TBPN Live - Alex Karp LIVE at Palantir AIPCon | David Glazer, Ben Harvatine, Danny Lutkus, Jonathan Webb, Nancy Cable, Ryan Asdourian, Drew Cukor, Zack Porter, Kyle Kirkwood, Matthew Jacoby

Starting point is 00:00:00 You're watching TBPN. Today is Thursday, September 4th, 2025. We are live from AIPCon. It's a Palantiers Conference. It's the, what do we call it? The Office of Ontology. That's right. The tent of tactical strategies.

Starting point is 00:00:16 Many people have been saying this. We have a great show for you today. Folks, we're interviewing Dr. Carp in just a few minutes. We're interviewing a ton of folks from Palantir, a ton of customers from Palantir, some founders, some folks who work at companies that use Palantir or should be an interesting day. But first, there is massive news

Starting point is 00:00:33 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. And I opened it. And I saw that they were getting acquired from their own announcement.

Starting point is 00:00:48 And I opened X and nothing had been shared. I kept scrolling. Randomly subscribed the browser company subsstack. I mean, they have actually a cool thing. It's their username is open. dot substack so the 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 yeah posted it a few minutes later I think they kind of woke up to it they announced it so sorry to front run them but Josh Miller

Starting point is 00:01:14 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 and 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 reflects our team's craftsmanship and relentlessness, the support of our

Starting point is 00:01:54 coaches, board members, and advisors in 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, trained 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 Alassian already has, they have a rovo, I think it's call or something like that.

Starting point is 00:02:25 Like 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 thing is just a fantastic company. Five billion in revenue, 82% margins, 1.5 billion in free cash flow, 1.4 billion in free cash flow.

Starting point is 00:02:42 I'm so glad we brought the sound board. We're back. And it just doesn't strike me as the like their last, the last few accuracy 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.

Starting point is 00:03:01 Jira tickets. They have Jira, which named after the poster. Yeah, 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.

Starting point is 00:03:18 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 well track some stuff but it's very at last you makes a lot of atlasian 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 and i think the timeline is generally like you saw the will to pew post uh like there are definitely people that are against this yeah and are saying that like well the vibe the vibes had turned on the browser company massively yeah over the last

Starting point is 00:03:53 at six to 12 months. Purely because of the valuation relative to the monetization and the and like the the progress of the business. Million yeah I think they had incredible incredible marketing yeah credible 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 fantastic but I mean we demo so it's cool I mean what I like to see is one it's a real acquisition yep they've like cleared the preft stack for sure. Massively, so the team, the whole team's getting paid.

Starting point is 00:04:27 There was some uncertainty about how much they'd raised, but it was somewhere between like 50 million or 75 million and 125 million. It was definitely not 300 million. And at 620 in cash, everyone's getting paid out, which is great. So yeah, I think. And put another way, it's only six months

Starting point is 00:04:47 of Atlassian's free cash flow. 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 a market that well I well I don't I am curious to see how they focus in the product on consumers versus enterprise like that 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

Starting point is 00:05:18 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? So here's a post here from Brad Jane. 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's a six $610 million purchase rhymes with that. The Atlassian problem they invented bottoms up sass. 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,

Starting point is 00:06:02 Notion, not Confluence. Cash Tag Team has near zero inroads with the next generation. They're 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 ARC, every startup using Loom. That's Atlassian buying Access.

Starting point is 00:06:23 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 understand the Loom acquisition so much more because Loom is an enterprise tool. It's used by startups. It's using a business context. Sure, it's probably used by some consumers.

Starting point is 00:06:40 It just feels like the price feels, it feels extremely steep given, like Loom had product market fit. It's just that it wasn't necessarily going to turn into this massive platform and compounding. But it's growing like crazy, actually, from within Atlassian,

Starting point is 00:06:56 they called that out on the earnings. Yeah. And so, like, I think that the loom... But it felt like a standalone... It felt like a standalone product, not a platform, but fit nicely into Atlassian's platform. Whereas paying $610 million

Starting point is 00:07:08 for a company that people use, 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. Hopefully, hopefully some...

Starting point is 00:07:19 I thought that was, like, total sign-ups. Yeah, but it's small. It's small. Yeah, and nobody, and 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 they kind of revitalize the vibes. 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, you know, Loom or something like that.

Starting point is 00:08:05 Like that, that's a possibility. And then 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'm if I'm like everyone's to get along to some degree. But the integration is probably really rough. We've heard about the data walls and the data the data wars. And so if you say,

Starting point is 00:08:56 hey, instead of trying to, you know, set up some API 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 be centralized. 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 Dia Browser, we're going to collectively redesign the browser to help knowledge workers kick butt in the AI era.

Starting point is 00:09:31 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. 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.

Starting point is 00:09:56 And there's, and, and Atlassian can underwrite, like, crazy opportunity more than BCs can. Anyway, we have Dr. Karp. Welcome to the stream. How are you doing? Great to meet you. I'm John. Happening. We're going to have you hold this microphone. Great. Where's the camera? The camera's right there. You can just see it wherever you want. What is the big announcement from today is, are you? Are you? Are you trying to tell more of a story around enterprise with this?

Starting point is 00:10:25 You know, we're kind of not, I think we're just, it's more like we're crushing it. Everyone tells us to be super modest about 93% growth in the U.S. and 94 rule of 40. 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 40 isn't real. It isn't really. 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 do think you have to judge humility by the delta between

Starting point is 00:11:10 performance and ego. And I would say somewhat ill modestly, I'm the most humble I've ever been. And, uh, and, uh, and, and now, and I just, I think it's like, so what we try to accomplish with, uh, we've been doing these kind of conference forever. Basically, because everything we've done at Palantir's like completely, uh, it, 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.

Starting point is 00:11:51 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 like be happy. 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 significantly less than the value creates good for us because there's a multiple in the value the flaw in the

Starting point is 00:12:22 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 entology at FDE structures where FDE are actually technical and internal orchestration which is largely artistic it 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.

Starting point is 00:12:55 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

Starting point is 00:13:33 us. They're like, great. That's a software business because the hating means they can't get rid of you. But 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's just all it's like all these things are hugely contradictory we are revenues going up our sales source 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 uh 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 a part of the value that we help

Starting point is 00:14:08 create actually it doesn't have to be the majority in fact it's usually the minority of the value to 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 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.

Starting point is 00:14:44 Don't believe anything we're saying. Talk to other people that have done it. We're not, we don't chaperoon the people here. So 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,

Starting point is 00:15:03 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 percent of AI trials in the enterprise aren't converting like what's going on

Starting point is 00:15:34 what does it look like when somebody sells someone well i mean that there's a technical reason These are LOMs are probabilistic. They're not precise. The value of L of them is when it's essentially in an ontology wrapper. Because 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 error bound is very, very, very narrow. And the way you actually do LMs 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.

Starting point is 00:16:22 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 workster. Yeah. Great.

Starting point is 00:16:43 Great. The damage that wokester'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.

Starting point is 00:17:02 There's no such thing as agency. You can get away with that BS. Well, if you want to, like, Palantier's. lifted. 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 late 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,

Starting point is 00:17:30 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, a, the CEO of a CRM company that was making some comments yesterday. Did you, did you catch? I, look, Pallantier, we structurally mind our own business. And I love that everyone minds our business. But I would say that what I, 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.

Starting point is 00:18:01 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 it's 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 it's like we are very focused on value creation and we ask to be modestly compensated by 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 Palatier right now in the U.S. is the market that counts. We don't have the people.

Starting point is 00:18:41 We don't have the time. We orchestrating completely perfectly at Palantir, which of course we don't do, as we're 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. It's 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. It's the 93, and that's not even our best number.

Starting point is 00:19:21 It's 94% rule of 40. It's like, and then people are like, oh, yeah, yeah, well, but we have all the skills. We have all the motion, but like somehow our ocean isn't working. It's so big, but 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.

Starting point is 00:19:48 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 competence, broadly defined, actually the big cultural shift on enterprises,

Starting point is 00:20:27 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 strategy. 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, you create X value, I'm going to pay you Y. And the central problem, a lot of the larger kind of less agile sclerotic

Starting point is 00:20:56 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 and 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

Starting point is 00:21:34 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 think 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, you know, I'm an unhappy Democrat, but running around

Starting point is 00:22:11 saying, oh, crime isn't an issue, when everyone knows crime is an issue, 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's 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

Starting point is 00:22:51 same products same everything so it's like and then i the other thing i the other thing i 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. 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 grad, you just got a 10x. So it's like, you know, it's like pound here's 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

Starting point is 00:23:25 five years represents 40 years. What if I'm saying in the next five years? It's 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. 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 long. Yeah, it's like, give them an extra decade.

Starting point is 00:23:41 There's steak dinners. But the real problem that they somehow don't understand in the DCF amount is a year is not a year for pound here. 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 Poundeer. But God, do they get me to go back to orchestration?

Starting point is 00:23:57 Because I'm going to fuck these people. And 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, if 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 the people that hate us.

Starting point is 00:24:18 Try them out. Yeah. Do you have advice for young people? I mean, you said like artists like people, not literally artists necessarily. You said the company is like an artist colony. Yeah. Did you just become an artist if you're in a person? People underestimate like their artistry because like from a young age, you get huge benefits for conforming.

Starting point is 00:24:36 And you can say, well, I don't. I mean the central advantage of being dyslexic, we can't conform. So that was, that ends up being a huge because you just can't. So you're going to 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 so you can't do wealth accumulation if you're in this country,

Starting point is 00:24:58 like in America. I think actually a lot of these things are true in other country. 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 all that to that, yeah, reject that this kind of mind. 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, 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,

Starting point is 00:25:33 you know, when I started at Palantir, I actually just, because I just wanted to be a 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 debauchous life. That was my goal. Like, I'm moving to Berlin. I thought I'd need 250K. I was like, a $250k is a minimum, a million dollars of maximum. I'm moving to Berlin. And it's going to be like tabottery forever. Bergheim. Yeah, well, I had to like, yeah. So it's a remote office there.

Starting point is 00:25:59 But like you then measure yourself and it's like, okay, well, I'm highly differentiated on measuring 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

Starting point is 00:26:32 and work back to discover if they're right or wrong. Not just, and like, in all these things, and like for example, on the Pallenture thing, it is a great lesson. Go listen to our critics. Whatever critic you love, we're a conspiracy theory. So like you can take the left-wing version, which is like, Palletier is stripping you of your civil liberties with some people on the right belief. Pallantir is a Jewish conspiracy run by a mutt somehow, okay, whatever, you know,

Starting point is 00:26:57 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 of doing this? How do you build a company? Do you think it's just like an allocation based on a conspiracy? Why did we work? Just pick your conspiracy and that's the strategy. 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 it, every single decision. If it was a decision anyone else would have made, you would not have worked because that's a commodity.

Starting point is 00:27:26 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.

Starting point is 00:27:46 If you're watching this podcast and you enjoy this, you've already passed a test. I don't care whether you're a welder, a plumber, a carpenter, an astrophysicist, or somebody who'd like to build a business or just want to get rich, or you want to get enough money and move somewhere and do what I want to.

Starting point is 00:28:02 Move to Berlin. It's not the right place anymore. But any case, 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?

Starting point is 00:28:15 Like, is American decline? Do we need to bring things back? like who are the power players how America is power payer number one right now and like all this media BS it's like you know you got to compare America to any 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 but like no tech industry yeah everyone rich was born rich basically or with almost no exceptions

Starting point is 00:28:50 the most important Germanic company, I hope someone from Germany is listening to this, Comte out of Palo Alto, is Pieter Tiel and Ish. It's like the only German company since SAP that's real. Like, they won't listen to us. Like, just think about that. You have Peter Thiel, like the most important venture person,

Starting point is 00:29:10 maybe that's ever lived. Co-founder of Pallenture and you have me. It was like some way, you know, basically partially German, did my PhD in German. And you have no tech industry. wouldn't you have us on fucking 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

Starting point is 00:29:26 what we're saying 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 like they will do you think that there's optimism around the idea of it's so pick up the phone call right oh no no I think I mean I pick up it's crazy who calls me it's like it's honestly like i i can't talk out of school calls me you'd be surprised a number people come and i'd begin every call with don't listen to me very few people have i'm gonna give you the freaksue answer you probably want to ignore it this is what i think and they're like huh okay yeah huh yeah okay

Starting point is 00:30:02 some callback some don't but um yeah of i mean i have a lot of i mean like honestly we have a huge retail a crazy thing about germany is a huge retail investor sure sure they don't admit it in public but in private they're like keep going keep going but uh but yeah no i'm just saying the point i'm saying is uh you know it's like uh oh so then it's like energy technical talent understanding how to manage the technical talent it's 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 no tall poppy syndrome yeah yeah well it's funny you mentioned that that's like yeah like we were very well this is the thing we have to fight for this yeah because that no tall

Starting point is 00:30:44 pop what that basically means and in every people may not realize this but in an every other culture I know of, and I lived broad into 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 here. The only thing is we have to fight for that because the thing that unifies the woke left and the work right is they don't like the consequences of meritocracy. They want to work back to the inputs. So in that, that like just will screw society. It's like you've got to be able to allow people to succeed wherever they go.

Starting point is 00:31:16 Now, I was kind of still progressive, if you know what believes it, I super would like the inputs to be fair. But the outputs, those are the outputs. It's the results of freedom. Okay, last question, we've got to get you out of here. I walked by your office. There were some kettlebells.

Starting point is 00:31:30 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 V-O-2 max and actually you need to be strong per unit of weight.

Starting point is 00:31:46 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 just started doing hang from a bar as a dead hang like four months ago. And I hit four minutes and 36 seconds. Four 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, you got to be a number of times.

Starting point is 00:32:19 This isn't just money. No, I mean, my goal for the year was, for actually the next 12 months was four minutes. Okay. But then there's the number two. We got to get those numbers out. Well, no, but the number two, the second best mountain climber in Norway, I don't know if you know his name. But he, I have a picture. He did four minutes and 22 seconds.

Starting point is 00:32:42 Ooh, there you go. There you go. We do. We did it part time. Thank you for having us. Bye. Appreciate your work. We'll talk to you soon.

Starting point is 00:32:51 Have a great rest of your dad. Congrats. You too. Congrats to you. Thank you. We will bring in our next guest in just a few minutes. Can you imagine the Fortune 500 CEOs that just want a meeting with Dr. Carp just to get energized? Yeah.

Starting point is 00:33:08 Oh yeah. 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. pays for the steak dinner. Fantastic. Well, I believe we have our next guest pretty much ready. Ben Harveteen from Palantir for a deployed engineer that has been a Palantir for nearly eight years. So many good quotes in there. I don't take holidays off. I don't take holidays off. Oh yeah,

Starting point is 00:33:33 the team is getting ready to post. Anyway, I'm excited for this one. Ben, welcome to the show. Good to have you. Have you. We are going to have you hold this microphone as much. as you can. But why don't you kick us off with an introduction on yourself and 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 for software. Right. Yeah. Worked for Anheiser-Busch. Oh, no way. Beer company for a year. That was a great sort of transition from college life to a jauntlet. What were you doing at Anheiser-Busch? It was a management training program. Okay, sure. Yeah, yeah. After that, random hardware startup. for a bit. Went to another hardware startup. But I had some buddies from college who had worked here. And the 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 of what's going on? I've got a little kind of toy demo though.

Starting point is 00:34:35 Yeah, one robot. Bringing a robot is a great sign of respect in our culture. 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. We're really trying to push the technology further and further down to the shop floor and 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.

Starting point is 00:35:11 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 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.

Starting point is 00:35:33 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,

Starting point is 00:35:45 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.

Starting point is 00:35:58 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's like anywhere you go, of data scattered all over the place.

Starting point is 00:36:10 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.

Starting point is 00:36:27 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 their head, they haven't written it down. Maybe it's in 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,

Starting point is 00:36:44 we're capturing it. Correct, yeah, and really like the whole point of any of these data exercises is you just won't 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,

Starting point is 00:36:56 it's how 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 to it okay um 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

Starting point is 00:37:19 arm and a camera 3d printed right yeah it's it's all yeah it's all 3d printed oh wow okay yeah yeah i didn't realize that yeah cool um and so you know it's it's kind of set up to be a dumb terminal that kind of works and looks like sure you know the robot arms you'd see on a factory floor yeah 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.

Starting point is 00:37:48 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 like a network sparse environment where you don't have that real time uplinked to the cloud, you can continue to run off of that ontology. Yeah, we were looking at semi-analysis. They put the five levels of robotics.

Starting point is 00:38:11 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 zero or level one, 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 F-150. And it's this huge arm and you can't go near it

Starting point is 00:38:28 because 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. And even in like this simple demo, we've got, you know, it'll trigger alerts

Starting point is 00:38:56 on, you know, it tries to execute move and you end up with like a block like jammed up here. It'll realize that. Okay. It'll say, okay, you got a jammed hopper. Yeah. 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 a 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 that I think we just talked about with Dr. Karp but like where do you see Palantir playing in the

Starting point is 00:39:32 stack you have a bunch of data the database you put Palantir on top but then at a 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 pre-agnostic about how far up or down the stack we go 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 an example of an edge node that one of our partners edge scale makes okay so this is that box so you can stick in the closet yeah yeah network to those existing machines that you have on the floor if you

Starting point is 00:40:06 just need a turnkey solution. And 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 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.

Starting point is 00:40:24 And really just like a bespoke piece of hardware running that ontology native software. It's a monument. So you know, if you've got like more nascent operations or Greenfield operations, you think about some of the companies who work with, defense tech is like they can go all the way down the stack if they want to sure for some of the you know the larger more established customers that we're

Starting point is 00:40:44 working with yeah the plug-in-play solution yeah what we know 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 a good I'd say it depends on the application like we've done we've done some some like examples of that even like previous AIP Where it's like do we need the like the local app served up with the chat bot for the line operator who can just be like what's going on? And it just talks to you. Yep, there's and 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 and kind of non-deterministic. Yeah. 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. They're 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

Starting point is 00:41:38 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 FDE yet or is it once an FDE, always an FD? I think it's once an FD, always an FD.

Starting point is 00:41:58 I try to keep my hands on keyboard as often as I can still. You know, still flying out to, you know, ever axle factories and rural. Kentucky or whatever yeah I think the closer you can stay 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 the customer yep 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 that really yeah

Starting point is 00:42:25 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 HTML 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 here he is what's going on welcome the show all right great to have you did you do it live demo always that is bold doing a demo is on a 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.

Starting point is 00:43:12 Yeah, baked into the training models of the future into the pre-training data. So be very careful. Don't leak anything. But introduce yourself. Tell us what you're going to show us. Yeah, absolutely. Microphone? Oh, yeah.

Starting point is 00:43:24 Sorry. My bad. What's going on, guys? My name is Danny. Yeah. Let's see here. I'm an engineer of Palantir. I've been a Palantir for about 12 years.

Starting point is 00:43:33 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 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, U.S. Special Operations, CIA National Counterterrorism Center. Sure. After my wife and I had her first kid, she was like, hey, could you not go to weird places in the world anymore? And I was like totally reasonable.

Starting point is 00:44:01 Reasonable requests. 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 Control. or Eaton or Molson Coors, Cummins Engine.

Starting point is 00:44:38 So it's a widgets factory. Yeah, they're making widgets, they're buying parts, they're assembling them, and you have to understand the flow rate, where's the rate limiting factor, how can we increase flow? This is where I think we have the most differentiation from a product perspective,

Starting point is 00:44:52 because it's 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.

Starting point is 00:45:16 So, like, the real magic behind all this, too, is like these, yes, they start off as like singular use cases that are like pretty great, like straight shot. But then, like, when you start to connect these workflows together and it's like, oh, the machine's down. and I have this material, like, 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 get a good sound on this? Okay, cool. Yeah, we're walking through it.

Starting point is 00:45:43 What I was going to demo is I think, like, one of the interesting things, and I'm sure you've talked to a lot of different palanteering 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.

Starting point is 00:46:22 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 and 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?

Starting point is 00:46:58 Yeah, you don't want them to go spend millions of dollars with some random group to then recommend a Pallendeer 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 the things that I could and should deploy to go solve this problem can I generate ideas with all the

Starting point is 00:47:36 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 the proposal but then like i don't know if you guys have talked to anyone about a i fde 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 a i 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?

Starting point is 00:48:30 Does that make sense? Yeah, it makes sense. I have some follow-up questions, but maybe jump into the demo first. Cool. I think, like, like, my immediate, I guess, question, maybe it's relevant is, like, how do you ensure kind of quality, right? 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.

Starting point is 00:49:02 Totally. And today, you know, just like code quality and product quality ends up popping up. But I'm sure that they already think about that. My take on this is like when you start doing anything with AI or a large, language models like it 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 a moment where like um where like you actually have built trust because like part of this is not

Starting point is 00:49:39 actually like a technology problem it's like a people in process problem where like people actually build trust in it and also you get all the tribal knowledge that's not in any system actually incorporate in some knowledge context that you can start to build off of over time but i think that's like that's the that's the trick is like humans always have to be in the loop right to begin but then like you build trust until you actually do the Jesus take the wheel moment yeah so yeah with this demo what is the uh is the design is like an internal tool or something that you would actually serve 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 to

Starting point is 00:50:21 implementation. So like is that for customers that are already using Palantir? Yeah. So like 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

Starting point is 00:50:57 oh yes yeah yeah go ahead yeah yeah let's do it no here we were 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, um, redesign. Hey, I'm a, um, oh yeah, for sure.

Starting point is 00:51:26 Go ahead. So like the problem, the problem that I'll type in basically is like, hey, I'm an aviation expert. Like, um, 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? okay basically to solve this problem sure so like you know you guys get to see me type yeah it's always fun yeah this is interesting um i yeah but a ton of questions i've always wanted to redesign the lax uh like uh like uh yeah the you streets yeah the the flow of traffic yeah that is a wild choice by lax just constant constant traffic uh wasn't too bad uh this morning fortunately but we did have

Starting point is 00:52:14 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 I would call I would called and texted and said you know this is no time to take shots of the dyslexic he had missed he had made a mistake and and confused gate nine for for gate six right and and there is no gate six and have a particular term anyways had it to a different terminal I've mine uh thank you for covering of course yeah yeah so it doesn't have to me so right now I just I typed in I like yeah a lot pretty rough problem

Starting point is 00:52:53 statement I'm an aviation expert I want to solve problems around EWR airport yeah there are two few runways and the runways are too short how do I optimize traffic flow okay 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 um you know my like misspelled problem statement like cleaning it out it's like a prompt engineer effectively that's right 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

Starting point is 00:53:30 like what the task prompt is what the lm 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, and efficiencies. It puts out, like, key requirements, like prioritize aviation safety standards. It gives out restraint, constraints. Nathan Fielder would be happy to hear that year. Yeah, yeah, yeah, right. 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,

Starting point is 00:54:08 like, is the initial problem description, way better. than like the garbily cook like two sentence thing that it 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 a potential approach is 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 the screen I can see that same like core objection objective function over on the left what it's working towards and then I can start to see as it's running on the left like research

Starting point is 00:54:46 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 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 ten dollars 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

Starting point is 00:55:21 optimizing an airport I think I can I think I can deal with a hundred dollar inference bill yeah I'm gonna be okay with that 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 build a set of logic that NLM actually orchestrates different parts of that logic

Starting point is 00:55:47 between, and it can use tools, like you know, deterministic tools, or it can write back, or it can access and query things that will 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,

Starting point is 00:56:07 like I wanna be able to give that chat, 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.

Starting point is 00:56:41 But in this case, right, I'm using GROC4. 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 they 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.

Starting point is 00:57:02 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 nap in 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 kind of like frontier level, but available broadly, but the Palantir value is 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 you have schematic. Well, the thing that stands out to me is like if you're a large enterprise, you want to work.

Starting point is 00:57:39 with foundry and have that ability to be model agnostic and like where does the leverage flow in that situation where when foundry can just sort of decide on the fly 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 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

Starting point is 00:58:22 logic sure 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 sure which is a ipp logic and so here i can just like set up a bunch different orchestrations for how I want to 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 same way 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,

Starting point is 00:59:01 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 against but then like as a backfall then i'm just like also giving it access to go inquiry 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 and 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 in it's writing as we like

Starting point is 00:59:42 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 links numbers and how the 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 is 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 the 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 then all the passengers that have middle seats and then all the passengers that have aisle seats and they have.

Starting point is 01:00:37 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 the flow. That's right. I think we're getting short on time here. One question. Let me like zoom forward. 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. Which is 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?

Starting point is 01:01:22 Which is like, hey, 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 like 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

Starting point is 01:01:45 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

Starting point is 01:01:59 and say like can I come up with like how I could calculate this and actually go and like run how close is this output? do you think to what a larger strategy I think it's like pretty I think it's like pretty aligned right because like they're not they 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 of 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

Starting point is 01:02:31 I need to come up with a proposal right and here's like the end proposal for what I 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 and the AI FTE and like start building right last last quick question are you feeling the reindustrialization yet are you seeing new entrance into the Midwest building things or is it more legacy players just trying to try to increase I think it's like a lot of what I work with are companies like Eaton, which are like 100 year old companies or like Johnson controls 100 euro 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

Starting point is 01:03:21 is interesting is like maybe five years ago this is really hard like people are 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 you guys brave to do a live demo next takes guests yeah great uh any great work I have a great listener thank you yeah love it have a great rest of the conference you're the man and we'll bring in our next guest John can live from the nuclear man himself welcome sorry to keep you waiting today is a great name to

Starting point is 01:04:00 to have a company that starts with the. I don't know if you saw the browser company. So 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?

Starting point is 01:04:24 What's the plan? So to my understanding, we're the only company in the Western world focused on the deployment 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 U.S. as the largest nuclear power in the world. 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. Does it. 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're talking about the Navy? I'm talking about the U.S. broadly. We have about 100 operating plants. I mean, today, 20% of the power in the U.S.

Starting point is 01:05:21 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? We're the deployment arm. And what does that mean? So, So think of if your 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. 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,

Starting point is 01:05:53 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&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

Starting point is 01:06:39 remembering how we used to build reactors as a country versus doing that new innovation. So there's really only two incumbents in the U.S. And that's Westinghouse and GE. And, you know, obviously we're talking to them. And then there's a lot of... And they built Vodal, the most recent nuclear power plants to come online that we're successful but over budget and over time correct oh man it was yeah hired everybody off that team so Georgia Vogel three and four first thing yeah what we wanted 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 like what in the what went wrong guys it's nuts

Starting point is 01:07:19 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 guys the last decade construction we're not talking 40 years ago I'm talking in the last this thing finished last year with wheel barrels 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 I lazy Americans. No, I'm not buying it. 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,

Starting point is 01:08:18 segmenting that data out. So if certain parties want to secure their data, they can't, then having LLMs and AI on top of that, giving predictive analytics, So when the supply chain's delayed the night before, a construction man or woman's waking up in an RV and a trailer at 3 a.m., okay, I'm going to be redirected at 315. I go there. At 345, 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 seven years instead of 12 years. China's building these one-gigated. gigawatt reactors for five billion in five years there's no reason we can't do it in five or four years i'm not going to name the number of my team will get really upset with me on the price side but um there's no reason yeah these two reactors took 12 years and 36 well let's talk about timelines in the industry broadly yeah because there's some recent i guess i don't i don't know if i can't remember if there's an eo or just a broad directive from the white house saying like we

Starting point is 01:09:21 want new nuclear breaking ground in the u.s in the next 12 months is that is that 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, but it's a $20 billion recovery project. Bringing old capacity back online. Yeah, yeah. So $9 billion walk away. They spent $9 billion on this nuclear, two gigawatt nuclear power plant, didn't finish it, walked away.

Starting point is 01:09:52 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 uh we'll 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 dirt certainly not nine billion but are you picking up a couple million it's a billion in legal fees yeah no it's it's a lot of big structure hopefully they port some concrete No, it looks like, I mean, if you walk on it, we're on, I'm not allowed to say we're

Starting point is 01:10:27 out, right? Yeah. Oh, God, I almost did. Um, so we're in America. We're in America. We're in America. We're in America. We're not afraid to say it. We're in America. But, uh, 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 yeah but we're going to go we're going to get that thing hopefully later this year early next year in construction uh we have an author dan wang on the show maybe last week he wrote a book called breakneck and he and he and he compares and contrast china to the united states and he calls china the engineering empire driven by an engineering

Starting point is 01:11:13 mindset the solution to everything in china is just more engineering uh 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 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

Starting point is 01:11:46 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 it's not just enough for you to be using AI to submit documents fast. You need review fast. So what's going to happen on the other side? God, I have so many comments on this subject. Just just rant. So how long do we have? Seriously, we got a couple of 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,

Starting point is 01:12:27 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 and it's working right the fleet's safe we have had in decades 100 operating nuclear power plants not one person in this country has died from radiation fallout zero point zero that is perfection so the the private sector needs to stop being a victim and just start doing what we're doing

Starting point is 01:13:16 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 costs, safer, higher quality than ever before. And I will say what we're doing with Palantir.

Starting point is 01:13:36 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, And we let 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.

Starting point is 01:13:56 That is a totally different world. Let us take your drawings, your great R&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 verse engineer thing. that's a whole other topic we could we could take on but we need the regulator to challenge us to be safe and we just as 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 carp rant together yeah yeah what what did Palantir show you that made you go

Starting point is 01:14:33 with them do was there was there a key case study that yeah so we started we're a two-year-old company that's about to be the first 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 techn, and agnostic. Is the other stuff managed by the government? Is that what you mean?

Starting point is 01:14:54 Or is it just older companies that manage? 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. I hired somebody over here a lot smarter than me.

Starting point is 01:15:13 was at Tesla, was at 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 aspects of data related to the project into a data lake, predictive analytics, our frontline teams. No one's even close, man. Yeah. This is it.

Starting point is 01:15:32 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 it would have been nice to look at a couple options and decide. Well, here's the good. Here's the good thing. I mean, it's just the most secure platform. The way it is configured, you know, 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.

Starting point is 01:15:57 And now we're just racing right now to go kind of build those solutions and it's working. Yeah. What's the, what's the structure of the financial milestones for you? Because I imagine that a lot of this doesn't look just like fund everything with venture capital. There's probably some project finance right and then 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 yeah 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 redwood how like the neoc clouds will go and build new data

Starting point is 01:16:39 centers but then there's there's project finance on the data center on the project on the project. You know, we're the ones getting it to completion. We can get an equity earn out in the project. We could 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 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 I could put 20 billing on our balance sheet yeah but I don't know maybe maybe in a couple years

Starting point is 01:17:13 let's talk let's see how this goes um so you know we're you know again I very just bullish on on Pallantier and I don't know whoever listened to that talk earlier it's I mean the binary outcome is it's us first China and all the tech bros and the badass CEOs and the badass Fortune 500 tech and here's what I would say we got to leave our ego at the door China is fucking kicking our ass that hope was not recorded everything's recorded they're live 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

Starting point is 01:17:50 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 first china it's not even close they're winning it's 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 now it's all going to be about performance on that construction site, on time, on budget, high safety. Well, and I love your position 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 staff. We got a partner, man. That's the thing, right?

Starting point is 01:18:35 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 here in the U.S. But I do think figuring out how we 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.

Starting point is 01:19:09 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. Thank you for so much for helping on. Thanks for doing this work.

Starting point is 01:19:28 We appreciate it. Have a good rest of your day. Up next, we have Nancy Cable from Ursa Major. we'll 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 right okay it's device we got an engine coming on the show it's shocking that it was clear through security we we we when we do these remote shows we sometimes have to bring a very very suspicious looking Wi-Fi hotspots Ben and the boys brought a Wi-Fi hot spots Ben and the boys brought a Wi-Fi hot spot

Starting point is 01:20:03 spot through the actually I think I had to walk it into the Capitol through a very odd place here maybe you pick up the microphone and we'll throw it on the table yep we can throw it on the table okay okay you set your end gently down okay incredible this is a wild demo we've first rocket first rocket engine nice to meet you I'm John I'm Nancy nice to meet you 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 a fun one we had so many promises a SpaceX engine yeah oh yeah we gotta follow up on that but

Starting point is 01:20:45 but this is the best demo we've gotten so far this is fantastic this is a good day for us yes so explain to us what is this and what's your business and introduce yourself yeah absolutely so 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 5,000-pound thrust class, proven hypersonic flight capability. So this thing right here has 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

Starting point is 01:21:29 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 this like a maneuverability is a piece of this for our customer. 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 there some major fit in? Yeah, absolutely. So we're doing, we aim to be disruptive. Yeah. And disruptive means that we want to break the mold of what some of the primes and the government

Starting point is 01:22:17 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, costs, speed, reliability.

Starting point is 01:22:50 Absolutely. And that is, most of my role is on the manufacturing side and making sure that I can take this excellent technology that our rocket scientists have developed and scale. so it's available to market. Right now, we're on, you know, 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 Pallenture partnership comes in. How does Pallenture for the news? 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.

Starting point is 01:23:29 a unique test system, you know, a different network drive, a different place to store the information. Different network drive? That is, I wasn't expected that. And that's, well, and I think this is the story of, 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 the interesting thing, right? It's just the natural chaos of things. Yeah. In every industry, rocket propulsion included. Yeah. And so feeling like, man, I'm 15 years behind. How could anyone possible? store something on a C drive. But when you're focused on getting the hardware to work,

Starting point is 01:24:04 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. Garpe 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,

Starting point is 01:24:24 and leveraging Palantier 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 Earth's Major actually interact with Palantir? Is it on an iPad, on a phone, on a computer,

Starting point is 01:24:46 while we're working on a test bench? In every phase, is it everywhere? Yeah, great question. So we've been with Palantir about three months now. Okay, so early, yeah. And right now, the daily interactions are mostly with our engineering and programmatic teams. We've built some inventory modules. We've built in, you know, looking at

Starting point is 01:25:02 our engineering line of balance, our change management systems. 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. Sure. 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. Yeah, you almost call it like an ERP almost.

Starting point is 01:25:46 Yeah, so we actually, we have an ERP, right? This is what everyone does. Everyone has, they have an ERP for an ERP. Yeah, but it's more like accounting. Accounting function, all of your work orders, the PLM, a product lifecycle management, and then an MES is the traditional thing, a manufacturing 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, because Palantir is better at them. 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

Starting point is 01:26:18 the team like spun out and built a business around that. Yeah, well, SpaceX, I Actually, so they have a product and it's kind of the gold standard. Everyone who's worked at SpaceS is like, I want that one. It's like, 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. Yep. Everything we need in one place.

Starting point is 01:26:39 Very cool. What's it, so what's it going to take to go for 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 gonna be forward with a robotic automation line.

Starting point is 01:26:58 So making sure we have the right tools, the right fixtures, the right machines, you know, 3D printing is critical to what we do here. Yeah, so 80% of the rocket, all of these metallic components are metal 3D printed. 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,

Starting point is 01:27:22 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. 80% of the line-down scenarios I've ever had where we stopped building product.

Starting point is 01:27:56 You want to guess what they're from? Lacking inventory. 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 not actually the hard part. The hard part is getting all the pieces

Starting point is 01:28:09 where they need to be to build a 1,200 component rocket engine. And it's things like that that the ontology is helping us off. A couple years ago I sat in a... It's funny. I don't know if this is Huber. but I feel like you could put this together, John. Well, that's kind of, that's the point, right? A manufacturing execution system.

Starting point is 01:28:27 So 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, like, Lego's for adults, but you can see it really just is a collection of fittings, fittings and fasteners. And that's kind of the point.

Starting point is 01:28:48 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. We've got young kids. I think they would enjoy putting one of these together. Yeah, yeah, a couple of years ago I sat next to somebody on a plane who was selling, it was pipe bending, pipe fitting, whatever this is. Tube bending. Yeah, he said, I mean, 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 there was a whole industry.

Starting point is 01:29:19 It's a whole, yeah. But it has to be there in person to make sure that they don't run out. Absolutely. It's a way 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.

Starting point is 01:29:31 There are some of the thinnest walled components on the rocket, right? This has a lot of mass to it. Sure. Tubes are often can be where failures happen. Sure, sure. So in an ecosystem, right, we need to test them. But also, where did this tube come from? What day was it bent?

Starting point is 01:29:45 What was the lot of stock material? What revision was ion? in my CAD model. Sure, sure. What testing did this engine undergo? All of that currently, I could find in our systems. Interesting. And it would take me hours.

Starting point is 01:29:57 Yeah, yeah, yeah. But if it's all in one place, yeah, it's all in one place, and we have a consolidated tool at set traceability. That's incredibly cool. Yeah. Thank you so much for bringing your baby on the track. This is a great sign of respect. Yeah, absolutely.

Starting point is 01:30:10 I mean, what's cooler than carrying around a hypersonic rocket engine, right? Everyone loves it, but the TSA. Oh, yes, yes, yes. Rough one to travel with. Yeah. Yeah, anyway, thank you so much for coming on. Yeah, absolutely.

Starting point is 01:30:20 We'll time soon. Thanks for coming on. Yeah, thank you so much. 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 can run?

Starting point is 01:30:31 I'd love to hear some ads. You want to talk about ramp.com? Ramp. Oh, you're playing the ramp song? Ram, ramp. Let's go through some. I did want to, while you pull that up, I did want to talk about Matt Huang. Oh, yeah.

Starting point is 01:30:46 The Paradigm and the Stripe team. introducing a new payments-first blockchain called Tempo. Matt says, as stable coins go mainstream, there's a need for optimized infrastructure. Tempo is purpose-built for stable coins and real-world payments born from Stripe's experience in 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, Kupang, Deutsche, Bank, DoorDash, LeadBank, Mercury, New Bank, OpenAI, Revolut, Shopify, Standard Charter, Visa, and more. Tempo's Payment First Design includes predictable low fees, payments, gas, and any stable

Starting point is 01:31:27 coin. Payments first U.X. Opt-in privacy, scale, 100,000 transactions per second, and EVM-compatible built on Reith. Tempo eases the path to bring real-world flows on chain, such as global payouts, pay ends, and payroll, embedded financial products and accounts, fast and cheap remittances, tokenized deposits for 24-7 settlement, micro-transactions, agentic payments, and more. Matt says we're building tempo with principles of decentralization and neutrality. That includes stable coin. Anyone can issue a stable coin. We might be able to have a TBPN coin. That sounds exciting. Oh, yeah, yeah, yeah. That was clearly a joke. No, but I was talking about a USD TPPN that's just a one for one stable coin that that that we issued to it does not move it does not move you can't make it move it won't budge

Starting point is 01:32:23 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 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 not so this is not downstream of like the stripe acquisitions directly privy and bridge so I have a poster from Zach Abrams founder of bridge okay 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

Starting point is 01:33:07 performant blockchain 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 stable coins 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 the Trump coin launched who's paying employees in Trump coin no no not not in Trump coin like that that day I think it was like a Saturday or it's okay exactly but when it launched you tried to pay

Starting point is 01:33:41 There was so much activity on-chain at that moment, like, good luck, you know, paying like a freelancer or something. So, yeah, the example would be like, I'm trying to pay a freelancer in stable coins, like, on-chain. Because, like, obviously, like, your default payroll providers are just using, like, you know, Web 2 rails or whatever. And that wasn't brought down by the Trump launch, right? Okay, got. Aid disbursements taking days due to low transactions per second and projects to later cancel due to six-figure upfront gas costs. Tempo is a new L1 built specifically for payments. And so anyways, quite the team they've put together here.

Starting point is 01:34:18 Yeah, we've got to get some of the folks on the show and have them break it down because I'm very interested in why not Salana, why not circle. You know, like it feels like there's a stable. The other question is 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, congrats to them on the launch, but we will want to know more.

Starting point is 01:34:42 Anyway, I believe we have our next guest. Welcome to the show. Ryan, I'm John. Ryan, it's pleasure. We're going to hold this microphone. Why don't you kick us off an introduction on yourself? All right. What brought you here today?

Starting point is 01:34:58 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. Lumens a telecom. Let's give it up for modernizing telecom.

Starting point is 01:35:13 Yeah, exactly. Finally. 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 decades.

Starting point is 01:35:39 We have basically been connecting the world. It starts with connection. And then in the last, in the last bit of time, the world has needed new ways of connecting. 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?

Starting point is 01:35:56 All fiber, right? Everything that's running across fiber. Those super fast connections you need, one port, one connection was the way of the world. We're changing that. We're getting it cloud ready, cloud enabled, remote control to all of those things. that give you that redundancy, all the things that power AI.

Starting point is 01:36:14 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 we're really focused on the enterprise. The enterprises that are building these new capabilities, data center operators,

Starting point is 01:36:30 hypers, 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, 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

Starting point is 01:36:54 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 as compute, storage, cooling, all those things that are needed. The missing link is connectivity. And realistically, it's something that has really

Starting point is 01:37:20 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 fiber and that is growing you know three to five x what we've had before okay and that is the capacity the world needs yeah so there's a some and is that capacity being used inefficiently today or or is or is demand still way out stripping supply the demand is completely

Starting point is 01:37:56 maxing out it's why we are putting these investments in the ground and we're not only the hyper scalers 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,

Starting point is 01:38:21 but the need is completely outpacing what the needs of the last couple decades have been. Yeah, I'm 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 Azure if I'm using GPD5 and then it sends back text this is not rich video

Starting point is 01:38:50 this is not VR I buy I immediately like intuitively understand like if we're in the metaverse world we're streaming 4k stereoscopic 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 airline you hear BP you hear some of these customers that are talking about their infrastructure all of the scheduling the inferencing the yeah the planning that is happening in real-time and adjusting that is not just people typing in their prompts into the text it is systems talking to systems and this is where

Starting point is 01:39:28 the data explosion has come from sure it's all happening in the background okay yeah yeah so so even though I fire off one query to GPT-5 it if it's doing deep research it might be pinging 75 different websites and that's driving up totally 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 like help optimize this airport and then it was like 20 minutes that's right okay yeah that's right so this is where the disruption in telecom sure and if you really think about what has changed in

Starting point is 01:40:04 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 in our, 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? Is that mean like more like multiple 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 what's yeah multi

Starting point is 01:40:46 multi tenant is a good way to think about some of the services on top of you know in the past yeah you've literally if you think even back to old telephone switches you've had you know the the one wire to one wire it's been one port to one service you had a service you out a port it's a truck roll it's a person coming out sure sure cloudifying it is bringing all of that technology to the 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.

Starting point is 01:41:15 Yeah, so a higher level of abstraction. Yes. And yeah, more, like almost like a virtual machine on top of the telecom infrastructure so 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

Starting point is 01:41:41 companies of today if you look at the sports industry manufacturing industry health care 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 the new shape of the telecom industry in your business like I imagine that there's some genius scientist that comes up with a faster fiber optic cable that is manufactured somewhere than someone

Starting point is 01:42:19 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 you know leasing or essentially charging a toll a lot of that toll road yeah do you sit all are we completely vertically integrated so we sit vertically integrated but I think what do you do R&D on on new fiber optic technology. We work with a number of partners on that and then we're also thinking about the AI optimizations 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

Starting point is 01:42:53 ground. Sure. Maybe it's on purpose, maybe it's not on purpose. Oh yeah, yeah, yeah, you need to be 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. Yep. 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 U.S. They have to seamlessly let those systems talk to each other.

Starting point is 01:43:26 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 of 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, you know, you've built

Starting point is 01:43:55 all these networks. We talked about fiber in the ground. Think about the systems over those decades that have been built up. One of the things Palantir is helping us with is this managing this operational. And 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. And you're like, why didn't they build a box that doesn't explode? And so you imagine that that's where the power lines work.

Starting point is 01:44:21 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. So you got to identify that early. There's that. And there's the software layer that is running all of those.

Starting point is 01:44:33 Got to make sure that that's up to date and not crashing. And Palantir is helping us optimize those, helping us bring them together. 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.

Starting point is 01:45:05 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 were running late, so thank you so much. This is great, thank you. Thanks for having me to be you.

Starting point is 01:45:18 All right, I'll grab this. Thank you. We have our next guest coming into the studio. Drew Kukor, I think we actually have multiple. We might need to pull up an extra chair. We have a lads, we have lads coming in. If we want to bring everyone in, we can. We can pass the mic around.

Starting point is 01:45:37 Whatever you guys want to do, we have multiple. Oh, okay. Hey, oh, hey. Just me. Oh, how are you doing? Sorry. What's happened? Welcome.

Starting point is 01:45:47 What's up? Great to meet you. Good. Good. How's the day? Could you kick us off with an introduction for those who don't know? Okay. I'm Dave.

Starting point is 01:45:56 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. Since like when our private or CFO retired, who's actually on the show recently. Yeah, Colin, I talked to him.

Starting point is 01:46:11 He retired in 2017 and since then, I've been leading to finance. 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 Jevin's paradox and we're doing more token inference than ever before. Reasoning models are kind of staying expensive.

Starting point is 01:46:37 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 this is one of the things

Starting point is 01:46:56 that we've been sort of saying is, like, LMs are commodity, commodity cognition, right? And so, like, essentially, it's like, it's getting, they're getting better and better, right? Elo scores better and better. Tocons are getting cheaper, right? And as Alex said, I don't know if you watch Kito, but, like, you know, he's talking about, okay, like, how do you actually derive value from that raw output of an O-M? 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 in Elo score.

Starting point is 01:47:24 And so these, like, things combined, I think are going to make it cheaper and 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 hyper-scaler 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 this and so I think like we will see but like I think that is it's much more about like how you're driving value from them then like well the cost is gonna be so overwhelming but they're super but it like totally it's like focus on the value and I do

Starting point is 01:47:52 think over time it's like people are gonna be able to manage this cost yeah yeah it feels like it feels like higher costs potentially but so much more value and it's pretty easy to tell you have 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. We got a product demo earlier. HiveMind 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 gvety wrapper and your product is really 4-0

Starting point is 01:48:33 and you're just kind of like reselling that right yeah yeah yeah yeah like and i do think it's like yeah like i think it's gonna be all about the value rather than like what the values there but the cost is superheritive yeah how are you how are you thinking about positioning uh palinger's story in commercial in the united states over the next couple years like what is the right framework People have always had the wrong mindset. It's a consulting shop. What do they even do, blah, blah, blah. Like what is the right frame of mind to be in?

Starting point is 01:49:01 Look, I think the right frame of mind is like we're delivering a tremendous amount of value to these customers with these customers, right? It's 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.

Starting point is 01:49:19 It's still relatively small, right? And it's like, we have, there's so much runway there, 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 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 had yeah do you do you think that uh people should be thinking about the commercial business as like a a bundle uh like a competitor to a bundle

Starting point is 01:49:50 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 people even wrap their mind around some version of all the above okay so it's like when you think about you know a you're not like head-to-head who are we competing with right and then everyone's like but I don't get it's like it's like it's a combination right we're not really we're competing against like the Frankenstein monster that almost every large corporation has yep and then you're also competing like particularly in government yeah but it

Starting point is 01:50:20 also applies in you know particular large corporations it's 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 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 point products. Yeah, yeah. It feels like it's like transformation, net new technology that would not get built in the enterprise otherwise. Correct.

Starting point is 01:50:45 And then once you've built that, once you've built that like compounding data asset, then perhaps you don't need some of the other products. Yeah, that exists. How is your framework or philosophy approaching the final, finance function at Palleteer changed because I feel like there's like very distinct eras where you know the change that every day does it does it do you do you feel like you have to update it every day like is it can in some ways like when you talk when we talked to carp earlier it's like yeah he's bringing that same energy and like philosophy uh it feels

Starting point is 01:51:12 like it's it's somewhat consistent uh even though you know numbers go up and down and and all that good stuff yeah look well I look I challenge any CFO working for carp to have here 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, 20th 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, we're very unique, right? And what that means is, like, we are constantly changing what we're doing, right?

Starting point is 01:51:49 And so, like, a lot of things, you know, you talk about Ford Deploying engineers in the early days. So that's consulting, that's this, obviously I'll just build the product that we have today, right? And so what you weren't optimizing on in those days was financial statements that Wall Street would want, right? Because it's, and then it's like, but because of what we built today, not because of what we built, we have financial statements Wall Street loves, but it wasn't built for that purpose. Sure, right? And which is crazy valuable, right? Because it means we're so differentiated and we're doing things the way that like we want to do them.

Starting point is 01:52:22 And the company was built that way. Can you tell me the story of how the COVID era changed the Palantir's financials? I remember seeing that T&E 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?

Starting point is 01:52:50 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 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 our headcount growth or 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

Starting point is 01:53:22 Yep, right? And you have to have products like AIFTE, like all these all these things that are constantly evolving and like that is a story pound here. It's like you're trying to do something you're either resource contrain or somehow constrained. It's like what do you do to meet that? And almost always is product led. Yeah, thanks a 10 cents. I know you have a busy day. So we'll let you go. Awesome. Thanks so much for help. Thanks for joining. We'll talk to you soon. We will bring in our next guests in a minute. Jord, do you have any breaking news? I got a update from Skook. Skook says Alex Karp trying his to get TBPN 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. There's a lot of fun. I'm glad that Scoots enjoyed the stream.

Starting point is 01:54:08 And thank you for YouTube for keeping us up. Keeping us live. Stream's going strong. Thank you to restream for keeping the stream live. Legends. Thank you. Couldn't do it without them. We will bring in our next guest.

Starting point is 01:54:21 guests we are ready to keep rocking and rolling here we got at got two chairs two chairs coming in come on in come on in pull what how you doing oh fantastic performance engineer very cool fantastic we don't have to sell me now I'm in how you doing good to meet I'm John hey I'm John I'm John how you doing Good to be you, guys. Lads, we got the lads here, take a seat. Take a seat. Do you guys want to share?

Starting point is 01:54:55 Here, yeah, we'll share it. We'll share my . 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 to sit up properly. Don't even try, it's not going to work. I already tried it.

Starting point is 01:55:12 Anyway, introduce yourselves. So I'm Zach Porter, a senior simulation engineer with Andreda Global on the IndyCar program. Cool. And I'm Kyle Kirkwood driver of the number 27 Honda for Andredi Global. Fantastic. Yeah, and I'm Drew from TWG. Fantastic. How do you, how do all of you fit together? We're all under the TWG umbrella. Okay.

Starting point is 01:55:31 Basically a bunch of different businesses within that. Drew can 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. Yeah. And, you know, sports, media, entertainment. Western lifestyle and of course the crown jewel of just about everything is the awesomeness of motorsports yeah in the Andretti team in Indy car how long have you been involved with Andretti that's my my fourth season at Andretti fourth season yeah also losing track of time here I think it's my fourth no it's my third

Starting point is 01:56:07 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 Indy lights and then I joined back with them in Indy car so really five seasons actually if you combined it all. Yeah. And I get to be this suit guy. Yeah. So I sit and watch this, but I've been here a year.

Starting point is 01:56:25 Oh, fantastic. Yeah. And walk me through the flow of like why you're here specifically at AIPCon. Why are you working with Palantir? Yeah. So in IndyCar, we have a ton of data. Yeah. In a ton of different siloed places.

Starting point is 01:56:37 Sure. It sits, you know, from stuff that we control, like our car setup database and stuff. But it also sits in like databases from IndyCar that we don't control. Sure. We have to consume all these things. 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

Starting point is 01:56:59 to all these disparate datasets into one place where our engineers can make better decisions faster sooner. Because in the end, you know, for practice one to practice two or practice to qualifying, whatever it is, there's this limited amount of time that we have to make a decision. The practice is coming whether you're ready or not. 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

Starting point is 01:57:40 in sailing there was this maybe it's a fake story i don't know but this idea that there was in the what's the big sailing cup that Allison races in America's Cup yeah yeah it's all it's all catamaran's now and and the in the story goes that they were all racing monoholes and someone looked in the 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 whole day I mean it would be like this again but you had the the fan car in F1 right tell me about this yeah yeah

Starting point is 01:58:20 yeah tell me the full story I don't know the full story I don't know if you do either we're in an era of motorport now that things are super tightly regulated sure really hard to find these big gains yeah what he's referencing back in the day there there was an era where where aerodynamics were kind of king and they 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 so they built a car that had big fans at the back of it shirts that ran down the side and the car literally sucked its way down so just so much extra down force I don't remember exactly how

Starting point is 01:58:49 long it existed but it wasn't very long I'm sure it got banned it's amazing but it was fundamentally dominant and 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 second these little micro moments and that's that's where being able to drill down through big data sure yeah yeah we're we'll be like we do a live show right so speed and 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

Starting point is 01:59:24 between different data sets and and systems of record i can imagine that's that's uh can be a disaster yeah and it's not just while while kyle's on track yes he's doing all of that but then as soon as he's back it's 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 race track okay talk about

Starting point is 02:00:04 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 and 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

Starting point is 02:00:50 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 way 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 like it's just going to go like a hot knife through butter. It's gonna be amazing. That's great Yeah, where

Starting point is 02:01:28 Walk me through some of the benefits and and try and give me some anecdotes about where games 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 clean and it is correct you know it's 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

Starting point is 02:02:02 touching walls and other cars and it's really touching it's really difficult sometimes to keep to make sure every system is working perfectly right It's a never-ending battle of trying to do that. And so we're working really hard with some ML models and some stuff to pick out sensor anomalies and flag them automatically so that our systems engineers don't miss them. And they can go drill down and figure out why that sensor's

Starting point is 02:02:22 failed or where and what their 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 budgetary

Starting point is 02:02:39 constraints. Is that something that's just said internally? Like, how do you work through? I'm happy that I don't have to worry about. Drew? But 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 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 got to be the last, the last phase 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 got to be careful here yeah right so if you come in I

Starting point is 02:03:18 mean obviously there's dollar budgets yeah 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 so apart from just the car and the magnificence of what I'm 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. Because frankly, every signal across the business is value. And by squeezing and

Starting point is 02:03:50 optimizing and making things run more efficiently, we end up with a better sport. And 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 the benchmark. And this is what we should manage off of. For us, we're going to ask for every tool we possibly can to make the car better. 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,

Starting point is 02:04:23 hey, it's your best job to go out and find that sponsorship, find those things. If we don't use this tool, our competitors will. Yep. 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 on 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 tried out some stuff and look at it and productionize it. Sure.

Starting point is 02:04:51 And in the end, try and get all of these, or at least the first initial use cases ready to go for St. Pete, 2026. 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'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 actually making a difference and we're actually able to progress with the data that we have. So that's where the engineers come in, right? We've got a massive group of engineers that take a lot of pride in their work,

Starting point is 02:05:28 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's very data driven. Yeah, what does the rest of your offseason look like? Are you training and running?

Starting point is 02:05:51 I saw the F1 movie and Brad Pitts. Yeah. Are you running or are you a technical guy or both? You know, training is important, right? Yeah. I mean, you have to be, as a racing driver, you've got to be like a certain. certain weight, certain size. You have to be, you got to 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 four or five Gs for an hour and 40 to two 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 pounds of downforce. So imagine driving your road car that weighs 8,000 pounds or something like that around without power steering. on the screen when you got the driver view so that you guys get a little credit yeah people assume

Starting point is 02:06:40 it's like turning the wheel of you know a tesla or whatever yeah no it's uh it's much tougher than people tend to realize i that's specific to indy car racing though indy car racing we don't have power steering f1 does a lot of sports cars that you see they do have power steering but indy car itself they do it for the sport um and they've kept it that way for many years so um it's a little bit old style but 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 it 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 now there's actually physical side to it so that's cool the off season is a lot of training uh preparation we do a lot

Starting point is 02:07:21 of sim work and driver in the loop simulators and yeah it's just being ready for for the next race that comes up it's hard it's hard though because you don't have g forces you can't you can't you You can't simulate G-forces for a driver. So having that involved is something that you get acquired to as a season progresses, if I'm being honest. What's your daily? I'm sorry? What's your daily driver? When you're not on the track?

Starting point is 02:07:45 My daily driver. So 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 have to own a car. You, so I race for. You get loaners or something? Yeah, exactly. So I race for Honda, right? In Indy car.

Starting point is 02:07:59 And I have a- That's 2000. word with underlight and glow you have glow on the S-2000 I have a Accura MDX they're their 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 yeah we'll talk to oh we'll say we need it and then I also erase sports cards for for Lexus okay cool and they also don't make in LFA anymore so yeah Yeah, just a million two-dollar car.

Starting point is 02:08:32 I can just go rip and depreciate real quick. Yeah. I mean, I have 500 at home. So that's the other car. That's great. Fantastic. Well, thank you guys for coming on. This is fantastic.

Starting point is 02:08:42 Anything else? We're sharing before you get out of here? Okay. Enjoy the rest of the conference. Thank you so much for helping on. Thank you. We'll talk to you soon. Have a good one.

Starting point is 02:08:51 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? Who do we have? We have someone else going on? Okay, okay, cool.

Starting point is 02:09:07 Yeah, yeah, yeah, we're good whenever. We kind of ran late. 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, possibly his own may shrink.

Starting point is 02:09:25 Our revenue is going up, our sales force is going down. He said on TBPN, 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 news maxing everybody what else I think we're ready for our next guest if you want to good lots of posts have fun welcome to the stream if you're ready we're good we can we're we're happy to have you how you do it's happening thank you so much for taking a time yeah welcome the show any relation to Brandon Jacoby with I don't think so I think you guys go differently we have a we have a buddy who

Starting point is 02:10:01 He's a designer and we like to poke fun of 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. 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.

Starting point is 02:10:17 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 or letting me come up here because we're technically on vacation this week. No way. crazy the grind never stops you couldn't miss AIPCOM about lock-in season yeah it's you gentlemen I couldn't pass up the chance we really appreciate it okay so it's great to have you

Starting point is 02:10:40 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 yep and golf a lot of people don't realize that we own golf Yeah, yeah. 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.

Starting point is 02:11:10 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 have a something about it. 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. way it was done years ago, right? So yeah, walk me through a little bit of the history, get me up to speak.

Starting point is 02:11:34 Oh, wow, well, I can't speak to all of it. 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

Starting point is 02:11:56 is converting from gut-based and tribal knowledge decision-making to data-driven, and therefore, after that, analytics and AI-based transformation. So, you know, 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, right? The proverbial next level. I promise myself I would avoid buzzwords in this conversation, but it may not happen. But, but yeah, it's been a conscious and concerted effort by our leadership top to bottom.

Starting point is 02:12:29 to really make that happen. And it's not easy at 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

Starting point is 02:12:54 with our team this far. Walk me through some of the concrete ways that you can use data to make a decision at racetrack. I remember there's this funny story. It might be apocryphal, but I heard that, as I always do this or I tell some story that it might be entirely hallucinates. You're not allowed me.

Starting point is 02:13:15 But so the story goes 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 beauty there. There's some, there's some hilarity there. But you can imagine that that's the type of very tractable problem. Where should I put a put a thing? Also 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 did last week or at the individual store level where it's like hey we're

Starting point is 02:14:02 out of this product yeah yeah what are the what are the problems what the most recent like case study you did yeah yeah great question look at you talking about planograms so 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 that's a really important use case um 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 I would actually argue it's even more painful when you

Starting point is 02:14:44 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 sandwich, is, 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 sets 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, we've got this pin on, we're celebrating 95 years, we've been here

Starting point is 02:15:24 a long time, and we expect to be here a lot longer. 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 a different experience.

Starting point is 02:16:03 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 there been optimism inside the company for the future? Well, we are certainly investing in the future. I was going to say people that are charging EVs, they want to, they still want to get fresh pizza, right? They do. Exactly.

Starting point is 02:16:24 Yeah, and we're actually taking a unique approach where we're developing that infrastructure 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%. It's a 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 we take a lot of pride and all exactly throw something else come get 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 so what lays ahead that's cool you know we have certain strategies and things that

Starting point is 02:17:07 we're talking about to make sure that we stay ahead it does feel like 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?

Starting point is 02:17:36 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 would 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 the store support center level but that's certainly not to say that there aren't implications at our stores because there certainly are

Starting point is 02:18:00 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 front line 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 each other and and I'm wondering in the retail context if like a POS system and an

Starting point is 02:18:38 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 and consolidating everything centrally and and really removing the need for kind of call it peer-to-peer communication of those of those platforms but because everything goes into data later exactly exactly and and you know again I I think that that team really deserves a shout-out to so while while our team is in the business the IT and the data team has really

Starting point is 02:19:10 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 the nerdy math that my team loves to do yeah awesome yeah 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

Starting point is 02:19:50 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 dashboarding, Tableau, Power BI, the diagnostic, which explains the descriptive.

Starting point is 02:20:09 And then my team really steps in on the predictive and the prescriptive front. So, you know, think about predictive maintenance or, hey, this, 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 that they have a better experience? And that drives a lot of it too. So yeah, there's a world of use cases out

Starting point is 02:20:50 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 the um so there's we like to joke a lot about um you know on my team and maybe others share the sentiment or don't but is it worse if a pump isn't working or 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 you know just don't even try there don't go there and I don't

Starting point is 02:21:38 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 can I just get but I'm getting like five times the amount of gas right I'm just like you weren't going to racetracks because we predict when that's down very off brand for race track to do anything slowly yeah speed is in the name this company race track for 93 years 95 years 95 years I can't wait for 100 you'll have to come back on yeah I would love to 100 years of racetrack data analysis break it down we'll do a hundred hour stream straight year by year I mean it must be fascinating name every data point. I mean just pulling like the revenue over a 93 year ramp like that's got to be

Starting point is 02:22:21 fascinating. That'd be interesting. Fascinating. Anyway, thank you so much for coming on and interrupting your vacation for us. Yeah, this is great. I'll talk to you soon. Have a great rest of your day. Cheers. Enjoy the conference. And that's our last guest for the day, right? That's our last guest for the day. This is fun. Started out with a bang. We should run out. We should run through a thank you to all the sponsors that make this possible. We told you about ramp.com time is money saved. Both. We are, of course, powered by Restream, one live stream, 30 plus destinations. Of course, we won't need to tell you about Figma.

Starting point is 02:22:55 Think bigger, build faster. Go to Figma.com for all your design needs. And get compliant on Vanta.com. Mone's risk, prove trust continuously. We also got graphite. Dot dev supporting us, code review for the age of AI. Polymarket, of course. Some big news out of Polly Market.

Starting point is 02:23:12 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. TurboPuffer, our newest sponsor, search every byte, serverless vector, and full-text search, built for first principles on object storage. Profound, if you want to get your brand mentioned in chat, you can too, get a profound.

Starting point is 02:23:36 Linear, of course, is a purpose-built tool for planning and building products. Big day for linear. Big day for linear. Big day for linear. Getting lots of shout-outs. Well, if Atlassian is paying 620, 610 for the browser company, they should get ready to pay $6 trillion for linear. I think so.

Starting point is 02:23:56 We are, of course, supported by Numeril, NumerHQ.com, sales tax on autopilot. Finn.A.I, the number one AI agent for customer service. Adio, customer relationship magic. Adio is the AI native CRM that builds scales and grows your company the next level. we of course sleep on eight sleeps you can go to eightsleep.com we also supported by public dot com investing for those that take it seriously uh and we always tell you about adquick.com out of public advertising made easy and measurable ad quick forever and if you noticed dr carp was wearing a fantastic petech Philippe aquanaut with an orange strap uh and if you want one for

Starting point is 02:24:40 yourself you can go to bezel get bezel.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 not for sure business insider has a scoop here that says Palantir CEO Alex Carp says top tech talent is about to get crazy valuable Alex Carp CEO of Palanttire set on quote unquote TV can why do they put us in quotes this is the dividing line this is the dividing line T.B. Close the laptop. Okay, so business insider. The website Business Insider. Wow. Says that top tech talent. I think I think we got to, I think we just got to put like the just one of the words in quotes.

Starting point is 02:25:26 It can't be quote business business. Business. Business. Business insider. Yeah, business insider. That is the way we talk. I got to look at. I actually have to look into this company because I love business. And I love, I love, I love, I love, Insider Traders. Insiders in business. Isn't that the lore? Henry Boggitt, the guy who started Business Insider. Loved Insider trading. 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 Swagg 2 tonight, the new album. What does that mean? And Meek Meek Mill posted to

Starting point is 02:26:11 two hours ago. Meek Mill becomes AI Founder. So according to Wikipedia, according to Wikipedia, Henry Bloggett was charged with civil securities fraud by the US SEC, settled the charges, with payment of $4 million. He was permanently barred from the securities industry by the SEC and the NYSE.

Starting point is 02:26:32 The charges rose during the dot-com boom at Merrill Lynch, which included issuing materially misleading research reports on internet companies and making exaggerator or unwarranted claims about them to customers. And then in 2007, four years later, he co-founded Business Insider, which is a fantastic hunt. It's so funny. He's the, he's leaning in the business of insider trading and he said, why did I combine? They didn't say insider trading.

Starting point is 02:27:04 They said civil securities fraud. It doesn't sound great, but, you know, I have to. There's so many good run. Jeff Bezos purchased the stake in Business Insider. And he had a great run, 2007 to 2023. Anyway, there's so many great quotes from the carb segment. This one, I would say, he says, I would say modestly, I'm the most humble I've ever been.

Starting point is 02:27:29 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 effed? 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 this and still no TVPN invite, we should we should probably have Mon. A lot of people were said no thanks because I guess he previously worked in military intelligence and and people didn't feel inclined to plug a hardware device into their into their phone.

Starting point is 02:28:09 but we're in the capital of military intelligence right now it looks like he's sold out the the initial batch so let's have on the timeline in turmoil anyone who puts the timeline in turmoil is welcome on the show I'm gonna follow right now and we will make it happen 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 of the United States and back to the United States. We do. Last thing, this is just because it is breaking and it's funny. Open AI plans to launch an AI powered hiring platform by mid-2026, putting the outfit in close

Starting point is 02:28:50 competition with LinkedIn. With LinkedIn. The company also wants to start certifying people for AI fluency. Are you AI fluent? This seems, this seems, yeah, this seems like more of a Mercore competitor than LinkedIn maybe I don't know like I 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 opening eyes IP so if they build something that's valuable but if they

Starting point is 02:29:29 build a network then that's a separate thing right because the that the IP doesn't matter as much like like the weight's to GVT5 or not as valuable as the as the chat GPT app so yeah maybe maybe there's something there I don't know well 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 Ultradome 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 okay let's go all right folks anyway thank you

Starting point is 02:30:01 we'll see you tomorrow today we love you back to a regular show tomorrow have a great afternoon bye Thank you.

TBPN Live - Alex Karp LIVE at Palantir AIPCon | David Glazer, Ben Harvatine, Danny Lutkus, Jonathan Webb, Nancy Cable, Ryan Asdourian, Drew Cukor, Zack Porter, Kyle Kirkwood, Matthew Jacoby

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