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

Summary

TBPN Podcast Summary

Episode: 🔴 Alex Karp LIVE from AIPCon 10
Date: June 4, 2026
Hosts: John Coogan & Jordi Hays
Featured Guests: Alex Karp (Palantir CEO), Peter Zaffino (AIG Executive Chairman), Chad Wahlquist (Palantir Architect), Sam Berry (USDA)

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Episode Overview

Broadcast live from Palantir AIPCon, this episode of TBPN explores the rapidly shifting landscape of AI, biosecurity, enterprise infrastructure, and future-proofing key businesses and institutions. Coogan and Hays bring in heavy-hitting guests—Palantir's Alex Karp, AIG’s Peter Zaffino, Palantir architect Chad Wahlquist, and USDA’s Sam Berry—to dig into the realities and myths of AI progress, bio-risk regulation, the evolving insurance and ag sectors, and what true organizational intelligence looks like. The overarching narrative: beneath the AI hype, major players are quietly building for defensibility, real-world impact, efficiency, and resilience.

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Key Topics, Insights, and Segments

1. The Bio Threat and AI’s Dual-Use Problem

[00:24–09:55]

• AI leaders, including Sam Altman, Demis Hassabis, Dario Amodei, and Alex Wang, have signed an open letter calling for mandatory nucleic acid synthesis screening and record keeping. This is to prevent AI-accelerated bio-risk scenarios.
  • Quote (B, 06:07): “The reason the letter is coming out now is that the threat of nucleic acid synthesis sequencing getting into the wrong hands has been enhanced by AI.”
• The polio virus and Spanish flu cases are discussed as precedents for how published genetic data can be weaponized.
• Current "voluntary" industry standards cover 80% of synthesis orders globally; however, the final 20% remain outside oversight.
  • Quote (A, 07:06): “So 20% still just hanging out? No, we’re good.”
• This push is “a Y2K-style moment” where industry self-regulates in anticipation of risk, aiming to preempt a crisis that could be fuelled by automated AI tools.

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2. Bio & Fintech Momentum—Contrasts and Parallels

[09:55–15:30]

• Biotech, once “left for dead” in terms of investor interest, is seeing fresh momentum with new companies (Isomorphic, Retro, Altos Labs) raising funds and being acquired—though still dwarfed by AI/semiconductor booms.
  • Quote (A, 10:21): “Biotech had been left for dead in some ways... but clearly there’s a lot of momentum.”
• Fintech highlights: Ramp raises $750 million at a $44 billion valuation, even surpassing PayPal’s market cap, signaling a shift in where growth and optimism reside in tech.

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3. The Palantir Philosophy—Alex Karp on AI, “Token Maxing,” and Enterprise Taste

[24:00–47:49]

AI “Token Maxing” and Value Creation

[28:26–36:52]

• Karp critiques shallow, high-usage (“token maxing”) LLM deployments, likening them to digital “masturbation” in the enterprise:
  • Quote (F, 28:37): “Internally, we call it the demasturatory—get off masturbation thing. Enterprises are like, okay, we believe this will create value, but we cannot have people just… rearranging deck chairs on their personal Titanic.”
• Solving business problems isn’t just about money and brute AI power, but “taste plus money.”
  • Quote (F, 29:32): “It’s taste plus money. And there is no like AI… all these things can be scaled in a very valuable but largely going to commodified way. But you can’t scale the taste of what is the business problem you want to solve.”

The Edge in Taste and Infrastructure

• Karp emphasizes that deep, robust business ontologies and enterprise-tuned infrastructure are Palantir’s core—an advantage hard to copy or commodify quickly.
  • Quote (F, 40:17): “LLM code appears like Palantir code but isn’t... these are essentially structures that are built deep into organizations that we own.”
• Karp references the phenomenon where others mimic Palantir language or methods, but this only expands Palantir's market and legitimacy.

Charisma, Likability, and the Risks of Popularity

• Karp draws a distinction between companies that are “popular with investors” (frontier AI labs) but lacking broad enterprise charisma—and those (like Palantir) that are “polarizing” but respected where it counts.
  • Quote (F, 33:34): “Yeah, we’re polarizing. These people have one side. They’re just—it is so…”
• Warns tech industry could see nationalization or heavy regulation if it continues to “free ride” on public goodwill without discipline, transparency, and pro-social integration.

Employment, Upskilling, and the Human Factor

• With AI advances, Karp and hosts argue that upskilling and thoughtful deployment increases worker value instead of simply enabling layoffs.
  • Quote (F, 46:14): “If you upscale somebody, they're more valuable... The modern enterprise is going to have... talented creative people with taste all up and down the stack.”

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4. Real-World AI in Enterprise: Peter Zaffino on AIG’s Transformation

[48:46–61:22]

• AIG’s international insurance business relies on Palantir’s ontology and forward deployed engineers to integrate data, speed up underwriting, and manage complex risks.
• Zaffino describes iterative, 90-day planning cycles and embedding Palantir engineers alongside AIG teams for real, incremental improvement.
  • Quote (E, 53:03): “We actually build our relationship on 90-day goals... That’s been incredibly effective.”
• Ontologies allow rapid post-M&A integration, e.g., integrating Everest’s $2B premium portfolio on top of AIG’s in four days.
• Zaffino stresses that AI enables faster, better decisions, more usable data, and agent-driven workflows, but real orchestration—turning data + AI into business value—is difficult and ongoing work.
  • Quote (E, 56:42): “I think companies... are struggling with the orchestration of how you actually drive agents, people and data into an organization.”
• On AI and jobs: “Our aspiration is not to implement anything that eliminates jobs. It’s about growth, reskilling, and opportunity.” [60:40]

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5. The Palantir Decomp Process: Chad Wahlquist on Ontology, OODA Loops, and Technical Culture

[61:49–82:18]

• As a forward deployed architect, Wahlquist explains Palantir’s unique approach (“decomp”)—embedding teams to break down real workflows, map business processes into ontologies, and create actionable feedback (OODA loop: observe, orient, decide, act).
  • Quote (C, 64:09): “The ontology provides that. When we talk about decomp, this is really about actually now I make more data computable...”
• Palantir’s Evolve tool analyzes logs to recommend optimal AI model selection, reducing “token maxing” and costs.
• Wahlquist notes many large companies actually don’t know, down to granular detail, how they make money or what products are truly profitable—Palantir’s modeling closes this “mystery gap.”
  • Quote (C, 80:13): “I joke that a lot of times companies make money by accident. They don't actually know what their most profitable product is…"

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6. USDA and Ag Tech: Sam Berry on Food Security, Data, and Modern Farming

[87:15–109:13]

• USDA’s reach extends far beyond food inspection—encompassing SNAP (food stamps), rural development, forest management, scientific research, and more.
  • $100B/year SNAP program is federally funded but state-administered, presenting data collection, fraud detection, and compliance challenges.
    • Quote (D, 99:40): “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… There's 12% improper payments, that's $12 billion a year.”
• Berry discusses ag-tech adoption: small and mid-sized farmers are surprisingly eager for automation to keep productivity high amidst aging workforce and labor shortages.
• The “dark side” of innovation: pests and biosecurity threats (e.g., New World Screwworm) require agile, science-driven government action, often in partnership with AI and tech companies.
• Data localization and trust are emerging issues, with Berry predicting interest in local, community-run or space-based data centers for sensitive agricultural and citizen data.
  • Quote (D, 108:55): “People might want their data to be hosted in a place that's aligned with their views... because it's like I can trust this local company…”

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Notable Quotes & Moments

• Alex Karp (on “token maxing” in AI):

  “Internally, we call it the demasturatory—get off masturbation thing. Enterprises are like, okay, we believe this will create value, but we cannot have people just... rearranging deck chairs on their personal Titanic.” [28:37]

• Alex Karp (on taste as differentiator):

  “It's taste plus money. And there is no like AI... you can't scale the taste of what is the business problem you want to solve.” [29:32]

• Peter Zaffino (on Palantir partnership):

  “We could build an ontology of Everest's portfolio on top of ours in four days.” [57:25]

• Chad Wahlquist (on unknowns in enterprises):

  “Companies make money by accident. They don't actually know what their most profitable product is.” [80:13]

• Sam Berry (on SNAP fraud):

  “There's 12% improper payments... That's $12 billion a year.” [101:08]

• Alex Karp (on potential regulation/nationalization):

  “I've been telling them for six months... we're going to be nationalized... The momentum is on the side of people in national life.” [43:01]

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Timestamps for Key Sections

• AI and Biothreat Open Letter — [00:24–09:55]
• Biotech & Fintech Momentum — [09:55–15:30]
• Alex Karp Interview — [24:00–47:49]
• Peter Zaffino (AIG) Interview — [48:46–61:22]
• Chad Wahlquist (Palantir) Interview — [61:49–82:18]
• Sam Berry (USDA) Interview — [87:15–109:13]

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Conclusion & Takeaways

This AIPCon special episode surfaces the complexities behind modern AI hype cycles, biosecurity risks, and the evolving interplay of technology, business, and public institutions:

• Regulation is catching up with science, driven by AI’s new risks (esp. in bio).
• Enterprise value isn’t just about technology, but hard-won operational “taste”, experience, and on-the-ground partnerships.
• AI’s greatest impact will show not in cost-cutting, but in augmenting and empowering specialized workers.
• Industries from insurance to agriculture are seeing these themes play out in data modeling, change management, and real-world deployment.
• Underlying it all is a call for proactive responsibility—by tech builders, business leaders, and public sector partners—to avoid both overreaction and complacency in a fast-moving era.

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(All references to specific segments, speaker names, and timestamps are based on the original episode transcript.)

Transcript

[A]

You're watching TVPN.

[B]

Today is Thursday, June 4, 2026. We are live from Palantir AIPCON. The Temple of technology is back.

[A]

Fortress of finance.

[B]

We will return to it, but it is also a state of mind. That's right. We are also sponsored by Ramp. Time is money save. Both easy use, corporate cards, bill pay, accounting, and a whole lot more all in one place. Big news from Ramp today. Massive fundraiser. We're going to cover it in a little bit.

[A]

But first you hear that.

[B]

Got to talk about. Oh, is it still going? I like it. The Ramp song's back. This was early days. We really talked about Ramp so much. Turned it into a song. Anyway, the topic of conversation in DC has it's still in AI world, but instead of talking about approving models before they're released, today it's about the Bio threat. Brandon Gorell wrote in the TVPN 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 signatories 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

[A]

was a very tangible Y2K style moment. Exactly where there was a, let's just say, powerful business strategy.

[B]

Yeah. Is it? Yeah, it was like, is it over? You start thinking about the consequences 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 creates full employment for Nikesh Arora over at Palo Alto Networks, who we had a chance to talk to yesterday, and he's been very fortunate in implementing the solutions to the cybersecurity threats posed by new AI systems. Some of the new AI capabilities that are rolling out, but Bio might be next. And so it's exciting to see that the great houses of AI are uniting behind the Bio threat. So let's take you through this. First, I'm gonna 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 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 a very, very problematic virus. It's an RNA virus, meaning that its nucleobases or building blocks are a C, G, U, if you're familiar with rna, adenosine, cytosine, guanine and uracil, put more plainly. Thanks, Brandon Gorell. 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, if there's a new virus or an existing virus or 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 collect a sample. So instead These researchers, in 2002, they were able to take the published sequence, chemically synthesize short DNA fragments, assemble them into 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 675,000Americans and had a 2 to 3% mortality rate among those infected. Very, very dangerous stu. 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 text in a text file, a bunch of atgu, 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 AI letter is all about. So that's the 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 Altman, 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 record keeping. You might have seen it on the timeline. And at first glance, Brandon here assumed, and I assume the same thing. Assumed it was another press release from a Frontier lab claiming it had just discovered discovered new capabilities in one of its internal models that would ultimately lead to catastrophe. A lot of this, like doom, fear based marketing has been happening. So that was sort of the natural reaction. And that's what Brandon.

[A]

Some people's reaction would be. Are we not doing record keeping here already?

[B]

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 is solved, yet 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? You said that you were trying to treat cancer, or you said that you were 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 dig into this. That's where they're going with that. And so they also need to verify 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. He gets into this. So he says the reason the letter is coming out now is that the threat of nucleic acid synthesis sequence 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 synthesis 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 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 board, but membership agreed.

[A]

So 20% still just hanging out? No, we're good.

[B]

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 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 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 flimsiness of the agreement. So it's not, you can opt into

[A]

this program by just saying that you're opting into it, but then even the reporting once you're opted in is voluntary.

[B]

So 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 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 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. So what else? Bunch of other factors contribute to the relative flimsiness of this agreement. 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 that the letter is not yet another warning of apocalyptic AI doom, which I think the public has unfortunately 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, a good breakdown, and I agree With a lot of that. Andrew Curran also has some deep dive on this with some more of the signatories. He shares screenshots of all of these and it really is everyone.

[C]

Yeah.

[B]

Y Combinator, Patrick Collison, Microsoft Interconnects, AI Harvard, tons of stuff. And then over in the nucleic acid synthesis industry you have Twist Bioscience, Anza, Emerald Cloud Lab and Kathleen McMahon from Valthos is on here, former guest of the show. So, good news, but obviously just an 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. The other news in the bio world.

[A]

Yeah, I mean the news is just that there's incredible momentum in biotech. It feels like early stage biotech after

[B]

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 at a trillion. Every chip stock is, you know, in the hundreds of billions trillions. This is much smaller, smaller but, but

[A]

it's notable because biotech had been 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.

[B]

Yeah.

[A]

But of course every asset class kind of go through, goes through that kind of phase and clearly there's a lot of momentum and they should be.

[B]

You would expect that biotech would be similarly Power law driven. Maybe not as extreme, but if you pull out SpaceX OpenAI anthropic from power Law, capital reversal. 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.

[A]

Yeah, we had that. Didn't we have a guy on that had sold like three companies. We didn't have $2 billion exit.

[B]

Yep. And then he joined another company and sold it for 3 billion like the next day.

[A]

And so, so anyways, you have Isomorphic Labs spun out of DeepMind Coinbase or not Coinbase, but Brian spun out or like founded New Limit. You have Retro.

[B]

They just raised a new round. We're going to get Jacob on the show as well.

[A]

Altos Labs from Jeff, the Chad from

[B]

Amazon, as this post puts it.

[A]

Anthropic obviously acquired Coefficient Bio as well.

[B]

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 gonna be able to cover this a lot more in the near future.

[D]

Yeah.

[A]

At what point do the. At what point does like a Pfizer or Johnson and John Johnson and Johnson start joining the press release economy of just coming. I'm not, I'm not saying it'd be a good thing, but coming, coming out and saying we believe we're, you know, right at the.

[B]

Because there are partnerships all the time that happens 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'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 agents to deploy web apps, servers, databases and more, while Railway automatically takes care of scaling, monitoring and security.

[A]

So Ramp, what's going on in ramp land?

[B]

$44 billion valuation.

[A]

Whoa.

[B]

Really, really solid traction. Just, you know, every 12, 18 months, sometimes much quicker, sometimes they do two rounds in two weeks. But really solid progress. They raised $750 million at a $44 billion valuation. Last time we grew this fast, we were 1 20th of the size.

[A]

So they're actually, this is the most notable thing to me.

[B]

Yeah.

[A]

Lots of chatter on the timeline around, you know, other fintech valuations.

[B]

You compare SaaS Apocalypse.

[A]

Yeah, well, yeah, you compare them to like, you know, Ramp is now worth more than PayPal.

[B]

Okay.

[A]

PayPal has 32 billion of revenue.

[B]

Yeah.

[A]

But PayPal certainly has, I would say, net, you know, probably negative momentum.

[B]

Yeah.

[A]

Whereas Ramp has incredible momentum. And this, this is the standout line. They were 1 20th the size the last time they were growing this fast. And so, yeah, just really, really, really, really impressive execution and incredible opportunity still.

[B]

Yeah. So Eric took to the timeline, posted an essay about the third pillar, comparing the previous eras of value creation. The two pillars, 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 is like marketing. I'm sure. And. 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 to the bank balance go up in this company.

[A]

All personal and business finance. Finance at the end eventually comes down to are we making more money than we're spending?

[B]

Yeah. And I think, 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? It is answering that question. 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 gotta do it. It's my number one advice for founders these days. There's some other fundraising news. Sabi, the Beanie BCI company is getting preempted at 35 million at 500 million post. This is a leak from R for Rock. We'll see where it goes.

[A]

This is huge for you.

[B]

Why?

[A]

Because you are a beanie guy.

[B]

I do like beanies.

[A]

You love to throw on a beanie.

[B]

A beanie in the morning keeps it together.

[A]

Yeah, yeah.

[B]

I like a beanie. Very, very.

[A]

It's interesting. I think that this format, of course I'm sure they can adapt it to other types of hats.

[B]

Yeah.

[A]

But this format certainly maybe makes it harder to build momentum in places like California at least Southern California, Arizona.

[B]

Be big amongst creative directors though.

[A]

Yeah, huge, huge, huge potential. Silver like silver like every.

[B]

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.

[A]

You can wear's what's interesting though. So are for rock.

[B]

Yeah.

[A]

Usually it's pretty dialed, pretty dialed, pretty dialed. It's almost like he has inside information. It's almost like he somehow got.

[B]

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 like, what's the benefit of leaking everything? Is he a lawyer that's seeing all the docs turn around?

[A]

I mean zero benefit for a lawyer.

[B]

Right.

[E]

Client.

[B]

The rush of getting likes on the timeline is pretty universal. Lawyers are just like, I need a

[A]

banger at a fund for sure.

[D]

Yeah.

[A]

And I Don't. I don't know anything else, but 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.

[B]

Sure.

[A]

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, but can certainly be annoying for teams as well. This was notable, though. So 200 million of LOI from B2B customers. And so very curious what the enterprise play is here. I don't know, but we can work on getting Rahul.

[B]

Does that mean through hospital networks or through the healthcare system? Or is it like Mark Zuckerberg wants to go further? He wants to track the brainwaves of the employees. Not just we're going to track your screen, we're also going to check your brain. I mean, it could go either way because you imagine Neuralink has had a bunch of traction and bunch of amazing. I saw Nolan, the first patient, P0 on Rogan, talking about playing COD with the Neuralink. Amazing. And you can imagine that at a certain point, some sort of partnership, they

[A]

have multiple hat form factors.

[B]

There we go.

[D]

Catboy.

[B]

Hat's coming.

[A]

We're good. I was getting really hung up on the beanie. And like, there's so many different enterprise or B2B contexts. You're in a. You're in a warehouse in Dallas, Texas, in the summer.

[C]

Yeah.

[B]

You don't know. Maybe this contract, maybe this $200 million LOI is from REI or Patagonia. You know, you don't know who makes beanies. What's the Carhartt? Carhartt makes a great beanie.

[D]

There we go.

[B]

You don't know any of this stuff. You're completely out to lunch.

[A]

I went through.

[B]

I think I went through on the Beanie Economy.

[A]

Beanie Economy. Anyway, Beanie market map. Work on it.

[B]

Let me tell you. Public.com public.com investing for those who take it seriously. Stocks, options, bonds, crypto, treasuries and more, all with great customer service.

[A]

They just launched a feature today that allows you to connect your favorite chat app to Public.

[B]

Yes, More important than ever, because with Public, you're going to be able to go and create the S&P499. If you don't like SpaceX or the S&P1. If you love SpaceX, you can express your opinion about SpaceX however you want.

[A]

Can you please help me build an index? Index for one company?

[B]

Yes. Index for one company or index for everything but one company SpaceX is very divisive. People are extremely optimistic in certain camps. Extremely pessimistic.

[A]

Goldman, Goldman very optimistic.

[B]

What'd they say?

[A]

Goldman expects SpaceX's AI revenue to surge 100 times by 2030. Huge, big number. I looked at this title and I was thinking like okay, what's Grox actual revenue today? If you take out X, what is

[B]

their AI revenue today? Is it just Grok subscriptions plus Grok Grok tokens? Do you include X subscriptions? Do you include cloud vendor 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 one scenario. Yeah, it's happened multiple times and so we have seen these charts many times and if, if they execute well this is entirely possible. It is, it is extremely.

[A]

Other other notable data points from the roadshow they the forecast anticipate SpaceX making about 360 billion of capital expenditures through 2028. Jensen somewhere fist pumping. Very excited about that number. Be a new hyperscaler and anyways very should be unsurprising, but very aggressive. Yeah, and the enterprise story is also live too.

[B]

The new Nvidia foundation model is also live. 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.

[A]

So moment of silence.

[B]

Moment of silence. Why is that?

[A]

For the end of an era, I guess.

[B]

They have been very focused for decades.

[A]

The last tier one there was a pure venture capital.

[B]

What did they get called again? Internet Boys or something? Oh, Soft Boys. There's some book about them that was very funny. But E Boys is a hit piece of a book title.

[D]

No, it's a fantastic book but the

[B]

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.

[A]

E Boys. The true story of the six Tall Men.

[B]

Yeah, clearly wrote the subtitle and was like I gotta take this, the edge off of this. It's too glazy. I gotta, I gotta take it down a notch. And so he, he, he threw the E Boys in there.

[A]

But anyways, big moves from Benchmark. Clark has a scoop in the journal. Benchmark has raised 2 billion across two new funds.

[B]

Wow.

[A]

And most notably their first ever dedicated growth fund.

[C]

Hmm.

[B]

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 pedigree, with that

[A]

kind of background would be fantastic.

[B]

Pretty good for growth investing.

[A]

Now that you say that though, yeah. Ev Randall.

[B]

Ev Randall, that's right.

[A]

They did pick up that.

[B]

They did pick him up. They're almost thinking two steps ahead there.

[A]

Are they building their fund strategy now, their entire platform strategy around Ev Randall?

[B]

Potentially. Potentially. Anyway, 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 aipcon here. We're going to bring him in just a minute.

[A]

While we wait, Austin based podcaster Joe Rogan reportedly being considered for 60 minutes.

[B]

60 minutes. They're going to have to call it 200 minutes, but because he records long podcasts in 60 minutes 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 technology 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 1,000 minutes a week. It's no problem.

[A]

We did consider that at one point early on we should do. We can we do basically a morning show.

[B]

Oh yeah.

[A]

Take a two hour break and come back, do another show.

[B]

Late night show. Late night show, maybe. Anyway, still we have Alex Garp here with.

[A]

Here we go.

[B]

Welcome to the show. Welcome back. Thank you so much for taking the time. We're gonna have you grab these headset, these headphones, right? Not these from you. You can sit.

[A]

No, no, get close.

[B]

Let's get in here.

[A]

We liked it last time the three of us were sitting here.

[F]

No, we can we can put the giant.

[B]

Let's put this up here. Right here.

[F]

All you can stand.

[B]

This always works.

[F]

Makes me feel. Yeah, yeah.

[B]

Get in here. Cl.

[F]

Good.

[B]

Okay. How is it going? How is AIPCON this time around?

[F]

What's changed? Well, we, we're in a phase.

[B]

Yeah.

[F]

All. Each one of these things like marks a time. First of all, you guys are even more baller, more successful.

[B]

Thank you. Thank you.

[F]

Some tendies in your pocket.

[B]

I think it might have been part two. We got to say thank you.

[F]

It's like blew us up.

[B]

You're looking biggest.

[F]

Guess you're looking bigger and stronger. Are you more attractive in your personal life now, randomly?

[A]

Well, here's what we're actually focused on. Dead hangs.

[D]

Yeah, yeah.

[A]

So you came on last time, you said your dead hangs around like five.

[F]

Oh, no, it's, it's. Well, it's plateaued in the last couple months at 5:30, 5:30.

[A]

Okay. So we, the thing is like people are going to hear that, they're going to think hanging on a bar five minutes, 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 under. We're still between.

[B]

Yeah, between one and two minutes.

[A]

Somewhere around a minute 30, you feel like your tendons are going to rip.

[F]

31 30. Dead Hang is respectable.

[B]

Okay.

[F]

Two minutes is super elite.

[A]

It does feel respectable. When you have the five minute number,

[F]

you're looking at the strength matters. No, 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 to say you can do

[A]

1:30, but multiple sets.

[F]

No, no. One day a week you do your maximum max.

[A]

Wow.

[F]

So like let's say you could do two minutes. You try to do at least 1:30. You fight to get to 130, but you don't fight to get to two minutes.

[B]

Got it.

[F]

That's your dead hang day. And then you can basically fuck around the next day. Do whatever you want, don't overdo it. But you could do two times one minute with a long break. 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 your day. What? The mistake people make is they hear my ball or time.

[A]

Fuck that guy. I mean, a mistake you're making is not doing a course course. This could be a whole new revenue line.

[F]

You guys have it more for you guys.

[B]

Yeah.

[F]

Call in. So, 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.

[B]

What about. What about breath hold underwater?

[F]

I don't do that. And I'm not sure I have it. Like, I grew up swimming and I think I'm weaker at that. Like, I bet you I'd be in your guys,

[B]

I'm a dive master.

[F]

I can hold my breath for three minutes. I'm just. Yeah, I'm just. Yeah. So I think, yeah, you'd be like. I think you'd be crushing me on that, honestly. But you have like the lung capacity.

[B]

That's true, that's true.

[F]

I mean, like, yeah, you got like,

[B]

if I'm not moving, I'm not using any oxygen.

[F]

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.

[B]

True.

[F]

So, you know, I say, I'll tell you the difference. God, they're always minding me out there. But like, okay, when we first met, it was like, AI, maybe real. Then I would say somehow, until about two weeks ago, there was like a holy fuck, this is real. But somehow it's not working. But we're not allowed to say it publicly because we'll look stupid.

[B]

Yeah.

[F]

And then there's a lot of investor hype.

[B]

Yep.

[F]

There still is, like investors printing 10 days. 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 and people are onto that. And then. So there's a whole value lecture there. You have a political situation where people who do not understand basic economics are winning the political argument. So we could talk about where there's a lot here.

[B]

Let's break it up. Let's start with the token maxing thing. Let's start with what's real. How are you actually thinking about deploying AI?

[A]

First of all, what is Palantir's philosophy around token consumption?

[F]

Well, okay, we have a product that will allow you to bail. I mean, internally it's called something, but externally. But really? We call it the demasturatory like get off masturbation thing internally.

[B]

Sure.

[F]

It's like people are just sitting there all day, kind of like a porn addiction. And enterprises are like, okay, we knew this. We believe this will create value.

[B]

Yeah.

[F]

But we cannot have people just like

[B]

some people checking the weather with it. Just like, and just rearranging deck chairs on their personal Titanic.

[F]

Literally like porn.

[B]

Okay.

[F]

Like people are like full.

[B]

It feels so.

[A]

Yeah. Tool shaped objects.

[F]

Yeah, tool shaped objects. You're looking at more than you want. You hope no one notices kind of before dinner.

[B]

Feels productive to have every email classified.

[A]

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.

[F]

So just to give you a weird analogy.

[A]

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.

[F]

But let me give you a thing that's too generous for you guys, okay? It, it, it's taste plus money.

[B]

Okay?

[F]

Yeah. And there is no like AI. Like if you look at like to pick any issue you want to talk about token max maxing, what's going on with deploy codes. Are other people going to build ontologies? Why does our political class not understand AI? Especially in Europe, it's like, yes, because all these things can be scaled in a very valuable but largely going to 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 the Ukrainians fighting the Israelis, commercial entities, there's somebody sitting there who's like, okay, but this problem is valuable. This problem isn't. And once that problem always has that problem, 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 this specialized way I underwrite. We're going to have a guest here. I want to understand the specialized way I drill for oil and gas that's both legal, ethical and reduces the cost of production. I want to change the supply chain of my industry. Whether that's military or whether that's building boxes or whether that's cars. These things require actual, precise, ongoing processes. They are enhanced by Large language models, they are not replaced by large language models. And then you get to security issues. For us, the whole mythos things is just a boon because, yeah, we could take any model, their model, OpenAI model, open model, we can now identify vulnerabilities at 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, there's 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, but you're not. So it's like, how do you have. How do you get. So all these problems are exposed, identified, and then you always have a thing of where's the charisma which people really underestimate. And it's not global. There's no global charisma now. So right now the large language models are very. Frontier companies are super charismatic with investors. I'll give you some news. They're super not charismatic with enterprises and

[B]

the people, even with enterprise.

[F]

No, no, because I understand what the people. No, the enterprise people. I have a secret. I have a secret. Like every company has a secret way of selling.

[B]

Yeah.

[F]

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 like, they're like, hey, I'll take your bad brand. We have a great brand in enterprise. But like, it's like, it's like secret knowledge. Because investors love this. They're like, hey, my stocks are all up. Everything's up. I mean, Palantir has done very well. Where like. But it's like. And you know, you guys are doing very well, I imagine, right? And it's okay, 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. It looks like masturbation at their. This 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 Palantir. We have. I Think we have like 50, 100 million global bands. We have like 5 million people that wake up in the morning literally calling me Satan. I didn't know I had that kind of warm hand. But you know, it's like that's what they believe.

[B]

Yeah.

[F]

And like. And they really believe it. Okay. What people are not allowed to really address is like, we have fans and enemies.

[B]

Yeah, yeah. These people.

[A]

We're polarizing.

[F]

Yeah, we're polarizing. Which means both sides.

[B]

Yep.

[F]

These people have one side.

[B]

Yep.

[F]

They're just. It is so it's like you and it's like it's a really.

[B]

Social media companies too have the same problem.

[F]

Yeah.

[B]

Everyone uses them, but no one likes that.

[F]

Yeah, but. But then they also live in a circle and that circle is printing money.

[B]

Yep.

[F]

So it's like, you know when you look in the mirror and you just printed a lot of money, you look pretty fresh.

[A]

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.

[F]

Well, no, 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 Palantir as a model, we have code that's basically infrastructure. So what are the Ukrainians using? What is the Department of War using? What do a lot of our enterprises, we call that primitives. It's basically hard coded things that. That understand the world the way do you do. It would take millions of technical hours and an understanding of all these enterprises to do it. So it's much more like how do you build a steel beam? Then you have code that is written by FDEs. Okay. So that's kind of managed. The reason why FDES work, the secret is it's actually managed in something that we as a product. So you're writing to a code base. We're managing that, we're increasing our product. It's not just random people writing. Then you have, let's call it free code. That free code is. That's magical. Like you can do it very quickly. It's almost right. It doesn't have to be exact dashboard dashboards, financial stuff, little flow probabilities, stuff

[B]

where you just have to one off analysis.

[F]

Magical, by the way. It's magical. Not only creates and it's magical in A way. I know people 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 I shouldn't do it.

[B]

Sure.

[F]

But it's like, it's like that. Right. And you just keep going and like. And if you're involved in the. That thing, you're also making money.

[C]

Yeah.

[F]

And then last, not least in certain circles, like if you have. You want to be a version researcher or you believe essentially it's a religion. So like, you know, and like one of the things. 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 and all the answers are there. But it's very, very successful at doing things that a company has to do. But it is not actually solving the problem that enterprises are. It is now. It can solve them. That's the trick. It's not binary. It's not like you can't say. They're not valuing. They're totally putting our business on steroids. Like, without LLMs, nobody would be talking about our ontology, about Apollo managing exploits, about our ability to manage an enterprise, essentially turning all these companies into FTEs. These deploy codes. We love them because now every company wants to deploy code. You know how you do that? You re platform on Palantir 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 just imagining they know how to do it.

[A]

Yeah. 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 people are promising AI could do for 20 years now. But actually doing it, finding all the really rough. Finding all the really rough edges and. And not. 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. Now there's maybe.

[B]

Oh, it's the wrong side. That's why flip it around.

[F]

Dyslexic.

[A]

Yeah, there you go. There you go.

[F]

Hopefully we got that. I don't know. All this stuff.

[A]

I trailed off, but no, I understand. Is part of it entertaining to you that it feels like, you know, Palantir has always been in some ways not had competitors because there's nobody with Alex Karp running a company that is. Does what Palantir does besides Palantir. But at the same time, there's been tens of billions of dollars deployed now to effectively do what Palantir does. But just selling the intelligence part, not selling all the underlying kind of infrastructure.

[F]

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 FT's. Now the, the very cool thing is when you've been in your basement doing your thing and everyone kind of use it as the freak show, it, it's really interesting and great to have adoption. The pretty ironic thing is half the people adopting now don't even know they're copying. But now the copying thing helps and hurts. Where it hurts is in the beginning, it puts clutter in the market.

[A]

Yeah.

[F]

And there's, there's no doubt about it where it helps and that we saw this with defense tech, honestly. So like in defense tech, we were the only people. We were the first people. Despite what I, I love these honestly, other podcasters, they're interviewing people or parroting things I said 20 years ago. They don't know it. And it's like, oh, that's so insightful. It's like, yeah, of course it's insightful. Carp 25 years ago. And like, but it says but. So that kind of, that part is super weird. But. And like, but, but it's. But what really happens when you see this 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,

[A]

first of all, you view it as like off balance sheet sales resources where

[F]

people are basically, well, it's off. Well, now that that's the large is they do two things. They increase the size of the market because de facto nobody wants to find an underwriting market where there's only one person.

[B]

Sure.

[F]

So like if you're the one person, the percentage of the defense budget you can get is much more. 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 and it does change like recruiting retention and like how you build a company. And we're always thinking, you have to think about how to being dyslexic. Huge advantage there because like you don't have a playbook. And now that we need you need things to shift and we're doing that. The, the. The central thing though that it's just cannot be developed Even if you understood the playbook. A lot of these things are like appear like it's like you know, LLM code appears like Palantir code but isn't for deployed thing appears like Palantir. 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 then three years we're in a completely different world. But there is this magical thing called taste. Like in the end of the day the reason why you guys have done so well. It's of course there's aptitude and diligence and showing up and all those things. Yeah. But you have to be able to differentiate between two people who are in business. One of whom is saying something that sounds weird, that is insightful, one of whom is parroting something that sounds weird. And that's all they're doing. And a lot of people, very few people can do that and do the same thing. Like the enterprises that succeed. There is a taste arbiter and at Palantir 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 sets should come in? What are the ways in which you protect? What should you push into the public crowd? What should be on prem? What should I mean leaving aside the law and war ethics, what do you want to protect? What should you protect? What should you not protect? Because quite frankly you want that to be out there so you can get more data. 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're popular with their friends. They don't. They really don't understand how unpopular they are in enterprise. They think it's like oh yeah, it's like the way I think I have a problem with like professors at Columbia, it's like, no, it's a real problem. Like they think I'm Satan. And you know, it's like, I think, you know, we grew up in the same community. Let's talk about Heidegger. They're like, they don't want to talk about Heidegger. So it's like, it's like, yeah. 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, they're the like partly because of this dynamic and very self inflicted because I tell you, I can't name names. I called many of the titans of this world and in like started the six months ago, like every couple days

[A]

we're going to be calling every couple of days.

[F]

Like some of them are like, yeah, we're going to be. I mean, you know, it's like, honestly, they're like the batch. They find me very entertaining. Like, I'm not sure. Like, so they call because. Yes. Like, yeah, it's like, oh, yeah, this is going to be entertaining.

[B]

You're going to pick up.

[F]

Yeah. So any case. So I've been telling them for six months. Six. We're going to be nationalized. Yeah, 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 life. And 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 gonna. Yes, they are gonna create opportunities. You have to talk openly about how these things are valuable because we have adversaries. You can't just say these, all that stuff. So the primary risk, honestly to Palantir and a lot of these other countries is. And then it's going to be nationalized before nationally it's going to be regulated by people who don't understand this. And now they'll tell you in private, I'm working on this. I'm Da da da. And this and 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.

[B]

Great.

[F]

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 do not want to 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 have a 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, 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 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, I have a, I have a full on very impressive corporate leader coming on. So I got it, I got.

[A]

Last question, last question. If we have time. How are your conversations going with Fortune 500 CEOs around headcount planning? There's been so many layoffs this last year that people were saying hey, we're getting so much out of AI we're able to, you know, cut back here or there. People inside tech often know like these, maybe there's just reduction because there needs to be a reduction or got bloated. Maybe they do need to fund some AI declining business model like getting out repeated by the businesses. But how are those conversations going? Like what does it look like?

[F]

Like 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.

[B]

Sure.

[F]

And like all these, whether it's people working on batteries, people driving trucks, people corporate leaders. And again, this is where I think we have to be very careful to be more disciplined on the corporate side. Like if you run around saying AI allowed you to fire two thirds of your workforce and you did it because maybe your competitors 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 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 modern enterprise is going to have like, we have a true like he very, very, very smart person coming on and it's like you're gonna 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, and, and then very talented creative people with taste all up and down the stack. Any case, I think this is time for me to.

[B]

This is time. We thank you so much.

[A]

Great to catch up.

[B]

Always fun. Oh, first.

[F]

Oh, they want me to stay for two minutes or what? I'm only going to stay. Look. But he's got to be the star.

[B]

The other headset.

[F]

Yeah. And I'm just gonna, I'm gonna take off after a minute here and why

[B]

don't you put that headset on Car,

[A]

why don't you introduce our guest?

[B]

Microphone on the left.

[F]

Well, I, you know, let him. One of the smarter people in business has developed unique ways to underwrite that did not involve firing people and someone. And someone I admire.

[E]

Thanks Alex.

[F]

With that I'm gonna let you guys go. Make sure to tell them that the ontology powers it

[A]

always selling. Hey, thanks for coming on the show. It's great to meet you.

[B]

Yeah. Please kick us off with, like, a bit of a more formal introduction.

[E]

Yes. Peter Zaffino, I'm the executive chairman, is effective on Monday of aig. He's ready to be the chairman and CEO 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, capital was challenging. So, you know, had a great team of people with me to transform the company.

[B]

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.

[E]

Global company.

[B]

Yeah.

[E]

With a little bit of a unique footprint. We're 50% international, 50% North America. But our second largest country after us is Japan.

[D]

Oh.

[E]

We have a big business in India.

[B]

Okay. Yeah.

[E]

And then we have a very big business in the uk. We do complicated risks. So you could think about what's happening in the Middle east now with shipping, marine, energy. We're heavily involved in that.

[B]

So something where there's not an existing futures contract that a company can just go and hedge. It's not, oh, I'm going to buy some oil futures because I fly planes around and I know I'm going to need diesel fuel in a couple of months. And so I'm going to hedge that out. This is for more complex risks.

[E]

It's for more complex risks. And, you know, think about the largest, you know, sort of customers in the world and, you know, big oil companies, you know, Fortune 500 companies. But we also have a personal insurance business which will cover things like accident, health.

[B]

Yeah.

[E]

That are distribution to consumers.

[B]

Yeah.

[E]

So we have a real balance.

[B]

Part of that feels like if you're talking about insuring a Fortune 500 company against a geopolitical risk, that feels like a meeting that takes place in a boardroom. It feels like there's a lot of folks with a lot of trust built up over years to understand each other's businesses. But then there's probably a lot of other underwriting happening and teams putting together comps and spreadsheets and data. And I want to know about the intersection there. It feels like the business is, and I don't know if it ever will be, just one click checkout for insurance products for Fortune 500 companies. But what is the interface between the quantitative, the qualitative, the relationship and the data? And then how is that changing?

[E]

So the quantitative, you have to start at the portfolio level.

[B]

Okay.

[E]

And you Want as much data as you possibly can to look at deterministic modeling, probabilistic and then stochastic. And 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, okay. Throughout, you know, the globe as well as ways in which you structure.

[D]

Yeah.

[E]

Insurance.

[B]

So for us, you can't look at, you can't look at an individual policy in, in isolation. You're managing portfolio risk, risk to the entire firm. And that's something that's happening probably 24, seven, I imagine. It's hard.

[E]

And that's what led me to Alex Karp. You know, it's hard to get the aggregation done. 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 properties going.

[B]

Yeah.

[A]

Take us back to your first meeting with Carp. Curious what the experience was like. Unique individual call you?

[E]

Yeah, no, I was actually introduced by a board member many years ago and it was really in this pursuit of not necessarily foundry or AIP or ontology, that's where it led us, but it was more on sort of the quantitative ways in which I was looking at the portfolio 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 intrigued. I mean, you see the brain, I mean he just thinks about things. He doesn't hold back. I mean, so, so I always knew where he stood with, with me, 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.

[B]

Okay.

[E]

And that's been incredibly effective.

[B]

What is, you know, a lot of the AI companies talking about scaling laws, exponential growth and token production or even revenue in many cases. But what's growing exponentially in your business? Are you bringing exponentially more data into the platform every year? Exponentially more compute resources, teams, number of policies? Like, what is the, what is the thing that's experiencing a boom right now?

[E]

The most important part, I believe in terms of business is that you have to have a business solution you're trying to solve. So for us it was more data.

[B]

Yeah.

[E]

Better data.

[B]

Yeah.

[E]

And then reduced 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.

[B]

Got it.

[E]

And then how do we actually make really cool.

[B]

What's, what's an example of distribution partner in this context?

[E]

So it'd be like a insurance broker or insurance agent or you know, someone who has their client is a customer product, effectively.

[B]

Exactly.

[A]

Okay, yes.

[B]

Yeah, that makes sense. What else? Jordy, do you have something?

[A]

Where was I going to go? The

[E]

Alex.

[A]

Yes.

[E]

So there's been ontology.

[A]

Yeah, we'll get there. So there's been, we, we primarily, I mean, we at least started covering early stage startups. There's been a debate in our kind of little sub industry right now around a bunch of new insurance focused startups that are growing incredibly quickly. And there's a debate going on As1, maybe AI makes it more possible to underwrite risk and if you can do that, well, grow very quickly. The other side says, hey, if you're hyperscaling an insurance company, maybe that's not, maybe you don't want to work with a company that is going through that hyper.

[B]

The iron law of the universe.

[A]

Yes, yes.

[B]

Maybe it goes up fast.

[A]

But yeah, talk about, talk about what AI has actually enabled, where you're excited about it, where it's failing broadly, maybe where it's overhyped and you can, I guess, tie that into everything you built.

[E]

With Palantir, there's never been a time, in my opinion, whether it was introduction to fintech and sure Tech, 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.

[A]

Yeah.

[E]

That I've seen the ability to change how an organization actually runs itself. And that can come from big companies like Palantir or Google or it could come from, 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 I think is happening, we talked about the sort of data ingestion portion, getting that into a digital workflow, using large language models to extract more data from what comes in, but also helping underwriters make decisions that are, 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, 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 on its way capabilities are there, then you start to think about the entire end to end chain being very different.

[B]

Yeah.

[E]

What I think about Palantir, while they've been such a critical partner, is one as we evolve 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, 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. And then we start to build out an ontology. And I really do think it's incredibly important. If there's one thing I look at for our organization, certainly the advancements of LLMs, their ability to do things more autonomously. Now where we started with the binary gen AI, now we're into a gentic AI where it 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 Palantir. I'll use the ontology example again. We did the full ontology of AIG and then we went to look at an acquisition called Everest, which had about $2 billion of premium. We got Palantir and to work with our team, we could build an ontology of Everest's portfolio on top of ours in four days. And quite frankly, what we started to learn again about that evolution is that you always relied on data lakes or global data repositories. 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.

[B]

I have a lot, I have one last question just on the actual change management, the organization, like how the office feels, how did you go about actually working with Palantir? Do you set up your own internal Palantir workforce? Who sits alongside fdes? Do you let Palantir 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 a unique situation

[E]

versus making sure Alex. And then, you know, two of the senior executives, Ryan and Ted, that everybody knows 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 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.

[B]

Yeah, that makes sense. Jordan, anything else? No.

[E]

Insurance has to be the most important topic.

[A]

No, no. If we do have a second, I don't know. I was not sure on timing. How are you thinking about, you know, workforce planning? Asked Karp about this and he said to ask.

[B]

We're token budgets.

[A]

We've stayed, you know, as, as you' this wave of AI layoffs, we've been over and over and over reminded people that if you have an individual and you give them more capability, you make them more productive, you make them more efficient. A thriving business will want to hire more people. Right. Because you can get more out of every individual. And so we've tried to remind people that over and over and over as companies that oftentimes are underperforming or bloated for whatever reason. But what's your kind of philosophy around hiring, headcount planning riffs, all that stuff in this kind of new era we've been focusing on.

[E]

I heard Alex at the tail end and I agree with him. So we're focusing on growth, we're focusing on reskilling and actually training our employees to be in a different part of the workflow. Now you would do this. I believe in all of this you have to still have great end to end production 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, 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.

[B]

That's a great optimistic vision. I love it. Thank you so much for taking time

[A]

to come chat with us for. Great to be with you.

[B]

Have a great rest of your time. Thanks. And up next, next we have Chad Walquist. First, I'm going to tell you about CrowdStrike. Your business is AI. Their business is securing it. CrowdStrike secures AI and stops breaches. Welcome to the show. How are you doing, Chad?

[A]

Great overcoat.

[B]

That's a new one.

[A]

That's an Eliano special.

[B]

It is. Oh, yeah. He is the master giving us a

[A]

run for our money.

[B]

Yeah, it's fantastic. Anyway, kick us off with an introduction on yourself, how you fit into Palantir. A little bit of backstory. I'm sure we have a ton of questions to run through.

[A]

First, how often do you guys do these things? Because it feels like this feels like an annual. It feels like an annual event, but

[B]

we're getting the call every three months.

[A]

Carp talks about, you know, manipulating time, working. You know, a quarter at Palantir is like a day somewhere else.

[B]

That is exactly.

[A]

So that it kind of makes sense.

[C]

Yeah, I'm like actually 23. The time warp is real. So we do these quarterly. So I'm a forward deployed architect. Technically, I do what is needed. And so doing the needful is kind of the Palantir way 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.

[B]

Can AI do decomp?

[C]

Yes.

[B]

Okay, unpack that. Because that feels like the secret sauce. That feels like the special thing about Palantir is actually being able to bring someone in who understands an organization. I think a lot of people see AI tools. A lot of people see AI tools. No, a lot of people see AI tools and they, and they think, okay, very defined workflow, input, output, but now instead of just math that Python can deal with, you can deal with some text. And that's great. But decomp to me has always felt less like, let's go into your HR system and understand the basic job description. Like, oh, someone Uploaded this resume vs oh, Steve actually does this completely outside of that system. And marketing has two platforms for this thing and engineering has three systems for Catbiles. And they're all the kludges that have built up over decades, sometimes hundreds of years for some of these organizations. Like that's what was so special about the Ford Deployed Engineer program. The Palantir model. Yeah, I'm surprised to hear you say I can do it at all. It feels like the final boss.

[C]

Well, this is where the really the Palantir thesis is. Humans and I'm working together. And so the way we think about this is modeling our business process. We heard some other people talking about this, of modeling my business process into the ontology. Because the LMS just necessarily have a worldview or world model of your business and your operations. The ontology provides that.

[B]

Okay.

[C]

And so when we talk about decomp, this is really about actually now I make more data computable as well. So we think about LMS on the agents and interact with it. Also we use LLMs to make more data computable and then model that in the ontology of how things are really working. And so what we are actually doing a lot of times now is building out that worldview and then running multiple agents over this. Actually being combative towards each other. Right. And so actually working against each other and having critiques. And so after you do that, you can also then give the human 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. It's an empowering thing.

[B]

Yeah. So how often like I'm interested in the like the pie in the sky Palantir pitch. Understand your entire business, run your entire business on Palantir. And then some of the nitty gritty where sometimes like the low hanging fruit is like, wait, there's a, like there's someone's job to just like take a form and type it into a sheet. Like we have, we've had image recognition for a long time. Let's actually go and, and implement that and get that into a database, get that into the ontology, get that into Palantir so then we can start building on top of it. And it feels like there might be a tension there. Obviously Both processes are speeding up. But how do you sort of like keep the project centered around the big goal while still chopping wood on all the things that actually need to happen?

[C]

Yeah. And I think this comes back to the forward deployed piece and like, what do we deliver? Outcomes. And we work backwards from that rather than, hey, I have this data, I'm going to build a data warehouse and then I'll build reports because all my

[B]

data is in one place.

[C]

That's the field of data dreams. And no one shows up.

[B]

Yep.

[C]

Right. And so really when we decomp things and work backwards from that, you know, the simple things like the form filling out, there's a lot of that.

[B]

Yeah.

[C]

Now the one approach that we see a lot is, you know, enterprise software is going to force you into their box.

[B]

Sure.

[C]

Right. Go fit into this box.

[B]

Yeah.

[C]

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.

[B]

Yep.

[C]

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 actually a middle ground because, you know, for a long time customization was kind of a four letter word. Right. 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? And that's so that when we decomp problems, thinking about not only the kind of the 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?

[B]

Is software getting more malleable? Because I can look at it two ways. I can look at one. Obviously AI agents are incredible at coding. They can make changes very, very quickly. That would take you a day and just a few minutes. At the same time, I see so many screenshots of people saying, I implemented this feature Again, the GitHub is plus a million lines of code. And at a certain point the context window is growing as fast as the code generation is growing. There's a. I'm a believer in the answer to bad slop is good slop and more slop. Maybe. But what are you actually seeing on the malleability of software? 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 2000 line repo and you can actually just put it in your own context window. So it becomes more malleable and you can use it as a building block. And that feels like that's going away. And I want to make sure that we've, that we're ready for when it goes away and it remains malleable.

[C]

Well, I think what, what's missing is the, the malleable enterprise scaffolding that you.

[B]

Okay.

[C]

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.

[B]

Sure.

[C]

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 malleable, malleability in the middle, where we can actually scale, we can enable people to do things differently while still building of creating enterprise grade, robust, secure, scalable software. And so it's actually a balance there about how I can enable that engineer that has been doing that now they can write code much faster, they can oversee things. And that enterprise scaffolding in the middle allows us to actually create the right guardrails, create a safe system of work for them to go develop things in. 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 OODA loop that I create. And really that OODA loop allows our customers, as they're doing workflows, they're giving feedback to agents. Now can agents help them do more based on the feedback? So both explicitly saying, hey, that was wrong and this sucked, or I chose this option. Now if you do that, enough agents can start to learn from 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. That's the frame OODA loop.

[B]

Observe, orient, decide, act. Right. I have a different question, but you can go.

[A]

If you had 30 minutes to give feedback to the AI labs, what are the kind of key areas, let's say the frontier labs, right, Leading models, what are the kind of key areas that you would be focused on?

[C]

Yeah, I mean, I think when we think about the enterprise space one, you're

[A]

like, don't compete with us.

[C]

No, actually, like, I think optionality is a good thing. Like I am agnostic to where you store your data, where you store what model you choose, what compute you. So, like, we can allow you to use any of that. Because the last thing that actually drives an outcome is re platforming, moving to another.

[B]

And that goes back to the on prem culture, the secure cloud culture, itar compliance. Like this is in the DNA of the company.

[C]

And so how do we actually enable people where they are instead of the focus on oh, if you re platform everything to Palantir, everything will be great. And like, well actually you've probably been re platforming for years. Can we enable what you have to go do these new things. So when we think about like the model companies and it's, you know, how do we ensure that we can give the feedback loops around, you know, tool usage and you know, yeah, that's the

[A]

kind of, that's the kind of stuff I was wanting to get your point of view on is like I'm sure you're getting into the nitty gritty with individual models where they're spiky, where there's shortcomings, et cetera.

[C]

Yeah, so 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 the meme around hey, make it exist first and then make it good. Most of the time I see people building with agents. They're using the latest frontier model. I just got it working. Then all of a sudden the token maxing and everything else and you're like oh my gosh, I just blew through my whole budget. So we built a tool called Evolve that will actually go analyze the logs in production about how these models are operating, what people are doing with them, the architecture over it and actually be able to swap out different models from different providers. Or hey, actually for most of this workflow you can use this model that's older and actually without thinking and test time compute it's more deterministic or even

[B]

cached models, cache models.

[C]

And then, or hey, if you actually just had this piece of data in the ontology, then you would eliminate all this in 50% of your cost. And so you know, some of these customers, McCarthy talked about this at our halftime. They were able to in two days eliminate 60% of their token cost by re architecting, picking a different model and prompt tuning. So it's the combination of all those, the permutations get really hard. Especially when it's in this probabilistic models. We have tools to do this in the deterministic world.

[B]

Prompt tuning 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 any mistakes. That's going to cost me a fortune.

[A]

Well, there was some chatter yesterday around something a model was doing to be more efficient. Was talking and like this bad.

[B]

Oh, caveman, caveman prompting. The caveman prompt method actually works.

[A]

How often are you working with a company that is having, call it like a mini chat GPT moment within their enterprise and then they're just like, let's not tell anyone about this. Because I imagine like there's all these, there's clearly places where.

[B]

What does that mean? Their product is taking off, like ChatGPT.

[A]

So they've found a way to apply AI in a way that is highly, highly effective and gives them an edge.

[B]

Oh, interesting. But yeah, like the theoretical, like within,

[A]

like technology, how to transform. So X People are very loud, right? Yeah, they're like, I just had.

[B]

I'm using everything.

[A]

Yeah, I just had a product work for 30 hours on this thing. They'll talk about it. But if you're a fortune 500 and you figure out how to do something, it's not like you want to like, put, put your hand up and say, like, guys, like, I figured something out.

[B]

Right.

[A]

Like, secrets are valuable. And these advancements and kind of breakthroughs are not going to be uniform.

[B]

The airline industry will never be the same when your direct competitor copies you

[D]

and you're like, yeah.

[A]

And so, and so part of why, you know, right now the meme is token maxing. And that's an obvious, going to be an obvious area of debate. People are happy to go talk about it. Say, you know, CEOs might say, hey, let's stop doing this. But there has to be all these other kind of pockets of interesting moments where we won't hear about them until they become kind of like standard operating procedure.

[C]

Or you see it in the earnings and the economics piece. Right. So, yes, unfortunately, X is not the real world and there's a lot of grift and noise and podcasting, PMing and that kind of stuff that goes on. But I think in the real world, yes, there is the haves and have nots. I mean, we were just talking about aig, when you can start to actually do the underwriting and have quotes back in hours or days instead of months on these highly complex enterprise kind of insurance agreements. If you don't have that, how are you ever going to compete? And so when we think about this, of The N of 1, those are the companies that we're going after. And we see where there are those moments that are not public.

[A]

It's the competitive advantage. Yeah, it's an interesting category because you can imagine AIG is working with a potential customer or renewing a policy, and that customer is going and talking to all of AIG's competitors. And if AIG is able to turn around, you know, a quote or a policy in 24 hours, and then it takes another player, you know, two weeks because it's, you know, complicated.

[C]

Email and spreadsheets.

[A]

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, there's that third, fourth, fifth. We'll kind of wait on those because we have a good option here.

[C]

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 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 least exciting thing on paper. But actually, if you're spending hundreds of millions, yeah, you guys get it. But hundreds of millions of dollars on a migration and we can cut it in half. That's a massive deal.

[B]

Back on the OODA loop, observe, orient, decide, act. On the observation side, what is the supply and demand imbalance for dashboards? And what I mean by that is, when you're working with a company, is there more demand for dashboards, more people asking, hey, we need a dashboard for this, we need a dashboard for that. And you have to back people off and say, I don't know if the dashboard's right for this. You might just want to do an ad hoc analysis or actually go and see versus you're seeing so much opportunity that you're like, okay, we want to push dashboards out everywhere. Like, 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.

[C]

Yeah, I want to kill all dashboards, okay? Dashboard. I mean, KPIs and dashboards should be a byproduct of operational applications where I'm making decisions. So we talk about the OODA loop. 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. That's Where I want those metrics, that should be a byproduct. If I go out with the goal of building a dashboard, it's going to be the field of dreams again. No one shows up. And so yes, it should be. You're going to have to build some of those things. The other side of this also is when you think about a data warehouse, literally. I won't go too deep into this technical riff, but Kimball and dimensional modeling was built in 96 for scaling databases and you're still modeling 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 in one single ontology. Which makes it the compound effect where as I add things in, I'm now compounding with each individual decision I'm working with gets better and better and better for the next use cases I connect across my business.

[D]

Yeah.

[B]

Is there an analogy there to just the deployment of AI tools? Currently I'm just reflecting on the NoSQL boom. And I don't know how strong this was, is probably just like an online take. But this idea of like why would you ever want a relational database? Why would you ever want a schema? Don't ever do a migration ever again. And the future looked like a win win almost like I think postgres installations probably grew and so did mongodb and other non relational databases. And people use redis for things and they use all sorts of different tools. And we 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, but I'm wondering like, as like are you seeing glimmers of, of the AI tools eating into different pieces of the technical stacks or is it all like yes. And across the enterprises?

[C]

I think it's yes. And in a couple different things there is. When you think about the real world, it is not just rows and columns. You can't describe everything with measures and attributes. 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 CV model and a 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. Whereas 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 these different things, to store them in ways. Well, we can do that all in the ontology, vectors, everything else. So that's really the goal around how do I model the real world, how it actually works and make that transparent so you're not having to figure out which technology they put in a time series thing for sensors on an oil platform. Don't care. And that's where we want to have the non differentiated heavy living truly in the platform to remove the friction about getting stuff done.

[B]

Done.

[A]

How common is it for a business with more than $100 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. But is some element of how much can chaos and mystery be reduced today? Because it feels like we're entering an era like you go back, you know, 50 years and 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, no email, all that stuff. And now mystery and chaos is probably reduced dramatically. But still there's companies that maybe before you start working with them, I'm curious

[D]

what those look like.

[C]

Yeah, I mean we work with a lot of different varieties of companies. I joke that a lot of times companies make money by accident. They don't actually know what their most profitable product is. And often they're trying to sell the thing that isn't actually the most profitable and actually not so selling the thing that actually is profitable. And it comes back to how they've modeled their data to aggregate it up to KPIs and other metrics. When you actually need to model at the finest grade in how your business operates to get a true cost of goods sold, for example, or true cost to serve. That's very complicated, it's very complex. So we really think about how do I embrace that complexity so that I can truly understand tactically at the edge, how do I do more of the things that are good and less of the bad? It's that simple. And those get peanut buttered across with KPIs and methods metrics. And people don't actually know how their businesses are operating. I can't tell you whether it's $100 million company or a $50 billion company. How many times I see this that they don't actually understand how they're making money at a fine grain.

[A]

Last question. Is there a world in the future where a company gets created, let's say on Stripe Atlas and the first account they sign up for other than that is, let's say a Palantir.

[C]

Oh yes, I would love that. And so we do have a Palantir for Builders program. We have small companies that there's people here that are two person startups, you know, that are working in their attic in Canada. I mean like so it is literally any size company come work, there's a free dev tier, people can come build. You actually, there's actually a Shopify integration in Palantir. You can go hook up to your Shopify and pull in Palantir. There are people doing this now. Are we always great at selling it or telling the store? 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, Mike, my business is dying. I was, you know, I was down 10% negative margin on what I was selling and through using Palantir they watched our YouTube videos and they built it themselves and increased to a 9 or 10% positive margin in three months.

[B]

That's great.

[C]

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, think we'll get there. It might take a little time.

[B]

I love it. Well, thank you so much for taking this time.

[C]

Thank you.

[A]

Great to catch up. Great to see you.

[B]

We'll talk soon. Our next guest is joining. In just 15 minutes we're going to go back to the timeline. First I'm going to tell you about Figma agents. Meet the canvas. Your AI agents can now create and modify Figma files with design system context.

[A]

It's so crazy how many companies, yeah, their whole strategy is like we're going to hire guys like Chad.

[B]

Yeah.

[A]

And they're going to, they're going to do stuff. He is final boss of fde.

[B]

What drove the FDE meme? Was it Palantir going public or was

[A]

it, it was Palantir going parabolic maybe. Yeah.

[B]

Because it just, it just because before it was like, okay, yeah, successful company but like no one really knows where the valuation is going. Now it's like, like my uncle just told Me that he made a bunch of money and so I gotta pay attention to this.

[A]

Well, that. But also they had been banging the FDE drum and getting the consulting.

[B]

But people had earplugs in to the banging of the drum and the earplugs came out.

[A]

Yeah. But when they were a 10 to 20 billion dollars company, a lot of people could still convince themselves that they were right. It's just a consulting business.

[B]

Yeah, yeah, yeah, exactly. But now it's.

[A]

And that gets harder to ignore. We covered this very briefly, but yeah. Very excited for Joe Rogan to be hosting his.

[B]

You know, it's rumored. This is a rumored leak. It is not confirmed by any means yet, but.

[A]

But I like the sound of it.

[B]

It would be. It's a very different direction.

[A]

This was a good post. I wanted to bring it up. Bucocapital 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 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 moments over the last month where somebody has sent me a deck for their company or materials and I can tell that. But 90% of the work that went into it was on prompting. And I have a very visceral reaction toward it. Especially for early stage companies where ideas and the way in which you go about doing things matter so much that it's almost like painting this initial vision and things like your go to market 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. But I just remember I got this deck, I was clicking through it and I very respectfully said go and do this yourself.

[D]

Yeah.

[A]

Because just because you've made something that. That looks like a deck.

[B]

Yeah.

[A]

But you didn't do the sort of like fundamental work to actually present this in a way. If you looked at each slide individually.

[B]

Yeah. Your eyes kind of glaze over.

[A]

Yeah.

[B]

And you just sort of like lose focus, stop paying attention. Yeah.

[A]

It's like it would have been more. It would have been more compelling to actually just have a bulleted list of like problems.

[B]

I mean a lot of times you can just opportunity send me the prompt because I can instantiate it in my head. I can Imagine the rest of the paragraphs. I have the context window preloaded for myself. Yeah, we should talk about the new Audi, the Nuvolari. Is this real? Motor one. This seems real.

[A]

It's real.

[B]

It's a big deal. It's the brand's first supercar since the R8. Twin turbocharged, 4 liter V8 hybrid. 217 mile per hour top speed. That is 10% faster than a Cayenne Turbo GT. What is the Cayenne Turbo GT market doing right now? Is it tanking? Depreciation must be just through the roof on this news because you have a car that's 10% faster. And so everyone was going to be rotating out.

[A]

I mean, I think the new Villari.

[B]

It's a really cool design. It's a really cool design.

[A]

Feels like somewhat cybertruck, cyberpunky, futuristic.

[B]

I don't know. It just checks the box for like the next supercar for me. And in a way that the.

[A]

Ben says it can't touch the R8.

[B]

Oh, can't touch the R8. Okay. Okay. Well, it goes zero to 60 in 2.6 seconds. Almost 1,000 horsepower. Let me tell you about Cisco. Critical infrastructure for the AI era. Unlock seamless real time experiences and new value with Cisco. And our next guest, Sam Berry is here from the usda. Welcome to the show. How are you doing? Good to meet you. Great to meet you. Thank you so much for coming on down. Let's throw this on. And just like that.

[A]

Cool.

[B]

On the left side.

[D]

So good.

[B]

Introduce yourself a little. Tell us about yourself.

[D]

All right. Yeah. My name is Sam Berry. I am proud to be working at the usda.

[B]

What do you do there right now on the cheese?

[A]

Nominative determinism. Do you know about nominative determinism?

[B]

No.

[A]

It's the idea that a person's name could possibly impact influence or. Or the. The. But. But Barry. And working at the Department of Agriculture is like, pretty perfect.

[B]

Yeah.

[D]

No, it's incredible, actually.

[A]

My.

[D]

The Berries came over here from France in like 1640.

[B]

Whoa.

[D]

So we've been here for a long time.

[B]

That's crazy.

[D]

And it was all farmers.

[B]

Yeah.

[D]

My grandpa then he became a materials engineer. Actually worked on jet engines.

[B]

Okay.

[D]

And so then his sons became engineers. My dad became an engineer and I was an engineer. So we're kind of trying to bring the two together now.

[B]

You're going back to the usda?

[D]

Yeah.

[B]

What is the shape of the usda? Like, what is the shape of the organization headquarters? Do you go in 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?

[D]

Well, actually, it'd be kind of interesting to ask you what you think. Like, what are the things that you

[B]

think USDA does, 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?

[F]

I don't know.

[D]

There's inspectors. There's a whole area that does that.

[B]

I imagine there's, like, a series of certifications, but what else is happening?

[D]

So all kinds of stuff. So do you know that, like, food stamps.

[B]

Yeah.

[D]

SNAP is inside of usda?

[B]

Oh, I didn't know that.

[D]

I didn't know that either. I figured it was in, like, HHS or something, but. But yeah, it's in USDA.

[F]

Yeah.

[D]

So that's $100 billion a year. It's kind of a big deal.

[B]

Yeah.

[D]

So we do. We have snap, that's in the Food Nutrition Service. Forest Service is inside of usda.

[B]

Okay.

[D]

Like crazy.

[B]

Yeah.

[D]

And then F PAC is like, what you would really think that usda, it's like the farmer facing, like, where farm programs are, where they do acreage reporting, like, the stuff I talked about today.

[B]

Got it.

[D]

Then there's rural development, which is like loans. Like a bank basically do loans for all kinds of things.

[B]

Okay.

[D]

Actually, in some of the reviews I came in on Doge, and 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.

[B]

Okay.

[D]

Yeah. And then there's, like, Food Inspection Service. And then there's actually a huge scientific arm that's inside of.

[B]

That Makes sense. Testing things.

[D]

Yeah.

[B]

Like labs advancing different pesticides.

[D]

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.

[B]

Yeah.

[D]

You know, but like, our. We are so uniquely positioned as a country because of the fact that we can feed ourselves.

[B]

Yeah.

[D]

And, like, that is not the case for a lot of. A lot of countries.

[B]

America basically a net exporter of food, too. You hear about this in the China debate all the time. Will they buy XYZ products from us as retaliation? And. Yeah. You just don't think about it. But yeah.

[D]

So, like, China can, like, minimally feed itself. Like, bare minimum, it could keep itself alive.

[B]

Yeah.

[D]

But, you know, they're getting like. Like, we just did a big deal with them to move a bunch of beef over there.

[B]

Yeah.

[D]

Kind of got some negative press on that. So it's important to know. It's. 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.

[B]

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

[D]

a little surprising that, like, food is actually part of that. I mean, and then in warfare, like agriculture and 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.

[B]

Okay.

[D]

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.

[B]

Sure.

[D]

Really important. So all this to tie back to us. I wanted to talk about the labs because this is like a whole area inside of usda, but we do all of 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. But GMOs are actually really important.

[B]

Yeah.

[D]

Because if we were hit with some kind of adverse event or something and we needed to create corn that could survive a drought better, like we have the science and the research to be able to do that.

[C]

Got it.

[D]

And it's a huge edge that we have, like, geopolitically.

[B]

Interesting.

[A]

Yeah, yeah. Talk about, over the years, I've read so many stories of, you know, this. This insect has been detected in some region of the US and there's speculation on. Is it, you know, kind of foreign interference, things like that. Is that. Is that in USDA domain. Is trying to help monitor and track and make sure that pests. Yeah, pests. Pests, like pests are obviously naturally occurring. Right. They flourish for their own reasons, or there can be some. Some sort of malicious intent as well. Is that your guys?

[D]

Yeah. Because they're not necessarily naturally occurring. Right.

[A]

Yeah.

[D]

And so one that we have going on right now, and I'm not saying this one's not naturally occurring, but the new world screw. Screw worm.

[A]

Yeah.

[D]

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. But New World Screwworm is something that's falling in USDA's, 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, like really hardcore.

[B]

Yeah.

[D]

So we're developing a lab Flush. No.

[A]

All sorts.

[D]

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. 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'd be very detrimental.

[B]

Yeah. So I'll have to go back and if it's a necessary technique that needs to be harnessed, it needs to be harnessed securely and it needs to be harnessed with the right teams in place to make sure that whatever's rolled out is rolled out effectively and safely.

[C]

Right?

[D]

Yeah. I mean, I think it's just so important. There's so much farther we can go with technology, but we have so much right now and so many people are just black pills. 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. Because, like, we have a long way to go. And when we're just like sitting feeling sorry for ourselves, like, it's not a good position to be in. This is the most incredible country on earth. And other countries are advancing though, you know, our edges, like, our edge doesn't come for free.

[B]

No. We got to work at it.

[D]

We got to keep pushing at these things. But when we do this, like, when there's a parasite that's to going, you know, coming into our country and we're able to just like use biology to combat it.

[B]

Yeah.

[D]

Like, that's incredible that our country can do that.

[A]

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

[D]

Yeah, I think it's a really important topic because you're essentially talking about democratizing access to technology. Right. And certainly with like AI becoming so much more widely available, that was a big step forward. But I mean this is a big point that's being hit on at this conference. And what Palantir is really focusing on is those LLMs become useless if they're not. If you're not deploying them in the right way with the right data boundaries. Right. So, you know, I think that's something that we're seeing even in our universities. We do a lot of university research and like all the, you know, kids or whatever the university students like, they're wanting to do experiments with LLMs and do like meat grading, like better meat grading because that's something that can happen at the farms. And if you can make that automated, then, you know, our ability to produce beef, you know, is greatly impacted. But there's a major issue in secession planning right now for farms. Right. Like this is a big thing that's happening.

[B]

Yeah.

[D]

Farmer generation is getting very old and kids don't want to go and run the farm.

[B]

A lot of them went to big cities.

[D]

Yeah.

[B]

Jobs and white collar work and stuff.

[D]

So, you know, this is a big thing that is H2A. Yeah. You know, these H2A visas where a lot of the farmers are actually still saying like we need the help from, you know, we need immigrants to come and help us and you know, the best way that we can solve that is through automation. So I think that that's something I would love to see USDA do more of or you know, it's something that needs to be answered. I don't have an answer for you right now, but in order for us to continue to, you know, remain self sufficient and providing food, whenever you have

[B]

a dwindling workforce, increasing the leverage and productivity of the existing workforce allows you to maintain overall aggregate productivity. This is general technological leverage. So it makes a ton of sense.

[D]

Do you know anybody that's becoming a farmer?

[B]

Well, we know some folks, we've had a number of entrepreneurs on the show who are getting into ag tech. Yeah.

[A]

Building.

[B]

We've had the founder of the laser weeder that uses. A lot of people don't like pesticides, but they don't mind if a pest is zapped with a laser because that's just heat that's being transferred to the particular plant right there. And the tomato plant continues flourishing. So it uses just cameras and lasers. Very cool sort of modern solution to something that people have had a lot of beer around. Around different pesticides.

[A]

Yeah, we had a fruit picking robotics company.

[B]

Yeah. Orchard as well. But mostly from tech side, usually with some family lineage sort of returning to the roots or tapping into their networks to go back. But I mean, truthfully, I don't know that many people that I grew up with. I mean, I grew up in la, so not much farming activity. I knew one family that had an avocado farm.

[D]

I mean it actually it would be super base to be a large scale farmer. Like more people should do it.

[A]

And maybe you could be the Alex Hormozi of farming.

[D]

Yeah, no, for real.

[A]

I mean, you can.

[D]

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 were actually doing a loan modernization effort right now, trying to make that better. But like USDA will fund it for you. You got to pay it back.

[B]

But you can like get the instruments low rate, subsidized. Yeah, yeah.

[D]

I mean, one of our administrators at usda, he like pull up his phone one day and he's like, look, it's a planting day for me. And it was this John Deere app. It's like the most advanced. Like 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.

[B]

Talk about data collection. I feel like data is the lifeblood of, you know, any decision making, any OODA loop, anything related to Palantir usda. And I'm wondering about like you mentioned that screw worm. You got to track that thing. It shows up on some cattle rancher's farm and they're detecting it or they're seeing symptoms. Maybe they know roughly what percentage of the herd is affected. But how do they actually get that information to you? Are they going to usda.govreportincident or are you pulling things from their filings? How do you Want that to evolve. I imagine that with more AI and technology, it's only as good as the data that we can actually put into the system. So just broadly, data collection, where is that going these days?

[D]

Well, if you don't mind, instead of screw them, I'd like to focus on snap.

[B]

That's a great idea. Yeah.

[D]

So SNAP is funded by the federal government.

[B]

Yeah.

[D]

But it's administered by the states.

[B]

Okay.

[D]

So when it comes to. So something that we're doing right now, and it's 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 funder of this program to understand the integrity. Like to verify the integrity of the program.

[B]

Yeah.

[D]

So we put a request out there, but it has to come from every single state.

[B]

Yeah.

[D]

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. 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, but the importance of. So that program, that's 100 billion taxpayer dollars a year, like, that's pretty substantial. That's an area where we really want to have all angles of the data available so that we can deploy AI and become really smart and detecting fraud. We want to get it to the point where if somebody's committing SNAP fraud, we should be able to. It's like your card. Right. If you. If somebody stole your card and did a transaction that wasn't recognized, like your card shut off.

[B]

Yep.

[D]

Right.

[B]

Yep.

[D]

So we want to get to the point where we're very intelligent and we're confident enough in the system that we can do that when there's fraud detected, it's off immediately because it's an important program. You know, we want to be able to support people that can't support themselves. But it's. It's not arguable that there's a massive amount of fraud in there. I mean, even the organization itself does, like, an audit every year, and they're at like, there's 12% improper payments. Improper payments is kind of a bad word.

[A]

12. You know, 12 billion a year.

[D]

Yeah. Right. And that's just like, kind of based on that's money that.

[A]

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.

[D]

And there's other, you know, you could, like, grok how SNAP has been used to fund, like, international crime organizations and, like, terrorist groups and everything. So it's. It's being exploited at a huge level. And, I mean, it's something that our secretary has prioritized. But that's probably our biggest f pack. What I talked about today is like, our most complex system of data. But the SNAP challenge is like, the biggest or like the SNAP environment is probably the biggest challenge on the data front.

[B]

What's next for you? Are you making a career out of this, or are you going to go be a farmer?

[D]

Hopefully both.

[B]

Okay. Yeah.

[D]

Yeah. I mean, yeah, I've got some farmland.

[B]

You do?

[D]

Trying to convert it. It's like woods right now. But what state.

[A]

Where is that?

[D]

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. And a bunch of chickens and ducks. You don't ever want to get. You don't want to get ducks. You don't want to get goats. Ducks are, like, really savage, actually. Yeah, Like, a chicken sleeps, you know, so, like, it's got a normal cycle. Like, at nighttime, it goes into the coop and it sleeps.

[B]

Ducks don't sleep.

[D]

No, ducks do not sleep. They, like, in our house was kind of this, like, really unique house. So the windows were, like, on the ground, and the ducks would come and just stare at us in the window. No, they're savage. They just, like. They sleep for, like, 10 minutes at a time. So they'll just like, waddle around and then sleep for 10 minutes. You have to have the right balance of female and male ducks.

[B]

Okay.

[D]

Otherwise, it's really ugly. Yeah.

[A]

Chickens are a lot.

[B]

I grew.

[A]

I grew up with chickens, and 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. On a timer to fill a bucket,

[B]

which would lift their.

[A]

Lift it up.

[B]

Yeah, Interesting.

[A]

But. But then I still. Core memories as a kid was waking up, my dad would yell like, there's a fox in the coop. 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. Yeah. We would just. Everybody would get up and try to go deal with satisfying. I wanted.

[B]

That's way more satisfying than some software bug.

[A]

There's so much fear and doom and, and black pilling around data centers. 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 water supplies, things like that. California went through, you know, probably many, many really rough years from a, from a water supply and a water scarcity standpoint. Thankfully, you know, had a lot of rains over the last few years. But how, how are you working with farmers or what is the situation around the kind of like tension between a lot of farmland could also be great land for data centers. Right. And there's been some pretty high profile stories where farmers either sold their land, but from your side you're trying to make sure that we have, you know, can produce an abundance of food, you know, from a, from a national security standpoint. So how are you guys thinking about that balance?

[D]

Yeah, I mean I think the best solution is putting the data centers in space, you know, like, which is totally led by Elon and people are jumping on that train. But it's going to be a couple of years it sounds like, before they're, to that point. We're actually, USDA is pursuing a partnership with SpaceX 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 aligned because we do care about conservation. You know, there was a lot of green stuff that was like, you know, not stuff that we care about, but we do care about conserving our land and putting data centers in space just makes a ton of sense. But that being a couple of years out, so for today, you know, I'm actually pretty passionate about this because in my hometown of Saline, 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. Like why not put a data center there where there's already the infrastructure, it's already developed land, but instead it's like taking these small townships and plopping them in the middle and the people don't really like it. Now the boards seem to like it for some reason the councils. So I don't Know what's up with that? But it doesn't align well.

[A]

It creates. It creates a massive amount of tax revenue that can be used to fund a bunch of other programs, but it's

[B]

got to actually flow back to the people who are in the town. I think that there's like a disconnect there sometimes.

[D]

Actually, this is kind of outside. But something that I do think is probably going to happen is there was this big shift to go to the cloud, right? It's like everybody kind of had their own servers, it's on prem, and now we're in the cloud and it's like, really, you just took. You moved it across the street, right? And now that people are becoming more aware of, like, what that means and when it's like, oh, my data's 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.

[F]

Interesting.

[D]

I think a good business opportunity would be. I. I think there's a world where there's a culture that comes up around data centers because, like, me personally, like, I want to build, like, my house is like, like, I'll have a kill switch for my wi fi. And then like, we've got the data

[A]

in the basement and got your raw milk supply.

[D]

Got raw milk? No, like, we're ready to go. I mean, I was ready to go off the grid before I came and joined the government. This is a much better option. But still, like, I care about my data. I don't really want to use YouTube Music anymore for my music because now my recommendations are getting worse. And you're, like, very beholden to that. It's like, I could very easily just have the music, buy my music and write a simple program to, like, make my recommendations. And it would be way better because there are certain artists that are not getting recommended because they're not, you know, prioritized behind the scenes. So. But not everybody's going to want to manage their own servers, Right.

[B]

Jensen just announced a data center that bolts onto the side of your house.

[D]

Oh, that's sweet.

[B]

And I mean, and there's more stuff that's coming that way. I mean, people are doing the Mac Minis. Can't really do the frontier AI on the Mac Mini just now, but in a few years, you know, the DGX desktops, like, it's all coming and I think it will be more of an option.

[D]

So just to, like, Kind of wrap this up. So there's this or this like topic. The 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 a thing inside of usda.

[B]

Interesting.

[D]

And so the payroll system that does that is a mainframe. 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 have the right environment to work. And it like all these things. I mean, like a dozen people came to me, told me all these things. So then I went and visited it. I was like really excited to, you know, encounter this being. And it's like a 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.

[A]

You're underwhelmed.

[D]

Yeah, it's like, you know, it's like this big.

[B]

Okay.

[A]

But I was expecting like a, like a small micro data center.

[B]

Exactly.

[F]

Yeah.

[D]

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.

[B]

Sure, sure. Yeah.

[D]

You know, because it's like I can trust like, I don't want this in my house, but I can like trust this like cool company, local company, that my data lives there because I don't need to distribute it across the globe. It's like, yeah, I'm here.

[B]

No, that makes sense. That's interesting.

[A]

Country intelligence.

[B]

Yeah. Yeah, we talked about this. This is the future. I love it. Anyways, so much for coming on the show.

[A]

Great to meet you. Thanks for doing this work.

[B]

Have a great rest of your day.

[D]

Thank you.

[A]

We will wrap up the show.

[B]

Yeah.

[A]

Thank you for tuning in with us today, folks. We will be back on Monday.

[B]

Yes.

[A]

And we look forward to it.

[B]

Some business to do tomorrow, but see you Monday. Leave us five stars on Apple Podcasts and Spotify. Sign up for the newsletter and have a wonderful weekend. We'll see you.

[A]

Love you.

[B]

Goodbye.

[A]

Goodbye.