Scott Wu, Cognition

Summary

Podcast Summary: David Senra with Scott Wu, Cognition (June 28, 2026)

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Overview

In this engaging and deeply personal episode, David Senra (Host, Scicomm Media) interviews Scott Wu, founder and CEO of Cognition, creator of the AI software engineer “Devin.” The conversation delves into Scott’s intensely competitive upbringing, the founding motivations and culture of Cognition, the company’s product evolution and business strategy, and Scott’s unique vision for the future of AI and work. The tone is candid, nerdy, ambitious, and sometimes philosophical, with plenty of insight into what drives great founders—and how Cognition is attacking the problem of building software through AI.

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Key Themes and Discussion Points

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1. Scott’s Background and the Origins of His Competitive Drive

• Early “Saltiness” and Family Dynamics

  • Scott was intensely competitive from a young age, recalling his first experience of losing at a middle school math competition as a second grader.
    • "I just remember being so pissed about that." (Scott, [00:39])
  • His family was steeped in competition:
    • Father: competitive Go player—immigrated to the US thanks to Go connections.
    • Mother: described as “the most salty” in the family, instilling confidence and a high value on being the best.
    • "Salty just means that you take offense to the idea of losing." (Scott, [04:38])
    • "She always told me that I was best and she was always extremely proud..." (Scott, [06:06])
  • Math and programming competitions defined his childhood. His closest friends were fellow competitors.

• Motivation and Ambition

  • Distinct archetypes of parental influence: some, like Elon Musk’s and Larry Ellison’s fathers, were negative and stern; others, like Scott’s mother, instilled a sense of special potential.
  • "My mom falls more into, like, the Estee Lauder category… she would tell you that … you could do even better." (Host, [05:43])

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2. The Drive to Win and the Pain of Losing

• Winning vs. Losing
  • The emotional driver for Scott is the aversion to losing, rather than the pleasure of winning.
    • "Losing feels way worse than winning feels good, but not by enough that it makes me want to stop trying." (Scott, [09:05])

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3. Founding Cognition and the Vision for Devin

• Team and Mission

  • Cognition was founded by a large group of experienced founder-programmers.
  • "This is the big one… We want to be a generational business. We want to build a hyperscaler." (Scott, [09:43])

• The Product Vision

  • Devin aims to be the interface between humans and computers—empowering anyone to turn instructions into working software.
    • "Devin is the way that humans can tell their computers what to do. Because that was the whole point of software engineering anyway." (Scott, [10:17])
  • The ambition is to help shift the world from “survival mode” to “creative mode”—a world where everyone has powerful tools to express themselves.
    • "We’ve been spending all this time living in survival mode as a species, and now we’re going to be living in creative mode." (Scott, citing his co-founder, [11:37])

• Devin Today and the Path Forward

  • Currently: used by enterprise dev teams to increase productivity by 10x (clients include Goldman Sachs, Mercedes, US government).
    • "Teams use Devin to ship 10 times faster and to do 10 times more." (Scott, [13:47])
  • Vision: a future where instructing your computer is entirely natural-language-driven, lowering the cost and friction to automate everything—including one-off, “single-use” software.
    • "The task is: you’re the one that’s deciding what to do. The agent does the execution." (Scott, [17:15])
  • Timeline: Scott believes dramatic progress will occur within the next five years.
    • "I honestly think we’ll have solved most of that over the next five years or so." (Scott, [18:00])

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4. Understanding Exponential Progress in AI

• Predictions and First Principles Thinking
  • Most people underestimate the speed and scale of AI’s self-improvement—humans aren’t wired to process exponential change.
    • "People often underestimate how fast things can change and how fast the world can change." (Scott, [19:01])
  • Critical difference in perspective: “first principles” thinking vs. simple historical pattern-matching.
    • "Rather than any kind of pattern matching, what really matters is just thinking about things from first principles..." (Scott, [21:12])
  • Dramatic improvement: time spent on a single “uninterrupted” AI work session has grown from 10–20 seconds to hours in months, and could soon be days or weeks.
    • "Why can’t that be days? Or why can’t that be weeks or months of work?" (Scott, [21:40])

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5. Product Evolution, Go-To-Market, and Enterprise Motion

• Early Product Launch, Criticism & Iteration

  • Initial demo videos drew polarized reactions—some awestruck, others deeply skeptical.
    • "There were some people who were like, this is the coolest thing ever... Then some people...this is the worst...or a scam." (Scott, [31:18])
  • The very first demo task (setting up MongoDB) was a breakthrough for Scott:
    • "I remember the first time that it did a real task. I could not sleep that night." (Scott, [32:23])
  • Cognition chose to “plant a flag” for working with AI as a coworker, not just a Q&A chatbot.
    • "The idea that you would work with AI as a coworker rather than as a tool or a chatbot... was important for our brand, for recruiting, for customer work." (Scott, [36:39])

• Enterprise Focus and Customer Onboarding

  • Cognition’s early growth depended on tackling monotonous, large-scale engineering challenges, such as codebase migrations, especially for massive enterprises (e.g. Nubank in Brazil).
    • "Building the product was almost like building for one company." (Scott, [44:29])
  • Revenue distribution: now ~75–80% enterprise, with a growing self-serve motion among real product-building teams.
    • "We don't focus at all on individual hobbyists...We focus on real teams building real products..." (Scott, [42:22])

• Deployment Tactics

  • Early deals required handholding and even “the whole company” flying out for the first customer. Now, customer deployment is faster (aim for 3 months), focused on deep customer education and enablement.
    • "We still do this where we'll fly out and ... point them to the right use cases, teach them how to get success..." (Scott, [46:05])

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6. Business Model, Alignment, and Technology Platform

• Incentive Alignment

  • ROI is rigorously defined in collaboration with each enterprise customer—measured in real output, not tokens used.
    • "Let's try and rank people by how much output they're actually producing or how much good work is actually getting done." (Scott, [49:13])
  • Neutrality: Cognition runs as a “compound model system,” leveraging the best model for each task (Anthropic, OpenAI, Google, or their own), rather than a single provider.
    • "Devon can use any of the different models...and will dynamically go and choose these models for these tasks." (Scott, [51:18])
    • "We like being Switzerland." (Scott, [50:06])

• Focus as a Startup Weapon

  • Cognition’s differentiation versus Big Tech is an obsessive, single-minded focus on building software tools, compared to the generalist approach of Microsoft or Google.
    • "The way that you build a real, lasting business is by really focusing and narrowing on one specific thing, making a very concentrated bet." (Scott, [52:39])
    • "We're just going to care so much more about it..." (Scott, [53:41])

• On Competing with Big Tech and Remaining Independent

  • Despite early skepticism (“Microsoft already has GitHub Copilot”), Cognition thrived by innovating faster and focusing tightly.
    • "There's so much more innovation and so much more to build..." (Scott, [55:42])
  • Scott is adamant about independence, despite acquisition interest.
    • "There's gotta be some crazy number somebody can throw at you where you're just like, fuck, I have to take this."
      "Not really." (Host and Scott, [60:43])
    • "We would sell if we thought it was the most ambitious thing to do ... But, you know, it's like my genuine answer in the sense that, like, that's, like, what we care about..." (Scott, [60:59])

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7. Founder Psychology, Motivation, and Culture

• Intrinsic Motivation

  • Scott echoes the “one life, go” philosophy—he does not care about personal wealth or cushy exits.
    • "I think I like eating sushi. That's fun. The sushi doesn't cost that much. You can do that off of an engineer salary as well." (Scott, [61:21])
    • "If we felt like, you know, we could have gone for it all, we could have pushed harder and we didn't... I don't think we would, like, live with ourselves..." (Scott, [64:03])

• Company Culture

  • Cognition is the manifestation of Scott’s social world; the company is “just all nerds” by design.
    • "Is like, I don't care if you're not a nerd… My friends are all, yeah, I agree." (Scott, [62:33])

• Reflections on Legacy and Regret

  • The pain for Scott would be not trying, not failing.
    • "If we tried and we gave it our all and we just weren't good enough… that, I could live with. But...we could have pushed harder and we didn't...I don't think we would live with ourselves." (Scott, [63:59])
  • Echoes founders like Steve Jobs and Todd Graves (Raising Cane’s): would never sell if driven by their mission.

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Memorable Quotes

• "Salty just means that you take offense to the idea of losing." (Scott, [04:38])
• “Losing feels way worse than winning feels good, but not by enough that it makes me want to stop trying.” (Scott, [09:05])
• “Devin is the way that humans can tell their computers what to do. Because that was the whole point of software engineering anyway.” (Scott, [10:17])
• "We've been spending all this time living in survival mode as a species, and now we're going to be living in creative mode." (Scott, [11:37])
• "I remember the first time that it did a real task. I could not sleep that night." (Scott, [32:23])
• "We would sell if we thought it was the most ambitious thing to do." (Scott, [60:57])
• "If we felt like, you know, we could have gone for it all, we could have pushed harder and we didn't...I don't think we would live with ourselves." (Scott, [63:59])

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Timestamps for Important Segments

| Segment | Topic | Timestamp | |-----------------------------------|------------------------------------------------------------|------------------------| | Defining “salty”; family stories | Sources of Scott’s competitiveness | 00:02 – 07:29 | | Pain of losing vs. thrill of winning| Emotional drivers for entrepreneurship | 08:35 – 09:33 | | Vision for Devin & creative mode | What successful AI/“Devin” unlocks | 09:43 – 13:50 | | How AI progress compounds | Understanding exponential growth in AI | 18:45 – 21:12 | | First product demo/early criticism| The MongoDB breakthrough and public skepticism | 31:17 – 33:05 | | Go-to-market and customer focus | Enterprise motion, first big customers | 37:17 – 47:18 | | Aligning incentives (ROI, neutrality) | Business model, compound model systems | 48:30 – 52:12 | | Culture and independence | Why Cognition remains independent; acquisition offers | 57:36 – 61:31 | | Personal motivation and legacy | Why true founders keep building | 62:31 – 64:44 |

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Notable Moments

• The “All Nerds” Company: Cognition is intentionally staffed by people who embody the founder’s culture (“just all nerds”). ([62:31])
• Flying the Whole Startup to Brazil: The founding team personally flew to Brazil to deploy their first enterprise customer (Nubank), participating directly in early product iterations. ([44:29])
• Commitment to Mission Over Money: Scott’s tone is unwavering: it’s about building something ambitious, not about personal wealth.
• On Innovation and Independence: Both host and guest reject the “too late, Big Tech will win” narrative, instead asserting that the opportunity for innovation is endless for those who deeply care.

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Closing Thoughts

This episode powerfully conveys what makes Scott Wu—and Cognition—distinct: an intrinsic drive to compete, relentless focus, a belief in the future potential of technology, and a culture that prizes curiosity and independence above all. Listeners come away with a richer understanding not just of how AI and software engineering are being transformed, but of the kind of mindset that distinguishes generational founders.

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For more conversations with the greatest living founders, subscribe to David Senra’s podcast, and explore in-depth profiles at Scicomm Media.

Transcript

[Host]

I want to know why you describe yourself as salty. What does that mean?

[Scott]

I've just always been this way as a kid. I. I just hated losing. Like, my first competitive memory ever is, like, when I was in second grade, I went to this seventh grade math competition. It was like, a middle school competition that was held at the, like, local university or whatever for middle schools.

[Host]

But you were seven?

[Scott]

Yeah, yeah, I was like, seven or eight years old. I was competing in, like, middle school math. And, like, I did the, like, math test and whatever. And then they were calling out the names of the, like, here's who got third place, here's got second, and I was kind of, like, waiting for my name to get called, and then I was none of them. And I just remember being so pissed about that. Yeah. I can't really give you a rational explanation for. For why it is.

[Host]

It doesn't have to be rational, but, like, how much of your brain is dedicated to competition?

[Scott]

I mean, it's all I do. Honestly, I don't know. I think the, like.

[Host]

Oh, what do you mean it's all you do?

[Scott]

Well, I think strategy game.

[Host]

I don't know.

[Scott]

It's the. The way even building a company, it feels the same. It's just like you're calculating the moves. You're thinking about, okay, if you do this and then this happens and you do that, and here are the different moves, and you're, like, calculating out what comes out to success. It's like a tree search where you're exploring the different options in the decision tree and you're trying to figure out how to lead to victory. That's the only thing I do in my life.

[Host]

And so this is basically just. You don't have memories when you weren't

[Scott]

like this, Basically, I think that's right.

[Sponsor/Ad Reader]

Yeah.

[Scott]

Yeah. I was a little brother growing up, and so my older brother was four or five years older than me, and naturally we'd play video games. And similarly, I would just always be super salty there as well. I don't know. It's just like.

[Host]

Yeah. So, like, I spent some time with Demis from DMind. And what was interesting is I draw a lot of, like, similarities between you two, because I've also spent some time with you, and I was like, well, they're both really smart. They're both articulate. They have, like, a friendly ui. Right. But then underneath that is, like, this, like, ruthlessly competitive drive. And Demis, I think he said this publicly, but I think he said, like, half his brain is dedicated to. To competition. And A lot of that comes from his early days in chess.

[Scott]

Yeah.

[Host]

What were you competing in when you were younger, besides math competitions?

[Scott]

Yeah, well, basically everything. So obviously, the main thing was math and programming competitions. And so ever since I was really young, that was like. That was like my, like, life. You know, my whole goal was to become, like, world champion of competitive programming. I would do that all the time as a kid. I would do. You know, the really great thing about these competitions, too, is, is, you know, you. You compete in your school competition, and if you do well enough in that, then you make it qualify for the local or, like, the city competition. And then if you do well in that, then you get to, like, the regional competition and state competition, and then you get to go to the national thing, the international thing. Right. And so it's. It's like a very nice setup where sooner or later you get to kind of meet people who are like you. Basically, when I was a kid doing these competitions, going for that, it was like all I really cared about. Those people that I met through these national, international competitions were honestly more like they were my childhood friends more than, like, the people around me in Baton Rouge, Louisiana, were. And it was like, we would hang out online, we would talk about math, we talk about problems, but all the other things, too. I mean, I played basically all the different competitive games. So, like, I. I played a lot of Super Smash Brothers. I used to go to tournaments for Super Smash Brothers. It was a lot of fun. I played melee, and then I played, like, Tetris. I played a lot of poker. I played some chess. I was okay at chess. I wasn't that good. I played some go. My dad was a competitive Go player. My parents came to the US in some sense because of go, which was kind of a funny coincidence because my dad was in grad school in China, and he had a professor who really liked him. The reason he liked him is because my dad was a really good Go player. He was a seven don at go, which, if you were to call it in chess, would be like, I don't know, 2300, 2400 rating equivalent or something like that. He would play with this professor on the weekends and stuff. And they were like, my dad would generally win, and they would talk about the games and stuff. And then that professor ended up moving to the US to come and teach. And at the time, this was super early on in immigration from China to the US and so it was not a very. It wasn't really a path that people knew that you could take. The professor wrote my dad and said, hey, like, I came. It's great. Like, there's so much more opportunity. It's so much better. Like, you should obviously come as well. Like, I'll help you with your, like, visa application. I'll help you, like, apply to colleges here and everything. And so my dad applied to grad school in the US and that's kind of how we ended up here in the first place. I was. I was born after we moved to the US Obviously.

[Host]

So your dad was competitive in Go. What was your mom competitive in, though? Because I think I read that you said that she might have been the most competitive person in your family.

[Scott]

Yeah, no, she was always. She was definitely the most salty, I would say, for sure. I mean, she would.

[Host]

What does salty mean?

[Scott]

Salty just means that you take offense to the idea of losing.

[Host]

Okay. I love that.

[Scott]

Yeah. She would always be, oh, no, no, I'm better at this, or I'm better, you know, I could beat you at this, you know, And. And I don't think she. I mean, she played ping pong a bunch growing up.

[Host]

Actually.

[Scott]

She played on her, like, school ping pong team. Obviously she studied some amount of math and so on, but. But it was just. It was more her personality than. Than any one thing that she really put all of her competitive energy into.

[Host]

I spent a lot of time, obviously, reading the biographies of history's greatest entrepreneurs.

[Scott]

Yeah.

[Host]

Always fascinated by, like, there's usually two different kind of archetypes for the parents. One, you have, like, the Larry Ellison and Elon Musk. Their dads would literally tell them, you know, you're worthless. There's stories in Elon's biographies where his dad just gets in his fucking face and yells at him for hours. Larry's adopted father would just tell him, you're never going to amount to anything. And so that they had this, like, inner fire to disprove, you know, saying that basically, no, fuck you, dad, you're wrong about this. Right? And then you have like, the essay lauders who, you know, their uncle or even their father is just like, you're really special. You have a lot of talents. If you put a lot of effort into this, you can do whatever you want. Your mom falls more into, like, the Estee Lauder, like, category where she would tell you that, like, hey, these people are doing amazing things. You could do even better than them. Correct.

[Scott]

I think they would have been happy enough if I just got like, a more traditional, cushy job and did all of that, like, I don't know, that they specifically steered me towards entrepreneurship and being a founder. But. But, no, they were always.

[Host]

But your mom gave you self confidence.

[Scott]

Yeah, I think she, she always told me that I was best and she was always extremely proud of the. You know, it's, it's the. I had these, like when you would go to these math competitions. Hold on.

[Host]

So you said she always told you that you're the best. Did she say that before there was evidence?

[Scott]

Yeah, I think so. I think that's right. I think even when I was like tiny, she would tell me that I was extremely talented. She was always a huge source of support and she always obviously believed in whatever I wanted to do. Because you would compete in math, you would give these trophies and stuff. And we didn't have, growing up, we didn't have pictures of our parents on the walls. We just had old math trophies. It was, my mom was very intentional about. No, no. The thing that we value in this

[Host]

household, other people's trophies are the ones you want.

[Scott]

Ours, Ours. The ones that me and my brother want. Of course we're going to put up

[Host]

pictures of other people's trophies and you'd better damn sure replace them with your own.

[Scott]

So like, you know, when I was pretty, my brother as well, you know, we both really like these competitions so like, you know, like, like accumulate these trophies. And she would always, like, every time we got one, she would hang it up and we're like, put it up on the, on the, on like the mantelpiece and everything. And it's like it was always, I think what she really valued, I think, I think education was really important to her and I, I think being best was very important to her as well.

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[Host]

what matters to you more? Like, what is a. Is it the pain of losing is like, losing is worse than the love. Like, the thrill of winning. Because that's how you started it. You're just like, I could not stand losing. Let me back up. Was reading another Larry Ellison biography. He says this. He's just like, listen, I'm addicted to winning, but I fear losing more than I love winning. And I actually talked to Michael Dell about it, and he's like, yeah, it's. It's just the fear of losing is the. The pain of losing is way worse than the. The. The good feeling of winning.

[Scott]

So I think that's definitely true in terms of how it feels. In order to get anywhere, you got to lose a lot, if anything. Almost like a lot of the, you know, I mean, a lot of the guys are talking about if anything, like the way they got to where they are is by. By losing a lot and having their share of wins along with that. But, like, you just have to put yourself out there and do a lot. So it's kind of an interesting thing. To your point of, like, I definitely feel the same way that, yeah, like, losing feels way worse than winning feels good, but not by enough that it makes me want to stop trying, if that makes sense.

[Host]

Yeah, no, I think it's impossible. I know your personality type. Like, there's just impossible for you not to do this. What is like, let's get into Devin. What is like winning with Devin look like to you?

[Scott]

So again, we're hyper competitive. But also, you know, the other thing about us is, like, we all had kind of started our own companies before this. So our founding team is a pretty big founding team, is nine people. And most of us had already founded our own companies before we had done different things. That was true for a lot of the early team. And we've always thought about this as, like, this is the big one. And so, like, we want to go for it all. You know, we want to be a generational business. Like, we want to build a hyperscaler and we want to go and do that. And like, maybe we'll succeed, maybe we won't. I don't know. But, like, that's what we're going after. And I think to me, what that means in Our field in building software is like people sometimes use the term coding agent or AI programming or something. I always hear that, I think a little bit about, well, we're not going to be interacting with code for that much longer or programs might not be the right level of abstraction. But I think what is always true probably is that it will be the human computer interface. And so what I mean by that is like the way that we think about Devon, if we're successful is, Devin, is the way that humans can tell their computers what to do. Because that was the whole point of software engineering anyway, right? It's just to be able to work with your computer and tell it what you want it to do. I mean, I think doing that for the world is like a massive opportunity and that's what we're really excited to go after.

[Host]

Why is that interesting to you? You just said we all did different things, we came together and we're like, no, this is the big one.

[Scott]

Yeah. I mean the simple answer is we're a bunch of nerds who, we're all programmers and built software and so on before and now this is the idea of teaching AI how to go do that.

[Host]

Wait, say more about that, what you just said. We're all nerds. This is the big one because we can actually teach AI how to do that.

[Scott]

We're all nerds who've spent the last, I don't know, 20 years of our lives just coding and making little things for the world. And the idea that you could teach AI how to go make things and have everybody have an AI that can help them make things and feel the wonder of that, I mean, it's pretty sick.

[Host]

Is that the idea, the single idea in the world that animates you the most, that gets you the most excited?

[Scott]

I think it is incredible. Yeah, I mean, well, I would go further too and just say like at a zoomed out level. It's. I honestly think that, you know, everyone talks about AGI, everyone talks about, well, what is the future going to be like? What, what are human lives going to be, you know, once AI can do everything. And I mean, to some extent, I think the thing that is most human obviously is self expression, creativity, like having things that you want to make happen in the world and being able to go do those things. Right. And so, I mean, a lot of how I think about this is basically we want to build the tool that gives everybody the power to go and make things that they want to make in the world. My co founder has a slime which I've always loved, which is we've been spending all this time living in survival mode as a species, and now we're going to be living in creative mode. And I think that's right. I think Minecraft survival mode is where you are like, you know, you're growing food, you're like, making sure you're safe from the monsters at night and whatever. And like, creative mode is just like, everything's up to you. You know, you have all the resources at your disposal. If you want something to happen, it'll happen. And the only question for you is, like, what you want to make happen. I think it's going to be amazing. And I mean, I think that is like the world that we're going towards and, like, we want to be the ones building that.

[Host]

Okay, so talk about what Devin does today and then I want you to flesh out that idea of, like, where you see it in the future. It's just the interface between humans and computers.

[Scott]

Sure, yeah. Devin is today what folks know as an AI software engineer, and basically what that means is that DEVON is a tool that anyone can use that will work with them end to end on building out software. And so we work with a lot of the biggest companies across the world. We work with Goldman Sachs, we work with Mercedes, we work with a lot of areas of the US Government at this point and so on. And we work with their software teams to just help them build more and do much more. And, you know, the thing about it is in the last 20, 30 years, obviously, I mean, software is eating the world, is the famous line. I think it's very much true. It's still like, it's still got a couple order of magnitudes to go. And in practice, what it looks like is that teams use Devon to ship 10 times faster and to do 10 times more.

[Host]

Okay, so that's where it's at today.

[Scott]

Yeah.

[Host]

How do you get to where you're saying you might. There is no, like, you're just the interface between humans and computers.

[Scott]

So now we're going to get into a philosophical discussion of what it means to be a programmer or a software engineer. Right. And I think, like, you know, if you go all the way back, there was a time where programming was like using the vacuum tubes, like plugging all those in and having it do the machine, do the arithmetic. Right. Like, the ENIAC was in some sense the first computer out there, although it was obviously very different. Or it would have been like filling out the punch cards and like setting up the or writing, putting down assembly or Writing in BASIC or something, right? So we've gone through a lot of generations already, is my point. And what does it look like going forward? When you talk about programming, all it really comes down to is how do you tell your computer what to do? And every single piece of software that you use, if you're using Instagram or TikTok or YouTube or whatever, that's a piece of software that somebody, or in these cases, some pretty big teams of engineers came together and thought through all these details of, okay, here's what I want it to do, here's how I want it to look, here's what I want this button to do, here's how I want to architect it. Every single little decision obviously was made by somebody, but the computer itself is then executing it accordingly to what the wishes of its creators was right? And I think what we'll start to see is that abstraction will continue to climb. And we kind of see this already at this point. You don't need to know a programming language, you don't need to know Python or Java or something like that in order to build your own software. And you can just say, hey, here's what I want. I want to make a cool website that does this, this and that, or for example, in my existing product, here's what we have today, and I want to change this thing or add this new plan or add this new feature and just have the agent go and do that for you. I think we're going to continue to go further down that axis. One important distinction I'd make, which is I think what we'll see a lot more of in the near future is software. Today, the math only really works out to create software if it's going to be used at least a million times or something. I'm giving maybe it's 10,000 times or whatever. And my point is, if you want to go build a product today, you need a whole team of engineers. Engineers are expensive. You got to pay salaries. You got to go build all this out, and you got to go and do that. You need that software to be used enough times to create enough value for that to be worth it. And something like YouTube passes that test because obviously so many, so many hours have been spent on building YouTube, but way more hours have been spent on using YouTube, and that's what's made that work out and made it feasible. But there's so many things out there which only very specific things that only need to be used a few times or even only need to be used once, right? And so all the white collar work that we talk about today even is very. All right, wake up in the morning, all right, I'm going to go look through these, like, 15 LinkedIn profiles. I'm going to look for this and that or whatever, or I'm going to go fill out these forms, or I'm going to do this data analysis and put this Excel sheet together with this research that I found. Right. All of these things are things that could be done with software. It's just, it obviously doesn't make sense to hire a whole team of people to go make you that piece of software which you're going to use one time and never again, versus just having the human go and do that themselves. Right. I think what we're going to get to is we're going to get to a point where you are just giving your instructions to that agent and the agent on the back end, you know, you don't even have to look at this, but on the back end, the agent is going to figure out, okay, here's. I'm going to write this code that's going to go do this. I'm going to put a script that automates this part. I'm going to do this and do this, and that's what's going to allow it to actually go and do all these things. But what you start to get to, as we're kind of saying, it's like this is really just how you control your computer and how you do what you want to go do. Right. And you wake up in the morning and it's like, here's what I want to go do. You talk to your agent about it. You figure out the task together. Once it has it, it can do the part of the literal, like, all right, put the pen to the paper on writing code. But that task is like, you're the one that's deciding what to do.

[Host]

So this ideal future that's in your mind, how far away do you think we are to that?

[Scott]

Yeah, I mean, we've made a lot of steps toward it. I would say we still have got a ways to go. You can use Devin today. You can use all the different kind of coding tools today. And you can do a lot more than you could have done 10 years ago, but certainly. Or even one year ago or six months ago. But, but, but certainly, you know, it's, it's, it's not at the level that we're talking about of you are neuralinked into the AI and you can tell it exactly what you want it to do and what you want to see in the world and just have the AI go and do that. When do we get there? It's. It's hard to say, but I honestly, I mean, I think we'll have solved most of that over the next five years or so. And in AI terms, five years is like a century. You know, in the rest of the world terms, obviously, it's like, it's kind of crazy to imagine that things can change that much in five years, but. But I really think it will.

[Host]

So this is kind of related to something I heard you say, where you're saying that humans just have a really hard time understanding exponential curves.

[Scott]

So true. I mean, you see this in the progress itself. You see this in the scaling laws with the data. You see this in the revenue curves of the companies that are building an

[Host]

AI, including your own.

[Scott]

And. And it's. It's a very. You know, it's like humans aren't really wired for this, right? Like, all of our, like, inherent, like, fight or flight response are kind of like our ability to kind of like things. To vastly oversimplify, if you're just fighting out there, foraging for food or whatever it is, a good hunt will bring you a couple days worth of food or something. But obviously, with the kind of exponential curves that we deal with, the equivalent of a good hunt here could be a thousand years worth of food. And we don't have that intuitive signal in our brains to really understand that at a really deep native level. People often underestimate, I think, how fast things can change and how fast the world can change. I mean, my parents even drilled this into me because they grew up in Communist China and they came to the US and even that we're talking about what were ultimately even much slower scales of progress in some sense, relative to, I think, what we're seeing today. But even that was like, as you can imagine, it was incredibly jarring to them. The idea that you come to the US and everybody has a car and all of these different. It's like everyone has all these household appliances and has all. It was a very different life when they grew up in the 60s in China and they were much, much poorer. And it was very, very different, right? And it's like, I mean, funnily enough, people got used to all these things pretty quickly, and now we can't live without them. But I think we'll kind of undergo the same period with AI, where five years from now, it's going to be insane to think about all these things that we're going to have, 10 years from now, we're going to have forgotten that we ever lived without them.

[Host]

Honestly, what do you think you understand about AI that other people don't? The reason I asked the question is because we have some mutual friends. I would describe our mutual friends as some of the most AGI pilled people that I know.

[Scott]

Yeah.

[Host]

And I feel every time I have a conversation with them, I'm like, oh, even though I pay attention to stuff, I feel like a toddler compared to like somebody like you. And so I'm very curious, like, what do you understand about AI that, that most people don't. And even people within the tech industry.

[Scott]

No, I. You're way too generous. I don't know that there's. I don't know that I have that anything that interesting or that deep of an insight. I mean, I think it's, it's you,

[Host]

you say that because you're used to it.

[Scott]

Here's what I'd say is the way that folks typically predict the future or think about what happens next is they pattern match based on what they've seen historically. And they say, okay, well, for 100 years it's always been like this and it's probably safe to assume that it will be. And 99% of the time that works great. And in these particular periods where things actually move and they're real things that are different, those are the 1% of times where it truly is different. Now you just kind of, rather than any kind of pattern matching, what really matters is just thinking about things from first principles like AI. There's the famous METR report which was saying a couple years ago, AI would do about 10 to 20 seconds worth of human work without interruption. And then you'd have to guide it or direct it or it would make a mistake or something like that. 10 seconds, 20 seconds. And that's just doubled every couple months, basically. And now we're talking about hours of work. So basically an AI can just take a task that would have taken humans hours of work to go do. If you go and describe that task well enough to the AI, it will just go and do the whole thing and come back to you with the result. And then you give it the next thing, the next thing. And if you just ask from a first principles question, well, why can't that be days? Or why can't that be weeks or months of work? And then what does the world look like if everybody has an agent that can just do months of work for them at a time? Then you get to a pretty different conclusion from what we've all seen and what we've all lived for the last several years. And I think that kind of first principles thinking is as different as it sounds and as crazy as it sounds. This is one of those times where it's actually more correct than the simple pattern match.

[Host]

I think you've even taken this further where you're like, well, what happens when they can work for a year unassisted?

[Scott]

Yeah, yeah, yeah. I think that's true. And I think we will get there.

[Host]

If Devin could work for a year without any human assistance.

[Scott]

Yeah.

[Host]

What would you have it do right now? Destroy your competitors?

[Scott]

All sorts of things. I mean, no, I mean, I still wake up and think about this in every different, like, you know, every little thing that I run, you know, dumb example, yesterday I was sending out, like, a bulk email and I was, like, trying to get the, like, email formatter to work. And it's, like, kind of painful. You know, some of these things are still, like, kind of hard to use or that doesn't support a certain, like, styling of the email that you wanted to go do. I think the thing I, like, had pasted something with indents and then it was like, just couldn't, like, unindent them because the editor, like, I don't know, there were some weird things where the editor, like, would not allow you to unindent one part, but not the other or whatever. And I was just thinking, like, it's really crazy that, like, that I'm still doing this basically, right? Like, in as much simplicity or honestly more as it would take you to explain this to another person. Like, hey, here's what I want to do. I'm just trying to make it look like this and then this and then that. The rest of that execution should just be done for you. And then you get to the point where you actually really just get to spend all your time thinking about, well, what do you want to do? What do you want to build? What do you want to create? What are the things that you want to see in the world that aren't there already?

[Host]

But I think what makes it interesting about what you were saying earlier is the more you increase the time, the more interesting it gets to me. There's this guy named Edwin Land who I won't shut up about, and he was the founder of Polaroid. Yeah, Steve Jobs hero. A lot of what we think of as Steve Jobs ideas literally just came from Edmund Land down to, like, the chairs and the table he would use for his presentations. It's like the same thing that when Land used in like the 70s in Polaroid and he thought of himself as a scientist, not as an entrepreneur. Edwin Land, he died with I think the third most patents. He's like Thomas Edison, some other dude. And then Edwin Land and what he did is he couldn't figure out he invented the industry of Insta photography. Yeah. Before you took a picture and you're like, hey, how's it look like we'll find out two weeks from now when we get back from Kodak. Like I have no idea. And he now he took a picture of a Polaroid, he's like, we'll find out in 60 seconds when it dries. But that was black and white forever. When I read that part where you're like, well, we're gonna have agents that can work on a sister for a year. I didn't think of what I would do, which is a question I just asked. I thought of Edwin Land hiring this guy. He's like, I want you to think about how we turn this from black and white into color. And before he could begin, the guy worked there and just thought for two years.

[Scott]

Sure.

[Host]

I'm like, that would be very convenient if I can have an agent attack this problem. And while I'm working in the background, cause I can't figure it out, they're just thinking about how to attack a single problem for two. In his case, it's the guy that fucking solved instant color photography. Just took him two years of thinking to do it. So this is like I'm going to push you on this a little bit more because I don't want your year long agent to send bulk email.

[Scott]

Oh, I agree. Just to be clear. And I think at some point it's kind of. You see this right where it's like when you're talking about seconds, you're literally talking about just a specific command. Right. When you're talking about hours, you're talking about giving it a task and having it do the task. Right. And I agree with you, obviously. Bulk emails for years. What you're talking about is like you're giving the agent permission basically. And it's like that sounds way more fun.

[Host]

Yeah, exactly.

[Scott]

And this is like what do I care about? And the answer might be, look, I want to give you a million different examples. The answer might be, look, there's this one societal problem which is really important to me and I think it's a solvable problem. I think we can all be happy, but we really needed to spread awareness about it. We really need to get folks to understand the points of it and we need to figure out how everybody should work together and coordinate on it. That's your point. That's a problem that an agent can think about, or even some of the sillier things too. Like, yeah, there's this video game that I really like, but I wish if I were making the game, here's exactly how I would think about all these things. And I feel like there's this really cool idea if you could combine elements from this one game that I really like, but then incorporate some of the elements from this other game and set the agent off on that mission of like, look, we're going to go and make the coolest thing ever and the coolest game ever, and we're going to incorporate those elements. We're going to think about how those nicely intertwined and work together. Right. Or if it's like, here's this piece of just novel science which I'm just really passionate about. Materials have been created this way for years and years. But here's this avenue of attack which I've been wondering about and thinking about of maybe there's a different novel construction of materials this way. Send your agent to go work on that for years or months and have it study that and explore these things and run its own experiments and trial. I think all of these are, I think, soon going to be very possible and to your point, is very different.

[Host]

Yeah, I like the framing of we're sending them on missions. I would have one that would pick the missions that I need to send other agents on.

[Scott]

Yeah, then you'll have the AI, which is like the manager AI of the missions. Yeah. And I think it's like, I think we will continue. My example of this is like, I always joke about how if you think about our ancestors from hundreds of years ago or thousands of years ago, imagine them looking at us and what we do. And it's like you're pushing buttons and you're sitting in a room and talking with other people and you call that a meeting. And that's like those things, that's work for you guys. And it's like, what do you mean that's work? You know, like I'm in the fields, like, I'm doing this every day, you know, I'm going and farming. I'm making sure you're making all of our, like, you know, clothes by hand or taking all. Taking care of all these things. And that's like work, you know, but like, how can you guys call, you know, and my point is just I think what we will have going forward is going to look that different from what we have today. We will look at people who are, as we say, just have these really interesting curiosities that they want to pursue. They have causes that they're passionate about, they have fun ideas or art that they want to create. And they're sending off their agents in pursuit of those missions. And they will think of that as work. And we will look at them and be like, wow, it's kind of crazy that that's what you get up and think about all day.

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[Host]

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[Host]

when you started cognition, did you know that you were going to try to make an automated software engineer? Was that your first idea?

[Scott]

I'll say it was always two things. One, it was always related to code and software, which again probably has something to do with, to do with us all being programming nerds. And then two is it was always around the idea that these would be like real multi step iterative processes. Which was I would say was a real hot take at the time. Like this was 20, 23 and 23.

[Host]

So this is when I met you guys and I was on a walk with a mutual friend of ours. And I remember first here you guys scaled from what, like a million in revenue to like 500 million or something like that in what, like 20 months or something?

[Scott]

Yeah, yeah, a lot more than eight

[Host]

times I remember before you had no revenue. And I heard the idea and it was pitched as like, essentially like an automated software engineer. And I was like, holy shit, this is like a huge. You're going after labor. This is a huge market. And then you released. Yeah, I remember the demo video.

[Scott]

Yeah.

[Host]

And then you got a lot of shit.

[Scott]

Yeah, yeah, yeah.

[Host]

Is that a good thing?

[Scott]

Well, it's kind of a mix of it, I think, is the. The full polar ends of the spectrum. Right. So there were some people who were like, this is the coolest thing ever. And like, you know, all this. And then there were some people who were like, you know, this is like the worst, you know, thing that you could buy. But similarly, like, in terms of the capabilities, there are some people who are like, oh, my God, everybody's going to lose all their jobs tomorrow. Which is not what we've ever really believed or thought of this as to also, like, dude, there's no way this is ever going to work. And, like, this is totally a scam, you know?

[Host]

So what was the criticism? Did you release the product earlier than you wanted to?

[Scott]

Well, look, it wasn't even a product at the time, to be honest. I mean, it was more just like a prototype or like a demo of what was possible. And we had been working with it and playing with it for a few months at that point and just wanted to show people some of the examples of what it was capable of, because there was a real. It was pretty insane for us to see as well. I remember the first time that it did a real task. I could not sleep that night. And so it's just showing that.

[Host]

Wait, wait, say more about that.

[Scott]

The first task that Devin did was it set up MongoDB for us. And it's like, it's. It's a standard thing. It's issues that a lot of people run into all the time when they're going and getting their kind of initial DB set up or whatever. But we would just run into errors. You have this whole flow where you find an error, you Google error, you find some message on Stack overflow or whatever, or you ask ChatGPT and it tells you, okay, here's what you should try. You try that thing, you run into a different error, paste that in. And for this one, we. At some point we're just like, okay, Devin, just. Just try to go fix it. Just go run commands, do whatever you need to go do. And then it worked.

[Host]

And you couldn't sleep.

[Scott]

It was truly like. Because again, it's like just seeing the exponential curve ahead. Because this was a very specific case. It was the one success that we had. It was very much like, definitely a way better than average run. But yeah, there was this feeling of like, why shouldn't all software and all products be built this way? Now you can just tell it what you want it to do and have it go do it. I mean, it's kind of funny because, yeah, to your point, we did get hate in the beginning. We did not feel motivated at all from the hate to be like, oh, yeah, no, maybe you guys are right. Maybe we should go and focus on the more kind of chatbot Q and A style project experiences. Maybe more than we should have. Maybe we should have done something in the middle. I don't know. But I think for us, when we had seen that and when we had done these different things, all of those demos that we showed in our launch announcement were actual runs of Devon that we had done ourselves and run into and been like, holy shit, this is insane for us. It was always kind of like, we can debate when or what level of effectiveness or whatever, but it's going to happen. And that's how we always felt about it.

[Host]

So explain the process of iteration to go from that product. You're getting a lot of hate. Actually, you know what? I called Jeremy Stern, who wrote this excellent profile of you in Colossus, which there's a lot of parts. I would just laugh my ass off, by the way. And I was asking, I was like, tell me the stuff that didn't make the profile. Yeah, and he said there wasn't that much because you're kind of like an open book and a lot of people, like, manage their media and, you know, you could have to talk about certain stuff off the record or whatever, and you were just like saying everything. But you did say something about, like, you made the point where, like, when you released the first product, what was the benchmark?

[Scott]

Swe bench. Yeah, and it was at like 13%.

[Host]

That was Devin.

[Scott]

Yeah. Yeah.

[Host]

And your point was that that's already better than.

[Scott]

Yeah, at the time, the best known was like 3 or 4% or something like that. But obviously, yeah, 13% still means you fail, you know, 87% of the time.

[Host]

Was there a pronounced benefit from releasing early like that?

[Scott]

For sure, yeah. I mean, so for us, by the way, you know, if you kind of think about this overall AI ecosystem, I mean, a simple way to put it, it's like, dude, we were late by a lot. You know, it's like OpenAI though, Google DeepMind or Google Brain. I mean, these are obviously more than a decade old already. OpenAI started end of 2015 or even the anthropic that folks talk about in the world had been around already for years. We were getting started in early 2024. There's like a year plus out from the ChatGPT launch. A lot of the existing players were already there. The same was true in code specifically. I mean, there was GitHub Copilot, which had, that people had already, engineers had already used for years. And that was very much the Q and A, the autocomplete style experience of working with AI. When you start a company, you kind of have nothing, you have no right to exist, is maybe one way to put it. It's an interesting truth about the world, which is obviously startups succeed. Startups succeed all the time. And startup versus big company has been played out for forever. But there's no reason, in terms of resources, in terms of people, in terms of brand awareness, you have none of the things that the big guys have. And so from that perspective, you have no right to win over what they're doing. The reason that you are sometimes able to anyway is if you really plant your flag in the ground and put a stake into what you think the future is and you run like hell towards that and if you turn out to be right on some of the core things. And I think we were wrong on a lot of things, to be clear. A lot of things. Definitely we were early, which is a very fair criticism. I think a lot of the details and the nuances we learned and adjusted over time. But the idea that you would work with AI as a coworker rather than as a tool or a chatbot, I think was, I mean, over the last couple years has obviously like really grown. And I think it was like very important for us, for our brand, for recruiting, for, you know, customer work, everything for us to be, you know, the first ones that actually planted that flag in the ground and said that.

[Host]

So when did you have this idea where you guys are going to take a run at very, I would say ferociously to like these giant, like Fortune 500 companies or even the, like the U.S. army uses. You sure. Like, where did that strategy come from?

[Scott]

So, funnily enough, so that launch was in March of 2024. And to your point, it went very viral. But again, we didn't have any customers, we didn't have a revenue. We didn't really have like, I thought

[Host]

your first iteration on the business model was like 500amonth. Or something, wasn't it? Am I misremembering this?

[Scott]

So we had that. That was actually later on, actually, that was end of 2024.

[Host]

Okay.

[Scott]

But you know, initially what we started was just like, you know, a bunch of people came, asked us for the product. We were like, guys, I don't know if this is a product that's ready for primetime, but if you really, you know, you can try it and like just let us know. And so people ran. We had to go and scramble to build a system of, okay, let's do a pilot or a poc. And we tried our best to not over promise and be like, guys, really, it's very early, but if you want to try it, you can try it. And we did all these and perhaps unsurprisingly, they were all just failing, which is kind of natural. It's like you could do some pretty cool things with it. You could do some pretty interesting toy demos or projects or whatever, but it was certainly not ready to work on an actual company is real code base and so on. And so from that point, this would have been like April or May of 2024. We were making agents work with GPT4. This was a very different era, so they were much more primitive agents. We kind of talked about this and got to this question of like, okay, well what do we think it actually does look like when the agents start to get good enough for adoption? And I think what we kind of came to was, well, different tasks are different, right? And so there are some tasks out there that are just really mundane and really repetitive and you're just doing the same tedious thing over and over again. And there are some tasks on the other end of the spectrum that are really tough architecture problems or deep issues that you have to really understand all the context and have all the know how to know how to fix. And like people were trying to use Devin for all of those and were failing at it, understandably. And so then the question was like, okay, what are the natural tasks that are like, if there's like a first task that is going to have PMF and real value from these kind of agent experiences where it can do the whole thing end to end, what does that be? We kind of said, okay, well it should be some of these really repetitive TDS1s. It's not so cut and dry that you can just have an automated script that does the exact thing every time. So it does take obviously some intelligence and some amount of meandering, but it is repetitive enough and scoped enough and on a tight enough feedback loop that you can have an agent do it and it would be able to go and diagnose and fix that problem. Right. And so that's kind of what brought us to some of these naturally to some of these initial use cases, which were things like migrations or version upgrades or helping People upgrade from Java 7 to Java 8 or something like that, which were kind of like, as you can imagine, enterprises had these massive code bases where they would go and do all that. And it's like a 50,000 file code base where you have to go. It's like the same eight things that you need to change in each one. You have to be a little bit thoughtful about how you'd make the trade offs, but like it's a very repetitive task. Right. Our first success ended up being with a company called nubank, biggest bank in Brazil by market cap at the time. And the use case was like one of these big migrations and we had kind of a custom Devon that was like extremely, extremely optimized for doing that. And as we grew from there later on we had kind of, as things got a little bit more mature, we've had both self serve business and enterprise business and so on. But I think from the beginning we had always seen this value that, that building software in the real world and managing massive, massive products that millions of people use every day was pretty substantially different from just building a cute website or a cute demo from scratch. And I think we really, really leaned into that. And naturally all the Fortune 500 or all the biggest companies in the world are software companies in 2026. Even the ones that folks don't necessarily think of that way. And like Walmart or CVS or JP Morgan or Mercedes or whatever, they're all software companies. They have massive, massive teams of software engineers. They have tons of things that they're building and shipping and maintaining. And that kind of became like a natural thing for us to work on because we had learned very early on that we want to work on real problems and we want to work on things that matter and that people actually care about.

[Host]

So wait, what percentage of your revenue is coming from enterprise then?

[Scott]

Today around 75, 80%.

[Host]

What are people using on the self serve? Like what are examples of?

[Scott]

Yeah, so we have a lot of teams who use it in self serve. I mean that's grown, actually that's grown quite a bit as well lately. But you know, we have Startup who, we have Exa who uses it a ton or openrouter or built. I ran into Someone in my apartment in the elevator the other day. Oh, you're the Devin guy. We use Dev.

[Host]

Are you Devin?

[Scott]

I've gotten that as well. Are you Devin? And I was like, I'm actually not, but that's okay.

[Host]

Why didn't we call it Scott?

[Scott]

There's both self serve and enterprise. With that said, it is entirely in both sides. It is still like actual engineering teams building real output. And so we don't focus at all on individual hobbyists who are just trying to make a cool thing or something like that. We focus on real teams who are building real products that they want people to use and getting output out of that.

[Host]

Okay, can you walk us through? I'm very curious. I'm a big enterprise. I contact you. Walk us through what happens to being a customer.

[Scott]

It starts with education and everyone's gone crazy over AI and agents and so on. And those are the buzzwords of the last six months, obvious. So they want to know more about this, but there's still a lot of detail and like, what does it actually look like to deploy them? So we'll show them what this looks like. We'll talk them through how we work with teams and how we partner, how we direct them to the right use cases, how we give them guidance on to maximize their roi, or what projects are or are not feasible with agents. And then from there, enterprises typically, obviously have some very messy processes. And so for most software, or most just generally like vendors that they'd want to work with, it's often, I mean, for, as you can imagine, for a massive bank, you know, adopting software, giving it access to all of their, you know, the repos, getting through security or whatever, that's like, usually for typical companies it's like a 12 to 18 month cycle. The thing that we do naturally is we just work with them to figure out how we go and do that as fast as is humanly possible.

[Host]

Did you send employees down to South America for Nubank?

[Scott]

Well, so nubank, I mean the first one. And honestly our entire team was the Ford deployed team. Like we all flew to Brazil. I mean it was like the first case, you know, it's like, it's like, let's be real here, okay? Agents did not work generally. Okay. And so there was, there was a lot of like, how do you make it work? Very specific. Like, we all flew to Brazil. I was there, the whole team was there. We were sitting there with their engineers understanding, okay, so this is what you do in that case. This is what you do in that case. And this is what Devin needs to know. And Devin needs to be able to read these things and like going and debugging their like exact problems. Now obviously it's not like that at all because, you know, cool, we'll get

[Host]

to where it is now. But idea of like, no, we didn't deploy a team. We deployed the whole company.

[Scott]

Oh yeah, yeah. I mean it was. Getting the first customer obviously was like a real, you know, it's like, I mean, I wonder what they thought of that. But, but, but like, yeah, it's like a. Literally like, okay, let's go through all these different things that you guys think could make sense. Let's go through each one. Let's try some of it manually ourselves to understand what the task looks like. Let's see if we can teach Devin to do this correctly and build in the right kind of like, you know, the, the right orchestration for Devin to be able to do this. And let's just basically building the product was almost like building for one company. And yeah, it was fun.

[Host]

So what do you do today?

[Scott]

So today it's obviously much more, it's much more self start and agents are so much more capable obviously. And we've figured out a lot more things with the unbreaking experience. But a lot of it is, we were saying these cycles typically take 12 to 18 months. We try to get deployed with folks in like three months, months. And a lot of that requires folks to, obviously, first of all, it requires them to really appreciate that it's a priority. I mean, if you have 25,000 software engineers that you're, you know, in an org that's running, that costs, you know, 10 billion a year or something that you think can move three times faster. You know, usually it is a priority, but then it's like figuring out how we get through the security reviews. You know, we have all of the, you know, we can deploy in their private cloud. We have, have very strict data agreements. We have like tight air walls on all these things, obviously.

[Host]

Do employees physically have to go? Are they working with. Are these deals so big that they're working with a specific company and only that company for a period of time or.

[Scott]

No, typically, no. We have a full forward deployed motion. But a lot of what that looks like, as we're saying, is a little bit more like user education and guidance. And so we will fly, we still do this where we'll fly out and kind of like go and see customers and work with them, point them to the right use cases, teach them how to get success, help Them with their setup and their playbooks, like all these things for how to use Devon. But it's much more a kind of like, look, we are here to assist you guys. We're giving you a lot of this kind of direction and so on, but you are yourself and your team is the one that's using Devon and running all of that, right? So that's a lot of what that looks like. We're set up to be as incentive, aligned with our customers as possible. And so a lot of it is, is not just like, oh, here's this tool, hand it over to your engineers and find out what they say about it. It's like, okay, well, let's figure out what are the initiatives that you guys really care about. And we will point you to for those initiatives. Here are each of the projects that we think Devin right now can make you 10 times faster on and we'll tell you for the ones that it's not. And here's what workflows you should use instead, or here's how you should get to value instead. But it looks more like that than a literal, you know, we are using Devon on your. On your. On your behalf or something.

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[Host]

wait. So tell more about how you align the incentives between your company and then your customers.

[Scott]

Yeah, so a couple of things here which I think are really important one, obviously is just like really working with them on just very clearly defining the roi, which I think is a very important. I mean, it's like everyone's talking about this, right? Over the last few months, people are going crazy on their token budgets and one engineer can spend so much, and you want to know that that's actually doing real value for you and you want to be able to identify where you are.

[Host]

Do you think all this stuff is a little crazy?

[Scott]

I think it is directionally correct, but I think there are definitely some places where people have gotten carried away. People talk about, oh, yeah, we rank our engineers by how many tokens they're spending. Well, let's try and rank people by how much output they're actually producing or how much good work is actually getting done. But obviously I think that the math works out in terms of the GPUs are expensive, but if your engineers are actually able to ship three times more, then it's very clearly worth it. You just want to make sure you're doing it the right way. Obviously a lot there on just like tying to specific outcomes, tying to, okay, what are the actual tickets that are getting done? What are the projects and the initiatives? And like, this project, which was scoped out for 18 months and was going to be handed off to an outsourced contractor and was going to cost you 15 million. Let's just talk about how you do this all internally with your own team and you do it for 1 million and you get it all done in three months, stuff like that, which I think is super important. The second thing, which I'll call out especially, is, is just being neutral a lot of discussion, obviously. What do you mean by that, being neutral with respect to all the labs, the models themselves?

[Host]

I think you said you like being Switzerland.

[Scott]

Yeah, we like being Switzerland, exactly. And so I think it's like an important thing of we are just as incentivized as they are to figure out how to make their token spend efficient. Right. And so Devin is purposefully meant to be a compound model system. And so all of these different things for the right, right task or even the right sub task of a task or something like that.

[Host]

Describe what a compound model system is.

[Scott]

Yeah, yeah. So for each different part of your use case, I mean, imagine, let's say you tell Devin, hey, customer just reported this bug. There's a JIRA ticket. Can you take a look at the ticket and go and solve the whole thing? Right. What does Devin actually go do? Right. First of all, Devin's probably Going and investigating, understanding what does the report say, what's going on? Second, well, probably as any engineer should, it's going to go and try to reproduce the bug itself. So it'll say, okay, let me spin up the product locally, we click around, try to follow the same steps that they followed, and see if I can make the bug happen as well. Then if you did that, then, okay, now you're looking at the logs, you're trying to figure out what went wrong, pinpointing what are the particular files or what was the flow of, what was the command flow that led to this. Then you do the debugging, then you go and test it again, make sure all these steps, then you put it up for review.

[Sponsor/Ad Reader]

And.

[Scott]

And it turns out that there are different models that are good for different parts of these tasks. Right. And even across different tasks, obviously. Also, it's like some tasks are these really crazy hard ones where you want to actually use max thinking and you want to use the very best models you can get your hands on in the world. Right. And then many other tasks are boilerplate enough or repetitive enough that what you care about is just getting it done really fast, getting an immediate answer, having the ability to verify that it was correct, but then beyond that, making sure that it was as cheap and fast and efficient as possible. And so Devin, rather than being pegged to one model and saying, oh, we're only going to serve you GPT for this, or we're only going to serve you opfiz for this, Devon can use any of the different models that has in its arsenal, which include all of these models from Anthropic, OpenAI, Google, et cetera, but also our own models or open source models out there. And it will dynamically go and choose these models for these tasks.

[Host]

How do you think about this? You're a customer of, of them, but also competitor with.

[Scott]

Yeah, no, I mean, look, I think in practice there's a lot of positive some work for us to do together. And so like, you know, the way that we think about ourselves, a couple things. One, software is the only thing that we care about, obviously. And there's a lot of value in focus and in building products and building our whole motions specifically around that.

[Host]

Say more about how you think about focus and the value in it.

[Scott]

You know, back to what we were saying about startups. Right? Right. Like why do startups ever win at all? And if you do everything, you will lose to Microsoft or Google, who does everything, but also has like trillions of dollars more resources and 100,000 more people than you. Right. And infinitely more brand name. Right. And like the way that you build, you know, like a real kind of like, you know, a real like lasting business or lasting product is by really, really focusing and narrowing on one specific thing, making a very concentrated bet and then obviously your bet has to end up being right, but from there it's a lot of just really tight execution. And so in software, you and I have talked about this. I love the quote from Daniel Ek in Spotify, right? People were saying, so there's YouTube's trying to go and do this and Apple has Apple music and why should there be? He was like, I can give you all the other reasons, but the truth is we're just going to care way more about music than they are. And I think for us it's true. It's like we are just going to care so much more about it. Like what does it look like to build software end to end at Goldman Sachs or at Mercedes Benz or something like that? And there's a lot of nuance in that and there's a lot of messiness in that. Right? It's not as simple as like, oh, here's a sandbox algorithms problem, go and code me the correct 30 line program that solves this problem or something. It's like how do you work with all the messiness of the real world? How do you understand the code base as it exists today? How do you collaborate with all the humans on it? How do you plug into their ticketing system? How do you give the agent the ability to test its own code and run everything locally? Right. All of these are obviously super hairy, messy problems and that's what we care a lot about. And so from that perspective it's a very kind of nice. The labs have their own products, I'm sure they'll continue to do more. But in practice there are a lot of nice ways for us to cooperate. Obviously on top of that being the Switzerland means that folks can work with us and kind of trust in us that we will route them to the right models, that we will optimize the price performance for them, that we will direct them to the right use cases and so on. Because. Because we're not incentivized for them to spend more on the models either. We're incentivized for them to get value and to get output out of it.

[Host]

I love that you use the example of the war between Spotify and the rest of the Apple music. For example, Jimmy Iovine who also came on the show and now is actually a friend of mine. He actually called me yesterday about this because anytime anything happens with AI Music Spotify, he calls me. But we talked about this because he's like, you don't understand. He had 40 years of experience in the music industry, had all the relationships. He's like, apple bought me for $3 billion. Spotify at the time, he's like, we're gonna go head to head with them. Spotify only had 3 million paid subscribers at the time. Jimmy's gonna fight the war. And he's like, I have one of the biggest companies in the world. And then he talked about. He's like, that wound up being a huge. He thought it was an asset, it was a huge liability because they're like, we don't give a shit about getting a couple million more, tens of millions or more of subscribers. We invented the most successful consumer product of all time. And he said something like, they just clipped his wings. He was trying to do something like, oh, this isn't important to us where Daniel was going to die if he was not successful. He just cared about a lot more.

[Scott]

Dude, even in the two years that we've been around, I've heard so many different versions of the same argument. Because when we started, as you can imagine, this was true for us. It was true for everybody in the space. Building the space. The number one pushback that you would always hear from investors, from other people is like, but Microsoft already has GitHub Copilot. Like, isn't that everybody's just going to use that, right? And it's like, it was a very reasonable thing to say in some sense because, you know, they did have all, you know, they did own all of GitHub and they did have a partnership with OpenAI and they did have like VS code and so on. I think in practice, the reality is there's so much more innovation and so much more to build that there was a lot of like, positive, you know, Microsoft's like a great partner of ours and we do a lot of things together and we build even more together, right? And I think the reality is, like, people have said this forever. Like, oh, like, yeah, like startups versus, you know, like, why should. You can give all the rationality.

[Host]

Ten years ago, it's like, Google would do this or Facebook could do this.

[Scott]

Yeah, sure. It's like, oh, why should Datadog exist? Or why should Snowflake or Databricks exist or whatever? You know, the clouds. The clouds have all of that. You know, the clouds care about observability too. The clouds care. And the reality is like it's in some sense a bit of an uncreative way to think about things. I think like, if there was like one thing that, okay, here's what we all know is going to be the end state future and everybody's just working toward it and whoever has the most resources toward it with. And of course, yeah, it's like we know who has the most resources today, right? But if there are millions of problems out there to solve, there's lots of different things. The world is dynamic. Things change all the time. There's lots of new ideas or opportunities or ways to build new products. Then the reality is like, of course, there's lots of different niches to own. There's different things to really bet on. There's different focuses to spend your time on. And I think that will continue. Yeah, I mean, I think it's like a for better or for worse. After the last couple months of news cognition, I think has been a bit more of the, like, we've become a bit more known as the, you know, the folks betting on independence in some sense because obviously there have been some high profile acquisitions.

[Host]

It's funny that you said that because one of the questions I'm going to ask you, which you probably won't answer, is like, how many different acquisition offers have you had?

[Scott]

I will not answer that question.

[Host]

Dozens more.

[Scott]

Dozens is a lot, dude. I don't know about dozens, but good time.

[Host]

It's probably only a handful that could actually afford to buy you if you would sell, but the amount of times they keep coming back.

[Scott]

Oh, I see.

[Host]

Well, yeah, anyway, this independence part is really interesting to me. We were on the phone, me, you and a mutual friend on three way. Like, I don't know, I think it was actually last summer and we're not going to say the company, but I was like, scott, what do you think about, you know, an acquisition with X? Not X, the platform, X, the blank company. You're like, I don't know how we'd be able to afford them. You just assumed I met you buying them and I just fucking cracked up laughing. It's hilarious.

[Scott]

No, so I think like, yeah, yeah. And it's like, folks, look, there are folks taking acquisitions and doing things. There's some big high profile ones. I think those are great, to be clear. And I think it's a very reasonable path for exit. As I mentioned for us, it's like we've all come into this like we want to build a generational business and someone we're really excited about. And I think people, I think today sometimes times I've seen some more of this nihilism where they say, oh, like, yeah, maybe it's just, maybe it's too late and maybe it's not possible anymore.

[Host]

And like, what does that mean? What's not possible?

[Scott]

Like, maybe it's not possible to build a new independent business because everything else, you know, it's like all the apps

[Host]

are going to do everything, all the

[Scott]

opportunities take it, you know, and it's

[Host]

like, guys, those people aren't founders. Founders are rationally optimistic.

[Scott]

Yeah.

[Host]

They're just not founders.

[Scott]

Yeah.

[Host]

They believe even there's no evidence that they should succeed. That they will succeed.

[Scott]

Yeah.

[Host]

So people like that just need to get a job.

[Scott]

Dude, I'd say this even with Devin. If you think that going onto the Devin web app and giving the or any other coding product out there today and giving the instructions the way that you do right now, and then you get the pull request out and then that's how you review it and that's how you build software. If you think that that's going to be the way that software is built forever, then, yeah, then nothing will change. But I think we have 10 more generations of these different product experiences to come, and building those and doing those is like, that is what innovation is and that's what's gonna happen.

[Host]

The advantage I have of reading for the last decade of all these biographies of physics entrepreneurs, you're studying somebody's life, but in many cases, if you were so successful they wrote a book about your life, it's almost like you get a minor in new industry creation every single time. It's just like, we're too late. It's over. There's no more opportunity. And the way I thought about this was the best definition of a business I ever heard. Actually came from Richard Branson where he says all businesses an idea that makes somebody else's life better. And if that's true, which I believe it is, then that means there's infinite opportunity in the future, now and in the future, because there's infinite ways to make somebody else's life better. And that's all a business is. This idea that's like, we got to the end of history is just bullshit.

[Scott]

Yeah.

[Host]

There. And you know, I don't mean to push you on this. Like, I am personally curious though, because, like, I know you're already rich. I don't think money is your North Star based on the conversations we had in the past. But, like, there's gotta Be some crazy number somebody can throw at you where you're just like, fuck, I have to take this.

[Scott]

Not really. I mean, it's like the. You know, people have asked me sometimes before. They've asked me, like, okay, but, like, really, would you guys.

[Host]

Like, this is what I'm doing right now.

[Scott]

And the way that I say it sometimes is like, we would sell if we thought it was the most ambitious thing to do. It's kind of an oxymoron, because obviously. But, you know, it's like my genuine answer in the sense that, like, that's, like, what we care about, you know, it's like. Like the. I mean, it's funny you talk about money. Like, I mean, I don't even. Dude, I don't have, like, I don't have a car.

[Host]

You live in an apartment.

[Scott]

I just realized I have merchant apartments. Yeah. It's like. I don't know. I think I like eating sushi. That's fun. The sushi doesn't cost that much. You can do that off of an engineer salary as well.

[Host]

Just so you know, if this is true.

[Scott]

Yeah.

[Host]

Like, then you are the type of entrepreneur that I find the most fascinating in. In the world. Because, like, when startup founders talk, talk. Come talk to me. It's just like, I don't give a. About your startup. I, Like, I asked the same question. It's like, is this your last business?

[Scott]

Yeah.

[Host]

Right. I asked Kareem from Ramp like this before we did this, like, deep partnership.

[Scott]

Yeah.

[Host]

And it's just like. It's like, okay, you could sell it. Like, the Zuck example, where they're like, why didn't you take a billion dollars? I think you own 25 of the company. You would have made 250 million. You're like, 22. He's like, well, what would I do with the money? I would just start another social network. I kind of like the one I have. Like, I just want to build shit anyways. Yeah. So, like, what do I do? And then the other element of this, which I think is almost tied into you, where it's like, I feel like you're just having a lot of fun, like, even being around here. It's just like, there's your company's weirder in the composition of the people because it's literally just all nerds. Like, and I think you, like, yeah, no, and I mean, but, like, you know, like, there's usually a mixture of obviously the nerds and some other people. And, like, you kind of built this, like, almost like your social network.

[Scott]

Is like, I don't care if you're not a nerd. Yeah, yeah.

[Host]

It's like a physical manifestation of your social network. But, like, the way I would describe

[Scott]

this is like, okay, cool, cool. My friends are all, yeah, I agree. I agree.

[Host]

And I mean that in obviously enduring way.

[Scott]

No, no, I love that. Yeah.

[Host]

But the people I'm most interested in is just like, there's no price. Right. Like, the example I use is, go ask Steve Jobs, I'll give you $2 trillion, but you can't work on Apple.

[Scott]

Yeah.

[Host]

What the fuck do you think he would have said?

[Scott]

Yeah, I agree.

[Host]

No, no, he's like, well, I want

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to work on Apple.

[Host]

That's what I want to do. There's a funny thing that I wanted to tie together, which I didn't find the opportunity till now, is you are currently the second wealthiest entrepreneur from Baton Rouge, Louisiana. I don't know if you remember. We've talked about this.

[Scott]

We've talked about this. Yeah, yeah, yeah.

[Host]

The first one, Chicken Fingers.

[Scott]

Great business. But I'm a customer of that business as well.

[Host]

The first one is Todd Graves.

[Scott]

Yeah.

[Host]

Owns Raising Canes. Yeah, I. I talked to him. He was on the show.

[Scott]

Yeah.

[Host]

You tell him you'll give $100 billion for your chicken finger empire.

[Scott]

Yeah.

[Host]

No. Oh, he.

[Sponsor/Ad Reader]

There's no.

[Host]

He's turned down. And I know this for facts. I've talked to him about it. He's turned down crazy acquisitions offers. He's like, I don't care about. It's not the money.

[Scott]

Yeah. No, I mean, for us, it's very like, like, again, this is not rational, but, like, the way that I would describe it, it's like I feel, and I think all of us do like, that it'd be one thing if we tried and we gave it our all and we just weren't good enough. Let me.

[Sponsor/Ad Reader]

Fine.

[Scott]

Like, it wouldn't be that, dude. I'd be. I'd be salty as hell.

[Host]

You know, it wouldn't be fine. But.

[Scott]

But, like. But, like, it would be like. It would be an outcome. It would be like an outcome that I could live with, you know? But if we felt like, you know, we could have gone for it all, we could have pushed harder and we did it. Like that, I think, is like, I just. I don't think we would, like, live with ourselves in that outcome. And that's like the. If you have me explain it, it's almost. It's kind of circular. I don't know. But. But it's like, like, why are we so excited to do this? Why do we do? You know, it's like we want to achieve our potential and build what we were meant to build, you know? And maybe that's something, and maybe that's nothing, but, like, you'd rather find out than see. Yeah.

[Host]

I think this idea of you have one life go.

[Scott]

Yeah.

[Host]

Is the perfect place. Then. Thanks for the time, Scott.

[Scott]

Yeah, thanks for having me.

[Host]

I hope you enjoyed this episode. Please remember to subscribe wherever you're listening

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and leave a review. And make sure you listen to my other podcast, Founders.

[Host]

For almost a decade, I've obsessively read over 400 biographies of history's greatest entrepreneurs,

[Sponsor/Ad Reader]

searching for ideas that you can use in your work. Most of the guests you hear on this show first found me through Founders.