley also on my shoulders because Paul Graham you know, Mark Andreessen and other people that I really respect invested in the company and like, they all have like very high hopes for it and they're all really excited about it. And I felt like I don't want to let people down. And I felt like if I sold the company, it would have been, it wouldn't be, I wouldn't have achieved the potential of it. And, and, and maybe I would regret it in the future. And, and it's like, okay, being rich is, is good. You know, I think, I think money is actually great and, and improves your lives in many ways. Allows you to focus on, on the things you love. But, you know, what are you going to be? You're going to be another, you know, rich Silicon Valley dude. And there's a lot of them, you know, gonna like, write, invest, do angel investing. It was like your life is kind of a little boring and, and you, you never kind of want to take the pain again unless you're Elon Musk to kind of like go start a, start another company and put your life's force and energy into it. And so for all these reasons, we decided to, to turn it down.

Now you have 40 million users.

Yes. And we achieved the valuation over what we were going to solve for. And I think the company is actually underpriced now.

That's a lot of grit to get here from six people. I want to talk a bit about your management style. You have a unique management style. I remember seeing one time on the Internet you said, I'm now the VP of Engineering of ReSplit. I feel like over the years there's always been, you're a hands on leader. Your leadership style maybe has become more popular, but it wasn't popular six, seven years ago and you were doing this like, how did you develop your leadership style? What advice do you have for founders as they think about running their companies?

In some way it's a deficiency. You know, Jensen now talks, talks a lot about him not being sort of a great manager and him having all these reports and only, you know, talking to everyone in these big meetings and being, you know, giving feedback publicly, not really doing performance reviews in the traditional way and all of that. And in some way I would say it is a perhaps lack of skill in terms of like how to do traditional management. The management style that I have is similar to like how I would like lead a open source project or how I would lead a sports team back home. I've always been a leader and I've always been sort of a hands on leader with where it is this duality of micromanaging and trusting people. Like it is actually not at odds to, to do these things. And the most inspiring leaders both can go dig into details and give very precise direction but then like really trust people to deliver on those things and also trust people to have their own innovation, their own ideas. The way I managed the company from the start is I had a text file inside Replit. I used it as a sort of a notebook and the text file had everyone's names and the one thing that I think they should be working on or the one thing that I expect them to deliver on. And every week when I meet everyone, we would go around the table and I would tell them, did you do this thing? And it's either yes or no or something go right or something like that. And then the other question, what are you going to do next week? And so it's like, okay, they did that last week or they didn't do it. Why did they fail? What happened? And here's what you do, what they're going to do the next week. By the way, my execs still write that everyone in the company every week on Friday they write, here's what I got done this week, here's what I'm planning to do next week. And so, and so I can still keep in my head what most of the company can do or is working on. Like I can walk around and tell you this, this guy's working on this, this person is working on this. So partly is I can keep a lot of complexity in my head and I can like really be able to kind of make these, these, you know, very deep decision about how like sort of one button should work inside the product or how certain marketing ideas we should run. And so I can go between all these different departments and be able to go all in into the details and then kind of zoom back out. And also, you know, just having really high expectations if someone if week over week that person who said they were going to get that thing done and they couldn't, it's like an obvious reason to let them go. It's like not that complicated. And so Replit has an high attrition rate, especially in the first few months of people joining. 20, 30% of people are kind of either leave or get let go because they're not able to keep pace of the environment or they get confused about how to work in that kind of environment.

The other thing I think that's unique about your culture, I remember I attended some all Team dinner at an all you can eat barbecue restaurant. I don't know if you remember this. And there's all these young people around the table and some of them didn't even go to college, graduate college. I mean you seem to recruit for raw talent.

Yes.

And that's something that a lot of people talk about doing, they want to do but it's hard to do. How do you do that? How do you filter people? How do you find these people, how do you give them leeway, how do you train them? Talk to us a little bit about how that, how that happens.

Yeah, first of all, I am. You need to be able to work with weirdos and misfits and I was able to do that whether it's instinctively or whether I relate to them being sort of myself a weirdo and misfit and, and I can see talent. Certain people, like even back in, in my college years, like finding those people that like have hidden talents and being able to harness it somehow I had this feeling of like if, if someone has a talent that is not being put to good use, I feel like a sense of waste and like, you know, this, this needs to be harnessed somehow and I think that's, it's sort of a good management skill. But in terms of. So first of all you shouldn't have allergy towards these people. I remember my wife and co founder Haya when we hired Mason he was like 18 year old kid. He, he was, you know, I joke that he's a runaway kid from, from Santa Cruz, which is partly true. He left Santa Cruz in high school. He wasn't happy, he wasn't happy with his family, with the school. He went up to the city, he went to one of those boot camps. Those boot camps were using, were using replit, all of them and one of them was selling me bug, bug reports. I'm like look, I don't have a lot of people who are working on this. Why don't you send me your best engineers. It's like look, I'm going to send you this kid. He's really awkward but he's one of our best. So he came in and I remember Haya one day was like, oh, this kid like doesn't, doesn't open the door, doesn't keep the door open behind him. He like literally slams the door on me. He's like, well he's not really thinking about you. He's like thinking about code as he's walking around. Right. So, and still to this day I see people doing that. But the first thing is most people, I think, just reject these people outright when they can't communicate with them or they can't relate to them. And then the other thing, they're going to spike on certain things and then they're going to be not great at some other things. Right. And so, but you know, you can construct a team where it all fits together, where the peaks of someone is the values of someone else. Right. Someone is really good at, you know, shipping thousands of lines of code and someone else is really good at testing it and very methodical about code reviews. And, you know, if you have one who's like a cowboy slinging code and another is like a little more careful and like meticulous and rigorous and perhaps a little annoying of how meticulous they are, put these two things together, there's a lot of tension, but at the end of the day, you get a good product. So you want to balance the team in a way and you want to go to hire from places that other people aren't going to and that's. That gives you an advantage because everyone's competing over the existing talents on the market. So for us, a lot of it was going to replit, a lot of it was running hackathons or running sort of prizes on replit. And, you know, some kids win and we fly them out to SF to work with us and you know, we've had anywhere from 16, 17, 18, 21 years old join the team and even on the older side, people who haven't had traditional jobs before or were indie game hackers that joined the team. So yeah, I mean, it's a sort of a diversity of sources. If I am starting a company today, I would try to like find niche communities. This is where you'll find like some really underrated talent. Whether it's a niche community around a niche crypto project or a niche program language. That's where I would look first.

You mentioned your wife Haya a couple times. What's it been like building a company with your wife?

Depends on the day. We've worked together. We met at work back in Jordan. We were working at this company. We recruited, actually a foreigner, came to Jordan as part of a job from Belgium and he decided that he loved Jordan a lot. He loved the desert and he wanted to build a company there. So I was the first employee there and Haya was the second. She was a designer, I was an engineer. And he pitched us on this idea. We're going to do consulting and then we're going to build a product on the, on the side and that. And then we're going to become a product company. By the way, every company that has this idea never becomes a product company. I'm sure you've seen some of them. And so the company was very dysfunctional. He was non present. And so it was like the inmates were running the asylum. So Haya and I ended up like working on a lot of projects together just for fun. And then we started dating, which dating in Jordan is not real dating. Like go out for a cup of coffee. She had like a 7pm curfew and so, and so and then by the time I started working on, on the open source replit, she actually contributed the, the logo and a few other designs. She helped with a few other things. And then when I got the visa to come to the U.S. we, we got married and very young, I was like 24 at the time. And then she, she joined me in the US we continued working on projects the whole time. We worked on art projects, worked on games, all these other things. And then when the time came to start the company, I was kind of like looking for co founders because I'm like, oh yeah, YC says that you need to have co founders and I guess it's like best to have co founders. And I was like going on these co founder dates and trying to meet people and all of that. And then she was like, well I can, you know, I can, I can start the company with you. I was like, it's gonna be really painful. Like it's really, really painful. Like are you sure you wanna go through that? And she's like, yeah, yeah, of course. Like I saw you, you know, with Code Academy and like, you know, I can do that. Three or four months later she's like, I underestimated how hard this thing is. But she, she obviously lived up to the, to the challenge and I think overall strengthened our relationship because when I was at Code Academy and I was working 12 hour, 14 hour days, she didn't really relate. What kind of job requires that kind of commitment? And then with replit, we were both doing that. So it's not like your partner is going off in the morning, not returning until midnight on the weekend, they're wasted and they can't really do anything fun. And so you don't really have this, this great relationship with them. If you have a startup founder and you're kind of, you have a regular job but if you're both founders, you're actually going through this together. And I think, I think that ends up Being, being good. And, you know, at the time, it was actually working against us because venture capitalists do not like, you know, husband, wife, founders. There wasn't a lot of examples at the time. Obviously, the most famous example is Paul Graham and Jessica. But then now there's like, they get Canva founders that a bunch of other companies that made it work. But there are challenges too. Like, you know, right now we're fully, sort of fully committed to the company, but also we have kids. And so how do you manage this? That adds quite a bit of stress and pressure on us. And then the other thing is, when something is going wrong in the company, like, a lot of people can go home and can disconnect because they find comfort in talking to their spouse about other things. But when Haya and I go home, we're talking about the problems, and the problems kind of percolate and they end up seeming bigger and worse. And so you start to need rules around. When do you actually talk about work? And you're constantly breaking those rules, obviously, because you're thinking about something. So I think it is a challenge and I think it is at the same time. There's something very, very unique and good about it. Yeah.

Well, it's impressive how well you made. It worked and you guys have a really great dynamic.

Thank you.

All right, lightning round. We have some lightning round questions. Are scaling laws going to hold?

Yes. Scaling different things. We're just going to keep finding things to scale.

What is the best piece of advice you've gotten?

Pull. Graham asked me this question. I was stressed at the time and he told me, is this your life's work? Are you going to be working on this 10, 20 years from now? And I said, yes. And he said, well, why are you worrying too much about the daily tribulations if they just settle into the fact that even if things are not great today, you're going to be able to fix them and you're going to be able to turn things around, obviously, as long as you're not dying. And that's why Paul always talks about not dying and surviving as a company.

What is your favorite new AI app?

I'm kind of a Luddite because I make a lot of things myself and I use Replit to make them. I use the basics, like ChatGPT, perplexity, but then I spin up pieces of software every day to use. I guess Manus is an interesting demo.

Yeah.

It's actually pushing on this idea that we talked about of, like, how long can models work? While saying, while Saying coherence. And I think they showed that they can go for an hour with some coherence.

Since you brought up Replit apps, what's a cool Replit app that you've seen recently?

There are a lot of business things that are fascinating. One cool thing that I went to New York. I was meeting a few investors and customers and I met with Sears Home Services. That's a century old company. I didn't know it still existed, but apparently the home services department still existed. I met this very cool trendy team. They kind of easily work at a startup. They're working at Sears and they told me that six months ago they finished the Cobalt migration and it took them six months. And then I started asking them like, what kind of other tools do you use? Do you use any SaaS software? And they don't have any ERP software, any of like these modern SaaS, SaaS software. And they leapfrogged an entire generation of software to start using Replit to create agents to manage their business. That's really cool. Yeah. So they have these field workers that every morning they wake up and they're like, oh, how do I optimize my routes to kind of earn the most and service the most customers? So they built like an AI tool that actually gives them the most optimal route that they use every day, for example. Yeah. By the way, that that team is non technical. They're all operations people.

It's insane that you go from Cobalt to Replit and you skip everything in between. I mean it does. We were talking about this in the beginning of the conversation, but it gives you a sense of the sectors of the economy that are going to get transformed by this technology. Favorite book.

I am a Strange Loop. Douglas Hofstadter.

Yeah.

I actually disagree with the conclusion slash premise perhaps of the book, but it's still like one of the best books exploring concepts like AGI, concepts like consciousness and the soul and what it means to be a human versus what it means to be a machine intelligence.

What is the foundation model that you're you like most or most impressed by?

I mean Claude is the. Is the best. I mean 3.7 is really the best at doing agent stuff.

Person who's influenced your life.

I think my mom had this supernatural belief in me. Like she, she would talk about all the great things that I was going to do even when I was a very small kid. And she, she gave me this again, unwarranted at times, confidence in myself.

Recommended reading on AI.

You know, Twitter. Like it's really awesome. You know, I. I think it got degraded recently. But you. But. But, like, there's so many papers that you find on Twitter, so many snippets of information. Just like. Like, go read those papers, skim them, and I think you'll learn about what's coming down the line by just reading the literature.

Last question. Who is the most underrated person in AI?

I think Michele Catasta, the head of AI now present at Replit. I don't think he's very public, but he was one of the early pioneers of LLMs for code, and he's a great leader as well, and visionary around where AI for Code is headed.

Super impressive guy. I love every conversation I have with him. All right, well, thank you for coming on the podcast. I really appreciate our friendship. Thank you for doing this.

Thank you. I mean, I appreciate your belief in us, and I think you saw something special real long time ago, and, like, all these things that you notice about what's special about Replit, and I really appreciate that.

Thank you. We're still 4% there. 40 million users. We got 960 million to go.

Yeah.

Exciting times ahead. Thanks, Abjad.

Thank you, Sam.