🔴 Alex Karp Live from AIPCon 10

You're surrounded by journals. Hold your position. Overnight success. Double leg, >> right? That's misinformation. >> Clearing order inbound. >> Let's just roll. >> We are surrounded by journals. Hold your position. >> Come, get up. >> Trust the experts. Five. We are founder. >> Five good. >> I see multiple journalists on the horizon. Stand by. UAV online. >> Blaze. Double blaze. Triple blaze. Double kill. Cook is team deathmatch. We are experts. Triple blades. Let's just roll. Right. Mark clearing order inbound. Get up. We are surrounded by journalists. Hold your position. >> Strike one. >> Strike two. Activate golden retriever mode. >> Mark clearing order inbound. >> Five good. I see multiple journalists on the horizon. Stand by. founder. >> You're watching >> You're watching TBPN. >> Today is Thursday, June 4th, 2026. We are live from Palunteer AIPCON. Uh with the Temple of Technology is back in Fortress of Finance. We will return to it, but it is also a state of mind. Um that's right. We are also sponsored by RAMP. Time is money. Save both. Easy to use corporate cards, bill pay, accounting, and a whole lot more all in one place. Big news from RAMP today. Massive fundraising a little bit. But first, we got to talk >> Oh, is it still going? I like it. The Ramp song's back. >> This was This was early days. We really talked about RAM so much. Turned it into a song. Anyway, uh the topic of conversation in DC has uh it's still in AI world, but instead of talking about uh approving models before they're released today, it's about the bio threat. Brandon Guerell wrote in the TBPN newsletter today, the great houses of AI have united behind the bio threat. There's actually a lot more to that because it was a big long list of signitories from AI, but also from the bio world and biotech and even startups. We've seen former guests of the show sign on. I'm excited to bring some of those folks back on the show in the coming weeks and hear more about this because I have this belief that as AI advanced, we got cyber because it was such a tight feedback loop, such a tight verifiable reward. Reinforcement learning works really well in that context. Bio has some similar characteristics. >> And it was a very tangible Y2K style moment. Exactly. Where there was it was a let's just say powerful business strategy. >> Yeah. Is it Yeah. It was like is it over? You start thinking about the consequence of this and you don't need to get to AGI super intelligence god. You can just have a really powerful tool that creates a new problem and that uh creates full employment for Nicashura over at Palo Alto Networks who we had a chance to talk to yesterday and uh he's been very fortunate in implementing the solutions to the cyber security threats posed by new AI systems some of the new AI capabilities they're rolling out. Um, but bio might be next. And so it's exciting to see that uh the great houses of AI are uniting behind the bio threats. So let's take you through this. First I'm going to tell you about console.com. Console builds AI agents that automate 70% of IT HR and finance support giving employees instant resolution for access requests and password resets. So uh in 1981 a group of researchers published the primary structure of the polio virus genome in the journal Nature. So they're basically open sourcing the sequence for making polio which just a few years earlier polio I think was on the decline by 1981 but uh a very very problematic virus. It's an RNA virus meaning that its nucleiobases or building blocks are ACGU if you're familiar with uh RNA adsine cytosine guanine and uricil. Uh put more plainly, thanks Brandon Guerell, uh he says when the researchers published the primary structure of the polio virus, they gave the world the literal sequence of polio virus building blocks in order from start to finish. By the mid- 20th century, before mass vaccination, polio was paralyzing and killing more than half a million people per year worldwide. So you have this pretty deadly virus killing more than half a million people per year worldwide. And you have just open sourced it. What happens? So in 2002, researchers synthesized infectious polio virus from its publicly available sequence data. So they didn't actually need any of the polio virus RNA to start. They didn't need it on hand. They didn't need to. It's not like they took a little sample and they just cloned it up and made it bigger. They they just took the data and they made the actual virus. So this is the this is the shape of the threat. If there's a new uh if there's a new virus or an existing virus or a forgotten about virus and you have the code to it, you can potentially print that RNA and then have the virus in your hands. Even if you don't have a sample, you weren't able to kill a sample. So instead, these researchers in 2002, they were able to take the published sequence, chemically synthesize short DNA fragments, assemble them into full length a full length DNA copy of the polio virus genome, and then use the DNA to make the viral RNA to fully recover the infectious virus. So in 2005, researchers used these same technologies to reconstruct the Spanish flu, a virus in 1918 that killed 675,000 Americans and had a 2 to 3% mortality rate among those infected. Very, very dangerous stuff. So basically, these two reconstructed viruses showed that having a physical virus on hand was no longer necessary as source material to create viruses. All you needed was the blueprints. as long as you have the code literally just like texted in a text file a bunch of ATGU uh you can go and make this as long as you have the equipment on hand but that is getting democratized as well and that's what this uh AI letter is all about. So that's a situation that we're still in today except now that we have AI there are easier ways to potentially reconstruct DNA sequences that could create new viruses. So yesterday, Demis Hassabis, Sam Alman, Dario Amade, Alex Wang, and dozens of other high-profile leaders across AI, tech policy, nucleic acid synthesis, and biotech signed an open letter called in support of mandatory nucleic acid synthesis, screening, and recordkeeping. You might have seen it on the timeline. And at first glance, Brandon here assumed, and I assumed the same thing, assumed it was another press release from a Frontier Lab claiming it had just discovered new capabilities in one of its internal models that would ultimately lead to cata catastrophe. A lot of this like doom fear-based marketing has been happening. So, that was sort of the natural reaction. Uh, and that's what Brandon >> Some people's reaction would be, were we not doing recordkeeping here already? >> That's a great question and Brandon actually did answer that. But, it's not just a PR stunt and it's not a new capability. They're not saying that the models can just create a novel virus, you know, one shot like that that that that is solved yet. Uh it's not there, but they see it as something that's coming down the pipe. And this letter is not this dangerous new capability. It's more asking the US government to force nucleic acid synthesis companies to screen orders for sequences of concern. So, hey, somebody just ordered this. Looks a lot like a virus. Like, what are we doing here? uh you said that you were trying to treat cancer or you said that you were, you know, trying to make a new peptide and all of a sudden you're asking for polio virus or something that looks like polio virus. Like let's let's dig into this. Uh that's where they're going with that. And so uh they also need to verify the legit the legitimacy of the customer and to keep a record of what they're sending and to whom. That's a crazy one that I'm sure you're like, wait, they weren't keeping records. They were a little bit. Uh he gets into this. So he says the reason the letter is coming out now is that the threat of nucleic acid synthesis sequences sequencing getting into the wrong hands has been enhanced by AI. So anyone with an AI tool in the future could in theory if the models don't have safeguards on them could uh synthes could create a sequence that then they go to a nucleic acid sequence company get printed send it to them mix it up boom they got a virus not good. So uh most of the global nucleic acid synthesis industry has already signed up to do some of this. They did they started this in 2009 with what's called the international gene synthesis consortium and roughly 80% of commercial synthesis capacity worldwide is on is is on board but membership >> 20% still just hanging out we're good >> 80% of nuclear weapons are safely stored. Don't ask about the other 20%. That's kind of what this letter is getting at because 80% it was a good first effort 2009 it's been 16 17 years. Uh we haven't had >> Yeah. But there's a new reason to go further. Let's get that last 20%. That's what they're asking for. So um >> me membership is not a strong guarantee that they're actually screening or keeping records of their customers because it's voluntary. The 80% number is also self-reported, for example, and a bunch of other factors contribute to the relative climsiness of the agreement. >> So, it's not >> you can opt into this program by just saying that you're opting into it, but then even the reporting once you're opted in is voluntary. So I I think the way this works is the international gene synthesis consortium is probably a nonprofit NGO you know non-governmental organization and every all the companies uh they they volunteer 80% of commercial synthesis volume has opted into this and then this organization the international gene synthesis consortium they say hey we've looked at the market and we're covering about 80% has opted into this we're we're on board with 80% and and the government isn't coming in and checking the records. They're not actually saying, "Okay, well, we we have a different number because we're the government and you have your this number. Let's verify this number." It's self-reported by that organization, but there's no reason not to trust that organization necessarily. Um, so what else? >> Bunch of other factors contribute to the relative flimsiness of this agreement. Uh, HHS also has guidance in place around the issue, but again, it's voluntary, meaning that the possibility of bad actors getting their hands on dangerous nucleic acid sequences, at least from American companies, still cannot be ruled out. Overall, it's good to see industry leaders signing this letter and doubly refreshing uh that the letter is not yet another warning of apocalyptic AI doom, which I think the public has unfortunately be come to expect from announcements like this. Hopefully, the relevant legislators are paying attention and can make this happen in short order. So, I thought that was a good uh a good breakdown and I agree with a lot of that. Um Andrew Curran also has some uh deep dive on this with some more uh of the signitories. He shares screenshots of all of these and it really is everyone. Uh yeah, Y Combinator, DeepMind, Microsoft, Interconnect AI, Harvard, uh tons of stuff. And then over in uh in the nucleic acid synthesis industry, you have Twist Bioscience, uh ANZA, Emerald Cloud Lab, and Kathleen McMahon from Boss is on here, former guest of the show. >> Um so, so uh good news, but uh obviously and just a early step. This is just an open letter to the government saying hey we think you should we want to support this we think that the government should start thinking about this. Uh the other news in the bio world, >> yeah, I mean the news is just that there's incredible momentum in biotech, early stage biotech. Uh after >> momentum, but not like volume, not scale yet because you're looking at $3 trillion IPOs going out this year potentially. So much news in AI, microns out of trillion. Every chip stock is, you know, in the hundreds of billions, trillions. This is much smaller, but >> but it's notable because biotech had been uh left for dead in some ways. We had a biotech investor on >> probably 14 months ago at this point who said I don't even know I mean just looking at the returns so far I don't know why you would invest in this asset class. Uh but of course every asset class kind of go throughs go uh goes through that kind of phase and clearly there's a lot of momentum >> and they should be you would expect that biotech would be similarly power law driven maybe not as extreme but if you pull out SpaceX open AI anthropic from capital >> yes but I feel like the biotech community has a little bit more of like a culture of like base hits doubles triples where they flip companies pretty pretty frequently in the >> Yeah we had that didn't we have a guy on that had sold like three companies. We didn't have when we were billion dollar exits. >> Yep. And then he joined another company and sold it for three billion like the next day. Uh and so >> so anyways, you have Isomorphic Labs spun out of uh Deep Mind uh Coinbase or not Coinbase, but but Brian spun out or like founded New Limit. Uh you have Retro >> a new round. We're gonna get Jacob on the show >> as well for that. >> Um Altos Labs uh >> from Jeff the Chad from Amazon. funny >> as this post puts it >> uh Anthropic obviously acquired coefficient bio as well >> but Jensen and Larry Ellison at Oracle are also doing stuff so there's a lot of activity it's very fun and I hope we're going to be able to cover this a lot more in the near >> future at what point do the uh at what point does like a Fizer or Johnson and John Johnson and Johnson start joining the press release economy of just coming I'm not I'm not saying it' be a good thing but coming coming out and saying, uh, we believe we're, you know, right at the because there are partnerships all the time that happen and they're always just like tucked a little bit deeper in the Wall Street Journal because AI is dominating and even private credit takes the front seat to the bio news, but there there's a whole bunch of deal making going on. Anyway, there's other deal making going on in fintech. We're going to talk about ramps raised today. But first, I'm going to tell you about railway. Railway is the all-in-one intelligent cloud provider. Use your favorite agent to deploy web app servers, databases, and more. while railway automatically takes care of scaling, monitoring and security. Um, so ramp, >> what's going on in ramp land? >> 44 billion valuation. >> Really, really solid traction just, you know, every 12 18 months, sometimes much quicker. Sometimes they do two rounds in two weeks, but uh uh really solid progress. They raised $750 million at a $44 billion valuation. Last time we grew this fast, we were 120th of the size. So accelerating. >> This is the most notable thing to me. uh lots of chatter on the timeline around uh you know other fintech valuations. You compare them >> apocalypse. Well, yeah. You compare them to you know ramp is now worth more than PayPal. >> Okay. >> PayPal has 32 billion of revenue. >> Yeah. >> Uh but PayPal >> uh certainly has I would say ne you know probably negative momentum. Yeah. >> Uh whereas RAMP has incredible momentum and this this is the standout line. Uh they were 120th the size the last time they were growing this fast and so >> um yeah just really really really impressive execution. Yeah. >> Uh and incredible opportunity still. >> Yeah. Uh so Eric uh took to the timeline posted an essay about the third pillar comparing uh the previous eras of value creation. The two pillars uh people and vendors dating back to 600 BCE. If you're not thinking in millennia, what are you doing here? Tokens emerged as the third pillar in 2026 AD. And he calls it the quadrillion token blind spot. Boil down 500 years of finance. And it's really just three questions. Who spent what? Was it worth it? What's the bill next month? I mean, people get caught up in all these crazy things. I mean, you see this in like marketing, I'm sure, and uh and ad buying where people will do all these crazy analyses and ROI ROAS and all this other stuff and like and it's always useful to zoom out and just be like, okay, we spent a bunch of money. Did the bank balance go up in this company or not? >> All all personal and business finance finance at the end eventually comes down to are we making more money than we're spending? >> Yeah. And I think uh yeah, Eric is is right to dive super deep into like token optimization and thinking about the tools that they're building, but then at the same time like not not don't get lost in the sauce and like actually zoom out and try and understand like what is the core value that you're delivering to your customer. Uh it is answering that question. Um so fantastic news over there. Let me tell you about the New York Stock Exchange. Want to change the world? Raise capital at the New York Stock Exchange. You got to do it. It's my number one advice for founders these days. Um, uh, there's some other fundraising news. Sabi, the Beanie BCI company is getting preempted at 35 million at 500 million post. Uh, this is a leak from R for Rock. We'll see where it goes. >> This is huge for you. >> Why? >> Because you are a beanie guy. >> I do like beanies. >> A beanie in the morning. >> Keeps it together. Yeah, I like a beanie. Uh very very >> I it's interesting. I think that uh this format >> Mhm. >> of course I'm sure they can adapt it to other types of hats. >> Yeah. >> But this format certainly >> maybe makes it harder to build momentum in places like California, at least Southern California, Arizona. >> Be big amongst creative directors though. >> Yeah. >> Huge. >> Huge. Huge potentially. Silver Lake. Silver Lake. Yeah. Every >> It's not too hard to change a beanie into a hat. cowboy hat. Like that's just extra leather around it. You could wrap the beanie in the in the cowboy hat. You can wear >> Here's what's interesting though. So R for Rock. >> Yeah. >> Uh usually >> it's pretty dialed. >> Pretty dialed. >> Pretty dile. >> Uh pretty dial. It's almost like he has inside information. It's almost like he somehow got >> Yeah. But I mean, we've talked about the game theory of like do does he work at a real like tier one venture capital firm that's is like what's the benefit of leaking everything? Is he a lawyer that's seeing all the docs turn around? Zero benefit for a lawyer, >> right? >> Um the rush of getting likes on the timeline is pretty universal. You're a lawyer, you're just like, "Ah, I need a banger." >> Out of fund. Okay. >> For sure. >> Yeah. >> And uh I don't I don't know anything else. >> Um but uh he's always taken the view that it can be helpful to the founder to build because a bunch of people are going to see this. >> Sure. that that this didn't sort of land in their deal flow or land on their desk and they're going to reach out, right? So, it does create momentum. Um, >> but uh can certainly be annoying for teams as well. Uh, this was notable though. So, 200 million of of LOI from B2B customers >> and so very curious what the enterprise >> play is here. I don't know >> but uh we can work on getting a whole >> Does that mean like uh like through hospital networks or through like the health care system or is it like Mark Zuckerberg wants to go further? He wants to track the brain waves of the employees not just track your screen. >> We're also going to track your brain. I I mean it could go either way because you you imagine like Neurolink has had a bunch of traction and bunch of amazing I saw uh Nolan the uh the first patient P0 uh on Rogan talking about playing COD with the Neurolink. Amazing. Uh and you can imagine that at a certain point like some sort of partnership. >> They have multiple hat form factors. >> There we go. Hats coming. >> We're good. I was getting really hung up on the beanie. I'm like there's so many different enterprise or or B2B contexts. You're in a You're in a warehouse in Dallas, Texas in the summer. >> Yeah. You don't know. Maybe this maybe this $200 million LOI is from REI or Patagonia. You know, you don't know who makes beanies. Uh what's the Carheart? Carheart makes a great beanie. >> Go. You don't know any of this stuff. You're you're completely out to lunch. I went through the beanie economy. >> Beanie economy. Anyway, beanie market map. Work on it. >> Let me tell you about public.com. Public.com. investing for those who take it seriously, stocks, options, bonds, crypto, treasuries, and more. All with great customer service. >> They just launched a feature today that allows you to connect uh your favorite chat app to public. >> Yes. >> And more important than ever, because with public, you're going to be able to go and create the S&P 499 if you don't like SpaceX or the S&P 1 if you love SpaceX. You can express your opinion about SpaceX however you want. Can you please help me build an index for one company? >> Yes, index for one company or index for everything but one company. Uh SpaceX is very uh divisive. People are extremely optimistic in certain camps. Extremely pessimistic. >> Very optimistic. Uh Goldman expects SpaceX's AI revenue to surge a hundred times by 2030. >> Huge. >> Uh big big number. I looked at this title and I was thinking like, okay, what's Grock's actual revenue today? If you take out >> Yeah. X, >> what what is their AI revenue today? Is it just Grock subscriptions plus GR Grock tokens? Do you include X subscriptions? Do you include uh cloud vendor and and neo cloud contracts? There's a bunch of different ways to measure it. The smaller the number, the easier it is to 100x. But we have seen other AI companies 100x revenues over two years, over three years, four years. Like the 100x has become, it's not a one-of-a-one scenario. >> Yeah. >> It's happened multiple times. And so uh we have seen these charts many times and uh if if they execute well there this is entirely possible. It is it is extremely >> other other notable data points from the road show. >> Yeah. >> They the forecast anticipate SpaceX making about 360 billion of capital expenditures through 2028. Uh Jensen somewhere fist pumping very excited about that number be a new hyperscaler. Um and uh anyways very >> should be unsurprising but very >> aggressive. Yeah. And um yeah the enterprise story is also live too. I saw the new Nvidia foundation model is also live. Uh we'll have to go check it out and look at the model card soon. See how it's benchmarking. But we got to move on to benchmark because there's new news in the benchmark world. First I'm going to tell you about MongoDB. What's the only thing faster than the AI market? your business on MongoDB. Don't just build AI, own the data platform that powers it. So, >> moment of silence. >> Moment of silence. Why is that sense? >> Uh the the end of an era, the last >> I guess I guess they have been very focused for decades. >> The last tier one that was a pure venture capital. >> What did they get called again? Internet boys or something. >> Oh, >> soft boys. There was there some book about them that was very funny. But e boys is a hit piece of a book book title. >> It's a fantastic book, >> but the subtitle makes up makes up for it. And it's a fantastic book and it's a very interesting story where they actually let a journalist come in and see how they true story of the six tall men. He clear he clearly wrote the subtitle and was like, I got to take the edge off of this. It's too glazy. I got to I got to take it down a notch. And so he uh uh he he threw the e boys in there. But uh >> anyways, big moves from Benchmark. Tate Clark has a scoop in the journal. Benchmark has raised >> two billion across two new funds. >> Wow. >> And uh most notably their first ever dedicated growth fund. >> H do they hire anyone who has experience growth investing? Who could possibly do growth investing there? Someone who's maybe like a bond capital and then founders fund then maybe Kleiner. Like someone with that. Somebody with that kind of background I think would be fantastic. >> Pretty good for growth investing. >> Now that you say that though. >> Yeah. >> Ev Randall. >> Ev Randall. That's right. >> They did pick up Ed. >> They did pick him up. They're almost thinking two steps ahead there. >> Are they building their fund strategy now? Their entire platform strategy around Ed Randall >> potentially. Potentially. Anyway, uh let me tell you about Shopify. Shopify is the commerce platform that grows with your business, lets you sell in seconds, online, in store, on mobile, on social, on marketplaces, and now with AI agents. And we are very fortunate to be joined by Alex Karp in just a minute. He's coming in to speak with us at AIP Con here. We're going to bring him in in just a minute. >> While we wait, Austin based podcaster Joe Rogan reportedly being considered for >> 60 Minutes. >> 60 Minutes. Uh, looks like >> they're gonna have to call it 200 minutes because he records long podcasts in 60 minutes isn't enough for him. >> Hundreds. It'll just be called >> Barry, if you're listening, put us in. Put us in the ring. We're ready to go. You need tech, business correspondent, someone who can just chop it up for 60 minutes. We do 60 minutes three times a day. We're ready to go. This is going to be light work for us. Barry, >> I'm ready. I'm ready. You could do 60 minutes right now. You could do 60 minutes tomorrow. You could do 60 minutes. You could do an extra 60 minutes easily >> when you're putting up. We're putting up a thousand minutes a week. It's no problem. >> We did consider that at one point early on. We thought, should we do basically a morning show? >> Oh, yeah. >> Take a two-hour break and come back and do another show. >> Late night show. Yeah, late night show, maybe. Anyway, we have Alex Garp here with us. >> Welcome to the show. Welcome back. Thank you so much for taking the time. Uh we're going to have you grab these headset. These headphones, right? Not these. >> You can sit close. Get in here. We got >> We liked it last time. The three of us were sitting here. >> No, we can. We can put the giant. >> Let's put this up here. Right here. I'll sit. You can stand. This always works. Makes me feel Yeah. Get in here close. This is good. Feel good. Okay. Feel good. Uh, how is it going? How is AIPCON this time around? What's changed? >> Um, well, we we're in a phase. All each one of these things like marks a time. First of all, you guys are even more baller, more successful. >> Thank you. >> Some tendies in your pocket. >> I think it might have been been part two. We got to say thank you. You know, it's like blew us up. You're looking you're looking bigger and stronger. So, hey, are you more attractive in your personal life now? Randomly. >> Well, here's what we're actually focused on. Dead hangs. >> So, you came on last time. You said your dead hangs around like five >> Oh, no. It's It's Well, it's plateaued in the last couple months at 5:30. >> 5:30. Okay. So, we The thing is like people are going to hear that. They're going to think hanging on a bar. How hard could it be? You got to go and do it. The audience has to go try to do it. We've started doing it. We're still in the >> We're still between Yeah. between and somewhere around a minute 30. You feel like your tendons are going to rip. >> 300 dead hang is respectable. >> Okay. >> 2 minutes is super elite. >> It does feel respectable when you have the five minute number. You're looking at the >> think strength matters. No. Um the the the thing is I don't want to go into rabbit hole in training. The single biggest mistake people make is they try to hang every day. You need recovery. It's like anything else. So if you want to mimic and get progress, you just do what I do, which is once a week you hang as long as you can. Doesn't have to be super macho. And then that's your day. So like just say you can do 130, >> but multiple sets or >> No, no, no. One day a week you do your maximum >> max. >> Wow. >> So like let's say you could do two minutes. Okay. >> You try to do at least 1:30. You fight to get to 1:30, but you don't fight to get to two minutes. Got it. >> That's your dead hang day. And then you can basically [ __ ] around the next day. Do whatever you want. Don't overdo it, but you could do two times one minute with a long break. >> Mhm. And then can't you just go around do less and less and less two days before if you two-minute dead hang you do like four times 15 seconds the day before you take off >> and you do that just keep doing that and you're dead. What the mistake people make is they hear my ball or time. >> [ __ ] that guy. I mean the mistake you're making is not doing a course. This could be a whole new revenue line. This could be a whole new revenue line of >> pound. You guys have it more for you guys. be like, "Hey, call in." So, um, yeah, I mean, the dead hang is a it's like and also some of it's just genetic. Like my other metrics are elite, but that this is somehow alien territory. >> God-given gift. >> What about uh what about breathold underwater? >> I don't do that. And I'm not sure I have like I grew up swimming. I think I'm weaker at that. Like I bet you I'd be in your guys range. >> I I'm a dive master. I can hold my breath for three minutes. >> And I'm just Yeah. I'm just Yeah. So, I think you would be I think you'd be crushing me on that, honestly. But you have like the lung capacity of a whale. That's true. I mean, like you got like I'm not moving. I'm not using. It's like you're like like you're like a like a like a whale floating out there under the ocean waiting to surface. True. >> So, no, I say I'll tell you the difference. Uh, God, they're always minding me out there. But like, okay, when we first met, it was like AI may be real. Okay. Then I would say somehow until about two weeks ago there was like a holy [ __ ] this is real but >> somehow it's not working but we're not allowed to say it publicly because we'll look stupid. Yeah. >> And then there's a lot of investor hype. Yep. >> Um there still is like investors printing attendees. So you have the investors on one side. I think people realize it's real but >> you know it's like you know you have the whole token maxing. Yep. >> And people are on to that and then so there's a whole value lecture there. Y >> um uh you have a political situation um where you know people who do not understand basic economics are winning the political argument. So you can you could we could talk about where >> there's a lot here. Let's break it let's break it up. Let's start with the token maxing thing. Let's start with what's what's real. Uh how are you actually thinking about deploying AI? >> First of all, what what is your what is what is Palanteer's philosophy around token consumption? >> Well, like we Okay. We have a product that will allow you to ba I mean internally it's called something but externally but really we call it the demastatory like get off masturbation thing internally. Sure. 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 just like and just re rearranging deck chairs on their personal Titanic >> literally like porn like people are like full on >> it feels it feels so >> yeah toolshaped objects tool-shaped objects you're looking at more than you want you hope no one notices you're kind of It feels productive to have every email classified with >> here'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 an >> no and I was going to say very 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 but >> let me give you a thing that's too generous for you guys >> okay it it it's taste plus money. >> Okay. Yeah. >> And there is no like AI like if you look at like pick any issue you want to talk about token mechanism maxim maxing uh what's going on with deploy codes are other people going to build ontologies why are why 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 commodified 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 in the for whether it's the Ukrainians fighting the Israelis commercial entities it's there's somebody sitting there who's like okay but this problem is valuable this problem isn't and once that value that problem is always has that problem >> always almost always but not always has attributes so there are some problems you could solve with this like I want to write a report on GDP growth in China right okay 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 the 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. They are not replaced by large language models. And then you get to security issues like but for us like the whole myth of things is just a boon because like yeah we could take any model their model open AI model sure >> open model we can identify we can now identify uh 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. >> Yeah. >> Well, you're not. So, it's like how do you have how do you 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 it's not global. There's no global charisma now. Like so right now the large language models are very frontier companies are super charismatic with investors. Mhm. >> I'll give you some news. They're >> super not charismatic with enterprises and the people like in a way. >> Even with enterprise. >> No, no. I mean, it's >> because I understand what the people No, no. The enterprise people. I have a secret. I have a secret like every company has a secret way of selling. You know what my secret way of selling is? Don't even call. Don't 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 They're like, "Hey, I'll take your bad brand." We have a great brand in enterprise, but like it it's like it's like secret knowledge because the investors love this. They're like, "Hey, my stocks are all up. Everything's up. I mean, Palunteer's done very well. We're like, but it's like and you know, you guys are doing very well, I imagine." Right? And it's okay. We're we're you know, we can 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, >> they are not happy. They do not like these people. They're tired of people token maxing. Yeah. That it looks like masturbation at their that's cost them money. They they're like And honestly, then you have something we're not allowed to talk about in this country. Likability. >> Like a palunteer. We have I think we have like 50 100 million global fans. 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 uh you know, it's like that's what they believe. >> Yeah. >> And like and they really believe it. Okay. What people are not allowed to really address is like we have fans and enemies. >> Yeah. >> Yeah. These people polarizing. >> Yeah. We're polarizing which means both sides. Yep. These people have one side. >> Yep. >> They're just It is. So it's like you and it's like it's a really big >> social media companies too have the same problem. >> Yeah. >> Everyone uses them but no one likes them. >> Yeah. But but then they also live in a circle and that circle's printing money. Yep. >> 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 is is part of it that 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 grow >> well no no they are magical at a certain kind of thing allowing you to write for example code now that code doesn't can't be used as a knowledge store so if you look at code in like three different ways like just using palent as a model we have code That's basically infrastructure. So what 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 that understand the world low way do you do? It would take millions of technical hours and an understanding of all these enterprises to do it. So it's it's much more like how do you build a steel beam? Then you have like code that is written by FDAs. >> Okay. So that's kind of managed. It's the reason why FDAs work. The secret is it's actually managed on something that we as a product. So you're writing to a codebase. We're managing that. We're increasing our product. It's not just random people write. 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, >> probabilistic stuff where you just have to get one off analysis. >> It was magical. >> By the way, it's magical. It 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 go and like and if you're involved in the 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 Russ researcher or you believe essentially it's a religion. So like you know and like one of the things is 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 a established religion Judaism, Christianity, Islam is like being filled. >> Yeah. >> And and all the answers are there but it 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 LM, nobody would be talking about our ontology, about Apollo managing SEC exploits, about our ability to manage an enterprise essentially as turning all these companies into FDES. These deploy codes, we love them because now every company wants a deploy code. You know how you do that? >> You replplatform on Palunteer and like and 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 like just imagining they know how to do it, right? >> Yeah. Is part is 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 it that people are promising AI can do for 20 years now but actually doing it finding all the really rough finding all the really rough edges and uh and not and and being at a point where you don't have to oversell the technology. You can sell both things. But now there's maybe >> Here we go. We got it together. Um, now there's maybe >> Oh, it's the wrong side. That's why. Flip it around. >> The dyslexic. >> There you go. There you go. Living the brand. Living the brand. >> We got that. I don't know. All that stuff wasn't recording. >> I I trailed off. But uh but is part of it entertaining to you that it feels like >> you know Palunteer has always been in some ways um not had competitors because there's nobody with Alex Karp running a company that is does what Palanteer does besides Palanteer. But at the same time, there's been tens of billions of dollars deployed now to effectively do what Palanteer does, but just selling the intelligence part, not selling all the underlying kind of infrastructure that you >> Well, they're doing two things. They're selling 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 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 it's really interesting and and 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. >> Yeah. >> Um and there's there's no doubt about it. where it helps and then we saw this with defense tech honestly. So like in defense tech, we were the only people. We were the first people despite what it's I I love these honestly other podcasters. They're interviewing people who are paring 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. Karp said it 25 years ago. Uh and like but it's but so that kind of that part is super weird but uh and like but um 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 offbalance sheet sales resources where other people are basically >> well it's off well no that 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 underwrite 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, if like but have you noticed the people who are serious buy it and then then it changed and then three it changes the standard. Now what you're seeing now is like that times 100x. >> Yeah. And it does change like recruiting retention and like how you build a company and we're we're always think you have to think about how to being dyslexic huge advantage there because like you don't have a playbook and now that need you need things to shift and we're doing that. Um the the 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 a deployed 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. And 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 pariting 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. And at Palunteer, we have taste art. 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 set should come in? What what are what are the ways in which you protect? What is what should you push into the public crowd? What should be on prem? What what should I mean leaving aside the law and like wars or 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. All those things are arbited 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 don't have they they're popular with their friends. They don't they really don't understand how unpopular they are in enterprise. Like they think it's like oh yeah like the way I think I have a problem with like professors at Colombia. It's like no it's a real problem. Like they think I'm Satan and uh you know it's like I I I think you know we grew up in the same community. Let's talk about Haidiger. They're like they don't want to talk about Haidiger. >> So it's like it's like Yeah. And 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. >> Yeah. >> There the like partly because of this dynamic and very self-inflicted because I I I tell you I can't name names. I called many of the titans of this world and and like started this six months ago like every couple days >> were going to be national every couple days. >> Like some of them I'm like, "Yeah, we're going to be I mean, you know, it's like honestly they're like the bat." They they find me very entertaining. Like I'm not sure. Like so they call cuz Yeah. It's like it's like, "Oh yeah, this going to be entertaining. You're going to pick up." >> Yeah. So any case, so I've been telling them for 6 months, six. We're going to be nationalized. >> Yeah. You talked about >> we're going to be nationalized. And they're like, "Why would anyone nationalize? 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 in national 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 Palenteer and a lot of these other countries is and And it's going to be nationalized before national 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 d this lobbyist. It's like not going to work. So like that's something like if you're listening to this and you're like look you know 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 like 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 you don't like my idea of like we all do a week in the park? Great. Come up with some other idea. We can have no idea, you know, and like and then they're like, "Well, I'm saying I I do not want a draft, just to be explicit." They'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 how few wars we fight. It's actually the anti-war position. But any case, disagree with everything. If you're we have on the right and on the left people uh 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. >> You know too many of us are chill waxing like it oh like nationalization it can't happen. America would never do that. >> 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, I'm sorry. Hey, I have a I have a full on very impressive uh corporate leader coming on. So, I got I got to turn it down. >> Last question. Last question if we have time. Uh >> 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, you know, cut back here or there. uh people inside tech often know like these maybe there's just a reduction because there needs to be a reduction or got bloated maybe they do need to fund some AI >> declining business model yeah the business just doesn't have momentum but how are those conversations going what does it look like >> like the the like I by the way I talk to fortune 500 companies I talk to unions I talk to soldiers I talk to fire it >> 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 twothirds of your workforce and you did it because maybe your competitor's kicking your ass. Yeah, >> that could that is a really like you might as well just go sign up for Bernie Bernie Sanders manifest. And 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 it's a 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 war fighting example is just the most neutral, not for everybody, but like the soldiers at the bottom have gotten much more valuable. And and I don't even just mean the special operators, which obviously they're in a different league, but like every the people doing a lot of the operations now are doing our product. They're high school vocationally trained. You see this everywhere. The the the modern enterprise is going to have like we have a a true like very very very smart uh 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 that's you can talk to him about that. But um uh um and uh and then very talented, creative people with taste all up and down the stack. Any case, I think this is time for me to >> I think this is time. >> We uh >> Thank you so much. >> Great to catch up. >> Always fun first. >> Oh, they want me to stay for two minutes or what? >> Oh, I'm only going to stay. Look, but just a minute. He's got to be the star. >> The other headset. >> Put it Put him Put you go in here. >> Yeah. And I'm just going to I'm going to take off after a minute. We're going to put here here and why don't you here put that headset on >> car. Why don't you introduce our guest >> microphone on the left? >> Well, I you know let he's he's >> one of the smarter people in in business um has developed um unique ways to underwrite that did not involve firing people >> and someone and someone I admire. >> Thanks, Alex. >> With that, I'm going to let you guys go. Make sure to tell them that the anttology powers are >> it's everything. >> Always selling. Hey, >> fantastic. >> Thanks for coming on the show. It's great to meet you. Pleasure. >> Uh yeah, please uh kick us off with like a bit of a more formal introduction. >> Yes. So, I'm Peter Zapino. Um I'm the executive chairman as effective on Monday of of AIG. I used to be the >> chairman and CEO. Um and have you know 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. Um, capital was challenging. Uh, so you know had a great team of people >> with me to transform the company. >> So give us the give us the shape of the business in terms of the different business lines, the different products, the international footprint, the workforce like what give 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 US is Japan. >> Oh. >> Um, we have a big business in India. >> Okay. >> Um, and then we have a very big business in in the UK. Uh, we do complicated risks. >> So, you could think about what's happening uh 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 I I fly planes around and I know I'm going to need diesel fuel in a couple months and so I'm going to hedge that out. This is for more complex risks. >> It's for more complex risks and you know you think about the largest you know sort of customers uh in the world and you know big oil companies um you know Fortune 500 companies but we also have a >> personal insurance business which will cover things like accident health >> uh that are distribution to consumers. Uh 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, uh 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. Uh but then there's probably a lot of other underwriting happening and teams uh putting together comps and spreadsheets and data. And I want to know about the the intersection there. It feels like the business is and I don't know if it ever will be just oneclick checkout for for insurance products for Fortune 500 companies. Uh but what 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. >> Um and you want as much data as you possibly can to look at deterministic y modeling probabilistic and then stochastic. I think once you understand like your mean and you understand the standard deviation around that then you have to apply it to you know sort of the widgets which is each policy >> throughout you know the globe as well as um ways in which you structure yeah insurance. So for us >> you can't look at you can't look at an individual policy uh in in isolation you you're you're managing portfolio risk risk to the entire firm and and that's something that's happening probably 247 I imagine >> it's hard and that's what led me to Alex Karp. Um, 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. I 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's going. >> Yeah. >> Take us back to your first meeting with Karp. Curious uh what the experience was like. >> It's a unique individual. >> Call you. >> Yeah. No. Um I was actually introduced by a board member many years ago and it was really in this pursuit of um 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 and could he help me think through computing >> and could he help me think through sort of portfolio optimization and I just got more and more uh intrigued. I mean, you see the brain. I mean, he just thinks about things. Um, yeah, >> he doesn't hold back. I mean, so he's so I always knew where he stood with uh 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 is too static. And so, we actually build >> our relationship on 90-day goals. Okay. And that's been incredibly effective. >> What is uh you know a lot of the AI companies talking about scaling laws, exponential growth in 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 what is the what is the thing that's experiencing a boom right now? most important part I believe in terms of business is that you have to have a business solution you're trying to solve for. So for us it was >> more data. >> Yeah. >> Um better data. >> Yeah. >> And then reduce cycle time. So 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. >> Um and then how do we actually make >> what's an example of distribution partner in this context? So it would be like a insurance broker or insurance agent um or you know someone who has >> their client is the product effectively. Exactly. Okay. Yes. Yeah, that makes sense. Um what else? Jordy, do you have something? >> Uh where was I going to go? The >> Alex ontologies. We'll get there. So So there's been uh we we primarily I mean we at least started covering early stage startups. There's been a debate uh in our kind of little subindustry right now uh around a bunch of new uh insurance focused startups that are growing incredibly quickly. >> Uh and there's a debate going on is one uh maybe AI makes it more possible to underwrite risk and if you can do that well grow very quickly. Uh the other side, you know, says uh hey, you know, if you're hypers scaling an insurance company, uh 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 goes up fast. >> But yeah, talk talk about um 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 uh everything you built with Palunteer. There's never been a time in my opinion whether it was you know introduction to fintech and sh how to use algorithms how to build data lakes and repositories for data there's never been a time in in my professional career so it's 35 years in big companies yeah >> that I've seen the ability to change how an an organization actually runs itself and that can come from big companies like Palunteer or Google or it could come from uh you know companies that are being funded by venture and have a very specific niche that can be you know additive to the organization. And what 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 uh helping underwriters make decisions that are you know 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 uh 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 it's certainly on it on its way capabilities are there um then you start to think about the entire endtoend chain being very different. >> Yeah. What I think about Palunteer while they've been such a critical partner is one as 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 in a fraction of the time helps us on the things I try to achieve is 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, and I really do think it's incredibly important. If there's one thing I look at for our organization, certainly the advancements of LLM's, their ability to do things more autonomously now where we started with the binary gen AI, now we're into a Gentic AI where we can just do things autonomously for so much longer. without the ontology of actually building like what the sort of 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 Palunteer. I'll use the ontology example again. We did the full ontology of AIG and then we went to look at an acquisition um called Everest which had about $2 billion of premium. >> We got Palunteering to work with our team. We could build an ontology of Everest's portfolio on top of ours in 4 days. Um and quite frankly what we started to learn again about that evolution is that you always relied on data lakes or global data repositories. What 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 in the way in which we are looking at our business. I have a la I have one last question. Um just on the actual change management, the organization like how the office feels. What how did you go about actually working with Palunteer? Do you set up your own internal Palunteer workforce who sits alongside FDES? Do you let Palunteer come in and plug in like one person per team that you have set up? Like was there a best practice? Did you go with the best practice? like what was the actual like experience of deploying the forward deployed engineers they get deployed into the organization that's got to be uh a unique situation >> first is making sure Alex and then you know 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 you know technology from AIG you would have some of the change management but you'd have the engineers sitting there with our teams throughout the entire process because >> 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 a ton of sense. Jordan, anything else? >> Uh no. Well, insurance has to be the most important topic. >> No, the last if we do have a second I I was uh uh >> not sure on timing. what how are you how are you thinking about you know workforce planning uh asked uh Karp about this and he said to ask >> token budgets um you know we we've stayed uh you know as as you've had this wave of AI layoffs we've been uh over and over and over reminded people that uh if you have a an individual and you give them more capability you make them more productive you make them more efficient a a thriving business will want to hire more people right because you get more individual and so we've tried to remind people of that over and over and over as you know companies that often times are you know underperforming or bloated for whatever reason but what's your kind of philosophy around uh hiring headcount planning uh riffs all that stuff in this kind of uh 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 uh we're focusing on reskilling and actually training um our employees to be in different part of the workflow. Now >> you would do this I believe in all of this you have to still have great endto-end process and so things that have been the humans been an LLM trained how to do things like outside of the normal workflow has to you have to get rid of that. I mean so I think that's just normal business. Yeah. >> Um but you know our aspiration is not to implement you know AI or anything that we're doing with our partners to eliminate jobs. I mean, it's about growth, reskilling, 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 with us. Thanks for coming. Great to be with you. >> Have a great rest of your time. >> Thanks. >> And up next, next >> we have Chad Walquist. First, I'm going to tell you about Crowd Strike. Your business is AI. Their business is securing it. Crowd Strike secures AI and stops breaches. Welcome to the show. How are you doing, Chad? >> Great overcoat. That's a new one. That's an Eleaniano special. It is. >> Oh, yeah. He is the master. >> Giving us a run for our money. >> Yeah, it's fantastic. Uh, anyway, kick us off with an introduction on yourself, how you fit into Palunteer, a little bit of backstory. I'm sure we have a ton of questions to run through. >> Uh, first, how often do you guys do these things? Because it feels like this feels like an annual It feels like an annual event. >> Yeah, >> but we're getting the call every month. Karp carp karp talks about you know manipulating time working you know a quarter at palunteer is like a day or a year at an exact so that it kind of makes sense. >> Yeah I'm like actually 23. >> Yeah time the time warp is real. So we do these quarterly. So I'm I'm a ford deployed architect technically. Yeah. >> I do what is needed and so doing the needful is kind of the palunteer way is 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. >> That's all what I'm doing. So really the goal is how do we help people decomp problems differently and apply the technology new ways. >> Can AI do decomp? >> Yes. >> Okay. Unpack that because that feels like the secret sauce. That feels like the special thing about Palunteer 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 and they think uh 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 demp to me has always felt less like let's go into your HR system and understand the basic job description and like oh someone uploaded this resume versus oh Steve actually does this completely outside of that system and marketing has two two platforms for this thing and engineering has three systems for CAD files and the all the clues that have built up over decades sometimes hundreds of years for some of these organizations like you that's what was so special about the forward deployed engineer program the Palunteer model y >> I'm surprised to hear you say I AI can do it at all it feels like the final boss >> well this is where the the really the palunteer thesis is humans and AI working together >> and so the way we thing about this is modeling our business process and we heard some other people talking about this of modeling my business process and the ontology um because the LMS don't necessarily have an a world view or world model of your business and your operations the ontology provides that okay >> and so when we talk about demp this is really about actually now I make more data computable as well so we think about LM on the agents and I'm interacting also we use LM 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 is building out that world view and then running multiple agents over this actually um 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 so 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 could do more with it right it's an empowering thing >> yeah So, uh, how often like I I I I'm interested in the, uh, like the pie in the sky, Palunteer pitch, understand your entire business, run your entire business on Palunteer. And then some of the nitty-gritty where sometimes like the lowhanging fruit is like, wait, there's a like there's someone's job to just like take a form and type it into a sheet. Like, we have we've had image recognition for a long time. let's actually go and implement that and get that into a database, get that into the ontology, get that into Palunteer 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 h how do you how do you sort of like keep the project centered around the big goal while still chopping wood on all the things that actually need to happen? >> Yeah. And I think this comes back to the forward deployed piece and like what do we deliver outcomes and 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 that's the that's the field of dreams and no one shows up >> right and so really when we demp things and work backwards from that you know the simple things like the form filling out there's a lot of that now the one approach that we see a lot is you know enterprise software is going to force you into their box. >> Sure. >> Right. You go fit you go fit into this box. >> Yeah. Well, then you know, okay, did I take away the special sauce which was my company because people were doing these 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 that 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 enable those people to do the things that made them special? >> Is is software getting more malleable because I I I can look at it two ways. I can look at uh one you know obviously AI agents are incredible at coding. They can run they they can make changes very very quickly that would take you a day in just a few minutes. Y >> at the same time I see you know so many screenshots of people saying I implemented this feature and 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's growing like there's a I 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 because 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 2,000line 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. Yep. And that feels like that's going away and I want to make sure that we've that we're ready for when it goes away and it remains malleable. >> Well, I think what what's missing is the the malleable enterprise scaffolding that you 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. So 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 malle malleability in the middle where we can actually scale. We can enable people to do things differently while still creating enterprise you know grade robust secure scalable software. And so it's actually a balance there about how >> I can enable that engineer that you know 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 guard rails create a safe system of work for them to go develop things in. Um and 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 udal loop that I create and really that udal 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 that sucked or I chose this option. Now if you do that enough agents can start to learn from that. So we actually store that in our ontology to allow it to scale. So it's really that human centric process around AI. AI 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. >> Udaloo, observe, orient, decide, act, right? Uh I have a different question, but you can you can go. >> Uh if you were giving if you had 30 minutes >> to uh give feedback to the AI labs, what are the kind of key areas? Let's say the frontier labs, right? Uh leading models. What uh 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, we >> one you're like don't compete with us. No, I actually like I I think optionality is a good thing. Like I am agnostic to where you store your data, where you store, how what model you choose, what compute you use. So like we we can allow you to use any of that because the last thing that actually drives an outcome is replatforming, moving to another >> and that goes back to the onrem culture, the secure cloud culture, ITAR compliance, like this is in the DNA of the company. >> Yeah. And so how do we actually enable people where they are instead of the focus on oh if you replplatform everything to Palanteer everything will be great. like well actually you've probably been replplatforming 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 are 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 a building with agents they're using the latest frontier model. I just got it working and then then all of a sudden the token maxing and everyone everything else and you're like oh my gosh I just blew through my whole budget. Yep. 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, um, 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 and test time compute it. It's more deterministic >> or even cached models >> cache models and then or hey if you actually just have this piece of data in the ontology then you would eliminate all this and 50% of your cost. Yep. And so you know some of the customers McCarthy talked about this at our halftime you know they they were able to in two days eliminate 60% of their token cost by rearchitecting picking a different model and in prompt tuning. So it's the combination of all those the permutations get really hard especially when it's in this probabistic models we've have tools to do this in the deterministic world >> prompt tuning it's a instead of 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 mistakes that's going to cost me a fortune. Well, there there were there's there was some chatter yesterday around uh uh something a model was doing to be more efficient was uh talking in in like this bad. >> Oh. Oh. Kan caveman caveman prompting. Um >> the caveman prompt method actually works. What how how often are you working with a company that is having call it like a mini chat GBT moment within their enterprise and then they're just like let's not tell anyone about this because I imagine like there's all these there's clearly places where >> what does that mean their product is taking off like Chad GBT or >> well so they've found a way to apply AI in a way that is highly highly effective and gives them an edge >> oh interesting >> uh but >> yeah like like the theoretical like technology transform. So, so X people are very loud, right? Figure uh they they're like, I just had this >> done 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, 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 and then your direct competitor copies you and you're like, >> "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 era 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 procedures >> or you see it in the the the uh earnings and the economics piece, right? Yeah. 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 I think in the real world, yes, there is the halves and have nots. I mean, we were just talking about AIG, 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 public. It's the competitive advantage. 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. Yep. >> And uh, 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. So many 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 this 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 think when you see people operating that with that level of efficiency what else can you do so I see this whether I'm doing you know SAP migrations the least sexy thing you could talk about but hey if I can cut your SAP migration >> let's give it up for >> yeah it's like the the least you know exciting thing on on paper but Actually, if if you're spending hundreds of millions of Yeah, you guys get it. But hundreds of millions of dollars on a migration and we can cut it in half. >> Yeah, >> that's a massive deal. >> Uh, back on the udaloop, observe, orient, decide, act on the observation side, what is the supply and demand imbalance for dashboards? Like, and what I mean by that is is when you're working with a company, is there 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. Like, you might just want to do an ad hoc analysis or actually go and see. Uh versus you're seeing so much opportunity that you're like, okay, we want to push dashboards out everywhere. Like what? Walk me through dashboarding right now because I've always been like sort of like, oh, the 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. dash I mean KPIs and dashboards should be a byproduct of operational applications where I'm making decisions. So we talk about the udaloop I have to actually act for things to hit the bottom line be valuable >> in the actual application >> in the application. So as I need those things and it's going to inform a better decision. Yeah, >> that's where I want those metrics. That should be a byproduct. Not 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 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 you know Kimble and dimensional modeling was built in 96 for scaling databases and you're still modeling in the same way in 2026 >> for 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 on 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 design working with gets better and better and better for the next use cases I connect across my business. >> Yeah. Is there an analogy there to just the deployment of AI tools currently? I'm I'm just reflecting on the 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 never do a migration ever again. Uh and the future looked like a win-win almost like I I think Postgress installations probably grew and so did MongoDB and other non-reational databases. uh and people use Reddus for things and they use all sorts of different tools and we built and we stood on the shoulders of giants and we got more giants and then you know that means full employment for you obviously but uh but I'm wondering like as like are you seeing uh glimmers of of the AI tools eating into different pieces of the technical stacks or is it all like yes and across the enterprises? Um I think it's yes and and in 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 >> and so it's actually multimodal and so like we think about this in our ontology 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 where in other places oh I have to have MongoDB 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 well we can do that all in the ontology vectors everything else so that that's really the 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 a you know oil platform >> don't care right and that 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 hund00 million 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? Because it feels like we're entering an era like >> you go back um >> you know 50 years and >> uh >> the level of like mystery in a large company would have been like is almost unconceivable today, right? because you have different time zones, different offices, you know, no email, all that stuff. And now like mystery and chaos is probably >> uh reduced dramatically, but uh still there's companies that that uh maybe maybe before you start working with them, I'm curious what those look like. >> Yeah, I mean we work with a lot of a lot of different varieties of companies. Um you know, I joke that a lot of times, you know, companies make money by accident. like they don't actually know what their most profitable product is and often they're trying to sell the thing that isn't isn't actually the most profitable and actually not selling the thing that actually is profitable and it comes back to how they've modeled their data is to aggregate it up to KPIs and other metrics when you actually need to model at the finest grade how your business operates to get a true cost of goods sold for example or true cost to serve like you that's very complicated it's very complex so like 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 businesses are operating. I can't tell you whether it's a hundred million dollar 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. >> Yeah. >> Uh last question. Uh, 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 Palunteer. >> Yes, that's interesting. >> I would love that. And so, we do have a Palanteer for builders program. We have small companies that there's people here that are two person startups, you know, that are working in their attict in Canada. I mean, like, so it it is literally um any size company. Come come work. There's a free dev tier people can come build. You actually there's actually a Shopify integration in Palunteer. You can go hook up to your Shopify and pull in Palanteer. Yeah, there are people doing this. Now, are we always great at selling it or telling the story? Sure. No. >> But 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, you know, there's some some guys here that have, you know, they hey, my my business is dying. I was, you know, I was down 10% negative margin on on what I was selling and through using Palanteer. I they watched our YouTube videos and they built it themselves and increase to a 9 or 10% positive margin in three months. >> That's great. >> And so like people can go do it. I think that's the great American story is like how do we enable that and I think we'll get there. Um it might take a little time. >> I love it. Well, thank you so much for taking the time. Great to see you. We will talk soon. Uh 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. So your AI agents can now create and modify Figma files with design system context. >> It's so crazy how many companies >> Yeah. >> are their whole strategy is like we're going to hire guys like Chad. Yeah. >> And they're gonna they're going to do stuff. >> He is he is he is the final boss of >> FD meme. What what what drove the FTE meme? Was it was it Palunteer going public or was it >> I think it was Palunteer going parabolic? >> Maybe. Maybe. Yeah. Once once. >> Yeah. because it just it just because before it was like okay yeah successful company but like no one really knows where the valuation's going. Now it's like my my uncle just told me that he made a bunch of money and so I got pay attention >> well that but also they had been banging the FTE drum >> or the and getting the consulting >> 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 to20 billion dollar company, a lot of people could still convince themselves that they were right. It's just a consulting business. >> Yeah. Yeah. Yeah. Exactly. But >> and that gets harder harder to ignore. We covered this very briefly. Um but uh but yeah, very excited for Joe Rogan to be hosting his uh you know, >> this is 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 wanted 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 I got this deck. I was clicking through it. Um, and I very uh respectfully said like go and like do this yourself 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 and you just sort of like lose focus, stop paying attention. >> Yeah. It's like it would have been more it would have been more compelling to actually just have a bulleted list of like problem. >> I mean a lot of times you can just send me the prompt because I can instantiate in my head. I can imagine the rest of the paragraphs. I I have the context window preloaded. Yeah. uh for for myself. Yeah. Uh we should talk about the new Audi, the Nuvo Lari. Is this real? Motor one. This seems real. It's real. >> Uh it's big deal. It's the It's the brand's first supercar since the R8. Twinturbocharged 4 liter V8 hybrid. 217 mph 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 and so why every everyone is gonna be rotating out. >> I mean I think they did I I think the new Ari design. It's a really cool design. >> Feels like somewhat cybert truck. >> Cyber punky futuristic. I don't know. It just checks the box for like the next supercar for me. And uh in a way that the >> Ben says it can't touch the R8. >> Oh, can't touch the R8. Okay. Okay. Well, it goes 0 to 60 in 2.6 seconds. Uh well >> almost 1,000 horsepower. Let me tell you about Cisco. Critical infrastructure for the AI era. Unlock seamless realtime experiences and new value with Cisco. And our next guest Sam Barry is here from the USDA. Welcome to the show. >> How are you doing? >> Very good. >> Good to meet you. Thank you so much for coming on down. Let's throw this on. >> And just like that. >> On the left side. >> So >> good. >> Introduce yourself a little. Tell us about yourself. >> All right. Yeah. My name is Sam Barry. I am uh proud to be working at the USDA. Would you do that right now? I'm the chief. >> Nominative determinism. Do you know about nominative determinism? >> No. >> It's the idea that you you know a person's name could possibly influence or or uh the but but Barry and working at the Department of Agriculture is like pretty perfect. No, it's incredible. Actually, my uh the berries came over here from France in um >> like 1640. >> Wow. >> So, we've been here for a long time. >> That's crazy. >> And uh it was all farmers. >> Yeah. >> Yeah. I was like all farmers until my grandpa then he became a materials engineer actually worked on jet engines. >> Okay. >> And so then his sons became engineers. My dad became an engineer. Then I was an engineer. So we're kind of >> okay >> trying to bring the two together. >> Now you're going back to the USDA. >> Yeah. >> What what is the shape of the USDA? Like what what is the shape of the organization headquarters? Do you go to the office? Is this you know US? You think just America international footprint? Like 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? >> Uh, they grade the milk in the states. 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. >> Um, there's like inspectors. There's a whole area that does that. >> There's like a series of certifications, but but but is but what else is happening? >> So, all kinds of stuff. Do you know that like food stamps? >> Yeah, >> SNAP is inside of USDA. >> Oh, I didn't know that. >> I didn't know that either. I thought I figured it was in like HHS or something, but uh yeah, it's in USDA. >> So, that's hundred billion dollars a year. It's kind of a big deal. >> Yeah. >> Um so, we do we have SNAP that's in the food nutrition service. >> Uh Forest Service is inside of USDA. 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 like a bank basically 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 of RD. 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. >> That's inside of >> That makes sense. Testing things and >> Yeah. Like labs, >> advancing different pesticides. >> So things that I mean actually become very passionate about it because I certainly didn't have an appreciation for I thought the same thing. It's like grading meat. Yeah. You know, >> um >> but like our we are so uniquely positioned as a country because of the fact that we can feed ourselves. >> Yeah. Yeah. >> And like that is not the case for a lot of a lot of countries. >> 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 a retaliation? And uh yeah, you just don't think about it. But uh >> yeah. So like China can like minimally feed itself like bare minimum it could like keep itself alive. >> 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. >> And 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 and like these are always like, you know, seven part negotiations. It's hard to look at anyone in isolation. >> But, I mean, I think it's a little surprising that aggro like food is actually part of that. I mean and then in warfare uh like agriculture the food supply is usually hit before anything like kinetic even happens you know and then before even the world knows that it's warfare >> you know okay >> uh because you can do that and you can do things to you know impact a nation's food supply in the future and so agriculture is like a really big deal >> sure >> um really important so all this to tie back to I wanted to talk about the labs because this is like a whole area inside of uh USDA but uh we do all of these these things like invest in figuring out. So 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. >> Um 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 would 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 uh over the years I've read so many stories of you know this uh this insect has been detected in you know some region of the US and there's speculation on is it um 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 >> um pests yeah pests pests like pests are obviously naturally occurring right they flourish for their own reasons or there can be um some uh some sort of malicious intent as well. Is that your guys' >> Yeah, cuz they're not ne necessarily naturally occurring, right? >> And uh so one that we have going on right now, and I'm not saying this one's not naturally occurring, but the uh new world screw screw worm that's coming up through Mexico. So 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. Uh, but New World's screworm 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. No 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. Um, and so we're developing a lab and like sterilizing flies, which again, like 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 like if we let this just come and flourish in our country, I mean, it' be very detrimental. >> Yeah. >> So, I'll go back and and if and if it's a necessary, you know, technique that needs to be harnessed, it needs to be harnessed securely and it needs to be harnessed with uh, you know, 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 uh like techn there's so much farther we can go with technology, but we have so much right now. >> And uh so many people are just black pill, right? And I think it's important. I think you should be like black pill on certain things, but you should probably take a lot of pills. Like you should be red pill and black pill and white pill at the same time. >> Uh because take >> 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 be in. 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 at 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 >> to combat it. Like that's incredible that our country can do that. >> Talk about uh these more SMB scale farmers and their approach to technology. Uh I think a lot of people would be surprised at how much uh how much these individuals at least from from what I've experienced are happy to lean into technology. I met uh a group in Texas that had developed this was years ago so pre-ai boom developed their own SAS product to help manage their operations like a a a tool that that they had built by discovering problems that they had uh on their property. And I just thought that was um that was really fascinating and and cool at the time because I think Silicon Valley would have maybe some expectation uh that uh that there might be an aversion to that until you get into the more like enterprisegrade uh scale. >> Yeah, I think it's a really important topic because you're essentially talking about like democratizing access to technology, right? And certainly with uh with like AI becoming so much more widely available, uh that was a big step forward. But I mean, and this is a big point that's being hit on at this conference and what Palanteer is really focusing on is those LLMs become useless if they're not uh if you're not deploying them in the right way with the right like data boundaries, right? So, um you know, I think that's something that we're seeing even in our universities. We do a lot of university research and uh like all the you know kids or whatever the 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 and uh if you can make that automated then you know our ability to produce beef uh you know is is greatly impacted. Um, but there's a major issue in secession 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 and run the farm. >> A lot of them went to big cities and jobs and white color work and stuff. >> So, uh, you know, this is a big thing that is a H2A. Yeah. You know, these H2A visas where like 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 >> and, uh, 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, uh >> it's something that needs to be answered. I don't have an answer for you right now. Yeah. But in order for us to continue to, you know, remain self-sufficient and 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 Yeah. general technological leverage. So, it makes a ton of sense. >> Do you know anybody that's becoming a farmer? >> Uh, well, we know some folks. We've had a number of uh entrepreneurs on the show who are getting into a tech building. Uh we've had the founder of the laser weeder that uh uses uh a lot of people don't like pesticides, but they don't mind if uh 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 beer around about around different pesticides. fruit picking robotics company. >> Yeah, Orchard as well. Uh but but mostly from tech side, usually with some family lineage uh sort of returning to the roots or or tapping into their networks uh to go back, but uh I mean truthfully, I don't know that many people that I grew up with. I mean, I grew up in LA, so not much farming activity. I know one family that had an avocado farm. >> I mean, it actually it would be super based to be a large scale farmer. Like more people should do it. And um >> we need a maybe you could be the Alex Hormosi of farming. >> Yeah. No, for real. I mean, you can. So, USDA, one of the great things that USDA does is you can get financial assistance. Like, you get big time like big time loans from USDA. You have to go through the process and they're actually doing a loan modernization effort right now to try and make that better. But >> like USDA, we'll fund it for you. You got to pay it back, but you can like get the interest rates low rate subsidized. Yeah. >> Yeah. I mean, one of our uh administrators at USDA, he like pulled up his phone one day and he's like, "Look, it's a planting day for me." And it was his John Deere app. It's like the most advanced, you know, like >> uh 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 wheat 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 udaloop, anything related to Palunteer, 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? Are they going to usda.govreport incident or are you pulling things from their filings? Like h how how do you want that to evolve? I imagine that that with more AI and technology we're 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 I'd like to focus on SNAP for that question. >> So SNAP is funded by the federal government but it's administered by the states. >> Okay. So uh when it comes to so something that we're doing right now and it was one of the first things that our secretary did like on our first day was she did a data call to all the states that you know we want all of your snap data to understand how because it's our responsibility as the funer of this program >> to understand the integ like to verify the integrity of the program. >> So we put a request out there but it has to come from every single state. >> Yeah. >> And a lot of the state programs they're not technical or they've got contractors that you know it's just a difficult thing to get us the data. Uh but then there's also a bunch of states that are just not complying, you know, for whatever reason, which it shouldn't be a problem. I don't understand what the problem is. >> Um >> but the importance of so that program that's a hundred billion taxpayer dollars a year. Like that's pretty substantial. Um 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. Like we want to get it to the point where um if somebody's committing snap fraud, we should be able it's like your card, right? If you if if somebody stole your card and did a transaction that wasn't recognized, like your card's shut off. Yep. Right. Yep. >> 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. >> Uh because it's an important program. You know, we want to be able to support people that can't support themselves, but it's uh it's not arguable that there's a massive amount of fraud in there. I mean, even the uh the organization itself does like an audit every year and they're at like there's 12% improper payments. Um improper payments is kind of a bad word. >> So 12 12 billion a year. >> Yeah. Right. And that's just like kind of based on >> that's money that that's 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 grock 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 at a huge level. And uh I mean it's something that our secretary has prioritized, but that's probably our biggest fpack. 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. Mhm. >> What's next for you? You making a career out of this or you going to go be a farmer? >> Hopefully both. Okay. Yeah. Yeah. I mean, um >> yeah, I've got some farmland. Um trying to convert it in It's like woods right now, but um >> uh where's that? >> Uh in Virginia, so actually when I lived in Michigan, we had like a little bit of a farm. We had some goats and sheep and a bunch of chickens and and ducks. Um you don't ever want to get you don't want to get ducks. You don't want to get goats. Um, ducks are like really savage actually. Yeah, like a chicken sleeps, you know? So, like it's got a normal cycle like at night time it goes into the coupe and it like sleeps. >> Ducks don't sleep. >> No, ducks do not sleep. No, 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. Otherwise, it's like 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. So, he would use like irrigation to >> uh on a timer to fill a bucket which would lift there would lift it up. Yeah. >> Okay. Interesting. Um but but then I still core memories as a kid was waking up my dad would yell like there's a fox in the coupe and then we'd be like running out. We'd be it would be like game on. Yeah. Yeah. Or you get like skunks in there and coyotes maybe. >> Yeah. We would just everybody would get up and try to go >> deal with be satisfying. That's way more satisfying than some software bug. there's so much uh fear and doom and and blackpilling uh around data centers. Uh I wanted to hear from you how you're I imagine your your role is to be an advocate for for farmers as well on 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 or what is the situation around um the the the kind of like tension between a lot of farmland could also be great land for data centers, right? And there's been some um uh 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 from a national security standpoint. or 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 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. We 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 align because we do care about conservation. um you know there was a lot of green stuff that was like you know sure not stuff that we care about but we do care about conserving our land and uh putting data centers in space just makes a ton of sense but that being a couple years out so for today you know I'm actually pretty passionate about this because in my hometown of Selen Michigan it's like small town mostly farmland they're putting a data center in there >> and it's like you know 30 miles from Detroit and Flint and like all these very industrialized areas and so it's very confusing to me why we wouldn't be putting these data center and they're like struggling areas. Detroit's doing all right, but like Flint struggling big time. >> Uh 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 and plopping them in the middle. Um 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. Um but it doesn't align. It creates it creates a massive amount of tax revenue that can be used to fund a bunch of other programs that >> 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. I actually this is kind of outside but something that I do think is uh 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 >> um 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. Interesting. So I think a good a b 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 we're I 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 cuz 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 like make my recommendations and it would be way better because there's certain artists that are not getting recommended because they're not, you know, prioritized behind the scenes >> or something. >> So, but not everybody's going to want to manage their own servers, right? >> Jensen just announced a data center that bolts onto the side of your house. >> Oh, that's sweet. >> And uh I mean and there's more stuff that's coming that way. I mean, people are doing with the Mac minis. Can't really do the Frontier AI on the Mac Mini just now, but in a few years, you know, the DJX uh desktops, like 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 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. So then I went and visited it. I was like really excited to you know encounter this being mainframe >> and uh it's like a >> uh five-year-old brand new like IBM server you know it's just like it's not there's no tapes. There's not like a team of people underwhelmed. Yeah. It's like you know it's like this big. Okay. >> Um, but I was expecting like a like a small micro data center or something. Exactly. Yeah. So totally modern and it like I like 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 data center 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 my data lives there because I don't need it distributed across the globe. It's like >> I'm here. >> No, that makes sense. That's interesting. >> Country intelligence. >> Yeah. Yeah. Yeah. >> Intelligence. This is the future. I love it. Anyway, thank you so much for coming on the show. >> Great to meet you for Thanks for doing this work. >> Have a great rest of your day. 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 Podcast and Spotify. Sign up for the newsletpn.com and have a wonderful weekend. We'll see you. Goodbye. Goodbye.