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You're watching TVPN. Today is Thursday, September 4th, 2025. We are live from AIPCON. It's Volunteers Conference. It's the. What do we call it? The Office of Ontology. That's right, the tent of Tax strategies. Many people have been saying this. We have a great show for you today, folks. We're interviewing Dr. Karp in just minutes. We're interviewing a ton of folks from Palantir, ton of customers from Palantir, some founders, some folks who work at companies that use Palantir. Should be an interesting day. But first, there is massive news because the browser company of New York has been acquired by Atlassian. This morning I was headed to the airport, I got a push notification from the browser company Substack. Substack. And I opened it. Yeah, and I saw that they were getting acquired from their own announcement and I opened X and nothing had been shared. That's actually I kept scrolling randomly. It was like browser company substack. I mean they have actually a cool thing. It's their username is open.substack. so. So URL is just open substack. Okay, interesting. So I open it up and I'm like, well, browser company's getting acquired for 600 million. Posted it a few minutes later. I think people kind of woke up to it. They announced it. So sorry to front run them, but Josh Miller shares. The browser company just signed a merger agreement to be acquired. We will remain independent. Our focus is dia. I've written and rewritten this post more times than I'd like to admit. But what I keep coming back to is simple. The work continues and we're grateful for this moment. The work continues because when I stop by the coffee shop near our office, nobody is using DIA yet. Very humble. Our Internet computer vision hasn't been realized. DIA has. Hasn't yet changed how you work on a Tuesday morning. This deal is about giving us the resources, distribution and monetization muscle to get there. At the same time, it feels disingenuous not to pause and briefly celebrate this milestone. It's that reflects our team's craftsmanship and relentlessness, the support of our coaches, board members and advisors, and the incredible effort from our deal team. Most of all, we're grateful for what this means for dia. It means we can hire faster, ship faster and bring DIA to more people. We can now invest in cross platform support and secure syncing, train custom AI models designed specifically for dia. We could see the company from down under getting into the foundation model game. I guess the weird thing about this is that Atlassian already has, they have a ro. Ro, I think it's called, or something like that. Like they, they, they, they haven't been asleep at the wheel in terms of AI. They definitely have been adding AI features. You were reading from the last earnings call, right? Yeah, I mean the last earnings call at last. Atlassian is just a fantastic company. 5 billion in revenue, 82% margins, 1.5 billion in free cash flow. 1.4 billion in free cash flow. I'm so glad we brought the soundboard. We're back. And so, and it just doesn't strike me as the, like their last, the last few acquisitions that they've done like loom just makes so much sense in the context of the rest of the product suite that they have, you know, they have Trello, they have hipcamp, which never really beat Slack JIRA tickets. Or they have Jira, which. Named after the. Named after the poster. Poster Jira tickets. And so all of that kind of makes sense as like a bundle you sell into one in the enterprise and then once people are tracking issues with Jira, you sell them on. Okay, let's do your project tracking, let's do your looms, let's do a whole bunch of other things. And then the DIA browser, sure. It could be a useful beneficiary for like if you're in an enterprise context, maybe you want to track some stuff. But it's very Atlassian. Atlassian makes a lot of tools that live in your browser. Yeah. But they all run really fine in the browser. So I think people are puzzled by this generally. And I think the timeline is generally like. You saw the Will Depew post. Like there are definitely people that are against this and are saying that like, well, the vibe. The Vibe. Vibes had turned on the browser company massively. Yeah. Over the last call it 6 to 12 months purely because of the valuation relative to the monetization and the, and like the, the, the progress of the business. Million. Yeah, they had incredible, incredible marketing. Incredible. Sort of like messaging, comm. The videos are incredible. Like I watched their announcement video and like the little details of the lens flares and they created taste. It's very tasteful. It's great. But I demo, so it's cool. I mean what I like to see is one, it's a real acquisition. They like cleared the prep stack for sure, massively. So the team, the whole team's getting paid. There was some, there was some uncertainty about how much they'd raised, but it was somewhere between like 50 million or 75 million and 125 million. Like it was definitely not 300 million. Yeah. And. And they. And then. And at 620 in cash, like everyone's getting paid out, which is great. So. So yeah, I think. And put another way, it's only six months of Atlassian's free cash flow. Yeah. Which is like. It feels like a lot, but at the same time it's like okay, like half a year of free cash to take a big bet on consumer. In an interesting way in an. In a market that I am curious to see how they focus in the product on consumers versus enterprise. Like at last. Very interesting. Is an enterprise software conglomerate. Yeah. Right. So you'd imagine that they would take the product in that direction. And I do think there's a lot of space to play in there. Right. It's like bringing AI into the browser where. Yeah. People do all of their work. Yeah. What's the steel man for this actually benefiting the Atlassian Enterprise suite. Something like. So here's. So there's a post here from Rod Jain. Yeah. He said Atlassian bought Vibes, not a browser. Never asked the best art collectors how they made their money or why they bought the art. Atlassian. $610 million purchase rhymes with that. The Atlassian problem. They invented bottoms up SaaS. Anyone could sign up for Jira. No procurement needed. They were the cool tool of 2010, but success forced them up market. Enterprise features, enterprise pricing, enterprise Vibes. Today, when founders start companies, they choose Slack, not Hipchat. Linear, not Jira. Notion, not Confluence. Cash tag team has near zero inroads with the next generation. Their Microsoft circa 2014. Rich, but irrelevant to anyone building something new. Why the browser company in Loom? These aren't product acquisition, they're guest list acquisitions. Every founder using Ark, every startup using Loom, that's Atlassian buying access to users they lost and might never get back. It's building a gallery in Brooklyn so you could get invited to the right dinners in Manhattan. I just understand the Loom acquisition so much more because Loom is an enterprise tool. It's used by startups. It's used in a business context. Sure. It's probably used by some consumers. It just feels like the price feels. It feels extremely steep given like Loom had product market fit. Yeah. It's just that it wasn't necessarily going to turn into this massive platform and compound. But it's growing like crazy actually from within Atlassian. They called that out on the earnings. Yeah. And so like I think that the limit. But it felt like standalone. It felt like a standalone product, not a platform that fit nicely at last. Agree with that. Yeah. Whereas paying 610 million for a company that, that people use. Yeah, but not a lot of people. It's a million DAUs, apparently. Something like that. I don't know. I think I thought that number was total. Maybe I thought that was like total signups. Yeah, but it's small. It's small. Yeah. Nobody was. The thing with Loom, people would adopt Loom and start embedding it in their work life in a way that they would be upset if they no longer had access to it. I'm not sure that DIA is quite at that level yet. So one bull case I can think is something like this where you bring in this team that clearly has taste, great design, and they kind of give the rest of the Atlassian product suite like a fresh coat of paint and, and they kind of revitalize the vibe. But the message, the messaging here is that they're going to continue operate independently and scaling the DIA team. Yeah, but that could just be something that they do for a little bit and then eventually they get interested in, hey, let's bring the team over and work on JIRA and work on a V2 of, of, you know, Loom or something like that. Like that. That's a poss. The other kind of maybe bull case, which I'm a lot less clear on, is, is there a world where if you have everyone in your organization using an enterprise AI powered browser, even if they're not on the full Atlassian stack, let's say they use two products and then they're, instead of using hipchat, they're using Slack. Can you scrape more easily the data out of the other enterprise products and centralize them somehow? Because I bet you if you're a company that's using Jira and Slack, those two companies don't get along because it's Salesforce versus Atlassian. But maybe if I, if I'm, if I'm like, they're kind of forced to get along to some degree. But the integration is probably really rough. We've heard about the data walls and the data, the, the data wars. And so if you say, hey, instead of trying to, you know, set up some API and, and scraping out your Slack data and dumping it into your JIRA instance every day, instead of that, have everyone on your team use this enterprise browser and no matter what tool they use, the data is going to centralize. So let's go over to Mike Cannon Brooks, the founder of Atlassian, he says, couldn't be more psyched to welcome Josh and Hirsch and the entire browser company team to Atlassian with DIABrowser. We're going to collectively redesign the browser to help knowledge workers kick butt in the AI era. It's a mission, a joint mission, a huge mission, and one I couldn't be more excited about joining with this team to get cracking on. Let's go. So, yeah, this just tells me, I mean, the most important line here. Collectively redesign the browser to help knowledge workers in the AI era. Yeah. The last option is that it's just, it just buys them time to kind of take some more shots on consumer AI, which is clearly a growing category. And there's. And Atlassian can underwrite like crazy opportunity more than VCs can. Anyway, we have Dr. Karp. Welcome to the stream. How are you doing? Great to meet you. I'm John Happening. We're gonna have you hold this microphone. Where's the camera? The camera's right there. You can just see wherever you want. What is the big announcement from today is, Are you, are you trying to tell more of a story around enterprise with this? You know, we're kind of not, We're. I think we're just. It's more like we're crushing it. Yeah. Everyone tells us to be super modest about 93 growth in the US and 94 rule of 440. They may be redefining the rule to, like, make sure the other people don't, like, have to live in shame. I keep seeing these articles, like, in the Wall Street Journal. It's like, Rule of 40 isn't real. It isn't real. Yeah, it's real because we're like crushing everyone. You were forced to be humble for a really long time. I was forced. Well, people were showering me with humble nuggets all day. It didn't really exactly work. But, you know, I, I do think you have to judge humility by the delta between performance and ego. And I would say somewhat ill. Modestly, I'm the most humble I've ever been. And, and, and, and, and now. And I just, I think it's like. So what we try to accomplish with the. We've been doing these kind of conferences forever, basically because everything we've done at Palantir is like completely. It's antithetical or at least orthogonal to what you would. How you would build a business. You guys look at a lot of businesses. You would never build a software downstream from value creation. It's all basically, how do I Make the client feel like they're getting laid when they're getting fucked. That's the whole way you build a software business. In our business, we began in the beginning, I used to tell people, you know, this is a, we're a mutually servicing business. Both sides should be happy. And, and the way we built the business was basically underlying metric. I always thought was, you know, the logic of software should be we charge you something downstream of value creation. That sum is a percentage of the value we create. It's better for both sides because it's, it's, it's, it's significantly less than the value creates. Good for us because there's a multiple on the value. The flaw in the logic was always that FDE model would basically mean that you'd get a one multiple. So we were structurally misaligned with everyone in finance. Everyone not at the Founders Fund, but basically everybody else because of that. Now what we've proven with ontology, FDA structures, where FDA are actually technical and internal orchestration, which is largely artistic, basically was now we got very lucky because without large language models, this would not be hypercharged. So it still didn't exactly make sense. But lo and behold, we have large language models. It hypercharges everything. So downstream value creation is an enormous amount of money. And because of our unit economics now, which some people believe are the best in the world, we actually get fairly valued. And what are we doing actually downstairs is we're saying America's central advantage is the plasticity of how we approach the pragmatism, right? So businesses have to move from businesses where it made sense to have parasitic software products that are like, basically helping you. It's like one of these things. It's like you believe you're learning to sell. They're selling you on something that is that you can't get rid of. You then run to Wall street and say, our clients all. We have 50,000 clients that all hate us. They're like, great, that's a software business. Because the hating means they can't rid. A platform business means that you're creating more value than you capture. Well, the way we do, the way we sell is like. And this is why it just all is like. All these things are hugely contradictory. Our revenue is going up, our sales orders going down. The number of people we plan to have in the future is less than now. We are very focused on, you know, everybody's like, high volume. The volume makes up for, you know, the fact that revenue decreases per client. We're not focused on that. All we believe we're going to make more from people in the future than in the past. Sizably more. Because it's like why should we not capture part of the value that we help create? Actually it doesn't have to be the majority. In fact it's usually the minority of the value create. We also believe that if for more kind of like kind of architectural implementation, technical perspective, the value is in high fidelity data captured in ontology with FDEs and where there's an enhancing factor with LLMs and that that's going to be very, very hard to replicate. But, but, but again all of this is kind of very non traditional. And so what we're really doing in these conferences is saying the same thing we say on the outside. Don't believe anything we're saying, talk to other people have done it. We're not, we don't chaperone the people here so they're like you can talk about things you like, things you don't like. People are on stage. But learn how to build the business of the future. What does the business of the future look like? Actually the interesting thing is workers become more valuable. Like actually trained workers become more valuable. This is exactly the opposite of what people are saying. But it's true. The person at the top is actually crazy valuable. People with technical expertise are crazy valuable. And everything else is going to be done in foundry ontology and something like an fda. So like the orchestration of the business is completely different. Where are Fortune 500 companies getting screwed by these AI pilots? We saw this stat like 95% of AI trials in the enterprise aren't converting. Like what's, what does it look like when somebody sells someone? Well, I mean that there's a technical reason LLMs are probabilistic, they're not precise. The, the value of LLM is when it's essentially in an ontology wrapper. Because to, to, to actually create value you have to be able to take the output, serialize it and deserialize it in the context of the business. So the logic, actions and security of the business and its tribal knowledge and what it's trying to accomplish. LLMs are vertically crucial. But the, but, but the error bound is very, very, very narrow. And the way you actually do LLMs in the real world, not in theory, not is like, is that you essentially put them in a concatenated chain where each single thing has to be done as a street unit because otherwise the underlying math is 95 times 100 separate chains. It's like totally unreliable. And if you do it any other way, you're getting a steak dinner. And that steak dinner is super tasty. It's not going to work. And even worse than the steak dinner, honestly, is that you're being taught how to do something incorrectly. It's like. It's like, okay, I'm going to learn how to learn from a Wokester. Great. Great. The damage that Wokster's doing, mostly on the left, but occasionally on the right. The real damage they're doing is they're teaching you how not to learn. Like, and if you just pick your favorite person, right, left, center, who's just selling complete garbage, it's all conspiracy, the whole thing. Yeah, it's like. It's like. It's like, there's no such thing as building. There's no such thing as agency. You can get away with eps. Well, if you want to. Like, Palantir is lifted. And one of the things I'm proudest about in the world is we've lifted people from their mom's garage to their own house. Millions of people. You want to stay in that garage. You listen to those people. And it's the same thing happens in enterprise. They're selling you something where you think you're getting laid and you're getting fucked. And once you're fucked like that, it's very hard to undo it. And like, yeah, you know, the crazy thing about my life is I'm like this wacky dyslexic. It's actually much harder to be dyslexic, but it's also much harder to get fucked because you don't believe. You don't, but you don't believe in any of this bs. It's like. So speaking. Speaking of sales, there was the CEO, founder, CEO of a CRM company that was making some comments yesterday. Did you. Did you catch? I. Look, Palantir, we structurally mind our own business. And I love that everyone minds our business. But I would say the. What? We constantly have people on tv. It always sounds like, you know the guy in high school who's like, but I'm so nice. Why don't I get laid? It's like, it's literally like. It's the same thing. I'm so nice. I'm so nice. I create all the value and I'm so nice, I'm begging to get laid. And no one was like, I have such a big this, I have such a big that. And we're like, yeah, we're not trying, dude. We're here, you know? And Yeah, I don't think about you at all. Well, I. It. It's like we are very focused on value creation and we ask to be modestly compensated for that value. And you know, if you disagree, you're like, you don't like us as a client or you love us as a client, but you think it's like, great, we're doing our thing, you know, in Palantir right now in the US Is the market cap that counts. We don't have the people, we don't have the time. We orchestrating completely perfectly at Palantir, which of course we don't do, and sort of like an artist colony. Right. We don't have a time to like actually focus on like what we need to, like extending certain components of ontology. We have to do extending maven for the sake of the West, Building things in classified environments, extending things with high value things like. Yeah, we're focused on that and we don't have the time. Like when you're growing 93% off of a very serious base with a de facto de minimis. Yeah, yeah, it's the 93. And that's not even our best number. It's 94% rule of 40. It's like. And then people. Then people are like, oh, yeah, yeah, well, but we have all the skills, we have all the motion, but. But like somehow our ocean isn't working. It's so big, but it's not. It's like, yeah, great, you have problems to. You have time to focus on us. We got things to focus on here that are crucial. You guys are. It feels like you're reacting to the changing world and actual like customer needs, whereas other players are reacting. Let me give you, Let me give you a more kind of slightly philosophical economic thing. What the large language model does models do in combination with ontology and FTEs, and knowing what you're doing is it creates period of optimality over time. We're not there exactly, but every single tech company in the world is going to be paid based on value creation. Maybe that's not completely true today, it will be true tomorrow. So when any company is saying something, you really have to ask. Given that the aspiration of LLMs are transparency and incompetence broadly defined, they've actually the big cultural shift on enterprises. People running enterprises believe that this thing should work. I should know the cost of the components in my business to the second. I should know how to rebuild things. If there's a macroeconomic, I should be able to put the bomb on your Head and not on his head. Okay, so that basically means every conversation in the future is going to be I, you create X value, I'm going to pay you. Why? And the central problem a lot of the larger kind of less agile sclerotic companies have is it's like they can't, it's very hard to move from I get paid because you can't get rid of me to I get paid because you could get rid of me but you don't want to because you're creating so much value. But that's where the future is going. And like people talk about like you know, how are we going to you know, get do 10x in revenue, blah blah blah with the same or less people. It's like yes, but the whole market's going to have to move to value creation and we're in the business of that and try to do it, you know, it's not. Yeah. Do you think long term that the gross margins of software companies will change materially because of like LLM inference costs, like token factory costs, that type of thing? Well you mean like enterprise software companies or if I, if I look at like the Fortune 500 right now, there's like a set number of gross margin that's out there. Should we expect like gross margin compression based on. Well I, I basically. Well first of all I think, let me just give you the trends. I think first of all skilled workers are going to become more valuable. Sure you're going to be paying them more, they're going to be happier. It's exact downstream. Politically it's very hard to argue for anything but high end immigration. So like why do you need more people? Like we got to make the people we have here work. So like politically it's like, like you know, I'm an unhappy democrat but running around saying oh crime isn't an issue when everyone knows crime is an issue is like it's like suicidal BS and no one believes it. And now that wokeism is luckily mostly at least in that way, you know, not as punishing. We can all just admit the obvious. So like transparency is going to be like, so the people are like workers are going to become more expensive, the overhead is going to become less truly basically artist shaped people are going to be incredibly valuable and they're going to demand to be very highly paid. So but the aggregate cost structure will come down. But more importantly the products you build are going to be much closer to what the market wants in real time. And then again just an obvious thing, this is happening like we have 10x growth in America compared to Europe. Same people, same product, same everything. So it's like. And then the other thing, the point that's a little less obvious, that I think people ignore, is time is not time. We always assume a minute of time is a minute of time. It's not like. It's like from the time you want to do something to the time it happens. If that's 10% of the time, you've just graduated, you just got a 10x. So it's like, you know, it's like Palantir is not. These kind of atrophied companies. They really, they every. It takes them three years, five years to get a year. It takes us a week to get a year. So it's like, you know, it's like that. That's actually what. What explains the numbers in a weird way is. Yes, but what if five years represents 40 years? What if. I'm saying in the next five years. Not. We're actually. It's like the whole problem with the DCF model, actually, that experts love is A, they don't understand product and then B, they kind of extend the DCF if they like you. So it's like, oh, I like the person. The DCF is super decades. They give them an extra decade of steak dinners. But the real problem that they somehow don't understand in the DCF is a year is not a year for Palantir. Like, a year is like, we don't do holidays. I'm working all the time. I'm working. Honestly, I sometimes hate the enemies of Palantir, but, God, do they get me to go back to orchestration because I'm like, I'm going to fuck these people. Like, like, you know, and the basic way I'm going to do it is, you know, going back to, like, dyslexic, you know, like organization, orchestration of. We're going to have the best products, the best people. I'm going to recruit those people. I'm going to make sure they're the most valuable and I'm going to put them in enterprises that value us. And if you don't value us, go, go work. Go, go work. The people that hate us, try them out. Yeah. Do you have advice for young people? I mean, you said, like, artists, like people, not literally artists. Again, you said, you said the company is like an artist colony. Yeah, well, people underestimate, like, their artistry because, like, from a young age you get huge benefits for conforming. And you can say, well, I don't. I Mean the central advantage of being dyslexic. We can't conform. Yeah. So that was. That ends up being a huge. Because you just can't. So you're gonna have to. So your basic thing, you have to emerge, do not conform. And by the way, the people who are telling you simplistic bullshit, that means, you know, like, meritocracy isn't going to matter. You're not going to judge all these conspiracies. It's. You can't do wealth accumulation if you're in this country, like in America, that I think actually a lot of these things are true in other countries, but in this country they're teaching you how not to learn, how to be complacent, how to give up your agency, how to fail and how to blame it on anyone else. And if you're. So you have to say, it's like, reject that. Yeah, reject that. That's kind of. And then you have to really, really look at people and judge them by their fruits. The best way to learn is to look at somebody and say, okay, well, you know, it's like, you know, you work with somebody like the co founding team at Palantir. So you have Peter, Joe, Stefan, Nathan. Like, part of what made us so good is it's like, okay, you can measure yourself. It's like, you know, when I started at Palantir, I actually, just because I just wanted to be left alone, I was like, yeah, I'm going to make some money. I'm going to move to Berlin. I'm going to live a debaucherous life. That was my goal. Like, I'm moving to Berlin. I, I thought I need 250k. I was like, a.250k is a minimum million dollars a maximum. I'm moving to Berlin. I'm going to like, debauchery forever. Berghain and yeah, well, I had to like, yeah, so it's. And then set up a remote office. But like, you then measure yourself and it's like, okay, well, I'm highly differentiated on measure, on, on managing complicated people who have to believe their opinion is their opinion, but still have to build a product that actually delivers value. That's my differentiation. And so like, you surround yourself and then remember, you have to remember the persuasion, being persuasive and being right are not correlated. So you have to really look at people who are historically right, rebuttably give them the rebuttable presumption that they are right, and work back to discover if they're right or Wrong. Not just. And like. And all these things. And like, for example, on the Palantir thing is a great lesson. Go listen to our critics, whatever critic you love. We're a conspiracy theory. So you could take the left wing version, which is like, Palantir is stripping you of your civil liberties with. Some people on the right believe Palantir is a Jewish conspiracy run by a mutt somehow. Okay, whatever. You know, it's like, okay, well, go. Actually, how does the product work? Does the product protect data? How does it protect it? Is it better than any other company in the world is doing this? How do you build a company? Do you think it's just like an allocation based on a conspiracy? Why did we. Yeah, just pick your conspiracy and that's the strategy. Yeah. And then, and then. But then unpack it and learn for yourself. Like, did this work? How did this work? How did they do it? Assume that at every single decision, if it was a decision anyone else would have made, you would not have worked because that's a commodity. Commodities aren't valuable. And then apply that to your life. What part of this do you understand? Like, you know, what part do you not understand? What part do you understand better than them? What part could you do better than them? And the weird thing about LLM Ontology Foundry is this actually will work for anyone watching this podcast. Yeah. If you're watching this podcast and you enjoy this, you've already passed the test. I don't care whether you're a welder, a plumber, a carpenter, an astrophysicist, or a somebody who'd like to build a business or just want to get rich or you want to get enough money and move to somewhere and do what I want to. Berlin, Germany. It's not the right place anymore. But any case. But. But you've already passed that test. Now go out and pass the test for life. Yeah, you said Germany's not the right place anymore. Like, what is your current mental model for the state of the world order? Like, is. Is. Is America in decline? Do we need to bring things back? Like, who are the power players? America is power pair number one right now. And like all this media bs, it's like, you know, you got to compare America to, and you can't compare America to some thing you're pretending in your head could be America. Compare it to Europe. Yeah, compare. I don't know what you want to compare it to. China? Like, you want to have no rights, you know, I mean, again, I'm actually not anti Chinese culture, but CCP you know, it's like compare it to Europe, like no tech industry. Yeah, everyone rich was born rich. Basically, or with almost no exceptions, the most important Germanic company. I hope someone from Germany is listening to this. Comte Auspolo Alto is Pete. Peter Thiel. Undischased. It's like the only German company since SAP that's real. Like. And they won't listen to us. Like, just think about that. You have Peter Thiel, like the most important venture person maybe that's ever lived. Co founder of Palantir. And you have me was like somewhat, you know, basic, partially dramatics. Did my PhD in German. And you have no tech industry. Wouldn't you have us on speed dial? Yeah, yeah. I mean, like on speed dial. Like, you don't have to listen to what we're saying. You don't have to agree with what we're saying. Who are you talking to? Who are you talking to? You're talking to your, like, I don't know, expert that came here and studied us. Trust the experts. Trust the experts. It's like so it's. Yeah, it's like energy. Like we're. They will. Do you think that there's. There's optimism around the idea phone call, right? Oh, no, no. Because I just. I mean, I pick up. It's crazy who calls me. It's like, it's honestly, like, I can't talk out of school calls me. You'd be surprised when people come in. I begin every call with, don't listen to me. Very few people have. I'm going to give you the freak show answer. You probably want to ignore it. This is what I think. And they're like, huh? Okay. Yeah, yeah, okay. Some callbacks, some don't. But yeah, of course I would. I mean, I have a lot of. I mean, like, honestly, we have a huge retail. Crazy thing about Germany is a huge retail investor base. They don't admit it in public, but like, keep going, keep going. But. But yeah, no, I'm just saying the point. I'm saying is, you know, it's like, oh, so then it's like energy, technical talent. Understanding how to manage the technical talent. That's an art. Like, we have the right venture people, the right entrepreneurs, the right spirit. We have generations of people who are entrepreneurial here. It's like Tall Poppy syndrome. Yeah. Yeah. Well, it's funny you mentioned that. That's like. Yeah, like you. We were very. Well, this is the thing. We have to fight for this because that. No tall pop. What that basically means. And in every. People may not realize this but in any, every other culture I know of and like, and I lived abroad in Germany, Europe, incredible cultures, but if you, your head sticks above the line, it gets cut off. There's one culture where that doesn't happen is here. The only thing is we have to fight for that because the thing that unifies the woke left and the woke right is they don't like the consequences of meritocracy. They want to work back to the inputs. So and that, that like just will screw society. It's like you've got to able to allow people to succeed wherever they go. Now I, I was kind of still progressive even though it believes it. I super would like the inputs to be fair. But the outputs, those are the outputs, my friends of freedom. Okay, last. We got to get you out of here. I walked by your office, there were some kettlebells. What, what are the kettlebells for? Oh, okay, well, this is slightly long. I'll give you a short version. So to be a cross country skier you've got to train year round. So you need substantial VO2 max. And actually you need to be strong per unit of weight. So as an example, I do three days a week of kind of above and below lactate threshold running, but mostly pretty far, and then once a week kind of at. And then I do two days of strength, one day of like endurance strength. And currently the thing I'm actually really proud of is I, I just started doing hang from a bar, so dead hang like four months ago and I, I hit 4 minutes and 36 seconds. 4 minutes and 36 seconds. What's the goal for the end of the year? What do we do? Well, actually my goal for the. Yeah, no, I mean my goal for the year was for actually the next 12 months was, was four minutes. Okay. But then there's the numbers. We got to get those numbers up. Yeah, yeah. Well, no, but the number two, the second best mountain climber in Norway. I don't know if we know his name, but he, I have a picture. He did 4 minutes and 22 seconds. What can I do? Thanks for having us. Appreciate your work. We'll talk to you soon. Have a great rest of your day. Congrats. Congrats. Thank you. Thank you. We will bring in our next guest in just a few minutes. We have. Can you imagine, can you imagine the Fortune 500 CEOs that just want a meeting with, with Dr. Karp just to get energized? Yeah. Oh, yeah, yeah. Like they don't, they're like, I'll pay for the steak dinner. Even though you're selling to me. I'll pay for the steak. You bring the energy. Yeah. Who pays for the steak dinner? Fantastic. Well, I believe we have our next guest pretty much ready. Ben Harvardine from Palantir for deployed engineer. That has been a Palantir for so many years. So many good quotes in there. I don't take holidays off. I don't take holidays off. Oh, yeah. The team is getting ready to post. Anyway, I'm excited for this one. Ben. Ben, welcome to the show. Good to have you. Have you. We are gonna have you hold this microphone as much as you can, but why don't you kick us off with an introduction on yourself and kind of. I'd love to know how you found your way to Palantir. That'd be super interesting. Yeah, it's kind of an odd path. I studied mechanical engineering and architecture in college, so. Not what you would think. Yeah, worked for Anheuser Busch. Oh, no way. Beer company for a year. That was a great sort of transition from college technology. What were you doing at Anheuser Busch? It was a, it was a management training program. Conversation based. So, yeah, after that, ran a hardware startup for a bit. Okay. Went to another hardware startup. But I had some buddies from college who had worked here and thing about Palantir seemed like everybody had just kind of like more autonomy and authority than I saw anywhere else. Yeah, yeah. Amazing. So what do you want to show us today? Can you give us a little tour? I've got a little. Brought a robot. Yeah, one robot. Bringing a robot is a great sign of respect in our culture, by the way, so. Thank you. Well, you know, you can imagine, you know, when we have, you know, events like this, there are a lot of demos, it's pretty screen heavy with software stuff and we've seen a lot of, I'd say, like increasing demand for our edge offerings and hardware offerings. Really trying to push the technology further and further down to the shop floor into the field. And so I wanted to put together something, you know, just a little kind of toy demo that made that a little bit more tangible for people who are here. Yep. So walk me from my understanding to how we get to the edge, how we get to robotics. Because my famous, like the case study that comes to my mind for Palantir in terms of like making things in the physical world is like, I think the Airbus example. So I, and, and, and whenever somebody says, oh, what does Palantir do? I'm like, okay, imagine a plane. There's a bunch of different parts. You got to have a certain amount of seatbelts, you got to have a certain amount of engines, you got to have a certain amount of fuel lines, you got to have a certain amount of chairs. And all those come from different places, and they all have different lead times and strengths, and they need different safety requirements. Did they get checked off? And so you put all of that instead of just in a loose database, you put it in a database, but then you have Palantir. That's actually tying everything together. So, you know, if there's a lead time on engines, you need to order more seatbelts in three weeks instead of two weeks. And that's kind of how I explain Palantir in terms of, like, make a big thing that's complex. Is that roughly right. And then how do you walk from that to, like, we need Palantir to somehow interface with, like, a robotic arm. Yep. Yeah. I mean, that's roughly right. Like, the way I think about it, it's like anywhere you go, people have data scattered all over the place. So the first step is, can we get that all into one place? Got it. Then can we model that data so it's as easy to work with it as is to talk about the concepts that represents? Right. Just, like, make it kind of. So there's this big meme in Silicon Valley and defense tech right now that, like, there's a whole host of manufacturing guys. They're all aging out. They're 65. And everything that they know about how to make a widget, whether it's a chair or a rocket motor, it's in there. Haven't written it down. Maybe it's some loose notebooks. And so this is kind of a way to jump and start getting more data online. Right. We're actually not throwing out the data, we're capturing it. Correct. Yeah. And really, like, the whole point of any of these data exercises is you just want to put the right data in front of the right person at the right time to make the right decision. Yep. And then just be able to close the loop and learn from it. And so if you're looking across the supply chain, so you do it. If you go down to a factory floor, the process is there. That's how you do it. And so when it comes to this robot, we're basically just, like, pushing that edge further. So instead of, you know, popping up an alert on a screen that tells somebody to go do something, what if you could actually just tell the robot to go do it? Okay. So Again, sort of a simple like toy example here. But the basic idea is that, you know, this is a little work cell that we made with a robot arm and a cable 3D printed, right? Yeah, it's all, yeah, it's all 3D printed in the arms. Oh, wow. Okay. Yeah, I didn't realize that. Cool. And so, you know, it's kind of set up to be a dumb terminal that kind of works and looks like the robot arms you'd see on a factory floor. You can give it moves to take. Maybe you can ask it for a picture, but past that it's not doing any heavy computation on board. But then you can push that data to an edge hub that can run embedded models, can run embedded ontology. So you can actually take that kind of model of the world in terms of objects, relationships, actions and models and you can push that down to the edge. And even if you have say like a, like a network sparse environment where you don't have that real time uplink to the cloud, you can continue to run off of that ontology. Yeah, we were looking at semianalysis. They put the five levels of robotics, I forget exactly how many levels there were, but they were trying to map the self driving car analogy to physical robotics. And I believe like level 0 or level 1, like the most basic was you have a pre programmed robotic arm that's doing the exact same move. It's taking the windshield and put it on the F150 and it's this huge arm and you can't go near it because it's, there's no cameras on it whatsoever. And if you step in that work cell, it will kill you if you don't, if you're not careful. And this seems like a step towards like level two. We're able to actually understand what different products mean. If there's, oh, this type of product shows up, there's going to be more likely that there's a defect or you need to adjust what the robot is doing. How can you actually get that data into something that's actionable? Yeah, yeah. And even in like this simple demo we've got, you know, it'll trigger alerts on, you know, it tries to execute a move and you end up with like a block, like jammed up. Okay. It'll realize, they'll say, okay, you got a jam top. That sort of stuff. Okay, interesting. Where does this play in like the stack of other software? I know when we talked to, what was it? Dirac, our buddy Phil, he was saying that like he's working with Automotive companies. But then they also have a lot of, there's a lot of like lower level control software on machine lines. Some of that's from German companies I think we just talked about with Dr. Karp. But like where do you see Palantir playing in the stack? You have a bunch of data, the database, you put Palantir on top. But then at a certain point there might be some robotics company that makes the robot and then they also might have some control software with kind of a messy API or something like that. Yeah, I think we can be pretty agnostic about how far up or down the stack. So we've got. I'll pull this box. Yeah, please. This is, this is the node that goes on the edge. Right. So this is a, this is an example of an edge node that one of our partners, edge scale makes. Okay. So this is that box that you can stick in the closet network to those existing machines that you have on the floor if you just need a turnkey solution. Yep. And then I think at the other end of the extreme, that's where we've got something like this, where this really, at the end of the day is an ontology defined piece of hardware in that the machine itself, its entire configuration, the state machine is running, everything about it is defined in the ontology, lives in the ontology. And like really just like a bespoke piece of hardware running that ontology native software, it's a monument. So yeah, you, you know, if you've got like more nascent operations or greenfield operations, you think about some of the companies we work with in defense tech, it's like they can go all the way down the stack if you want to. Sure. For some of the, you know, the larger, more established customers that we're working with. The plug and play solution. Yeah. What's the sweet spot for the specs on an edge scale? Like edge node, like something on the edge. I think it really. Do you need to be running like a large language model that feels like something that you could do on? It depends on the application. Like we've done some examples of that even previous AIPcons. It's like, do we need the local app served up with a chatbot for the line operator who can just be like, what's going on? And it just talks to you? Yep. There's, it's not just purely deterministic. Okay. If, if the block is blocked, then send the error message instead. It's, it's actually interpreting a bunch of data in a kind of a non deterministic way. So I'd say it's like, you know, I think like anything, it really depends on the application and the users because again, there are a lot of guys that are working on these lines, guys and girls where they don't need another screen in their life. And so it's really finding like what's the right way to interface with those operators to ultimately just drive the better decision making. How much is, like how much is the. What is the role of the FDE in this kind of new era, new territory? Because it feels like. Yeah. Are you graduated from being an FD yet or is it once an fd, always an fd? Yeah, I think it's once an fd, always an fd. I try to keep my hands on keyboard as often as I can. Still, still flying out to whoever. Axle factories in rural Kentucky or whatever. Yeah, I think the closer you can stay to that stuff, the better. I think really like the role of the FD is like just like it always has been. Go on site with customer, don't just understand, but internalize their problems, their challenges, you know, and so go create some value. Yeah. Well, thank you so much for hopping on the stream. We appreciate it, really. Congratulations on everything. Thanks for bringing your baby. Yeah, yeah, you can definitely take this out here. I will grab this and we will have our next guest, Danny Lucas from Palantir coming in. He also has a demo. Do you guys know if the demo is, is going to need the HDMI cable, is that right? Okay, so we will bring in Danny whenever you get a chance. Yeah, let's, let's bring in our next guest. There he is. What's going on? Welcome to the show. How are you? Great to have you doing live demo always. That is bold. Doing a demo is on a live stream. This is live. So literally anything you share on your screen potentially will go out to the Internet forever to be baked into the future super intelligence. Yeah. Baked into the training models of the future, into the pre training data. So be very careful, don't leak anything. But, but, but introduce yourself, Tell us what you're going to show us. Yeah, absolutely. Microphone. Oh yeah. What's going on guys? My name is Danny. Yeah, see here, I'm an engineer, Palantir. I've been a palantir for about 12 years. In terms of like my role, it's hard to describe. Like I'm sure everyone at Palantir said that. I guess like if I had a role or a title, I, I do a lot of our business in the Midwest at this point. So first six years of Palantir, I was on the government side. I did work with Department of Justice, US Special Operations, CIA, National Counterterrorism Center. After my wife and I had our first kid, she was like, hey, could you not go to weird places in the world anymore? And I was like, totally reasonable. Reasonable request. We, we moved back to the Midwest and I switched over the commercial side. And that's kind of like what I do now is like grow our business in Midwest. Yeah. What's like a, what's a like just line drive solution that you like just total wheelhouse solution for? You know, I imagine like a large enterprise customer in the Midwest. Yeah. What I focus on a lot is manufacturing in the Midwest. So you can like, there's huge manufacturers in the Midwest, whether that's like Johnson Controls or Eaton or Molson, Coors, Cummins engine. So it's a widgets factory. Yeah, they're making widgets, they're buying parts, they're assembling them. And you have to understand that's right. Where's the, where's the rate limiting factor? How can we increase flow? This is where I think we have the most differentiation from product perspective. Because it's like, like I can actually affect the physical world and then I can measure how I affect it and then I can learn and improve how I affect the physical world the next time. Right. Whether that's like, hey, I'm in supply chain and I'm short on inventory, like, how do I solve that problem in the most effective and optimized way versus like I'm trying to manufacture something and like, how do I make sure my machines are running, I have the right labor, I'm trying to do the right thing. So like, the real magic behind all this too is yes, they start off as singular use cases that are pretty great straight shot. But then when you start to connect these workflows together and it's like, oh, the machine's down and I have this material, what do I do and how do I go do it? What do you want us to show us today? I can kind of hold this for you if you want. We're getting good sound on this. Okay, cool. Yeah, walk us through it. What I was going to demo is, I think like one of the interesting things, and I'm sure you've like talked to a lot of different volunteers today is like, we are never going to purport to be like a strategy consulting type of thing when we engage with customers. Like, we're never going to purport to be like, oh, like a, hey, we're experts in X, Y or Z. And the great thing about that, right, is like, we're true to like who we are. The bad thing about that, right, is like, companies will identify and the organizations that we work with will identify, like, hey, I know this is a problem, right? But like, there's a huge amount of time between like, hey, there's a problem and then let's go like, implement a solution. And the dependencies on actually getting to that faster are like, I have the internal SMEs that can actually like understand the problem and come up with the right solution and do the feasibility and all that great stuff. Or I go work with like strategy consulting. I pay millions and millions of dollars to get a deck that tells me like, hey, this is the solution that we think you should employ with the right, like, ROI in this approach. And we've done this feasibility study and we think that you should go do that. And so like, we find that as a huge impediment to like our own growth, right? Like, why should I wait months? Yeah, you don't want them to go spend millions of dollars, some random group to then recommend a palantir product that's 100% right. And so like, what we've been exploring more is just like, well, why can't I use AI to do that? Like, why can't I like, give a fairly haphazard business, like a description of business problem and use agents essentially to like, structure that into a better business problem description to do the necessary research about, like, what are the potential solutions of things that I could and should deploy to go solve this problem? Can I generate ideas with all the requisites of how I actually employ those ideas and actually generate a proposal where then I also have like agents as critiques on that proposal to be like, is this technologically feasible? Is this like financially feasible? All the things that you would expect, like strategy consultants to do for you. Like, I should just be able to do that in a day and come up with a proposal. But then like, I don't know if you guys have talk to anyone about AI fte, but then, like, I should just then be able to use the output of like this to then go build it. Yeah, like, I should just be able to say, like, cool, here's the solution. I need to go build input into AI fde. Build it right? And go from like, you know, what would have taken six or nine months until we ever get engaged to like, well, I think this is a problem. Like, let's just go do it like in the next week. Right. Does that make sense? Yeah, yeah, it makes sense. I have some follow up questions, but maybe, maybe jump into the demo first. Cool. I think like, my immediate, I guess question, maybe it's relevant is like, how do you ensure kind of quality? Right. Because like, you didn't say this, but like someone else in another context might call this like vibe coding. Sort of like generating like a deep research report on like a problem and a potential solution and then like, you know, sort of prompting your way to an implementation. And today, you know, just like code quality and product quality ends up popping up. But I'm sure that you're already thinking about that. My take on this is like when you start doing anything with AI or large language models, like there has to be a human in the loop, right. Not only to make sure that quality is coming out of the other side, but also to ensure feedback loops are occurring and. Right. And then, and then you can take that context and start getting closer and closer to a Jesus, take the wheel moment where you actually have built trust. Because part of this is not actually, I think, a technology problem. It's a people in process problem where people actually build trust in it. And also you get all the tribal knowledge that's not in any system actually incorporated in some knowledge context that you can start to build off of over time. But I think that's the trick is like humans always have to be in the loop, right, to begin, but then you build trust until you actually do the Jesus, take the wheel moment. Yeah. So yeah, with this demo, what is the, is it designed as like an internal tool or something that you would actually sort of out. A lot of our customers are starting to use this to start to shorten the cycle time of going from like initial problem identification application to implementation. So like, and is that for, is that for customers that are already using Palantir? Yeah, so like we. We've started using this primarily with like a lot of existing customers. Right. But then the cool thing about it is I don't know if you guys have heard where like all of the things I'm going to show you are kind of like native components of the platform. But then we've developed this capability where we can say like, hey, this is actually a really repeatable workflow. What if we package this up and then just. It's way easier to deploy where we can just like deploy there, deploy there, deploy anywhere, basically. Cool. Yeah. So walk us through, pull it up and maybe bring it a little bit closer so oh, yes, we see it. Oh, yeah. Yeah, go ahead. Yeah, let's do it. No saying text messages or anything like that. All right, cool. I used to work in the aviation space a lot, and I fly in and out of Newark, which, like, if you guys do that, you know, that's a real pain in the ass. Yeah, yeah. So let's. Let's start there. Let's just say, like, redesign. Hey, I'm a. Oh, yeah, for sure. Go ahead. So, like, the problem. The problem that I'll type in basically, is like, hey, I'm an aviation expert. Like, we're seeing significant delays around, like, Newark Airport because there's not enough runways and the runways are too short. Like, what should I do to optimize my flow, basically, to solve this problem. Sure. So, like, now you guys get to see me type. It's always fun. Yeah. This is interesting. Yeah. A ton of questions. I've always wanted to redesign the lax. Like, streets, like, the flow of traffic. Yeah, that is a wild choice by lax. Just constant, constant traffic. Wasn't too bad this morning, fortunately. But we did have a funny incident with a member of our team who, first day John arrived, got through security. Oh, yeah. And almost managed to miss his flight because he was getting a breakfast by a former guest and friend. I was called. I would call and texted and said, you know, this is no time to take shots at the dyslexic. He had missed. He had made a mistake and confused Gate nine for gate six. Right. And there is no gate six particular terminal. Anyways, headed to a different terminal. I've mind. Thank you for covering. Of course. Yeah, yeah, it doesn't have to be. So right now, I just. I typed in I like. Yeah, pretty rough problem statement. I'm an aviation expert. I want to solve problems around EWR airport. There are too few runways and the runways are too short. How do I optimize traffic flow around it to minimize disruptions? Okay, so that's kind of like the first point. And what's happening here is like, the first set of agents is basically taking that as a problem description and actually, like, putting more structure around it. So it's not like my, you know, my, like, misspelled problem statement, like, cleaning it up. It's like a prompt engineer effectively working. That's right. And so you. On the left side of the screen, you can actually see some of the logic of, like, what happened. The train of thought here of, like, hey, here's the problem statement. I can see the system prompt, like, what the task prompt is. What The LLM like responded to when they saw this to them actually then creating and structuring this problem, which is like, hey, the core objective is I want to optimize air traffic flow around Newark Liberty International Airport to minimize disruptions, delays, inefficiencies. It puts out like key requirements, like prioritize aviation safety standards. It gives out restraint constraints. Nathan Fielder would be happy to hear that you're. It gives out constraints like limited number of existing runways, restrict simultaneous operations, et cetera, et cetera. So like, this looks pretty good to me. Like, as the initial problem description went way better than like the garbly cook, like two sentence thing that I did. So now I want to like start to get into the phase of like actually starting to do research on this. To say like, what are potential tools, what are potential approaches to actually solve this problem. Yep. And so what's happening right now is like now we're going into, kicking off into more of like an agent. Yeah. Just branching a bunch of agents to go do deep research. And so. Yeah, exactly. So like now on this screen I can see that same like core objection objective function over on the left, what it's working towards. Yep. And then I can start to see as it's running on the left, like, like research topics as it's doing research pop up and modeling. This is all built in like native foundry tooling. Sure. How, how inference heavy is this? Because it feels like it's going to town right now. Yeah, I'll show you. I'll show you. Kind of like the under of how we're actually doing the research. Yeah, it is a unique, it is a unique like, like, I don't know, like problem set. Because it's like going to town is something we worry about when we're talking about like, oh yeah, you have a billion consumers and $10 really adds up. Yeah. But if it's like a problem as important as this, if you're talking about, if you're talking about, you know, optimizing an airport, I think I can, I think I can deal with $100 inference, Bill. Yeah, I'm going to be okay with that for sure. So the other thing that I think is interesting here is that like, I think agent is like a very. There are a lot of definitions for what an agent is. I think at this point in time, like one definition is like, and this was like kind of our first approach was like, hey, let's, let's build a set of logic that an LLM actually orchestrates different parts of that logic between and it can use tools like deterministic tools, or it can write back, or it can access and query things to ultimately do some type of automation. I think the other definition of like what an agent right now is like more of a chat interface. And then in that regard, right, like I want to be able to give that chat interface like access to tools. Right. And so in this case like what I've given the agent access to is a bunch of different tools first. Like I can see the model that I'm using behind the screen here. And like, for our, from our perspective, like we think the models are mostly like commoditized at this point. There might be certain models that are better at different things and you actually probably want to use these things interchangeably and actually have an evaluation framework that based on the tasks that you're asking it to do will like select the right model for that particular task. But in this case, right, I'm using Grok 4 and then like for the tools in particular, like I've given it access to like conduct research. So I've given it some ways in which it can actually reach out and use different, either internal or proprietary information of the organization that we're working with or reach out and use something like perplexity to do like more AI based search. I've given it the ability to like generate like create code blocks if it's like coming up with an ROI and it needs to do napkin math. Like I want to say like I want you to allow you to actually like generate the code but also then run the code to see like what, what the result is. And then I mean it seems like all of this is, all of this is kind of like frontier level, but available broadly. But the palantir, I mean the thing that you actually have like data that isn't just available on the web. And so like if I'm actually an airport and I actually have specific data about, the thing that stands out to me is like if you're a large enterprise, you want to work with, with Foundry and, and have that ability to be model agnostic. Yep. And like where does the leverage flow in that situation where when Foundry can just sort of decide on the fly what, what, what form of intelligence do I want to use for this problem set. Very cool. So I can see like kind of like the train of thought on the right like what it's doing and so it's going to go, it's already using the research kind of tool and you can already see the research topics starting to like pop up here. So like this is an example of an application, right, that like a user would use. They would, they know nothing about Foundry, right? They're, they're logging into an application. Their job is like go do this thing, right? But then behind the scenes you have a lot of different options for how you're setting up this logic. I don't know how much you guys have seen Foundry, but this is an example of what we call AIP logic. I could write all of this orchestration and code if I wanted to. I'm fairly lazy. So I use the lower code tool, which is a logic. And so here I can just like set up a bunch of different orchestrations for how I want a function to run. In this case, I'm putting in inputs for what I want the query to be, which is around like that problem statement we talked about. And I'm setting up functions for how it can like reach out to different types of sources. So like the first one is like if I had an internal kind of like proprietary information on schematics of a Runway or planes or what types of runways planes can land on, things like that. Like that's all information that then I can make available to the LLM to go to a combination of like semantic and keyword search against it to find the right information to go do research again. But then like as a backfall then I'm just like also giving it access to go in query perplexity, right. And go say like, hey, go find what's out, what else is out on the Internet to actually go do this research about this particular problem. Right. And then bring that back. And then the last part of this is like an action then to like go capture all that information and store it back into the ontology layer in Foundry. Awesome. So this is kind of like what it's doing live is like it's still working. It's working like. And it's, and it's writing as we like, as it's doing research. Right. So it's like what is the current Runway configuration, operational capacities and key limitations at ewr, including details on Runway lengths, numbers and how they impact aircraft operations. Sure. And so then it actually gives me like this is, this is pretty good information to cite the sources, where it's coming from and everything like that. Right? Yeah. What are effective non infrastructure strategies for optimizing airport throughput? Right. And so in this case, right. It's actually saying like, hey, there's this performance based navigation as a cornerstone, right? Yeah, I remember hearing that if you, if you have the plane board from the back to the front. It'll load way faster. But no one wants to do that because it's a business model thing. Yeah. Because people pay to be at the front of the plane and they want to get on the plane first. But if there was another proposal that was like, load all the passengers that have window seats seats, then all the passengers that have middle seats, and then all the passengers that have aisle seats, and they all kind of just flow in. No one's quite figured that out. But yeah, I mean, I could imagine that it could come up with a bunch of different proposals for, you know, similar. Just kind of like, rethinking of this. The flow. That's traffic. I think we're getting short on time here. One question. Let me, like. Yeah, I'll show you kind of like an end product here, please. Which is like, let's go. I already ran this today. I was, like, hanging out with the American Airlines guys because, like, we're making fun of EWR as one does, not their hub. But yeah, this is like an idea that it generates, and then, like, I get a summary of what that idea is, and then it automatically develops critique agents that are, like, looking and evaluating on different type of, like, different criteria. Right. Which is like, hey, can I. What's the risk assessment and mitigation evaluation? What's the economic feasibility of actually doing this? Like, what is the safety and regulatory compliance evaluation? And then it's going to run, like, those evaluations using that agent as a task criteria to actually then say, like, I can see the guidance that we gave the agent. Right. And its task. And then it has to go evaluate to see if it makes sense from that perspective. Right. And it even, like, generates its own models and its own code to say, like, hey, is this feasible from, like, it. Can I do basically nap, like, napkin math and say, like, can I come up with, like, how I could calculate this and actually go and like, run how. How close is this output, do you think? To what a larger. Yeah, strategy. Pretty. I think it's like, pretty aligned, right? Because, like, they're not in normal times. Like, these strategy consulting firms aren't getting access to all the data. And so they're like, being like, okay, come up with the idea, do the research, generate the idea, guess a little bit. Then, like, I need to do some napkin math on, like, how I would think about actually, like, critiquing this idea. And then ultimately, like, I need to come up with a proposal. Right? And here's like, the end proposal for What I think you should go do same framework where I have agents then writing portions of that proposal and then from there, right, it's just like copy paste that proposal in the AI FTE and like start building. Right. Last, last quick question. Are you feeling the re industrialization yet? Are you seeing new entrants into the Midwest building things or is it more legacy players just trying to, trying to increase. I think it's legacy. A lot of what I work with are companies like Eaton which are like 100 year old companies or like Johnson Controls, €100 companies that are saying like how do I actually use this as an advantage to do, to do better. Right. Like, and that's like where I think is interesting is that like maybe five years ago this was really hard. Like people were like yeah, I don't trust it or I don't believe in it. I think now what's interesting is they're like I trust it, let's go. Like it's just, you can give them, you can sit down and give them a demo. That's right. Well, thank you so much for coming on. Thanks so much for joining. Thanks for having me guys. Brave to do a live demo take sketch. Yeah, thank you so much. Hey, great work. Have a great listener. Thank you. Yeah, love it. Have a great rest of the. Thanks for tuning in. You're the man. And we will bring in our next guest, Jonathan Webb from the Nuclear man himself. Welcome. Sorry to keep you waiting. Today is a great name to have a company that starts with the. I don't know if you saw the browser company the Free Press sold for $200 million. The browser company sold for $620 million. Everyone is all in on companies that start with the today. There we go. But give us the intro on the nuclear company. What's the plan and where are you in that plan? What's the plan? So to my understanding, we're the only company in the western world focused on the deployment of new nuclear. What does that mean? I assume some of your communities probably followed the nuclear industry a little bit. I mean there's no AI without power. I just talked in that talk earlier about, you know, China is about to pass the US as the largest nuclear power in the world. Yeah. Our thesis is the reactor is not the problem. There's a lot of legacy reactors that are operating in the U.S. there's some of the best performing reactors on planet Earth. There's a lot of startups, dozens designing new reactors that are all going to be great reactors. The problem is being able to Deploy those reactors on time, on budget. We have the safest operating nuclear fleet. The highest performing operating nuclear fleet. You talk about the Navy or. I'm talking about the U.S. we have about 100 operating plants. I mean, today, 20% of the power in the U.S. comes from nuclear. That's nuclear. That was built in the 60s and 70s. We've built two reactors in 30 years. So what are we with the deployment arm and why? What does that mean? So think of if you're American Airlines or Delta, you don't call GE or Rolls Royce. You don't just call to buy a jet engine. You call Boeing or Airbus. What if I handed you a jet engine or a Ferrari engine or a Bugatti engine? No matter how great that engine is, you're going to be like, what are we doing? So we want to be the full solution to deliver that power plant to either a hyperscaler, to a utility, to a foreign government, or potentially to operate those on our own. And the good thing is we're not competing with any of those reactor companies in the market. We're a partner of them. So once they go from R D to, you know, manufacturing, to design, to implementation, there's a big difference between white lab coats designing projects in an R and D lab to living in a construction site where, you know, I've done. Much of our team's done. I mean, I've built 8 million square feet of stuff at the last thing, you know, got a team of builders that worked for Elon building gigafactories, built the last nuclear power plants here. We want to be that team that when you're ready to go deploy your reactor, you know, we can partner with you. Get that reactor in the field and get it up and operate. Your partners on the reactor side, how much of what they're doing is just remembering how we used to build reactors as a country versus doing that new innovation. So there's really only two incumbents in the US and that's Westinghouse and Georgia. And, you know, obviously we're talking to them, and then there's a lot. They built Vodal, the most recent nuclear power plants to come online that were successful but over budget and over time. Correct. Oh, man, it was. Yeah, I hired everybody off that team. So Georgia Vogel, three and four, first thing nuclear. Yeah, what we want to. No, no, no. We wanted to hire, like, if people look at that and go, abject failure, I go, no, no, no. These are lessons learned. This is. Is like, what. What went wrong, guys? It's nuts, man. Like it took 10,000 people at the peak of construction on that construction site. Guys go to a rock concert, look at 10,000 people and think they're showing up to work every day. You don't want an amphitheater just to meet your team. 10,000 people managing the project with paper. No way, dude. Last decade construction, we're not talking 40 years ago, I'm talking in the last. This thing finished last year with wheelbarrows and wagons of paper. So you're looking at 10 to 20% efficiency for the people working and you know, the audience and the larger viewership might go, ah, lazy Americans. No, I'm not buying it. Yeah, we are not giving our teams and people the advantages to win the American spirit and fight alone. God, I'm believing it as much as anyone. It's not enough. We got to bring technology, tools, capability. That's where we're partnering with Palantir. So I'm taking hundreds of thousands of pages of documents, which is what it takes to build one of these power plants, putting into a data lake, segmenting that data out. So if certain parties want to secure their data, they can then having LLMs and AI on top of that giving predictive analytics. So when the supply chains delayed the night before, a construction man or woman's waking up in an RV in a trailer at 3am okay, I'm going to be redirected at 3.15 I go there. At 3.45- I go there. Giving our frontline teams all the tools, technology and information. We can do it. We're not splitting an atom. We're not going to Mars. We're just building the most dominant AI enabled platform on planet Earth and we're going to slash that 10,000 down to 5,000. We're going to go to 7 years instead of 12 years. China's building these 1 gigawatt reactors for 5 billion in 5 years. There's no reason we can't do it in 5 or 4 years. I'm not going to name the number. My team will get really upset with me on the price side. But there's no reason these two reactors took 12 years and 36. Let's talk about timelines in the industry broadly, because there's some recent, I guess I don't know if I can't remember if it was an EO or just a broad directive from the White House saying like, we want new nuclear breaking ground in the US in the next 12 months. Is that, is that brother? It could be us. So we are imminently close to a recovery project. That I'm not supposed to talk about. So I'm not going to name the state and. But it's a 20 billion dollar recovery. Recovery. Bringing old capacity back online line. Yeah. Yeah. So $9 billion walk away. They spent $9 billion on this nuclear 2 gigawatt nuclear power plant. Didn't finish. It walked away. So we are getting brought in. We're imminently close. If we win that, you all should definitely come. This tiny little team that's two years old that partnered with Palantir to go recover this animal and finish it. Would love to have you all. Yeah, yeah. When, when you think about what they spent, what is the value that's just sitting there on the. Certainly not 9 billion, but you're picking up a couple billion in legal fees. Yeah, no, it's, it's structure. Hopefully they poured some concrete that's still there. It looks like. I mean, if you walk on it, we're on. I'm not allowed to say where we're at, right? Yeah. Oh, God, I almost did. So we're in America. We're in America. Play that American sound effect. We are in America. We're not afraid to say it. We're in America. But the, when you walk this site and you look at it, it looks like, you know, aliens landed and just left. Because it's in rural America where this big infrastructure. So there's a lot of value there. There's been some value that's, you know, not, not quite where it should be. But we're going to go, we're going to get that thing hopefully later this year, early next year construction. We had an author, Dan Wang, on the show maybe last week. He wrote a book called Breakneck. And he, and he, and he compares and contrasts China to the United States. And he calls China engineering empire driven by an engineering mindset. The solution to everything in China is just more engineering. Build a train to nowhere, build a bridge, just build housing, build everything. Build, build, build, build, build. And in the United States, he calls us the lawyerly society. And, and we are to everyone in politics is lawyerly or a lawyer lineage. And so one of the problems that I've heard in nuclear is that oftentimes you go to build something, you think, okay, I got a plan. It's compliant with all the laws, and the laws change and all of a sudden you're back to square one. You got to rip out all the pipes because they said no copper. Now you got to use lead pipes again or whatever. How much of that do you think is. Is real? Or how much do you think? Because that feels like something that you can speed up by analyzing all the legal code constantly and the regulatory filing speeding that up. But some of it also has to happen on the other side, right? Like. Like it's not just enough for you to be using AI to. To submit documents fast. You need review fast. So what's going to happen on the other side? I have so many comments on this rant. So how long do we have? A couple five minutes. So, yeah, I mean, this is the hot button issue for me. We have the safest operating nuclear fleet in the world and the highest operating capacity this industry. Don't get me wrong, the legal bs, yes, we all agree. But the victim mentality of the industry, the victim mentality of entrepreneurs in San Francisco acting like high school kids, blaming the regulator. Brother, it ain't that hard. We hired the number two at the nrc, Laura Dudes. She's on our team. We're walking into the NRC going, what do you need? We're going to be fully transparent. We're going to be fully compliant. They should be incredibly critical. It's nuclear, for God's sakes. If there is one, and here's the other one, big misnomer view. And it's working, right? The fleet's safe. We have had, in decades, hundred operating nuclear power plants. Not one person in this country has died from radiation fallout. 0.0. That is perfection. So the private sector needs to stop being a victim and just start doing what we're doing and figure out how to partner with the regulator. We're seeing no problem. So the other kids that want to cry on Twitter, go for it. You want to sue the regulator, go for it. We're just going to go in and partner with them and figure out how to build bigger, faster, lower cost, safer, higher quality than ever before. And I will say what we're doing with Palantir. Well, here's the good news to the people designing reactors, and you're ready to go deploy them. What you're doing and what I'm doing have nothing in common. I have a team again. Me and my wife were living in an rv, got engaged on the last construction site. I've got guys that were building Vogel 3 and 4 had heart attacks on the construction site, had people living at the gigafactories. That is a totally different world. Let us take your drawings, your great R and D, drag it into reality, and we're going to build that trust with the regulator, with you. But I do think we got to Go. Pencils down, swords down on blaming the regulator. Now the legal, you know, that's a whole versus engineer thing. That's a whole other topic we could take on. But we need the regulator to challenge us to be safe. And we just as an industry have to figure out how to comply and get the job done. Yeah. What great rant. I would love to see you and Karp rant together. Yeah, yeah. What did Palantir show you that made you go with them? Was there a key case study that. So we started. We are a two year old company that's about to be the only company in the US with commercial nuclear under our watch. I'm like, what did we do right? What are others doing? We're just building a team to go build and kind of reactor tech and agnostic. Is the other. Is the other stuff managed by the government? Is that what you mean like, or is it just older companies that there's no one that's actually focused on building? Everyone's designing new reactors. I just want to go build stuff so I could build a Westinghouse, a GE reactor, you know, any one of the new advanced reactors we just want to build. So then the last year what we did is we looked at everything, hired somebody over here a lot smarter than me. Was it Tesla? Was it Microsoft? Looked at all the different AI platforms. What can we do? We knew what we wanted. Nuclear os. So nuclear OS is the, you know, again, all, all aspects of data related to the project into a data lake. Predictive analytics to our frontline teams. No one's even close, man. Yeah, this is it. I'm not trying to be like a sales job. I would like to get like a commission. Yeah. I was going to guess that there's not another great alternative. That would have been nice to at least look at a couple options and decide, well, here's the good thing. I mean, it's just the most secure platform. The way it is configured. We're going to go build the most dominant AI enabled nuclear platform and we're doing it with Palantir. So it took us about a year of study, it took us a couple months of planning and now we're just racing right now to go build those solutions and it's working. Yeah. What's the structure of the financial milestones for you? Because I imagine that a lot of this doesn't look just like, like fund everything with venture capital. There's probably some project finance and there's actually a customer who might be, not you, that's paying you just to Manage the construction for our business model. So Topco, you know the nuclear company, you're investing your VC dollars into technology and team which this town knows that big, you know, buckets of capital, project capital. I hired a big boy CFO that's raised 10 billion in his life. He was CFO with JB at red. How like the Neo clouds will go and build new data centers. But then there's, there's project financ equity on the project. You know, we're the ones getting it to completion. We can get an equity earn out in the project. We can get a fee during construction. Sure. And then there's multiple. Either we could build on transfer to a large utility, we could build on operate for a hyperscaler, we could build on transfer to a foreign government or we could, we could operate it ourselves. So you know our, there's a few ways we get there but the debt and equity is going on the project, not through us. Now I mean our valuation's not to a point to where could put 20 billion on our balance sheet. Yeah. But I don't know, maybe, maybe in a couple years. Let's talk, let's see how this goes. So you know we're you know, again I very just bullish on paler and I don't know whoever listened to that talk earlier. It's, I mean the binary outcome is it's US versus China and all the tech bros and the badass CEOs and the badass 5 Fortune 500 tech executive. Here's what I would say. We got to leave our ego at the door. China is kicking our ass. That I hope was not recorded. Everything recorded. So the look it is. Look, the reality is it's not even a competition. We're losing so bad and we've got to work together. So I would say to the community watching, you know, push me, be hard on me, critical on me, that's fine. But let's figure out how to challenge each other and work together because it's a binary outcome. Right now it's us versus China. It's not even close. They're winning at so many categories and we've got to figure out how to work together. And that's what I think. Palantir and a unique framework they're bringing not only the technology but the mentality of how do we work together and win and you know, now it's all going to be about performance on that construction site, on time, on budget, high safety. And I love your position in the, in the nuclear kind of market broadly and that if somebody can build great reactors, you can help them actually become a real business based on it and not have to worry about every single point in the stack. We got a partner, man. That's the thing, right? This is where China is going into the Middle east fully vertically integrated, going mbs. We will do it all one shop stop. They don't want to work with three constructors and somebody selling a reactor. No. So like how do we partner together? Go as a coalition. We're going to deliver power globally. We're going to deliver power in the, in here in the US but, but I do think figuring out how we, you know, bring down this ego of like there's so many silos and we need to challenge each other. But that's what I would say to you all because there's a lot more people on this. Listen to you than listen to me. How do we bring our tech community Together, our big CEOs who are important and great, but if you compare them to China, we're not winning. So it's like how do we do that and go win collectively? Fantastic. Well, I think we have our next guest here. We're going to take a look at some rocket motor. So thank you for, thank you so much for helping us. Thanks for joining us. Thank you for doing this work. Have a good rest of your day. Up next we have Nancy Cable from Ursa Major. We will bring her in. And do you want us to try and bring that in here? What are you thinking? I'm happy to bring it in. Bring in the engine. Bring in the engine. Engine, right. Okay. It's device. We got an engine coming. It's shocking that it was clear through security. We, we, when we do these remote shows we sometimes have to bring very, very suspicious looking WI fi hotspots. Ben and the boys brought a WI FI hotspot through the. Actually I think I had to walk it into the capital through a very odd place here. Maybe pick up the microphone and we'll throw it on the table. Yep, we can throw it on the table. I think we'll be okay. Yeah. Set your own gently down. Okay. Incredible. This is a wild demo. First rocket, first rocket engine. Nice to meet you, I'm John. John, I'm Nancy. Pleasure. We're going to hold this as much as you can. We've had, we've had people brought bring fish to the show. Sushi that was extracted or the fish was killed with a robot. Shinkay that was. We had somebody promise us a SpaceX engine too. Yeah. Oh yeah. We Gotta follow up on that. But this, this is the best demo we've gotten. SpaceX, this is fantastic. This is a good day for us. Yes. So, so explain to us what is this and what's your business? Introduce yourself. Yeah, absolutely. So I'm Nancy Cable, I am the director of operations for Ursa Major and we are an aerospace and defense company. So we are deploying primarily right now hypersonic rocket technology, which is what this is. This is our Hadley engine. So a 5000 pound thrust class proven hypersonic flight capability. So this thing right here has a flown Mach 5 really critical in the defense space right now. We must field technology and we must do it faster. And that's what Hadley and some of our next gen products are enabling. Now correct me if I'm wrong, the value of the hypersonic missile is that it has the maneuverability of a cruise missile with like the speed of an icbm and it's not. And so is maneuverability a piece of this is. Is this like a. Maneuverability is a piece of this for our customers? So a lot of interceptor technology is what current applications and for our next gen products the maneuverability and the storability of the fuels are also front of mind. Yeah. And help me understand where Ursa Major fits in the overall stack of like the primes and the different supply chain. Like are you developing whole weapons systems that sell directly to the DoD? Are you partnering with other companies that we might be familiar with? Where does Ursa Major fit in? Yeah, absolutely. So we're doing, we aim to be disruptive and disruptive means that we want to break the mold of what some of the primes in the government have traditionally done, which is these like years or even decades long deployment cycles of development and qualification. And to do that we do want to push the industry. So that does mean not necessarily fielding the weapon system ourselves, although that is on the horizon, but putting ourselves in the position where we're partnering with the government, partnering with the primes and forcing them to push the envelope on how fast we can get these products into the spaces that they need to be. So right now, huge focus on just manufacturing excellence, cost, speed, reliability. Absolutely. And that is most of my role is on the manufacturing side and making sure that I can take the same excellent technology that our rocket scientists have developed and scale it so that it's available to market. Right. Right now we're on looking at the order of tens to hundreds of units a year. That needs to be tens of thousands of units a year. And that's really where The Palantir partnership comes in. How does Palantir fit in this? Yeah, absolutely. You might think that engineers are great at data flow, but if we were to look at this rocket engine here, different engineers designed the turbo machinery and the injector and the chamber, and all of them came up with a unique way to process their data. A unique test system. You know, a different network drive, a different place to store the information, and different network drive. That is. I wasn't expecting that. And that's. Well, and I think this is when I think about, you know, we have a small company here, maybe 10 people, and we probably do have like six different, like Google Drives and different folders for different data. Well, that's interesting. It's just that it's the natural. Everyone in every industry, rocket propulsion included, ends up feeling like, man, I'm 15 years behind. How could anyone possibly store something on a C drive? But when you're focused on getting the hardware to work, you're not necessarily focused on the efficiency. And so putting the data efficiencies front and center. Even before Palantir, our aim was right data, right people, right time, right decisions. I loved what Dr. Karp was saying about people happiness. People are not happy when they feel behind. They are happy when they feel ahead, when they can make real time decisions. And leveraging Palantir out onto the shop floor and into the back end of our data structures means that we can get the information to people so they can be real time and then even predictive about how we're doing manufacturing. Yeah. So how does someone at Ursa Major actually interact with Palantir? Is it on the iPad, on a phone, on a computer while they're working on test bench? Every phase. Yeah. Great question. So we've been with Palantir about three months. Okay. So. And right now, the daily interactions are mostly with our engineering and programmatic teams. Okay. We've built some inventory modules, we've built in, you know, looking at our engineering line of balance, our change management systems. Sure. But. But like we were hearing from our nuclear, you know, from nuclear, the people on the floor doing the work are actually the most important people in the factory. If my technicians can't build an engine, we cannot deliver to our customers. So that is the next endeavor that we are a few weeks into, with amazing results so far, is to actually make Palantir a manufacturing execution system. Make it the shop floor portal. One data source, one source of truth, one program from raw material, ordering, ordering all of the parts, producing all of the parts internal through Fielded data at our customers. You almost call it like an erp. Almost. Yeah. So we actually, we have an erp. Right. This is what everyone does. Everyone has, they have an ERP for intersection resource plan. Yeah. Accounting function, all of your work orders, the PLM product, life cycle management. And then an MES is the traditional thing, a manufacturing account execution system. And we have said, why not use Palantir? It's already integrated. I don't want one more monolithic software, connect it with the erp, actually pull some of the functions out of the erp. I remember hearing a story, I don't know how true it is, but something about like SpaceX built like a ton of custom software for everything they needed to do and then eventually I think the team like spun out and built a business around that. Yeah, yeah, well SpaceX actually, so they, they have a product and it's kind of the gold standard. Everyone who's worked at SpaceX is like, I want that one, I want that one. And that really is the, you know, the magic of that software is everything in one place, which is what ontology brings everything we need in one place. Very cool. What's it, so what's it going to take to go from making tens or hundreds of these to tens of thousands? The physical process matters, of course. Right. We are a hardware company. You look at the complexity of this and you can understand why we're not going to be forward with a robotic automation line. So making sure we have the right tools, the right fixtures, the right machines. 3D printing is critical to what we do here. 80% of the rocket, all of these metallic components are metal 3D printed. Fascinating. Yeah. Developing some of our own unique alloys. So scaling the machines is probably the longest lead time for us. And then setting up the correct tools, fixtures. As you can imagine, test stand infrastructure is really big, but not having the data around that in silos. So when we need to build hundreds of these, I need to know where every piece part is at every moment so that we can make the best real time decisions possible for quality for the customers. So the physical infrastructure is really what we're most familiar with. And now Palantir is helping us with that digital infrastructure side of things. I've been in manufacturing my whole career. Yeah, 80% of the line down scenarios I've ever had where we stop building product, you want to guess what they're from. Lacking inventory or it's lacking inventory, it is not having a component. And so we think about like, yeah, a rocket engine is really physically complex. That's actually the hard part. The hard part is getting all the pieces where they need to be to build a 1200 component rocket engine. And it's things like that that the ontology is helping us solve. A couple years ago I said it's funny. I don't know if this is hubris, but I feel like you could put this together, John. Well, that's kind of. That's the point. Right. But it's just like actually putting the pieces together is the easy part. But it's like making the parts and making sure you have them at the right time is the real challenge. So it's like doing a puzzle over like, you know, 20 days type of thing. Yeah. I mean, we joke. It's like, right. Lego Legos for adults. But you can see it really just is a collection of fittings. Fittings and fasteners. I can. And that's kind of the point. How can we have a system that makes it so easy and so obvious how we manufacture these, that I could pull the two of you in and say, build a rocket engine. And you could do it with confidence. You've got young kids. I think they would enjoy putting one of these together. Yeah. Yeah. A couple years ago, I sat next to somebody on a plane who was selling. It was pipe bending. Pipe fitting, whatever this is. Yeah. Tube bending. Yeah. He said, I'm in my. My business is tube bending. And I was like, what? And he was like, yeah. He was going to SpaceX specifically to sell two bending machines to them. I didn't realize it was a whole industry. He made his money in bending machines in person to make sure that they don't run out. Absolutely. It's a weight limiting factor. If the tube isn't bent, you can't make the rocket. If the tube isn't bent, you can't make the rocket crazy. And tubes actually carry some risk. They're some of the thinnest walled components on the rocket. Right. This has a lot of mass to it. Tubes are often can be where failures happen. So in an ecosystem. Right. We need to test them. But also, where did this tube come from? What day was it bent? What was the lot of stock material? What revision was I on in my CAD model? What testing did this engine undergo? All of that currently I could find in our systems. Interesting. And it would take me hours. But if it's all in one place, if it's all in one place and we have a consolidated tool, it's that traceability that's incredibly cool. Fantastic. Anything else? Thank you so much. For bringing your baby on the show. This is a great sign of respect. Yeah, yeah, absolutely. I mean, what's cooler than carrying around a hypersonic rocket engine? Everyone loves it. But the tsa, they don't. Oh, yes. Rough one to travel with. Yeah. Anyway, thank you so much for coming on the show. Absolutely. Thanks for coming on. Thank you. We have our next guest ready or should I talk? We have a couple minutes. Why don't you tell us about some ads? Do you have some ads you could run? I'd love to hear some ads. You want to talk about ramp.com? oh, you find their AM song. Ramp, ramp, ramp. Let's go through some of. I did want to. While you pull that up, I did want to talk about Matt Huang, Paradigm and the Stripe team introducing a new payments first blockchain called Tempo. Matt says as stablecoins go mainstream, there's a need for optimized infrastructure. Tempo is purpose built for stablecoins and real world payments born from Stripe' global payments and Paradigm's expertise in crypto to ensure Tempo serves a broad array of needs. We're excited to be working with an incredible group of initial design partners including Anthropic, coupang, Deut, Deutsche Bank, Door Dash, Lead Bank, Mercury New Bank, OpenAI, Revolut, Shopify, Standard, Charter, Visa and more. Tempo's payment first design includes predictable low fees payments, gas and any stablecoin payments first UX, opt in privacy scale 100,000 transactions per second and EVM compatible. Built on wreath, Tempo eases the path to bring real world flows on chain such as global payouts, pay ins and payroll, embedded financial products and accounts, fast and cheap remittances, tokenized deposits for 24. 7 settlement, microtransactions, agentic payments and more. Matt says we're building Tempo with principles of decentralization and neutrality. That includes stablecoin neutrality. Anyone can issue a Stable coin. We might be able to have a TVPN coin. That sounds exciting and any. Oh yeah, yeah, yeah. That was clearly a joke. No, but I was talking about a USD TVPN one for one stablecoin that we issued to. It does not move. It does not move. You can't make it move. It won't budge. Independent and diverse validator set with a roadmap toward a permissionless model. So apparently they're already in a private test net and anyways two. Two power players, Paradigm and and Stripe coming together. It sounds like they're. They're positioning. I guess Matt is running Tempo but they're positioning this as they're both investors in Tempo, so I think they really do want to take a decentralized approach. So this is not downstream of like the Stripe acquisitions directly. Privy and Bridge. I have a post here from Zach Abrams, founder of Bridge. He says Bridge was one of the first companies to use blockchains to solve core payments problems. During our journey, we've seen how even the most performant blockchains struggle with basic financial services use cases. A few examples, a payroll transaction consistently failing when. When Trump launched. That's interesting. So when the Trump Coin launched, apparently people that were running payroll, like, you know, couldn't get Bridge with stablecoins. No, no, no, he's not talking about, he's not talking about Bridge specifically, but he's saying like, if you were trying to pay employees at the time that Trump Coin launched, who's paying employees in Trump Coin? No, no, no, not, not in Trump Coin. Like that, that day, I think it was like a Saturday or was Friday. I forget. Exactly. But when it launched, if you tried to pay, there was so much activity on chain at that moment, like, good luck, you know, paying like a freelancer. So yeah, the example would be like, I'm trying to pay a freelancer in stablecoins like on chain because like obviously like your default payroll providers are just using like, you know, web two rails or whatever. And that wasn't brought down by the launch. Right. Okay. Got a. Disbursements taking days due to low transactions per second and projects to later Cancel due to 6 figure upfront gas costs. Tempo is new L1 built specifically for payments. And so anyways, quite the team they've put together here. Yeah, we got to get some of the folks on the, on the show and have them break it down because I'm very interested in why not Solana, why not Circle? You know, like it feels like there's a. The other question is why? Why not another L? Like why not an L2? Exactly. Exactly. But this is something unique and they must have put a lot of time and effort into it. So, yeah, congrats to them on the launch, but we will, you know, want to know more. Anyway, I believe we have our next guest. Welcome to the show. You hold this microphone. Why don't you kick us off with an introduction on yourself? All right, what brought you here today? Perfect. I'm Ryan as Dorian. I'm the chief marketing and strategy officer for Lumen. Okay. And we're here at aipcon talking about all the great things we're doing together to modernize telecom. Lumen's a let's give it up for modernizing telecom. Yeah, exactly. Finally it's, it's fun because it's decades of complex operational. I think Palantir is helping us modernize into this new world that you need for AI ready, multi cloud world. That is what everyone's here talking about. Yeah. How do you define, break down more of what you do in telecom specifically? Yeah. So Lumen is, you know, for, for decades we have basically been connecting the world. It starts with connection and then in the last, in the last bit of time. Yeah, the world has needed new ways of connecting. Yeah. We're bringing that infrastructure, we're bringing control. If you think about the way it was before, it was like fiber cables in the ground, all fiber. Right. Everything that's running across fiber, those super fast connections, you need one port, one connection, was the way of the, was the way of the world. We're changing that. We're getting it cloud ready, cloud enabled, remote controlled. All of those things that give you that redundancy, latency, all the things that power AI. Yeah, that's what Lumen is doing. And we're connecting the world. Okay, who's the customer right now we have lots of customers. So it start. So we're really focused on the enterprise, the enterprises that are building these data center operators. Data center operators, hyperscalers, of course. And so we've announced some of the work we've done on the backbone, the infrastructure backbone of the AI economy. But what we're really doing is enabling businesses new things, new technologies that they want to give them a technological advantage. We're disrupting this industry to help them disrupt their industry. Yeah, yeah, yeah. So I mean, obviously there's like an immense amount of money flowing into data centers. Is a lot of that actually going into like new bandwidth requirements between data centers? Like the basic narrative is like, yeah, they might spend a billion dollars training something, but it's all happening within one data center. Well, so the thing you hear about a lot, and you guys have talked about a lot as well, is computer storage, cooling, all those things that are needed. The missing link is connectivity. And realistically it's something that has really emerged as of recent to say there are new types of connectivity, new next gen fiber that has way more capacity than the world has ever needed before. We're growing leaps and bounds. By 2028, we'll have about 66 million route miles of fiber. And that is growing, you know, 3 to 5x what we've had before. Okay. And that is the capacity the world Needs. Yeah. So there's some sort. And is that capacity being used inefficiently today or. Or is demand still way outstripping supply? Demand is completely maxing out. It's why we are putting these investments in the ground. And we're not only the hyperscalers, I'd say the tip of the spear. They're consuming a lot of this, they're looking for a lot of this data center to data center connectivity. But it's really enterprises everywhere that are now saying, you know what, we also need that type of bandwidth. And some will take it dedicated, some will take it shared, but the need is completely outpacing what the needs of the last couple decades have been. Yeah. Trying to make that more concrete for me, like, because I feel like most people's interaction with AI is I send the most condensed packets possible across the Internet, just a couple lines of text and then a bunch of GPUs light on fire at the AWS data center. Or Azure If I'm using GPT5. And then it sends back text. This is not rich video, this is not VR. I buy. I immediately intuitively understand, like, if we're in the Metaverse world and we're streaming 4K stereo microscopic, that's super bandwidth heavy. How is AI bandwidth heavy? So it's actually great listening to the customers that have been here at aipcon, because you hear American Airlines, you hear bp, you hear some of these customers that are talking about their infrastructure, all of the scheduling, the inferencing, the, the planning that is happening in real time and adjusting, that is not just people typing in their prompts into the tech, it is systems talking to systems. And this is where the data explosion has come from. It's all happening in the background. Okay, yeah, yeah, yeah. So. So even though I fire off one query to GPT5, it, if it's doing deep research, it might be pinging 75 different websites. And that's driving up total Internet use. Yes. And the systems fanning out are also creating their own queries. Yes. Like, yeah, we saw that with the demo from Palantir. Like, you know, he typed one line of text to help optimize this airport and then it was working 20, like 20 minutes. That's right. Okay. Yeah, that's right. And so this is where the disruption in telecom, and if you really think about what has changed in telecom over the last 25 years, the answer is not much. When you can take one port and you can put lots of services on that port and put the control in the customer's hands, you've changed the way people interact. It's cloudifying telecom and in this new world of what is happening with Cloud, like Cloud 2.0, that is the necessary bandwidth, control and precision that you need in connectivity. What does cloudifying telecom mean? Does that mean like more like multi tenant on the actual fiber lines? Like instead of a hyperscaler owning one route, then they're, they're bidding it out and spot rates or something? Yeah, multi, multi tenant is a good way to think about some of the services on top of, you know, in the past you've literally, if you think even back to old telephone switches, you've had, you know, the one wire to one wire, it's been one port to one service. You add a service, you add a port, it's a truck roll, it's a person coming out. Cloudifying it is bringing all of that technology to the users, giving them that interface, that portal where they can say, I need these services, I need them in these locations, I need this speed, I need the bandwidth turned up. It's network as a service. Yeah. So a higher level of abstraction and yeah, more like almost like a virtual machine on top of the telecom infrastructure so it can be provisioned like on an ad hoc basis. Yeah. And one of the biggest changes I think in the economics, this AI economy is also, if you think about a network subscription, if you will, of the past, you sign up, you get a certain amount of bandwidth. But if you look at the companies of today, if you look at the sports industry, manufacturing industry, healthcare industry, they have these spikes that are massive. And so we're providing that network as a service where it turns up, turns down, and then customers are paying for what they. It's a consumption model. And again, that's part of this cloudifying model which has not hit telecom till what we're looking to transform. So yeah, help me understand the new shape of the telecom industry in your business. Like I imagine that there's some genius scientists that comes up with a faster fiber optic cable that is manufactured somewhere. Then someone purchases that, they buy some land, they bury it in the ground, maybe they get some rights and then at a certain point someone's leasing or essentially charging a toll along that toll road. Yeah. Do you sit all, Are we completely vertically integrated? So we, we sit vertically integrated, but I think what do you do R and D on, on new fiber optic technology? We work with a number of partners and then we're also thinking about the AI optimizations on, on that fiber. So if you think about intelligent routing. If you think about redundancy, if you think about all those things where you could have something as simple as a fiber cut in the ground, sure, maybe it's on purpose, maybe it's not on purpose, but something aware of that and then you need to dispatch someone to go fix it. You can't have any interruption to the services you're running, so we have to have that redundancy. On top of that, our customers and enterprises everywhere, I think they started mostly building with one cloud. Now if you think about this multi cloud world where they're hitting Azure, gdc, aws, they're hitting all of them at the same time with the same applications in different regions across the us they have to seamlessly let those systems talk to each other and they don't want a direct connection to each of them. That's where we started, but now they want to be able to live in this fabric where their systems can talk to all of these in all the regions, get all the data and process faster, because that's part of the disruption they want. Last question for me, how does Palantir fit into that? Yeah. So if you think of the operational complexity of the decades of past. Yeah. You know, you've built all these networks, we talked about fiber in the ground. Yeah. Think about the systems over those decades that have been built up. Yep. One of the things Palantir is helping us with is this, managing this operational complexity. You sort of see an abstraction of this in LA when there's the fire and like the, the boxes with the telephone lines just explode. Yeah. Like why didn't they build a box that doesn't explode? And so you imagine that. Okay. That's where, that's how the power lines work. The fiber optic lines. Yeah. They're newer. Yeah. But there's probably still some stuff that might go wrong if it was installed 30 years ago. Yeah. Well, you got to identify that early. There's that and there's the software layer that is running all of those. Got to make sure that that's up to date, not crashing. And Palantir is helping us optimize those, helping us bring them together. And, and, and what we are building for customers is then a system that they don't have to think about the optimization they need in their network. We're going to help automate that. We're going to help bring AI to that network and that's part of this partnership. And it's also, frankly, the most exciting part about disrupting Telco. It's not an industry that too many have talked about disrupting for a while. It's ripe for it, it's needed. And this AI Multi cloud era lumen's here for it. That's very exciting. Anything else, Jordy? Love it. We're running late, so thank you so much for wrapping up. Thanks for having me. All right, I'll grab this. Thank you. We have our next guest coming into the studio, Drew Cukor. I think we actually have multiple. We might need to pull up an extra chair. We have lads. We have lads coming in. If. If we want to bring in everyone in, we can. We'll. We. We can pass the mic around. Whatever. Whatever you guys want to do. We have multiple. Oh, okay. Hey. Oh, hey. Just me. Oh. How you doing? Sorry. What's up? I'm John. Welcome. What's up? Great to meet you. How you doing? Good, good. How's the day? Could. Could you kick us off? Grab the mic? Kick us off with an introduction for those who don't know. Okay. I'm Dave. Dave Glazer. Been a palantir for 12 years. And I'm a CFO pre IPO. Pre IPO? Yeah, like, basically. Or DPO. Right. GPO. Yeah. Like, when our prior CFO retired, who's actually on the show recently. Yeah, Colin. Talked to him. He retired in 2017, and since then I've been leading the finance team. Yeah. So my big question for you. Gross margins for the Fortune 500 in the AI era, are we going to see a structural shift? You know, the inference bills are skyrocketing. Inference per token is dropping. But then Jevons Paradox, and we're doing more token inference than ever before. Reasoning models are kind of staying expensive. And we saw in the Journal earlier this week, maybe last week, software company called Notion said that they saw their gross margins drop from 90 to 80%. Not bad still. But there is. Does seem to be some sort of impact. And I'm wondering how you think it might play out for the really big companies. Yeah, look, I think one of the things that we've been sort of saying is like, LMS are commodity. Commodity cognition. Right. And so essentially, it's like they're getting better and better. Right. ELO scores better and better. Tokens are getting cheaper. Right. And as Alex said, I don't know if you watch Keynote, but, like, you know, he's talking about, okay, like, how do you actually derive value from that raw output of an om? And so it's like. I think it's like the raw output, it is getting cheaper. We're still, like, very early days on these models and you're seeing them just sort of like up into the right and ELO score. And so these things combined, I think, are going to make it cheaper and cheaper over time. And I think we'll see sort of on. On gross margin. I think you look at some of the other things, like hyperscaler costs, right. From a lot of these places. I think, like, people. The people's gross margins have survived. Right. They're more efficient. They're all. And so I think, like, we will see. But like, I think that is. It's going to be much more about, like, how are you deriving value from them then? Like, well, the cost is going to be so overwhelming, but they're super, like, totally. It's like focused on the value. And I do think over time it's like people are going to be able to manage those costs. Yeah, yeah. It feels like. It feels like higher costs potentially, but so much more value. And it's pretty easy to tell. Yeah, I'm spending a lot on inferencing a certain LLM API, but obviously I'm delivering more value and so I'm charging also. You have to think about the position that Palantir sits in. And we got a product demo earlier. Hive Mind was leveraging like a bunch of different models and like, that position of having leverage and being like, we are the product, we have the data, we have the customer relationship and we can vend in whatever intelligence sources we need in order to accomplish the task. Like, that's a better position than being. If you're a GBT rapper and your product is really 40 and you're just kind of like reselling that. Right? Yeah, yeah, yeah, sorry. Yeah, yeah. Like, and I do think it's like, yeah, like, I think it's be going to be all about the value rather than like, well, the values there. But the cost is superhitive. Yeah. How are you. How are you thinking about positioning Palantir story in commercial in the United States over the next couple of years? Like, what is the right framework? People have always had the wrong mindset. It's consulting shop. What do they even do? Blah, blah, blah. Like, what is the right frame of mind to be in? Look, I think the right frame mind is like, we're delivering a tremendous amount of value. Yeah. To these guys with these customers. Right. It's like, like, and they're needed to in this. Right. And it's like you deliver that value and we're like, just at the beginning, so you look at like our US commercial business like grew over 90% last quarter. It's still relatively small. Right. And it's like we have, there's so much Runway there. Yeah, right. Like we, it's like just that that business has like sub 400 customers. Yeah, right. Like that is when you, when you look sort of across a lot of other companies, it's like that's, you know, so it's like we're doing all this with such a, like a small customer base and obviously it's rapidly growing, but you know, it's like it just shows the amount of Runway that's ahead. Yeah. Do you, do you think that people should be thinking about the commercial business as like a bundle, like a competitor to a bundle of products that already exist or something that's entirely net new or displacing an entirely different class of spend in the enterprise? Like, how can, how should people even wrap their mind around some version of all the above? Right. So it's like when you think about, you know, you're not like head to head, who are we competing with? Right. And then everyone's like, but I don't get it. It's like it's a combination. We're not really, we're competing against like the Frankenstein monster that almost every large corporation has. And then you're also competing like particularly in government, but it also applies in, you know, particularly large corporations is like custom built software. So it's like those two, you're competing against that and over time you're obviously going to sort of eat into a lot of, into a lot of the spend. But it's like only because of the value that's being delivered. And then it's like you don't maybe need some of these. Yeah, yeah. It feels like the, it's like, it's like transformation new net, new technology that would not get built in the enterprise otherwise. Correct. And then once you've built that, once you've built that compounding data asset, then perhaps you don't need some of the other products. Yeah. How is your framework or philosophy approach approaching the finance function at Palantir changed? Because I feel like there's like very distinct eras where you know, it changes like every day. Does it, does it, do you, do you feel like you have to update it every day? Like, because in some ways, like when you talk, when we talked with Carp earlier, it's like, yeah, he's bringing that same energy and like philosophy. It feels like it's, it's somewhat consistent even though, you know, numbers go up and down and all that good stuff. Stuff, yeah. Look. Well, look, I challenge any CFO working for CARP to have hair, right? So, look, I think you've got to step back and say, okay, like, how do we approach finance? Right? And it's like, this is a company, like. And, you know, people have said it a lot, like, we don't have a playbook, right? And obviously, there's a way that's run. The company's been built over the last, you know, 20, 20 years. Like, I've been lucky enough to be here for 12 of them. But, like, you know, and because of that, it's like. Like, we're very unique, right? And what that means is, like, we are constantly changing what we're doing, right? And so, like, a lot of things, you know, you talk about for deployment engineers in the early days, oh, that's consulting. That's. This obviously helped us build the product that we have today, right? And so. But you weren't. What you weren't optimizing on in those days was financial statements that Wall street would want, right? Because it's. And. And then it's like. But because of what we built today, not because or because of what we built. We have financial statements Wall street loves, but it wasn't built for that purpose, right? And. Which is crazy valuable, right, because it means we're. We're so differentiated, and we're doing things the way that, like, we want to do them, right? And built. The company was built that way. So can you tell me the story of how the COVID era changed Palantir's financials? I remember seeing that tne fell off a cliff, and it never really came back. And that was at the time. I was talking to some people who were looking at the company. They were pretty excited about what that meant, and it felt like it was almost like a structural shift for the company. But is that a reasonable story to tell? Is that apocryphal look? It's part of the story, right? And so I think, like, what would happen with COVID it was we could no longer, like, you just couldn't be as much at a customer site, right? And so then it's like, well, we got to extend the product further, right? And, like. And this is a story that keeps happening in Palantir. It's like, well, you know, we only have, you know, around 4,000 people, right? And. Or you. Or you look at sort of our headcount growth. Like, if you go back two years, it's up 12% from two years ago. Revenue is up 88. It's like, well, how do you do that? It's like, well, the product's got to be better, right? And you have to have products like aifde, like all these. All these things that are constantly evolving. And like that is a story of Palantir. It's like you're trying to do something. You're either resource constrained or somehow constrained. It's like, what do you do to meet that? And almost always is product led. Yeah. Thanks a ton of sense. I know you have a busy day, so we'll let you go. Awesome. Thanks so much for having me. Thanks for joining. We'll talk to you soon. We will bring in our next guests in a minute. Jordy, do you have any breaking news from Scoops? Skook says Alex Karp trying his best to get TVPN banned from YouTube. I will say I think it was like the least family friendly 10 minutes segment of the hundreds of hours that we put out. But it was some of the best. Some of the best. Some of the best. It was a lot of fun. I'm glad that Skooks enjoyed the stream and. And thank you for YouTube for keeping us up. Keeping us up. We might. Everything's going strong. Thank you to Restream for keeping the stream. Thank you. Couldn't do without them. We'll bring in our next guest. Guests, we are ready to keep rocking and rolling here. Who we got at? Got two chairs. Two chairs coming in. Come on in, Come on in. Pull over. What? How you doing? We've got an IndyCar driver for you. Oh, fantastic. Performance engineer. Very cool. Fantastic. You didn't have to sell me. Now I'm in. How you doing? Good to meet you. I'm John. Hey, pleasure. I'm John. How you doing, lads? We got the. Take a seat. Do you guys want to share? Yeah, we'll share, we'll share. Great. So, yeah, why don't you to kick us off with the introductions, Let us know who you are. I'm sorry you got stuck with a rough chair. I couldn't figure out how to get the chair up properly. I should know. Don't even try. It's not going to work. I already tried it anyway. Introduce yourselves. So I'm Zach Porter, a senior simulation engineer with Andrea global on the IndyCar program. Cool. And I'm Kyle Kirkwood, driver of the number 27 Honda for Andretta Global. Fantastic. Yeah. And I'm Drew from twg. Fantastic. How do. How do all of you fit together? We're all under the TWG Umbrella. Okay. Basically a bunch of different businesses within that, and Drew probably speak to it a little better than I can. Yeah. I mean, it's a family. It's a great holding company. We have tons of businesses from insurance to asset management, investment banking, and, you know, sports, media, entertainment, Western lifestyle. And of course, the crown jewel of just about everything is the awesomeness of motorsports. Yeah. And the Andretti team in IndyCar. How long have you been involved with Andretti? That's my. My fourth season at Andretti. Fourth season? Yeah. Losing track of time here. I think it's my fourth. No, it's my third. It's my third season with them. But I've also. I've been a part of the family for longer than that. I was with them in Indie Lights, and then I joined back with them in IndyCar. So really, five seasons, actually. If you combined it all. Yeah. You know, I'm. I get to be this suit guy, so I sit and watch this. But I've been here a year. Oh, fantastic. Yeah. And. Yeah. And walk me through the flow of, like, why you're here, specifically at AIPCON. Why are you working with Palantir? So, in IndyCar, we have a ton of data. Yeah. In a ton of different siloed places. Sure, it sits, you know, from stuff that we control, like our car setup database and stuff. And it. But it also sits in, like, databases from IndyCar that we don't control. Sure. We have to consume all these things, and they're all connected. They all represent performance. They all represent the pieces of the car and how they go around the track and how we get faster and how we're relatively performing against the competitors. So we came to Palantir and worked down this path to try and connect all these disparate data sets into one place where our engineers can make better decisions faster, sooner. Because in the end, you know, from practice one to practice two or practice two to qualifying, whatever it is, there's this limited amount of time that we have to make a decision to practice this coming. Whether you're ready or not. Yeah. So the more informed we can be, the better decision we can make, in theory, the faster we can iterate and be more competitive. So, yeah, it feels like the. Maybe we're just in the era of, like, you know, small micro optimizations just add up to greatness. Are there any stories from your career or just racing in general that stand out to you, where someone just discovered some secret that just gave them a massive advantage? I'm thinking of in sailing. There was this. Maybe it's a fake story. I don't know. But this idea that there was in. In the. What's the big sailing cup that Allison races in? America's Cup? Yeah. Yeah. It's all. It's all catamarans now. And the story goes that they were all racing monohulls, and someone looked in the rule book and said, there's nothing that says you can't bring a catamaran. And then one day, somebody brought a catamaran and just beat everyone. And it was just one of the most fantastic stories. Have there been any eras that you've studied where someone's just figured out something that just rewrote the. It would never be like this again. But you had the fan car in F1, right? Tell me about this. Yeah, yeah. Tell me the full story. I don't know the full story. I don't know. You do either. We're in an era of motorsport now that things are super tightly regulated. It's really hard to find these big gains. But what he's referencing, back in the day, there was an era where aerodynamics were kind of king, and the guys did a similar thing. They looked at the rulebook and said, hey, there's nothing that says we can't power the air inside the car on our own. And so they built a car that had big fans at the back of it and skirts that ran down the side, and the car literally sucked its way down. So just so much extra downforce. I don't remember exactly how long it existed, but it wasn't very long. I'm sure it got banned, but it was fundamentally dominant. And there's. There's been a lot of those kind of things now and. And over time, but now we're kind of in this era of fighting for these hundreds of seconds, these little micro moments. Yes. That's where being able to drill down through big data is powerful for us. Yeah. Yeah. We're. We'll. We'll be like. We do a live show. Right. So speed, timing is important, and sometimes we're like, oh, this document isn't here. We don't have this link and things like that. You guys are racing around a track where every millisecond matters. And so if you're jumping between different data sets and systems of record, I can imagine that can be a disaster. Yeah. And it's not just while Kyle's on track. Yes. He's doing all of that, but then as soon as he's back, it's between sessions as well. It's the clock's always ticking. We're competing on the track and off the track. Yeah. I mean, we just have such little time to go through so much data and to be able to piece it all together and understand a full picture, you have to do a lot of different things, which our engineers are very good at. But it's time consuming. So if there's a way to actually consolidate it, simplify it, and make things more efficient, then it's going to allow our engineers to make better decisions down the road, which is optimizing performance on the racetrack. Okay. Talk about the tension between the three of you. I imagine that you only care about speed. You care about speed and manufacturing. Can we make it? And you care about speed. We all care about manufacturing capability. Cost, maybe. Cost. Look, so what are the trade offs? Obviously, everyone cares about speed and winning, but. But there are layers to the trade offs because you can't. You can't just always turn every dial to 11, right? Well, I mean, look, you know, I spent 30 years in the Marines. Yeah. And, you know, we got tired of fighting wars on PowerPoint. And, you know, for business, we're getting tired of, like, making decisions off of rudimentary and incomplete systems that provide only partial solutions. And it just takes forever to get data together. Yeah. And so, you know, from a business perspective, we have to look at it and basically say, look, we want to transition to something better. And the cost of that is not just material, like dollars, it's also change. It's changing mindset. And as you can see from Andretti, like, they're all into this. Like, this team is ready to make that transformation, but it'll still come at a cost. Right. There's people who are stuck in their ways. Look, I like to do things this way. I'm not used to that much data coming at me. I can't make decisions that fast. Like, this is transformational. And really fundamentally, it's people money, it's organizational. And obviously, when you got a great team, it's just going to go like a hot knife through butterfly. It's going to be amazing. That's great. Yeah. Where. Walk me through some of the benefits and try and give me some anecdotes about where gains have come from throughout your career. Yeah. I mean, like, for us, we take in so much time series data on the car specifically. That's the representation of what Kyle's doing on the track and what the car is doing and all of that. And being able to connect that data to his feedback and ensure also that that Data is, is clean and it is correct. You know, it's not like a car that's just rolling down the road and it's hanging around and putting some sensor data out. Like he's flogging the thing around the racetrack and occasionally touching walls and other cars. And more than touching, it's really difficult sometimes to make sure every system is working perfectly. It's a never ending battle of trying to do that. And so, you know, we're working really hard with some ML models and some stuff to pick out sensor anomalies and flag them automatically so our systems engineers don't miss them and they can go drill down and figure out why that sensors failed or where and what the knock on effects are. And in the end just get that part replaced immediately so that the next outing, the next time we're on track, we know the data is going to be as good as it can be. That's been the earliest, easiest wins for us is kind of in that space. Yeah, yeah. Is there a How do you think about budget, Budgetary constraints? Is that something that's just set internally, like how do you work? I'm happy that I don't have to worry about, worry about it, Drew. But I mean, even zooming out, for those who might not be familiar, like, I mean we saw some, we saw some drama earlier this week about salary caps and, and different ways to get around things. Like how do you think about setting the budget for the team and then actually executing against that? Because that's gotta be the last, the last phase of against. How do you actually deliver something that you can deliver on race day every single day with reliability and not need to cut the cost later? Let me, let's talk like this is innovation. Yeah. Okay. So we gotta be careful here. Yeah. Right. So if you come in, I mean, obviously there's dollar budgets. Right. Because it's not unconstrained. Yeah. But at the end of the day, like what we want to do is we're talking about a fully connected business here. Sure. So they've got an HR shop, they've got a tech team, they've got engineering, they've got a ton of groups that all need to be brought together. Yeah, yeah. So apart from just the car and the magnificence of what we're doing, you've got to bring it all together. And so we need room and space to be able to build out a complete connected business. Yeah. Because frankly every signal across the business is value. And by squeezing and optimizing and making things run more Efficiently we end up with a better sport. Yeah. And like I think at this point we're in that journey and so costs are going to be, you know, not giant but constrained. And we're going to deliver and we're going to watch and see as this evolves until we land somewhere where we can finally say this is it, this is the benchmark and this is what we should manage off of. For us, for us. We're going to ask for every tool we possibly can to make, to make the car better. He's going to, he's expecting us to do that, to do that job. And in turn we turn around to the commercial side of our business and look at them and say, hey, it's your guys job to go out and find that sponsorship, find those things. Because if we don't use this tool, our competitors will and we're in the business of winning and if we're not going to try to do that, then why are we here? Take us through the next few months in the calendar, the rest of the year, the next year. So we literally just ended the last race of the season like three days ago. Four days ago. So we officially start our off season and this is where we sort of take some of our use cases and our ideas that we've sort of half baked and trialed some stuff and look at it and productionize it. Sure. And in the end try and get all of these, or at least the first initial use cases ready to go for St. Peter beat 2026. Yes. That's kind of the target. And there's a ton of prep from here to there. Yeah. And I'd say in the off season racing is so expensive that you, you're limited on how much testing you can actually do on a racetrack. Right. So it's very important that all the data that we collect and we utilize is, is actually making a difference and we're actually able to progress with, with, with the data that we have. So that's where the engineers come in. Right. We've got a, a massive group of engineers that take a lot of pride in their work and they have five, six months from now until the start of the next season that they dig in through maybe one or two tests that we get maybe some wind tunnel stuff, maybe some various other things, shaker rigs we call it. But we can't really get on track that much because of how expensive it is. So a lot of what we do is in the sim world and it is very data driven. Yeah. What does the rest of your off season look like. Are you training? Running? I saw the F1 movie and Brad Pitt's running around. Are you running or are you a guy or both. You know, training is important, right? Yeah. I mean, you have to be. As a racing driver, you got to be like a certain weight, certain size. You have to be. You gotta have good endurance, but you also need to have some strength to be able to wheel the car around. Right. We don't have power steering. You're hitting the brake pedal as hard as you possibly can. And we're pulling up to 4, 5 GS for an hour and 40 to 2 hours at a time. So it can get very physical very fast. No power steering. No. In the car. And the car makes over 5,000, £6,000 of downforce. So imagine driving your road car that weighs 8,000 pounds or something like that around without power steering. Flash that on the screen when they're. When you got the driver view so that you guys get a little credit. Yeah. People assume it's like turning the wheel of, you know, a Tesla or whatever. Yeah, no, it's. It's much tougher than people tend to realize. That's specific to IndyCar racing, though. IndyCar racing, we don't have power steering. F1 does. A lot of sports cars that you see, they do have power steering, but IndyCar itself, they do it for the sport and they've kept it that way for many years. So it's a little bit old style, but at the same time it's good because it really translates it a little bit, right? Yeah, it's like. It creates a sport out of it. Right. It's a little bit more physical. People don't look at it as much as like, oh, you're just driving a car around some roads. Right. Pushing pedals, turning wheels. No, there's actually physical side to it. So the off season is a lot of training, preparation. We do a lot of sim work and driver in the loop simulators. And yeah, it just being ready for the next race that comes up. It's hard though, because you don't have G forces. You can't simulate G forces for a driver. So having that involved is something that you. You get acquired to as the season progresses, if I'm being honest. Yeah. What's your daily. I'm sorry, what's your daily driver when you're not on the track? My daily driver. So that is one. Is. That is one of the great things about being a racing driver is you don't have to own a car. Oh, you don't yes, you. So I, I raised for loners or something. Yeah, exactly. So I race for Honda. Okay. An Indy car. And I have a 2000. No word with Underlight and Glow. You have glow on the S2000? No, I have a Acura MDX. Very cool. Since they're, they're sister companies. Right. And then I also. They're not sending you nsx. They don't make the NSX anymore. So they still got them laying around. Give them a call. We'll, we'll, we'll talk to them. We'll say we need it. We need them ripping around in nsx. And then I also race sports cars for, for Lexus as well and LFA every day. Obviously they also don't make an LFA anymore. So. Yeah, just a million two dollar car. I can just go rip and depreciate real quick. Yeah, I mean is 500 at home? So that's the other car. That's great. Fantastic. Well, thank you guys for coming on. This is fantastic. Anything else worth sharing before you get out here? Okay. Enjoy the rest of the conference. Thank you so much for hopping. Thank you. We will talk to you soon. Have a good one. Thanks. Goodbye. Jordy. Any other breaking news going on? We have our next guest coming into the studio in just a minute. Who do we have. We have someone else going on. Okay. Okay, cool. Yeah, yeah, we're, we're good. Whenever. We kind of ran late. Now we're, now we're running a couple minutes early. We will keep it going. Oh, yeah. Palantir CEO Alex Karp thinks the value of skilled workers is spiking even as big tech companies, Companies, possibly his own, may shrink. Our revenue is going up. Our sales force is going down. He said on tvpn the number of people we plan to have in the future is less than now. Very cool. We scoop. We're scoop maxing. We're newsmaxing everybody. What else? I think we're ready for our next guest. If you want to scroll the timeline, we're looking good. Lots of posts. Having fun. Welcome to the stream. If you're ready. We're good. We can. We're. We're happy to have you. How you doing? You. Thank you so much for taking the time. Yeah. Welcome to J. Thank you. Any relation to Brandon Jacoby? I don't think so. I think you guys. We have a buddy who works, he's a designer. We like to, we like to poke fun at him because he is. We call him Jacoby. And whenever we have a design problem, we Always call him the last name. Sticks with that one. Yeah, yeah. Anyway, please introduce yourself for the stream. Who are you? What do you do? Happy to. Sorry, I'm out of breath. You're good, you're good. So, Matt Jacoby. I'm the head of data science and analytics at Racetrack. Okay. Southeast based fuel and convenience retailer. Yeah. And shout out to my wife for letting me come up here because we're technically on vacation this week. I heard this. This is crazy. The grind never stops. You couldn't miss. AI got the memo about lock in season. Well, yeah, it's you gentlemen. I couldn't pass up the chance. We really appreciate it. Okay. So it's great to have you. Yeah. So. So break down the business a little bit more. Give me a sense of the scale, what the day to day is like. Customer. You know, obviously we have a general idea, but give us more. Yeah, yeah, Happy to share. So roughly 700 retail locations across our family of brands of racetrack, raceway and golf. A lot of people don't realize that we own golf. Yep, yep. Cool. 10,000 employees, associates in our stores and people at our store support center in Atlanta. A lot of people don't know either. We're top five largest privately held company in the state of Georgia and We are top 15 in the United States. Thank you. We ever said Minecraft. So walk me through a little bit of the history of the company because I imagine that what we're going to talk about in terms of like, you know, software, artificial intelligence is, you know, a revision to the way it was done years ago. Right. So yeah, walk me through a little bit of the history. Get me up to speed. Oh, wow. Well, I can't speak to all of it. I. I've been there about two years. But what I can say is that we've done a really great job of focusing on transformation, specifically data enabled transformation. Actually just wrapped up a conversation about this downstairs. But if you ask me, one of the purest use cases for transformation is converting from gut based and tribal knowledge based decision making to data driven and therefore after that, analytics and AI based transformation. So. So we've really focused heavily, even before my time, on making the best decisions we can with data. And so our partnership with Palantir has really allowed us to take that to the next level, the proverbial next level. I promised myself I would avoid buzzwords in this conversation, but it may not happen naturally, but yeah, it's been a conscious and concerted effort by our leadership, top to bottom, to really make that happen. And it's not easy at times. Times. Right. You're asking people to step out of what they've done in the past and to trust data and math that may or may not be right, if we're just being candid. And so we've really grown and focused and developed on building that muscle with the organization top to bottom. It's been a really, really interesting and impactful two years with our team thus far. Walk me through some of the. The concrete ways that you can use data to make a decision. At Racetrack. I remember there's this funny story. It might be. Might be apocryphal, but I heard that I always do this where I tell some story that might be entirely hallucinates. You're an LLM. But, but so, so the story goes is that one shot is that McDonald's needed to figure out how to place a bunch of restaurants. I'm sure that this is something somewhat related to what you have to do. You decide where the restaurants go. And they did a ton of analysis and they figured out this street corner was the best and that street corner was the best. And they spent millions of dollars in consulting and they put them all there. And then Burger King came along and said, yeah, just put one next to McDonald's. And there's some, there's some beauty there. There's some, there's some hilarity there. But. But you can imagine that that's the type of very tractable problem. Where should I put a. Put a thing also, like, like store layout, planograms, figuring out what goes on, promotion when pricing, dynamic pricing. There's a whole bunch of things that I could imagine you do. But like, walk me through what you. Or even at the individual, at the individual store level, where it's like, hey, we're out of this product. Yeah. What are the, what are the problems? What's the most recent, like, case study you did? Yeah, yeah. Great question. Look at you talking about planograms. So, yeah, we like to say that we're always focused on, on the customer, right. At the end of the day, it's our customers and it's our associates that make this massive business continue to run and drive. And so you're hitting on inventory. That's a really important use case. But even more important than that is making sure that we have the right levels of people at our stores to meet that customer demand. There's nothing worse than when you go up to a gas station to fill up your gas tank and there's a yellow bag on the handle. Or, Or I would actually argue it's even more painful when you put it into your. And then it's slow. Or. Yeah, so there's that and there's also the inside experience. Right. We take pride in our food offering. So fresh pizza, fresh sandwiches, breakfast sandwiches. And that takes people. That takes time and that takes hours. And making sure that we have the right level of people in the store, right number of hours and the right skill set as well. It's not just. You can't just throw hours at these problems. You need to understand the skill set to meet that demand and meet those expectations of the customer. Because at the end of the day, it really is that customer that makes us continue to thrive. And, you know, got this pin on. We're celebrating 95 years. We've been here a long time and we expect to be here a lot longer. 95 years ago, software didn't exist. It truly did not exist. And now you're sitting here implementing AI and the largest enterprise software platform possible. Switching gears, a little bit of a hot take. Have you been surprised by the developments in just how the electric car has rolled out? Like, there was a moment when everyone was like, do not get in the gas station business at all. It's going to be all electric. All these companies are cooked. And then we saw the consumer kind of pull back from that and want to. Different experience. And maybe they have a daily. That's a, you know, Tesla and it's great. But then they also still are in the gas world in some ways. Have you, has, has. Has there been optimism inside the company for the future? Well, we, we are certainly investing in the future. We. Yeah, I was going to say people that are charging, they want to, they still want to get fresh pizza, right? They do, yeah. Yeah. The. And we're actually taking a unique approach where we're, we're developing that infrastructure and, and those customer venues on our own. So we've chosen to really understand the customer and do it in a way that meets their expectations because we can't predict what the future is going to hold. 100%. So different experience right now because you might be stopping for 20 minutes instead of two minutes or five minutes. That's a great point too. So you have a more captive audience for a longer period of time and a lot of pride and all that. Exactly. Throw something else. Come get some racetrack swag in the gas station. Yeah. Or anything. Fresh pizza or what have you. But, but yeah, we're certainly not turning a blind eye to what lays ahead. That's Cool. You know, we have certain strategies and things that we're talking about to, to make sure that we stay ahead. It does. It's a unique opportunity now to actually take that seriously. You've seen where this market stabilizes and there's also just the standardization around nacs now. Like the actual charging port is standardizing. So that probably makes the infrastructure cost a lot, a lot less or a lot less risky, I guess for you. Yeah, very, very exciting. So walk me through the actual like scale of the Palantir implementation. Are you early days? Are you trying to roll this out to all the employees? You said 10,000, wasn't it? Something like that. Do you want everyone to interface with this or is this more of like a managerial tool that will be used to like make decisions about how to run the business? Yeah, that's a great question. I think right now we've really focused in on use cases that are driven at the managerial level or the head kind of store support center level. But that's certainly not to say that there aren't implications at our stores, because there certainly are. And I think as we, as we progress and as we deploy more and more use cases, I very easily could see getting the technology in our frontline associates hands as a real value add and frankly a differentiator. Yeah. Have you, have you had any problems with different enterprise software companies not playing nicely together? You don't have to name names, but we've just been tracking this story that there's now some AI companies that come out and say, hey, we want to take your, you know, your Google Docs and get it to talk to your Slack. And Slack is owned by Salesforce, so they don't want to talk to each other. And I'm wondering in the retail context if like a POS system and an inventory management system, like there might be some similar sharp elbows or is it all pretty copacetic? Yeah, I think it's fairly copacetic, but mostly because of our IT team and the really great work that they've done from a data architecture standpoint, consolidating everything centrally and really removing the need for kind of call it, peer to peer communication of those platforms. Because everything goes into data lake. Exactly. And again, I think that that team really deserves a shout out too. So while our team is in the business, the IT and the data team has really been an enabler for us. We have a wealth of information and data that we can make some of these really complex decisions with and without it. We would be severely hamstrung and would be working on challenges like pulling out of POS systems or what have you. And so we've kind of, we're past that level and we have a really strong data lake and infrastructure and architecture to support all of the nerdy math that my team loves to do. Yeah, awesome. Yeah. What, what, what, what else are you trying to identify going forward? Is. I mean, I imagine that like the base case is just like, I want to know what stores are over performing underperforming, but then ideally you want to be able to predict which stores are going to start underperforming and intervene beforehand. Is that roughly? Yeah, roughly. I think it depends on the use cases. And again, not to throw buzzwords out there again, but we break down analytics into four main types. There is the descriptive. So the old school reporting and data dashboarding tableau, power bi the diagnostic, which explains the descriptive. And then my team really steps in on the predictive and the prescriptive front. So think about predictive maintenance or, hey, this fuel pump is predicted to go down in the next two or three weeks. That predictive and prescriptive approach allows us to pivot again transformationally away from being reactive to being proactive with things that really impact our customers. So we like to really focus on, hey, where are the customer pain points? How can we peel that onion? How can we solve some of those so they have a better experience and that drives a lot of it too. So, yeah, there's a world of use cases out there and we're really just scratching the surface. Very cool. One last question for me. Are there bad actors in the gas station business that intentionally pump the gas slow to drive people into the convenience store. Oh, my gosh. That flies in the face of everything that we think. Well, just because. So there's. We like to joke a lot about, you know, on my team and maybe others share this sentiment or don't. But is it worse if a pump isn't working or is it actually worse if a pump is slow? And I actually think my experience are the most painful when I go up to a pump and it just, it's slowly ticking. At least when, when you see a bag, you see the yellow handle, then, you know, just don't even go there. Yeah, don't go there. And I don't think. I just remember, maybe, maybe it was because when I was a kid and I was broke and I'd put like $20 on pump five and it just felt like it'd go fast. And now as an adult, I'm, I can, I Just get. But I. But I'm getting like five times the amount of gas. Like you weren't going to racetracks. We predict when that's very brand for racetrack to do anything slowly. Yeah. Speed. Speed is in the name of this company. Racetrack for 93 years. 95 years. 95 years. I can't wait for 100. You'll have to come back on. I would love to. 100 years of racetrack data analysis. Break it down. We'll do 100 hour streams year by year. I mean it must be fascinating. Name every day data point. I mean just pulling like the revenue over a 93 year ramp. Like that's got to be fascinating. It'd be interesting. Fascinating. Anyway, thank you so much for coming on and interrupting your vacation. Yeah, this is great. We'll talk to you. Enjoy the conference. Have a great rest of your day. Enjoy the conference. And that's our last guest for the day. Right? That's our last guest for the day. Fun started out with a bang. We should run out. We should run run through. A thank you to all the sponsors that make this possible. We told you about ramp.com Time is money, save both. We are of course powered by restream1 live stream. 30 plus destinations. Of course we won't need to tell you about Figma. Think bigger, build faster. Go to figma.com for all your design needs and get compliant on vanta.com. manage risk trust continuously. We also got graphite.dev supporting US code review for the age of AI polymarking. Of course some big news out of Polymarket. There was a major trade deal. We'll talk about that tomorrow. Okay? Okay Julius, what analysis do you want to run? You can chat with your data and get expert level insights in seconds. Turbo Puffer, our newest sponsor. Search every bite serverless vector and full text search built from first principles on object storage. Profound. If you want to get your brand mentioned in chat. Linear of course is a purpose built tool for planning and building products. Big day for linear. Big day for linear. Getting lots of shout outs. If atlassian is paying 62610 for the browser company, they should get ready to pay 6 trillion for linear. I think so. We are of course supported by numeral numeralhq.com sales tax on autopilot. Fin AI, the number one AI agent for customer service. Adeo customer relationship. Customer relationship magic Adio is the AI native CRM that builds scales and grows your company to the next level. We of course sleep on eight sleeps. You can go to sleep.com. we are also supported by public.com investing for those that take it seriously. And we always tell you about AdQuick.com ad advertising made easy and measurable ad quick forever. And if you noticed, Dr. Karp was wearing a fantastic Patek Philippe aquanaut chronograph strap. And if you want one for yourself, you can go to bezel getbezel.com your bezel concierge is available now to source you any watch on the planet. Seriously, any watch. And they would love to find you a orange band. An orange band. Aqua dot for sure. Business Insider has a scoop here that says Palantir CEO Alex Karp says top tech talent is about to get crazy valuable. Alex Karp, CEO of Palantir, said on quote unquote. Again, why do they put us in quotes? This is the dividing line. This is the dividing line. TV Close the laptop. Okay. So close. Business Insider, the website Business Insider. Wow. Says that top. I think. I think we gotta. I think we just got to put like the. Just one of the words in quotes. It can't be, quote. Business. Business. Business. Business Insider. Business Insider. That needs to be the way we talk. I gotta look at. I actually have to look into this company because I love business and I love. I love insider trading. Insider insiders in business. Isn't that the lore? Isn't that the lore? Henry Bogga, the guy who started Business Inside, loved insider trading. I think he lost his. I think he lost his license. I'm not kidding. I'm not kidding. Okay, look this up. Business Insider Insider history. History and more breaking news. Justin Bieber is launching SWAG two tonight. The new album. What does that mean? And Meek. Meek Mill posted 2 hours ago. Meek Mill becomes a AI founder. So according to Wikipedia. According to Wikipedia, Henry Bloggett was charged with civil securities fraud by the U.S. sEC. Settled the charges. 4 million. He was permanently barred from the securities industry by the SEC and the nyse. The charges rose during the dot com boom. Merrill lynch, which included issuing materially misleading research reports on Internet companies and making exaggerated or. Or unwarranted claims about them to customers. And. And then in 2007, four years later, he co founded Business Insider, which is a fantastic pun. It's so funny. It's so funny. He's the. He's. He was in the business of insider trading. And he said, why did I combine. They didn't say insider trading. They said civil securities fraud. Okay, it doesn't sound great, but you know, there's so many run. Jeff Bezos purchased a stake in Business Insider and He, he had a great run. 2007 to 2023. Anyway, there's so many great quotes from the. The carb segment. This one I would say, he says, I would say modestly, I'm the most humble I've ever been. You would never build a software company downstream from value creation. It's all, how do I make the client feel like they're getting laid while they're getting f'd? So good. The founder, Adam, who introduced AI Key, a small device that lets AI control your entire phone. Just plug it in and ask it to complete a task. He's saying all of this, all of us, and still no TVPN invite. We should, we should probably have mine. A lot of people, A lot of people were said, no thanks, because I guess he previously worked in military intelligence and people didn't feel inclined to plug a hardware device into their, into their phone. But we're in the capital military intelligence right now. It looks like he sold out the initial batch. So let's have him on the timeline in turmoil. Anyone who puts the timeline in turmoil is welcome on the show. I'll give him a follow right now and we will make it happen. We're, we're. A lot of people are having fun with the stream. This is a great reaction. Anyway, that's our show. We got to get out. Whoa. The United States and back to the United States. We do last thing. This just because it is breaking and it's funny. OpenAI plans to launch an AI powered hiring platform by mid-2026, putting the outfit in close competition with LinkedIn. With LinkedIn, the company also wants to start certifying people for AI fluency. Are you AI fluent? This seems. Yeah, this seems like more of a mercour competitor than LinkedIn. Maybe. I don't know. Like, yeah, we need to dig in more to that. But. But the other odd thing is that wouldn't Microsoft get a copy of whatever they build? So wouldn't. Wouldn't Microsoft get access? Like if they build a new. I mean, that's the deal. That's the nature of the deal is that they get, they get the rights to AI OpenAI's IP. So if they build something that's valuable, but if they build a network, then that's a separate thing, right? Because the IP doesn't matter as much. Like the weights to GVD5 are not as valuable on the platform as the chat GPT app. So yeah, maybe there's something there. I don't know. People have been complaining about LinkedIn for a long time. So maybe. Maybe there's breaking news. What is this? Donald Boat says that he has art for the Ultra dome. Oh, yeah, Yeah. I was. I was talking to him about that. I'm very excited. Great. He made something so. Well. I wish we could keep streaming, but we got to get back to the Ultra. We gotta go. Okay, let's go. All right, folks. Anyway, thank you. We will see you tomorrow today. We love you. Back to a regular show tomorrow. Have a good rest of your day. Bye.