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Technology's daily show (formerly the Technology Brothers Podcast). Streaming live on X and YouTube from 11 - 2 PM PST Monday - Friday. Available on X, Apple, Spotify, and YouTube.
You're watching TBPN. Today is Thursday, 09/04/2025. We are live from AIPCon.
Speaker 2:It's the
Speaker 1:volunteers conference. It's the what do we call it? The office of ontology.
Speaker 2:That's right.
Speaker 1:The tent of tactical strategies. Many people have been saying this. We have a great show for you today, folks. We're interviewing doctor Karp in just We're a few interviewing a ton of folks from Palantir, ton of customers from Palantir, some founders, some folks who work at companies that use Palantir should be an interesting day. But first, there is massive news because the browser company of New York has been acquired
Speaker 2:by Atlassian. This morning
Speaker 3:Yeah.
Speaker 2:I was headed to the airport. Yep. I got a push notification from the browser company Substack.
Speaker 1:Substack,
Speaker 2:that's And I opened it. Yeah. And I saw that they were getting acquired from their own announcement. I opened X and nothing had been shared.
Speaker 1:That's actually
Speaker 2:I kept scrolling. Randomly It was like was
Speaker 1:browser company Substack.
Speaker 2:I mean, they have actually a cool thing. Their username is open dot Substack. So so the URL is just open Substack.
Speaker 4:Oh, okay.
Speaker 1:Okay. Interesting.
Speaker 2:Yeah. I opened it up and I'm like, woah, browser company's getting acquired for 600,000,000. Yeah. Posted it a few minutes later. I think they
Speaker 5:kind
Speaker 2:of woke up to it. They announced it. So sorry to front run them. But Josh Miller shares the browser company just signed a merger agreement to be We will remain independent. Our focus is Dia.
Speaker 2:I've written and rewritten this post more times than I'd like to admit. But what I keep coming back to is simple. The work continues and we're grateful for this moment. The work continues because when I stop by the coffee shop near our office, nobody is using Dia yet. Very humble.
Speaker 2: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 crafts craftsmanship and relentlessness, the support of our coaches, board members, and advisers, and the incredible effort from our deal team.
Speaker 2:Most of all, we're grateful for what this means for Dia. It means we can hire faster, ship faster, and bring Dia to more people. We can now invest in cross platform support and secure syncing, train custom AI models designed specifically for Dia. Mhmm. We could we could see the the company from down under getting into the foundation model game, I guess.
Speaker 1:The weird thing about this is that Atlassian already has, They have a ro rovo, I think it's called or something like that. Like, they they they they haven't been asleep at the wheel in terms of AI. They definitely have been adding AI features.
Speaker 2:You were reading from the last earnings call. Right?
Speaker 1:Yeah. I mean, the last earnings call Atlassian Atlassian is just a fantastic company. 5,000,000,000 in revenue, 82% margins, 1,500,000,000.0 in free cash flow, 1,400,000,000.0 in free cash flow. I'm so glad we brought the soundboard.
Speaker 2:We're back.
Speaker 1:And so and it just doesn't strike me as the like, their last the last few acquisitions that they've done, like, Loom just makes so much sense in the context of the rest of the product suite that they have. You know, they have Trello. They have Hipcamp, which never really beat Slack.
Speaker 2:Jira tickets.
Speaker 6:Or they
Speaker 2:have Jira, which named after the
Speaker 1:Named after the poster. The poster Jira tickets. And so all of that kind of makes sense. It's like a bundle. You sell into one in the enterprise.
Speaker 1: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. Yep. Let's do a whole bunch of other things.
Speaker 1:And then the Dia browser, sure, it could be a useful beneficiary for, like, if you're in an enterprise context, maybe you wanna Well track some stuff, but it it's very accurate.
Speaker 2:Atlassian makes a lot of tools that live in your browser.
Speaker 1:Yeah. But they all run really fine in the browser. So I think people are puzzled by this generally. And I think the timeline is generally like, you saw the Will DePue post. Like, there are definitely people that are against this and are saying that, like
Speaker 2:Well, the Vibe Not a good woofer. The Vibe's had turned on the browser company massively Yeah. Over the last, call it, six to twelve months.
Speaker 1:Purely because of the valuation relative to the monetization and the and, like, the the the progress of the business. Million Yeah.
Speaker 2:I think they had they had incredible incredible marketing
Speaker 1:Yep.
Speaker 2:Incredible sort of like messaging, comms.
Speaker 1:The videos are incredible. Like, I watched their announcement video and like the little details of the lens flares and
Speaker 2:They created a taste.
Speaker 1:It's very tasteful. It's fantastic. But I mean, we demoed
Speaker 2:So it's cool. I mean, what I like to see is one, it's a real acquisition. Yep. They've like cleared the prep The prep stack
Speaker 1:for sure.
Speaker 2:Massively. So the team, the whole team's getting paid.
Speaker 1:There was some uncertainty about how much they'd raised, but it was somewhere between like 50,000,000 or 75,000,000 and 125,000,000. Like, it was definitely not 300,000,000. And at $6.20 in cash, everyone's getting paid out, which is great.
Speaker 2:Yeah, I think
Speaker 1:And put another way, it's only six months of Atlassian's free cash flow, Yeah. Which is like, it feels like a lot, but at the same time, it's like, okay. Like, half a year of free cash to take a big bet on consumer in an interesting way Yeah. In a in a market that
Speaker 2:Well, I well,
Speaker 1:I don't think so.
Speaker 2:I am curious to see how they focus in the product on consumers versus enterprise. Like, at last, the industry is an enterprise software conglomerate. Yeah. Right? So you'd imagine that they would take the product in that direction.
Speaker 2:And I do think there's a lot of space to play in there. Right? It's like bringing AI into the browser where
Speaker 1:Yeah.
Speaker 2:People do all of their work.
Speaker 1:Yeah. What's the steel man for this actually benefiting the Atlassian enterprise suite? Something like
Speaker 2:So here's so there there's a post here from Rod Jane. Yeah. He said, Atlassian bought vibes not a browser. Never asked the best art collectors how they made their money or why they bought the art. Atlassian's a $6,610,000,000 purchase rhymes with that.
Speaker 2:The Atlassian problem, they invented bottoms up SaaS. Anyone could sign up for Jira. No procurement needed. They were the cool tool of 2010, but success forced them upmarket. Enterprise features, enterprise pricing, enterprise vibes.
Speaker 2:Today, when founders start companies, they choose Slack, not HipChat, Linear, not Jira, Notion, not Confluence. Hashtag 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.
Speaker 2:They're guest list acquisitions. Every founder using Arc, every startup using Loom. That's Atlassian buying access to users they lost and might never get back. It's building a gallery in Brooklyn so you could get invited to the right dinners in Manhattan.
Speaker 1:I just I I understand the Loom acquisition so much more Yeah. Because Loom is an enterprise tool. It's used by startups. It's used in a business context. Sure.
Speaker 1:It's probably used by some consumers.
Speaker 2:But just feels like the the price feels it feels extremely steep given like, Loom had product market fit. Yeah. It's just that it wasn't necessarily gonna turn into this massive platform and compounding.
Speaker 1:Growing like crazy, actually, from within Atlassian, they called that out on the earnings. Yeah. And so, like, I think that the
Speaker 2:looming But felt like a standalone it felt like a standalone product Yep. Not a platform that fit into Atlassian's Yeah. Whereas paying 610,000,000 for a company that that people use Yep. But not a lot of people.
Speaker 1:It's a million DAUs apparently, something like that.
Speaker 2:I don't know. I think I I thought that number was total.
Speaker 1:Maybe. Maybe it's total that month.
Speaker 2:I don't I thought that was like total sign ups.
Speaker 1:Yeah. But it's small. It's small.
Speaker 7:Yeah. And nobody
Speaker 1:and and nobody was using it
Speaker 2:at a 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 I'm not sure that Dia is quite at that level yet.
Speaker 1:So one bull case I can think is something like this where you bring in this team that clearly has taste, great design, and they kind of give the rest of the Atlassian product suite like a fresh coat of paint, and and they kind of revitalize the pods.
Speaker 2:The message the messaging here is that they're gonna continue operate independently and scaling the Dia team.
Speaker 1: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 v two of
Speaker 3:Yeah.
Speaker 1:Of, you know, Loom or something like that. Like, that that's a possibility. And then the other 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.
Speaker 1:But maybe if I'm like They're kind forced
Speaker 2:to get along to some degree.
Speaker 1:But the integration's probably really rough.
Speaker 5:We've heard
Speaker 1:about the data walls and the data wars. And so if you say, 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's gonna be centralized.
Speaker 2:So let's go over to Mike Sure. Cannon Brooks, the founder of Atlassian. He says, couldn't be more psyched to welcome Josh and Hirsch and the entire browser company team to Atlassian. With DiaBrowser, we're going to collectively redesign the browser to help knowledge workers kick butt in the AI era. It's a mission, a joint mission, a huge mission and one I couldn't be more excited about joining with team to get cracking on.
Speaker 2:Let's go. So, yeah, this just tells me, I mean, the the the most important line here, collectively redesign the browser to help knowledge workers in the AI era.
Speaker 1:Yeah. The last option is that it's it just buys them time to kinda take some more shots on consumer AI, which is clearly a growing category. And there's and and Atlassian can underwrite, like, crazy opportunity more than VCs can. Anyway, we have Doctor. Karp.
Speaker 1:Welcome to the stream. How are you doing? Great to meet you. I'm John.
Speaker 8:What's happening?
Speaker 1:Hey, John. We're gonna have you hold this microphone. Great. Where's the camera? The camera's right there.
Speaker 1:Can just see wherever you want. What what is the big announcement from today? Is Are you trying to tell more of a story around Enterprise with this?
Speaker 7:You know, we're kind of not. Think we're just It's more like we're crushing it. Yeah. Everyone tells us to be super modest about 93% growth in The US and Yeah. 94 rule of four 40.
Speaker 7:They 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 as real because we're like crushing everyone.
Speaker 2:You were forced to be humble for a really long time.
Speaker 7:I was forced well, people were showering me with humble nuggets all day. It didn't really exactly work. But you know, I I do think you have to judge humility by the delta between performance and ego. Mhmm. And I would say somewhat ill modestly, I'm the most humble I've ever been.
Speaker 7:And I I think it's like, so what we try to accomplish with we've been doing these kind of conferences forever, basically because everything we've done at Palantir is like completely, it's antithetical or at least orthogonal to how you would build a business. You guys look at a lot of businesses, you would never build a software downstream from value creation, it's all basically how do I make the client feel like they're getting laid when they're getting fucked. That's the whole way you build a software business, in our business we began in the beginning, used to tell people, we're a mutually servicing business, both sides should be happy. And the way we built the business was, basically underlying metric I always thought was, the logic of software should be, we charge you something downstream value creation. That sum is a percentage of the value we create.
Speaker 7:It's better for both sides because it's significantly less than the value creates. It's good for us because there's a multiple on the value. The flaw in the logic was always that FDE model would basically mean that you'd get a one multiple. So, we were structurally misaligned with everyone in finance, everyone not at the founders fund, but basically everybody Now, else because of what we've proven with ontology, FD structures, where FD are actually technical, and internal orchestration, which is largely artistic, basically was, now we got very lucky, because without large language models, this would not be hyper charged. So it still didn't exactly make sense, but lo and behold, we have large language models, it hyper charges everything.
Speaker 7:So downstream value creation is an enormous amount of money. And because of our unit economics now, which are, 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 set 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. Yeah.
Speaker 7:You then run to Wall Street and say, our clients all we have 50,000 clients that all hate us, They're like, great. That's a software business because the hating means they can't
Speaker 2:rid of it. But a platform business means that you're creating more value than you capture.
Speaker 7: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 contrary. We our revenue's going up, our sales force is going down, the number of people we plan to have in the future is less than now. We are very focused on, everybody's like high volume. The volume makes up for the fact that revenue decreases per client. We're not focused on that at all.
Speaker 7: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 create? Actually, it doesn't have to be the majority. In fact, it's usually the minority of the value you create. We also believe that, from more kind of like, kind of architectural implementation, technical perspective, the value is in high fidelity data captured in ontology with FTEs, and where there's an enhancing factor with LLMs. And that's going to be very, very hard to replicate.
Speaker 7:But again, all of this is kind of very non traditional, and so what we're really doing in these conferences is saying the same thing we say on the outside. Don't believe anything we're saying. Talk to other people who have done it. We're not, we don't chaperone 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.
Speaker 7:What does the business of the future look like? Actually, the interesting thing is workers become more valuable. Like, actually trained workers become more valuable. This is exactly the opposite of what people are saying, but it's The person at the top is actually crazy valuable. People with technical expertise are crazy valuable.
Speaker 7:And everything else is going to be done in foundry, ontology, and something like an FDA. So the orchestration of the business is completely different.
Speaker 1:Where are Fortune 500 companies getting screwed by these AI pilots? We saw this stat, 95% of AI trials in the enterprise aren't converting, like what's What going on does it look like when somebody sells someone
Speaker 7:Well, mean, there's a technical reason. These LMs are probabilistic, they're not precise. The value of LLM 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 error bound is very, very, very narrow.
Speaker 7:And the way you actually do LLMs in the real world, not in theory, not is like, is that you essentially put them in a concatenated chain where each single thing has to be done as a street unit, because otherwise the underlying math is 95 times 100 separate changes, 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 gonna learn how to learn from a Wokester.
Speaker 1:Yep. Great.
Speaker 7:Great. The 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, there's no such thing as building, there's no such thing as agency. You can get away with FBS.
Speaker 7:Well, if you want to Palantir'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.
Speaker 7:They're selling you something where you think you're getting laid and you're getting fucked. And once you're fucked like that, it's very hard to undo it. And like, yeah, you know, the the crazy thing about my life is I'm like this wacky dyslexic. It's actually much harder to be dyslexic, but it's also much harder to get fucked. Because you don't believe you you don't but you don't believe in any of this BS.
Speaker 7:It's like
Speaker 2:So speaking speaking of sales, there was a the CEO, founder CEO of a CRM company that was making some comments yesterday. Did you did you catch?
Speaker 7:Look, Palantir, 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. Why don't I get laid? It's like it's literally like it's the same thing.
Speaker 7:I'm so nice. I'm so nice. I create all the value, and I am so nice. I'm begging to get laid and no one was like, I have such a big this. I have such a big that.
Speaker 7:And we're like, yeah. We're we're not trying, dude. We're here. You know? And I
Speaker 2:don't think about you at all.
Speaker 7:And it well, I it it it's like we are very focused on value creation and we ask to be modestly compensated for that value. And you know, if you disagree, you're like, you don't like us as a client or you love us as a client but you think it's like, great. We're doing our thing. You know, in Palantir right now in The US is the market that we don't have the people, we don't have the time. We orchestrating completely perfectly at Palantir, which of course we don't do, because we're like an artist colony, right?
Speaker 7: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. Mhmm. Extending things with high value things like, yeah, we're focused on that and we don't have the time.
Speaker 7: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. It's 94% rule of 40%. Then people are like, oh, yeah, yeah, well, but we have all the skills. We have all the motion.
Speaker 7:But somehow our ocean isn't working. It's so big, but it's not it's like, yeah, great. You have problems to you have time to focus on us. We got things to focus on here that are crucial.
Speaker 4:And you
Speaker 2:guys are it feels like you're reacting to the changing world and actual like customer needs, whereas other players are reacting
Speaker 7:to 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 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 aspiration of LLMs are transparency and competence, broadly defined.
Speaker 7:Actually, the big cultural shift on enterprises, people running enterprises believe that this thing should work. I should know the cost of the components in my business to the second. I should know how to rebuild things if there's a macro economic trajectory. 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.
Speaker 7:And the central problem a lot of the larger, less agile, sclerotic companies have is, it's like, they can't it's very hard to move from I get paid because you can't get rid of me to I get paid because you could get rid of me, but you don't want to because you're creating so much value. But that's where the future's going. And like people talk about like, how are we going to do 10x in revenue, blah, blah, blah, with the same or less people. It's like, yes, but the whole market's going to have to move to value creation. And we're in the business of that and try to do it.
Speaker 1:Do you think long term that the gross margins of software companies will change materially because of LLM inference costs, like token factory costs, that type of thing?
Speaker 7:Well, you mean like enterprise software companies?
Speaker 1: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, the eyeballs basically?
Speaker 7:Well, first of all, I think Yeah. 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.
Speaker 7: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 you know, I'm an unhappy Democrat. But running around saying, oh, crime isn't an issue when everyone knows crime is an issue, it's like suicidal BS.
Speaker 7:And no one believes it. And now that wokeism is luckily, mostly, at least in that way, 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.
Speaker 7:And then, again, just an obvious thing, this is happening, we have 10x growth in America compared to Europe. Same people, same products, same everything. So it's like, and then the other thing, the point that's a little less obvious that I think people ignore is, time is not time. We always assume a minute of time is a minute of time. It's not.
Speaker 7:It's like from the time you wanna do something to the time it happens, if that's 10% of the time, you've just got a 10x. It's like PoundShare's not these kind of atrophy companies. They really take 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, that's actually what explains the numbers in a weird way is, yes, but what if five years represents forty years?
Speaker 7:What if I'm saying in the next five years? Not where actually, it's like the whole problem with the DCF model actually that experts love is, a, they don't understand product. And then b, they kind of extend the DCF if they like you. So it's like, oh, like the person. The DCF is too big Give them an extra decade of steak dinners.
Speaker 7:But the real problem that they somehow don't understand in the DCF is a year is not a year for Palantir. Like a year is like, we don't do holidays. I'm working all the time. I'm orchestrating. Honestly, I sometimes hate the enemies of Palantir.
Speaker 7:But God, do they get me to go back to orchestration because I'm like, I'm going to fuck these people. And the basic way I'm going to do it is going back to dyslexic 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 with the people that hate us. Try them out.
Speaker 1:Yeah. Do you have advice for young people? You said artists like people, not literally artists.
Speaker 2:Again, you said the company is like an artist's colony. Yeah.
Speaker 1:They just become an artist if you're young person.
Speaker 7:People underestimate their artistry because from a young age, you get huge benefits for conforming. And you can say, well, don't. I mean, the central advantage of being dyslexic, we can't conform. So that ends up being huge cause you just can't. So you're to have to, so your basic thing to emerge, do not conform.
Speaker 7:And by the way, the people who are telling you simplistic bullshit, that means meritocracy isn't going to matter, you're not going to judge, all these conspiracies, so you can't do wealth accumulation if you're in this country, like in America. I think actually a lot of these things are true in other countries. But in this country, they're teaching you how not to learn, how to be complacent, how to give up your agency, how to fail, and how to blame it on anyone else. You have to say it's like, Reject
Speaker 1:to that.
Speaker 7:Yeah, reject that. That's kind of a 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 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.
Speaker 7:It's like, you know, when I started at Palantir, I actually just, because I just wanted to be left alone. I was like, yeah, I'm gonna make some money. I'm gonna move to Berlin. I'm gonna live a debaucherous life. That was my goal.
Speaker 7:Like, I'm moving to Berlin. I I thought I'd need 250 k. I was like, a 250 k is a minimum, a million dollars a maximum. Yeah. I'm moving to Berlin.
Speaker 7:It's like debauchery forever. Berghein. Yeah. Yeah.
Speaker 1:Well, I had
Speaker 7:to like yeah.
Speaker 2:A So Set up a remote office
Speaker 7:But like, you then measure yourself, and it's like, okay, well, I'm highly differentiated on measure, on managing complicated people who have to believe their opinion is their opinion, but still have to build a product that actually delivers value. That's my differentiation. And so like, you surround yourself and then remember, you have to remember, being persuasive and being right are not correlated. So you have to really look at people who are historically right, rebuttably give them the rebuttal presumption that they are right 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 Palantir thing, it's a great lesson.
Speaker 7:Go listen to our critics. Whatever critic you love. We're a conspiracy theory, so like, could take the left wing version, which is like, Palantir is stripping you of your civil liberties, which some people on the right believe. Yep. Palantir is a Jewish conspiracy run by a mutt somehow.
Speaker 7:Okay. Whatever. You know, it's like, okay. Well, go actually, how does the product work? Does the product protect data?
Speaker 7: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?
Speaker 2:Yeah, just pick your conspiracy and that's the strategy.
Speaker 7:Yeah, and then, but then unpack it and learn for yourself. Like, did this work? How did this work? How did they do it? Assume that at every single decision, if it was a decision anyone else would have made, you would not have worked because that's a commodity.
Speaker 7:Commodities aren't valuable. And then apply that to your life. What part of this do you understand? Like, what part do you not understand? What part do you understand better than them?
Speaker 7: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. If you're watching this podcast Yeah. And you enjoy this, you've already passed the test. Mhmm.
Speaker 7:I don't care whether you're a welder, a plumber, a carpenter, an astrophysicist, or a somebody who'd like to build a business or just wanna get rich or you wanna get enough money and move somewhere and do what I would do.
Speaker 2:Move to Berlin.
Speaker 7:It's not the right place anymore. But any case but but you've already passed that test. Now go out and pass the test for life.
Speaker 1:Yeah. You said Germany is not the right place anymore. Like, what is your current mental model for the state of the world order? Like, is is is America in decline? Do we need to bring things back?
Speaker 1:Like who are the power players? How how America
Speaker 7:is power pair number one right And like all this media BS, it's like, you you know, you gotta compare America to and you can't compare America to some thing you're pretending in your head could be America, compare it to Europe. Compare, I don't know, you want to compare it to China? You want have no rights? You know, I mean, again, I'm actually not anti Chinese culture, CCP, you know, it's like, compare it to Europe, like no tech industry. Everyone rich was born rich basically or with almost no exceptions.
Speaker 1:Yeah.
Speaker 7:The most important Germanic company, I hope someone from Germany is listening to this, Komped aus Palo Alto. It's Peter Thiel undiche. It's like the only German company since SAP that's real.
Speaker 1:SAP has
Speaker 7:been to say. They won't listen to us.
Speaker 1:Yeah.
Speaker 7:Like, just think about that. You have Peter Thiel, like the most important venture person maybe that's ever lived. Co founder of Palantir, and you have me. It was like somewhat basic, dramatic, my PhD in German. And you have no tech industry.
Speaker 7: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.
Speaker 7:You don't have to agree with what we're saying. Who are you talking to?
Speaker 2:Who
Speaker 7:are you talking to? You're talking to your, like, I don't know, expert that came here and studied us?
Speaker 2:Trust the experts.
Speaker 7:Trust the experts. It's like so it's yeah. It's like energy. Like we're
Speaker 1:like Do you think they will? Do you think that there's optimism around the idea
Speaker 2:of can't pick
Speaker 1:up a
Speaker 2:phone call, right?
Speaker 7:No, no, no. I mean, I pick up it's crazy who calls me. It's like it's honestly like I can't talk out of school who calls me. You'd be surprised the number of people come in. I begin every call with don't listen to me, very few people have.
Speaker 7:I'm gonna give you the freak show answer, you probably wanna ignore it, this is what I think and they're like, okay, yeah, okay. Some call back, some don't. But yeah, of course, mean, have a lot of, mean, like honestly, we have a huge retail, the crazy thing about Germany is they have a huge retail investor base, they don't admit it in public, but in private they're like, keep
Speaker 3:going, keep
Speaker 7:going. But yeah,
Speaker 1:no, I'm
Speaker 7:just saying, the point I'm saying is, you know, it's like, oh, so then it's like energy, technical talent, understanding how to manage the technical talent, that's an art, like we have the right venture people, the right entrepreneurs, the right spirit, we have generations of people who are entrepreneurial here.
Speaker 1:It's like The poppy syndrome?
Speaker 7:Yeah, well, it is funny you mentioned that. That's like, yeah, we're very, well, this is thing, we have to fight for this. Because that no tall poppy, what that basically means, people may not realize this, but in every other culture I know of, I lived abroad in Germany, Europe, incredible cultures. But if your head sticks above the line, it gets cut off. There's one culture where that doesn't happen.
Speaker 7:It's here. The only thing is we have to fight for that because the thing that unifies the woke left and the woke right is they don't like the consequences of meritocracy. They want to work back to the inputs. So, and that just screw society. It's like you've got to be able to allow people to succeed wherever they go.
Speaker 7:Now, was kind of still progressive, you know, believes it. I super would like the inputs to be fair. Yeah. But the outputs, those are the outputs.
Speaker 1:Yep. They're results
Speaker 2:of freedom.
Speaker 1:Last question. We've to get you out of here. I walked by your office. There were some kettlebells. What are the kettlebells for?
Speaker 7:Oh, okay. Well, this is slightly longer. I'll give you a short version. Please. So to be a cross country skier, you've got to train year round.
Speaker 7:Yeah. So you need substantial VO2 max. And actually, you need to be strong per unit of weight. So, as an example, I do three days a week of kind of above and below lactate threshold running, but mostly pretty far and then once a week kind of at, and then I do two days of strength, one day of like endurance strength. And currently, the thing I'm actually really proud of is I just started doing hanging from a bar, so dead hang like four months ago and I hit four minutes and thirty six seconds.
Speaker 1:Four
Speaker 2:What's minutes and thirty six the goal for
Speaker 1:the end of the year? What do we do?
Speaker 7:Well, actually, my goal for the yeah. You gotta you gotta
Speaker 1:I gotta hit the soundboard.
Speaker 7:This isn't just money. No. Is no. I mean, my goal for the year was for actually the next twelve months was was four minutes.
Speaker 2:Okay.
Speaker 7:But then
Speaker 1:there's the the number
Speaker 2:two We gotta get those numbers up.
Speaker 7:Yeah. Yeah. Well, no. But the number two, the second best mountain climber in Norway. Yeah.
Speaker 7:Don't if you know his name. Yeah. But he I have a picture. He did four minutes and twenty two seconds. Oh,
Speaker 3:there you go. What can I do?
Speaker 2:We did it part time. For having us.
Speaker 1:Hi, appreciate your work. We'll talk to you soon. Have a great rest of your day, congrats.
Speaker 7:Yeah, you too. Congrats to
Speaker 1:you guys. Thank you, thank you. We will bring in our next guest in just a few minutes. We have
Speaker 2:Can you imagine can you imagine the the fortune 500 CEOs that just want a meeting with with doctor Karp just to get energized?
Speaker 1:Yeah. Oh, yeah. Yeah.
Speaker 2:Like, don't they're like, I'll pay for the steak dinner even though you're selling to me. I'll pay for the steak. Yeah. Yeah. You bring the energy.
Speaker 1:Yeah. Who pays for the steak dinner? Fantastic. Well, I believe we have our next guest pretty much ready, Ben Harvatine from Palantir, Ford deployed engineer that has been at Palantir for
Speaker 8:nearly
Speaker 2:So many ten
Speaker 1:What was
Speaker 2:So many good quotes in there. I don't take holidays off.
Speaker 1:I don't take holidays off. Yeah. The team is getting ready to post. Anyway
Speaker 2:I'm excited for this one. Ben? Ben, welcome to the show.
Speaker 1:Good to have you.
Speaker 2:Good to have you.
Speaker 1:We are gonna have you hold this microphone as much as you can, but why don't you kick us off with an introduction on yourself and kind of, I'd love to know how you found your way to Palantir, that'd be super interesting.
Speaker 9:Yeah, it's kind of an odd path. I studied mechanical engineering and architecture in college. Not what you would
Speaker 1:Not think for software, software yeah.
Speaker 9:Worked for Anheuser Busch.
Speaker 1:Oh, way.
Speaker 9:Beer company for years, that was a great transition from college
Speaker 2:Beer technology. What were you doing at Anheuser Busch? Yeah.
Speaker 9:It was a management training program. Okay. Creation based. Yeah. Yeah.
Speaker 9:After that,
Speaker 2:ran Okay. A hardware startup for a
Speaker 9:Went to another hardware startup. Yeah. But I had some buddies from college who had worked here and thing about Palantir seemed like everybody had just kind of like more autonomy and authority than I saw anywhere else.
Speaker 1:Yeah, yeah, amazing. So what do you wanna show us today? Can you give us a little tour
Speaker 9:of what's Yeah, going I've got a little
Speaker 10:kind of
Speaker 2:Brought a robot. Yeah,
Speaker 9:one robot. Slide in.
Speaker 2:Bringing a robot is a great sign of respect in our culture, Brad. Yeah, So, thank you.
Speaker 9:Well, you know, you can imagine, you know, when we have, you know, events like this,
Speaker 6:there are
Speaker 9:a lot of demos, it's pretty screen heavy
Speaker 1:with
Speaker 9:software stuff.
Speaker 1:Yep.
Speaker 9:And we've seen a lot of, I'd say like increasing demand for our edge offerings, hardware offerings, 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.
Speaker 1:So walk me from my understanding to how we get to the edge, how we get to robotics, because my famous like, the case study that comes to my mind for Palantir in terms of, like, making things in the physical world is, like, I think the Airbus example. So I and and and whenever somebody says, oh, what what does Palantir do? I'm like, okay. Imagine a plane. There's a bunch of different parts.
Speaker 1:You gotta have a certain amount of seat belts. You gotta have a certain amount of engines. You gotta have a certain amount of fuel lines. You gotta 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.
Speaker 1:Did they get checked off? And so you put all of that instead of just in a loose database, you put it in a database, but then you have Palantir that's actually tying everything together. So, you know, if there's a lead time on engines, you need to order more seat belts in three weeks instead of two weeks. And that's kind of how I explain Palantir in terms of like make a big thing that's complex. Is that roughly right?
Speaker 1:And then how do you walk from that to like, we need Palantir to somehow interface with like a robotic arm?
Speaker 9:Yep. Yeah. I mean, that's roughly right. Like the way I think about it, it's like anywhere you go, people have data scattered all over the place. So the first step is can we get that all into one place?
Speaker 2:Got it.
Speaker 9:Then can we model that data so it's as easy to work with it as it is to talk about the concepts that represents. Right, just like Yeah,
Speaker 1:So there's this big meme in Silicon Valley and defense tech right now that like there's a whole host of manufacturing guys. They're all aging out. They're 65. And everything that they know about how to make a widget, whether it's a chair or a rocket motor, it's in their
Speaker 4:head It's kind
Speaker 2:a tire wizard.
Speaker 1: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, we're capturing it.
Speaker 9:Correct. Yeah. And really like the whole point of any of these data exercises is you just wanna 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.
Speaker 9:And so if you're looking across the supply chain, that'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 do it? Okay.
Speaker 9:So, again, sort of a simple like toy example here. The basic idea is that, you know, this is a little work cell that we made with a robot arm and a camera It's
Speaker 2:three d printed, right?
Speaker 9:Yeah, it's all three d Even
Speaker 1:the arms are three, oh wow. Okay, yeah, I didn't realize that. Yeah. Cool.
Speaker 9:And so, you know, it's kinda set up to be a dumb terminal that kinda works and looks like Sure. You know, the robot arm jutsi on a factory floor. Yep. 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. Yep.
Speaker 9:But then you can push, you know, that data to an edge hub that can run embedded models, can run embedded ontology. So you can actually take that that kind of model of the world in terms of objects, relationships, actions, and models. Yep. And you can push that down to the edge. And even if you have, say like a network sparse environment where you don't have that real time uplink to the cloud, you can continue to run off of that ontology.
Speaker 1:Were looking They at semi put the five levels of robotics. I forget exactly how many levels there were, but they were trying to map the self driving car analogy to physical robotics. Yep. And I believe, like, level zero or level one, like, the most basic was you have a preprogrammed robotic arm that's doing the exact same move. It's taking the windshield and putting it on the f one fifty.
Speaker 1:And it's this huge arm, you can't go near it because it's there's no cameras on it whatsoever. And if you step in that work cell, it will kill you if you don't if you're not careful. And this seems like step towards, like, level two where we're able to actually understand what different products mean. If there's oh, this type of product shows up, there's gonna be more likely that there's a defect or you need to adjust what the robot is doing. Yeah.
Speaker 1:How can you actually get that data into something that's actionable?
Speaker 9:Yeah. Yeah. Even in, like, this simple demo, we've got you know, it'll trigger alerts on you it know, tries to execute move and you end up with like a block, like jammed up
Speaker 3:up Okay.
Speaker 1:It'll realize that.
Speaker 9:They'll say, okay, you got a jammed hop,
Speaker 1:need be clear, that
Speaker 9:sort of stuff.
Speaker 1:Okay. Interesting. Where does this play in like the stack of other software? I know when we talked to what was it? Dirac, our buddy Phil, he was saying that, like, he's working with automotive companies, but then they also have a lot of there's a lot of, like, lower level control software software on machine lines.
Speaker 1:Some of that's from German companies that I think we just talked about with Doctor. Karp. But like where do you see Palantir playing in the stack? You have a bunch of data, the database, you put Palantir on top, but then at a certain point, there might be some robotics company that makes the robot. And then they also might have some control software with kind of a messy API or something like that.
Speaker 9:Yeah. I think we can be pretty agnostic about how far up or down the stack we So we've got I'll pull this box.
Speaker 1:Yeah. Please. This is the node that goes on the edge, right? This So is a
Speaker 9:this is an example of an edge node that one of our partners, Edge Scale, makes.
Speaker 1:So
Speaker 9:this is that box that you can stick in the closet and factory network to those existing machines that you have on floor if you just need a turnkey solution. Yep. And then I think at the other end of the extreme, that's where we've got something like this where Yep. This really, at the end of the day, is an ontology defined piece of hardware. And that the machine itself, its entire configuration, the state machine is running, everything about it Yep.
Speaker 9:Is defined in the ontology, lives in the ontology. And it's like, really just like a bespoke piece of hardware Mhmm. Running that ontology native software.
Speaker 2:It's a monument.
Speaker 9:So, you you know, if you've got like more nascent operations Sure. More greenfield operations, you think about some of the companies we work with in defense tech. Yeah. It's like, they can go all the way down the stack if they Sure. Want For some of the, you know, the larger more established customers that we're working with, the plug and play solution.
Speaker 1:Yeah, what's the sweet spot for the specs on an edge scale, like edge node, like something on the edge? Like, do you need to be running like a large language model that feels like something that you could do on a You could.
Speaker 9:I'd say it depends on the application. Like, we've some like examples of that even like previous AIP concert. It's like, do we need the local app served up with a chat bot for the line operator who can just be like, what's going on?
Speaker 1:It just talks to you. Yep. And it's not just purely deterministic. Okay, if the block is blocked, then send the error message instead it's actually interpreting a bunch of data in a kind of a non deterministic Yeah.
Speaker 9:I'd say it's like, you know, I think like anything, it really depends on the application and the users. Because again, there are a lot of guys that are working on these lines, guys and girls, where they don't need another screen in their life. And so it's really finding like what's the right way to interface with those operators to ultimately just drive the better decision making. How
Speaker 1: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
Speaker 2:Yeah, are you graduated from being an FDE yet or is it once an FDE, always an FDE?
Speaker 9:I think it's once an FDE, always an FD. I try to keep my hands on keyboard as often as I can still. Yeah. You know, still flying out to whoever axle factories in rural Kentucky or whatever. That's awesome.
Speaker 9:Yeah, think the closer you can stay to that stuff the better. I think really like the role of the FD is like, just like it always has been go on-site with the customer. Yep. Don't just understand, but internalize their problems, their challenges, you know, and solve.
Speaker 1:Go create some value. Yeah. Well, you so much for hopping on the stream. Yeah. We appreciate having Congratulations on everything.
Speaker 1:Thanks for
Speaker 2:bringing your baby.
Speaker 1:Yeah. Yeah. You 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.
Speaker 1:Do you guys know if the demo is is gonna need the HDMI cable? Is that right? Okay. So we will bring in Danny whenever you get a chance. Yeah.
Speaker 1:Let's let's bring in our next guest.
Speaker 2:Here he is. What's going on? Welcome to the show. Hey. How are you?
Speaker 2:Great to have you. Yeah.
Speaker 1:Do a live demo. Always. That is bold. A demo is on a livestream. This is live.
Speaker 1:So literally anything you share on your screen potentially will go out to the internet forever to be baked into the future super intelligence. Yeah. The training Baked models of the future into the pre training data. So be very careful. Don't leak anything.
Speaker 1:But but but introduce yourself. Tell us what you're gonna show us.
Speaker 2:Yeah. Microphone?
Speaker 6:Oh, yeah. Sorry. I'm dumb.
Speaker 1:My bad.
Speaker 6:What's going on, guys? My name is Danny. Yep. Let's see here. I'm an engineer at Palantir.
Speaker 6:I've been a Palantir for about twelve years. In terms of like my role
Speaker 2:Overnight.
Speaker 6: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 at Palantir, I was on the government side, I did work with Department of Justice, US Special Operations, CIA, National Counterterrorism Center.
Speaker 5:Sure.
Speaker 6:After my wife and I had our first kid, she was like, hey, could you not go to weird places in the world anymore? And I was like, totally reasonable. Yep. Reasonable request. We moved back to the Midwest and I switched over to the commercial side and that's kind of like what I do now is like grow our business in Midwest.
Speaker 1: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.
Speaker 6:Yeah. What I focus on a lot is manufacturing Sure. In the Midwest. So you can like, there's huge manufacturers in the Midwest, whether that's, like, Johnson Controls or Eaton or Molson Coors
Speaker 1:Yeah.
Speaker 6:Cummins engine.
Speaker 1:So it's a widgets factory.
Speaker 2:Yeah.
Speaker 1:They're making widgets. They're buying parts. They're assembling them, and you have to understand the flow rate. Where's the where's the rate limiting factor? How can we increase flow?
Speaker 5:This is
Speaker 6:where I think we have the most differentiation from product perspective, because it's like, like, I can actually affect the physical world. Sure. 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.
Speaker 6:Like, how do I solve that problem in the most effective and optimized way versus like, I'm trying to manufacture something and like, how do I make sure my machines are running? I have the right labor. I'm trying to do the right thing. So like, the 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 What
Speaker 1:do you want us to show us today? Oh, yeah. I I can kinda hold this for you if you want. So We're getting good sound on this? Okay.
Speaker 1:Cool. Yeah. Walk us through it.
Speaker 6:What I was gonna demo is I think, like, of the interesting things, and I'm sure you've, like, talked to a lot of different Palantirans today, is, like, we are never gonna purport to be, like, a strategy consulting type of thing when we engage with customers. Like, we're never gonna purport to be, 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, like, that we work with will identify, like, hey.
Speaker 6:I know this is a problem. Right? But there's a huge amount of time between like, hey, there's a problem, and then let's go implement a solution. And the dependencies on actually getting to that faster are like, I have the internal SMEs that can actually 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.
Speaker 6:I pay millions and millions of dollars to get a deck that tells me like, hey, this is the solution that we think you should employ with the right, like, ROI in this approach, and we've done this feasibility study, and we think that you should go do that. And so, like, we find that as a huge impediment to like our own growth. Right? Like, why should I wait months?
Speaker 2:Yeah. You don't want them to go spend millions of dollars
Speaker 9:No.
Speaker 2:To some random group to then recommend a pound to your products.
Speaker 6:That's a 100% right. And so like, what we've been exploring more is just like, well, why can't I use AI to do Like, why can't I, like, give a fairly haphazard business like, a 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 of of things that I could and should deploy to go solve this problem? Can I generate ideas with all the requisites of how I actually employ those ideas and actually generate a proposal where then I also have, like, agents as critiques on that proposal to be like, is this technologically feasible? Is this, like, financially feasible? All the things that you would expect, like, strategy consultants to do for you, like, I should just be able to do that in a day and come up with a proposal.
Speaker 6:But then, like, I don't know if you guys have talked to anyone about AIFTE, 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 AIFDE, build it. Right?
Speaker 6:And go from what would have taken six or nine months until we ever get engaged to, well, I think this is a problem, let's just go do it in the next week.
Speaker 1:Right? Okay.
Speaker 6:Does that make sense?
Speaker 1:Yeah.
Speaker 2:Yeah. Makes sense. I have some follow-up questions, but maybe jump into the demo first. Cool. I think my immediate, I guess, question, just maybe it's relevant, is, like, how do how do you ensure kind of quality?
Speaker 2:Right? Because, like, you didn't say this, but, like, someone else in another context might call this, like, vibe coding. Right? Sort of, like, generating, like, a deep research report on, like, a problem and a potential solution and then sort of prompting your way to an implementation.
Speaker 6:Totally.
Speaker 2:And today, just like code quality and product quality ends up popping up, I'm sure that
Speaker 1:you're already
Speaker 6:thinking about that. My take on this is, like, when you start doing anything with AI or 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 Jesus take the wheel moment where, like, where, like, you actually have built trust.
Speaker 6:Because part of this is not actually, I think, a technology problem. It's like a people and process problem where people actually build trust in it. And also you get all the tribal knowledge that's not in any system actually incorporated in some knowledge context that you can start to build off of over time. But I think that's 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.
Speaker 1:Yeah. So yeah, with this demo, what is the is this designed as like an internal tool or something that you would actually sort of out?
Speaker 6:A lot of our customers are starting to use this
Speaker 2:to start to
Speaker 6:shorten the the cycle time of going from like initial problem identification to implementation. So like, exactly.
Speaker 1:And is that for customers that are already using Palantir?
Speaker 6:Yeah. So like, we've started using this primarily with a lot of existing customers, But then the cool thing about it is, don't know if you guys have heard, where all of the things I'm gonna 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.
Speaker 1:Cool. Yeah. So walk us through it. Cool it
Speaker 2:up and maybe bring it a little bit closer. So
Speaker 7:Oh, yeah.
Speaker 2:See you as well. See it.
Speaker 6:Can you share your whole screen? Oh, yeah. Yeah. Go ahead.
Speaker 1:Are you ready?
Speaker 3:Yeah.
Speaker 1:Okay. Let's do it.
Speaker 6:One ever saying text messages or All anything like 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 start there. Let's just say like
Speaker 2:It's redesign.
Speaker 6:Hey, I'm a
Speaker 7:How'd you just say
Speaker 6:Oh yeah, for sure, go ahead. So like the problem that I'll type in basically is like, hey, I'm an aviation expert. Like, we're seeing significant delays around like Newark Airport because there's not enough runways and the runways are too short. Like, what should I do to optimize my flow?
Speaker 1:Okay.
Speaker 6:Basically to solve this problem.
Speaker 1:Sure.
Speaker 6:So like, now you guys get to see me type, which is always fun.
Speaker 1:Yeah, this is interesting. A ton of questions
Speaker 2:about I've always the wanted to redesign the LAX streets, like the flow of traffic.
Speaker 1:Yeah. That is a wild choice by LAX. Just constant, constant traffic. It wasn't too bad this morning, fortunately, But we did have a funny incident with a member of our team who, first day
Speaker 2:John arrived, through security
Speaker 1:Oh, yeah.
Speaker 2:And almost managed to miss his flight because he was getting a breakfast.
Speaker 1:By a former guest and friend
Speaker 2:of the show. Call and I text and and said Well,
Speaker 1:you know, this is no time to to take shots at the dyslexic. He had missed he had he had made a mistake and and confused Gate 9 for for Gate 6.
Speaker 6:Right.
Speaker 1:And and there is no Gate 6. There's a particular terminal.
Speaker 2:So we've
Speaker 3:headed to
Speaker 1:a different terminal.
Speaker 2:I've mind
Speaker 6:Thank you for covering so everyone.
Speaker 1:Yeah. Yeah. So It
Speaker 6:doesn't have to be. So right now, just I typed in I, like pretty rough problem statement. I'm an aviation expert. I want to solve problems around EWR Airport. Yep.
Speaker 6:There are too few runways and the runways are too short. How do I optimize traffic flow around it to minimize disruptions?
Speaker 1:Okay.
Speaker 6:So, that's kind of like the first point. And what's happening here is like the first set of agents is basically taking that as a problem description and actually like putting more structure around it so it's not like my, you know, my like misspelled problem statement.
Speaker 1:Yeah. Course. Like cleaning it up.
Speaker 2:It's like a prompt engineer effectively. That's right.
Speaker 6:That's right. And so Yeah. You on the left side of the screen, you can actually see some of the logic of, like, what happened, the train of thought here of, like, hey, here's the problem statement. I can see the system prompt, like, what the task prompt is, what the LLM 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.
Speaker 6:It gives out restraint constraints.
Speaker 2:Nathan Fielder would be happy to hear that you're. Yeah,
Speaker 6:It gives out constraints like limited number of existing runways, restrict simultaneous operations, etcetera, etcetera. So like, this looks pretty good to me, like is the initial problem description, way better than like the garbly gook, like two sentence thing that I did. So now I want to like start to get into the phase of like actually starting to do research on this to say like, what are potential tools, what are potential approaches to actually solve this problem? Yep. And so what's happening right now is like, now we're going into kicking off into more of like an agent
Speaker 1:Yeah, just branching a bunch of agents to go do deep research.
Speaker 6:And so, yeah, exactly. Like now on the screen, I can see that same like core objective function over on the left.
Speaker 1:What it's working towards.
Speaker 6:Yep. And then I can start to see as it's running on the left, like research topics as it's doing research pop up and modeling. This is all built in like native foundry tooling. Sure.
Speaker 2:How how inference heavy is this? Because it feels like it's going to town Yeah. Right
Speaker 6:I'll show you kind of like the under of how we're actually doing the research.
Speaker 1:Yeah. It is a unique, I don't know, problem set because it's like going to town is something we worry about when we're talking about, oh yeah, you have a billion consumers and $10 really adds up. Yeah. But if it's like
Speaker 2:A problem as important as this.
Speaker 1:You're talking about optimizing an airport, I think I can deal with $100 inference bill. Yeah. I'm going be
Speaker 2:Okay with that.
Speaker 11:For sure.
Speaker 6: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 Sure. I think at this point in time. Like, one definition is, like and this was, like, kind of our first approach was, like, hey, let's let's build a set of logic that an LLM actually orchestrates different parts of that logic between and it can use tools like the new deterministic tools, or it can write back, or it can access and query things to ultimately do some type of automation. Yeah. I think the other definition of like what an agent right now is like more of a chat interface.
Speaker 6:Yeah. And then in that regard, right, like, I wanna be able to give that chat interface access to tools. Yep. Right? And so in this case, what I've given the agent access to is a bunch of different tools.
Speaker 6:First, I can see the model that I'm using behind the screen here. And from our perspective, 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 wanna use these things interchangeably and actually have an evaluation framework that based on the task that you're asking it to do will, like, select the right model Yeah. For that particular task. But in this case, right, I'm using Grok four.
Speaker 6:And then like for the tools in particular, like, I've given it access to like conduct research. So I've given it some ways in which it can actually reach out and use different either internal or proprietary information of the organization that we're working with or reach out and use something like Perplexity Yeah. To do, like, more AI based search. I've given it the ability to, like, generate, like, create code blocks. If it's, like, coming up with an ROI and it needs to do napkin math Yeah.
Speaker 6:Like, I wanna say, like do that terms you to allow you to actually, like, generate the code, but also then run the code Yep. To see, like, what what what the result is.
Speaker 1:And then, I mean, it seems like all of this is all of this is kind of like frontier level but available broadly, but the Palantir level is that you actually have data that isn't just available on the web. So if I'm actually an airport and I actually have specific data
Speaker 2:Yeah, about you have schematics that stands
Speaker 1:out to
Speaker 2:me is like if you're a large enterprise, you wanna work 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, form of intelligence do I want to use for this problem Very
Speaker 1:cool.
Speaker 6: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 kinda tool, and you can already see the research topics starting to, like, pop up here. So, like, this is an example of an application, right, that, like, an a user would use. They would they know nothing about Foundry. Right?
Speaker 6:They're they're logging in to an application. Their job is like, go do this thing. Right? But then behind the scenes, you have a lot of different options for how you're setting up this logic.
Speaker 1:Sure.
Speaker 6:I don't know how much you guys have seen Foundry, but this is an example of what we call AIP logic. Mhmm. I could write all of this orchestration and code if I wanted to. I'm fairly lazy, so I use the lower code tool
Speaker 1:Sure.
Speaker 6:Which is AIP logic. And so here, I can
Speaker 2:just like set up a
Speaker 6:bunch of different orchestrations for how I want a function to run. In this case, I'm I'm putting in inputs for what I want the query to be, which is around like that problem statement we talked about. Mhmm. And I'm setting up functions for how it can reach out to different types of sources. Mhmm.
Speaker 6:So the first one is if I had an internal proprietary information on schematics of a runway or planes or what types of runways planes can land on, things like that, like, that's all information that then I can make available to the LLM to go to a combination of, like, semantic and keyword search against it to find the right information to go do research against. But then as a backfall, then I'm just also giving it access to go inquiry perplexity. Right? And go say like, hey, go find what else is out on the internet to actually go do this research about this particular problem, right? And then bring that back.
Speaker 6:And then the last part of this is an action then to go capture all that information and store it back into the ontology layer in Foundry. Awesome. So this is kind of like what it's doing live. It's like
Speaker 1:It's still working.
Speaker 6:It's working. And it's writing as it's doing research, right? It's like, what is the current runway configuration, operational capacities, and key limitations at EWR, including details on runway lengths, numbers, and how they impact aircraft operations. Sure. And so then it actually gives me like, this is pretty good information.
Speaker 6:It'll 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 it's actually saying, like, hey. There's this performance based navigation as a cornerstone.
Speaker 6:Right?
Speaker 1:Yeah. I remember hearing that if you if you have the plane board from the back to the front, it'll load way faster, faster, but no one wants to do that because the
Speaker 2:It's a business model thing.
Speaker 1:Yeah, because people pay to be at the front of the plane and they want to get on the plane first. But 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 all kind of just flow in. No one's quite figured that out. But yeah, I mean, could imagine that it could come up with a bunch of different proposals for similar just kind of rethinking of the flow
Speaker 6:of That's right.
Speaker 2:Think we're getting short
Speaker 1:on time here.
Speaker 2:Already cut off.
Speaker 6:Me zoom forward. I'll show you kind of an end product here, which is like, let's go I already ran this today. I was hanging out with the American Airlines guys because we're making fun of EWR.
Speaker 2:As one does. Which is
Speaker 6:not their hub.
Speaker 9:Yeah, yeah. But
Speaker 6:yeah, this is an idea that it generates. Okay. And then I get a summary of what that idea is. Sure. And then it automatically develops critique agents that are like looking and evaluating on different type of
Speaker 2:like Interesting.
Speaker 6:Different criteria, right? Which is like, hey, can I, what's the risk assessment and mitigation evaluation? What's the economic feasibility of actually doing this? Like, what is the safety and regulatory compliance evaluation? And then it's going to run like those evaluations using that agent as like a task criteria to actually then say like, I can see the guidance that we gave the agent, right?
Speaker 6:And it's task. And then it has to go evaluate to see if it makes sense from that perspective.
Speaker 1:Yep.
Speaker 6:Right? And it even generates its own models and its own code to say like, hey, is this feasible from like, can I do basically like napkin math?
Speaker 7:Yep.
Speaker 6:And say like, can I come up with like how I could calculate this and actually go and like run and
Speaker 2:see How how close is this output do you think to what a larger Structured Yeah? I
Speaker 6:it's like pretty aligned, right? Because in normal times, these strategy consulting firms aren't getting access to all the data. And so they're being like, okay, come up with the idea, do the research, generate the idea for me.
Speaker 2:Guess a little bit.
Speaker 6:Then I need to do some napkin math on how I would think about actually critiquing this idea. And then ultimately, I need to come up with a proposal. And here's the end proposal for what I think you should go do. Same framework where I have agents then writing portions of that proposal. And then from there, right, it's just like copy paste that proposal in the AIFTE and like start building.
Speaker 2:Right? Last That's very cool. Last quick question. Are you feeling the re industrialization yet? Are you seeing new entrants into the Midwest building things?
Speaker 2:Or is it more legacy players just trying to trying to increase think it's legacy.
Speaker 6:Lot of what I work with are companies like Eaton, which are like 100 year old companies or like Johnson Controls, 100 year old companies that are saying like, how do I actually use this as an advantage to do better? Right? And that's like where I think is interesting is that like maybe five years ago, this was really hard. Like people were like, yeah, 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.
Speaker 6:Like it's just You
Speaker 2:can give them a time to You can sit down and give them a demo.
Speaker 3:That's right.
Speaker 1:Well, you so much for coming on.
Speaker 8:Thanks Thanks so much for for
Speaker 1:having me guys.
Speaker 6:Brave to
Speaker 2:do a live demo.
Speaker 1:We're next
Speaker 2:Thanks guys.
Speaker 1:Yeah, Thank you so much.
Speaker 6:Hey. Great work.
Speaker 8:I a
Speaker 6:great listener.
Speaker 1:So Thank you.
Speaker 2:Yeah. Love it.
Speaker 1:Have a great rest of the conference. In. See you all.
Speaker 2:You're the man.
Speaker 1:And we will bring in our next guest,
Speaker 2:Jonathan Webb from the nuclear. Man himself. Welcome. Thank you.
Speaker 1:Sorry to keep you waiting. Good to meet you, Jonathan.
Speaker 2:Today is a great name to have a company that starts with the.
Speaker 6:Yes. Don't know if
Speaker 2:you saw the browser company.
Speaker 1:I exactly press sold for $200,000,000. The browser company sold for $620,000,000. Everyone is all in on companies that start with the today.
Speaker 2:There we
Speaker 1:go. But give us the intro on the nuclear company. What's the plan? And where are you in that plan?
Speaker 5:What's the plan? So to my understanding, we're the only company in the Western world focused on the deployment of new nuclear. What does that mean? I assume some of your communities probably follow the nuclear industry a little bit. I mean, there's no AI without power.
Speaker 5:I just talked in that talk earlier about China is about to pass The US 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 US. They're some of the best performing reactors on planet Earth. There's a lot of startups, dozens, designing new reactors that are all going to be great reactors.
Speaker 5: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 talking about the Navy?
Speaker 1:Or just I'm talking about The
Speaker 2:US. Probably.
Speaker 5:We have about 100 operating plants. Mean, today, 20% of the power in The US comes from nuclear. That's nuclear that was built in the sixties and seventies. We've built two reactors in thirty years. So what are we?
Speaker 5:We're the deployment arm. And why do what does that mean? So think of if you're American Airlines or Delta, you don't call GE or Rolls Royce.
Speaker 7:You don't
Speaker 5:just call to buy a jet engine. You call Boeing or Airbus. What if I handed you a jet engine or a Ferrari engine or a Bugatti engine? No matter how great that engine is, you're going be like, what are we doing? So we want to be the full solution to deliver that power plant to either a hyperscaler, to a utility, to a foreign government, or potentially to operate those on our own.
Speaker 5: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 and d to, you know, manufacturing to design to implementation, there's a big difference between white lab coats designing projects in an r and d lab to living in a construction site where, you know, I've done, much of our team's done. I mean, I've built 8,000,000 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.
Speaker 5:We wanna 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.
Speaker 2:Your partners on the reactor side, how much of what they're doing is just remembering how we used to build reactors as a country versus doing that new innovation?
Speaker 5:So there's really only two incumbents in The US and that's Westinghouse and GE. Yep. And, you know, obviously, we're talking to them. And then there's a And lot
Speaker 1:they built Votel, the most recent nuclear power plants to come online that were successful, but over budget and over time. Correct?
Speaker 5:Oh, man. It was yeah. I hired everybody off that team. So Georgia, Vogtle three and four, first thing. What we wanted Ocean.
Speaker 5:No. No. No. We wanted to hire like, if people look at that and go abject failure, I go, no. No.
Speaker 5:No. These are lessons learned. This is like what in the what went wrong. Guys, it's nuts, man. Like, it took 10,000 people at the peak of construction, on that construction site.
Speaker 5:Guys, go to a rock concert. Look at 10,000 people and think they're showing up to work every
Speaker 10:day.
Speaker 2:You don't want an amphitheater just to meet your team.
Speaker 5:10,000 people managing the project with paper.
Speaker 2:No way. Dude, non the non construction
Speaker 5:We're not talking forty years ago. I'm talking in the last this thing finished last year with pay wheelbarrows and wagons of paper. So you're looking at 10 to 20% efficiency for the people working and you know the audience and the larger viewership might go, lazy Americans. No, I'm not buying it. We are not giving our teams and people the advantages to win.
Speaker 5:The American spirit and fight alone, god, I'm believing it as much as anyone. It's not enough. We gotta bring technology tools capability. That's where we're partnering with Palantir. So I'm taking hundreds of thousands of pages of documents, which is what it takes to build one of these power plants, putting into a data lake, segmenting that data out.
Speaker 5:So if certain parties wanna 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 Yeah. A construction man or woman's waking up in an RV in a trailer at 3AM. Okay.
Speaker 5:I'm gonna be redirected at 03:15. I go there. At 03:45, I go there. Giving our frontline teams all the tools, technology, and information, we can do it. We're not splitting an atom.
Speaker 5:We're not going to Mars. We're just building the most dominant AI enabled platform on planet Earth, and we're gonna slash that 10,000 down to five thousand. We're gonna go to seven years instead of twelve years. China's building these one gigawatt reactors for 5,000,000,000 in five years. There's no reason we can't do it in five or four years.
Speaker 5:I'm not gonna name the number. My team will get really upset with me on the price side, but, there's no reason
Speaker 1:Yeah.
Speaker 5:These two reactors took twelve years and 36,000,000,000.
Speaker 2:About timelines in the industry broadly
Speaker 1:Yeah.
Speaker 2:Because there's some recent, I guess I don't I don't know if I can't remember if it was an EO or just a broad directive from the White House saying, like, we want new nuclear breaking ground in The US in the next twelve months. Is that is that better?
Speaker 5:It could be us. So we are imminently close to a recovery project that I'm not supposed to talk about,
Speaker 1:so I'm not
Speaker 5:gonna name the state. And, but it's a $20,000,000,000 recovery project.
Speaker 1:Bring bring old capacity back online.
Speaker 5:Yep. Yep. So $9,000,000,000 walk away. They spent $9,000,000,000 on this nuclear two gigawatt nuclear power plant. Didn't finish it.
Speaker 5:Walked away.
Speaker 2:Yeah. So
Speaker 5:we are getting brought in. We're 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. I would love to have you all on
Speaker 2:that consensus. Yeah. When when you think about what they spent, what is the value that's just sitting there on the dirt? Certainly not 9,000,000,000, but are
Speaker 1:you picking up a couple million It's 9,000,000,000 in legal fees
Speaker 4:and Yeah.
Speaker 1:It's it's The actual infrastructure. Hopefully, ports of concrete that's
Speaker 5:It still there or looks like, I mean, if you walk on it, we're on, I'm not allowed to say where we're at. Right? Yeah. Oh god. I almost did.
Speaker 2:Good catch.
Speaker 1:We're we're in America.
Speaker 2:We're in America.
Speaker 1:We're that American sound effect. We are in America. We're not afraid to say it. We're in America.
Speaker 5:But the when you walk this site and you look at it, it looks like, you know, aliens landed
Speaker 1:Yeah.
Speaker 5:And just left. Yeah. 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.
Speaker 5:But we're gonna go we're gonna get that thing hopefully later this year, early next year and
Speaker 1:be under construction. Had author Dan Wang on the show maybe last week. He wrote a book called Breakneck, and he and he and he contra compares and contrasts China to The United States, and he calls China the engineering empire driven by an engineering mindset. The solution to everything in China is just more engineering. Yeah.
Speaker 1:Build a train to nowhere. Build a bridge. Just build housing. Build everything. Build, build, build, build, build.
Speaker 1:And in The United States, he calls us this the lawyerly society. Mhmm. And and we are to everyone in politics is lawyerly or a lawyer lineage. And so one of the problems that I've heard in nuclear is that oftentimes you go to build something, you think, okay. I got a plan.
Speaker 1:It's compliant with all the laws, and then the laws change. And all of a sudden, you're back to square one. You gotta rip out all the pipes because they said no copper. Now you gotta use lead pipes again or whatever. How much of that do you think is is real, or how much do you think?
Speaker 1:Because that feels like something that you can speed up by analyzing all the legal code constantly and with the regulatory filing speeding that up. But some of it also has to happen on the other side. Right? Like like, we we it's not just enough for you to be using AI to to submit documents fast. You need review fast.
Speaker 1:So what's gonna happen on the other side?
Speaker 6:Oh god.
Speaker 5:I have so many comments on this.
Speaker 1:Please just just rant.
Speaker 5:So, how long do we have?
Speaker 1:Seriously, got a couple of minutes.
Speaker 5:Five minutes. So, yeah. I mean, this is the hot button issue for me. We have the safest operating nuclear fleet in the world and the highest operating capacity. This industry don't get me wrong.
Speaker 5:The legal BS, yes, we we all agree. But the victim mentality of the industry, the victim mentality of of 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 at the NRC, Laura Dudes. She's on our team.
Speaker 5:We're walking into the NRC going, what do you need? We're gonna be fully transparent. We're gonna be fully compliant. They should be incredibly critical. It's nuclear for God's sakes.
Speaker 5:If there is one and and here's the other one. Big misnomer. Voodoo
Speaker 2:is working. Right? The fleet's safe.
Speaker 5: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 the private sector needs to stop being a victim and just start doing what we're doing and and and figure out how to partner with the regulator.
Speaker 5:We're seeing no problem. So the other kids that wanna cry on Twitter, go for it. You wanna sue the regulator, go for it. We're just gonna go and partner with them and and figure out how to how to build bigger, faster, lower cost, safer, higher quality than ever before. And I will say what we're doing with Palantir.
Speaker 5:Well, here's the good news. To the 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, we let me and my wife were living in an RV, got got engaged on the last construction site. I've got guys that were building Vogtle 3 And 4, had heart attacks on the construction site, had people living at the gigafactories.
Speaker 5:That is a totally different world. Let us take your drawings, your great r and d, drag it into reality, and we're gonna build that trust with the regulator with you. But I do think we gotta go pencils down, swords down on blaming the regulator. Now the the legal, you know, that's a whole verse engineer thing. Well, that's a whole another topic we could we could take on, but we need the regulator to challenge us to be safe.
Speaker 5:And we just as as as an industry have to figure out how to comply and get the job done.
Speaker 1:Yeah. What?
Speaker 2:Great rant. I would love to see you and Karp rant together.
Speaker 1:Yeah. Yeah. What, what did Palantir show you that made you go with them? Do was there was there a key case study that Yeah.
Speaker 5:So we we are a two year old company that's about to be the the only company in The US with commercial nuclear under our watch. Mhmm. I'm like, what did we do right? What are others doing? We're just building a team to go build and and kind of reactor techno agnostic.
Speaker 1:Is the other is the other stuff managed by the government? Is that what you mean? Like Mhmm. Or is it just older companies that that manage
Speaker 5:There's no one that's actually focused on building. Everyone's designing new reactors. I just wanna go build stuff. You just wanna go build I could build a Westinghouse, a GE reactor. You know, any one of the new advanced reactors.
Speaker 5:Yeah. We just wanna build. So then the last year, what we did is we looked at everything. Hired somebody over here a lot smarter than me. He was at Tesla.
Speaker 5:He 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 all aspects of data related to the project into a data lake, predictive analytics to our frontline teams.
Speaker 5:No one's even close, man. Yeah. This is it. I'm not trying to be like sales job. I would like to get like a commission Yeah.
Speaker 2:I was gonna guess that there was not another great alternative that it did would have been nice to
Speaker 7:at least
Speaker 2:look at a couple options and decide. No.
Speaker 5:I mean, just well, here's the good thing. I mean, it's just the most secure platform the way the way it it is configured. You know, we're gonna go build the most dominant AI enabled nuclear platform, and we're doing it with Palantir. So it took us about a year of study. It took us a couple months of planning, and now we're just racing right now to go kinda build those solutions, and it's it's working.
Speaker 1:Yeah. 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. And then there's actually a customer who
Speaker 9:might be
Speaker 1:not you that's paying. They're paying you just to manage
Speaker 5:the construction. Our business model, so Topco, you know, the nuclear company, you're investing your VC dollars into technology and team, which this town knows.
Speaker 1:Yep.
Speaker 5:That big, you know, buckets of capital, project capital, I hired a big boy c CFO that's raised 10,000,000,000 in his life. He was CFO with JB at at Redwood.
Speaker 1:How, like, the neo clouds will go and build new data centers, but then there's there's project finance on the data center.
Speaker 5:Project. Yep. You know, we're the ones getting it to completion. We can get an equity earn out in the project. We can get a feeder in construction.
Speaker 5:Sure. And then there's multiple either we could build own transfer to a large utility. Yeah. We could build own operate for a hyperscaler. We could build own transfer to a foreign government, or we could we could operate it ourselves.
Speaker 5:So, you know, our there's a few ways we get there, but
Speaker 1:Mhmm.
Speaker 5: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 20,000,000,000 on our balance sheet. Yeah. Yeah. I don't know.
Speaker 5:Maybe maybe in a couple of years, let's talk. Let's see how this goes. Fantastic. So, you know, we're, you know, again, very just bullish on on Palantir, and I don't know whoever listened to that talk earlier. It's I mean, the binary outcome is it's us first China.
Speaker 5:And to all the tech bros and the badass CEOs and the badass five Fortune 500 tech executives, here's what I would say. We gotta leave our ego at the door. China is fucking kicking our ass. That hope was not recorded.
Speaker 1:Everything recorded. We're live.
Speaker 5:So the look. It is look. The reality is it's not even a competition. Yeah. We're losing so bad, and we've gotta work together.
Speaker 5:So I would say to the community watching, you know, push me, be hard on me, critical on me. That's fine, but let's figure out how to challenge each other and work together because it's a binary outcome right now. It's us versus China. It's not even close. They're winning at so many categories, and we've gotta figure out how to work together.
Speaker 5:And that's what I think Palantir and a unique framework they're bringing, not only the technology, but the mentality of how do we work together and win. And, you know, now it's all gonna be about performance on that construction site on time, on budget, high safety.
Speaker 2:Your position in the in the nuclear kind of market broadly and that if somebody can build great reactors, you can help them actually become a real business based on it and not have to worry about every single point in
Speaker 5:the stack. Got a partner, man. That's the thing. Right? This is where China is going into The Middle East fully vertically integrated going, MBS, we will do it all.
Speaker 5:One shop stop. They they don't wanna work with three constructors and, somebody selling a reactor. No. So, like, how do we partner together, go as coalition? We're gonna deliver power globally.
Speaker 5:We're gonna deliver power in the in here in The US. But I do think figuring out how we, you know, bring down this ego of, like, there's so many silos, and we need to challenge each other. But that's what I would say to you all because there's a lot more people on this listen to you than listen to me. How do we bring our tech community together, our big CEOs who are important and great? But if you compare them to China, we're not winning.
Speaker 5:So it's like, how do we do that and go win collectively?
Speaker 1:Fantastic. Well, I think we have our next guest here. We're gonna take a look at some rocket motors. So
Speaker 2:Thank you for Thank
Speaker 1:you so much for helping us.
Speaker 8:Thanks for joining us.
Speaker 2:Thank you for doing this work.
Speaker 1:Thanks for supporting us, guys. Have a good rest of your day. Up next, we have Nancy Cable from Ursa Major. We will bring her in. And do you want us to try and bring that in here?
Speaker 1:What are you thinking? I'm happy to bring it in.
Speaker 2:Bring in the engine. Bring in the engine. In the engine.
Speaker 1:It's the engine. Right? Okay. It's device.
Speaker 2:We got an engine coming
Speaker 1:up. It's it's shocking that it was clear through security. We we we when we do these remote shows, we sometimes have to bring, very, very suspicious looking Wi Fi hotspots. Ben and the boys brought a Wi Fi hotspot through the actually, I think I had to walk it into the
Speaker 2:capital capital.
Speaker 1:Through a very odd place.
Speaker 2:Here. Maybe pick up the microphone and we'll throw it on the table.
Speaker 10:Set it gently.
Speaker 1:Yep. We can throw it on the table.
Speaker 10:Think we'll be okay. Yeah. Just sit your end gently down.
Speaker 2:Okay. Incredible.
Speaker 1:This is a wild demo. We've
Speaker 2:First rocket engine. Nice to
Speaker 1:I'm John.
Speaker 10:John, I'm Nancy.
Speaker 1:Great to meet you. Pleasure. Thanks coming on. Hold this as much We've as you had we've had people brought bring fish to the show, sushi that was extracted or the fish was killed with a robot, Shinkai. That was a We
Speaker 2:had somebody promises a SpaceX engine too.
Speaker 1:Yeah. Oh, yeah. Gotta follow-up on that. But but this is this is the best demo we've gotten
Speaker 3:so far.
Speaker 10:Hate SpaceX. This is fantastic. This is a good day for us.
Speaker 1:Yes. Incredible. Explain to us what is this and what's your business and introduce yourself.
Speaker 10:Yeah. Absolutely. So I'm Nancy Cable. I am the director of operations for Ursa Major. Cool.
Speaker 10: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 five.
Speaker 10:Okay. Wow. Really critical in the defense space
Speaker 2:Yeah.
Speaker 10:Right now. We must field technology
Speaker 1:Mhmm.
Speaker 10:And we must do it faster.
Speaker 1:Yeah.
Speaker 10:And that's what Hadley and some of our next gen products are enabling.
Speaker 1:Now, correct me if I'm wrong. The the value of the hypersonic missile is that it has the maneuverability of a cruise missile with, the speed of an ICBM. And it's not and so is maneuverability a piece of this? Is this, like, a
Speaker 10:Maneuverability is a piece of this for our customers. So a lot of interceptor technology is what current applications. And for our next gen products, the maneuverability and the storability of the fuels are also front of mind.
Speaker 1:Yeah. And 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 weapon systems that sell directly to the DOD? Are you partnering with other companies that we might be familiar with? Where does Ursa Major fit in?
Speaker 10:Yeah. Absolutely. So we're 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 in the government have traditionally done, which is these, like, years or even decades long deployment cycles Mhmm.
Speaker 10: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.
Speaker 1:So so right now, huge focus on just manufacturing excellence, cost, speed, reliability?
Speaker 10:Absolutely. Yeah. And that is most of my role is on the manufacturing side
Speaker 1:Yep.
Speaker 10:And making sure that I can take this excellent technology that our rocket scientists have developed and scale it so that's available to market. Right? Yep. Right now, we're on, you know, looking at the order of tens to hundreds of units a year. Yep.
Speaker 10:That needs to be tens of thousands Yeah. Of units a year. And that's really where the Palantir partnership
Speaker 1:How comes does Palantir fit in this?
Speaker 10:Yeah. Absolutely. You might think that engineers are great at data flow. But if we were to look at this rocket engine here, different engineers designed the turbo machinery and the injector and the chamber. And all of them came up with a unique way to process their data, a unique test system, a different network drive, a different place to store the information.
Speaker 1:Different network drive? That is was an
Speaker 10:expecting Well, and I think
Speaker 1:this is We the have a small company here, maybe 10 people, and we probably do have six different, like, Google drives and and different folders for different data.
Speaker 10:Well, that's interesting. Just that it's just natural chaos. Everyone Yeah. In every industry, rocket propulsion included Yeah. No one's up feeling like, man, I'm fifteen years behind.
Speaker 10:How could anyone possibly store something on a C drive? Yeah. Yeah. Yeah. When you're focused on getting the hardware
Speaker 1:They're to trying work move fast.
Speaker 10:You're not necessarily focused on the efficiency. And so putting the data efficiencies front and center Yep. Even before Palantir, our aim was right data, right people, right time, right decisions. I loved what Doctor. Garp was saying about people happiness.
Speaker 10:People are not happy when they feel behind. They are happy when they feel ahead, when they can make real time decisions. And leveraging Palantir out onto the shop floor and into the back end of our data structures means that we can get the information to people so they can be real time and then even predictive Yeah. About how we're doing manufacturing.
Speaker 1:Yeah. So how does how does someone at UrsaMajor actually interact with Palantir? Is it on an iPad, on a phone, on a computer, while they're working on test bench, in every phase?
Speaker 10:It Yeah. Great question. So we've been with Palantir about three months
Speaker 1:now. Okay. So it's so early.
Speaker 10:Yeah. And right now, the daily interactions are mostly with our engineering and programmatic teams. Okay. We've built some inventory modules. We've built This in looking at our engineering line of balance, our change management systems.
Speaker 10:Sure. But like we were hearing from our nuclear from Nuclear, the people on the floor doing the work are actually the most important people in the factory.
Speaker 1:Yep.
Speaker 10: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
Speaker 6:Mhmm.
Speaker 10:Ordering all of the parts, producing all of the parts internal through fielded data on our at our customers.
Speaker 1:Yeah. Is is is you almost call it like an ERP almost?
Speaker 10:Yeah. So we actually we have an ERP. Right? This is what everyone does. Everyone has they have an ERP Yeah.
Speaker 10:For an EnerSys resource plan. Yeah. Accounting accounting function, all of your work orders. The PLM, product life cycle management. And then an MES is the traditional thing, a manufacturing execution system.
Speaker 10:Yeah. And we have said, why not use Palantir? It's already integrated.
Speaker 1:Yeah.
Speaker 10:I don't want one more monolithic software. Yeah. Connect it with the ERP. Actually, pull some of the functions out of the ERP. Yeah.
Speaker 10:Palantir is better
Speaker 1:hearing a story. Don't know how true it is, but something about, like, SpaceX built, like, a ton of custom software for everything they needed to do. And then eventually, I think the team, like, spun out and and and built a business around that. Yeah.
Speaker 10:Yeah. Well, SpaceX actually so they they have a product and it's kind of the gold standard. Everyone who's worked at SpaceX
Speaker 1:Is like, I want is that one. Like,
Speaker 10:I want that one. Yeah. And that really is the, you know, the magic of that software is everything in one place Yep. Which is what Ontology brings. Yep.
Speaker 10:Everything we need in one place.
Speaker 1:Very cool.
Speaker 2:Yeah. What's it so what what's it gonna take to go from making tens or hundreds of these to tens of thousands?
Speaker 10: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.
Speaker 1:Yeah.
Speaker 10:So making sure we have the right tools, the right fixtures, the right machines, three d printing is critical to what we do here. What's three d printed on metal? 80% of the rocket, all of these metallic components Wow. Are metal three d printed. Fascinating.
Speaker 10:Yeah. Developing some of our own unique alloys. So scaling the machines is probably the longest lead time for us. And then setting up the correct tools, fixtures, as you can imagine, test and infrastructure is really big. Yeah.
Speaker 10:But not having the data around that in silos. Mhmm. So when we need to build hundreds of these, I need to know where every piece part is at every moment Yep. So that we can make the best real time decisions possible for quality for the customers. So the the physical infrastructure is really what we're most familiar with.
Speaker 1:Yep.
Speaker 10:And now Palantir is helping us with that digital infrastructure side of things. I've been in manufacturing my whole career. Yeah. Eighty percent of the line down scenarios I've ever had where we stopped building product. You wanna guess what they're from?
Speaker 2:Lacking inventory or
Speaker 10: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 where they need to be to build a 1,200 component rocket engine.
Speaker 10:Yeah. And it's things like that that the the ontology is helping us solve.
Speaker 1:A couple of years ago,
Speaker 2:said It's funny. I I don't know I don't know if this is hubris, but I feel like you could put this together, John.
Speaker 10:Well, that's kind of that's
Speaker 1:kind That's the point. Right?
Speaker 4:A manufacturing
Speaker 2:But it's just like the so so actually putting the pieces together is the easy part. But it's like making the parts and making sure you have Yep. Them at the right time is the real challenge. Make It's like doing a puzzle over like, you know, twenty days type of thing.
Speaker 10:Yeah. I mean, we joke it's like, right, Lego Legos for adults. But you can see it really just is a collection of fittings fittings and fasteners. I and that's kind of the point. How can we have a system that makes it so easy and so obvious how we manufacture these that I could pull the two of you in and say, a rocket engine, and you could do it with confidence.
Speaker 2:That's all kids, I think they would enjoy putting Yeah. One of these
Speaker 1:Yeah. A couple of years ago, I sat next to somebody on a plane who was selling wait. It was pipe bending, pipe fitting,
Speaker 10:whatever this is. Tube bending?
Speaker 1:Tube bending. Yes. He said, mean, I'm in my my business is tube bending. And I was like, what? And he was like, yeah.
Speaker 1:He was he was going to SpaceX specifically to sell tube bending machines to them. I didn't realize it was a whole industry.
Speaker 10:It's a simple He made his
Speaker 2:money in bending in
Speaker 1:person to make sure that they don't run out.
Speaker 10:Absolutely. Because
Speaker 1:yeah. It's limiting factor. If the tube isn't bent, you can't make the rocket.
Speaker 10:If the tube isn't bent, you can't make the Crazy. Tubes actually carry some risk. They're some of the thinnest walled components on the rocket. This this has a lot of mass
Speaker 1:to it. Sure.
Speaker 10:Tubes are often can be where failures happen.
Speaker 1:Sure. Sure.
Speaker 10:So in an ecosystem, right, we need to test them, but also where did this tube come from? Yep. What day was it bent? What was the lot of stock material? What revision was I on in my CAD model?
Speaker 1:Sure. Sure.
Speaker 10:Sure. What testing did this engine undergo?
Speaker 1:Yeah.
Speaker 10:All of that currently, I could find in our systems.
Speaker 1:Interesting.
Speaker 10:And it would take me hours.
Speaker 1:Yeah. Yeah. But But if it's all in one place.
Speaker 10:If it's all in one place, and we have a consolidated tool, it's that traceability.
Speaker 1:That's incredibly cool. Yeah. Fantastic. Anything else
Speaker 8:Thank you
Speaker 12:so much for
Speaker 2:bringing your baby on the track.
Speaker 1:This is a great sign of respect.
Speaker 2:Yep.
Speaker 10:Yeah. Absolutely. I mean, what's cooler than carrying around a hypersonic rocket engine? Absolutely. Right?
Speaker 10:Everyone loves it but the TSA.
Speaker 1:They don't Oh, yes.
Speaker 2:A rough one. Rough one
Speaker 1:to travel with. Yeah. Anyway, thank you so much
Speaker 10:for coming on this team. We'll talk to you
Speaker 2:for coming on.
Speaker 10:Nice meeting you. You.
Speaker 2:We have our next guest ready
Speaker 1: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? I'd love to hear some ads.
Speaker 1:We are talking about You wanna talk about ramp.com? Ramp. Ramp. Ramp. Ramp.
Speaker 1:Oh, you're bringing the ramp song? Ramp. Ramp. Let's go through some of
Speaker 2:last quarters. I did wanna, you pull that up, I did wanna, talk about, Matt Huang Oh, yeah. 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 stablecoins and real world payments born from Stripe's experience in global payments and Paradigm's expertise in crypto.
Speaker 2:To ensure Tempo serves a broad array of needs, we're we're excited to be working with an incredible group of initial design partners including Anthropic, Coupang, Deutsche Deutsche Bank, DoorDash, Leadbank, Mercury, Newbank, OpenAI, Revolut, Shopify, Standard Charter, Visa, and more. Tempo's payment first design includes predictable low fees, payments gas, and any stable coin, Payments first UX, opt in privacy, scale a 100,000 transactions per second, and EVM compatible built on wreath. Tempo eases the path to bring real world flows on chain such as global payouts, pay ins and payroll, embedded financial products, and accounts, fast and cheap remittances, tokenized deposits for twenty four seven settlement, micro transactions, agentic payments, and more. Matt says, we're building tempo with principles of decentralization and neutrality that includes stablecoin neutrality. Anyone can issue a stablecoin.
Speaker 2:We might be able to have a TBPN coin. That sounds exciting. And any Oh, yeah. Yeah. Yeah.
Speaker 2:That was clearly a joke. No. But I was talking about a a a USDT VPN.
Speaker 1:This is a one for
Speaker 2:one stable coin that that that we issued to
Speaker 1:It does not move.
Speaker 2:It does not move. You can't make it move. It won't budge. Independent and diverse validator set with a roadmap toward a permissionless model. So, apparently, they're already in a private test net.
Speaker 2:And, anyways, two two power players, Paradigm and, and Stripe coming together. It sounds like they're they're positioning I guess, Matt is running Tempo, but they're positioning this as they're both investors in Tempo. So I think they really do want to take a decentralized approach.
Speaker 1:But so not so this is not downstream of, like, the Stripe acquisitions directly, Privy and Bridge?
Speaker 2:Yeah. So I have a post here 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 performant blockchains struggle with basic financial services use cases.
Speaker 2:Mhmm. 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
Speaker 1:get On bridge with stablecoins?
Speaker 2:No. No. No. He's not talking about not talking about Bridge specifically, but he's saying like, if you were trying to pay employees at the time
Speaker 1:that Trumpcoin launched Who's paying employees in Trumpcoin?
Speaker 2:No. No. No. Not not in Trumpcoin.
Speaker 1:Oh, okay.
Speaker 2:Like that that day, I think it was like a Saturday or
Speaker 1:it a
Speaker 2:Friday. Forget exactly, but when it launched, if you tried to pay, there was so much activity on chain at that moment that like good luck, you know, paying like a freelancer or something.
Speaker 1:So yeah, the example would be like, I'm trying to pay a freelancer in stablecoins like on chain because like obviously, like your default payroll providers are just using like Yeah. You know, web two rails or whatever. And and that wasn't brought down by the
Speaker 2:Trump launch.
Speaker 7:Right? Okay. Got it.
Speaker 2:Aid disbursements taking days due to low transactions per second and projects to later canceled due to 6 figure upfront gas costs. Tempo is new l one built specifically for payments. And so Mhmm. Anyways, quite the team they've put together here.
Speaker 1:Yeah. We gotta get some of the folks on the on the show and and have them break it down because I'm very interested in why not Solana. Why not Circle? You know? Like, it feels like there's a
Speaker 2:The other question is why why not another l, like, why not an l two
Speaker 1:Exactly. Built on. Exactly. But this is something, unique, and they must have, put a lot of time and effort into it. So Yep.
Speaker 1:Congrats to them on the launch, but we will, you know, want to know more. Anyway, I believe we have our next guest. Welcome to the show. Hi. I'm Johnny.
Speaker 1:Hi. Hi. It's a pleasure.
Speaker 2:It's it's Welcome to the show. You for
Speaker 1:this microphone. Why don't you kick us off an introduction on yourself and what brought you here today?
Speaker 12:Perfect. I'm Ryan Azdorean. I'm the chief marketing and strategy officer for Lumen.
Speaker 7:Okay.
Speaker 12:And we're here at AIPCon talking about all the great things we're doing together to modernize telecom. Lumen's a
Speaker 2:Let's give it up for modernizing telecom. Yeah. Exactly. Right? Finally.
Speaker 12:It's it's fun because it's decades of complex operational. I think Palantir is helping us modernize into this new world that you need for AI ready multi cloud world that is what everyone's here talking about.
Speaker 1:Yeah. How do you define break down more of what you do in telecom specifically.
Speaker 12:Yeah. So Lumen is, you know, for for decades, we have basically been connecting the world. Okay. It starts with connection and then in the last in the last bit of time Yeah. The world has needed new ways of connecting.
Speaker 1:Yeah.
Speaker 12:We're bringing that infrastructure. We're bringing control. If you think about the way it was before it
Speaker 1:was It's like fiber cables in the ground?
Speaker 12:All fiber. Right? Everything that's running across fiber.
Speaker 2:Got
Speaker 12:it. Those super fast connections you need. One port, one connection was the way of the the way of the world. Mhmm. We're changing that.
Speaker 12:We're getting it cloud ready, cloud enabled, remote controlled. All of those things that give you that redundancy, latency, all the things that power AI.
Speaker 1:Yeah.
Speaker 12:That's what Lumen is doing and we're connecting the world.
Speaker 1:Okay. Yeah. Who's the customer right now?
Speaker 12:We have lots of customers. It start So we're really focused on the enterprise. The enterprises that are building these
Speaker 1:Data center operators.
Speaker 12:Capabilities, data center operators, hyperscalers, of course. And so we've announced some of the work we've done on the backbone, the infrastructure backbone of the AI economy. Mhmm. But what we're really doing is enabling businesses new things. New new technologies that they want to give them a technological advantage.
Speaker 12:Mhmm. We're disrupting this industry to help them disrupt their industry.
Speaker 1:Yeah. Yeah. Yeah. So, I mean, obviously, there's, an immense amount of money flowing into data centers. Yeah.
Speaker 1:Is a lot of that actually going into, like, new bandwidth requirements between data centers? Like, basic narrative is like, yeah, they might spend a billion dollars training something, but it's all happening within one data center.
Speaker 12:Well, so the the thing you hear about a
Speaker 1:lot Yeah.
Speaker 12:And you guys have talked about a lot as well as compute, storage, cooling, all those things that are needed. Yeah. The missing link is connectivity.
Speaker 1:Mhmm.
Speaker 12:And realistically, it's something that has really emerged as of recent to say there are new types of connectivity, new next gen fiber Yeah. That has way more capacity
Speaker 1:Sure.
Speaker 12:Than the world has ever needed before.
Speaker 1:Sure.
Speaker 12:We're we're growing leaps and bounds over by 2028, we'll have about 66,000,000 route miles of fiber. Okay. And that is growing, you know, three to five x what we've had before.
Speaker 1:Okay.
Speaker 12:And that is the capacity the world needs.
Speaker 1:Yeah. So there's some
Speaker 2:And sort of is that capacity being used inefficiently today or or is or or is demand still way outstripping supply?
Speaker 12:The demand is completely maxing out. It's why we are putting these investments in the ground. And we're not only the hyperscalers, I'd say, the tip of the spear.
Speaker 1:Mhmm.
Speaker 12:They're consuming a lot
Speaker 6:of this.
Speaker 12:They're looking for a lot of this data center to data center connectivity. But it's really enterprises everywhere that are now saying, you know what? We also need that type of bandwidth. And some will take it dedicated, some will take it shared, but the need is completely outpacing what the needs of the last couple decades have been.
Speaker 1:Yeah. Try and make that more concrete for me, like because I feel like most people's interaction with AI is I send the most condensed packets possible across the internet, just a couple lines of text. And then a bunch of GPUs light on fire at the AWS data center or Azure if I'm using GPT-five. And then it sends back text. This is not rich video.
Speaker 1:This is not VR. I buy I immediately, like, intuitively understand, like, if we're in the metaverse world and we're streaming four k stereoscopic, that's super bandwidth heavy. How is AI bandwidth heavy?
Speaker 12:So it's actually great listening to the customers that have been here at AIPcon because you hear American Airlines, you hear BP, you hear some of these customers that are talking about their infrastructure. All of the scheduling, the inferencing, the
Speaker 1:Yeah.
Speaker 12:The planning that is happening in real time and adjusting, that is not just people typing in their prompts Yeah. Into the text. It is systems talking systems, and this is where the data explosion has come from.
Speaker 1:Okay. Sure.
Speaker 12:It's all happening in the background.
Speaker 1:Okay. Yeah. Yeah. Yeah. So so even though I fire off one query to GPT five, it it if it's doing deep research, it might be pinging 75 different websites, and that's driving up Yeah.
Speaker 1:Total Internet usage.
Speaker 12:Yes. And the systems
Speaker 1:It's fanning out.
Speaker 12:Are also creating their own queries. Yes.
Speaker 1:Like Yeah. We saw that with the demo from Palantir. Like, you know, he he typed one line of text, like help optimize this airport
Speaker 2:And then it was working for like twenty minutes.
Speaker 1:That's right. Okay. Yeah. That's right.
Speaker 12:So And so this is where the disruption in telecom Sure. Comes. And if you really think about what has changed in telecom Yeah. Over the last twenty five years, the answer is not much.
Speaker 1:Yeah. Yeah.
Speaker 12: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
Speaker 1:Mhmm.
Speaker 12:You've changed the way people enter it's cloudifying telecom. And in this new world of what is happening with cloud, like cloud two point o, that is the necessary bandwidth control and precision Yeah. That you need in connectivity.
Speaker 1:What what does cloudifying telecom mean? Does that mean like more like multi tenant on the actual fiber lines? Like, instead of a hyperscaler owning one route, then they're they're bidding it out in spot rates or something?
Speaker 12:What's going on? Multi multi tenant is a good way to think about some of the services on top of, you know, in the past.
Speaker 1:Yeah.
Speaker 12:You've literally, if you think even back to old telephone switches. Yeah. You've had, you know, the the one wire to one wire. Mhmm. It's been one port to one service.
Speaker 12:You add a service, you add a port, it's a truck roll, it's a person coming out.
Speaker 1:Sure. Sure.
Speaker 12:Cloudifying it is bringing all of that technology to the users, giving them that interface, that portal where they can say, I need these services. I need them in these locations. I need this speed. I need the bandwidth turned up. It's network as a service.
Speaker 1:Yeah. So a higher level of abstraction
Speaker 12:Yes.
Speaker 1:And, yeah, more like almost like a virtual machine on top of the the the telecom infrastructure so it can be be provisioned, like, on an ad hoc basis.
Speaker 12:Yeah. And and one of the biggest changes, I think, in the economics of this AI economy is also, if you think about a network subscription, if you will, of the past, you sign up, you get a certain amount of bandwidth. But if you look at the companies of today, if you look at the sports industry, manufacturing industry, healthcare industry, they have these spikes that are massive. Mhmm. And so, we're providing that network as a service where it turns up, turns down
Speaker 1:Yeah.
Speaker 12:And then customers are paying for what they it's a consumption model.
Speaker 1:Mhmm.
Speaker 12:And again, that's part of this cloudifying model which has not hit telecom till what we're looking to transform with.
Speaker 1: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 Yeah. That is manufactured somewhere, then someone purchases that, they buy some land, they bury it in the ground, maybe they get some rights, and then at a certain point someone's, you know, leasing or essentially charging a toll along that toll road. Yeah. Do you sit all are we completely vertically integrated?
Speaker 12:So we sit vertically integrated, but I think what
Speaker 1:Do do R and D on new fiber optic technology?
Speaker 12:We work with a number of partners on 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 ground. Sure. Maybe it's on purpose, maybe it's not on purpose. Yeah.
Speaker 1:Yeah. You need to dispatch someone to go fix it. You
Speaker 12:can't have any interruption to the services you're running. We have to have that redundancy. Yep. On top of that, our customers and enterprises everywhere, I think they started mostly Yep. Building with one cloud.
Speaker 1:Yep.
Speaker 12:Now, if you think about this multi cloud world, where they're hitting Azure, GDC, AWS, they're hitting all of them at the same time with the same applications in different regions across The US. Sure. They have to seamlessly let those systems talk to each other. And they don't want a direct connection to each of them. Mhmm.
Speaker 12:That's where we started. Mhmm. But now, they want to be able to live in this fabric where their systems can talk to all of these in all the regions, get all the data and process faster Mhmm. Because that's part of the disruption they want.
Speaker 1:Question for me. Yeah. How how does Palantir fit into that?
Speaker 12:Yeah. So if you think of the operational complexity of the decades of past past
Speaker 1:Yeah.
Speaker 12:You know, you've built all these networks. We we talked about fiber in the ground. Yeah. Think about the systems over those decades that have been built up. Yep.
Speaker 12:One of the things Palantir is helping us with is this managing this operational
Speaker 1:You sort of see an abstraction of this in LA when there's the fire and like 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 So said that's where
Speaker 2:the first
Speaker 1:that's how the power lines work. The fiber optic lines, yeah, they're newer. Yeah. But there's probably still some stuff that might go wrong if it was installed thirty years ago. Yeah.
Speaker 1:Well, So there's you got to identify that early.
Speaker 12:There's that and there's the software layer that is running all
Speaker 1:of those. Gotta make sure that that's up to date and not crashing.
Speaker 12:And Palantir is helping us optimize those, helping us bring them together. Yeah. And and what we are building for customers is then a system that they don't have to think Mhmm. About the optimization they need in their network.
Speaker 1:Yep.
Speaker 12:We're gonna help automate that. We're gonna help bring AI to that Yep. And that's part of this partnership. And it's also, frankly, the most exciting part about disrupting telco. It's not an industry that too many have talked about disrupting for while.
Speaker 12:It's ripe for it. It's And this AI multi cloud era, Lumen's here for
Speaker 1:it. That's very exciting. Anything else, Jordy? Love it. We're running late.
Speaker 1:So thank you so much for coming out.
Speaker 2:Thanks for
Speaker 1:having me. Alright. Cheers. This. Thank you.
Speaker 1: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. Lads. We have lads.
Speaker 1:We have lads coming in. If we want to bring everyone in, we can. We can pass the mic around. Whatever you guys want to do, we have multiple.
Speaker 11:Oh,
Speaker 1:okay. Hey. Oh, It's just me. Oh, how are you doing?
Speaker 2:Sorry. What's up?
Speaker 3:What's up? Great to meet you.
Speaker 1:How are doing?
Speaker 3:Good. Good.
Speaker 1:How's the day? Could you kick us off Grab the mic. Grab the mic. Kick us off with an introduction for those who don't know.
Speaker 3:Okay. I'm Dave, Dave Blazer. Been a Palantir for twelve years, and I'm a CFO.
Speaker 1:Pre IPO.
Speaker 3:Pre IPO? Yeah. Like, basically
Speaker 1:Or DPO.
Speaker 10:Right?
Speaker 3:DPO. Yeah. Since, like, '2 like, when our our prior CFO retired, was actually on the show recently.
Speaker 1:Yeah. Colin. I talked to him.
Speaker 6:Great guy. He
Speaker 3:retired in 2017, and since then, I've been leading the finance team.
Speaker 1:Yeah. So my big question for you, gross margins for the Fortune five hundred in the AI era, are we going to see a structural shift? The inference bills are skyrocketing, inference per token is dropping, but then Jevan's paradox and we're doing more token inference than ever before. Reasoning models are it's kind of staying expensive. And we saw in the journal earlier this week, maybe last week, a software company called Notion said that they saw their gross margins drop from 90 to 80%.
Speaker 1: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.
Speaker 3:Yeah. Look. I I think this is one of the things that we've been sort of saying is, like, LMs are commodity commodity cognition. Right? And so, like, essentially, it's like they're getting better and better.
Speaker 3:LEGO scores better and better. Tokens are getting cheaper. And as Alex said, I don't know if you watched Keto, he's talking about, Okay, how do you actually derive value from that raw output of an OM? So I think the raw output, it is getting cheaper. We're still very early days on these models.
Speaker 3:And you're seeing them just sort of up and to the right in Elo score. And so these things combined, I think, are going to make it cheaper and cheaper over time. And I think we'll see on gross margin. I think you look at some of the other things, like hyperscaler costs from a lot of these places, I think people's gross margins have survived. They're more efficient.
Speaker 3:They're always and so I think we will see. But I think that is it's going be much more about how are deriving value from them than, well, the cost is going be so overwhelming, but they're super
Speaker 1:Totally.
Speaker 3:It's focused on the value. And I do think over time, it's like people are going be able to manage those costs.
Speaker 1:Yeah. Yeah. It feels like higher costs potentially, but so much more value. And it's pretty easy to tell, yeah, I'm spending a lot on inferencing a certain LLM API, but obviously, I'm delivering more value and so I'm charging
Speaker 2:Also, to think about the position that Palantir sits in. We got a product demo earlier. Hive Mind was leveraging like a bunch of different models. And, like, that position of having leverage and being, like, we are the product. We have the data.
Speaker 2:We have the customer relationship, and we can vend in whatever intelligence sources we need in order to accomplish the task. Like, that's a better position than being if you're a GBT rapper Yeah. And your product is really four o and you're just kind of like reselling that. Right?
Speaker 1:Yeah. Yeah. Yeah. Sorry.
Speaker 3:Yeah. Yeah. Like, and I do think it's like, yeah. Like, I I think it's gonna be all about the value Yeah. Rather than like, well, the value is there, but the cost is super prohibitive.
Speaker 1:Yeah. How you thinking about positioning Palinger's story in commercial in The United States over the next couple of years? Like, what is the right framework? People have always had the wrong mindset. It's a consulting shop.
Speaker 1:What do they even do? Blah, blah. What is the right frame of mind to be in?
Speaker 3:Look, I think the right frame of mind is we're delivering a tremendous amount of value with these customers. And they're needed too in this. And it's like, you deliver that value and we're just at the beginning. So if you look at our US commercial business, it grew over 90% last quarter. It's still relatively small.
Speaker 3:And there's so much runway there. It's just that business has sub 400 customers. Yeah. Right? That is when you look sort of across a lot of other companies, it's like that's know, and so it's like we're doing all this with such a small customer base.
Speaker 3:Yep. And obviously, it's rapidly growing, but it just shows the amount of runway that's ahead.
Speaker 1:Yeah. Do you do you think that people should be thinking about the commercial business as, like, a a bundle like, a a competitor to a bundle of products that already exist or something that's entirely net new or displacing an entirely different class of spend in the enterprise? How should people even wrap their mind around
Speaker 6:that?
Speaker 3:Some version of all
Speaker 1:of above.
Speaker 3:So it's like when you think about not head to head, who are we competing with? And then everyone's like, but I don't get it. It's like, is this a combination of it? We're competing against the Frankenstein monster that almost every large corporation has. And then you're also competing, particularly in government.
Speaker 3:But it also applies in particularly large corporations is 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 only because of the value that's being delivered. And then it's like you don't maybe need some of these point products.
Speaker 1:Yeah, yeah. It feels like the it's like transformation, net new technology that would not get built in the enterprise otherwise.
Speaker 3:Correct. And then once you've built that, once you've built that compounding data asset, then perhaps you don't need some of the other products. Yeah.
Speaker 2:That makes How is your framework or philosophy approaching the finance function at Palantir changed? Because I feel like there's like very distinct eras where, you know
Speaker 3:They change them every day.
Speaker 2:Does it does it do you do you feel like you have to to update it every day? It consistent in some ways like when you talk when we talked to Karp earlier, it's like Yeah. He's bringing that same energy and like philosophy. It feels like it's it's somewhat consistent even numbers go up and down and and all that good stuff.
Speaker 3:Yeah. Look. Well, I look, I challenge any CFO working for carp to have hair. Right? So, look.
Speaker 3: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, and people have said it a lot, we don't have a playbook. And obviously, there's a way that the company's been built over the last twenty ish years. I've been lucky enough to be here for twelve of them.
Speaker 3:Because of that, we're very unique. And what that means is we are constantly changing what we're doing. And so a lot of things, you talk about four deployment engineers in the early days. Oh, that's consulting. That just obviously helped us build the product that we have today.
Speaker 3:And so what you optimizing on in those days was financial statements that Wall Street would want. And then it's like, but because of what we built today, not because or because of what we built, we have financial statements Wall Street loves, but it wasn't built for that purpose.
Speaker 1:Sure.
Speaker 3: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. Yeah. Right?
Speaker 3:And and built the company was built that way.
Speaker 1:Can you tell me the story of how the COVID era changed Palantir's financials? I remember seeing that T and E fell off a cliff, and it never really came back. And that was at the time, 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.
Speaker 1:But is that a reasonable story to tell? Is that apocryphal?
Speaker 3:Look, it's part of the story. And so I think what happened with COVID, it was we could no longer you just couldn't be as much at a customer site, right? And so then it's like, well, we've got to extend the product further, right? And this is a story that keeps happening in Palantir. It's like, well, we only have around 4,000 people.
Speaker 3:Or you look at our headcount growth. 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.
Speaker 3:And you have to have products like AIFTE, all these things that are constantly evolving. And that is the story of Palantir. It's like, if you're trying to do something, you're either resource constrained or somehow constrained. It's like, what do you do to meet that? And almost always is product led.
Speaker 1:Yeah. That makes a ton of sense. I know you have a busy day, so we'll let you go. Awesome. Thanks so much for helping me.
Speaker 2:Thanks for joining.
Speaker 1:We'll talk to you soon. We will bring in our next guests in a minute. Jordy, do you have any breaking news?
Speaker 2:I got updates from Skook. Skook says Alex Karp trying his best to get TBPN banned
Speaker 1:from YouTube right now.
Speaker 2:I will say That is great. Think it was like the the least family friendly ten minutes segment of the hundreds of hours that we put out.
Speaker 1:But it was
Speaker 2:it was some of the best.
Speaker 3:Enjoyable. Some of the best.
Speaker 1:Some of the best.
Speaker 7:It was
Speaker 1:a lot of fun. I'm glad that Scooks enjoyed enjoyed the stream. And and thank you for YouTube for keeping us up.
Speaker 2:Keeping us up. Might Jimmy's
Speaker 1:going strong. Thank you to Restream for keeping the stream
Speaker 2:Thank
Speaker 1:you. Couldn't do it without them. We will bring in our next guest. Guests, we are ready to keep rocking and rolling here.
Speaker 2:Who do got? United States. Two chairs coming in.
Speaker 1:Come on in. Come on in. Pull over.
Speaker 2:What? How
Speaker 1:are doing? We got we got an IndyCar driver for you. Oh, fantastic.
Speaker 9:Amazing. Performance engineer.
Speaker 1:Very cool.
Speaker 11:And then a finance guy.
Speaker 1:Fantastic. We Dream dream come have to sell me. Now I'm in. How are you doing? Good to meet you.
Speaker 1:I'm John.
Speaker 7:Hey, Pat.
Speaker 1:Pleasure. I'm John. Nice to meet you. How are you doing?
Speaker 2:Good to meet you. Nice, guys.
Speaker 1:Lads. We got the lads. We got
Speaker 2:the lads.
Speaker 1:Take a seat.
Speaker 2:Take a seat. Seat. Do you guys wanna share
Speaker 1:Here. Yeah. We will share. We'll share
Speaker 11:my monitor.
Speaker 1:I'll be on the note. Great. So, yeah, why don't you two, kick us off with the introductions? Let us know who you are. I'm sorry you got stuck with a rough chair.
Speaker 1:Couldn't figure out how to get the chair to sit up properly.
Speaker 4:I should know.
Speaker 1:Don't even try. It's not gonna work. I already tried it. Anyway, introduce yourselves.
Speaker 11:So I'm Zach Porter, senior simulation engineer with Global on the IndyCar program. Cool.
Speaker 4:And I'm Kyle Kirkwood, driver of the number 27 Honda for Andrade Global. Fantastic.
Speaker 13:Yeah. And I'm Drew from TWG.
Speaker 1:Fantastic. How how do how do all of you fit together?
Speaker 11:We're all under the TWG umbrella.
Speaker 4:Okay.
Speaker 11:Basically a bunch of different businesses within that and Drew could probably speak to a little better than I can.
Speaker 13:Yeah, mean, it's a family, it's a great holding company. We have tons of businesses from insurance to asset management, investment banking and sports, media, entertainment, Western lifestyle. And of course the crown jewel of just about everything is the awesomeness of motorsports Yeah. And the Andretti team in IndyCar.
Speaker 1:How long have you been involved with Andretti?
Speaker 11:It's my fourth season at Andretti.
Speaker 1:Fourth season?
Speaker 4:Yeah. All
Speaker 2:Third season. Yeah.
Speaker 4:Losing track of time here. Think it's my fourth. No. It's my third. It's my third season with them, but I've also I've been a part of the family for longer than that.
Speaker 4:I was with them in Indie Lights and then I joined back with them in Indie Car. So really five seasons actually if you combined it all.
Speaker 13:Yeah. And I get to be this suit guy. So I sit and watch this but I've been here a year.
Speaker 1:Oh, fantastic. Yeah. And, yeah, and and walk me through the flow of, why you're here specifically at AIP Con? Why are you working with Palantir? Yeah.
Speaker 1:Yeah.
Speaker 11:So in IndyCar, we have we have a ton of data. Yeah. In a ton of different siloed places. Sure. It sits, you know, from stuff that we control, like our car setup database and stuff.
Speaker 11:It but it also sits in like databases from IndyCar that we don't control.
Speaker 1:Sure.
Speaker 11:We have to consume all these things and they're all connected. They all represent performance. They all represent the pieces of the car and how they go around the track and and how we get faster and how we're relatively performing against the competitors. Yeah. So we came to Palantir and worked down this path to try and connect to all these disparate data sets into one place where our engineers can make better decisions faster, sooner.
Speaker 11:Because in the end, for practice one to practice two or practice two 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. Yep. So the more informed we can be, the better decision we can make in theory that the faster we can iterate Yep. And be more competitive.
Speaker 1: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 mass advantage? I'm thinking of, in sailing, there was this maybe it's a fake story, I don't know. But this idea that there was in the what's the big sailing cup that Alison races in, America's Cup? Sail GV.
Speaker 1:Yeah. It's all catamarans now. And the story goes that they were all racing monoholes. And someone looked in the rule book and said, there's nothing that says you can't bring a catamaran. And then one day, somebody brought a catamaran and just beat everyone.
Speaker 1:And it was just one of the most fantastic stories. Have there been any eras that you've studied where someone just figured out something that just rewrote the
Speaker 4:whole
Speaker 2:I
Speaker 4:mean, it would never be like this again. But you had the fan car in F1, right?
Speaker 1:Tell me about this. Yeah. Yeah, yeah, tell me the full story.
Speaker 4:I don't know the full story.
Speaker 10:I don't
Speaker 4:know you do either. We're in an
Speaker 1:era of Yeah.
Speaker 11:Motor sport now that things are super tightly regulated. Sure. It's 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 king.
Speaker 11:And they Yeah. The guys did a similar thing. They looked at the rule book and said, hey, there's nothing that says we can't power the air inside the car on So our they built a car that had big fans at the back of it and skirts that ran down the side. And the car literally sucked
Speaker 1:its way down. So just so much extra down force.
Speaker 11:I I don't remember exactly how long it existed,
Speaker 1:but it wasn't very long. It got banned. It's amazing.
Speaker 11:But it was fundamentally dominant. And there's been a lot of those kind of things now over time. But now we're kind of in this era of fighting for these hundreds of seconds, these little micro moments. And that's where being able to drill down through big data
Speaker 1:is so powerful for us.
Speaker 2:I always imagine 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.
Speaker 2:And so if you're jumping between different data sets and systems of record, I can imagine that can be a disaster.
Speaker 11:Yeah. And it's not just while Kyle's on track. Yes. He's doing all of that. But then as soon as he's back, it's between sessions as well.
Speaker 11:The clock's always ticking. We're competing on the track and off the track.
Speaker 4:Yeah. Well, I mean, we just have such little time to go through so much data. And to be able to piece it all together and understand a full picture, you have to do a lot of different things, which our engineers are very good at. But it's time consuming. So if there's a way to actually consolidate it, simplify it, and make things more efficient, then it's going to allow our engineers to make better decisions down the road, which is optimizing performance on the racetrack.
Speaker 1:Okay. Talk about the tension between the three of you. I imagine that you only care about speed. You care about speed and manufacturing. Can we make it?
Speaker 1:And you care about speed
Speaker 4:We all
Speaker 1:care manufacturing about capability and and cost maybe? Cost. Cost? Look, so what are the trade offs? Obviously everyone cares about speed and winning but there are layers to the trade offs because you can't just always turn every dial to 11, right?
Speaker 13:Well, I mean look, I spent thirty years in the Marines.
Speaker 1:Yeah.
Speaker 13:And we got tired of fighting wars on PowerPoint and for business, we're getting tired of like making decisions off of rudimentary and incomplete systems that provide only partial solutions and it just takes forever to get data together. Yeah. And so, you know, from a business perspective, we have to look at it and basically say, look, we want to transition to something better. And the cost of that is not just material like dollars, it's also change. It's changing mindset.
Speaker 13:And as you can see from Andretti, like they're all into this. Like this team is ready to make that transformation, but it'll still come at a cost, right? There's people who are stuck in their ways. Look, I like to do things this way. I'm not used to that much data coming at me.
Speaker 13: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've got a great team, like it's just gonna go like a hot knife through butter. It's gonna be amazing.
Speaker 1:That's great. Yeah. Walk me through some of the benefits and try and me some anecdotes about where gains have come from throughout your career.
Speaker 11: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. 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.
Speaker 11:He's flogging the thing around the racetrack and occasionally touching walls and other cars and
Speaker 4:More than touching.
Speaker 11:It's really difficult sometimes to keep to make sure every system is working perfectly. Right? It's it's a never ending battle of trying to do that. And so, you know, we're we're working really hard with some ML models and some stuff to pick out sensor anomalies and flag them automatically. Yep.
Speaker 2:So that
Speaker 11:our our systems engineers don't miss them. Yep. And they can go drill down and figure out why that sensor's failed or where and what their knock on effects And 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 gonna be as good as it could be. That's that's been the the the earliest easiest wins for us is is kinda in that space.
Speaker 1:Yeah. Yeah. Is there a a how do you think about budget budgetary constraints? Constraints? Is that something that's just said internally?
Speaker 1:Like how do you work I'm through
Speaker 11:happy that I don't have to worry
Speaker 1:about that. You don't have to
Speaker 2:worry about that. Drew?
Speaker 1:Look. But even zooming out for those who might not be familiar, I mean, 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 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 costs later.
Speaker 13:Me, let's talk like this is innovation. Yeah. Okay, so we gotta be careful here. Yeah. Right, So if you come in, I mean, obviously there's dollar budgets, right?
Speaker 13:Because it's not unconstrained.
Speaker 6:Yeah.
Speaker 13:But at the end of the day, like what we wanna do is we're talking about a fully connected business here.
Speaker 1:Sure.
Speaker 13:So they've got an HR shop, they've got a tech team, they've got engineering, they've got a ton of groups that all need to be brought together. Yeah, yeah. So apart from just the car and the magnificence what we're you've got to bring it all together. And so we need room and space to be able to build out a complete connected business. Yeah.
Speaker 13:Because frankly every signal across the business is value. Sure. And by squeezing and optimizing and making things run more efficiently, we end up with a better sport. And like, I think at this point we're in that journey. And so costs are gonna be, you know, not giant but constrained and we're gonna deliver and we're gonna watch and see as this evolves until we land somewhere where we can finally say, this is it, this is the benchmark and this is what we should manage off of.
Speaker 11: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 job and in turn we turn commercial side of our business and look at them and say, hey, it's your best job to go out and find that sponsorship, find those things. Because if we don't use this tool, competitors will. And we're in the business of winning and if we're not gonna try to do that, why
Speaker 1:we here? Take us through the next few months in the calendar, the rest of the year, the next year.
Speaker 11: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 case and our ideas that we sort of half baked and Yeah. Trialed some stuff and look at it and and productionize it. Sure. And and in in the end, try and get all of these or at least the first initial use cases ready to go for Saint Pete twenty twenty six.
Speaker 11:That's kind of the target, and there's a ton of prep from here to there.
Speaker 4: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,
Speaker 1:right?
Speaker 4: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 a lot of pride in their work. 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.
Speaker 4: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.
Speaker 1:Yeah, what does the rest of your off season look like? Are you training and running? I saw the F1 movie, and Brad Pitt's
Speaker 4:always Are running you running,
Speaker 1:are you attacking the guy, both?
Speaker 4:Training is important, right? Yeah. Mean, you have to be, as a racing driver, you gotta be like a certain weight, certain size. You gotta have good endurance, but you also need to have some strength to be able to wheel the car around, right? We don't have power steering.
Speaker 4:You're hitting the brake pedal as hard as you possibly can. And we're pulling up to four or 5Gs for an hour and forty to two hours at a time. So it can get very physical very fast. No power steering. No power car steering.
Speaker 4:Makes over 5,000, 6,000 pounds of downforce. Imagine driving your road car that weighs 8,000 pounds or something like that around without power steering.
Speaker 2:Just flash that on the screen when you got the driver view so that you guys get a little credit? People assume it's like turning the wheel of a Tesla or whatever.
Speaker 4:Yeah, no. It's much tougher than people tend to realize. That's specific to IndyCar racing, though. IndyCar racing, don't have power steering. F1 does.
Speaker 4:A lot of sports cars that you see, they do have power steering. But IndyCar itself, they do it for the sport. And they've kept it that way for many years. So it's a little bit old style. But at the same time, it's good because it really translates It's
Speaker 2:from the boys.
Speaker 4:A little bit, right? Yeah. It creates a sport out of it, right? It's a little bit more physical. People don't look at it as much as like, oh, you're just driving a car around some roads, pushing pedals, turning wheels.
Speaker 4:No, there's actually a physical side to it. The off season is a lot of training, preparation. We do a lot of sim work and driver in the loop simulators. And yeah, it's just being ready for the next race that comes up. It's hard though because you don't have g forces.
Speaker 4:Can't simulate g forces for a driver. So having that involved is something that you get acquired to as the season progresses, if I'm being honest.
Speaker 1:Yeah. What's your daily?
Speaker 4:I'm sorry?
Speaker 1:What's your daily driver? When you're not on the
Speaker 4:track, My we daily will driver. So that is one of the great things about being a racing driver is that you don't have to own a car.
Speaker 1:Oh, you don't own
Speaker 3:a car? Okay.
Speaker 4:I race for Did
Speaker 1:you get loaners or something? Is
Speaker 4:that Yeah, what exactly. So I race for Honda, right, in any car. I have a
Speaker 1:S2000 lowered with underlining glow. You have glow on the S2000?
Speaker 4:No. Have a Acura MDX
Speaker 1:Very cool.
Speaker 4:Since they're sister companies, right? And then I
Speaker 1:also They're not sending you NSX? They don't make
Speaker 4:the NSX anymore.
Speaker 1:They still got them laying around. Give them a call. We'll talk to them. We'll see.
Speaker 3:We need
Speaker 1:you ripping around at NSX.
Speaker 4:And then I also race sports cars for for Lexus as well. And
Speaker 1:To LFA every day, obviously.
Speaker 4:They also don't make an
Speaker 3:LFA anymore. Yeah. Just a
Speaker 2:million $2 car. I can just go rip
Speaker 4:and depreciate real quick. Yeah. I mean, I have 500 at home.
Speaker 1:Okay. That's the other car.
Speaker 8:That's great.
Speaker 1:Fantastic. Well, thank you guys for coming on. This is Anything else you worth sharing before you get out here? Okay. Enjoy the rest of the conference.
Speaker 1:Thank you so much for helping out.
Speaker 2:Thank you.
Speaker 1:We will talk to you soon. Have a good one.
Speaker 3:Thanks.
Speaker 1:Goodbye. Jordy, any other breaking news going on? We have our next guest coming into the studio in just a minute. I believe we have on.
Speaker 2:What do we got?
Speaker 1:Who do we have? We have someone else going on. Okay. Okay. Cool.
Speaker 1:Yeah. Yeah. Yeah. We're we're we're we're good whenever. We kind of ran late.
Speaker 1:Now we're now we're running a couple minutes early. We will keep it going.
Speaker 2:Quarantine. So Palantir CEO Alex Oh, Palantir CEO Alex Karp thinks the value of skilled workers is spiking even as big tech companies possibly his own may shrink. 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.
Speaker 2:Very cool. Scoop. We're scoop maxing. We're news maxing everybody. We're news maxing.
Speaker 2:What else?
Speaker 1:I think we're ready for our next guest if you wanna this is from the timeline. Looking good. Lots of posts. Welcome to the stream. If you're ready, we're good.
Speaker 1:We can we're we're we're happy to have you. How you doing? What's happening, John? Nice to meet you. Thank you so much for taking Yeah.
Speaker 1:The Welcome to Jacoby. Thank you. Any relation to Brandon Jacoby with him?
Speaker 2:I don't think so. I think
Speaker 1:you guys are different. We have we have a buddy
Speaker 2:who works.
Speaker 1:He's a designer. We like to we like to poke fun at him because he is we call him Jacoby. That And and whenever we have a design problem, we always call him.
Speaker 9:The last
Speaker 8:name sticks with that one.
Speaker 1:Yeah. Yep. Anyway, please introduce yourself for the stream. Yeah. Who are you?
Speaker 1:What do you do?
Speaker 8:Happy to. Sorry. Out of breath. Just lost you.
Speaker 7:You're good.
Speaker 8:You're good. So, Matt Jacoby. I'm the head of data science and analytics at Racetrack. Okay. Southeast based fuel and convenience retailer.
Speaker 8:Yeah. And shout out to my wife for letting me come up here because we're technically on vacation this
Speaker 1:week. Heard this. This is crazy. The grind never stops.
Speaker 2:You couldn't miss it.
Speaker 1:Yeah. Think memo about lock in season.
Speaker 8:Well, it's you it's you gentlemen. I couldn't pass up the chance to
Speaker 1:We really appreciate it. Yeah. Okay. So It's great to have you. Yeah.
Speaker 1: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 we have a general idea, but give us more.
Speaker 8:Yeah. Yeah. Happy to share. So roughly 700 retail locations across across our family of brands of Racetrack Yep. Raceway Yep.
Speaker 8:And Golf.
Speaker 2:A lot
Speaker 8:of people don't realize that we we own Golf.
Speaker 1:Golf.
Speaker 8:Yep. Yep. Cool. 10,000 employees Yep. Associates in our stores and people at our our store support center in Atlanta.
Speaker 8:A lot of people don't know either. We're top five largest privately held company in the state of Georgia and we are top 15 in The United States. Yes.
Speaker 1:Thank you. We have a So walk me through a little bit of the history of the company because I imagine that what we're gonna talk about in terms of like, you know, software, artificial intelligence is, you know, a revision to the way it was done years ago. Right? So so, yeah. Walk me through a little bit of the history.
Speaker 1:Get me up to speed.
Speaker 8:Oh, wow. Well, I can't speak to all of it. Yeah. 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.
Speaker 8:Yep. Actually, just wrapped up a conversation about this downstairs. But if you ask me, one of the purest use cases for transformation is converting from gut based and tribal knowledge based decision making to data driven Yep. And therefore after that analytics into AI based transformation. So you know, we we've really focused heavily even before my time on making the best decisions we can with data.
Speaker 8:Yep. And so our partnership with Palantir has really allowed us to to take that to the next level. Right. The proverbial next level. Promised myself I would avoid buzzwords in this conversation Yeah.
Speaker 7:But may
Speaker 1:not happen naturally.
Speaker 8:But but but yeah, it's it's been a conscious and concerted effort by our leadership top to bottom Mhmm. To to really make that happen and it's not it's not easy at times. Right? You're you're asking people to step out of what they've done in the past and to trust data and math Yeah. That may or may not be right if we're just being candid.
Speaker 8:Yeah. And so we've we've really grown and focused and and developed on on building that muscle with the organization top to bottom. It's been a it's been a really really interesting and impactful two years with with our team thus far.
Speaker 1: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 might be apocryphal, but I heard that and as I always do this, where I tell some story that might be entirely hallucinating.
Speaker 2:You're an LLM.
Speaker 1:But but Yeah. Was taking hope of you. So so the story goes is that
Speaker 2:One shot.
Speaker 1: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 And one next to there's some beauty there, there's some hilarity there, but you can imagine that that's the type of very tractable problem, where should I put a thing.
Speaker 1: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 could do, but like walk me through what you
Speaker 2:do Or even at last the individual store level where it's like, hey, we're out of this product.
Speaker 1:Yeah, are the problems? What's the most recent case study you did?
Speaker 8:Yeah, yeah, great question. Look at you talking about planograms. So yeah, we like to say that we're always focused 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 And so you're hitting on inventory, that's that's a really important use case.
Speaker 8:But even more important than that is making sure that we would 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 your gas tank and there's a yellow bag on the handle.
Speaker 1:Or Yeah.
Speaker 8:Or I would actually argue it's even more painful when you you put it into your
Speaker 4:And then
Speaker 8:you're And you're gonna have to yeah Slow. Or
Speaker 1:it's out.
Speaker 8:So there's there's that and there's also the inside experience. Right? We take pride in our in our food offerings. So Totally. Fresh pizza, fresh sandwiches, breakfast sandwiches.
Speaker 8:And that takes people. That takes time and that takes hours. And making sure that we we have the right level of people in the store, right number of hours, and and the right skill sets as well. It's not just an you can't just throw hours at these problems. You 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 that makes us continue to thrive.
Speaker 8:And you know, we've got this pin on, we're celebrating ninety five years. We've been here a long time, and we expect to be here a lot longer.
Speaker 1:Ninety five years ago, software didn't exist. It truly did not exist. And now you're sitting here implementing AI in in 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?
Speaker 1: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. And maybe they have a daily that's a Tesla and it's great, but then they also still are in the gas world in some ways.
Speaker 1:Have you has has has there been optimism inside the company for the future?
Speaker 8:Well, we we are certainly investing in the future. Yeah.
Speaker 2:We we I was gonna say people that are charging EVs, they wanna they still wanna get fresh pizza. Right?
Speaker 8:They do. Exactly. Yeah. The and and we were actually taking a unique approach where we're we're developing that infrastructure and and those those customer venues on our own. So we've we've chosen to to really understand the customer and do it in a way that that meets their expectations because we can't predict what the future is gonna hold 100%.
Speaker 1:That's a different experience right now because you might be stopping for twenty minutes instead of two minutes or
Speaker 8:That's five a great point too. So you have a more captive audience
Speaker 1:Exactly.
Speaker 8:For a longer period of time and
Speaker 1:take a lot
Speaker 2:of pride
Speaker 8:and all. Exactly.
Speaker 1:Throw something else
Speaker 8:in Come some racetrack swag in the gas station.
Speaker 1:Yeah, anything.
Speaker 8:Fresh pizza or what have you but Yeah, you can sit
Speaker 1:down for a minute.
Speaker 8:We're certainly not turning a blind eye to what lays ahead.
Speaker 1:That's
Speaker 8:cool. We have certain strategies and things that we're talking about to to make sure that we stay ahead.
Speaker 5:It does feel like it's
Speaker 1:it's a 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 less or a lot less risky, I guess, for you. Yeah. Very, very exciting.
Speaker 1:So walk me through the actual, like, scale of the Palantir implementation. Are you early days? Are you trying to roll this out to all the employees? You said 10,000, wasn't it? Something like that?
Speaker 1:Do you want everyone to interface with this, or is this more of, like, a managerial tool that will be used to, like, make decisions about how to run the business?
Speaker 8: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 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. And I think as we as we progress and as we deploy more and more use cases, I very easily could see getting the technology in our frontline associates hands as a real value add and frankly a differentiator.
Speaker 1:Yeah. 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 Google Docs and get it to talk to your Slack. And Slack is owned by Salesforce, so they don't talk to each other. And and and I'm wondering in the retail context if, like, a POS system and an inventory management system, like, there might be some similar sharp elbows, or is it all pretty copacetic?
Speaker 8: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 really removing the need for kind of call it peer to peer communication of those platforms.
Speaker 1:Because everything goes into data like you're Exactly. Going
Speaker 8:and you know, again, I think that that team really deserves a shout out too. So while while our team is in the business, the IT and the data team has really been an enabler for us. We have a wealth of information and data that we can make some of these really complex decisions with. And without it, we would be severely hamstrung and would be working on challenges like pulling out of POS systems or what have And so we we've kind of we're past that level and we have a really strong data lake and infrastructure and architecture to to support all of the the nerdy math that my team loves to do.
Speaker 2:Yeah. Awesome.
Speaker 1:Yeah. What what else are you trying to identify going forward? I mean, I imagine that like the base case is just like, wanna know what stores are over performing underperforming, but then ideally you wanna be able to predict which stores are gonna start underperforming and intervene beforehand. Is that roughly the
Speaker 8:main 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, explains the descriptive.
Speaker 8:And then my team really steps in on the predictive and the prescriptive front. So think about predictive maintenance or hey, this fuel pump is predicted to go down in the next two or three weeks. That predictive and prescriptive approach allows us to pivot again, transformationally away from being reactive to being proactive with things that really impact our customers. So we like to really focus on hey, where are the customer pain points? How can we peel that onion?
Speaker 8:How can we how can we solve some of those so that they have a better experience? And that that drives a lot of it too. So so yeah. It it there's a world of use cases out there and we're really just scratching the surface.
Speaker 1:Very cool.
Speaker 2: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?
Speaker 8:Oh my gosh. That flies in the face of everything that
Speaker 2:we think.
Speaker 1:Well just Just because you wanna
Speaker 8:So there's we like to joke a lot about my team and maybe others share this sentiment or don't but is it worse if a a pump isn't working? Or is it actually worse if a pump And is I actually think my experience are the most painful when I go up to a pump and it's slowly ticking. At least when when you see a bag and you see the yellow
Speaker 1:handle And you know just don't even try.
Speaker 8:Don't go there. Yeah. Don't go there. And I I don't think
Speaker 2: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 go fast and now as a as an adult, I'm I can just get I but I'm getting like five times the amount of gas. Right. I'm just like,
Speaker 8:you weren't going to racetracks
Speaker 1:because we predicted that's need to be brand for racetrack and do anything slowly. Yes. Speed is in the name. That's how you name This company.
Speaker 8:Race track.
Speaker 1:For ninety three years. Five. Ninety five years. Yep. Ninety five years.
Speaker 2:I can't wait for a 100. You'll have to come back on. Yeah. That's hundred years of race track data analysis. Break it down.
Speaker 2:We'll do a hundred hour stream
Speaker 1:Year by year. I mean, it must be fascinating.
Speaker 2:Name every data point.
Speaker 1:I mean, just pulling like the revenue over a ninety three year ramp. Like that's gotta be fascinating.
Speaker 8:That'd be interesting.
Speaker 1:Fascinating. Anyway, thank
Speaker 2:you so much for coming on and interrupting your vacation. Yeah.
Speaker 1:This is great. We'll talk to Enjoy
Speaker 2:the conference.
Speaker 1:Have a great rest of your day. Cheers. Enjoy the conference.
Speaker 2:And that's our last guest for the day, right?
Speaker 1:That's our last guest for the day.
Speaker 2:This was fun. Started out with a bang.
Speaker 1:We should run out we should run well, 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 livestream, 30 plus destinations. Of course, we won't need to tell you about Figma.
Speaker 1:Think bigger, build faster. Go to figma.com for all your design needs and get compliant on vanta.com. No problem. Prove trust continuously. We also got graphite.dev supporting us code review for the age of AI, Polymarket, of course.
Speaker 1:Some big news out of Polymarket. There was a major trade deal. We'll talk about that tomorrow.
Speaker 2:Okay. Okay.
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Speaker 1:Linear, of course is a purpose built tool for planning and building products.
Speaker 2:Big day for Linear. Big day for
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Speaker 1:Getting lots of shout outs at Atlassian.
Speaker 2:Atlassian is paying $6.20 $6.10 for the browser company, they should get ready to pay 6,000,000,000,000 for linear.
Speaker 1:I think so. We are we are of course supported by Numeral, numeralhq.com, sales tax on autopilot. Fin.ai, 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 to the next level. We, of course, sleep on Eight Sleeps.
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Speaker 2:Adquick forever.
Speaker 1:And if you noticed Doctor. Karp was wearing a fantastic Patek Philippe
Speaker 2:Aquanaut chronograph.
Speaker 1:And if you want one for yourself, can go to bezel, getbezel.com. Your bezel concierge is available now to source you any watch on the planet, seriously any watch. I'm sure that they would love to find you a
Speaker 2:Orange band. An orange band Aquasite for sure. Business Insider has a scoop here that says Palantir CEO Alex Karp says top tech talent is about to get crazy valuable. Alex Karp, CEO of Palantir said on quote unquote TV Why did they put us in quotes?
Speaker 1:This is the dividing Why
Speaker 2:put us in quotes?
Speaker 1:This is the dividing line. TV ads. The laptop.
Speaker 2:So Business Insider. Wow. The website Business Insider
Speaker 1:Wow.
Speaker 2:Says that top
Speaker 1:I I think I think we gotta I think we just gotta put like the just one of the words in quotes. It can't be quote business Business.
Speaker 2:Business. Business Insider.
Speaker 1:Business Insider. That is the way we talk.
Speaker 2:I gotta look at, I actually have to look into this company because I love business and I Insider trade Insiders in business.
Speaker 7:Isn't that
Speaker 1:the lore? Isn't that the lore? Henry Boggett, the guy who started Business Insider?
Speaker 6:Loved insider
Speaker 1:trading. Think he lost his license. I'm not kidding. I'm not kidding.
Speaker 2:Okay. Look this up.
Speaker 1:Business Insider, insider history history.
Speaker 2:And more breaking news. Justin Bieber is launching Swag two tonight, the new album.
Speaker 1:What does that mean?
Speaker 2:And Meek Meek Mill posted two hours ago. Meek Mill becomes a AI founder.
Speaker 1:So according to Wikipedia according to Wikipedia, Henry Bloggett was charged with civil securities fraud by the US SEC, settled the charges. There you 4,000,000. He was permanently banned barred from the securities industry Oh. By the SEC and the NYSE. The charges rose during the dot com boom and Merrill Lynch, which included issuing materially misleading research reports on Internet companies and making exaggerated or or unwarranted claims about them to customers.
Speaker 1:And and then in 02/2007, four years later, he co founded Business Insider, which is a fantastic It's so funny. It's funny.
Speaker 2:He was in the business of insider trading he said, why did I combine
Speaker 1:They didn't say insider trading. They said civil securities doesn't sound great, but you know, a seven year run, Jeff Bezos purchased a stake in Business Insider and he had a great run 02/2023. Anyway.
Speaker 2:There's so many great quotes from the the carb segment. This one I would say, he says, I would say modestly, I'm the most humble I've ever been. You would never build a software company downstream from value creation. It's all, how do I make the client feel like they're getting laid while they're getting f. So good.
Speaker 2: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
Speaker 7:out. This
Speaker 2:He's us still know TVPN invite. We should we should probably have Mon. A lot
Speaker 1:of people have Mon.
Speaker 2: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
Speaker 8:but But we're we're
Speaker 1:in the capital of military intelligence right now.
Speaker 2:It looks like he sold out the initial batch.
Speaker 1:Let's have him on. You put the timeline in turmoil. Anyone who puts the timeline in turmoil is welcome on the show.
Speaker 2:I'll give him a follow right now and we will make it happen.
Speaker 1:We're we're a lot of people are having fun with the stream. This is a great reaction. Anyway, that's our show. We got to get out of The United States and back to The United States.
Speaker 2:We do. Last thing this just because it is breaking and it's funny. OpenAI plans to launch an AI powered hiring platform by mid twenty twenty six putting the outfit in close competition with LinkedIn.
Speaker 1:With LinkedIn?
Speaker 2:The company also wants to start certifying people for AI fluency. Are you AI fluent?
Speaker 1:How many MNeshes? Yeah, this seems like more of a Merkur competitor than LinkedIn maybe. I don't know. Yeah, we need to dig in more to that. But the other odd thing is that wouldn't Microsoft get a copy of whatever they build?
Speaker 1:So wouldn't Microsoft get access? Like if they build a new I mean, that's the deal. That's nature of the deal is that they get the rights to OpenAI's IP. So if they build something that's valuable, but if they build a network, then that's a separate thing, right? Because the IP doesn't matter as much.
Speaker 1:The weights to GBT5 are not as valuable as the ChatGPT app. So yeah, maybe there's something there. I don't know. People have been complaining about LinkedIn for a long time. Maybe there's
Speaker 2:some Breaking news.
Speaker 1:What is this?
Speaker 2:Donald Boat says that he has art for the UltraDome.
Speaker 1:Oh yeah, yeah, was talking to him about that. I'm very excited.
Speaker 6:Great.
Speaker 1:He made something.
Speaker 2:Well, wish we could keep streaming but we gotta get back to
Speaker 1:We do, we We gotta go. Let's go.
Speaker 2:All right folks.
Speaker 1:Anyway, thank you. Thank We'll see you
Speaker 2:Today, we love you. Back to a regular show tomorrow.
Speaker 1:Have good great rest rest of your of