Ep 147: Scott Wu & Russell Kaplan on the New Era of Software Abundance

21:37It's almost like we're getting multiple years done in one year, right? So what does that mean for society? How do you think about that? Yeah, I think about this a lot. Have you ever seen the graph of inflation by different sectors? And you can see, you know, the sort of highly regulated constrained sectors at the top, you know, like tuition, health care spending. The cost of these sectors are broken and everything else gets cheaper. Right. And then the plasma screen TVs are just way down. Japanese taught us what to do. Exactly. Yeah. I kind of think that's that chart is going to happen for like all of society, basically.

22:06And in particular, if you think about where is software, we're going to enter this like hyper deflationary cycle of software where it's it's so easy to build. It's so abundant. And that doesn't mean that people are going to, you know, stop producing software. They're actually going to produce thousands of times more software, right? And so you're going to have this total software abundance. But basically, any problem that's kind of solvable in the digital realm should just get solved in the digital realm. And so I think what you're left behind with is, you know, now how do we direct a lot of this new energy to go improving the physical world, go improving everything else?

22:36You actually have to do things with real operations, real people in the real world. Now, I guess robots could change that too. But for now, anyway, with you to put robots aside, the returns to actually do things in the real world should go up by comparison because it's harder to make those cheaper, right? Yeah, we think about that a lot at Cognition of how do we not just improve software for software's sake, but how do those improvements basically translate into real world benefits for everyone? Ultimately, these things are tools in service of our own lives, our companies, our businesses, and our livelihood.

23:03The real world is messy and you have to interact with that. It's super messy. It's super messy. And I think a lot of engineers fall into the trap of, oh, I want to solve this very pure problem. And there's nothing wrong. It's actually really fun to solve. A lot of our team, they spend their entire programming careers just optimizing their ability to solve the purest algorithmic programming problems in the world. But it's an interesting contrast to one of the things that really differentiates cognition in the market right now. Now, what we keep hearing from customers is, you know, we have this forward-deployed engineering team that will go partner really deeply with large organizations and say, hey, it's not just about the tools we're delivering, but how do we get into the weeds together to actually go drive, like, big structural changes.

23:40Tell me a bit more about that. You guys have obviously deployed with a lot of the biggest companies in the world. What are some of the more impressive results you guys have seen in the wild? Yeah, so the earliest results that we saw and we were like, oh, there's something here, where they started with basically, like, modernization programs. So people that had large legacy existing things, they needed to transform. And if you sort of did the math to scope out how long it would take, maybe it would be like a two year project. And, you know, relatively quickly, by like late 2024, we were measuring, you know, somewhere between a six to 12 X productivity gain for those types of projects.

24:11Meaning that, you know, one hour of human time spent managing Devin was worth like six to 12 hours of that human time doing the work themselves. And so that was a big that was like a big early result. And we started doing lots of lots of engagements where our customers would use would use Devon to just refactor, migrate, modernize these large systems. Now, the interesting trend that we're seeing is a shift from really reactive to proactive engineering work. So if you think of the early days of the Internet, most of the packets that were sent on the Internet, it was like a human clicking a button or visiting a link or initiating some requests.

24:45And then at some point it totally flipped. And now most of the packets are initiated by machines talking to other machines. And I think we're now seeing the sort of the flippening there for software itself, which is the process of deciding what code to write. It used to be entirely a human scoped thing. Now we have people, you know, we have people wiring up Devin to all sorts of events and alerts inside their organizations to say, hey, let's just start the engineering work right away when something happens. I'll give you one example. One of the largest regulated firms in the world. they do very thorough sort of security vulnerability scanning on their code and they make sure to understand is this potentially insecure here or there and there's great existing tools for scanning that you know SonarCube, Veracode, Sneak there's this whole market of tools and what they did was they hooked up all of those alerts they were getting from those tools and they just started piping them to Devin saying Devin can you take the first pass triage because if you're a human engineer at a company you're drowning in these alerts it's not a really fun part of your job and they're remediating 70 % of these automatically now in production with Devin.

25:46And so it's really flipping the, you know, kind of flipping the. I've been thinking a little bit about the pyramids with regard to this lately, where it's like almost impossible to see how people with like no tools at all built the pyramids. And it does seem amazing if you look at the software that we have built up until like a couple of years ago, that all of this exists all by people. And then I feel like that'd be like something you'd look at 20 years from now when everyone just uses AI. You've built all that by hand. Can you imagine that? It seems like totally impossible. Yeah, yeah. No, I think to your point too, I mean, I think there's sometimes a question about, you know, we're talking about, okay, this is going to get way easier to build, it's going to be way cheaper to build these things and so then what happens and I think the reality is like we have so much more to build.

26:23I mean, I was thinking about this even like today, you know, it's like you wake up, you're like, you know, you're like, let's say you're like logging in to check your medical records and it's like not a very good experience. You're like logging into your bank, it's like not a very good experience. There's so much to fix. There's so much to fix. So many of these things and to your point, I think the reality is there's so much more that we can do with software. I mean, I think some of the things that people have started talking more about, which is I think what we're going to get to over the next little bit, are things like even like generative UIs or like single-use software, right?

26:51If you get to a point where, you know, so much of the work that you want to do, it can be done in code. It just doesn't make sense to like write code for a single, you know, for something that you're only going to do one time or even something that you're only going to do like 10 or 20 times, right? I love that the AI can actually write code for this instance coming up and give you the right UI for what's going on. It gives you exactly the right thing and builds it. Yeah. And so this is, I mean, people talk about Jevon's paradox and I think, I mean, in software, it is perhaps, I think there's nowhere that it is more true than in software, right?

27:20As we have produced more and more and as it's gotten easier, the reality is that actually demand has only gone up. That's actually a really fun idea. Like right now, luxury is like, it's like Vacuna. It's really nice. The stitching is right. But what if luxury was like, it's a perfect UI for you just for this instance in your life where you happen to need it, right? It's kind of a funny idea. Either that or actually luxury might flip the other side because um artisanal handcrafted software is going to be so rare it's like you know the in the early days of the industrial revolution was like oh great mechanized mechanized labor for goods like that was the higher status good because of course the machines were more precise the the bags were more you know were more perfectly made and now it's totally flipped if something's handmade that's um that's obviously much higher status because it's so rare and like how could you expend the resources for that this website was handmade you can tell because and all the little bugs.

28:07I think we're going to see that in software. Yeah, it's like handcrafted artisanal code. It's like the Amish, except that they're doing software. Yeah, yeah. It's like a 2020 society, frozen in time, of people making you handmade goods. No, that's very silly. So speaking of things that are broken and that there's infinite need to fix, government, to me, is one of those areas where I think if you were to graph everything or in terms of getting better or worse and efficient or less efficient, it probably unfortunately comes off as really, really messed up. And I'm always starting to hear some pretty interesting things in government.

28:42My friend Jared Kushner, who's obviously been involved in these administrations, he worked with Qatar, with Elad, and they built something where the permits only take 120 minutes there. So if you want a permit to build something, it'll get back to you in two hours. It's pretty cool. So there's all these ways government could do things better with software. You guys just launched Cognition for Government. What's the goal with that? What's going on? Yeah, I mean, I think at a high level, I mean, we talk about places where there is so much more software to build, so many things to fix. And I mean, government is an obvious example of that in all of these departments.

29:14Similarly, you know, things that we used to do by hand, not even that long ago, honestly, I mean, 20, 30 years ago, like lots of these things were done by hand at the Treasury. Now, so much of that is software. And yet so much of that software, obviously, still has such a such a long way to go. And I think from our perspective, when we think about how do we make sure that the US stays in pace with what it needs to be, how do we make sure that the breakthroughs that are coming through in AI in the private sector are also coming through in the public sector, it's something that's a really important problem for us.

29:42I also think there's something poetic about it because I think people don't appreciate this, but the government is really responsible for a lot of modern technical innovation. You know, going back decades and centuries, Scott drew the analogy earlier to punch cards. And I think people don't appreciate this, but really the first wide-scale production use of punch cards was the 1890 census. In the 1880s, they did the census by hand. They tallied it. It took like seven years and they were doing the math and they realized that 1890, if they did the census the same way, it was going to be longer than 10 years.

30:14And the census is every 10 years. So they were screwed. And so the government basically put out this call for technology help. You know, can we solve this problem with technology? And there was a guy by the name of Hollerith who invented what later became the Hollerith machine to use punch cards for actually running the 1890 census. And it was on time. It was under budget. And it kind of kick-started actually a lot of modern software, where I studied at Stanford. A lot of the Silicon Valley ecosystem was actually really invested in by the government back decades ago, partially for defense. And now we're in this flipping point, even by the self-driving.

30:54I mean, DARPA doesn't get enough credit, I think, for really kick-starting the self-driving revolution with the DARPA Grand Challenge. And now we're kind of in this state of the world where the government spends$100 billion a year on just IT modernization. We have huge amounts of government still written in COBOL with people who have left or no longer understand how the code works. And it's really holding us back. And I mean, you feel it as a citizen day to day. Think about your interaction with the DMV or trying to pay your taxes. Or waiting for a permit to come for something. Waiting for a permit.

31:24Yeah. I mean, these things have real world consequences. And that's one of the things I'm personally really excited for is, you know, you can bring technology like this across the private and public sector. It's like a great equalizer for entering work. Can you deal with the COBOL stuff? Is that something, Devin? COBOL is actually a massive use case for us. Yeah. One of the early bets and investments we made was in what we call code base intelligence. So, you know, if you think about like a modern language model, there's a limited context window. Right. But a lot of the largest organizations in the world, they have very, very complex code bases that do not fit inside a single context window.

31:57We've done a lot of work, both on the model training and RL side, but also on the sort of harness engineering around that and the indexing around that to figure out how do you work with really messy custom languages. And so COBOL, it's actually not even the hardest one for us. One of the examples I like is Goldman Sachs has invented some of their own programming languages. People don't know this, but Goldman has a pretty insane internal engineering team. They've literally written their own programming languages. And they've been able to sort of customize and tune Devon to work on those internal languages, too.

32:25So COBOL is actually easier in some sense than that. So in the government, I mean, they spend$100 billion a year in IT. It's ironic because you're right. Sometimes government in the past, especially when innovation was really expensive, they pushed some new things that otherwise wouldn't have happened. Today, most of that$100 billion is spent on special interests that don't seem to be using the money well. So it's a giant mess. What are the types of projects you're working on? Totally. I mean, the incentives are obviously super screwed up for a bunch of reasons that your listeners are probably familiar to.

32:51One of the less known ones that I think we actually might be able to just sidestep is the government is a really unique buyer of software for a bunch of reasons. But one of them is that a lot of times they want to own the IP of the software they're using. And this has a really big implications for most SaaS businesses. You know, if you make scheduling software and your business is a SaaS business, you don't want the government to own your IP. You want them to have a license to it, to use it for scheduling. And actually that desire is literally incompatible with how a lot of government contracting has worked and happened historically.

33:22So you end up in this situation where the government says, oh, you know, this scheduling provider is a real example. This scheduling provider that has great SaaS that can do scheduling. I can't use it because I wouldn't own the IP. So I have to go work with the systems integrator and completely custom build my own. Right. Insane. Now, now what we could, could we lobby and go try to convince the government to change their policies? Yes. But actually, I think easier for us to just sidestep the problem and say, look, Devin can just write this thing for you. You know, just build it for you. We're actually, because of AI agents, you know, you're in the you're in the regime where actually now everyone can own their own own their own IP more easily than before.

33:55So I think that's one of the ways we're sort of trying to sidestep some of this. Can you can you be your own government contractor? Are you going to have your own government contract doing this then? Are you just going to power others? How are you thinking about it? I mean, right now we have dozens of of kind of Fed RAM deployments of cognition, both with agencies and with primes. You know, we work with U.S. Army, U.S. Navy, the Treasury. we work with folks like Palantir, with Andrel and other primes. And so we just want to build sort of the most capable, most useful agentic software engineering platform and then, and then work like heck to kind of get in the hands of people to make it useful.

34:27And then Cognition for Government, this is a fast growing business for you then right now? I would say government is one of those things that it really takes some time to kind of build and be compliant and work in the way that people want to work. And then once you're there, Once you're there, it's much easier to be helpful. And so we've, over the past year, we've really done a lot of the legwork to, you know, how do you get your FedRAMP certification? How do you understand the needs of these agencies, which are actually pretty different in a lot of ways? Again, just in the civilian sector, they're operating under completely different trade-offs, right?

34:55A lot of the software they use is actually by statute, not allowed for them to write themselves. Talk about regulatory capture and interest. There are literally laws saying, you, government agency, are not allowed to maintain your own website. You have to bid this out to a contractor. And so it kind of sounds insane, but that's the way the system works. And I guess my experience working on AI with technology is that a lot of times it's actually easier to solve a frontier science or engineering problem than it is to sort of change the molasses of the existing world. When we work on self-driving at Tesla, a lot of folks would ask us, hey, why don't you make the cars talk to each other?

35:32Wouldn't that be way easier for self-driving if all the cars could talk to themselves? And the answer is, yeah, it totally would. if you got every car talking to itself. But until then, you're going to have to deal with human-driven cars. And so then you have to actually solve the much harder science problem of predicting the motion of all those human cars. I think it's the same for our work with government. So yeah, I mean, right now, we have dozens of these deployments. I think folks are giving us really good feedback on how much it's accelerating their work. And some of the missions are really exciting.

35:56Think of an organization like NASA JPL. Who doesn't want to help us get back to space faster? I love it. Yeah, we just actually interviewed Jared Isaacman, who's running NASA. So it's a great guy to partner with. It is true in government. A lot of times people say, well, this solution would work if we just put this thing in the middle and made everything talk to it. And I'm like, sure. Everyone always tries to do in government. But these guys are never going to all work together. They're all going to debate. And so you actually have to design it knowing it's distributed. It's very interesting.

36:19The real world is messy and you deal with it as it is. Yeah, it's like the XKCD comic. We have a dozen standards. This is a mess. We need one more. Now you have one more. Yeah, exactly. So it's very honorable to go work in government. America needs that. You're fixing it. Obviously, you're growing even faster in the enterprise in general. So these are both big businesses. This is obviously a very competitive time right now. There's a lot of the smartest people in the world. You have a lot of them here. There's some of them at other places as well. I think very famously, some of the big labs like Anthropic have been, I think they've doubled into the tens of billions in the last few months.

36:48It's obviously the highest growth thing right now in the general area. You guys are obviously, don't say your exact revenue, but you're likely to get into the billions soon if you're not already there. What's the competition? Do people use that with you? Does it help you when they do well? How do you think about these things? Yeah, yeah, for sure. So no, I mean, it's an exciting one, obviously, And software engineering and code is just so big that I think there's a ton to do. I mean, we've seen a ton of growth as well. I mean, our usage, for example, of dev in our customers has, I think, roughly tripled already since the start of this year.

37:16So just inside the customers alone. Inside the customers alone. That's right. But what I would say, I think a couple of thoughts here. One, again, there's so much different work to go do in code. And so you'll see a lot of others who are, for example, building products that will help you make a quick little website or something like that or build something like fun. which is, you know, you can use Devin for that, but I think that's not necessarily what we specialize in. On the other hand, a lot of what we really, really focus on, to Russell's point, is working with enterprises, governments, you know, regulated industries, working with massive, massive code bases and trying to make sense of that and work in all of those systems, right?

37:52And so a lot of the problems that you have to go deal with and work on are, how do you, you know, absorb all of the messy context and the knowledge of this code base? How do you work with a massive, you know, something that has hundreds of thousands of different files and work across that? How do you test and iterate against your, you know, your existing unit testing framework? Or how do you, you know, click around and use these products yourself and make sure that the edits that you made were good? And that's a lot of what we've always focused on with Cognition. And so, you know, some of the, I mean, basically all the guys that you mentioned here in terms like the foundation labs and so on, we actually partner with them and we work very closely with them.

38:26I think what we tend to find is that, you know, for obviously their kind of like base research and the work that they do with models, there's a ton of interesting work for us to do together. Whereas I think for a lot of this work with really enterprise transformation, you know, we want to be like on the ground working very deeply with folks and figuring out with them how we use software to help them achieve their goals. So Palantir, originally, we created this thing called forward deployed engineers, which didn't really make sense to people 10 or 15 years ago. And it turns out there was like certain types of workflows where you could build a product to do a lot.

39:03But then the forward deployed engineer would actually have to understand the business value and kind of connect the dots and then take things that they created that oftentimes go back to the core. So the core gets better. So the core could do it next time. Like, do you have to sound like you have something similar to this? Do you have a lot of these? Is that it's a big part of the value you're providing? Yeah, so it's interesting. There's some similarities and some differences for us with sort of the Palantir model. So one of the interesting differences is that a lot of times, you know, our customers, they just use our product on their own, even without us talking to them or without us discovering them.

39:33You know, people bring in our tools and they just start using them immediately. But once Devin is inside an organization, the ceiling of what you can accomplish if you're a world-class agent manager versus a new engineer who's just sort of learned the tools, it's an enormous delta. And so there's kind of some parallels to our business that are more like kind of a Databricks or Snowflake type thing where you just kind of get in and then people start using your products more and consuming more. On the other hand, if you actually want to go drive major outcomes, like real business change that results in like structural, you know, oh, I can launch this entire product line that I wouldn't have had the capacity to, you know, otherwise like that, that type of outcomes, you know, our four deployed engineers are among the best in the world at managing agents at really high scale, because that's like exactly what they sort of focus on and do every day.

40:22So at Palantir, we had these frameworks of like, like building an ontology of all the data and making it talk to each other, ontology, the processes. And we had all sorts of different frameworks over time that are conceptual frameworks we'd use when you go in. Do you guys have like your own conceptual frameworks for business value? And do you have something called ontologies? Like, not to get the secret sauce, but other things like this you could tell us. We really look at it from the perspective of the software development lifecycle. So we go inside an organization. They have a way of doing things, right?

40:49Of developing software, starting from planning and deciding what they even want to write, understanding all of their existing code and process to then maybe scoping it out and maybe writing some code, testing it, fixing it when it's wrong, iterating on it, deploying it in production, monitoring it. You know, there's a pretty standardized software development lifecycle at this point. And what's happening is agents are just eating more and more of the cycle. And it kind of started with the writing of the code. And now we're like well past that. Right. And so, in fact, one of the more recent products we shipped is called Devon Review.

41:16it's because we observed that there's this totally new bottleneck in the software development in the software development life cycle that wasn't the case previously which is there's this abundance of code being written by ai now how can humans even keep up with it all to understand what's going on again we work with you know a lot of like regulated large complex organizations that are they're running mission critical systems and you can't just sort of vibe code you know your way and like yolo merge uh the treasury shouldn't be vibe could no it's like actually really important And so eventually, you know, are we going to have English as a source of truth and people are just going to be collaborating on specs?

41:49Yes. I think, you know, March 2026, no. You still need to understand the code that you're merging. And so we, you know, a big thing we care about at Cognition is we're building tools that are like future looking but still meet people where they are. Right? We still want to meet people where they are. And so Dev and Review, it's not just having like an AI auto comment on every PR and say, oh, this was good or not good. It's actually tools for humans to really deeply understand huge quantities of AI-generated code. Is there some sense that at some point one of the AI models, if it gets a little too tricky, could sneak something into the things that are being built to do something we didn't want it to do?

42:23Yeah, so it's a great question. Obviously, I think with a lot of these things, the simple answer in software is you want to be working with the same review process and the same QA processes that we all have. And so any big enough engineering organization, frankly, already has to think about this just with their humans. And it doesn't have to be on purpose, obviously. Maybe you accidentally introduced a security risk or so on. And that's why you have review. And that's why you have QA. And that's why you have user testing. And that's why you have release cycles and all these other things. And I think, you know, I do think this is one of the things that often comes up, which is like, how do you make sure that your agent behaves correctly, which, of course, is like a very important problem.

43:04The reality is that I think in software, we have actually a lot easier or maybe at least a lot more grounded of a path to do that because we have the same thing already with all of our humans. Right. And so when you work with Devin, for example, Devin is making commits, is submitting, you know, code diffs and so on. Devon is not allowed to go and deploy your code to production by itself or anything like that, right? It works in all those same systems and has the same guardrails. And so let's just fast forward for fun a few years in the future. I talked to some of the people running the top labs.

43:33They're pretty convinced things are going to keep getting better at a pretty high pace for at least two or three years. It's kind of like with Moore's Law. You can't really see out what's going to get there in five years. But it feels like things are going to change a lot. So tell us about 2028, 2029. design like is there certain things that just look very different or certain things that are that are like we have to do now we don't have to do it all they're like what are the unsolved problems for dev and to just be like doing a massive projects on itself in three years yeah i think a couple shifts i mean one of the obvious ones which i'll just call out is just much more widespread usage of all of this and just good knowledge on how to use these things i think right now you know you have this core group of we'll call it like agent forward engineers right or agent forward companies that understand how to use this and they are seeing these you know 5x, 10x productivity gains as a result.

44:17I mean, obviously, you know, all of these big organizations or these companies or governments or things like that are seeing those same results and realizing, wait, we can't just sit here and be five times slower. And, you know, we have to go learn how to do this right now. And so that's really happening. I mean, even this year, I would say. In terms of the continued capabilities gains that we're going to see, yeah, I mean, I think there's, I think the models are going to get better and better. You know, one of the stats that people talk about a lot is this METR report, which basically says for each different model that comes out, roughly how much human work can it do in an automated fashion before you have to go interrupt it and say, oh, that was wrong.

44:59Let's go do this. Right. And so it's like, you know, just two or three years ago, the answer was like 10 seconds or something. You know, you would have it write one line and then it's like, all right, the next line is already wrong. Let's stop here. Right. And at this point, it's already gotten to the point where it's in the scale of, 10, 20 hours is what the latest one has been. I think Opus 4.6, for example, I think was around 18 hours. This is always very weird to me because it's implying that models work in human time though, right? Which is so weird. Why are they a hundred times faster? Yeah, so the answer, they do typically do the tasks in less time than a human would.

45:29And then the 18 hours is basically, this is how long it would take a human to do that amount of work in between each of the interruption points, right? And the AI might be doing that in one hour or two hours or something like that, right? And the thing that's really crazy about this stat is you just see it very consistently double. And I think for the last few years, it's doubled about four or five times every year, which is insane, which means you wait two or three months and it's already doing twice as much work. So the whole world is changing every two or three months in terms of what's possible.

45:56Yeah. And this is what we've kind of seen as well. And I think we're going to see even more of that. and to some of Russell's previous points, the thing that's kind of interesting for us is that the form factor of what you want to deliver or how you want to work with the AI changes a lot as you're going through that, right? And so when we're saying, okay, you do 10 seconds of work, obviously the answer is you as a human need to be staring at your file of code and like shepherding it and handholding it with every single step, right? If you're talking about it's doing days of work or weeks of work, now you're actually giving it whole, you know, output level tasks of like, hey, you know, we really need to go make this app much faster?

46:31Can you go and run this whole thing and then, you know, do a smoke test and make sure all the changes look right? And it's going to go off and do that entire project, right? Versus, or, you know, even bigger initiatives of like, yeah, can you auto respond to all of the, you know, the upgrades or the potential vulnerabilities that are coming in with our reporting and just make sure all of that, right? So you're going to see a lot more, I think, proactive work. You're going to see a lot more basically like event-driven kickoff work where it doesn't have to be human. That's moderating it every step of the way.

46:58And then in terms of like, what does that mean for society or where does it go? I actually think one thing I'll go on the record on is I think there's going to be an explosion in small businesses. I think AI is actually like an extremely small business enabling technology in particular, where you think about what's hard about building or starting a small business. It's, you know, you don't have the resources of a large company that specialization of labor in each part of the in each part of the process. And I think AI, it's extraordinarily enabling. Think about the quality of quick legal gut check you can get from a chatbot, from a frontier lab, the quality of analysis of your financials, the quality of software that you build.

47:35It's all coming together to empower each individual person, again, if you exercise that agency, to just do so much more on their own. I love this. I actually have a small thing on the side where I'm trying to help create tens of thousands of small business owners, so I'm totally in line with this. This is a really good theme right now for us to do. One last thing I want to ask you guys about the business that I'm just so impressed by. So I hired a lot of the first 300 people at Palantir. I spent a lot of time on talent. Obviously, I even hired Scott at one point a long time ago with one of my companies with Vib's help.

48:03How did he do it in his internet? He's very, very, very impressive. Come on, you got to give the report. But I did not see him at the time as someone who was like a CEO person. So he really grew a lot, which is good. I mean, you know, he's just like he's learning and growing as he goes. And I definitely see him as a CEO and founder now. But one thing you guys have done is like a significant percent of the cognition. Hires are actually former founders. So not only are you hiring the best people in the world, you're hiring a ton of former founders. Like, why are you doing that? How are you doing that?

48:30Tell us a little bit about the talent stuff. Yeah. Yeah, for sure. No, I mean, I think the reality is we just have such a massive problem that we're going after, which is, you know, solving all of code. And I think the way we even started this company is, you know, Russell was a founder before this. I was a founder before this. All of us were. I think of our kind of initial crew. And the idea for us, I think, was let's make this one the big one. We're going to go for it all. We're going to go for the most ambitious. I mean, the play of solving software engineering feels like a big enough one that we can all do that together.

49:02And I think that's a lot of what it comes down to, honestly, is just like, yeah, are we working on something and doing something that's exciting for folks who, to your point, are, you know, I think a ton of the folks at Cognition are, could very easily go off and start their own companies and get funded and build their teams and so on. And the question, I think, for us always has been about how do we make this the place that makes more sense for them to do that? And it's honestly easier to do that now than ever before. Also, as a company, I'm thinking, we have one team at the company. They're called Special Projects Engineers.

49:35And basically, every person on that team is a former founder, like every single one. And they do a really interesting mix of engineering work, of product work, of talking to customers, of driving commercial outcomes. The problem space, to your point, is so big that actually if you're, again, a high agency person, you're going to take initiative and you're working at a small, fast-growing company with a problem space so big that your only constraint is your own ambition. Well, it does seem like there's this renaissance or revolution going on in a world where the capabilities are doubling every two or three months.

50:05I'm like, this is a place you can come and be around some of the other smartest people in the world who are part of growing something with that, which I guess is pretty fun for people. We have a good time. One thing about the interview process or the selection process, to your point, which I think is kind of interesting to call out, is I think one or two years ago, I think a lot of people had this mentality in terms of how you interview of like, okay, there's all these AI tools. How do we interview people in a way that, how do we make sure that people aren't using AI while we're interviewing them?

50:35And I think that has totally flipped. Honestly, I think that was wrong. And I think if you're asking the question of like, how do we evaluate people on exactly the thing that AI can already do? That's kind of the wrong. And so, you know, I think for us, and this has always been the case for us, you know, our interview process has always been, you can use as much AI as you want to use. You know, it's just like, we're going to give you a few hours, just build your whole own product surface, right? And build your own. A lot of these are projects that frankly is like, if you were trying to do this by hand, you would not be able to get done in a few hours.

51:07So you kind of have to use AI for this, right? But in reality, what we actually want to test is, in addition to kind of how familiar you are with these tools, what we actually want to test is, yeah, what do you think is the right thing to build? Or how do you make these product decisions? How do you make these trade-offs? How do you decide or collect information about what you should be doing? And so we found that that's helped us a lot. I love it. Well, we started the American Optimist podcast to try to push back on cynicism and pessimism in our country. What's the best case for an optimistic AI future?

51:35What inspires you about what you're seeing? Yeah, no, it's a great question. You know, so funnily enough, I think recently we had this whole Cetrini piece come out, which I thought was frankly ridiculous. I mean, I think it's I think it gets a lot of the basic economics wrong is maybe a simple way to put it. And I think it goes off of some of the same things that we are calling out, which is it's going to be easier to build things, it's going to be cheaper and so on. And then somehow concludes that the outcome is going to be much worse for all of us. And I think maybe that, you know, from an economics perspective, there's a distinction between what is real versus nominal, you know, deflation, which I think is maybe one thing to call out here, which is, of course, prices are going to get cheaper.

52:13But why should that mean that we're all worse off? Right. But but but I think the simple thing that I'd call out about AI is I think we right now are at a point where so many of the things that we want to build and so many of the things that we want to do are just hard bottlenecks by pure execution. Right. And I think we're pretty quickly getting to a point where that's not the case. And my favorite line on this is, you know, our co-founder Walden says this, which is for so long, we've all been living in Minecraft survival mode. and now we're going to be in creative mode. And I think that's really, I think that's what we're going to see over the next, honestly, the next five or 10 years is getting to a point where you're really only limited to your ideas and to your imagination where you can kind of just turn things into reality.

52:57And I think that's going to be a great future. Awesome. Well, that seems probably not to leave it on. Thanks, guys. Cool. Thanks for having us. Thanks for having us. Yeah.