The Exchanges

Q any country in minutes, hire anyone anywhere, get visas handled fast, and get back to building. Visit deal.com slash sorcery. That's deel.com slash sorcery. So you started this company post COVID. It's nearly four years old, and it was AI native from the start. So I just want to know, like, what, what is like fundamentally different about building a company today in the AI era versus the SaaS cycle?

A Yeah. Um, I mean, I think pace is really important. And one thing with pace that I think people struggle with is like, You have to just assume that most things are two-way doors, and people really hate this. Like, they really like to kind of, like, think about a decision, get to, like, 90% certainty, and then make a decision. The reality is, like, you're probably gonna have to make a bunch of decisions at, like, 51% certainty, um, which means you're being wrong, right, right? And can you deal with being wrong and then quickly pivoting, right? And I think you have to do that way more than you used to in the past, A. B, I think there's a huge difference on enterprise, like, massive difference on enterprise, which is you cannot get away with, like, going in a room, building some product, and then selling it, and then never improving the product over the next, like, XYZ years. The users matter so much. Like, the alternative is Quad, ChatGPT, these other things, like, those are great products too. And so I think that the product bar for enterprise is astronomically higher than it used to be, where like you a hundred percent have to be constantly innovating and creating the best product for the end user. And I think in the past you could kind of get away with doing some things like really long sales cycles and things like that.

AI assessment note: “I think pace is really important... there's a huge difference on enterprise”

Q We're going to be interviewing Gabe after this. What question do you think I should ask him?

A Um, I think that the best question actually is why are benchmark, like, why are, why are the current benchmarks for most verticals bad, right? Um, and if you, if you look at, like, the benchmarks that have existed for legal for a long time, I mean, half of them are, like, can it pass the bar? Multiple choice questions on, like, community property law and things like that, right? And I think that we haven't actually until now had, here is a very, like, Very good set of data that doesn't a legal task from end to end, and we're missing this in most articles other than coding. Basically, coding is the only one that has like a good saturated benchmark.

AI assessment note: “I think that the best question actually is why are the current benchmarks for most verticals bad”

Q things about SpaceX in this picture because they've run tenders every six months. They have a pool of employees that are not just software engineers, but people who are welding machines that are electricians. And these are traditional blue collar jobs that have not seen tech like equity exits. So I'm curious on SpaceX's standpoint because they also Buy back their shares. So like what, what are the dynamics internally?

A I think it's great that they've, I mean, they've really shared equity across the entire company, which is incredible. And to your point, they have lots of people on the shop floor who literally make all of the stuff that matters. So the fact that this is life changing for so many people, I really think is incredible. Um, you know, the tenders are an opportunity, like no one has to sell anything, right? It's just an opportunity for people to sell something. Um, The company has bought back shares, I think, just to manage dilution, right? If you look at public companies, they buy back shares, and that's, you know, relatively normal. So the only difference is that this was a private company.

AI assessment note: “they've really shared equity across the entire company, which is incredible”

Q things about SpaceX in this picture because they've run tenders every six months. They have a pool of employees that are not just software engineers, but people who are welding machines that are electricians. And these are traditional blue collar jobs that have not seen tech like equity exits. So I'm curious on SpaceX's standpoint because they also Buy back their shares. So like what, what are the dynamics internally?

A I think it's great that they've, I mean, they've really shared equity across the entire company, which is incredible. And to your point, they have lots of people on the shop floor who literally make all of the stuff that matters. So the fact that this is life changing for so many people, I really think is incredible. Um, you know, the tenders are an opportunity, like no one has to sell anything, right? It's just an opportunity for people to sell something. Um, The company has bought back shares, I think, just to manage dilution, right? If you look at public companies, they buy back shares, and that's, you know, relatively normal. So the only difference is that this was a private company.

AI assessment note: “The company has bought back shares, I think, just to manage dilution”

Q So I want to break into the strategy component of this too, because this does bleed into your investment thesis and like how you're looking at companies, I would believe. So, like, what, what are the main strategies that you go after? Like, what are the competitive forces, and like, how do you look at maneuvers?

A Well, I don't look at maneuvers. I look at when I was doing the, the venture investing thing, you know, 90 eight percent plus of my decision was based on the strength of the founder. Um, like, do I have, when I have a conversation with that founder, do I believe that they would just like crush at what they are doing? Do I believe that they are the best in the world at something novel? I mean, it's still what I'm looking for right now, even though I'm not doing direct investing, but do I think that they are the best in the world at something, whatever that is, and do I think they can leverage that To build one of the world's greatest companies.

AI assessment note: “Well, I don't look at maneuvers. I look at... strength of the founder.”

Q That's so interesting. So what is, I mean, I guess to distill that down further, what is the sales experience in the AI world? Like, people just don't, like, they don't really know what they want? We have to be pretty prescriptive.

A We have to say, like, oh, we've seen this from other customers, like, this, these are, like, best practices are. Um, a lot of times they don't have experience with like partnerships for these different integrations. They're not sure like what the best end user experience is. So we'll just, we have to use our experience to kind of guide them through the best way to build. Um, but also a lot of these AI companies, they purchase much faster, uh, like a large financial services, like the deal cycles are definitely just longer. Um, for SaaS platforms, shorter because sometimes we're selling to an existing product that already has product market fit. If it's a newer product, they're also like a little bit unsure of what adoption might look like. And for these large AI companies, it's, it's really fast. Um, because the competition is just so fierce.

AI assessment note: “we have to use our experience to kind of guide them through”

Q So I think you guys have been a little bit humble. I know, and I think these are some of your customers. So some of your customers are names like OpenAI, Perplexity, Netflix, Uber, Mistral, Dropbox, Freshworks, and more. So how did that happen?

A Yeah, a lot of work. Like, it was very, a lot of work. Yeah. Um, when we first got started, especially because we were infrastructure, a lot of startups, like, were very scared to use us. I remember talking to Ramp, and they were, I think, like, a hundred employees at the time, um, and we were really scared to onboard them because our product was so, like, early. And, um, yeah, it's, it's, we've obviously gone, like, a long way since then, and obviously they've grown a lot on us as well. And, yeah, we just, We've had to adapt the company a lot. Like, I think after our Series B, we made a really concerted effort to move up market, segment our team, um, have a more mature sales motion, and also just make sure our product was really enterprise ready, and that was really hard, but, like, last year was really when it all started kicking in.

AI assessment note: “after our Series B, we made a really concerted effort to move up market”

Q Did you also notice when you were doing that the vulnerability of your business before?

A I, I mean, I think absolutely. I, I think we realized in that moment, it was the classic innovators dilemma where we had, you know, this product, it was growing really fast. It continues to grow really fast, but we also knew where the space was going and we wanted to put as many resources as possible in our newer products. Um, and so during that month Shensi was talking about, we also realized like we do not have this ability to just put unlimited resources on this new thing. How can we get more with less? And that was why we, we knew we had to invest in AI. We knew we had to be able to build New products and, and, you know, adapt our existing products with just a couple of people. So honestly, actually building our, our next product agent handler, we had one engineer and me and Shensi, and we were helping on nights, weekends. We did whatever we could have because we needed to contribute. We needed to help. And the other half was if we are the leaders of this company, we have to know everything there is to know about how AI works, how you build with AI so that one, we are more effective and two, we're building for where the puck is going.

AI assessment note: “I think absolutely. I, I think we realized in that moment, it was the classic innovators dilemma”

Q like, cybersecurity has become a hot topic, and we're seeing prevalent breaches over and over and over again, and a lot of times it's through integrations and different APIs connecting things together, whether it's, like, someone's using, I don't know, like, Vercel was talking about theirs openly, like, we saw the Merkur one, we've seen So many. Okay. So what is the state of cybersecurity in this layer of AI?

A Yeah. I mean, I think one of the biggest problems, so what caused, you know, Mercore and a bunch of others is the supply chain attacks where everyone's using these same open source packages, but now the number of pull requests or code change requests that are being sent to GitHub, I haven't seen the chart in a little bit, but it's like massively soaring to the point where it's, you can tell it's all agentic, right? Agents are pushing a ton of code. You don't have enough humans to read all that code, and so things are slipping by, things are getting in, and one of them was a vulnerability that gets injected into an open source package that everybody relies on and uses, and so all of a sudden, that, that little, you know, virus or that file goes into all the code bases, and people are just getting really screwed over by that, so I think there is a need for, for seriously, like, slowing down, especially with these core packages, being incredibly careful, um, Yeah, I think that's, that's one of the, the big ones we're seeing, and we're only gonna see more of that as more AI generated code continues to get pushed out.

AI assessment note: “one of the biggest problems... is the supply chain attacks where everyone's using these same open source packages”

Q What are his key leadership traits? I know you just mentioned a few, but like, like, What is, what is Jack Dorsey like? I have no idea.

A So I loved working with Jack. You know, he's, he's more of a normal person than you, ah, would think because people portray him as this iconoclastic, quirky character, and there are elements of his personality that are quite iconoclastic. However, he's deeply thoughtful. He's very caring for his family. He's very healthy. He's very curious, and he's an amazing listener, and I think a few of the things that I've loved about him is, um, when he has an idea He actually says, like, I kind of want to see this come to fruition, and he just makes it happen. Like, how many of us have dreamed about an idea, and then you just let it sit, and you let it stew in your brain, and you do nothing about it? That is not Jack. When Jack has an idea that he thinks is going to change the world, well, of course we're going to put people behind it and go execute against it. That's just the way he is. And so sometimes he's wrong on timing, But he's never wrong about the idea, and so following his instincts is a great way to follow what to expect in consumer products. Um, his, his listening skills are unparalleled. You know, he'll sit in meetings and not say a word. Like, not say a word. And you can either think that he's really freaky, and you're like, you know, off put by it, or You kind of watch why he's doing it, what he's synthesizing, what he's capturing, and then realize there's real wisdom in h…

AI assessment note: “he's deeply thoughtful. He's very caring for his family. He's very healthy.”

Q A-sixteen Z. So in terms of all of the events coming up, I guess the number one topic that I was asked was on tax optimization. Can you break down? I know this is a loaded topic too, because there's so many different things, but okay. Let's say you have a liquidity event. 90% of your net worth is in one position. What do you do? What is step one?

A So again, if you remember, I talked about three sort of components, but step one is absolutely your trust and estate optimization construction. So, um, There's a lot of nuance and complexity around there, but if you sort of step back, it's all about creating trusts, and there is, as you probably know, like a whole bunch of different trusts you can create. Charitable remainder trusts, spousal access trusts, uh, revocable or irrevocable trusts, grantor, non-grantor trusts, there are all these different things you can do. I'm not an attorney, so I, I don't know all the details about them, but I know enough to tell you that it's a very, um, complicated Situation. In parallel, you can also take some of your proceeds and put them in a different strategy to generate losses, so-called tax loss harvesting, and there, there's several flavors of that too that you can do. Um, uh, where I find that people have difficulty is you hire attorneys, right, and they're going to tell you everything about, or CPAs, about these trusts, but they're not going to have the knowledge To compare that and contrast that to an investment strategy that generates loss. The real challenge I think for people is how do I trade these things off? And that's where having a multidisciplinary kind of background is really important because each of those silos doesn't know enough about the other one to trade them off. Ri…

AI assessment note: “step one is absolutely your trust and estate optimization construction.”

Q agent tooling, and model orchestration, so your teams ship product, not plumbing. Mistral, Dropbox, and Drada already trust Merge in production. Start building at merge.dev. Founders scale faster on Deal. Set up payroll for any country in minutes, hire anyone, anywhere, get visas handled fast, and get back to building. Visit deal.com slash sorcery. That's D E L dot com slash sorcery. Is there anything we're missing out on taxes?

A Yeah, I think there's one, again, one element which I think is really important to me. Super interesting thing happens, happen when, um, you, you bring two different disciplines and examine sort of the multidisciplinary aspect to it. So I kind of touched on this before, but it's like, again, you, you hire attorneys. They're going to talk to you all about trust. You hire sort of liquid asset managers. All they're going to talk to you about is tax loss harvesting. No one, Is spending the time to look at the two and know enough about the two. Very few people know enough about the two to help you trade the two off. Uh, you know, we've done some work on that, and it turns out that there are many situations where the trusts Are not necessarily the right answer. They save you tax, but you can achieve the same or even better outcomes using these tax loss harvesting strategies. So I think that's where, um, I would caution people to not just jump into one of the silos and look at all of them and be sure they're advised properly on all the different components. Charity, liquid strategies, and trusts, and trade them all off appropriately.

AI assessment note: “Very few people know enough about the two to help you trade the two off.”

Q A-sixteen Z. So in terms of all of the events coming up, I guess the number one topic that I was asked was on tax optimization. Can you break down? I know this is a loaded topic too, because there's so many different things, but okay. Let's say you have a liquidity event. 90% of your net worth is in one position. What do you do? What is step one?

A So again, if you remember, I talked about three sort of components, but step one is absolutely your trust and estate optimization construction. So, um, There's a lot of nuance and complexity around there, but if you sort of step back, it's all about creating trusts, and there is, as you probably know, like a whole bunch of different trusts you can create. Charitable remainder trusts, spousal access trusts, uh, revocable or irrevocable trusts, grantor, non-grantor trusts, there are all these different things you can do. I'm not an attorney, so I, I don't know all the details about them, but I know enough to tell you that it's a very, um, complicated Situation. In parallel, you can also take some of your proceeds and put them in a different strategy to generate losses, so-called tax loss harvesting, and there, there's several flavors of that too that you can do. Um, uh, where I find that people have difficulty is you hire attorneys, right, and they're going to tell you everything about, or CPAs, about these trusts, but they're not going to have the knowledge To compare that and contrast that to an investment strategy that generates loss. The real challenge I think for people is how do I trade these things off? And that's where having a multidisciplinary kind of background is really important because each of those silos doesn't know enough about the other one to trade them off. Ri…

AI assessment note: “step one is absolutely your trust and estate optimization construction.”

Q Uh, it's critical. So where does accuracy and reliability sit in decision making?

A Yeah. I, I think the, Where you see rollouts get completely stopped or companies go to zero overnight is in cases where, you know, a model or a tool hallucinates or performs, uh, incorrectly and ends up impacting patient care. And so, it ends up being maybe the most important thing. And the only way to truly ensure that is, one, great evals. You need to have a massive data set of historical, uh, uh, documentation or historic, whatever the task might be, coding, voice calls, To then measure your model iterations on, because when you release a new version of a model or a new version of an agent, it does some things amazing, but then it does a couple of things that are kind of weird, and there might be regression and performance deterioration on the fringes and in categories that you weren't even thinking about. And I think in smaller companies or in companies that don't have a lot of engineers, you can ship quick and get this stuff out, but that performance degradation goes unseen and then turns into cast like just cascading problems for organization later. So our, our belief is We work hand in hand, one book with the regulators, but I think more importantly with the customers to show them that the edge cases they care about always work in the test harnesses, always work in their back testing data, um, and then make that super public. And then at the same time, like when there ar…

AI assessment note: “And so, it ends up being maybe the most important thing.”

Q lots of data. I've had a couple conversations with leaders at COTU previously, Michael Barton, Thomas LaFont, and we've walked through all the different stages of the tech cycles. So, We walked through internet, mobile cloud. We're now in the AI era. What is going on here? I feel like every six weeks we need an update at this point. It's just moving so fast. So what are you seeing?

A Yeah. I mean, honestly, what you said is, is the reality that AI is big and everyone can make these grand statements about how big it is. And we've also tried to do that a little bit in our slides about trying to size the TAM. But the most exciting part is the pace of the innovation. And you can look at that across. You know, how quickly companies have reached, you know, ten billion dollars, 30,000,000,050 billion dollars of ARR, which, you know, we talk about quite a bit, how OpenAI reached, you know, almost a billion users in the fastest time in history. Like all of the curves are so much steeper and faster of adoption, whether it's at the consumer level, the enterprise level, revenue level, like however you think about it. And so to us, we always think about rate of change. Like that's how you really define technology and how quickly it's, you know, catching on and how big it can be. And a lot of times you see quick rate of change and then flattening, right? A lot of times when we look at apps that used to be popular, there was an app that might've done something, it gets to like ten million users, and then it all of a sudden flatlines. But the fact that you're hitting these metrics, whether it's, you know, top-down metrics or user metrics that just continue to go and continue to grow at such hyper rates, and sometimes they actually are seeing a, almost like a little bit of …

AI assessment note: “most exciting part is the pace of the innovation... all of the curves are so much steeper”

Q liquidity, access your funds anytime. Companies like Scale AI, DoorDash, Service Titan, HIMS, Anthropic, Flexport, Robinhood, and Plaid trust and use Brex. Start today at brex.com slash sorcery. That's B-R-E-X dot com slash sorcery. Turing is training the next generation of AI with tasks that require real expertise and real world judgment. You've talked about how agents have become one of the biggest unlocks. So what is the difference there?

A So if you think about your initial chat GPT interaction, you would ask it a question. It would think for some amount of time, depending on the complexity of the question, and then it would come back to it with an answer, or not even a lot of times an answer. Maybe it comes back with an intermediate step of, okay, I searched all of X, Y, Z. I did this. Am I on the right track? Do you want me to go more in detail? Do you want me to do less? Yeah. And it was a lot of human in the loop. An agent now, when you give it a problem, like the, the, the, the amazing part of Opus Um, when it launched at the end of last year was that an agent can now spawn its own agents, and that can increase the depth of work, the time of work, and the quality of the work. And so all of those things, that was a huge model unlock. Agents, agents launching agents, I think is one of the most underappreciated unlocks that has happened, because you can give an agent a project now, and you can just go away, and you can come back, and it has done If not all of it, really, really far along. And the amazing thing is you can also, when you instruct it and prompt it initially, you can say, go do this, launch as many agents as you want, and then launch another 10 agents to check all of these agents work. So not only like, it's so collaborative and iterative now, where the human is almost out of the loop. And if you t…

AI assessment note: “An agent now, when you give it a problem... an agent can now spawn its own agents”

Q As the CIO of KOTU's public sector, I would be remiss not to ask you about risks. What are the biggest risks that you're watching?

A Um, look, the, the biggest risk is that there is some technology on the other side that materially changes where some of these shortages exist today. Um, and that risk exists only in the grand scheme of, okay, the types of stocks that would be impacted by that. If there was, let's say, if let's say that the deep seek moment that happened, uh, you know, last year, another moment like that happens where someone figures out a model that can do all the calculations with less power, less Semiconductors, less memory, all of that. Um, if that happens, it is probably good for the long run of AI, because that means that the adoption is going to happen faster, more people are going to use it. If AI becomes materially cheaper, there's the whole Javon's paradox argument, which is like, yeah, the cheaper it becomes, the more people will find creative ways to use it and do other things, right? Humanoids are still kind of in the back burner, but maybe if it gets cheap enough, like, there'll be way more dollars that get put behind solving that problem, and all of a sudden, like, Other things get accelerated, and so I think that, like, that is a risk that I'm looking at as, you know, someone who has a mark-to-market portfolio every day that I'm thinking about, um, and then, you know, there's, could there be some regulation that comes out of nowhere, um, that maybe changes the, the, or dampens t…

AI assessment note: “the biggest risk is that there is some technology on the other side”

Q What was it like building this out in step with Jeff Bezos and Andy Jassy? What were the biggest lessons that you learned from them?

A Well, the first lesson I learned when I got there, never put a PowerPoint presentation in front of anybody. I will, we did not use PowerPoint. And I don't know if you've heard that before, but we did not use PowerPoint inside. Everything was a written document. You had to be prolific at writing. And telling a story. Things were a storytelling inside the company, and I will never forget the first meeting I did with Andy, and I, they, he walked in with some of his leaders, and we were, we were a smaller team, but I had a beautiful PowerPoint. Nobody would even look at it. I was like, I was just mortified, but I never made that mistake again. Um, so I, I had to actually realize they were a company that dove really deep into the details. And you had to be more prepared. I think my brain worked harder when I was there because I had to go relearn things I'd forgotten as a leader running a public sector business. Like, they would ask me very detailed things about contracting and compliance and why. They wanted to know why and understand why I was asking for something and what it would achieve and what would be the risk. And you, you know, your brain, you just really have to Be on top of your game constantly, and I feel like they helped me stay on top of my game, and I learned that What you write down and how you tell that story needs to be crisp. Don't use a bunch of extra words. Tell…

AI assessment note: “Well, the first lesson I learned when I got there, never put a PowerPoint”

Q Yeah. So, what did you learn from Barry Diller?

A Ah, I learned so much, uh, from Barry Diller, but, you know, one of the things that I learned from him is that I'm comfortable going against the grain. You know, Barry was the ultimate counterpuncher. He kind of took on, you know, ABC, NBC, CBS. Uh, you didn't grow up with him, but a lot of people grew up with him, and he, he built Fox, which was a new, uh, a new product. I've always been comfortable going against the grain, and it's one of the things that encouraged me to join Uber. From the outside, things looked really, really difficult, uh, but we got in there, and, uh, at first it was, you know, climbing uphill, uh, but the desire to go against the grain was something that I learned from him.

AI assessment note: “one of the things that I learned from him is that I'm comfortable going”

Q You're almost four years old. How did you scale up this fast? What was the process like?

A Um, yeah, I've been building companies for like about 20 years now, uh, scaled a software company up pretty fast, sold it, scaled Archer up pretty fast, took it public. And so, um, you know, every time, you know, I'm scaling, I'm doing, I'm in this phase, I get, I get to sit back and say, how do, what did I learn from past experiences and how do I do it better? And, um, you know, at Figure, we took a very differentiated approach to basically vertically design everything. I don't think there's any group in the world that I wouldn't think on the robotic side that makes the designs more parts than we do on the robot. We design the motors, basically every part within there, the rotor stator, everything. Um, the sensors, the structure, the kinematics, the joints, uh, we like this, you know, the batteries that you saw today and the battery packs that, that I think has really enabled us to like control our destiny. We get to like build our own supply chain. And, uh, without that, you're like left at the mercy of like some vendor. And then if that has an issue, like how are you gonna go solve it? If it's got a code problem, do you understand it? Can you QA it? Can you fix it? Can you patch it? Um, So we understand the whole stack from top to bottom. There was enormous lift up front to get the right people here that could do that. And then we've now been iterating through, as you can se…

AI assessment note: “we took a very differentiated approach to basically vertically design everything”

Q And so whenever a new war breaks out, do you have to go back to the drawing board and edit? Like, what was that like?

A It was there, I'll tell you what, it's actually, it was, it was dueling forces of urgency, because on the one hand, the global climate was changing frequently, and there was frequent things we had to change. At the same time, Anderil is probably one of the, the most prolific shippers of product, um, new iterations of things, and new versions of stuff, entirely new projects, things that they weren't talking about that we thought we had kind of like a sneak peek. And then by the time we got around to putting it in print, they'd already talked about it and stuff, like, so Anduril's pace of execution against the backdrop of like a global environment that's changing super fast, you know, I think we were on draft 35 by the time we were like, okay, lock, like we have to, at some point we have to actually print the book, um, but it was, it was a lot of iteration.

AI assessment note: “we were on draft 35 by the time we were like, okay, lock”

Q Um, so after reading This book and writing this book. What are the key takeaways that you learned from Anduril's leadership and their ability to perform over and over again?

A When I reflect back on the last couple of years of, of feeling like I was just spending a lot of time with the Anduril founders, you know, indirectly of like hearing what they have to say and how they think about things and how they articulate things in different contexts. Um, you know, throughout the book we had, I think, over 500 individual sources that we've referenced a thousand plus times, but, um, specifically if you look at the dozen or so interviews with the founders, uh, I went back and looked at, at transcripts of those things, there are, the, the word hope appears 44 different times, and there is this fundamental hopefulness in everything that Anderil, that the company builds and the, and the mission behind the company, Is this idea that if we thought all hope was lost, we wouldn't be doing this. We're doing this because we, we continue to have hope, and I think that that speaks fundamentally to what a real mission is. Trey Stevens talks incredible essay about choosing good quests, talks about it all the time. Um, I think that this speaks truth to what a good quest is, and it is not a specific product. It is not a specific solution. Even for Anduril, it is not defending a specific nation, right? It is not It's not just making the U.S. capable of protecting itself. It is protecting a specific subset of ideals and values that represent this sort of idea of Western demo…

AI assessment note: “there is this fundamental hopefulness in everything that Anderil, that the company builds”

Q How is the headquarters set up? We're going to go through a couple of different things today. What's the flow?

A Yeah, so I wanted to start here because this is the end result. Making all this stuff work the way that it is right now is super hard, and that's what the vast majority of the company is focused on, is just making these things work, uh, in a reliable way. Uh, we will, uh, give you a feel for what it's like to actually fly one of these things remotely. We'll look at some of the new vehicles we're developing, which I'm super excited about. Uh, we'll show you kind of the, like, ultimate stress testing rigs, That we use to put these things through the paces. Uh, and then I'm excited to talk about, you know, the impact that this is already having today with customers, because I think because the industries we serve are a little bit out of sight, you know, don't, people don't think a lot about what is Caltrans doing to keep the road safe, or what is PG&E doing to inspect their infrastructure. Um, people don't really understand how deployed and impactful these things already are, so I'm excited to talk about that.

AI assessment note: “We'll look at some of the new vehicles we're developing, which I'm super excited about.”

Q The sun is out and shining. Um, so I also heard that you opened up a new New York City office. So what's going on there?

A Well, we've been in New York for, um, Almost 14 years now, and, uh, we were, we have been growing, and I think as you know, one of the teams now at General Catalyst is, uh, this company called Precepta that's focused on, uh, driving transformation of enterprises, and, uh, it was really important for us to co-locate all of our, uh, teams in the same place. So, so GC's now got a much bigger space in New York where one floor is actually engineers, and they're building a, Lots of cool stuff. I'm sure we'll talk about that, and then we have our investment team, and it's really designed to be, ah, welcoming for founders, and be sort of a builder environment in general.

AI assessment note: “co-locate all of our, uh, teams in the same place. So, so GC's now got a much bigger space”

Q One of the, like, very unique things about General Catalyst has been your global growth. There are other funds that like to tout American dynamism that kind of baskets this, like, national interest, but you have global resilience. So why did you choose global resilience as a strategy?

A Yeah, I guess a few things. I think global TAM, last I checked, was bigger than the American TAM. So it allows us to play, um, in a, um, bigger market. Uh, it comes down to our values. Uh, we believe in inclusive prosperity, so if, if we're gonna have AI diffuse, uh, you know, we wanna make sure it's captured all around the world. It's like a genuinely, genuinely something we care about. And the other thing is, um, if you think about our two trends, global resilience and artificial intelligence, the geopolitics is going to force supply chains to shift. Uh, does Europe really want to buy, uh, American defense products? Maybe some countries will, but the biggest ones won't. They want to create resilience. Does India really want to rely on Russia or America for their defense? Not really. They want to do it on their own. So our view was, well, defense primes are going to emerge everywhere in the world. Okay? And we want to help founders create those everywhere. So, you know, we were in the seed. We've been invested in, uh, Ann Wilson's seed. We are, Deeply invested in, uh, uh, Helsington Seed, uh, and that's a project that Jeanette and Daniel Ek and Paul Kwan, uh, have worked on. And, uh, and then we're invested in Rafi in India, that Neeraj has been, uh, working on as well. So, so our belief is that, uh, the opportunity is everywhere, and we want to help, uh, uh, founders, and we …

AI assessment note: “I think global TAM, last I checked, was bigger than the American TAM.”

Q Part of Glasswing 40 is Nikesh Arora, who you just brought on. So how does that work? And congratulations.

A So Nikesh has just joined as a lead independent director, and, uh, he's been, um, first of all, Nikesh is incredibly brilliant. He's, uh, he's an entrepreneurial soul running a fortune 500 company, and he's done an incredible job building that. Um, uh, he's also a great investor mind. So as I think about our, you know, GC ecosystem, which is a collection of investors and builders, I just think he, he'd be a great mentor for all of us. And, uh, I've had many conversations with about where this is going, and every one of those conversations is extremely provocative, and so it made sense to lean on him just like, uh, I and we lean on Ken. And I, I just believe in, uh, you know, great Uh, leaders that are deep thinkers, uh, and, uh, uh, have seen scale to just be guiding, you know, guiding the team here at GCN. So that's been, that's the purpose. So really excited that, uh, a lot of us are going to work closely with them.

AI assessment note: “Nikesh has just joined as a lead independent director... be a great mentor for all”

Q So most recently, you have been going viral on and off with these new product launches. Can you talk about the recent product launch?

A So we just lost agents for investing. Um, and what that helps you do is to essentially automate any type of workflow in your brokerage account. That could be a trading strategy, that can be money movements, that can be alerts you want to set up to some regard. And it really moves the way you even just like interact with the platform away from just like clicking buttons to just expressing intent. And how we think about it is that it kind of helps people to really kind of move up in the, in the sophistication. You know, like one example we always make is there's a lot of people who have a decent understanding of that you could generate some income using covered calls on your portfolio. And that's like high level what they understand, but now to actually go find the right contracts, see what the income potential would be, et cetera, that's where it starts to break down. And so people just like start freezing because now they have to educate themselves and it's going to be really hard. Um, with Asians, you can literally just be like, okay, look at my portfolio. Um, show me if I could do, you know, five grand a month income potential on it. Um, let me just like, let me see what's there. And then it scans the portfolio, shows you Um, you know, opportunities for, for, for cover calls and income potential behind it, and then you can just like activate it and it runs as a workflow. And …

AI assessment note: “So we just lost agents for investing. Um, and what that helps you do”

Q terms of you in your role, Co-CEO, founder of Public, Sorcery is sponsored by Brex, and they're all about performance, and so as you think about performance for yourself as Co-CEO, who are, I think performance really has to do with who you surround yourself with, or who you admire, who you are mentored by, who are some of those people for you, and how do you keep yourself motivated?

A So Janneke and I have a little bit of a circle of, like, other founder friends, and a few of those are further along in their journey than we are, and so on, and I think that is always, first it's great just for advice and so on, to have other founder friends that, like, in similar situations, similar size companies, and all that kind of stuff, or even further along than you are, um, because I think that's immensely important and helpful, but there's also a sense where, like, You know, I don't know, you're sitting on your buddy's private jet, and you're like, fuck, there's always another level. And so there is always this, like, man, and so you get, if you always get, like, uh, uh, exposed to, like, your friend's successes, you know, um, on the one side, you're obviously rooting for them, and you're happy for them, but on the other side, you're also always a competitive being, and you're like, argh, okay, gotta work harder. Gotta work harder. Yeah.

AI assessment note: “Janneke and I have a little bit of a circle of, like, other founder friends”

Q What are you looking for in these research papers? Has it gotten redundant at this point? Like what is new?

A Um, a lot of it is, I just look for inspiration. I think the thing that's happening right now across computer science through at large is these glimpses of what the future will look like. And for a long time in my professional career, academia was always Either too abstract or too far into the future. You'd read a paper. So I spent a lot of my younger years looking at things like cryptography, which was sort of the first love that I, that I've had in, in CS. And you'd look at like homomorphic encryption and be like, wow, that's a fantastically interesting concept, but we don't really have nearly the computes. Like it's not going to be a thing in my immediate professional future, or you'd look at Quantum computing and like quantum attacks and post quantum encryption. And you're like, that's cool. But like, this is really gonna happen in my lifetime. Will they have enough qubits to be dangerous? And with AI, it's different where you're literally reading a paper, like, yeah, somebody's gonna implement it tomorrow and it's gonna be available. And then everyone's gonna take advantage of it. And so you read the papers, like what, what might come next? What, what will be the important news of tomorrow? So it's, you, you have to stay current.

AI assessment note: “a lot of it is, I just look for inspiration.”

Q So Peter, you were on stage and you walked through the timeline of the company and some of the biggest milestones. For people who didn't get to see that, maybe they will, but could you share some of those biggest milestones?

A Yeah. So, uh, we, we started in 2017, uh, in, uh, I call it the, the early era of self-driving cars. And, ah, and back then, for strategy reasons, we didn't think it was going to be smart for us to really go directly into self-driving at that moment. Um, but we started out building tools. We built simulators and data management offerings and, and large-scale distributed compute offerings, and we sold these out to the industry, and, ah, that was sort of our, our early success as a company, and then, ah, they gave us resources. Yeah. Yeah. And we just learned and learned and learned. Um, and we really expanded out and became very horizontal. We started working in a bunch of different industries and, um, and we were very much on top of all of the, the latest AI breakthroughs at every step. And so we could sort of see when, when something interesting was happening, should that actually change our strategy? And, and so there were a few interesting things that happened, which, which evolved the strategy. One was transformers. Um, that were originally successful for large language models, like you heard about Anthropic and OpenAI, um, those started to have an impact on self-driving technology and robotics, and we thought that was really interesting, and then on top of that, there was, uh, some breakthroughs in what's called end-to-end deep learning, which is where you can now take lot…

AI assessment note: “we started in 2017... We built simulators and data management offerings”