Lenny's Podcast: Product | Career | Growth - Behind the product: Replit | Amjad Masad (co-founder and CEO)

Episode Date: November 21, 2024

Amjad Masad is the co-founder and CEO of Replit, a browser-based coding environment that allows anyone to write and deploy code. Replit has 34 million users globally and is one of the fastest-growing developer communities in the world. Prior to Replit, Amjad worked at Facebook, where he led the JavaScript infrastructure team and contributed to popular open-source developer tools. Additionally, he played a key role as a founding engineer at the online coding school Codecademy. In our conversation, Amjad shares:• A live demo of Replit in action• How Replit’s AI agent can build full-stack web applications from a simple text prompt• The implications of AI-powered development for product managers, designers, and engineers• How this might reshape companies and careers• Why being “generative” will become an increasingly valuable skill• “Amjad’s law” and how learning to debug AI-generated code is becoming ever more valuable• Much more—Brought to you by:• WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs• Persona—A global leader in digital identity verification• LinkedIn Ads—Reach professionals and drive results for your business—Find the transcript at: https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad—Where to find Amjad Masad:• X: https://x.com/amasad• LinkedIn: https://www.linkedin.com/in/amjadmasad/• Website: https://amasad.me/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Amjad Masad and Replit(02:41) The vision and challenges of Replit(06:50) Replit’s growth and user stories(10:49) Demo of Replit’s capabilities(16:51) Building and iterating with Replit(25:04) Real-world applications and use cases(30:13) The technology stack(33:48) The evolution of Replit and its capabilities(39:36) The future of AI in software development(44:04) Skills for the future: generative thinking and coding(47:26) Amjad’s law(50:36) Replit’s new developments and future plans—Referenced:• Replit: https://replit.com/• Cursor: https://www.cursor.com• Aman Mathur on LinkedIn: https://www.linkedin.com/in/aman-mathur/• Node: https://nodejs.org/en• Claude: https://claude.ai/• Salesforce: https://www.salesforce.com/• Wasm: https://webassembly.org/• Figma: https://www.figma.com/• Codecademy: https://www.codecademy.com/• Hacker News: https://news.ycombinator.com/news• Paul Graham’s website: https://www.paulgraham.com/• Jevons paradox: https://en.wikipedia.org/wiki/Jevons_paradox• Anthropic: https://www.anthropic.com/• Open AI: https://openai.com/• Amjad’s tweet about “society of models”: https://x.com/amasad/status/1568941103709290496• About HCI: https://www.designdisciplin.com/p/hci-profession• Taylor Swift’s website: https://www.taylorswift.com/• Andrew Wilkinson on LinkedIn: https://www.linkedin.com/in/awilkinson/• Haya Odeh on LinkedIn: https://www.linkedin.com/in/haya-odeh-b0725928/• Amjad’s law: https://x.com/snowmaker/status/1847377464705896544• Ray Kurzweil’s website: https://www.thekurzweillibrary.com/• God of the gaps: https://en.wikipedia.org/wiki/God_of_the_gaps—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [e-mail].—Lenny may be an investor in the companies discussed. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.lennysnewsletter.com/subscribe

The idea behind Replit is that making software today is very difficult. We want to make it easier. People view this as a developer in their pocket, essentially. We have 34 million users globally. There's people everywhere learning to code on Replit, building startups, building personal software, personal tools. For people building products, they product managers, founders. Like, what skills do you see will matter more, matter less?

Typically, your bottlenecked where your ideas are, not fitting in because they need to be made and they need to be made quickly. Now you open up that problem. So now actually makes you things is a lot easier. Actually, you become limited by how fast you can generate ideas. I think people are unaware of just how far things have gone. I could imagine, whatever, five years from now, someone running a billion dollar company with zero employees where it's like the support is handled by AI, the development is handled by

AI and you're just building and creating this thing. Man, the future is wild. Today, my guest is Amjad Massad. Amjad is the co-founder of Repliment, an AI-powered software development and deployment platform for building and shipping software. It's one of the fastest growing developer communities and AI products in the world. There's a lot of talk these days about how AI is changing, how products will be built, how product teams are going to operate, which functions will be more and less valuable over

time. But I feel like very few people have actually seen what modern AI tools can do, and have fully grasped how much you can get done with very little technical skill now and in the future. And so I'm going to do an experiment with this podcast where I'm going to do a series of behind the product episodes where we go deep on important products that product builders should be aware of and should probably start playing with. In our conversation, Amjad does a demo of what you can do with Replit today, which is going to blow your mind. And then we spend most of the conversation talking about the implications of this on the future of product development, on the future of product management, and on the future of startups and founders. It's a very exciting time.

It's also a very scary and destabilizing time for a lot of people. And my thinking is the more you are aware of what's possible today and where things are going, the better position you'll be in to thrive in this very wild and crazy future that is coming very fast. If you enjoy this podcast, don't forget to subscribe and follow it in your future. favorite podcasting app or YouTube, it's the best way to avoid missing future episodes, and it helps the podcast tremendously. With that, I bring you Amjad Masad. Amjad, thank you so much for being here. Welcome to the podcast. It's more pleasure. I thought it would be great to start with just having you explain what is Replit, what's the vision, where is this going, what job does it do for people?

The idea behind Replit is that making software today is very difficult, and we want to make it easier. One of the reasons for the difficulty is that it is very fragmented. So you would need to download what's called an IDE. That's basically a code editor. You need to download the runtime, basically Python or JavaScript. You need to figure out a package manager to configure your kind of open source packages. And once you've done all of that, you need to figure out how to deploy it, how to share it, how to. And so it's a very hard process. And that's one of the ways where people get stuck and never learn how to code because it just feels like this cumbersome IT process. And so the vision for Replisd has always been is like, okay, making software is fun is great.

More people should do it. But so for more people to do it, it needs to be easier to do. It needs to be in one place. And it needs to be learnable. It's easy to learn. And so that's the product today is, I think, one of the easier idees slash environment slash deployment environment on the internet. And I think we make it really easy for people to just jump in, even without prior experience

of coding, especially now with the new AI products that we built. This episode is brought to you by WorkOS. If you're building a SaaS app, at some point your customers will start asking for enterprise features like Samo Authentications. and skim provisioning. That's where WorkOS comes in, making it fast and painless to add enterprise features to your app. Their APIs are easy to understand

so that you can ship quickly and get back to building other features. Today, hundreds of companies are already powered by WorkOS, including ones you probably know, like Versel, Webflow, and Loom. WorkOS also recently acquired Warrant, the Fine Grain Authorization Service. Warrant's product is based on a groundbreaking authorization. authorization system called Zanzibar, which was originally designed for Google to power Google Docs and YouTube.

This enables fast authorization checks at enormous scale while maintaining a flexible model that can be adapted to even the most complex use cases. If you're currently looking to build role-based access control or other enterprise features like single sign-on, skim, or user management, you should consider WorkOS. It's a drop-in replacement for Auth Zero and supports up to 1 million monthly active use. users for free. Check it out at workos.com to learn more. That's workos.com. This episode is brought to by Persona, the adaptable identity platform that helps businesses fight fraud, meet compliance requirements, and build trust. While you're listening to this right now, how do you know that you're really listening to me, Lenny? These days, it's easier than ever for fraudsters to steal PII, faces, and identities. That's where Persona comes in. Persona helps leading companies. Persona helps leading

companies like LinkedIn, Etsy, and Twilio, securely verify individuals and businesses across the world. What sets Persona part is its configurability. Every company has different needs depending on its industry, use cases, risk tolerance, and user demographics. That's why Persona offers flexible building blocks that allow you to build tailored collection and verification flows that maximize conversion while minimizing risk.

Plus, Persona's orchestration tools automate your identity process so that you can fight rapidly shifting fraud and meet new waves of regulation. Whether you're a startup or an enterprise business, Persona has a plan for you. Learn more at withPersona.com slash Lenny. Again, that's with P-E-R-S-O-N-A dot com slash Lenny. What's the scale of REPL at this point?

How large has gotten? How many people are using it? We have 34 million users globally. We have a large global presence. There's people everywhere learning to code and replet, building startups, you know, building personal software, personal tools or internal tools of companies. More recently, we've been expanding to companies. We released our kind of B2B package in July, and that's been growing really fast.

It's been really fun to see people bring Replit to work as well. Damn, I knew it was popular. I didn't realize it was that large, actually. As I was preparing for this podcast episode, there's this Twitter. tweet that kind of that went viral where this guy Jevin, who I actually know, I know this guy from Canada, he's awesome, tweeted about how his 11-year-old girl build an app in Replit. She just, like, had an idea and she built it. And the best part of it is someone in the, like, reply to him. And they're like, like, you have to, like, launch an app. You have to, like, host it somewhere.

You have to build a database. You have to, like, deploy it. There's no way to that. And he's like, no, that's exactly what Replit did. Yeah, that's what we do. Everything that, that, that commenter was talking about it. And he's right, right? Like, it's, the surprising thing about an 11-year-old building an app is not so much even the coding. It is like all the nonsense around it. And so we just abstract all that away.

I love that. And that's, I struggle with that myself when I was an engineer way back in the day. Oh, you were an engineer. I didn't know that. I was. I was an engineer for 10 years. I was an engineering manager. And then I jump ship into product.

Wow. I'm happy I did, but I do miss that. I was. I was not an amazing engineer. I was like a good enough startup engineer. So this is the kind of stuff that I would have left to use. So we're going to jump into a demo of what this actually looks like. I thought maybe actually before we get into it, there's other tools that people are aware of that help you build stuff.

And so to kind of put a finer point on like what this does and how it's different from other things you may have heard of, say cursor comes up a lot these days. Just talk about like a little bit about the competitive landscape of who. who else is out there that helps you build product? Again, we go back to this idea of like end-to-end platform for making software. So that's like from writing code all the way to deploying it and monetizing it and all of that. Now, every step in the process of the software development lifecycle, there are a lot of different tools. So cursor is a fork of VS code that's made that has really awesome AI tools. But that's an editor.

You still need runtime. You still need a deployment environment. Actually, quite a few users use cursor in tandem of the REPLED because RETLIT just simplifies the runtime and deployment environment. And so you have products, AI products in different places in the software development lifecycle. But really, what differentiates REPLIT is that we do everything. But also, you know, that makes it harder to adopt for certain people. people, like if you're at a big company, it's very easy to bring a new editor and start coding with that.

It's quite hard to bring, you know, something that's quite opinionated about everything from the, from how the code runs to how the code deploys. But that's a tradeoff we're willing to make. It's like, yeah, we're not going to get into the enterprise, you know, main software development pipeline, but we want to empower everyone to be able to build software. And that means product managers, designers, we have operations people, sales ops, HR ops. We have lawyers using Replierp. And so it is democratizing the act of software engineering. Amazing. And that's why you're here. Let's do a demo. While you're pulling it up,

you're going to share your screen and show us what this product can do. And the reason I I'm excited about doing a demo, and this is an experiment, kind of a new type of podcast episode I'm doing where we're diving into specific products and what they can do. I feel like there's so much talk about AI and what's doing and people keep reading about, oh, yeah, I can do this and AI can do that.

And I feel like not many people actually see this stuff in action, especially the most cutting edge stuff. Like, I think people are unaware of just how far things have gone and how much is actually possible, especially when someone that knows what they're doing is using the product. So I'm excited to show. people what is actually possible, and especially because this is going to impact the future

of product management and product teams. So I'll turn that over to you. Give us a demo. Awesome. So this is Replitt's homepage. You can create what's called a REPL, which is a project. We have all sorts of languages you can you can pick from really in the hundreds. But most recently, and this is how Replit became like a thousand times easier, is you can just, describe what you want to make. So you go on this homepage, we have this text box, and you can write something like, make me a cool app or what have you. But, you know, a more descriptive prompt is better. And so I asked RPM at Replit, Amman Mather, who's a fan of the show, to tell me what PMs like to build. And so he came up with a prompt. He kind of really crafted a great prompt. So I'm going to just

put it here. And basically what we're asking for is we want to build a web application. You can you can say what stack you want to use or you can leave it up to the AI to decide. Here we're saying, you know, build it in Node.js for product managers to track feature requests on a public dashboard. So say, you know, I have a product I'm growing. I have a community. I want that community to engage with building the product. I want them to submit feature requests, vote on them. I want to be able to manage. that. So we're talking here about the features, avoiding system, feature requests.

Read a few of them just for folks that aren't watching on YouTube. You give them sense of some of the stuff in this prompt. So feature request submission, so allowing the users to add features, avoiding system, so allowing users to upvoting system, so allowing users to upvote these features, feature requests, and status tracking, being able to, it's like a canband style board with columns like planned and progress. So that way the admin can, can kind of share with the community with their building. And we want it to be user-friendly design.

So make it modern and all that nice kind of prompty things. And then admin controls for product manager. So, you know, as a product manager, I want to be able to kind of really manage this community. I love that it builds internal tools too, not just the front end. Exactly. Exactly. All right.

So we're going to start building. Since this is like a pretty big, big prompt, the initial coding might take a while. There's different styles of using Grapplet agents. I often go with like minimalist prompts. That's also

how I code as well. Like I have like a vague idea for what I want to build and iterate from there. Other people, you know, product managers like to like write PRDs and like more descriptive things. And you can do either of those things. The AI now responded

and then said, you know, I'll build all of that for you. I'm going to build up the initial prototype, and you can tell me how it feels, and then we can make it better from there. AI is also suggesting adding comment threads, implementing email notifications, and so I can select those, and it's being creative.

It's telling me what else I could build. But for now, I'm just going to go with a prototype, and then we can assess from there. So as you see, as the prototype is starting, you can see this progress pane where we can watch the AI doing its thing. So here it's created a Postgres database. Obviously, when we're building a full stack application, you need to be able to save things. So this is one of cool things about Replit.

We have all these services, storage, database. So now it's coding. It's building the database schema. Now it's building the home page. And it's actually quite fun. and edifying to watch it build us because you can really start to learn how to structure web apps. And, you know, if it runs into a problem and, you know, as things get complicated, it might run into a problem. And you want to be able to help debug and things like that.

It's good to be able to have an idea of what's going on. But it's not necessary. I think a lot of people just don't care about the code and are still able to build things. but we want to make the process transparent. You want to show people exactly what the agent is doing. You're basically sitting there behind an engineer on a computer and just watching them code is what the experience feels like. Yeah, and actually the way we built it is like it's a multiplayer system.

So Replit has real time what we call multiplayer coding. And we reused the multiplayer system to build the agent. So the agent in the code is structured as another user of the platform. So basically we're both coding together. So I can go into the files here. And that's the thing that makes Replit really cool. I think people are familiar with some of the more like chat interfaces, like V0 and others, where it's purely chat.

But this is like a full IDE where you can like go and look at the files and edit them yourself or ask the AI for an explanation. What's kind of the limitation of what this can do today? Like, what can't you do? Say you're like, you have zero coding experience. What sorts of products can you not yet build with something like this that might be possible in the future? How far does this take you now?

You know, you can build MVPs. I think you can also start to get some initial users. I think when you start iterating on the product, like large iterations, you might run into problems. For example, you know, it's not very good at database migrations. And so we're trying to fix that. So, you know, a lot of when you're iterating on the product, a lot of the times you're actually, you know, changing the structure of the app. And that requires database migrations. So now, like, it might change the database in a way that creates an error that's unrecoverable.

And at that point, you might get stuck, especially if you don't know how to code. Some people will figure it out by going to chat, GPT, and Claude and, like, asking questions. And, like, I actually am really inspired about how persistent some of our users are, which is really amazing. But I think, yeah, that's like you'll get an MVP, past the MVP where it's like a product that's working and you need to change it and iterate on it. It's still a struggle now. But I expect, you know, over the next few months,

we'll continue. It's like if you think about it, it's like sort of we're building, you know, we're building as you're building. So we're building out the agent so that it can continue getting better as our users are also building their applications. Got it. So what I'm hearing is it's really good at building like the first version and helping you get to something that you can even have people use. It's not amazing yet at evolving from there. Like using AI to help you make the product better and better and better and iterate. But you can get in there if you have if you know how to code and take it from there. Right. Yes. Or you can hire someone. We have a feature on the site called Bounties where you can hire human coders to kind of. help you or finish finish it for.

That's going to be our job for humans for, like that'll remain for a while. You know what we want to do? We want to get to a point where the agent can go grab a human when it runs into a problem. I think that would be sick. Oh, my God. It's like everything's reversed. I love it. Oh, look, I think it might be done.

Check that out. Yeah. So now the agent is asking us is the application running and showing the homepage. I like that. Confirming. Yeah, almost asking us to do QA. I'll just say yes. So it found an error.

So there's an error here. And it's like there's a DOM warning. I'm going to fix it. So in the meantime, as it's fixing it, so it can be proactive, right? Because it looks at all the errors and things like that. But in the meantime, we can use it. I just created an account.

It's coding. It's fixing the buck. That's cool. Yeah, restart. Okay, well, we'll wait for it. How long would you say it would take an engineer to build this like a, you know, like a typical engineer? A few days, I would say, to a week.

I mean, if you're really good at, it might be hours, but, but, you know, it probably would take me a few days. I would say I'm like a decent engineer. It'll take a few days. Yeah. And it took how much like five, ten minutes? Yeah.

And probably like cost us something in the sense. Wow. In terms of compute. Yeah, in terms of compute, yeah. Like probably, you know, I would estimate it like 15 cents or something like that. Wow. Okay.

So here it is. Here it is. And the agent was like, okay, this is looking good. Completed it if you want to deploy, deploy it. But I'm like, okay, I'm going to test. at first. And so currently it's living just locally on your local host. Yeah, it's not local host, it's so a Ruppload, but yes, it's the equivalent of local host. Because it's really easy. I can even invite you to this session. And you know, you can be here with me. And so it's all

online. So let's admit a feature. So make the product prettier. That's what a typical user might say. So we have this here. you can upvote it. I guess I can't upload it because I'm the user that created it. But I've created another user, you can upload it. But now, you know, we need to be able to move things around, right, as the admin. So I don't know how to log into the admin panel. So I'm going to ask the agent, how do I log in to the admin panel?

So it might have already built the feature and it's not exposed in the right way. it'll be able to. What I love about, just like watching you interact with this thing and just real quick all throughout, it feels like an engineer that is behind the scenes building this thing, like on Slack.

And you're just talking to them. They built this thing. They're like, I'm going to check this out. I'm done. And you're like, oh, okay. But how do I log into this admin panel?

And they're like, okay, here you go. Yeah. So it says, it says, you know, it's going to, would you like me to help you register account? So it's creating an account,

an admin account for me. So it's not only builds things, it's also, it also maintains things, right? So in this case, it's actually doing SQL queries. It's not writing code to create an admin account for us. It's insane. I want to talk about the implications of this on product development and product management and founders, but just like what we just witnessed is somebody, I know you do have technical abilities, but someone that didn't have to,

didn't have to have any technical skill build like a real product that people can use in five minutes. That looks good and works. And you could keep making it better by talking to this agent. I'll tell you from our experience, like what we're seeing.

Like, you know, there's so many products that are empowering developers. Like, it's a very easy calculation to say we're going to make engineers 20% better. and we're going to like sell it to companies and we're going to, you know, take 10% of that value, right? Like that's why there's so many startups now that are just trying to make engineers a little better. Our calculation is like, well, you know, what if you made everyone a developer? Like, what does that, what does that look like?

And so when we released the agent and really made programming a lot easier, what we're seeing is that people, exactly like you said, people view this as a developer. in their pocket essentially. What we're hearing from customers is that I'm doing things I would otherwise have to go hire a developer. But also because the activation energy is lower than going to hire developer, whether upwork or other places, I'm building a lot more ideas that otherwise I wouldn't have built. So, you know, it is, I think it was called the Javelin's paradox or something like that, which is like when the cost of things go down.

the total consumption of it goes up, which I'm not sure why they call it a paradox, but the cost of electricity goes down. Maybe you would expect that, the total spend goes down, but actually total spend goes up because people consume more of it. And so I think that's going to be the case of software.

Like as the costs go down, people will just like make a lot more software to improve their lives and to improve their work. and start more startups and all of that. So to follow that thread, what are you seeing inside of startups or even big companies in terms of how folks are already using this?

Knowing this is like the worst it will be and it will only become smarter and better. Right. These days, how are people actually using this, say, that are product managers or just like non-technical people within startups or bigger companies? On the SMB side of things, a lot of people are building kind of back office tools, right?

So we have real estate agents that have a lot of data, have a lot of things they want to manage in their business, that are building a lot of these tools that they otherwise would have to buy. But typically when you buy, it's actually not exactly what you need. And that's kind of the problem with SaaS. It's like it's like one size fits all. And so a lot of people are seeing it as sort of a SaaS replacement for in-house tools and things like that. And then when you go to the bigger companies, it's anywhere from prototyping to actually production apps to tools as well. So we've seen product managers build, like I said, like a V1 of an app and actually go out and test it with users.

And I can't name the company, but there's a public company that have, user applet to test a V1 of an app. And obviously, after that sort of works, they take it to the engineers and they're like, okay, we built this thing. We think it's a great thing. We test it with some users. Let's go actually put it on the roadmap and built it and build it into the actual product. So you are sort of unblocking product managers from having to need engineers for everything

that they want to build. So they can really build the V0 or V1 of the product. And that's super empowering for them. We saw it also with like marketing departments. Like Spot Hero has a marketing, head of marketing that actually can code decently well but use Replit to build as apps. And they built like a competitive analysis application that looks at, at competitors pricing and make sure that they are benchmarked correctly.

And so it's a full stack app, use database and everything, and it runs on a continuous fashion. And we see sales engineers use Replit to spin up prototypes really quickly. So actually someone at X, formerly Twitter, is on the sort of partner engineering side of things. and he uses Replit agent to spin up applications and prototypes for customers to see how they can use the X API. I love this. I love these examples. By the way, the demo, is there anything else you want to share about the demo before we close that out? So it created an admin account.

We can ask it with the username, password, and kind of go into it and manage it. But basically, that's it. The app is complete in terms of what we ask for. we can like send it out. I can give you a URL. Let's actually just deploy it really quickly to show people how you can deploy. Maybe in the show notes will link to the app. You could check it out.

Sounds good. Okay, cool. That's amazing. So this is deploying it onto some like cloud provider. I don't know what you use, but. We use Google Cloud. Okay. So we abstract all of that away from you.

But we use Google Cloud behind the scenes. Imagine a place where you can find all your potential customers and get your message in front of them in a cost-efficient way. If you're a B2B business, that place exists, and it's called LinkedIn. LinkedIn ads allows you to build the right relationships, drive results, and reach your customers in a respectful environment. Two of my portfolio companies, Webflow and Census, are LinkedIn success stories. Census had a 10x increase in pipeline with a LinkedIn startup team. For Webflow, after ramping up on LinkedIn and Q4, they had the highest marketing source revenue quarter to date. With LinkedIn ads, you'll have direct access to and can build relationships with

decision makers, including 950 million members, 180 million senior execs, and over 10 million C-level executives. You'll be able to drive results with targeting and measurement tools built specifically for B2B. In tech, LinkedIn generated a 2-5x higher return on ads spend than any other social media platforms. Audiences on LinkedIn have two times the buying power of the average web audience and you'll work with a partner who respects the B2B world you operate in. Make B2B marketing everything it can be and get $100 credit on your next campaign. Just go to LinkedIn.com slash pod Lenny to claim your credit. That's LinkedIn.com slash pod lenny.

Terms and conditions apply. Let's go down this thread actually while this is happening. Just like what allows for this to be possible technology-wise? What is the kind of the stack, whatever you can share that enables the. to exist. Yeah, for sure. First of all, it's all the abstractions that we built. So the way Replit works is, you know, at the very bottom layer, it's our runtime.

So this is the operating system. This is the package manager. This is the language runtimes. We built a system that is able to install packages in any language, including native packages. So the AI anytime it needs a package, I can go here and show one of those. By the way, the AI can take screenshots as well so that it checks it work. So here you can see it's taking screenshots to make sure the homepage is rendering. Here you can see, you know, it wanted to drag and drop library. And so it installed that. And so it has access to all the packages across all

languages, including Linux and all of that. And then the layer on top of that is the editor and the infrastructure that runs the editor, including what I described as the multiplayer editor. And then we expose all of that infrastructure to the AI. And there's almost like a new discipline called AI computer interfaces. So sort of like HCI is now ACI. And turns out like LLMs need interface. that are actually quite different than humans.

They're trying to make them use human interfaces like Anthropics computer use, but those are really expensive and you need to kind of process all this images and video. So instead we, you know, for the shell, for example, we give it a, you know, a sort of a text representation of what the shell is doing at a certain increments. For package installation, we give it a certain tool for editing. we give it like an editor tool that when it's writing the code, it's getting feedback on whether there are errors or not similar to what a human sees, but it's actually like old text just to make it easier. So that's AI computer interface. And obviously all of that is sitting on foundation models.

So the improvement in foundation models has allowed us to build this. the most important model that we use is the Sonnet model from Claude, from Anthropic, and it is the best model at coding. So that's the model we use for coding. But we use models from OpenEI as well because it's a multi-agent system. And so we have models that are critiquing. We have manager, editor model, and we have like critique model. and different models will have different powers.

We also train some of our models, like the embedding model for search, is something we trained internally. So, you know, I actually wrote about it back in like 22, I said it's going to be society of models, like products will be made of a lot of different models. And, you know, it's quite a heavy engineering, engineering project.

to say the least. We were talking offline and you said you've been working on this since 2009 when you first built the first idea of Replit. Is that right? Yes. Oh, my God. Here's the deployed app.

I can send it to you and you can use it. You can see my request even on the logged out page. So I can register, upvoted, log in as admin and move things around. We can see what's in progress, what's completed. This looks like a product. I could see designers, spending like days to, you know, designing.

Yeah. Passing it to engineering, PMs, you know, having feedback. Engineers taken a few days to build it. Yes. And here's just a prompt. Here's what I want. That's right. That's right.

And we can iterate on it very easily. We can also iterate on the UI. We can say, you know, I don't like this or that. And it'll do a good job. So we can go here. We can start a new session or like a new session to create an entirely new feature here. And it'll just. do the right thing.

And it builds from that code base. It understands. Here's what you've built. I want to add this thing. Yes. Okay. And that becomes your sort of your history, right? Like this was the V1 and now I'm working on this new feature.

And, you know, and, you know, it's almost like what engineers do in Git commit messages. By the way, it generates get commit messages for every, for everything that it does. So you can roll back as well. And so we're trying to make it so that, yes, it's for everyone, but we're trying not to abstract too much away. We want to build tools, right, for you to learn to use. And so we want power users to be able to understand the full power of Rapplet. And it's really deep product.

I think you can spend a couple of years to kind of master it. I want to talk about implications, but I want to come back to something you mentioned that is incredible that people may have missed. You basically built a computer specifically designed for the AI agent to use. That is a different version of a computer, specifically optimized for how AI wants to use a computer. Yeah.

Yeah, yeah. So, you know, there's an entire discipline called like HCI, right? Like it's like how to do that. Yeah. So now there are papers about AI, computer interfaces and interactions. And so large language models are trained on large tax corpus from the internet, but they're still like kind of alien creatures.

So they're not like humans. So they have different behaviors. It's unclear what's the best way to give it an editor. So there's so many experimentation about like what's the best way to give it a view, on what's editing, how many files can you show it, like, you know, before it starts to hallucinate. And right now it's like more of an art than science, but it's becoming more and more like a science. This is insane. So it's a simple way to think about it.

There's a foundational model. Here's what I want you to build. And here's a computer to use to build it. Yes. How am I? Here's a computer with a set of tools. Here's a tool to install a package. here's a tool to edit the code.

Here's a tool to run a SQL query. And also services. Here's a bunch of services you can grab from. Here's a database service. He's an object store service. He's an auth service. So you can think about it as a bunch of external services, the computer with a bunch of tools,

and they're all interfacing with the foundation model. It's funny listening to this how it starts to feel like the fact that we might be living in a simulation is not as far-fetched as it may feel. Like this feels like the beginnings of what a simulation computer would be. Yes. Yes. You know, it's pretty, like, you know, you can go really sci-fi on this and it's like, where is it headed, right?

Like, you know, if we give it enough tools, like let's say you can, I can drop it in Slack. And instead of interfacing with it in this fashion, I want to interface with it in a totally autonomous way. So we actually have this feature coming up where instead of me testing it, we give it another agent. So here, instead of me interfacing with it and saying, you know, this is running or not running, we can give it another agent that is actually testing the application.

And so, and then let's say interface with it entirely through Slack. and I'll say something like, give me, you know, give me Taylor Swift tickets the moment they land. And so it'll build an app that continuously monitors the web for when Taylor Swift tickets land.

And there's like an agent that's using the app to be able to get that. And then you can imagine it has some kind of wallet or credit card. and then the moment it lands, it kind of gets it. I mean, what I'm trying to say is that software, like agents being able to do software is how AI gets more general because software runs our lives, runs the internet, runs our businesses. And so the more competent AI becomes that software, the more general they are in terms

of what they can do. Okay. this can go in so many directions. I'm going to bring us back to the implications for people building products. They product managers, founders. How does this change that function, that skill set? Like, what skills do you see will matter more, matter less, which functions are maybe in some danger and they should start thinking about a different career path?

One interesting persona that we're seeing is the CEO, the CEO of startup, the CEO of you know, Andrew Wilkinson from Tiny is a big user. And so these people are typically creatives, right? They built a company. They hired people. A lot of them can't code. A lot of them are designers or product managers or something else. And you can imagine a bottleneck.

You can imagine a bunch of ideas in their head. And the ideas have to translate through them, talking and then someone else listening to them and like assuming that someone else actually understands what they say and then that's someone else going and trying to build what they want to what they want built and also assuming that person has has time right because a lot of times your engineers are kind of stuck building the current thing they're not thinking about the future thing and so uh what gets to me excited is a lot of these CEOs are building the future concept the next company the next product they're going to build, the next, say, company they're going to build. And so it unlocks the creativity,

and again, sort of unblocks them from that. And look, it's, you know, it's a V1 of the product, but it can push things forward. You can touch it, you can feel it, you can say, okay, this is, this really has lags and we should, we should work on it. You give it to your engineers and they can improve on it from there. So that's one persona, but I'm really excited about it. The CEO slash founder. In sort of companies, one of the things that I think is sort of hard about tech companies. Is sort of these silos between designers, product managers and engineers. And everyone feels that pain.

of kind of we have low bandwidth communication, which is language and text on Slack and Zoom calls. And it leads to a lot of frustration because it's really easy to misinterpret people. And again, leads to sort of siloing, where people working on something and then you pass it on to the next team. And it's not really what they expect. That happens a lot between designers and engineers. But like the common language that everyone shares is code, right?

Like ultimately in software tech companies, everything that we're talking about need to eventually flush out in terms of code. And so what if the language becomes actually working prototypes and working applications? For example, we have the Figma extension that translate a, you know, Figma mocks into React that runs on on Replit. So instead of, instead of, you know, giving, giving the engineers, you know, just just moks or screenshots, whatever, you just say, oh, here's, here's a bunch of React code, you know, just make sure it runs on our infrastructure, but like, don't mess with it.

Don't move the pixels around, right? And so I think it just like opens, opens up, you know, silos of the, you know, silos of the companies make communication around product a lot more concrete because I can I can give you a working prototype. And that'll change how people work. If you can imagine that everyone can make software, it's really kind of a radical reimagining of not just what tech companies are, but really what most companies are because everyone can be more, more, general. So say you're a PM listening to this, an engineer, designer, what skills do you think, if you were one of these folks, if you were in building Replit right now, what kind of skills would

you suggest folks focus on more and what you think are just like, okay, this is going to be less valuable in the future. Don't worry about these sorts of things. You can either pick one of those three functions or all three. I think a very important scale that's like perhaps harder to develop, but it's worth working on is being generative, being more generative, being able to generate new ideas quickly because, you know, you can think about it as like a factory line, right? Like, so, so you have ideas, you have the production of these, of these ideas, or like the initial kind of production of these ideas. and then you have other people that want to consume these ideas or work with you on these ideas.

And so typically you're bottlenecked by the middle kind of part where your ideas are kind of like, there are a lot of them and they're not fitting in because they need to be made and they need to be made quickly. And so now you open up that bottleneck. So now like actually making things is a lot easier. actually you become limited by how fast you can generate ideas. And I find that true of myself as well. Like, you know, I consider myself quite generative, but now I have this tool and I can like build,

build a lot more and explore a lot more. And I'm finding that, well, actually, I'm running out of ideas sometimes. And so, and so, you know, training that, that muscle, I think, is a good thing. I think, like, learning a little bit of coding, and, like, not the traditional way of learning coding. Like, when you go, like, if you go to, like, a coding boot camp, they're going to start with, like, what is Git?

Actually, my co-founder, Ohio was a designer when we were first building your replet together, she went to web assembly to do like a coding course. And the first day, they were like spent this whole time on Git. And she's like, what is that? Like, well, what does it do? Like, I still don't know what to get exactly this. But it's like, you're you're inverting the process, like you're giving the tool before the actual problem. And so I think all of that stuff you don't have to worry about. So things that you don't have to worry about, I think a lot of the, you know, as a PM, as a designer, as someone who's not like in your code editor every day, don't worry about all the

tooling. And if you learn a little bit of coding just by, you know, talking to an AI, doing a little bit of debugging, building something with Replit, you know, running into a problem and trying to fix it just using AI, you'll learn a bit of coding. And, you know, I have this, I have this that's been called, Not by Me, dubbed as I'm Jed's Law, which is the returner investment for learning a code is doubling every six months. And really, just learning a little bit of that skill, learning a bit of skill about how to,

you know, prompt AI, how to read code and be able to debug it. every six months that's netting you more and more power because you're going to be able to create a lot more you're going to be able to it's going to be easier to create you're going to be able to create a lot you know a lot more complete things

so that's that's another skill that I think could be necessary this is super interesting okay so this last point you made Amjad's law it's interesting because when people like as someone's listening to this I could see them being like engineers are in trouble. Why do you need engineers at this point? These agents are building the code.

Your point is specific engineering skills are going to be incredibly valuable and more and more about how often are they doubling? Would you say every year you said? No, every six months. Every six months, these specific engineering skills are becoming more valuable. And the idea is this you don't need to like know. everything. You don't need to know the foundation, like to build the app as much. It's more to unblock the agent and understand the mental model of how this stuff is built. So you can move forward fast. That's right. That's right. Understanding the basic components of it.

Yeah. So it's like we need new engineering schools to teach you these very specific skills. Yes. Versus spending gears on like algorithms and I think I think no one has done that yet. Right. And I think this is this is like a, you know, big business probably ready to get built. It's like, AI native coding. It's totally different than like traditional coding. That's why on hacker news, there's so much skepticism about like AI native coding tools

because they're like, yeah, it's a glorified auto-complete. And I understand like if you're, you know, writing operating system kernels, you know, it's not really doing that much for you. But if you're building product, it's building it for you at this point, right? And so, you know, if you're starting a school to teach AI native coding, you would skip so much of computer science and the basic tools. And you would teach the basic idea of how to structure an app.

And then you would teach prompting. And then you would teach, I think, a little bit of debugging. I think debugging is quite a good skill right now to learn. And interestingly, if you want to be good at debugging, there's a lot you need to understand, which is basically what you're saying is like that's the subset of things to understand, is things that break. And to do that, you have to understand how all works,

what are servers, what are APIs, all these things. Okay. How far? So we've been talking about how this is very good right now, building a prototype, building a V1, MVP, people can use it. You can deploy this app. People can start using it. And there's like a scale.

it can reach. Do you see a future where you can build like a sales force size business fully replete or other tools that can scale to hundreds of billions of dollars of value? Or is there just going to always be some limit of like you need like actual engineers and designers sitting on this thing building and thinking it also? If like my law is like you know, directionally correct, even even if the months are not, I don't know exactly correct, the duration is correct. you're going to see a compounding effect of the power. It's actually quite hard to convince yourself. But if you really convince yourself that we are on a massive scale of improvement in

AI, then the answer is yes. And it's like absurd to my engineering mind that I'm saying this. But, you know, Ray Kurzweil, this like, you know, futurist, you know, talks about how exponentials are really hard for humans to grasp. And so actually when we started building the agent, you know, I told the team, it's easy, and we've fallen in the strap before, it's easy to build and optimize for today. You know, in 22, we built like, you know, copied like thing and autocomplete. We train our own models.

We optimize the hell out of them. But at some point, like that modality was kind of, you know, not the right mentality, which is like the autocomplete modality. And the right modality is actually this, I think, for now as being able to, the chat inside the programming environment and for the agent to create things for you. But in order for us to make that bat, you know, a year ago, the models were actually not there. Like the models could not do this. But we were like, okay, we're going to build for the models that are landing in six months.

And truly like six months later, the models started to land that are capable of this, of the reasoning that we need and whatever. And so that was like, you know, sonnet fee weren't. which is, oh, wow, like we switched to it and the reasoning improved so much. And six months later, you have son of you, too. And so it's really almost like a six months cadence. And so if we're really on this trajectory, then, you know, I would say, you know, next year you're able to scale. And maybe you get, you know, thousands of users paying you.

The AI can do maintenance. You know, we already showed the AI doing like SQL queries and doing migrations. so they will be able to do maintenance, debugging, things like that. I think where it gets really tough is that, you know, when you're hitting scale and you're an architect a system that is resilient, so that means, you know, you would start, you know, sharding databases, and we would start, like, using different Q systems and components and things like that. And I think, you know, the AI needs to have access to the Internet,

entire suite of tools to be able to do this. And I think that's going to be the next bottleneck. I think the AI needs to be a lot more reliable at doing that. But I could imagine, like, whatever, five years from now, someone running a billion-dollar company with zero employees where it's like the support is handled by AI, the development is handled by AI. And you're just building and creating this thing that is, you know, that people are finding valuable and are paying you for it. That being said, it's worth like thinking about the economics of it. Like if the, you know, if the cost of software goes down a lot, like, then what is, you know,

what is the price that you can charge on software, right? So can you actually build the next Salesforce? if anyone can generate self-force. And then the question is like, what is the, you know, and this is why I emphasize being generative, because I think then the thing that would make you better is like by being able to iterate and improve the thing really quickly and generate new ideas.

And stay ahead of all the other people building these tools so quickly. Yeah. Oh, my God. An interesting other kind of mental model I'm seeing as you talk about this sort of thing is not to offend religious folks, but there's this concept of God of the gaps. I imagine you've heard that. Yes.

Where it's like God explains all the things that we don't yet understand. And over time, that kind of space shrinks and God's like, all the things we don't get yet, those gaps, that was God. That's what, that proves there's need to be a God. And it feels like right now humans are like the gaps in these tools. Yes. For these agents you talk about that you can hire within Replit or like fixing these little gaps And over time, AI will fix these things themselves.

That's right. And these gaps will shrink. I mean, unless we hit some fundamental limit in the current regime of AI, which, you know, I'm not, I'm not an expert about like how far transformers could scale. But I feel like it is, you know, we found the thing that could scale pretty far. but maybe there are limitations in data or other things like that that we could be surprised by. But if there isn't, then we are on a massive trajectory of removing these gaps quickly. Yeah, very true. We have no idea. We keep thinking it's just going to keep going, but maybe it'll stop at some point. I could keep going and going, but I think we should also let people go play with these things and process all the things we've been talking about.

is there anything else they think might be helpful for folks to think about or learn or study? You know, I'll give advice to sort of founders or leaders at companies. The way we work is going to change rapidly, and it's important to be sort of resilient to that change. One thing that I think is really difficult now is having roadmaps, especially if you're doing anything in AI. but really anything that AI could affect, you want to be able to react to it really quickly. And so, you know, when the Anthropic dropped the computer use sort of capability, you know, we slot it in our roadmap because we don't really have an explicit roadmap.

We, like, immediately jumped on it and started building things and we launched some things around it. We're going to be doing more with it. But like, there's going to be capabilities that, are going to drop. And you want to really, in some cases, if it really affects your business, you want to be able to jump on it really, really quickly. So being agile, not being sort of stuck with, with roadmaps, being able to kind of just just say, oh, we're just going to switch priorities right away is going to be super important.

Not being, you know, like I said, with silos. at Replit, there's so many people that are on the scale of like, you know, designer to engineer, designer, product manager. Actually, I mentioned Amman earlier. He started as a designer at Replit and now as a product manager.

We have people who start as designers become engineers. And we have people in the middle and we're comfortable with that, like design engineers and that fit at different parts of the scale. And the design engineers go to the design,

create meetings and some designers go to the engineering meetings. And you just got to be fluid, right? Because, you know, again, when designers can code and engineers can design, I mean, it's really becomes, you can't have a lot of structure around that. So you want to build a culture and you want to build an environment or milieu that is, like, really, really flexible, which is uncomfortable for a lot of people. Man, the future is wild. Everyone's a hybrid person now.

Let me just actually double down on what you just said, which I think is really interesting. It's almost like if you're an engineer, where your skill set will become most valuable is unblocking these AI tools and knowing debugging and figuring out to allow it to go further and further and further. Within PM and Design Land, based on what you're describing,

where the skills will become more valuable, is generating ideas, almost like finding opportunities, discovery, finding what problems need to be solved, and then articulating that as clearly as possible to the AI tooling. That's right. Yeah, this is a very crisp sort of advice that people can follow today, I think. Oh, man. What a world. Okay, I'm Judd.

This is incredible. My mind is racing. I've got to go build some apps immediately. Give us feedback. I will do that. I will do that. So just to leave listeners with a couple things. One is just what should they know? Where do they find you? How do they try Replit? Anything else other than just go to replit.com? Yeah, just go to replit.com. It's an open beta right now. We're kind of quickly

improving and going to exit beta, I think, in a few weeks. But if you're comfortable testing, something that's like not perfect, go to rapplet.com. If you subscribe to our core plan, you should be able to access the agent and start using it. And we are, you know, I think the place where we're most active is Twitter. So Twitter or like X, the handle Replit, R-E-P-L-I-T or my handle, A Mossad. Oh, yeah. One other thing I wanted to make sure we had a chance to touch on is you're working on something new, something that's coming in the very new future.

Maybe the day this episode drops. Talk about that. All right. So depending on when the episode is coming out, This could be the first time people hear about it. But we have this product called agent. It is sort of high agency, does everything from setting up the project and all of that, right?

And so now we are working on assistant. So assistant is, like let's say the cousin of agent. It is a little less powerful, but a lot more controllable. So you can like focus on features or areas of the code that you want to change and you still don't have to know how to code, but it is a lot more manageable and it is a lot faster. So you saw how it took some time to kind of create the project and code some of the things. Assistant is in the order of milliseconds and seconds to be able to respond to you.

And so again, as I talk about the idea of tools, we want people to have as much power and autonomy as possible. And so there are certain instances where agent is the best, it's going to do the debugging for you, it's going to create the database for you. But if you want more control, assistant is going to give you that. Just so folks totally understand what this is going to do for them.

What's like the mental model for what this is like, if it's like a person we're helping you out? Agent is like having a developer work. You give them the PRD, right? And they're going to go and build the thing. Assistant is like you're sitting next to them. So they built the thing and now you walk over to their desk and you say, let me move this button three, three pixel to the left.

Let me change this thing. So like small increments of changes that you want happen really quickly and you want it reliably, that will give you that. So it's just like much faster iteration on UI and things like that. incredible the future is wild final question i always ask everybody how can listeners be useful to you come work at replet uh we have we have a PM role i think up if you if you're product manager we're hiring engineers and and and and product managers so so come work at replet or refer someone to replet especially if you're like our tools and you want them to get better the best

way to do that is to get us great people we can hire well you're about to get a a product managers applying. Amazing. I love that. Good luck. I'm John. Thank you so much for being here. This was incredible.

Thank you. Thank you for your podcast and the community that you've built and newsletter and everything. It's been awesome to watch. Thanks, man. Appreciate that. Bye, everyone. Thank you so much for listening.

If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny'spodcast.com. See you in the next episode.