🔴 Alex Karp LIVE from AIPCon 10 | Alex Karp, Peter Zaffino, Chad Wahlquist, Sam Berry

27:56And then so there's a whole value lecture there. You have a political situation where people who do not understand basic economics are winning the political argument. So we could talk about where AI is coming from. There's a lot here. Let's break it up. Let's start with the token maxing thing. Let's start with what's real. How are you actually thinking about deploying AI, ROI? First of all, what is Palantir's philosophy around token consumption? Well, like we, okay, we have a product that will allow you to be, I mean, internally it's called something, but externally, but really we call it the demasturbatory, like get off masturbation thing internally.

28:38Sure. It's like people are just like print, like sitting there all day, kind of like a porn addiction and enterprises are like, okay, we knew this, we believe this will create value, but we cannot have people just like some people. Checking the weather with it and just rearranging deck chairs on their personal Titanic. It is literally like porn. Okay. Like people are like full on. It feels so. Yeah. Tool-shaped objects. Yeah. Tool-shaped objects you're looking at more than you want. You hope no one notices. You're kind of before dinner. It feels productive to have every email classified with tag.

29:12Here's what it comes down to. Like business problems can never be. I mean, sometimes they can be solved purely with money and just spending more. But very often. Actually, I think it's the opposite. So just to give you a weird analogy. No, and I was going to say, very often it's more the opposite, where it's about figuring out the right way to do something, and then you can use capital to fuel that process. Well, let me give you a thing that's too generous for you guys. Okay. It's taste plus money. Okay. And there is no, like AI, like if you look at, like pick any issue you want to talk about.

29:44Token maxing, what's going on with deploy codes, Are other people going to build ontologies? Why does our political class not understand AI, especially in Europe? It's like, yes, because all these things can be scaled in a very valuable but largely going to commodify way. But you can't scale the taste of like, what is the business problem you want to have to solve and need to solve? At the end of the day, whether it's the Ukrainians fighting, the Israelis, commercial entities, there's somebody sitting there who's like, okay, but this problem is valuable. This problem isn't. And once that value, that problem always has, that problem almost always, but not always has attributes.

30:28So there are some problems you could solve with this. Like I want to write a report on GDP growth in China, right? But if it's a problem that requires a knowledge store, like I want to understand the specialized way I underwrite. We're going to have a guest here. I want to understand the specialized way I drill for oil and gas that's both legal, ethical, and reduces the cost of production. I want to change the supply chain of my industry, whether that's military or whether that's building boxes or whether that's cars. These things require actual, precise, ongoing processes. They are enhanced by large language models.

31:03They are not replaced by large language models. And then you get to security issues. Like, for us, like, the whole mythos thing is just a boon. Because, like, yeah, we can take any model, their model, open AI model, open model. We can identify, we can now identify vulnerabilities at, like, 10, 100x. Yeah. But then who patches them? How do you patch them on-prem? How do you patch them on-prem so that your specialized knowledge stays on-prem? Like if you're any business or Intel service, a lot of these things are very similar. Like you're not putting your classified data in a public cloud. Same thing if you're like you have a special way of farming soybeans.

31:42But you're not. So it's like how do you have – so all these problems are exposed, identified. And then you always have a thing of where's the charisma, which people really underestimate. And it's not global. There's no global charisma now. So right now, the large language models are very frontier. Companies are super charismatic with investors. I'll give you some news. They're super not charismatic with enterprises and the people. Even with enterprise? No, no. Because I understand what the people are. No, no. The enterprise people. I have a secret. I have a secret. Like every company has a secret way of selling.

32:18You know what my secret way of selling is? Don't even call it. Don't come talk to us. There's a frontier company. Go spend two days with them. and if you're lucky after you're done, I'll let you in my door. They're like clamoring. They're like, hey, I'll take your bad brand. We have a great brand in enterprise. But it's like secret knowledge because the investors love this. They're like, hey, my stocks are all up. Everything's up. I mean, Palantir's done very well. But it's like, and you guys are doing very well, I imagine, right? And it's okay. But I'll tell you what, you go down the street, you talk to a Marine, you talk to a bus driver, you talk to the person who owns the bus driving company.

32:55they are not happy. They do not like these people. They're tired of people token maxing. It looks like masturbation that's cost them money. They're like, and honestly, then you have something we're not allowed to talk about in this country, likability. Like Palantir, I think we have like 50, 100 million global bands. We have like 5 million people that wake up in the morning literally calling me Satan. I didn't know I had that kind of warm hand. But, you know, it's like that's what they believe. and they really believe it. Okay, what people are not allowed to really address is we have fans and enemies.

33:33You're polarizing. We're polarizing, which means both sides. These people have one side. They're just... It's a really big... Social media companies too have the same problem. Everyone uses them, but no one likes them. But then they also live in a circle and that circle is printing money. So it's like, you know, when you look in the mirror and you just printed a lot of money, You look pretty fresh. Is part of it that some element of the technology, let's just say LLMs, is so magical that the companies involved, that the companies that are making and selling frontier intelligence can be bad at a bunch of other things and still great?

34:11Well, no. No, no. They are magical at a certain kind of thing, allowing you to write, for example, code. Now, that code can't be used as a knowledge store. So if you look at code in, like, three different ways. like just using Palinthias as a model. We have code that's basically infrastructure. So what are the Ukrainians using? What is the Department of War using? What do a lot of our enterprises, we call that primitives. It's basically hard-coded things that understand the world. What do you do? It would take millions of technical hours and an understanding of all these enterprises to do it. So it's much more like how do you build a steel beam?

34:43Then you have code that is written by FDEs. So that's kind of managed. The reason why FDEs work, the secret is, It's actually managing something that we as a product. So you're writing to a code base. We're managing that. We're increasing our product. It's not just random people writing. Then you have, let's call it free code. That free code is, that's magical. Like you can do it very quickly. It's almost right. It doesn't have to be exact. Dashboards. Dashboards, financial stuff. Little flow. Probabilistic stuff where you just have to get it. One-off analysis. Magical. Yep. By the way, it's magical.

35:17It not only creates, and it's magical in a way. I know people don't like the porn thing, but it's also addicting. It's like, you know, it's not good for you, but, you know, it may lead to damage. One more dashboard. One more time. It can't hurt that much. I know my doctor says it. I shouldn't do it. But it's like it's like that. Right. And you just keep going. And like and if you're involved in that thing, you're also making money. Yeah. And then last not least, in certain circles, like if you have you want to be a researcher or you believe essentially it's a religion. So, like, you know, and like one of the things, it's very charismatic, especially to people who've never had a religion, because all of a sudden that hole in your heart that was yearning for, I don't know, I would say, you know, a established religion, Judaism, Christianity, Islam is like being filled.

36:03And all the answers are there. But it's very, very successful at doing things that a company has to do. But it is not actually solving the problem that enterprises are. it is now it can solve them that's the trick it's not it's not binary it's not like you can't say they're not valuing they're totally putting our business on steroids like without llms nobody would be talking about our ontology about apollo managing secure exploits about our ability to manage an enterprise essentially turning all these companies into fdes these deploy codes we love them because now every company wants to deploy code you know how you do that you re-platform on Palantir.

36:43And it actually works. It's not somebody with no taste who's never done enterprise, who has no earthly clue how these things work, who's done something else and is just imagining they know how to do it. It's part of this moment quite entertaining for you because you guys have been working on understanding businesses at a deep fundamental level, creating you guys have effectively been doing the work that people are promising AI could do for 20 years now, but actually doing it, finding all the really rough edges and being at a point where you don't have to oversell the technology. You can sell both things.

37:25But now there's maybe... Here we go. We got it together. Now there's maybe... Oh, it's the wrong side. That's why. Flip it around. There you go. It's kind of like the dyslexic. There you go. There you go. There you go. Living the brand. Hopefully we got that. I don't know. All this stuff. I trailed off, but is part of it entertaining to you that it feels like, you know, Palantir has always been in some ways not had competitors because there's nobody with Alex Karp running a company that does what Palantir does besides Palantir. But at the same time, there's been tens of billions of dollars deployed now to effectively do what Palantir does, but just selling the intelligence part, not selling all the underlying kind of infrastructure.

38:14Well, they're doing two things. They're trying to sell the intelligence part, and they're trying to pretend if you just hire a bunch of people and let them run around their FDs. Now, the very cool thing is when you've been in your basement doing your thing and everyone kind of views it as the freak show, it's really interesting and great to have adoption. The pretty ironic thing is half the people adopting now don't even know they're copying. But now, the copying thing helps and hurts. Where it hurts is in the beginning, it puts clutter in the market. And there's no doubt about it. where it helps.

38:53And then we saw this with defense tech, honestly. So like in defense tech, we were the only people were the first people, despite what I, I love these, honestly, other podcasters. They're interviewing people or parroting things. I said 20 years ago, they don't know it. And it's like, Oh, that's so insightful. It's like, yeah, of course it's insightful. Carp said it 25 years ago. And like, but it's, but so that kind of, that part is super weird. But, and like, but, but it's, but what really happens when we see is like, it expands the market. So like in defense tech, we would not be doing this well in just purely in government, unless there weren't 50 companies that were doing similar things, because then the people are like, okay, first of all, you view it as like off balance sheet sales resources.

39:37Well, well, it's off. Well, no, that's the large, it's, they do two things. They increase the size of the market because de facto, nobody wants to find it under a market where there's only one person. Sure. So, like, if you're the one person, the percentage of the defense budget you can get is much smaller. And two, they set up a comparator. It's like, you know, you may not like the freak show. Okay. But have you noticed the people who are serious buy it? And then three, it changes the standard. Now, what you're seeing now is, like, that times 100x. And it does change, like, recruiting, retention, and, like, how you build a company.

40:16and we're always thinking, you have to think about how to, being dyslexic, huge advantage there because like you don't have a playbook and now you need things to shift and we're doing that. The central thing though that is just cannot be developed, even if you understood the playbook, a lot of these things are like, appear like, it's like, you know, LM code appears like Pounder code, but isn't for deploy thing, appears like Pounder, it isn't ontology. You could theoretically copy parts of it, but they're essentially structures that are built deep into organizations that we own. And by the way, take you three years.

40:49And in three years, we're in a completely different world. But there is this magical thing called taste. Like in the end of the day, the reason why you guys have done so well, it's of course there's aptitude and diligence and showing up and all those things. Yeah, but you have to be able to differentiate between two people who are in business, one of whom is saying something that sounds weird, that is insightful. One of whom is parroting something that sounds weird. and that's all they're doing. And a lot of people, very few people can do that. And you have the same thing, like the enterprises that succeed, there is a taste arbiter.

41:25And at Palantir, we have taste in every product, taste in every deployment, taste in every casting. Who puts the people there? How do you put them there? How do you organize the thing? Our ontology then does that technically. How do you manage the whole org with taste? Who should be in charge? What data sets should come in? What are the ways in which you protect? what should you push into the public crowd? What should be on-prem? I mean, leaving aside the law and wars, war ethics, what do you want to protect? What should you protect? What should you not protect? Because quite frankly, you want that to be out there so you can get more data.

42:00All those things are arbitrated by taste, and then you have to have the credibility of having taste. That's a real problem for a lot of these places because they're popular with their friends. They really don't understand how unpopular they are in enterprise. They think it's like, oh, yeah, it's like the way I think I have a problem with like professors at Columbia. It's like, no, it's a real problem. Like they think I'm Satan. And, you know, it's like I think, you know, we grew up in the same community. Let's talk about Heidegger. They're like, they don't want to talk about Heidegger. So it's like, it's like, yeah.

42:30And so that's just a, it's a weird thing. It's going to be a super, the one thing I would say for anyone listening, if you're listening to this and you're chillaxing and not active, I'm not saying you have to agree with me politically or anything. Partly because of this dynamic and very self-inflicted because I'll tell you, I can't name names. I called many of the titans of this world and started this six months ago. Every couple of days, we're going to be national. You call them every couple of days? Some of them are like, yeah, we're going to be. I mean, honestly, they find me very entertaining.

43:06I'm not sure. So they call because it's like, oh, yeah, this is going to be entertaining. You're going to pick up. So any case, I've been telling them for six months, we're going to be nationalized. We're going to be nationalized. And they're like, why would anyone nationalize? It never happened in America. It's never. Why would anyone nationalize us? We're so likable. We're creating so much value. Like, okay, I'm not going to debate that. I know how likable I am. I'm not going to tell you how likable you are. But I am telling you, and you know, the momentum on this is on the side of people who are nationalized.

43:38And we don't get our act together and figure out ways we can say, hey, look, there are problems here we're going to deal with. These things are not going to – yes, they are going to create opportunities. You have to talk openly about how these things are valuable because we have adversaries. You can't just say these – all that stuff. So the primary risk, honestly, to Palantir and a lot of these other countries is – and then it's going to be nationalized. Before nationalized, it's going to be regulated by people who don't understand this. And now they'll tell you in private, I'm working on this, I'm da-da-da, and this is in this lobby.

44:07It's just like not going to work. So that's something, if you're listening to this and you're like, look, you don't have to agree with me on all my proclamations. I got a lot of, by the way, there's some people who think I'm saying we should have a draft, too lazy to read. I'm just saying we should. In a world where everything is changing, everything is changing, don't we have to find some communal structure to remember we're American? You don't like my idea of we all do a week in the park? Great. Come up with some other idea. We can have no idea. And then they're like, well, I'm saying I do not want to draft just to be explicit.

44:40They're like, oh, that's pro-war. No, honestly, you know what? Most of our wars are fought because no working class person is making a decision. You start making sure everyone is involved in everything. I'll see you have few wars we fight. It's actually the anti-war position. But in any case, disagree with everything. We have on the right and on the left people, people who have no earthly clue what they're talking about, right and left. All they're talking about is how much they hate us. and those of us who are sensible in the middle, too many of us are chill waxing. Like nationalization, it can't happen.

45:12America would never do that. Sleepwalking. Sleepwalking into, and you guys have tendies to protect now. You guys should be on the front line of this. Like you got full, oh, sorry, I have a full on very impressive corporate leader coming on. So I got to turn it down. Last question, if we have time. How are your conversations going with Fortune 500 CEOs around headcount planning? There's been so many layoffs this last year that people were saying, hey, we're getting so much out of AI. We're able to cut back here or there. People inside tech often know maybe there's just a reduction because there needed to be a reduction or got bloated.

45:54Maybe they do need to fund some AI initiatives. Or it's a declining business model. getting out competed by someone. Yeah, the business just doesn't have momentum. But how are those conversations going? What does it look like? By the way, I talk to Fortune 500 companies. I talk to unions. I talk to soldiers. I talk to fire. If you upscale somebody, they're more valuable. And like all these, whether it's people working on batteries, people driving trucks, people, corporate leaders. And again, this is where I think we have to be very careful to be more disciplined on the corporate side. Like, if you run around saying AI allowed you to fire two-thirds of your workforce and you did it because maybe your competitor is kicking your ass, that is a really – like, you might as well just go sign up for Bernie Sanders' manifest.

46:38And part of the thing is they really believe that can't happen. So they're free riding on the fact that it could. Like, we have – and it just cannot work anymore. These things are very, very explosive. The American people sense that there is something dangerous here. And when people are playing with that fire, it's like they assume the fire won't burn their hands. That's not the world we're in. That fire is going to consume us. And what we see, again, the warfighting example is just the most neutral, not for everybody. But, like, the soldiers at the bottom have gotten much more valuable. And I don't even just mean the special operators, which obviously they're in a different league.

47:13But, like, the people doing a lot of the operations now are doing our product. They're high school, vocationally trained. You see this everywhere. The modern enterprise is going to have, like, we have a very, very, very smart person coming on. And it's like you're going to have a very smart executive. He's much better at hiding it than I would be if I were him. But you can talk to him about that. But and then very talented, creative people with taste all up and down the stack. In any case, I think this is time for me to. I think this is time. Thank you so much. Great to catch up. It was fun. First.

47:54Oh, they want me to stay for two minutes or what? I'm only going to stay. Look, but he's got to be the star. The other headset is right here. Put him. Put him in here. Yeah, I'm just going to take off after a minute. We're going to put him in here. And why don't you put that headset on? Car, why don't you introduce our guest? Microphone on the left. Well, he's one of the smarter people in business. has developed unique ways to underwrite that did not involve firing people, and someone I admire. Thanks, Alex. With that, I'm going to let you guys go. Make sure to tell them that the ontology powers it.

48:36It's everything. Always selling hate. Thanks for coming on the show. It's great to meet you. Thank you. Yeah, please. Kick us off with a bit of a more formal introduction. Yes, I'm Peter Zofino. So I'm the executive chairman as effective on Monday of AIG. I used to be the chairman and CEO and have worked with the company for nine years to help transform it. It was in a place where underwriting profitability was challenging. Operations were challenging. Data was challenging. Capital was challenging. So I had a great team of people with me to transform the company. So give us a shape of the business in terms of the different business lines, the different products, the international footprint, the workforce?

49:20Give us the scope and the scale here. Global company with a little bit of a unique footprint. We're 50 % international, 50 % North America, but our second largest country after U.S. is Japan. We have a big business in India, and then we have a very big business in the U.K. We do complicated risks, so you can think about what's happening in the Middle East now with shipping, marine energy. We're heavily involved in that. So something where there's not an existing futures contract that a company can just go and hedge. It's not, oh, I'm going to buy some oil futures because I fly planes around and I know I'm going to need diesel fuel in a couple of months.

49:58And so I'm going to hedge that out. This is for more complex risks. It's for more complex risks. And, you know, think about the largest, you know, sort of customers in the world, big oil companies, you know, Fortune 500 companies. But we also have a personal insurance business, which will cover things like accident health. Yeah. that are distribution to consumers. So we have a real balance. Part of that feels like, if you're talking about insuring a Fortune 500 company against a geopolitical risk, that feels like a meeting that takes place in a boardroom. It feels like there's a lot of folks with a lot of trust built up over years to understand each other's businesses.

50:35But then there's probably a lot of other underwriting happening and teams putting together comps and spreadsheets and data. And I want to know about the intersection there. It feels like the business is, and I don't know if it ever will be, just one-click checkout for insurance products for Fortune 500 companies. But what is the interface between the quantitative, the qualitative, the relationship, and the data? And then how is that changing? So the quantitative, you have to start at the portfolio level. Okay. And you want as much data as you possibly can to look at deterministic, modeling, probabilistic, and then stochastic.

51:10I think once you understand your mean and you understand the standard deviation around that, then you have to apply it to the widgets, which is each policy throughout the globe as well as ways in which you structure insurance. You can't look at an individual policy in isolation. You're managing portfolio risk, risk to the entire firm, and that's something that's happening probably 24-7, I imagine. It's hard, and that's what led me to Alex Karp. You know, it's hard to get the aggregation done in anything that looks like real time. It's usually static. It can be 30, 60, 90 days. And your portfolio could change.

51:49I mean, it's not going to change dramatically. But having the ability to, you know, sort of assess risk and use the quantitative data to make better decisions on a daily basis is the aspiration of the way the company is going. Yeah. Take us back to your first meeting with CARB. Curious what the experience was like. The unique individual. Can we call you? Yeah. No. I was actually introduced by a board member many years ago, and it was really in this pursuit of not necessarily foundry or AIP or ontology. That's where it led us, but it was more on sort of the quantitative ways in which I was looking at the portfolio.

52:25Could he help me think through computing, and could he help me think through sort of portfolio optimization? And I just got more and more intrigued. I mean, you see the brain. I mean, he just thinks about things. he doesn't hold back. So I always knew where he stood with me and with AIG, but just developed a very strong trusting relationship. And there's such a tremendous partner that we're able to iterate with them almost like no other company because we do things in 90-day increments because going out like a year or two years is too static. And so we actually build our relationship on 90-day goals.

53:03And that's been incredibly effective. What is, you know, a lot of the AI companies talk about scaling laws, exponential growth and token production or even revenue in many cases. But what's growing exponentially in your business? Are you bringing exponentially more data into the platform every year, exponentially more compute resources, teams, number of policies? Like what is the thing that's experiencing a boom right now? So most important part, I believe, in terms of business is that you have to have a business solution you're trying to solve. So for us, it was more data, better data, and then reduce cycle time.

53:44So in other words, like when we get the data that comes in from our distribution partners, how fast can we get it with higher quality data and more data to the underwriter to make decisions? Got it. And then how do we actually make the adjustments? What's an example of distribution partner in this context? So it would be like an insurance broker or insurance agent or someone who has their client as a customer. You're going to sell your product effectively. Exactly. Yes. Yeah, that makes sense. What else? Jordy, do you have something? Where was I going to go? Alex wants us to cover ontology. Yeah, so we'll get there.

54:20So we primarily, I mean, we at least started covering early stage startups. There's been a debate in our kind of little sub industry right now around a bunch of new insurance focused startups that are growing incredibly quickly. And there's a debate going on is one, maybe AI makes it more possible to underwrite risk. And if you can do that, well, grow very quickly. the other side, you know, says, hey, you know, if you're hyper scaling an insurance company, maybe that's not, maybe you don't want to work with a company that is, you know, going through that hyper. The iron law of the universe. Yes, yes, maybe.

55:03What goes up fast must come down fast. Talk about what AI has actually enabled, where you're excited about it, where it's failing broadly, maybe where it's overhyped. And you can, I guess, tie that into everything you built with Palantir. And there's never been a time, in my opinion, whether it was, you know, introduction to FinTech and SureTech, how to use algorithms, how to build data lakes and repositories for data. There's never been a time in my professional career, so it's 35 years in big companies, that I've seen the ability to change how an organization actually runs itself. And that can come from big companies like Palantir or Google, or it could come from companies that are being funded by venture and have a very specific niche that can be additive to the organization.

55:58And what I think is happening, we talked about the sort of data ingestion portion, getting that into a digital workflow, using large language models to extract more data from what comes in, but also helping underwriters make decisions that are more comprehensive. You also have the ability in the way in which you service customers to be much better through the use of AI. I think companies generally, my observations, are struggling with the orchestration of how you actually drive agents, people, and data into an organization. And once that is solved and is certainly on its way, capabilities are there, then you start to think about the entire end-to-end chain being very different.

56:43What I think about Palantir, while they've been such a critical partner, is one is we evolved together. but in that data ingestion to be able to take structured unstructured text all sorts of data and get into a workflow and a fraction of the time helps us on the things i try to achieve it's like we have now data that we probably wouldn't have used before because it wasn't good or we couldn't translate it couldn't get it into the digital workflow um and then we start to build out an ontology and i really do think it's incredibly important if there's one thing i look at for our organization, certainly the advancements of LLMs, their ability to do things more autonomously now where we started with the binary gen AI, now we're into agentic AI where we can just do things autonomously for so much longer.

57:28Without the ontology of actually building what the digital twin of your business looks like, where you take it and how you evolve it becomes very challenging. So we've been able to do things with Palantir. I'll use the ontology example. Again, We did the full ontology of AIG, and then we went to look at an acquisition called Everest, which had about$2 billion of premium. We got Palantir into work with our team. We could build an ontology of Everest's portfolio on top of ours in four days. And quite frankly, what we started to learn again about that evolution is that you always relied on data lakes or global data repositories.

58:06What we found is that we could get, you know, sort of foundry and start to build out this ontology with going to the admin platforms. All of a sudden, these repositories and the central places of getting data and make sure it's scrubbed wasn't as relevant. So I think we continue to advance that in the way in which we are looking at our business. I have one last question. Just on the actual change management, the organization, like how the office feels, how did you go about actually working with Palantir? Do you set up your own internal Palantir workforce who sits alongside FDs? Do you let Palantir come in and plug in one person per team that you have set up?

58:50Was there a best practice? Did you go with the best practice? What was the actual experience of deploying the four deployed engineers? They get deployed into the organization. That's got to be a unique situation. First is making sure Alex and then two of the senior executives, Ryan and Ted, that everybody knows what we're trying to do together. So we start there. Then we wanted to embed the engineers with our team. So if we had a business leader that was trying to drive the underwriting output, you'd have technology from AIG. You would have some of the change management, but you have the engineers sitting there with our teams throughout the entire process.

59:25Because the iteration is really important in terms of translating what you're trying to achieve from the business side and the engineers actually helping us think through the application of some of the LLMs or ways in which we could circumvent some of the things that we were doing. Yeah, that makes sense. Jordy, anything else? No. Well, yes, but I think that has to be the most important topic. No, no. If we do have a second, I was not sure on timing. How are you thinking about workforce planning? Asked CARP about this, and he said to ask you. Or token budgets. We've stayed, as you've had this wave of AI layoffs, we've been over and over and over reminded people that if you have an individual, you give them more capability, you make them more productive, you make them more efficient, a thriving business will want to hire more people, right?

1:00:19Because you can get more out of every individual. And so we've tried to remind people that over and over and over as companies that oftentimes are underperforming or bloated for whatever reason, but what's your kind of philosophy around hiring, headcount planning, rifts, all that stuff in this kind of new era? We've been focusing on, I heard Alex at the tail end and I agree with him. So we're focusing on growth. We're focusing on reskilling and actually training our employees to be in a different part of the workflow. Now, you would do this, I believe in all of this, you have to still have great end-to-end process.

1:00:56And so things that have been, the human's been in LLM trained how to do things like outside of the normal workflow, you have to get rid of that. So I think that's just normal business. But our aspiration is not to implement AI or anything that we're doing with our partners to eliminate jobs. I mean, it's about growth, re-skilling, and finding ways in different markets to have exponential growth and opportunity and having a lot more insight in the business that we run. That's a great optimistic vision. I love it. Thank you so much for taking time to come chat with us. Thanks for coming on. Thanks for coming on.

1:01:26Thanks for coming on. Have a great rest of your time. Thanks. And up next, we have Chad Walquist. First, I'm going to tell you about CrowdStrike. Your business is AI. Their business is securing it. CrowdStrike secures AI and stops breaches. Welcome to the show. How are you doing, Chad? Great overcoat. That's a new one. It's a popular one. That's an Eliano special. It is. Oh, yeah. He is the master. Giving us a run for our money. Yeah, it's fantastic. Anyway, kick us off with an introduction on yourself, how you fit into Palantir, a little bit of backstory. I'm sure we have a ton of questions to run through.

1:01:57First, how often do you guys do these things? Because it feels like this feels like an annual event. Yeah. Quarterly. But Palantir. We're getting the call every three months now. Karp talks about, you know, manipulating time. You know, a quarter at Palantir is like a day, a year at another company. That is exactly right. So that kind of makes sense. Yeah, I'm like actually 23. The time warp is real. So we do these quarterly. So I'm a forward-deployed architect technically. I do what is needed. And so doing the needful is kind of the Palantir way. It's like there's no job below me. And so no matter if I'm out on the edge with customers, I'm talking to executives, explaining the ontology, doing YouTube videos.

1:02:36That's all what I'm doing. So really the goal is how do we help people decomp problems differently and apply the technology? Can AI do decomp? Yes. Okay. Unpack that because that feels like the secret sauce. That feels like the special thing about Palantir is actually being able to bring someone in who understands an organization. I think a lot of people see AI tools. A lot of people see AI tools. No, a lot of people see AI tools and they think, okay, very defined workflow, input, output. But now instead of just math that Python can deal with, you can deal with some text, and that's great. But decomp, to me, has always felt less like let's go into your HR system and understand the basic job description.

1:03:17Like, oh, someone uploaded this resume versus, oh, Steve actually does this completely outside of that system. and marketing has two platforms for this thing, and engineering has three systems for CAD files, and all the kludges that have built up over decades, sometimes hundreds of years for some of these organizations, that's what was so special about the forward deployed engineer program, the Palantir model. I'm surprised to hear you say AI can do it at all. It feels like the final boss. Well, this is where really the Palantir thesis is humans and AI working together. And so the way we think about this is modeling our business process.

1:03:57We heard some other people talking about this, of modeling my business process in the ontology. Because the LMs don't necessarily have a worldview or a world model of your business and your operations, the ontology provides that. And so when we talk about decomp, this is really about actually now I make more data computable as well. So we think about LMs on the agents that I'm interacting with. Also, we use LMs to make more data computable and then model that in the ontology of how things are really working. And so what we're actually doing a lot of times now is building out that worldview and then running multiple agents over this actually being combative towards each other right and so actually working against each other and having critiques and so after you do that you can also then give the human human in the loop feedback about this and iterate on this and also what we find is that's really a scaling mechanism it's like a new power tool right i think you guys were just talking about this the kind of the perspective around jobs and all this stuff it's like when you gave carpenters power tools there weren't less carpenters there were more i can do more with it it's an empowering thing yeah so uh how often like i i i'm interested in the uh like the pie in the sky palantir pitch understand your entire business run your entire business on palantir and then some of the nitty-gritty where sometimes like the low-hanging fruit is like wait there's a like there's someone's job to just like take a form and type it into a we've had image recognition for a long time.

1:05:22Let's actually go and implement that and get that into a database, get that into the ontology, get that into Palantir, so then we can start building on top of it. And it feels like there might be a tension there. Obviously, both processes are speeding up, but how do you keep the project centered around the big goal while still chopping wood on all the things that actually need to happen? Yeah, I think this comes back to the forward deployed piece and what do we deliver outcomes. And we work backwards from that rather than, hey, I have this data. I'm going to build a data warehouse and then I'll build reports because all my data is in one place.

1:05:52That's the field of dreams and no one shows up. And so really when we decomp things and work backwards from that, the simple things like the form filling out, there's a lot of that. Now, the one approach that we see a lot is enterprise software is going to force you into their box. Sure. Right? Go fit into this box. Yeah. Well, then, okay, did I take away the special sauce, which was my company? Because people were doing all these kind of amalgamations. hey 40 ways to do a po yep well maybe it is okay to do 40 ways but my software can't handle it and it's fragmented right and so there's there's actually a middle ground because you know for a long time customization was kind of a four-letter word right no no one wanted to do that and i think that's where we think about malleable software actually how do we help you be more different not more similar interesting and that's so then when we decomp problems thinking about not only the the kind of the uh quantitative piece but the qualitative piece and the people and process around this, how do we actually enable those people to do the things that made them special?

1:06:47Is software getting more malleable? Because I can look at it two ways. I can look at, one, obviously, AI agents are incredible at coding. They can make changes very, very quickly that would take you a day and just a few minutes. At the same time, I see so many screenshots of people saying, I implemented this feature. Again, the GitHub is plus a million lines of code. And at a certain point, Like the context window is growing as fast as the code generation is growing. Like there's a, I'm a believer in the answer to bad slop is good slop and more slop maybe. But what are you actually seeing on the malleability of software?

1:07:26Because sometimes the most malleable software in the past has been, oh, well, there was a really incredible engineer who figured out this problem and baked it down to a 2 ,000 line repo. and you can actually just put it in your own context window so it becomes more malleable and you can use it as a building block. And that feels like that's going away and I want to make sure that we're ready for when it goes away and it remains malleable. Well, I think what's missing is the malleable enterprise scaffolding. And that's what we think about the ontology and foundry and the platform and then Apollo that allows us to go deploy these changes.

1:08:00So it gives us the right amount of structure but the right amount of freedom. So I think that's the balance we try to find is that malleability in the middle where we can actually scale, we can enable people to do things differently while still creating enterprise-grade, robust, secure, scalable software. And so it's actually a balance there about how I can enable that engineer that has been doing that. Now they can write code much faster, they can oversee things, and that enterprise scaffolding in the middle allows us to actually create the right guardrails, create a safe system of work for them to go develop things in.

1:08:32And then it's also the feedback loop. So the other thing that we do with our ontology and our platforms is implicit and explicit feedback from users using it. So the OODA loop that I create, and really that OODA loop allows our customers, as they're doing workflows, they're giving feedback to agents. Now, can agents help them do more based on the feedback? So both explicitly saying, hey, that was wrong and this sucked, or I chose this option. Now, if you do that enough, agents can start to learn from this. We actually store that in our ontology to allow it to scale. So it's really that human-centric process around AI.

1:09:00AI is not like we shouldn't be thinking about AI from the sake of AI for AI. It's AI to enable humans to do more. Yeah. That's the frame. OODA loop, observe, orient, decide, act. Yep. Right? I have a different question, but you can go. If you were giving, if you had 30 minutes to give feedback to the AI labs, what are the kind of key areas? Let's say the frontier labs, right? Leading models. what are the kind of key areas that you would be focused on? Yeah, I mean, I think when we think about the enterprise space, you know. One, you're like, don't compete with us. No, actually, like, I think optionality is a good thing.

1:09:40Like, I am agnostic to where you store your data, what model you choose, what compute you use. So, like, we can allow you to use any of that. Because the last thing that actually drives an outcome is replatforming, moving to another thing. And that goes back to the on-prem culture, the secure cloud culture, itar compliance like this is in the dna of the company and so how do we actually enable people where they are yeah instead of the focus on oh if you re-platform everything to palantir everything will be great and like well actually you've probably been re-platforming for years can we enable what you have to go do these new things so when we think about like the model companies and it's you know how do we ensure that we can give the feedback loops around you know tool usage and um you know yeah that's the kind of that's the kind of stuff i was uh wanting to get your point of view on is like i'm sure you're getting into the nitty-gritty with individual models where where they're spiky where there's you know where there's shortcomings etc yeah so we we actually just launched i just put a youtube video out last week on this new tool called evolve we talked about it in the kind of the halftime show where customers are using actually ai to help them understand which model so like maybe you know the the the the meme around hey make it exist first and then make it good yeah most of the time i see people building with agents they're using the latest frontier model.

1:10:53I just got it working. Then all of a sudden the token maxing and everything else, you're like, oh my gosh, it just blew through my whole budget. So we built a tool called Evolve that will actually go analyze the logs in production about how these models are operating, what people are doing with them, the architecture over it, and actually be able to swap out different models from different providers or hey actually for most of this workflow you can use this model that's older and actually without thinking and test time compute it, it's more deterministic. Or even cached models. Cash models. And then, or, hey, if you actually just have this piece of data in the ontology, then you would eliminate all this in 50 % of your cost.

1:11:28Yep. And so, you know, some of these customers, McCarthy talked about this at our halftime. They were able to, in two days, eliminate 60 % of their token cost by re-architecting, picking a different model, and prompt tuning. So it's the combination of all those. The permutations get really hard, especially when it's in this probabilistic models. We have tools to do this in the deterministic world. Prompt tuning. It's don't make mistakes. It's okay to make some mistakes. If the mistake is going to cost just a little bit, I'm fine because don't make any mistakes. That's going to cost me a fortune.

1:11:59Well, there was some chatter yesterday around something a model was doing to be more efficient was talking in like this bad. Oh, caveman. Yeah, caveman prompting. Yeah, the caveman prompt method actually works. How often are you working with a company that is having, call it like a mini chat GPT moment within their enterprise, and then they're just like, let's not tell anyone about this? Because I imagine there's clearly places where... What does that mean? Their product is taking off like chat GPT? So they've found a way to apply AI in a way that is highly, highly effective and gives them an edge.

1:12:39Oh, interesting. uh but like like the theoretical like within like technology yeah yeah so so x people are very loud right yeah they're like i'm using everything yeah i just had a product work for 30 hours on this thing they'll talk about it but if you're a fortune 500 and you figure out how to do something it's not like you want to like put put your hand up and say like guys like i figured something out right like secrets are valuable and these advancements and kind of breakthroughs are not going to be uniform the airline industry will never be the same when your direct competitor copies yeah and so and so part of part of why you know right now the meme is token maxing um and that's an obvious going to be an obvious area of debate people are happy to go talk about it say you know ceos might say hey let's stop doing this um but there has to be all these other kind of pockets of interesting moments where we won't hear about them until they become kind of like standard operating procedure or you see it in the the uh earnings and the economics piece right yeah so i yes unfortunately x is not the real world you know and there there's a lot of grift and noise and you know podcasting pming and you know that kind of stuff that goes on but i i think in the real world yes there is the haves and have-nots i mean we were just talking about amg like when you can start to actually do the underwriting and you know have quotes back in hours or days instead of months on these highly complex enterprise you know kind of insurance agreements if you don't have that how are you ever going to compete yeah and so when we think about this of the n of one right you know that those are the companies that we're going after and we see where there are those moments that are not yeah it's such an interesting category because you can imagine aig you know is um you know working with a potential customer or renewing a policy and that customer is going and talking to all of AIG's competitors.

1:14:34Yep. And if AIG is able to turn around, you know, a quote or a policy in 24 hours and then it takes another player, you know, two weeks because it's, you know, complicated. Email and spreadsheets. So many teams will just say like, hey, we, you know, you know, especially once you have two bids, you can basically say like, okay, that third, fourth, fifth, we'll kind of wait on those because we have a good option here. Well, it builds trust. The other piece here, so when you see people operating with that level of efficiency, what else can you do? So I see this whether I'm doing SAP migrations, the least sexy thing you can talk about.

1:15:09But hey, if I can cut your SAP migration. Let's give it up for you. It's like the least exciting thing on paper. But actually, if you're spending hundreds of millions of dollars on a migration and we can cut it in half, that's a massive deal. So back on the OODA loop, observe, orient, decide, act. On the observation side, what is the supply and demand imbalance for dashboards? And what I mean by that is when you're working with a company, is there more demand for dashboards? More people asking, hey, we need a dashboard for this. We need a dashboard for that. And you have to back people off and say, I don't know if the dashboard's right for this.

1:15:51You might just want to do an ad hoc analysis or actually go and see. versus you're seeing so much opportunity that you're like, okay, we want to push dashboards out everywhere. Walk me through dashboarding right now because I've always been like, sort of like, oh, there's too many dashboards. You build them and then no one looks at them. Yeah, I want to kill all dashboards. Okay. That's my perspective. Dashboard, I mean, KPIs and dashboards should be a byproduct of operational applications where I'm making decisions. So we talk about the Oodaloo. I have to actually act for things to hit the bottom line and be valuable.

1:16:21In the actual application. In the application. So as I need those things and it's going to inform a better decision, that's where I want those metrics. That should be a byproduct. If I go out with the goal of building a dashboard, it's going to be the field of dreams again. No one shows up. And so, yes, it should be – you're going to have to build some of those things. The other side of this also is when you think about a data warehouse, like literally I won't go too deep into this technical riff, but like Kimball and dimensional modeling was built in 96 for scaling databases. and you're still modeling the same way in 2026 or your dashboard your tableau whatever those things are and like that's not actually how the world works in rows and columns you need complex things to model how the world really works and that's what we think about the ontology which means i can reuse it for an operational application kpis agents all in one single ontology which it makes it the compound effect where as i add things in i'm now compounding with each individual decision I'm working with gets better and better and better for the next use case as I connect across my business.

1:17:20Yeah, is there an analogy there to just the deployment of AI tools currently? I'm just reflecting on the NoSQL boom. And I don't know how strong this was. This is probably just like an online take, but this idea of like, why would you ever want a relational database? Why would you ever want a schema? Don't ever do a migration ever again. And the future looked like a win-win almost. I think Postgres installations probably grew and so did MongoDB and other non-relational databases. And people use Redis for things, and they use all sorts of different tools. And we stood on the shoulders of giants, and we got more giants.

1:17:55And then that means full employment for you, obviously. But I'm wondering, are you seeing glimmers of the AI tools eating into different pieces of the technical stacks, or is it all like yes and across the enterprises? I think it's yes and. And in a couple different things, there is when you think about the real world it is not just rows and columns you can't describe everything with measures and attributes yeah and so it's actually multimodal and so like we think about this in our anthology where you can have one semantic object that actually has a cad file and an image a cb model and tabular stuff in one semantic thing of a plant yeah which means i'm starting to talk in the language of my business so being able to have the multimodal representation worse in other places oh i have to have mongo db and i have to have a sql database here and i have to have an S3 bucket here to put all of these different things to store them in ways.

1:18:48Well, we can do that all in the ontology, vectors, everything else. So that's really the goal around how do I model the real world, how it actually works, and make that transparent so you're not having to figure out which technology to put in a time series thing for sensors on an oil platform. Don't care, right? And that's where we want to have the non-differentiated heavy lifting, like truly in the platform to remove the friction about getting stuff done. how common is it for a business with more than a hundred million dollars of revenue to have very little understanding of how their business actually works like maybe they own maybe they own maybe they know like the main thing which is like you know we make a product and try to sell it for more than it costs to deliver yeah um but uh but but is is some element of uh how how much can chaos and mystery be reduced effectively today.

1:19:40Because it feels like we're entering an era, like you go back 50 years and the level of mystery in a large company would have been, is almost unconceivable today, right? Because you have different time zones, different offices, no email, all that stuff. And now mystery and chaos is probably reduced dramatically. but still there's companies that maybe before you start working with them, I'm curious what those look like. Yeah, I mean, we work with a lot of different varieties of companies. I joke that a lot of times companies make money by accident. They don't actually know what their most profitable product is, and often they're trying to sell the thing that isn't actually the most profitable and actually not selling the thing that actually is profitable.

1:20:29And it comes back to how they've modeled their data to aggregate it up to KPIs and other metrics when you actually need to model at the finest grain how your business operates to get a true cost of goods sold, for example, or a true cost of serve. That's very complicated. It's very complex. So we really think about how do I embrace that complexity so that I can truly understand tactically at the edge how do I do more of the things that are good and less of the bad. It's that simple. And those get peanut buttered across with KPIs and metrics. And people don't actually know how their business is hopping.

1:20:58I can't tell you whether it's a$100 million company or a$50 billion company, how many times I see this that they don't actually understand how they're making money at a fine grain. Last question. Is there a world in the future where a company gets created, let's say on Stripe Atlas, and the first account they sign up for other than that is, let's say, a Palantir? Yes, I would love that. And so we do have a Palantir for Builders program. We have small companies. There's people here that are two-person startups that are working in their attic in Canada. So it is literally any size company, come work.

1:21:37There's a free dev tier. People can come build. There's actually a Shopify integration in Palantir. You can go hook up to your Shopify and pull into Palantir. There are people doing this. Now, are we always great at selling it or telling the story? Sure. No. But there are companies doing this, and I do think there's a day where it's going to be ubiquitous. Because I also think there's some guys here that have, hey, my business is dying. I was down 10 % negative margin on what I was selling. And through using Palantir, they watched our YouTube videos, and they built it themselves and increased to a 9 % or 10 % positive margin in three months.

1:22:10That's great. And so people can go do it. I think that's the great American story is how do we enable that? And I think we'll get there. It might take a little time. I love it. Well, thank you so much for taking the time. Thank you. Great to catch you. Great to see you. We will talk soon. Our next guest is joining in just 15 minutes. We're going to go back to the timeline. First, I'm going to tell you about Figma. Agents meet the canvas. Your AI agents can now create and modify Figma files with design system context. It's so crazy how many companies, their whole strategy is like, we're going to hire guys like Chad, and they're going to do stuff.

1:22:48He is the final boss of FTEs. What drove the FTE meme? Was it Palantir going public? or was it? I think it was Palantir going parabolic. Maybe. Yeah, because it just proved. Because before it was like, okay, yeah, successful company, but no one really knows where the valuation's going. Now it's like, my uncle just told me that he made a bunch of money. That, but also they had been banging the FDE drum and getting the consulting accusations. Yeah, but people had earplugs in to the banging of the drum and the and the earplugs came out yeah but when you're when they were a 10 to 20 billion dollar company a lot of people could still convince themselves that they were right just a consulting business yeah yeah yeah exactly but and that gets harder harder to yeah ignore we covered this very briefly um but uh but yeah very excited for joe rogan to be hosting his uh you know it's rumored this is a rumored leak it is not confirmed by any means yet but but i like the sound of it it would be it's a very different direction uh um this was a good post i want to bring it up buco capital says it's really incredible the absolute ai garbage in all caps that people are comfortable sending to their co-workers and bosses there's a good chance productivity will actually decrease as ai adoption increases because everyone is busy waiting through uh ai slop i don't think i don't think it'll actually i don't think it'll actually get there but i have had uh i have had moments over the last month where somebody has sent me you know a deck for their company or materials and i can tell that uh 90 of the work that went into it was on prompting yeah and uh i have a very like visceral reaction toward it especially for like early stage companies where uh ideas and the way in which you go about doing things matter so much that uh it's almost like you know painting this initial vision and things like your your go-to-market um product differentiation why you'll actually win like use ai to make your team slide that's great right just taking like a set of facts and making it look good right you're giving somebody a bio something like that um but i just remember uh i i got this deck i was clicking through it um and i very uh respectfully said like go and like do this yourself yeah because uh just because you've made something that that looks like a deck yeah but you didn't do the sort of like fundamental work to actually present this in a way uh if you looked at each slide individually yeah your eyes kind of glaze over yeah and you just sort of like lose focus Yeah, it's like it would have been more compelling to actually just have a bulleted list of like problem.

1:25:46I mean, a lot of times you can just send me the prompt because I can instantiate it in my head. I can imagine the rest of the paragraphs. I have the context window preloaded for myself. Yeah, we should talk about the new Audi, the Nuvo Lari. Is this real? Motor one? This seems real. It's real. It's a big deal. It's the brand's first supercar since the R8. Twin turbocharged 4-liter V8 hybrid. 217 mile per hour top speed. That is 10 % faster than a Cayenne Turbo GT. What is the Cayenne Turbo GT market doing right now? Is it tanking? Depreciation must be just through the roof on this news. Because you have a car that's 10 % faster.

1:26:27And so everyone is going to be rotating out of the GTs. I think the new Volari looks amazing. It's a really cool design. It's a really good design. Feels like somewhat Cybertruck inspired. Cyberpunky, futuristic. I don't know. It just checks the box for like the next supercar for me. And in a way that the - Ben says it can't touch the R8. Oh, can't touch the R8. Okay. Okay. Well, it goes zero to 60 in 2.6 seconds. Almost 1 ,000 horsepower. Let me tell you about Cisco. Critical infrastructure for the AI era. Unlocks seamless real-time experiences. A new value with Cisco. And our next guest, Sam Berry, is here from the USDA.

1:27:03Welcome to the show. How are you doing? Good to meet you. Thank you so much for coming on down. Let's throw this on just like that on the left side. Good. Introduce yourself a little bit. Tell us about yourself. All right. Yeah, my name is Sam Berry. I am proud to be working at the USDA. What do you do there? Right now. I'm the chief. Nominative determinism. Do you know about nominative determinism? No. It's the idea that a person's name could possibly influence or the, but Barry and working at the Department of Agriculture is like pretty perfect. Yeah. No, it's incredible. Actually, my, the Barry's came over here from France in like 1640.

1:27:44Whoa. So we've been here for a long time. That's crazy. And it was all farmers. Yeah. There you go. Yeah, yeah. It was like all farmers up until my grandpa. Okay. Then he became a materials engineer, actually. And worked on jet engines. Okay. And so then his sons became engineers. My dad became an engineer and I was an engineer. So we're kind of trying to bring the two together. There you go. That's the USDA. Yeah. What is the shape of the USDA? Like, what is the shape of the organization? Headquarters? Do you go to the office? Is this, you know, U.S.? You think just America, international footprint?

1:28:15Like, do you travel for work? What's it like working there? Well, actually, it'd be kind of interesting to ask you what you think. Like, what are the things that you think USDA does? They grade the milk and the steaks. Yeah, okay. That's what I think about it. So I imagine that at some point, farmers send the cows to you and you kind of inspect them and say, this is a good cow. Is that what happens? I don't know. There's inspectors. There's a whole area that does it. I imagine there's like a series of certifications. But what else is happening? So all kinds of stuff. So do you know that like food stamps?

1:28:48Yeah. Snap is inside of USDA. Oh, I didn't know that. I didn't know that either. I figured it was in like HHS or something. But yeah, it's in USDA. Yeah. So that's$100 billion. a year it's kind of a big deal yeah um so we do we have snap that's in the food nutrition service yeah uh forest service is inside of usda okay like crazy yeah yeah uh and then fpac is like what you would really think that usda it's like the farmer facing like okay where farm programs are where they do acreage reporting like the stuff i talked about today got it um then there's rural development okay which is like loans it's like a bank basically they do loans for all kinds of things okay um actually in some of the reviews i came in on doge and uh uh there's like beachfront hotels that are being funded out ofrd so there's like a lot of things that need to be cleaned up okay um yeah and then there's like food inspection service and then there's actually a huge scientific arm yeah uh that's inside of that makes sense testing things and yeah like labs advancing yeah different pesticides so things that i mean i actually become very passionate about it because I certainly didn't have an appreciation for it.

1:29:53I thought the same thing. It's like grating meat, milk, you know. But we are so uniquely positioned as a country because of the fact that we can feed ourselves. And that is not the case for a lot of countries. Yeah, isn't America basically a net exporter of food too? You hear about this in the China debate all the time. Will they buy XYZ product from us as retaliation? Yeah, you just don't think about it. but yeah so like china can like minimally feed itself like bare minimum it could like keep itself alive yeah um but you know they're getting like like we just did a big deal with them to move a bunch of beef over there yeah we kind of got some negative press on that so it's important to know it's uh i forget exactly what it's called but it's like the parts of the cow that we don't eat here so it's a little misleading to say like the amount that we're sending over there also all these trade deals are like very complex and there's like six different moving parts we get batteries or they get the chips.

1:30:49These are always seven-part negotiations. It's hard to look at anyone in isolation. I think it's a little surprising that food is actually part of that. In warfare, agriculture and the food supply is usually hit before anything kinetic even happens. And then before even the world knows that it's warfare. Because you can do that and you can do things to impact a nation's food supply in the future. And so agriculture is like a really big deal. Sure. Really important. So all this to tie back to, I wanted to talk about the labs. Yeah. Because this is like a whole area inside of USDA. But we do all of these things, like invest in figuring out.

1:31:28So like personally, I try and avoid like GMOs and we eat, you know, like we drink raw milk and we get our meat from a local farm. But GMOs are actually really important. Yeah. Because if we were hit with some kind of adverse event or something and we needed to create corn that could survive a drought better. Like we have the science and the research to be able to do that. Got it. And it's a huge edge that we have like geopolitically. Interesting. Yeah. Yeah. Talk about over the years, I've read so many stories of, you know, this, this insect has been detected in, you know, some region of the U.S.

1:32:02and there's speculation on, is it, you know, kind of foreign interference, things like that. Is that, is that in USDA domain is trying to help monitor and track and make sure that. Pests. Yeah, pests are obviously naturally occurring, right? They flourish for their own reasons. Or there can be some sort of malicious intent as well. Is that what your guys are doing? Yeah, because they're not necessarily naturally occurring, right? Yeah. And so one that we have going on right now, and I'm not saying this one's not naturally occurring, but the New World Screw Worm that's coming up through Mexico.

1:32:37So our secretary, which, by the way, I couldn't say enough good things about Secretary Rollins. I mean, she's incredible. just an actual like genuine good and like it's unbelievable what she's able to accomplish but new world screw worm is something that's falling in usda's uh you know responsibilities and this is like a parasite basically that's coming up through mexico and it's like a flesh eating parasite so it's like really hardcore yeah so we're developing a lab flesh i know all sorts no but you know i don't think you want to be around it but no it's for like cattle mostly is what it impacts.

1:33:10And so we're developing a lab and sterilizing flies, which, again, personally, I don't really like any of this stuff, but it's better to be doing this and be able to protect our nation than if we let this just come and flourish in our country. I mean, it would be very detrimental. So I'll have to go back. And if it's a necessary technique that needs to be harnessed, it needs to be harnessed securely, and it needs to be harnessed with the right teams in place to make sure that whatever's rolled out is rolled out effectively and safely, right? Yeah, I mean, I think it's just so important. You know, there's like tech, there's so much farther we can go with technology, but we have so much right now.

1:33:48And so many people are just blackpilled, right? And I think it's important. I think you should be like blackpilled on certain things, but you should probably take a lot of pills, like it should be red pill and blackpilled and white pill at the same time. Because like, we have a long way to go. And when we're just like, sitting feeling sorry for ourselves, like, it's not a good position to being this is the most incredible country on earth and other countries are advancing though you know our edge is like our edge doesn't come for free no we got to work on it we got to keep pushing at these things but when we do this like when there's a parasite that's you know coming into our country and we're able to just like use biology yeah to combat it yeah like that's incredible that our country can do that talk about uh these more smb scale farmers and their approach to technology.

1:34:35I think a lot of people would be surprised at how much these individuals, at least from what I've experienced, are happy to lean into technology. I met a group in Texas that had developed, this was years ago, so pre-AI boom, developed their own SaaS product to help manage their operations, like a tool that they had built by discovering problems that they had on their property. And I just thought that was really fascinating and cool at the time because I think Silicon Valley would have maybe some expectation that there might be an aversion to that until you get into the more enterprise-grade scale.

1:35:19Yeah. I think it's a really important topic because you're essentially talking about democratizing access to technology, right? And certainly with AI becoming so much more widely available, that was a big step forward. But I mean, this is a big point that's being hit on at this conference and what Palantir is really focusing on is those LLMs become useless if they're not, if you're not deploying them in the right way with the right like data boundaries, right? So, you know, I think that's something that we're seeing even in our universities. We do a lot of university research and like all the, you know, kids or whatever, the university students, like they're wanting to do experiments with LLMs and do like meat grading, like better meat grading Because that's something that can happen at the farms.

1:36:00And if you can make that automated, then, you know, our ability to produce beef, you know, is greatly impacted. But there's a major issue in succession planning right now for farms, right? Like, this is a big thing that's happening. Like, the farmer generation is getting very old. And kids don't want to go and run the farm. A lot of them went to big cities. Yeah. Jobs and white-collar work and stuff. So, you know, this is a big thing that is H-2A. Yeah. You know, these H-2A visas where a lot of the farmers are actually still saying, like, we need the help from, you know, we need immigrants to come and help us.

1:36:32And, you know, the best way that we can solve that is through automation. So I think that that's something I would love to see USDA do more of or, you know, it's something that needs to be answered. I don't have an answer for you right now. But in order for us to continue to, you know, remain self-sufficient in providing food. Yeah, whenever you have a dwindling workforce, increasing the leverage and productivity of the existing workforce allows you to maintain overall aggregate productivity. This is general technological leverage, so it makes a ton of sense. Do you know anybody that's becoming a farmer?

1:37:01Well, we know some folks. We've had a number of entrepreneurs on the show who are getting into ag tech and building. We've had the founder of the Laser Weeder that uses – a lot of people don't like pesticides, but they don't mind if a pest is zapped with a laser because that's just heat that's being transferred to the particular plant right there, and the tomato plant continues flourishing. So it uses just cameras and lasers. Very cool sort of modern solution to something that people have had a lot of fear around, around different pesticides. Yeah, we had a fruit-picking robotics company. Yeah, orchard as well.

1:37:35But mostly from tech side, usually with some family lineage, sort of returning to the roots or tapping into their networks to go back. But, I mean, truthfully, I don't know that many people that I grew up with. I mean, I grew up in L.A., so not much farming activity. I knew one family that had an avocado farm. I mean, it would be super-based to be a large-scale farmer. More people should do it. Maybe you could be the Alex Hormosey of farming. Yeah, no, for real. So USDA, one of the great things that USDA does is you can get financial assistance. You get big-time loans from USDA. USDA, you have to go through the process.

1:38:09And they were actually doing a loan modernization effort right now trying to make that better. But USDA will fund it for you. You've got to pay it back. But you can get the interest rates super low. But it's a very, very low rate. It's subsidized, yeah. Yeah. I mean, one of our administrators at USDA, he pulled up his phone one day. And he's like, look, it's a planting day for me. And it was this John Deere app. It was like the most advanced. He had all these tractors going. And there's still people sitting in the tractors. But it's to the point where it basically could be fully automated. so i mean you can get yourself a couple thousand acres and just start you know growing corn or weed or cotton like cotton and then you know whatever uh talk about data collection i feel like data is the lifeblood of uh you know any decision making any ooda loop anything related to palantir usda and i'm wondering about like you mentioned that screw worm you got to track that thing it shows up on some cattle rancher's farm and they're detecting it or they're seeing symptoms Maybe they know roughly what percentage of the herd is affected, but how do they actually get that information to you?

1:39:10Are they going to USDA.gov slash report incident, or are you pulling things from their filings? How do you want that to evolve? I imagine that with more AI and technology, it's only as good as the data that we can actually put into the system. So just broadly data collection, where is that going these days? Well, if you don't mind, instead of Screw Room, I'd like to focus on Snap. for that question? That's a great idea, yeah. So SNAP is funded by the federal government, but it's administered by the states. Okay. So when it comes to, so something that we're doing right now, and it was one of the first things that our secretary did on our first day, was she did a data call to all the states that we want all of your SNAP data to understand, because it's our responsibility as the funder of this program to understand the integrity, to verify the integrity of the program.

1:39:56So we put a request out there, but it has to come from every single state. And a lot of the state programs, they're not technical, or they've got contractors is that it's just a difficult thing to get us the data. But then there's also a bunch of states that are just not complying for whatever reason, which it shouldn't be a problem. I don't understand what the problem is. But the importance of, so that program, that's 100 billion taxpayer dollars a year. That's pretty substantial. That's an area where we really want to have all angles of the data available so that we can deploy AI and become really smart in detecting fraud.

1:40:27We want to get it to the point where if somebody's committing snap fraud, we should be able to, It's like your card, right? If somebody stole your card and did a transaction that wasn't recognized, like your card's shut off, right? So we want to get to the point where we're very intelligent and we're confident enough in the system that we can do that. When there's fraud detected, it's off immediately because it's an important program. You know, we want to be able to support people that can't support themselves. But it's not arguable that there's a massive amount of fraud in there. I mean, even the organization itself does like an audit every year, and they're at like, there's 12 % improper payments.

1:41:04Improper payments is kind of a bad word. So$12 billion a year. Yeah, right. And that's just like kind of based on samples. That's money that could actually be going towards the intent of the program, which is to provide food to people that otherwise wouldn't be able to get it. and there's other you know you could like grok how snap has been used to fund uh like international crime organizations and like terrorist groups and everything so it's it's being exploited at a huge level and uh i mean it's something that our secretary has prioritized but that's probably our biggest f-pack what i talked about today is like our most complex system of data but the snap challenge is uh like the biggest or like this the snap environment is probably the biggest challenge on the data front.

1:41:53What's next for you? Are you making a career out of this or are you going to go be a farmer? Hopefully both. Yeah, I mean, I've got some farmland. You do? I'm trying to convert it. It's like woods right now. What state? Where is that? In Virginia. So actually, when I lived in Michigan, we had a little bit of a farm. We had some goats and sheep and a bunch of chickens and ducks. You don't want to get ducks. You don't want to get goats. Ducks are like really savage, actually. Yeah. Like a chicken sleeps, you know? So like it's got a normal cycle. Like at nighttime, it goes into the coop and it sleeps.

1:42:28Ducks don't sleep. No, ducks do not sleep. They like, in our house was kind of this like really unique house. So the windows were like on the ground. And the ducks would come and just stare at us in the window. No, they're savage. They just like, they sleep for like 10 minutes at a time. So they'll just like waddle around and then sleep for 10 minutes. You have to have the right balance of female and male ducks. okay otherwise it's like that's really ugly yeah chickens are a lot i grew i grew up with chickens and uh most of the time they're they're cool my dad would build these sort of like complex contraptions to automate the opening enclosure interesting so he would use like irrigation to uh on a timer to fill a bucket which would lift there lift it up yeah interesting um but but then I still core memories as a kid was waking up.

1:43:14My dad would yell like, there's a fox in the coop. And then we'd be like running out. We'd be like game on. Yeah. Yeah. Or you get like skunks in there. Yeah. We would just, everybody would get up and try to go deal with it. That's going to be more satisfying than some software bug. There's so much fear and doom and blackpilling around data centers. uh i wanted to hear from you how your i imagine your your role is to be an advocate for for farmers as well on on water supplies things like that california went through you know probably many many really rough years from a from a water supply um and a water scarcity standpoint thankfully you know had a lot of rains over the last few years but how how are you working with farmers or what is the situation around the kind of like tension between a lot of farmland could also be great land for data centers, right?

1:44:18And there's been some pretty high profile stories where farmers either sold their land, but from your side, you're trying to make sure that we have, you know, can produce an abundance of food, you know, from a national security standpoint. So how are you guys thinking about that balance? Yeah, I mean, I think the best solution is putting the data centers in space, you know, like, which is totally led by Elon and people are jumping on that train, but it's going to be a couple of years. It sounds like before they're to that point, we're actually, uh, USDA is pursuing a partnership with SpaceX. Um, and that, that part isn't, isn't ready yet.

1:44:49We don't really have a need for that, but it's, there's a partnership on the technical side, but there's also just on the like conceptual side of the fact that like we're aligned. Cause we do care about conservation. Um, you know, there was a lot of green stuff that was It's not stuff that we care about, but we do care about conserving our land. And putting data centers in space just makes a ton of sense. But that being a couple years out, so for today, I'm actually pretty passionate about this because in my hometown of Saline, Michigan, it's a small town, mostly farmland. They're putting a data center in there.

1:45:18And it's like 30 miles from Detroit and Flint and all these very industrialized areas. And so it's very confusing to me why we wouldn't be putting these data centers. and they're like struggling areas. Detroit's doing all right, but like Flint's struggling big time. Like why not put a data center there where there's already the infrastructure? There's like it's already developed land. But instead, it's like taking these small townships and plopping them in the middle. And the people don't really like it. Now, the boards seem to like it for some reason, the councils. So I don't know what's up with that, but it doesn't align with what the people want.

1:45:55It creates a massive amount of tax revenue that can be used to fund a bunch of other programs. But it's got to actually flow back to the people who are in the town. And I think that there's like a disconnect there sometimes. Actually, this is kind of outside. But something that I do think is probably going to happen is, you know, there was this big shift to go to the cloud, right? It's like everybody kind of had their own servers. You know, it's on-prem and now we're in the cloud. And it's like, really, you just took, you like moved it across the street, right? and now that people are becoming more aware of like what that means and when it's like oh my data is in aws or you know it's like and maybe this is a global company and how much can i really trust this company that there's going to be a shift back to caring actually actually caring about where your data is living i think a good business opportunity would be i i think there's a world where there's a culture that comes up around data centers because like me personally like i want to build like my house is like uh like i'll have a kill switch for my wi-fi and then like we've got the data in the basement you got your raw milk supply got raw milk no like we're ready to go i mean i was ready to go off the grid before i came and joined the government this is a much better option but um still like i care about my data i don't really want to use youtube music anymore for my music because now my recommendations are getting worse and you're like very beholden to that it's like i could very easily just have the music buy my music and write a simple program to make my recommendations and it would be way better because there are certain artists that are not getting recommended because they're not prioritized behind the scenes.

1:47:23But not everybody's going to want to manage their own servers. Jensen just announced a data center that bolts onto the side of your house. Oh, that's sweet. There's more stuff that's coming that way. People are doing it with the Mac minis. Can't really do the Frontier AI on the Mac mini just now but in a few years, the DGX, desktops, it's all coming and I think it will be more of an option so just to like kind of wrap this up so there's this um uh or this like topic the uh one of the things that usda does is we pay 600 000 federal employees so like we pay secret service we pay dhs we pay it's like it's like a thing inside of usda interesting and so the the payroll system that does that is a mainframe okay and people literally explained it to me like this thing has a personality like you have to like you can't touch it the wrong way you have to it has to have like the right environment to work and it like all these things i mean like a dozen people came to me and told me all these things.

1:48:14So then I went and visited it. I was really excited to encounter this being. Mainframe. And it's like a five-year-old, brand-new IBM server. There's no tapes. There's not a team of people. You're underwhelmed. Yeah, it's like this big. Okay. But I was expecting a small micro data center or something. Yeah, exactly. So totally modern. And I formed this connection with it. And I was like, we have had so many conversations. about you and i just thought that like this is potentially a future where it's like a coffee shop you know like people might want their data to be hosted in a place that's like aligned with their views sure sure yeah you know because it's like i can trust like i don't want this in my house but i can like trust this like cool company local company that my data lives there because i don't need it distributed across the globe it's like yeah i'm here no that makes sense that's interesting country intelligence yeah yeah we talked about this this is the future i love it Thank you so much for coming on the show.

1:49:13Great to meet you. Thanks for doing this work. Have a great rest of your day. Thank you. We'll talk to you soon. We will wrap up the show. Yeah. Thank you for tuning in with us today, folks. We will be back on Monday. Yes. And we look forward to it. Some business to do tomorrow. But see you Monday. Leave us five stars on Apple Podcasts and Spotify. Sign up for the newsletter at tbpn.com. Have a wonderful afternoon. And have a wonderful weekend. We'll see you later. We love you. Goodbye. Goodbye.

1:49:41Thank you.