Inside Devin: The world’s first autonomous AI engineer that's set to write 50% of its company’s code by end of year | Scott Wu (CEO and co-founder of Cognition)
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Our whole team is only like 15 engineers. We use a ton of Devon when we're building Devon. Most folks on the team are definitely working with up to five Devons at once. And so Devon merges like several hundred pull requests into production in the Devon codebases every month. What percentage of your PRs are Devon versus humans right now? It's in the neighborhood of a quarter or so. Where do you think this will be at the end of the year? Honestly, we expect it to be a decent bit more than that.

You guys are so ahead of how companies work with AI engineers. AI is going to be the biggest technology shift of our lives. So most of the big tech revolutions that we've had over the last 50 years, like personal computer and the internet and mobile phone, they all had this big hardware component that was a big part of the distribution. Folks who were building for those industries kind of saw their market grow and grow and grow basically steadily year over year as the number of people with mobile phones increased, right? As the number of people connected to the internet increased.

One of the things which is already, I'd say, different in AI is just how explosive the technology can be. There's no weight on hardware distribution. It means that the space is just growing so exponentially. How is the fact of being an engineer and building changing? I think there's going to be way more programmers and way more engineers a few years from now. Pretty quickly, the form factor of what it means to be a programmer obviously is going to change. But at the end of the day, of course, the discipline is all about just being able to tell your computer what's due. And so in that lens, I really think that programming is only going to become more and more important as AI gets more powerful.

Today, my guest is Scott Wu. Scott is the co-founder and CEO of Cognition, which makes a product called Devon, the world's first autonomous AI software engineer. Unlike other AI tools that I've highlighted on this podcast, Devon is designed to act like an actual remote engineer that you chat with like you would with any other human engineer through Slack or through its dedicated website. When Devon launched about a year ago, it was very much a junior engineer. Over the past year, they've made a lot of progress, and Devon is now being used by tons of companies in production.

We chat about how their engineering team of 15 uses Devins to build Devon, including how every engineer uses about five Devons each to help them code and move faster. How a quarter of their pull requests today are committed by Devons and that they expect this to be over 50% by the end of the year. We also talk about how Scott imagines software engineering is going to look in the future and how the role of an engineer changes from a coder to an architect.

We also get into the eight pivots that they went through before landing on this path, why Scott believes AI tools like this will lead to more engineer hiring versus less, also where the name Devon comes from, and so much more. This episode is going to blow your mind. I highly recommend you listen to it if you're at all interested about where engineering, product building, and AI is going.

A huge thank you to Claire Vo for suggesting a bunch of great questions for this conversation. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. Also, if you become an annual subscriber of my newsletter, you get a year free of linear superhuman notion perplexity and granola. Check it out at lenny'snewsletter.com and click bundle. With that, I bring you Scott Wu.

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Many of you are building AI products, which is why I'm very excited to chat with Brandon Fu, founder and CEO of Paragon. Hey, Brandon. Hey, Lenny. Thanks for having me. So integrations have become a big deal for AI products. Why is that? Integrations are mission critical for AI for two reasons. First, AI products need contacts from their customers' business data, such as Google Drive files, Slack messages, or CRM records. Second, for AI products to automate work on behalf of users,

AI agents need to be able to take action across these different third-party tools. So where does Paragon fit into all this? Well, these integrations are a pain to build, and that's why Paragon provides an embedded platform that enables engineers to ship these product integrations in just days instead of months across every use case, from RAG data ingestion to agentic actions.

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Scott, thank you so much for being here and welcome to the podcast. Thanks so much for having me. Excited to be on. I'm really excited to have you here because you are building and you've been building something that is very different from what a lot of other AI companies have been doing for a long time, although they are starting to converge to where you guys are now. We're going to talk about that. And it's also just such a unique point in the history of AI and just the journey of AI. And so it's really cool to be chatting right now.

And I feel like we're going to chat again in a few years and be like, wow, we were so right about so much and so wrong about so much. Yeah. And so I'm excited to have you here. Let's start with talking about Devon, giving people an understanding of just what the heck Devon is. This is the main product that you guys build. What is the simplest way to understand what is Devon? Absolutely. And so Devon is a fully autonomous software, isn't it?

that is going to work on tasks end-to-end. And so there are a lot of great tools for all parts of the stack of the AI code workflow. What Devon does is it is a full asynchronous workflow. And so you can tag Devon on an issue in Slack. You know, you're talking about an issue and you tag Devon. You can tag Devon in linear. You can have Devon. And Devon will make pull requests in your GitHub. And so it's very much built to work with engineering teams as your junior engineer. Amazing. Okay.

Okay. So I remember when you guys launched this, there was like this big pitch of this is your new AI engineer. And it was really good at a lot of stuff. It wasn't great at other things. It's been a year now about since you guys launched. Is that right? Yeah. Yeah. What's the best way to think about like the level of seniority that engineer had back in the day when you guys launched? And then the level of seniority of engineer today, if that's, I don't know, measure of how to think about it.

Yeah. And it's crazy to think about, by the way, because, you know, a year ago when we did the initial launch, I mean, people didn't really believe that an agent was possible. And it was a very I mean, it was a very different time. You know, it's like start of 2024. You know, things with model capabilities were definitely quite a bit earlier on. Reasoning especially was was quite a bit earlier on. And and yeah, I mean, in the time since then.

It's obviously developed a lot, I think, in terms of practical skills. You know, there are some comparisons we make. Sometimes we kind of say, well, when we got started, it was kind of like a high school CS student. And then as time went on, it became more of like a college intern. And now it's like a junior engineer. But I would say, though, that those are more like rough guidelines because I really like the phrase jagged intelligence, for example, because there are obviously there are certain things that it is much better at than a human. There are certain things that it's much worse at than a human.

And I think over the last year, we've learned a lot, especially about not just coding agents, but agents in general, just like really building out like how all of us should be working and interacting with agents as part of our flow. And so a lot of the things that we built, I mean, it's, you know, there was no Slack

uh there was no github integration there was no linear there was no interactive planning phase working back and forth there was no way to touch up devin's code and so a lot of the the the features that we built on the product side since then have really been about basically yeah figuring out how to how to make working with devin and handing off tasks to devin as smooth of an experience as possible that's so interesting so a lot of the work has gone not into how do we just make devin the best possible engineer but it's how to

work with this new type of entity that we haven't ever worked with? I think it's a 50-50 of both. I think the capabilities obviously have improved a ton and we've seen these get better and get measurably better. But I think the other side of it is everything to do with really the product interface and the tools and so on. And I think today...

folks generally know how to use chatbots and to work with chatbots, right? And that's an interface that people are familiar with. And obviously with agents, it's still like a real curve, I think, to learn how to use them and how to get the most out of them. And so it's really exciting to see a lot of others starting to build and do a lot more in the agent space as well. But I think this is the kind of thing that we're all really figuring out together as a space.

What can you share about just the scale of Devon at this point, whatever you're comfortable sharing? And then just where do you think the level of Devon's coding abilities will be in a year?

So we work with companies of all stages and sizes. On the smallest end, it goes to startups of just one or two people who are using Devon to build out a lot of their initial prototypes or initial products, all the way up to big public companies, Fortune 100 companies, or public banks or things like that who are using Devon across their engineering teams.

In general, we've seen a huge range of the use cases there. And obviously, the kinds of engineering work that you're doing at a one or two person startup is very different from the kind of work that you're doing at a public bank. But throughout, it's all been basically, yeah, being that junior buddy of yours that it

that makes you go faster and really multiplies you, I would say. I think it can multiply you as an engineer, obviously, by just letting you work with your own team of Devins instead of having to be fully synchronous on a single task. And then it's also multiplying your team and multiplying your team's knowledge base because Devin really accumulates a lot of the knowledge from working with every member of your team and is able to bring that into each new session.

Awesome. We're going to show people how it actually works later in the podcast. We're going to do a few live demos. But let's actually go to the beginning of the journey. What's just the origin story of Devon? How did this all begin? The founding team, I mean, most of us have known each other for years and years and years, actually. And for almost everyone, this is our first time working together, but we've known each other a long time. And we all actually had our own kind of journeys in AI for the last decade or so.

And so for myself, I ran a company before this called Lunch Club, which was an AI for a professional networking product. And I ran that for about five years. And one of my co-founders, Steven, was one of the first engineers at a company called Scale AI, which has obviously grown a lot and done very well. My other co-founder, Walden, was an early engineer at a company called Cursor.

which has also obviously grown a lot and done really well. And our whole team was kind of like that. Many of us knew each other from competitive programming and math competitions, but we had stayed very closely in touch in the decade since then. And we've all kind of all had our own journeys. And so we had one person who was running teams at Neuro. We had one person who was at Waymo, someone who had their own YC tool startup for machine learning. And we were really excited to build something together.

And this was around like late 2023, so about a year and a half ago at this point. And yeah, when we got started, I mean, I think there were a couple of things that we felt really strongly about. And one was that reinforcement learning was really working and was going to be the next big paradigm shift in capabilities. You know, back then it was impossible.

you know, the initial chat GPT launch in 2022. And those models were to first order where, you know, what we would call imitation learning in AI, right? Which is basically, you know, you have the model, read all the texts that you can find on the internet and then train it to talk like somebody on the internet would talk, right? And they're kind of...

obviously a lot more details on top of that, but that's kind of the first order pass of what was really done. And it was amazing, right? I mean, it passed the Turing test. It was able to respond and to have encyclopedic knowledge about a lot of things. And I think this new paradigm, which we've gotten into over this last year or year and a half is really high compute RL, which is a very different paradigm, right? Which is basically the ability to go and

do work on tasks and put something together and then be evaluated on whether that was correct or incorrect and use that knowledge to decide what to do and to learn from that. And so we felt very strongly that that was going to happen. I think for us, code was the natural thing to work on for a couple of reasons. One, because we're all programmer nerds ourselves. And so teaching AI to code is about as cool as it gets for us. But also because code has this whole automated feedback loop where you can run the code.

And that is the kind of automated feedback that really feeds into the RL, which makes these models so great at coding. And then the other thing that we felt very strongly about was that the product experience was going to shift from, you know, what I'll call like text completion to agents, basically, right? And to first order, I would kind of say, you know, there have been a lot of great experiences in text completion. You know, it's been used for marketing. It's been used for customer support. It's been used for education and in code, obviously, as well as, you know, the GitHub Copilot was fantastic.

was kind of really the dominant product of that initial wave, right? But I think that the big shift that we really felt we would see is moving from kind of this text-to-text model to an actual autonomous system that can make decisions, that can interact with the real world, that can take in feedback, that can iterate and take multiple steps to solve problems. And now we call that agents, but that was what we were really excited about at the time.

So it was always coding, it was always agents. And in some ways that kind of feels like it should have been, you know, like been clear from the start. But even with that, it's, I feel like we've pivoted like eight times or something within coding agents, you know, over the last year and a half. I just noticed recently all the AI, top AI companies, sort of, not all, but many of them, the

product that is winning is different. It has a different name from the company, which is not typical. Cursors, AnySphere, Bolt, StackBlitz, you guys are Cognition Labs, like V0s for sale. And it just tells me these all emerge later in the company's journey and they tried a bunch of stuff and like, oh wow, this thing worked.

And it's so interesting that it's so common amongst these top AI companies. Yeah, and there's even, I mean, OpenAI, ChatGPT, Anthropic, and Cloud, and Google. Yeah, it's funny. Yeah, yeah, I agree. So, you know, when we got started, it wasn't even really a company. I mean, it was more like a project or a hackathon almost. You know, we got a bunch of... We booked an Airbnb basically for a couple weeks as it was around Thanksgiving time and just got...

a bunch of people together who were just excited to hack on some projects and build something cool. And it's funny, actually, the first thing that we were building for actually was more like solving like these more like contest programming problems and using like an agentic loop to really do better on that. And so obviously, if you run your code on test cases, you can evaluate, you know, there's a lot of agentic work that you can do there to try and do better. And that we spent some time on that initially. And then, you know,

And, you know, we've kind of gone from, I mean, the story of the whole company for us in some sense has been going from hacker house to hacker house, you know? So after that, you know, we had another hacker house and that's where kind of some of the initial ideas for Devin came and really building like a software engineering agent and not just like a coding agent and having it interact with a lot of these tools. But even then there were so many iterations and, you know, even like,

the idea of talking to Devin, for example, was something that we had to come up with. Initially, it was just like you hand off a task and then it works, and then it shows you this whole finished code. And now, obviously, it's like you can jump in at any time, you can get feedback on the plan, you guys can scope out the task together when you're working with Devin. And a lot of these things we had to develop, obviously. And certainly, we've learned a lot about the use cases, the form factor, we've made a lot of big improvements. And

and step function improvements on the capabilities and Devin's ability to use tools and debug and make decisions. And so it's, yeah, it's been a fun journey. I mean, I think like,

I would say that the grounding question for us really, which is one that we think about all the time, is really just like, what is the future of software engineering? And how should we be working with AI to write code? Because I think at the end of the day, of course, that's what underlines all of the product decisions that we make. I like that you're asking the juicy question I wanted to get to. Before I ask it...

Just for the history books, when did you guys start kind of hacking around and when did Devon launch? How long was that period? Yeah, so we started in November of 2023, which was just like hackathon mode. We officially made it into a company around the start of 2024.

And then our initial launch was in March. And so it was like nonstop. I mean, it's been nonstop for the entire last 17 months. But it's getting to the launch and then obviously working with enterprises and developing the product a lot more, building in...

building it and getting it to work for a lot of practical use cases and then doing, you know, getting it, making it fully available self-serve in December of last year. And now we've rolled out 2.0, obviously just a few weeks ago. And so it's been a very busy time for us.

Understatement of the century. Let me ask this question because you touched on it a bit. This whole idea of Devin as a person and this idea of creating a personality for Devin, it's unlike any other, I believe, AI app. No one else has like a name and like you don't think of it as a person. What made you guys decide to go that approach and just how do you design it to work well that way?

I would say it's a decision we're pretty proud of, I would say. I mean, I think there's a lot of different product experiences out there. And I think the thing that really makes Devin unique in what it does is that you can really hand off. And more and more what we've seen, honestly, is that

that I think a lot of kind of explaining that the Devon experience to folks is really just explaining to explain it as, yeah, this is your junior buddy, you know, and, and that goes for a lot of the parts of the flow where in the onboarding, for example, you know, initially I would say like, we've definitely had a lot of users come in and just kind of see the blank screen and not really know, or, or they'd, they'd ask, Hey, like, I'm going to do this whole big rearchitecture of the whole code base. And, and basically, you know, what, what, what we've learned over time is to basically get folks to think more like, well, well, well, you know,

let's work on getting the repository set up first. Let's make sure we hand Devin a couple one-pointer tasks so it can get familiar with the code base. Let's get it the thing. If Devin needs to be able to test the code or run the linter or CI or things like that, obviously we want to make sure Devin's got its own virtual machine set up to be able to do that. And similarly, I think the usage pattern, I think

Often it wasn't clear. And obviously you can sit and just kind of watch Devin do it action by action and work that way. But we found that the best workflow really as a team building a lot of stuff was to work with multiple Devins and to run them asynchronously and to kick them off and to only jump in basically as you needed to provide feedback or steer the plan or anything like that.

And so in many ways, I think it's, I think Devin as a name really is our attempt to kind of capture the soul of that as a product where it really is, you know, treating it like a bit more of an autonomous entity that you can hand off tasks that you can work with, that you should be teaching and learning with over time. I want to come back to an area you started us down and then I took us away from, which is impact on software engineering and then how software engineering is going to change. So there's kind of two parts of this, just like

When people are using Devon today, say in this year, how is the act of being an engineer and building changing for those companies? What does that look like? By the way, we're all software engineers ourselves. I'm a programmer by training and still a programmer at heart, certainly. I think the way that we've always thought about it is there's layers of abstraction and there's tools.

And one way I would say it at a high level is kind of, you know, I think of AI in general as, yeah, I mean, computers are obviously getting more and more intelligence and are able to do more and more. And, you know, it's possible there may come a day where computers truly do everything that we do and humans are not responsible for any of it. You know, I don't expect that to come particularly soon. But I guess what I would say is, you know, until that point,

For as long as we're still part of the equation, one of the most important things to do, obviously, is, you know, for us as humans is to instruct our computers on what we want and what we want to build and what we want to do. Right. And software engineering is, you know, we think of it today, obviously, as Python and C++ and JavaScript and all these things. But at the end of the day, of course, the discipline is all about just being able to tell your computer what to do.

And so in that lens, I really think that programming is, if anything, is only going to become more and more important as AI gets more powerful. And I think the thing that's really exciting for us is, yeah, it's like really like seeing that kind of iterative transformation. And so you asked what things look like today. And I would say, yeah, it really is like having like a junior buddy or really a team of junior buddies that you can work with, right? And so every engineer on our team, we use a ton of Devin when we're building Devin and

And so Devon merges like several hundred pull requests into production in the Devon code bases every month, you know, which is, I mean, our whole team is only like 15 engineers. And so it's a pretty sizable fraction of all the code that we write.

And the way that we use it is basically, yeah, everyone's got their whole team of Devins. If you're going to be looking through various issues, if you're going through feature requests, if you're going through bugs, if you're going through new paradigms that you want to build, then it is naturally the case that there's a lot of handoff points where you just say, hey, Devon, here's what's going on. Can you please take a pass at this? And sometimes Devon will be able to do the task 100% autonomously and just makes the PR and then you merge the PR and that's great.

Sometimes you want to be able to jump in for the 10 or 20% that really needs your help. Maybe there's a few details with how exactly you want to scope it or how you're architecting this feature, or maybe you want to go and test the front end at the end yourself to make sure it looks exactly the way that you want and give your one or two lines of feedback after that.

But a lot of it is really, yes, it's kind of like, yeah, learning to work with Devin to be able to just do more in parallel and build more. What percentage of your PRs are Devin versus humans right now? Yeah, I'd have to look, but it's in the neighborhood of a quarter or so of all of us. Yeah, yeah.

And then what was it like six months ago? Oh, yeah, it's grown a ton. I mean, we've seen it grow exponentially internally ourselves as well. And so it's kind of an interesting one where, again, it's always both the capabilities and the product interface. And so I think the intelligence has increased a lot. But the other thing, of course, is that we've spent a lot of time in figuring out how to build and

to really kind of build for an interface where you can get Devin's value on tasks where Devin is able to do the 80 or 90%. So Devin is obviously not perfect and it'll make mistakes and so on. And a lot of the question is basically, yeah, how do you scope out your initial task with Devin and then just kind of set Devin off and

and have it go and do the things that you want to do. How do you come in at the end and review and give feedback? How do you make sure Devon learns over time? How are you able to kind of just check in as needed and course correct if you want to? - Okay, so today about quarter of your PRs are Devon's.

Where do you think this will be at the end of the year? What would you guess? I think by the end of this year, we expect it to be more than half. And I mean, as time goes on, one of the things that we've seen is just you're able to do more and more and more work asynchronously, right? And you're able to hand off more and more. I think the soul of programming, the soul of software engineering has really been about

through all the areas, not just now, but even when it was assembly, right? And even when it was Pascal and even when it was punch cards or whatever. I think the soul of it has really been basically just about defining the problem that you're facing and really thinking through exactly what is the solution that you want to build, thinking through the architecture, thinking through the details, and really kind of mapping out in your mind exactly what you want to build, basically, and what you want to have your computer do.

And I think that's what makes software engineering really great. And I think that's like the funnest part of software engineering. I think at the same time, that's probably in the neighborhood of 10%.

of the average software engineer's time, right? Because 90% of the time is, you know, you've got this Kubernetes error that you've got to debug and you have to see what went wrong and the system crashed or, you know, you left some port open and this is, you know, messing up or, you know, there's a bug report that you have to take care of or you've got to migrate your code or you've got to upgrade to a new version or things like that. You know, a lot more kind of like implementation.

And one of the ways that we've kind of thought about Devin and building Devin is really allowing engineers to go from bricklayer to architect, so to speak. And a lot of it is just getting to the point where you can do the high level directing and you can basically specify things exactly how you want. I think it is very much about still having the human in control and having the human able to do the full specification, but just multiplying the magnitude of

of what you can do and what you can build in one day or one hour or however long. So in the future, say someone is trying to get into software engineering, thinking about becoming an engineer. First of all, do you think people should, classic question everyone's getting these days, should you still learn to code? So that's just, I'd love your perspective there. And then two, for people that are engineers today, what skills do you think will be more and more important and then less important in this discussion of moving from bricklayer to architect?

Yeah, for sure. I love this question. First of all, the question of, you know, whether you should still learn to code, my answer would be absolutely yes. I think to a large extent, you know, computer science, when you take computer science classes and when you learn these fundamentals, sure, you're learning a little bit about, you know, how a particular language's syntax works or something like that. But honestly, most of what you're learning really is about the ability to logically break down problems for number one.

And two, I would say, is just, yeah, the model of a computer and a lot of these decisions and a lot of the abstractions that we've built over time, right? Like, what is a database and how should you think about a database? You know, what is a garbage collection system and how do those work? And all of these different pieces. And the reason I think that's important is because

It's the same with a lot of these other, arguably we've already gone through these phases in programming. And I think this next one is going to be somewhat faster and somewhat bigger, but in many ways a similar flavor, which is when you work with Python today, obviously a lot of things are already abstracted away from you. And in some sense, someone from 50 years ago might already call Python, you just get to explain in English what you want and now the computer does it for you, right?

And that's great. And I think it's really powerful. It's opened it up. I mean, we have far more programmers, obviously, than we ever have before because of that. But I would say, certainly, as you're building your skills as an engineer, it really helps a lot to understand the abstractions and to be able to peel the layers beneath. And so folks will use assembly, for example, if they're really performance optimizing a piece of code.

But also, in order to build good systems and to understand these things, you certainly want to understand these abstractions of how does networking work? What is TCP/IP exactly? Or what happens with this Python code when it gets interpreted or all of these details? And I think similarly,

I think we will get to a state where, yeah, with no experience at all, you're going to be able to build some pretty cool stuff and to do some pretty amazing work just by explaining what it is that you want. But I think that for quite some time, you'll really want to be able to think precisely about the details, to peel back the abstractions, to be very precise about what it is that you want to build and how. And then...

for skills that you think are more and more valuable for engineers? Like where should engineers today be leaning more and more into and versus like, you know, forget and forget this. I don't need to think about this anymore. For sure. And I think architect, I mean, it's, you know, we already have a term for architect and engineering, and I think it is directly the directionally the right term. And,

And I think a lot of it is really, you know, it's, I think, one thing to kind of just do a routine implementation and write boilerplate code and things like that. And I would say that, you know, in many ways, AI coding has already made us much faster at that, right? But I think a lot of the core questions of understanding very complex systems and working in the context of the whole company and thinking about, you know, the product that you're

building or the work that you're doing and understanding, okay, what are the problems that we want to solve? How do we want to solve those problems? What is exactly the solution that we want to build? What are all these key decisions and trade-offs that we're going to be making? Basically, I think folks who are able to do that really, really well are just going to be able to leverage themselves more and more. And so if anything, I think there's going to be way more programmers and way more engineers a few years from now than there are today. And I think pretty quickly, the form factor of what it means to be a programmer, obviously, is going to change. And

In some sense, it already has. But I think there's just going to be so much more for us to build. I think one of the great things, folks talk about Jevons Paradox all the time. I mean, software is truly the shining example of Jevons Paradox, where we have always managed as a society to find more and more things that we want to build software for and build more code for. And I really think there's a lot more out there to do.

For people that don't know Jevons Paradox, can you briefly explain it? Absolutely. Yeah. So Jevons Paradox just says that as the price of something goes down, it can still be the case that the total spend on it actually goes up.

And so you can think about this with money, you can think about this with time or resources, but the direct version here is I think as it becomes easier and easier to program and as programming becomes more and more effective, I think we're going to have a lot more programmers. I think in a kind of zero sum view, you might say, "Well, we're going to be 10 times faster at software engineering. And so it means that we're going to need 10 times fewer software engineers." But I think in practice, what really is going to happen is actually

We're going to build even more than 10 times as much code. And because all of the work that we do is so capped, obviously, on our ability to actually build and execute and iterate. We're going to have so many great ideas out there. We're going to have so many great products out there. People are going to build a lot more personalized experiences, for example. And there's going to be a lot to do. Going back to the way you guys use Devon, so you said that every engineer has kind of this fleet of Devons.

How many Devins per engineer do you find most people are working with these days at your company? Yeah, so it's very asynchronous. And so obviously you can kick them up

and start them up and shut them down basically as you see fit. But most folks on the team are definitely, yeah, are often working with up to five Devins at once, I would say. And it's a nice flow where it's, you know, you think through, all right, what are the five things that we want to get done today? One, two, three, four, five. You have Devin one do number one. You have Devin two do number two, Devin three. And the thing about it is,

A lot of it is, and for what it's worth, I think it's taken us some time to really kind of like to adjust to it and get to the point where it's really intuitive for us. But I think it's, yeah, it's definitely a different experience where you're handing off most things asynchronously and the goal for each of your tasks is to

be there for the parts that really need your expertise, where either you really, really need to define exactly what it is that you're solving for and what you're building, or maybe some of the more complex parts where you want to steer Devon towards particularly what kinds of changes you want to make. I want the class to be set up this way, and we should go and change all the downstream references to this as well, or whatever. But basically having Devon do the bulk of the work asynchronously with you. And then how many engineers do you guys have, roughly?

Yeah, so our engineering team today is about 15 people. 15. One five. One five, yeah. Holy moly. Okay, and then each one has five-ish Devins. Yeah. So there's five times the number of Devins as engineers. What I love about this is this is just like a glimpse into where the future is going. You guys are so ahead of...

of how companies work with AI engineers. And so seeing how you operate is going to be, it's essentially how most companies will end up operating. Yeah, and for what it's worth, we've already seen this shift, I'd say ourselves, where it's...

In terms of the team, obviously, folks don't spend that much of their time just writing out boilerplate or just kind of doing pure implementation of features. And people get to spend much more of their time focused on really just

Yeah, thinking about the core questions of, yeah, how do we make Devon better? What is the right interface for Devon? What is the right flow or the right set of features that's really going to make this as great of an experience as possible? And that's how we like things, obviously. When is the point you reach where there's takeoff of this being the, you know, like your Devon story?

starts moving so much further ahead of everyone else. Like once you have enough Devons doing all these things, they're just like, we're in your 10 years, 20 years, 30 years, 100 years ahead. Honestly, I think as a community, you know, I think the kind of all of us as engineers around the world, I think we're going to have to think about this and build for this and kind of adapt to these new technologies. But what I would say is, yeah, I think more and more, and especially as capabilities get better, but certainly, you know,

even in steady state today, I think more and more, I think things are going to shift towards this kind of asynchronous flow. And one of the reasons I would say for that is in the real world, you're just capped by real world constraints, right? And I think that one way to put it is kind of like,

And don't take these numbers exactly. But, you know, it's kind of like the first order math of it is, of course, you know, being able to write files or to complete this function or complete this line or things like that. You know, it helps a ton. It's a really great experience, right? There's a lot of parts of building software that obviously...

are almost not that at all, right? It's, you know, you have a bug that you're trying to fix. And so you spin up the local server, you click around on your own products on the front end and try to reproduce the bug yourself. You know, once you have the error, you take a look at Datadog and you see what happened and you try to find other errors in the logs. You know, you look at those files and you see what went wrong. You make some edits. Maybe you go and like rerun the whole process again now that you, you know, just to make sure your change looks right, right? And that's a lot of, you know, what it means to be a software engineer, right?

And these are processes that take real time. I think we're going to shift more and more towards this agentic workflow because in some ways, it's kind of like the way to really get to that 200%, 500%, 1,000% gains that we'll be getting to with software engineering over the next few years.

Okay, enough talk. Let's show people what the heck this actually looks like. Let's do it. You've got a couple of demos prep that show a few use cases that you found helpful. So you're going to pull up your screen and then we'll kick it off and then we'll talk as it's happening. And so the whole process, obviously, of working with Devin is...

working asynchronously. And so I thought it'd be cool for us to actually just watch Devin a little bit in action. And then we can go through some other examples of work that Devin's done or things that Devin does for us, even on our team, for example. But then we can check back in asynchronously with our Devin after. So I'll share this real quick. And the key thing that I would just emphasize here is a lot of it, obviously, is really just about

Yeah, thinking about as a software engineer or as engineers ourselves or engineering teams, engineering teams, PMs, and so on, what are the things that we would want to build that we would want to hand off? And so we have Devon set up with our own Devon code base, for example. And so I'll go ahead and kick off at Devon for that. And so I'll just say, hey, at Devon, I'm on with my friend, Lenny. Hi, Devon. Can you modify Devon web app to...

Let's feature your newsletter as part of the Devon website. Let's do it. Like on the real Devon website. Lenny's site. You're going to lose all your users. And so we're going to kick this off. As you can see, Devon gets started instantly and goes ahead and responds. And again, you can work with this asynchronously. You can work with it synchronously as well. For this, we'll just kind of go in a little bit and see exactly what's going on. But

As you can see here, Devin's going through files and taking a look through a lot of stuff. And so we can follow here basically as we need to and see what makes sense. You can see Devin's already called out a few particular pieces, right? There's the sidebar, which we have implemented on the front end, and there's pieces there. And we're going to have a new component, and that component's going to link to Lenny's website. That all sounds good. Devin's asking us any questions if there's anything that we have here.

Same story here where you can let Devin make its own decisions and hand off, or you can go ahead and give some more thoughts. Should the button open in a new tab or within an application, I'll say let's open it in a new tab. And you could answer these at any point? You can answer these at any point. You can hand off, hand back off. But it's not going to be like, just goddammit, I just wrote it this way. Why didn't you tell me earlier? That's right. Yeah, one of the main...

One of the big pieces with Devin is Devin will always be enthusiastic, will always be ready to put in the hours. Thanks for changing scope. Thanks, guys. And so we'll give Devin a chance to work, and it's going to go through these files, and it'll make a pull request for us, and we'll see and go from there. But I thought it'd be fun to show some other examples of Devin in action as well. One of the examples, actually this morning, which I just used Devin for, is...

I asked Devin to help me brush my own facts up for this podcast. And so obviously a huge fan of the podcast and the newsletter. I asked Devin, hey, Devin, going to be on the podcast. Could you please research everything you can about him and make a nice website quiz for me so that I can make sure I know my facts. Right. And so, Devin, this is just this morning. I asked him to do this and I'll kind of just show what Devin did. It looks like.

Yeah, went to Wikipedia first. Unfortunately, it's not a page on Wikipedia, which is Lenny will work on that. I guess they did you dirty. I mean, we need a patron. And so then it went and found it on Spotify.

So you're watching what it's researching live? Yeah, yeah, yeah. So this was this morning, obviously. And this is a playback of what Devon did. This is part of Devon. You could just watch what it did. Yeah, yeah. And so especially when you're building engineering projects or something like that, you can see kind of like each of the steps that Devon was doing. Or if Devon tested the code locally, obviously you want to be able to go and look and see what Devon was clicking around with and testing or things like that. So it found the newsletter. It's going and looking at this, right?

And it's going and reading all of this. And then it says, okay, let's get started with putting the code together, right? And so it says, hey, I've researched. It's going through and writing all of this, putting the app together. It plays its own quiz itself, actually, which we should just play this quiz, actually. Let's see how much I know. What is the name? What is the name of the podcast? Lenny's Podcast. Okay.

For people that are watching, so to say, approximately how many subscribers? A million. Very good. Yeah. What are three main topics Lenny's focused on? Oh, product growth and career. Very good.

It's a good quiz. What does Lenny's do besides podcasting? Okay. Writing, angel investing, and advising. How often does Lenny publish? Once a week, right? And so, yeah, so we can go through all of these and do all of these. And I took this quiz, by the way, obviously to make sure that I was well-prepped, but yeah, this is kind of one of the more fun examples, obviously, of just like- Scott, how many subscribers do I have at my newsletter? Over a million, actually. Yeah.

And then one last one I'll show, and then maybe we can come back to our initial run after. But like I was saying, a lot of this is really built to work with all of the existing code workflows out there. And so for example, we were doing some exploration with the DeepSeq repository on GitHub.

And we imported it into Devon and we got our own fork of it set up in Devon. And a couple of things I just wanted to show here. One is Devon sets up its whole Wiki with all of its internal understanding. And so when Devon indexes the code base, obviously,

building a representation of the code base and learning it and improving it over time is one of the big things that Devon does. And funnily enough, we found that naturally humans really are interested to understand this code base representation as well. And so, you know, Devon Wiki is something that we built here and you can take a look at all of these different pieces and see each of these different things. Here are the FP8 operations, you know, here's an SG lag integration. There's diagrams of how the different layers are built and put together. There's, you know,

deployment operations. There's a lot of details about the architecture as well. And you can ask questions about it as well. And so, for example, you can say, how does DeepSeq handle multi-token prediction designed for spec deck? And it'll go through and it's able to kind of search through the entire code base and give you an informed answer based off of that. And so we use this a lot. And it helps when you're scoping out a task for Devon and doing an initial prompt. It also helps, obviously, just in a vacuum. You often have questions about your code base that are really nice, right?

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Something that I've learned as I've been talking to more and more AI building companies and apps is there's a big difference in how large of a code base they could integrate into. And that's a big deal for companies that are existing versus startups, people that have large existing code base. How should people think about what kind of code base Devin can plug into?

Yeah, yeah. So we go all the way to the biggest code bases possible, right? And one way I'd kind of put it is, you know, how the way that we as engineers would think about a large code base is certainly, you know, when you're making changes or when you're thinking about a particular task, you're not

You're not bringing in every single line of the code base at once. You have a high level of abstraction that you're able to think about and look into. And then you're obviously able to zoom in and get to higher resolution on each of these different things. And so Devon works in much the same way, where the first thing it'll do is it's going to figure out the high level architecture of what's going on here and what this is built for and so on.

But within each of the components, it's obviously also going to be able to zoom in and give some more detail about each of these. And so here's FPA to BFloat 16 and how exactly a lot of that is set up. Here's each of the different parts of the code base. And so similarly, we've built this to be scalable. It's essentially coming back to the engineer as architect is now it's helping you understand the architecture.

Yeah. It's kind of circling back to that. Yeah, yeah, exactly. And one of the fun use cases that we've seen actually with folks is they'll often actually use Devin, get Devin's help to onboard new engineers on the team.

Right. And, you know, when you're new and you're joining, there's obviously a lot of questions that you have about the code base or about how things are set up. It also sometimes can be a little bit awkward to, you know, to ask your mentor or your manager the questions. And if you're worried that they're going to be really dumb questions. Right. And so it's nice to just be able to ask Devin and to go through Devin's wiki and to understand these internal representations. Right.

I think that's really interesting because it comes back to your point that Devin is not just a junior engineer. It's what you call a jagged, jagged engineer. Jagged intelligence. Jagged intelligence where it's almost like a staff engineer at understanding the code base. Usually you have to ask an engineer that's been there a long time. What does this do? Where's this thing? How does this work? And it feels like Devin's very good at that.

Yeah, yeah, yeah. Obviously, the retrieval and kind of processing a lot of code and a lot of tokens at once is something that language models are really great at, right? And so basically being able to get those gains in the places that you need them is really great. Sweet. All right. You got a couple more uses. Yeah, one last flow I'll show is just, you know, we just rolled this out last week, actually, but it's a full kind of dev and automation setup with linear, right?

And so if you have tasks that you're doing on the DeepSeq repository, for example, and it's all set up, all you have to do is you just add the Devin label. And Devin will come through and it'll give you this. And it's going to give you its thoughts on what the task looks like. And you can take a look at each of the particular files that you see or it'll point out snippets that it thinks are important.

And from there, if you feel good about what was built or the conclusions that we came to, then you can just start off a Devon session that will go and actually do that work. That is insane. Like, that sounds like such a simple idea. But essentially what you're saying is there are tasks in Linear that are fixes and features.

And now Devin just goes off and can just do them for you. Yeah, yeah. And so it's definitely like it's a hands-on process. You know, you certainly want to be involved when Devin is scoping out the task or giving you its thoughts. And the nice thing, too, by the way, is Devin will give you its confidence level. And, you know, here's how likely I think I am to really understand this piece or that piece or whatever. Right.

But it helps make things a lot faster, right? And to your point, you know, it's like a lot of product managers, for example, obviously love to be able to use Devon and Linear to understand things better, the code base better, or things like that. And, you know, Claire Vo, for example, from LaunchDarkly is a big Devon user, and she loves basically going and scoping out tasks or asking data questions or asking, hey, what's going on? Or like, is this merged into production yet? Or, you know,

is this a feature flag right now or what percent of people are getting this or that feature. It's a clean way basically to make that intelligence much more accessible.

I love just like with the integration with linear that you can still keep it really simple. You know, you add a little ticket like, hey, this link to this homepage would do this. And Devin will be really good at understanding what you mean and then show you here's what I'm thinking. Is this right? Yeah. Yeah. Cool. OK, so. So, yeah, so Devin did finish working. It seems like there's something going on with the CI and it's debugging that right now. But it went ahead and put up the initial first pass pull request and we can take a look.

Let's do it. This is the Devon website, obviously, in this custom deploy. And we have Lenny's newsletter right here. Let's ship this to production. Let's go. Don't be so confused. Yeah. That's amazing. Okay, show it again real quick. So I just added it to the homepage of Devon's cognition labs. Yeah, yeah. So Devon obviously has access to our Devon code base. It does a lot here. And so it's super familiar with all the pieces here. Beautiful. And it said, yeah. I like how that looks. We've got Devon search. We've got Devon wiki. This is a winner.

And we got Lenny's newsletter. I'll link to your site. You link to my site. We'll get some page rank going. Yeah, yeah, yeah. Okay. Is that a good example? Oh, there it is. What a beautiful website for my newsletter. Is that just like a good example of the kind of thing Devin is very good at? Yeah.

Like, here's a very specific thing to change on the website. How does your people think about what Devin is very good at and maybe where it starts to fall apart? You know, the way that we often describe it is I think Devin is best when it is working on tasks that are well-defined. You know, it's one way to put it is you want to be giving Devin tasks, not problems. Right.

And a lot of these things, like what you just saw, which was kind of like a quick front-end feature request or a bug fix or adding testing and documentation or things like that. One of the things that makes a loop really nice, obviously, is a quick way to iterate and test.

And so with something like this, obviously super easy for us, for example, to just go pull up the preview and see that the link works, right? Obviously it would be easy for Devin to do as well. Devin will often go and log into Devin and start a Devin session and make sure it's, you know, when it's working on our own code base, which is,

kind of hilarious. But yeah, you generally want something that is kind of like easy to verify and easy to test is the main thing. And you can work on bigger projects or bigger asks as well, obviously. But in that case, you should certainly expect to need to steer Devon more to make sure you're really, you know, to make sure it's going the right direction. It's interesting because that's very similar to the way people talk about synthetic data and reinforcement learning, creating data that's very easy. There's like a very definitive answer, yes and no. Yeah.

That's very clear. Yeah. Okay. Let me ask you this question. What's something that you guys debated a lot as you were designing and building Devon? I'll give a couple that come to mind. One I would say is, yeah, a question of

I'll call it how opinionated we should be. We had the workflows that we used to Devon for, which was very much, as you can see, for basically integrating to our Slack and GitHub and making pull requests for us in our repos, responding to issue reports or things like that. And naturally, we've had...

certainly a lot of other, you know, kind of different things that have come up that folks have tried. I mean, we have folks who like order their DoorDash with Devin, for example, even we have folks who certainly, you know, a lot of people who are kind of building cool, cool websites from scratch or working on things like that. And yeah, I mean, it's been an interesting trade-off for us where I think

The way that I would describe it is in our products, certainly the large bulk of the features that we build are for this kind of like making pull requests and engineering teams use case. But I think basically our kind of general stance with all the others is obviously if folks want to use Devon for that, that's great. And we want to just kind of make sure that they're fully aware about the limitations and about where things get caught up.

It's funny with AI, especially because I would say the most common pieces of startup advice out there, I would say, is focus on a really niche cohort, do things that don't scale, make one use case that's really great, and then you grow from there. And I think that's great advice across the board. But yeah, it's kind of interesting because I think with generative AI, you naturally see this where a lot of product experiences can turn out to be more general.

Right. And so it's an interesting trade-off for us. This is something that we still always go back and forth on. How much do we want to do more to support all the other kind of use cases out there and to handle other things that folks might want to do with Devon?

I think another one that comes to mind is how much Devon should be, let's say, like a single comprehensive project experience versus like a suite of tools. And as you can see here, you know, we have Devon Search, we have Devon Wiki, you know, we have the linear ticket scoping. And certainly, you know, these tools interact with each other. But I think as time has gone on, we've seen it more and more as really building this suite of tools. And I think that, you know, I think the core agent experience and the core kind of agent that will go off and build each of these

build things for you, for example, is always, of course, going to be, you know, that's Devin and that is like the core piece. I think that will always be what's really special about our work. But I think that all of the other features out there, you know, there is a complex suite of work that's required for real world software sharing and engineering is just messy at the end of the day, right? Yeah.

And so I think there are a lot of different flows and a lot of different use cases that make sense. And an obvious thing to point out is you could ask the same questions to Devon Search as you could to Devon, right? And Devon will go through and it'll do the same thing. It'll go through and look through the files and give you an answer and stuff. But with that said, on the one hand, I think on the capability side, there's certainly a lot you can do to really optimize things for very specifically question answer about this repository. Right.

And that made sense to really kind of build into a specific kind of feature. And then on the other side, I would say we found that users actually really like having this access of control, right? Sometimes you have a task that you're thinking about, but you actually don't want Devin to get started on the task just yet. You want to ask Devin and understand what parts of the code base might

be relevant, right? And so you want to be very direct about saying like, this is just an ask, and I just want to see the snippets of code base that relate, right? Or I just want to look at the wiki and understand the existing representation. And so it's kind of on both the capabilities and on the UX side, we've found that that's kind of what's naturally made sense over time.

Let's talk about the landscape then of just other companies in the space, which is something a lot of people are always thinking about. There's all these different approaches. You guys are going full on AI engineer. There's obviously ID companies. There's also just like models being built that are really good at engineering. Everyone's kind of starting to build agents now. You guys are ahead on this in a lot of ways. Like OpenAI just recently said they're going to build a software engineering agent. Anthropix got something there.

you know, cursor and wind surf have their own little agents and replet thoughts on just kind of where you guys fit in, in the landscape and then where you, how you think you win long-term. How do you think about that?

Yeah. Yeah. And for what it's worth, you know, I think all of these are incredible teams. I think, you know, really smart and really forward thinking folks who are building a lot of great products out there. And it's I think there's there's there's a lot to do, honestly, you know, over the next few years with with the advent of AGI or whatever you want to call it. You know, I think one of the quotes that I love is is.

In 2017, if you asked if we had AGI, the answer is no. And in 2025, if you asked if we have AGI, the answer is, well, you have to define AGI and it depends on your subject. And I think it does kind of get to the point of, I mean, there is a lot of really amazing stuff happening. I think that it's easy to underrate, I would say, just how big of a shift it is that we're seeing, right?

I think there are a lot of great products out there, for example, over the last 10 years, 20 years, 30 years that have made each of these different niches of the lifecycle of building a product a little bit easier, for example. There's great products out there for instant response. There's great products out there for logging. There's great products out there for billing. There's all these different tools. And the obvious thing is what we're seeing with AI is

All of these spaces are going to be moving multiple times faster, you know, and it's going to be like an order of magnitude shift, if anything. Right. And so I think from our perspective, you know, we've obviously had a very specific lens that we've been on this whole time. And that is, you know, autonomous coding agents. And there's there's a lot of problems solved there, to be honest. Right. Like the the.

There's still a ton to do on the core capabilities, certainly. And we see cases all the time where it's like, wow, why did Devon make that decision? That seems-- no human engineer would have ever done that. There's all sorts of spots where it

with the product interface, there's obviously a lot to think about. And I think it's, it's, by the way, not just like a single thing that we're working towards, but something that will change with every addition of capabilities. Like I kind of think of it as like, there's, there's 20 generations of agent product, you know, agent coding experiences to come. You know, I think that, I think the one that we'll get to over the course of several years is probably something where you

you don't even look at the code at all, right? And you're actually just looking at your own product and you're just able to look and specify and say, hey, you know, this button should be a little bit rounder. Let's do that. And by the way, let's add a new tab here and maybe we should save this information. Let's start up a database table and let's index it on X, Y, and Z columns. And you're just, you know, basically working with your products in real time and having your agent build out those things for you. Obviously, there's going to be a lot of generations, you know, in between that.

here and there. But I think the product experience itself is going to change every single time. And then obviously, you know, there's all of the practicality of just getting it out there in the world. And so...

you know that folks obviously need to need to learn how to use the new technology there's a lot to do to to deploy into all of the messiness of real world software you know there's a lot of cobalt out there still there's a lot of fortran out there still there's you know there's lots of kind of abstractions and and details that folks have done and so i think from our perspective you know we have we have been been since the beginning have been laser focused on aj

agentic coding. And that is the one thing that we've really believed in. It's the one thing that we've designed for. And that goes all the way to even the revenue model with ACUs and having the usage-based setup. It goes into, obviously, all the product experiences of thinking how, okay, where do you want to talk to Devin? You want to be able to talk to Devin in Slack. You want to be able to spin this up from your issue tracker. You want to be able to... All of these things. And then, of course, the capabilities. And so I

I don't think there's any one easy answer. I think it's obviously a combination of things, but this has been the space that we've lived in and spent all of our time in for the last year and a half, and it's going to be that way for the next five or 10 years too. Along these lines, a big question everyone always has in AI is moats and defensibility.

It's a question I've been asking every founder that comes on. How do you just think about how to build a moat in this space when it's so much easier to build and these models are, you know, so much is built on these models that are themselves advancing so quickly? I'd give one slight kind of tweak on that, which is I think it's often less about moats and more about stickiness. And what I mean by that is, you know, moats are in some sense, typically what folks mean by a moat is something that means that a competitor couldn't even enter the market.

And I agree that at a high level, a lot of different folks had different layers of the AI spectrum, the foundation labs or the application layer or so on. I don't think there's any kind of hard barrier that would prevent others from entering. I think what does exist is stickiness, which I would kind of define as once you have a product experience that you really like,

are you excited to keep using that experience or is there kind of like, you know, is there an effect where it is just as easy from now on to just switch on to a new one and learn a new one and so on, right? And I think

From that perspective, I think there's a few things that are really great about coding agents in particular. One I would say is there is a lot of just inherent stickiness and learning and buildup over time, which is that as you use Devon and as your whole team uses Devon, it's the same thing with an engineer. If you're joining on day one versus... You've been at the company for five years, you wrote half the code yourself. You've touched every file, you've built every single piece, you know all the engineers.

And so similarly, it's like Devon will really learn and build its representation of your code base and of your stack and of your process over time. And we'll be able to do a lot more with that. And then the other piece of it, which I think is really exciting, I'd say, is

There really is a lot to do of what I would kind of call like a multiplayer aspect of code, which if you think about it is how a lot of things get done in the real world, certainly. And so it's one thing to have your own experience, which you use yourself as just an engineer. But for example, ourselves, we see this all the time where

some engineers are working with Devin and teaching Devin things. And as I mentioned, like folks will have Devin onboard their new engineers and kind of convey that knowledge to them, right? Or similarly, it's like, you know, I'll start a session with Devin in Slack and I'll say, hey, you know, it'd be cool if we could do this thing. And some other engineer will chime in and say, oh, by the way, like the reason we did it initially was X and Y. And so,

Devin, just make sure when you do this change that you still support that workflow. And Devin will say, okay, sounds great, right? Or Devin will make a PR. I'll be working with Devin. We'll make pull requests in GitHub and somebody else will be reviewing that PR or give some comments and Devin will work on that too, right? You'll be in linear. And so all these kind of spaces, it really does just kind of set up for an experience where

Basically, where Devin can just grow in the value that it can provide for your whole work over time. And so I think from that perspective, if anything, we want there to be a lot of innovation and a lot of new products and so on. I don't think that the goal is to try to lock other people out of a building. There's a lot of stuff to build, and I think there's going to be a lot of different experiences. I think from our perspective, what we think about is more like, how can we make Devin more and more and more useful as you're using it more?

It's very similar. We had Michael from Cursor, the CEO of Cursor on the podcast, and he had a similar point of just he thinks moats are just kind of like consumer, like Google is the way he thinks it's like Google, where people can easily switch. You just have to say the best. And that's the answer. Yeah. And it feels like you're adding to that of just like, but also if you can create some stickiness where it is very hard to leave because it's so good at what it's doing and it's built knowledge and integrated to your workflows, that ambivalence.

and builds on that. Yeah. And I think one of the things that's nice about our space too is software engineering, for better or for worse, has a very clear tie to value. And what it means is, I guess one way to put it is, there is always kind of like a clear next level, at least for the next while. I think there could be some point where you're just like, all right, just build the entirety of YouTube for me. And Devin just does the whole thing. It's like,

there's probably been like a hundred million hours of human engineering time. It built, building YouTube, building the algorithm, building all the infrastructure, all of the, the everything, every little detail. And like, you know, maybe there's some time where Devin just does that out of the box, you know, that that's, that's obviously going to be a long time from now. I think on the interim, on every level in between, obviously it's,

It makes a difference, the quality of software engineering. And I think one of the cool things with developers, obviously, is developers are really willing to learn new experiences and to put in effort if it means that they're able to have a higher and higher quality experience. Awesome. I'm going to spend a little time on the tech that enables Devon without divulging anything.

trade secrets, just what allowed you to make Devon so good? Was there like an unlock with a certain model? Some folks have shared like 3.5 was a huge unlock for a lot of their products. Just what's kind of the key to the way you've architected or built Devon that makes it work so well? We obviously, you know, we've been betting on agents for a long time. I think that agents were doable and workable like a lot earlier than most folks might have thought.

But certainly, I think as the community has really rallied around it, I mean, you see the impacts of that in the pre-training. You see the impacts of that in...

in a lot of the work that's done with these models, I actually don't think there's been any, like from our perspective, I don't think there's been any single like step function based model shift or anything that has been kind of like a night and day difference in Devon. But I certainly think that the curve of, you know, every point on the chart, I mean, there's a new model that comes out every week now, has obviously made a big difference in terms of what we've been able

to do. And then obviously on top of that, we work with the research teams at all these foundation labs to do a lot of our work on top. And so I think that my hot take here, which I would give, is I think the

I think in terms of base intelligence, we're honestly basically already there. And I think a lot of what we see actually and what we spend our time on is less so. Obviously, we don't pre-train our own models or things like that. It's less so like increasing the base IQ of a model, for example, and more about kind of like teaching it all of the idiosyncrasies of real world engineering. Right.

Right. And and thinking about here's how you use Datadog and do this. And and here's how you might diagnose this error. And here are the different things that you could run into. And here's how you handle each of those. And when you're ready, here's how you make GitHub PR. And, you know, this is true in engineering. It's true in every other space as well. I mean, there's so much detail and idiosyncrasy to to the work that we all do, obviously, day to day. And and a lot of it is kind of like.

like teaching the model to mirror the complexity of the real world, I would say, rather than kind of like getting it to some higher fundamental level of problem solving, which I think the foundation labs are doing a really great job. There's something you shared when we were chatting before we started recording around the growth of

previous kind of transformative technologies were very hardware oriented and there was like a limiting factor to their growth and AI is not that. Can you just share that insight? For a number of reasons, I think AI is going to be the biggest technology shift of our lives. But I think one thing, which is what we were just talking about before this, which is AI

Most of the big tech revolutions that we've had over the last 50 years, I'm thinking about personal computer and the internet and the mobile phone. They all had this

This big hardware component that was a big part of the distribution. And so you had the internet. And initially, it was just these universities that were talking with one another. But obviously, over time, we got the whole world plugged into the internet. And it took years and years and years. The same thing was true with mobile phones. The same thing was true with PC. And the thing that's interesting about that in particular, which is I would say we're already seeing the effects of that, is...

In these hardware distribution regimes, obviously there's a lot that depends on real time. And so folks who were building for those industries saw their market grow and grow and grow basically steadily year over year as the number of people with mobile phones increased, as the number of people connected to the internet increased. And many of those businesses

it's still crazy to think of, but many of those businesses got started right in the beginning. I mean, like Apple and Microsoft were started like right around the same time, you know, and same is true for a lot of the great internet businesses or wherever. But certainly it was kind of like a,

It was something that touched the whole world with time or a large fraction of the whole world. And it had a really massive impact, but it took place over several years because of the time that it took. And I think one of the things which is already, I'd say, different in AI is just how explosive the technology can be. Once AI could, and I think we're firmly past the inflection point in AI could, where it's

As an engineer, if you're not using AI at all to write code, you're falling behind, honestly. And it is a technology that everyone should have and should be using. And there's no weight on hardware distribution that is causing that. And it means that the space is just growing so exponentially, basically.

Michael Pollan has this interesting point that cliches are cliches because they're so true. And that's why you they're like cliches. Like I heard that a million times. And I think it's like people hear this like, yeah, I know, but.

Like, it's actually insane what is happening. Yeah, that's what that's why you're here to help us through this transition. Yeah, no, I mean, it's a fun time. And I think there will be, you know, real, real investment and real work that it takes. But I think from the perspective of us as engineers, for example, I think it just means it's it's it's so important to to to stay in the loop with everything that's happening.

And, you know, as we're seeing, it's not only because of your learning and your ability to work these technologies, but it's also, you know, about basically like teaching the AI, you know, what there is to know about your code base in order to make it really effective at building with you and doing more of the things that you would want it to do. So along those lines for people listening that they're like, hey, we should be using Devon at our company.

What are things you've found to be helpful in helping an engineer at a company get adoption and be able to use Devon, either culturally or logistically?

So a pattern which we often see with folks is there will be a few folks at the team who are really excited and want to try out the new thing and they want to put in the investment and are really excited to get it going. And they'll go through all the setup. They'll give Devin the repos. They'll teach Devin how to run the lint and the CI and all of those details, right? And they'll start off by giving it those initial tasks and kind of like help Devin build a foothold, basically. And as time goes on,

Eventually, folks will see, wow, Devin's writing all these PRs. Who's this Devin person that just joined the company is just knocking out PRs? Yeah, and they'll see that and then naturally they'll get on and they'll get an account. And one of the cool things, of course, is by the time they join, Devin already knows a good amount of detail about the repositories that they're already working in.

And they're working with that. And so one of the really cool things which we often see is, yeah, that the early adopters themselves can really pave the way, I think, for everyone else on the team. But yeah, I think the main thing I would just kind of call out is, yeah, it really does take, it's a very different product experience, right?

And I think it's for what it's worth. I think there's still a lot more that we should be, you know, that we can do to to make it as intuitive and as clear as possible to folks like how to use Devin and what the right steps are and how to really maximize value out of Devin.

But I think that, yeah, it's the kind of thing where if you put in the investment and understand exactly what it takes to get Devon to be successful, we've found ourselves that as time has gone on, we just use Devon more and more and more with every next update. So let me follow that thread. There's a question I ask every year.

AI app building founder, which is if you could sit next to every new user of Devon and whisper something in their ear to help them be successful with Devon, like one or two tips, what would those tips be? I think the biggest thing I would say is it really is just, you know,

treat Devin like your new junior engineer. I think that's the biggest thing. I think folks come in and they see the blank page and they think of all sorts of various things that they want to try out. They think of lots of... I think typically the flow that we see that works best is obviously you can try demos, you can do things, but a lot of it is just like

Yeah, let's figure out what tickets we want to get done today or this week and let's have Devon get started on those. And let's start with the easier ones and then work with Devon and understand what things Devon needs to get set up to be able to test its own code and do this well. And then let's scale up over time. And then obviously, as you work with your engineer, you understand better how to communicate with them or what are the right tasks or projects to bring them in on. Yeah.

But I think that really is the one-liner for us. Okay. There's a question I've been meaning to ask. I just want to get back to this because it's something I think a lot about with Devins. Everyone's going to have five Devins, let's say 10 Devins. Everyone's kind of turning into basically an eng manager with a bunch of junior engineers, which isn't necessarily the best job in the world. It's just a bunch of reviews. At least you don't have to do performance reviews and not once. But it's like sitting around checking a lot of PRs all day.

there's a sense of you become an architect and which is a kind of what every engineer wants to become eventually right there. Like, I just want to think about the architecture. I don't want to code all these fixed bugs. So I get that. That's, you know, there's a good side to that, but just how do you,

I imagine you're thinking a lot about this, just like how do you make life pleasant and fun and enjoyable as basically an engine manager of say 500s Devins in the future? Yeah, I can just imagine the performance. You know, it's, Devin, you've done a really great job on your task, but I really would like you to be more proactive in the team meetings. So what I would say,

It's funny, actually, because it's something that, you know, in terms of the wording that we thought a lot about as well, it's just, you know, we've used the term like manager of Devon's in the past, which of course I think is a big part of it. But I think that the only thing I would point out here is I do think

I think that the bricklayer versus architect is closer to the experience than being a manager, because I think a lot of the difficulty of, you know, management or, you know, the reason that, you know, that people shy away from it is more because of, you know, a lot of the kind of various, let's say, like.

there's, there's kind of like all of the kind of like the context and the ownership and kind of like the responsibility and stuff. And then there's also kind of all of the, the, the emotional aspects of it. Right. Where I think working with Devin is a little more like just being kind of like having more, it's like having an interface to, to hand off tasks and build tasks. Right. And,

And so, you know, the parallel that I would kind of draw is, you know, when we invented Python, obviously, like, we didn't, you know, it's like, in many ways, you know, the description and the outlining of tasks, it was obviously, it was like a different paradigm. But I think certainly it was nowhere near, you know, what folks typically think of as kind of like management bureaucracy today, right? And I think that with Devin, a lot of it is just kind of like, it's more like,

finding the right levels of abstraction that you could work with Devin on and just kind of like finding the workflows that work really well. And the obvious thing to say here is it's a very, you know, it's like you can always have Devin take a first pass at things, right? And so, you know, you can take the first pass, you have Devin take the first pass, you know, if it's great, you merge it right away. If it's, you know, if it needs some touch up, you can obviously give that feedback, for example. But a lot of it is like, it's more about

basically making Devon part of your flow than it is losing control, which I think is the main thing that folks are scared of with management. Are you thinking about a manager Devon, like a Devon that manages other Devons? Yeah. So for what it's worth, Devon can start other Devons through the API. And so we've seen this happen quite a bit of times where naturally if you have some big task that you want to do, Devon will do this all the time. It'll chunk up and

and then I'm parallelized into smaller Devins. And so it's the kind of thing that you need to give down the credentials to be able to do that. It's not currently something that is kind of like default enabled, but I can certainly imagine as time goes on that there's more and more of that. Devins all the way down. Yeah, yeah. I think the thing that's kind of interesting too is like, you know, with humans, the way I almost say it in technical terms is like, there's this coupling of a context and a thread.

And what I mean by that is, is like, basically, each human can only operate, you know, single threaded on the work that they do, and they have their set of contexts. And then there's other humans, obviously, who can do other stuff at the same time, but they have their own context, right? With agents, one of the cool things is you can have an agent that's doing multiple lines of exploration at once, but is sharing all of the context of everything that they find.

And so I think that this is very early and I think we'll see this, but folks obviously love to talk about basically like systems and agents communicating with one another. And I think that there will be a lot of new paradigms to build for once we get there. And it's so interesting what you said about the decision between having one Devon and only one Devon do all the things and you just tell them things and they kind of fire off jobs versus you have five Devons and they're each doing individual things. It's such an interesting decision to make.

Yeah, for sure. Two more questions.

What's maybe the most counterintuitive thing you've learned so far building Devon that maybe goes against startup wisdom, common startup wisdom? Something I've thought about a lot lately as we've built this is, you know, this is not my first company. Actually, for a lot of us, it's not our first company. Like I think of our 26 or 27 people total on the team, like I think 18 of us have started our own company before this. And yeah, like one of the things I think about is, you know, there's actually your point about cliches, I think really important.

really spoke to me as well, which is there's kind of the really common things which you hear all the time in startups where it's like, you know, you got to move fast or you got to hire great people. It's like, okay, well, obviously you do. You know, I wasn't planning on not hiring great people. You know, I wasn't planning on going slow. And similarly, it's like, yeah, you really got to build something that people want, right? And there's kind of these like three to five things which are always repeated and they're always the common wisdom in startups.

And I definitely had this idea as a founder when I was starting initially that

All right. So those are the kind of the three to five basic things. But as you get really deep into it, you spend a lot of years into it. You know, you learn all of the thousands of other things that you have to learn to be, you know, to build a company. Right. And I think to some extent, that's, of course, true. And there's lots of little details that you'll get into with all these different things. Right. Including including team building and product and strategy and engineering, decision making and fundraising and and

and sales and every other component, right? But I also realized that as time has gone on, more and more, I felt like building companies well sometimes just comes down to doing those three to five things just right.

even more than you could possibly expect. And so with us, it's like, everyone says to go fast, but it's like, yeah, we had a hackathon in November. We had another hackathon in December. We started the company officially in January. We got the prototypes out to initial users in February. We did a launch in March. We got our first customers in April. It's just basically truly pushing the pace in every spot where we possibly could has really made a difference for us.

And similarly, it's like, yeah, everyone always says, you know, you should hire great people. But I think that the truth within that truth is basically like you should fight

to all ends, basically, you know, to get the folks that you really want to bring in. And it's, you know, one of my favorite stories to share is we had a candidate who came and interviewed. He was a junior at MIT. So he was very, very young. And we gave him our interview. He did way better than almost any of the full-time, like the full-time candidates that we had ever talked to. And so we said, hey, you know, what do you think about

taking some time off of school and working with us and building out Devon, like we really think you're just going to be able to come in and just have a ton of impact already from day one. And he thought about it for a while and he came back and he said, no, like I'm down. I want to do it, but my parents really want me to graduate from school and I'm just not sure there's a way to make it work. And so, and so, so we talked to him more and kind of just understood the situation. And then, you know, and then we,

We flew to North Carolina, went straight from the airport to his parents' house, had dinner with him and his parents. We talked a lot. It's a really, really nice Gujarati family. We kind of gave them some gifts and just talked to them about it and tried to understand, all right, what would it take and what will we need to make work? And they just said, you know, it sounds like a great opportunity, but we really want our son to be able to graduate, right?

Right. And and we talked that through and we figured out a setup basically where he could work for us essentially full time, but then come in for his required classes and and do what he needed to do to get the diploma, basically, but no more than that.

And we talked that through and then, you know, we got to a point where everyone was happy with that. And then, you know, went straight back to the airport and flew right back basically. And that was, you know, that was the first and only time that I've ever been in North Carolina. It was just a great trip, you know, and it's the kind of thing where it's like, you know, it's,

Hiring great people is one thing, but truly just never giving up and really giving it everything that you can to make it work for people who really make sense to be on the team. And he's been with us on the team for over a year now, and he's been an incredible, incredible engineer, and we wouldn't be here without him.

him. And similarly, we had someone else who was, again, really, really talented candidate, did amazingly well, very young and had a lot of great offers at a lot of other companies. And we were talking to them about, he wanted to start his own company someday as well. And we were talking to him about

certainly a lot of the obvious things, which are kind of like having him meet our investors or get to do work with customers or see a lot of these other components so that when the time came, that he would have all the experience he needed to start his own company. But one of the other things that was big is he was talking with a lot of great companies already. He didn't want to burn any bridges. And so we actually worked with him and basically hand wrote all of his

responses to each of the other companies and kind of worked with him on it to say like, you know, here's how you should say it in a way that's

you know, going to come off as like, you know, that you really did appreciate the time with them and that you obviously, you know, you want to remain close with them and stay in touch. And it was the kind of thing, obviously, where it's like, look, obviously, it's our job is to make sure that he's happy enough that he doesn't want to leave anytime in the near future. But I think it's the kind of thing where, you know, the way that you put together a really, really great team is by really what's, by fighting for what's right for them, too.

So, wow, those are incredible stories. And it makes so real these, you know, as you say, cliches, hire the best people. Like this is what it sounds like to hire the best people. This is what it takes.

Yeah, no, and I was just saying, it's just like, you know, a lot of things, yeah, we've, we've, we've fought very hard to just kind of like, yeah, like reimagine things from the ground up, because it's, you know, a lot of it really is just thinking about like, yeah, like, where, where do we think the technology is going over the next five to 10 years? And, you know, what is that the place that we want to have in the future? So

I wonder if people are going to be fighting for the best Devon someday. I'll give you overtime pay benefits, you know, free health care and everything. And then it's like, yeah, it's like magic, the gathering cards. Yeah. And then just going back to your three to five things. So essentially, this is incredible advice. Essentially, it's like you always hear hire the best people, move fast, build things people want.

Yeah, build something people want, you know, stay as close as possible to your customers, right? And then I think the other thing is just always think about where things are going, not where they are today. I feel like those are kind of like the five things, which is, you know,

And especially in AI with things moving so fast and there's so much great talent, I feel like a lot of these are even more true where it's kind of like, it's not just thinking about where things are going to be in 10 years. It's like thinking about what's going to happen next week. And obviously things are moving very quickly and it is very hard to predict, but you really have to be very rigorous with yourself, I'd say, about like,

thinking through those things and evaluating all of the decisions that you make in that lens. And staying focused is the big takeaway to me here. It ends up feeling like there's a thousand things you should do, but it's always these five things. Scott, we covered a lot of ground. We went through every question I had, which is great. Is there anything else that you want to share? Anything else you want to leave your listeners with? Maybe a final nugget or something really you want to double down on that we said before we...

let you go. The biggest thing that comes to mind for me is there's a lot of different perceptions about AI. There's, I think, basically every emotion under the sun right now. There's a lot of fear, for example. There's also a lot of skepticism. We're very skeptical types as well, and we always want to try it ourselves to really see it and believe it.

I think the main thing that comes to mind for me is, you know, I'm honestly really optimistic about what we're building here with AI and, you know, not just with code and with Devin, but the whole space and everything that's getting done. And I think one of the cool things that is really actively happening is just the ability for everyone to multiply themselves. And that's how we've always thought about it. It's how we've thought about what we're building. And it's, you know, I think the...

There is a lot more to do out there in the world. You know, I'm not too worried about us running out of things to do. And from that lens, it's it's I think that the thing that we've always been most excited about is is how can we all do more? I hear you, Scott. Well, with that optimism, we've reached our very exciting lightning round. Are you ready? Yeah, let's do it. OK, here we go.

First question, what are two or three books that you find yourself recommending most to other people? In terms of nonfiction, I think...

for folks in startups, I think one of the things that I've really enjoyed is just learning and understanding the history of Silicon Valley. And there's, you know, it's all these things that we think about. Somebody invented them. I mean, it's one of the great realizations I feel like is like, you know, somebody invented the idea of a seed route, right? Somebody invented the idea of venture capital. Somebody invented the idea of like product market fit, you know, and all of these different principles that we talk about. And so for that, there's a book called The Power Law by Sebastian Malaby, which I

I really like it. Basically, it's just kind of like a tour of many of the great businesses and the great products that have been built over the last 60, 70 years in Silicon Valley, which I really love. I think in terms of fiction, I actually have always really liked The Great Gatsby by F. Scott Fitzgerald. It's one of my personal favorites as a fiction person.

Do you have a favorite recent movie or TV show that you've really enjoyed? I have to admit, I have not watched. I can't think of a single movie or TV show that I have watched in the last while. So I'm sure there's I'm looking forward to watching a lot of great ones post AGI. That's got to be in the trailer. That's great. I like that. And that just shows how hard you're working, just how much it is going on and how fast everything's moving.

Do you have a favorite product you've recently discovered that you really love? Could be an app, could be something physical, could be toothbrush, toothbrush.

One I would say is, you know, I got an Aura frame recently. It's just like, you know, a frame that shows photos. And you can show a new photo every day or every hour or, you know, every 15 minutes or whatever you like. I've actually, I've really enjoyed it a lot. I think it's a nice way to just have, basically, just have a picture frame with memories that come up. And then the other thing I would say as like a general purpose thing, you know, it's,

It's not particularly new, but I would say I think AirPods are actually extremely well built and well designed. I realize now that it's like, I basically use them for all sorts of... I'm taking calls on a walk and I'm using AirPods. Obviously, I'm doing work at my computer at my desk, I'm plugged into AirPods. And it works quite well, I don't see, for a lot of different situations. And they're very comfortable, they're very consistent.

I'm going to double down on the Aura Frame. I got one of these for my mom and my mother-in-law, and they're so great for just sharing photos of your kids with your family.

And people have like, you know, they've heard of picture, like digital picture frames, but the Aura just does it really well. And it's really easy to add photos and they're just really nice looking. You can imagine, you know, not that long from now, we'll have the Aura frame accepted, you know, Studio gibblifies every photo that you have in it. And then, you know, it's, yeah. Or just imagines things you've done that are really cool. Like a sweet life.

Yeah, cool. And it's Aura, I believe is how you spell it. If folks want to check it out, we'll link to it. Not affiliated. Okay, two more questions. Do you have a favorite life motto that you often come back to and find useful in work or in life? Yeah, you know, something I've thought about a lot is a lot of the...

A lot of the proverbs out there are actually contradictions. It's like birds of a feather and then you also have opposites attract. And it's kind of funny because you feel that both of them are true and often they both are true and a lot of it is about understanding why. And one of those that I feel like, especially in the world of startups that I think about all the time, is I think it is true.

very important to be focused and driven and to really kind of maximize your potential. And then at the same time, it's also very important

to not let your own personal emotion get tied up in your success or failure. And I think especially with startups, because there's always ups and downs, honestly, even in the most successful companies ever. It is just like, it's a rocky road. There's a lot that happens and a lot that goes down. And I think one of the things which I've thought a lot about is that somehow you really want to

do your best and put everything you can into it and do everything you can to basically, you want to put it all out on the field, you know? But at the same time, you want to be okay with both wins and losses, right? And you want to be able to move on and go into the next one each time. And something, yeah, I mean, it's funny, but what I've found personally is that

Obviously, it's really important for your own emotional state and mental state to be able to do that. And we've had lots of mistakes. I had my first company, which was cool, but there were a lot of tricky spots there. And then over the course of cognition, it feels like it's been already eight years compressed into one year. And it's still going at that pace. But somehow, it also actually makes you

more successful, I think, too. You know, it's like you are just more able to give it your best and to do the things that will lead to success if you're not tying it up in your own personal worth. So that is so interesting. I just had a podcast recording recently where with an executive coach, Jerry Colonna, that I think will come out before this might be after this.

That's one of his big pieces of advice. And it's a very Buddhist approach of just not clinking and attaching to an outcome. Yeah. Okay, final question. I'm curious if there's a story here, but we could keep it short. Is there a story behind Devin as the name and or is there another contender for Devin?

for Devin being the agent? Devin was the name from pretty early on. We were interested, you know, we were working on coding agents from the beginning and, you know, my co-founders are Steven and Walden, for example, and we had this idea, all right, let's get started. And, you know, let's not, let's try to kind of like, kind of like,

expand the box as much as we can. So have everyone kind of think out of the box and do their own thing. And let's have everyone do their own thing first for a bit. And then we'll kind of like consolidate and take everything that we've learned. And so, you know, Walden made a virtual, you know, developer version of him, which was called Dev Walden. And then Steven made one of him, which is called Dev Steven. And, you know, we had all of these. And then we were kind of combining it all into one thing. And we're like, okay, you know what? It's Devon.

And that was the thing. And so Devin was like, yeah, Devin stuck for us quite early on, I would say. One thing which we did have a big decision on there actually is what the...

what the image of Devon would be. And so as folks know, there's the hexagons and then people might have seen this more recently, but there's actually also an otter, like a little otter with a laptop in its lap that is Devon as well. And we had this debate over what to go with and what not to go with and stuff. It's been a while now, but somehow we still have both the hexagons and the otter.

You skipped over where the Devon is. Oh, so Devon is a dev. Yeah. And so it's kind of like when we were consolidating all the names, it just kind of seemed clear then that this would be the universal dev that we all like to work with. Incredible. Scott, this was so much fun. Oh, my God. I learned a ton, which is always a really good sign.

Two final questions. Where can folks find you slash Devin slash anything else you want to point them to? And how can listeners be useful to you? Awesome. Yeah, no, we're at app.devin.ai. And, you know, you can find us as well on Twitter or a lot of other social media. We'd obviously love to hear any feedback you have about the Devin product. Like there's so much to figure out. And I think the

Like I said, I think we're all still like 20 steps away from really the future of software engineering. And so it really means a lot to hear what folks think about the product as they're trying it out. And so please, please let us know anytime if there's things that we can do to make it better. Scott, thank you so much for being here. Thank you so much for having me. I had a great time. Me too. 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's podcast dot com. See you in the next episode.