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4:32
>> Today is Thursday, June 4th, 2026.
4:32
We are live from Palantir AIP Con.
4:38
Uh the temple of technology is back in our lives.
4:41
We will return to it, but it is also a state of mind.
4:42
Um we are also sponsored by Ramp. Time is money. Save both.
4:46
Easy-to-use corporate cards, bill pay, accounting, and a whole lot more all in one place.
4:51
Big news from Ramp today.
4:51
Massive fund raise we're going to cover in a little bit, but first we've got to talk.
4:57
>> Oh, is it still going? I like it. The Ramp song is back.
4:58
This was This was early days.
5:01
We really talked about Ramp so much. Turned it into a song.
5:03
Anyway, uh the topic of conversation in DC has uh it's still in AI world, but instead of talking about uh approving models before they're released, today it's about the bio threat.
5:17
Brandon Garrett wrote in the TVBN newsletter today, "The great houses of AI have united behind the bio threat."
5:24
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.
5:33
We've seen former guests of the show sign on.
5:34
I'm excited to bring some of these 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.
5:50
Bio has some similar characteristics.
5:52
>> And it was a very tangible Y2K style moment. >> Exactly.
5:57
>> Where there was a it was a let's just say a powerful business strategy.
6:01
>> Yeah, is it Yeah, it was like is it over?
6:03
You start thinking about the consequences of this, and you don't need to get to AGI super intelligence God.
6:08
You can just have a really powerful tool that creates a new problem, and that uh creates full employment for Nikesh Arora over at Palo Alto >> Networks.
6:15
>> had a chance to talk to yesterday.
6:15
And uh he's been very fortunate in implementing the solutions to the cybersecurity threats posed by new AI systems through the new AI capabilities they're rolling out.
6:25
Um but bio might be next, and so it's exciting to see that uh the great houses of AI are uniting behind the bio threat.
6:32
So, let's take you through this.
6:34
First, I'm going to tell you about console. com.
6:36
Console builds AI agents that automate 70% of IT, HR, and finance support giving employees instant resolution for access requests and password resets.
6:43
So, uh in 1981, a group of researchers published the primary structure of the poliovirus genome in the journal Nature.
6:51
So, they're basically open-sourcing the sequence for making polio, which uh just a few years earlier, polio I think was on the decline by 1981, but uh a very, very problematic virus.
7:03
Uh it's an RNA virus, meaning that its nucleobases or building blocks are A, C, G, U if you're familiar with uh RNA, uh adenine, cytosine, guanine, and uracil.
7:13
Uh put more plainly, thanks Brandon Groll, uh he says, "When the researchers published the primary structure of the poliovirus, they gave the world the literal sequence of poliovirus building blocks in order from start to finish."
7:26
By the mid-20th century, before mass vaccination, polio was paralyzing and killing more than half a million people per year worldwide.
7:34
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?
7:40
So, in 2002, researchers synthesized infectious poliovirus from its publicly available sequence data.
7:46
So, they didn't actually need any of the poliovirus RNA to start.
7:52
They didn't need it on hand.
7:52
They didn't need to It's not like they took a little sample and they just cloned it up and made it bigger.
7:56
They They just took the data and they made the actual virus.
7:58
So, this is the This is the shape of the threat.
8:01
If there's a new If there's a new virus or an existing virus or a forgotten about virus and you have the code to it, you can potentially print that RNA and then have the virus in your hands even if you don't have a sample.
8:13
You weren't able to collect a sample.
8:14
So, instead, these researchers in 2002, they were able to take the published sequence, chemically synthe- synthesize short DNA fragments, assemble them into full-length a full-length DNA copy of the poliovirus genome, and then use the DNA to make the viral RNA to in fully recover the infectious virus.
8:30
So, in 2005, researchers used these same technologies to reconstruct the Spanish flu, a virus in 1918 that killed 675,000 Americans and had a 2 to 3% mortality rate among those infected.
8:42
Very, very dangerous stuff.
8:44
So, basically, these two reconstructed viruses showed that having a physical virus on hand was no longer necessary as source material to create viruses.
8:52
All you needed was the blueprints.
8:54
As long as you have the code, literally just like text in a text file, a bunch of ATGU, uh you can go and make this as long as you have the equipment on hand, but that is getting democratized as well.
9:03
And that's what this uh AI letter is all about.
9:07
So, that's the situation that we're still in today.
9:10
Except now that we have AI, there are easier ways to potentially reconstruct DNA sequences that could create new viruses.
9:16
So, yesterday, Demis Hassabis, Sam Altman, Dario Amodei, 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.
9:33
You might have seen it on the timeline.
9:34
And at first glance, Brandon here assumed, and I assumed the same thing, assumed it was another press release from a Frontier lab claiming it had just discovered new capabilities in one of its internal models that would ultimately lead to catastrophe.
9:46
A lot of this like doom fear-based marketing has been happening.
9:49
So, that was sort of the natural reaction.
9:50
Uh and that's what Brandon said.
9:52
>> people's reaction would be, "Were we not doing record keeping here already?"
9:56
>> That's a great question.
9:56
And Brandon actually did answer that.
9:57
But, it's not just a PR stunt.
9:59
It's not a new capability.
10:01
They're not saying that the models can just create a novel virus, you know, one shot it like that that that that that is solved yet.
10:07
Uh it's not there, but they see it as something that's coming down the pipe.
10:11
And this letter is not this dangerous new capability.
10:15
It's more asking the US government to force nucleic acid synthesis companies to screen orders for sequences of concern.
10:22
So, "Hey, somebody just ordered this.
10:25
Looks a lot like a virus.
10:27
Like, what are we doing here?
10:27
Uh you said that you were trying to treat cancer or you said that you were, you know, trying to make a new peptide.
10:33
And all of a sudden you're asking for polio virus or something that looks like polio virus.
10:37
Like, let's let's dig into this."
10:39
Uh that's where they're going with that.
10:41
And so, uh they also need to verify the legit- the legitimacy of the customer and to keep a record of what they're sending and to whom.
10:48
That's a crazy one that I'm sure you're like, "Wait, what?
10:50
They weren't keeping records?" They were a little bit. Uh he gets into this.
10:53
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.
11:02
So, anyone with an AI tool in the future could in theory, if the models don't have safeguards on them, could uh synthesis could could create a sequence and 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.
11:16
So, uh most of the global nucleic acid synthesis industry has already signed up to do some of this.
11:23
They did They started this in 2009 with what's called the International Gene Synthesis Consortium.
11:27
And roughly 80% of commercial synthesis capacity worldwide is on is is on board, but membership in the consortium >> is still [laughter] just hanging out. Now, we're good.
11:40
>> 80% of nuclear weapons are safely stored.
11:43
Don't ask about the other 20%.
11:45
That's kind of what this letter is getting at.
11:47
Because 80% it was a good first effort, 2009. It's been 16 17 years.
11:53
Uh we haven't had Yeah, but there's a new reason to go further. Let's get that last 20%.
11:57
That's what they're asking for.
11:58
So, um membership is not a strong guarantee that they're actually screening or keeping records of their customers because it's voluntary.
12:06
The 80% number is also self-reported, for example, and a bunch of other factors contribute to the relative flimsiness of agreement.
12:15
So, it's not government verified.
12:18
>> by just saying that you're opting into it, but then even the reporting once you're opted in is voluntary.
12:23
>> So, I I think the way this works is the International Gene Synthesis Consortium is probably a non-profit NGO, you know, non-governmental 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.
12:47
We're we're on board with 80% and and the government isn't coming in and checking the records.
12:53
They're not actually saying, "Okay, well, we we have a different number cuz we're the government and you have your this number.
12:58
Let's verify this number."
13:00
It's self-reported by that organization, but there's no reason not to trust that organization necessarily. Um so, what else?
13:06
A bunch of other factors contribute to the relative flimsiness of this agreement.
13:09
Uh HHS also has guidance in place around the issue, but again, it's voluntary, meaning that the possibility of bad actors getting their hands on dangerous nucleic acid sequences, at least from American companies, still cannot be ruled out.
13:20
Overall, it's good to see industry leaders signing this letter and doubly refreshing uh that the letter is not yet another warning of apocalyptic AI doom, which I think the public has unfortunately come to expect from announcements like this.
13:34
Hopefully, the relevant legislators are paying attention and can make this happen in short order.
13:38
So, I thought that was a good a good breakdown and I agree with a lot of that.
13:42
Andrew Curran also has some deep dive on this with some more of the signatories.
13:47
He shares screenshots of all of these and it really is everyone.
13:52
Yeah, Y Combinator >> Patrick Collison >> Microsoft, Interconnects AI, Harvard, tons of stuff.
13:57
And then over in in the nucleic acid synthesis industry, you have Twist BioScience, Ansa, Emerald Cloud Lab, and Kathleen McMahon from Altos is on here.
14:08
Former guest of the show.
14:10
Um, so So, good news, but obviously and just a early step.
14:14
This is just an open letter to the government saying, "Hey, we think you should we want to support this.
14:19
We think that the government should start thinking about this."
14:23
The other news in the bio world >> Yeah, I mean the news is just that there's incredible momentum in biotech, early stage biotech after >> Momentum, but not like volume, not scale yet.
14:35
Because you're looking at $3 trillion IPOs going out this year potentially, so much news in AI, Micron's at a trillion, every chip stock is, you know, in the hundreds of billions trillions.
14:47
This is much smaller, but >> But it's notable because biotech had been left for dead in some ways.
14:54
We had a biotech investor on probably 14 months ago at this point who said, "I don't even know.
15:02
I mean, just looking at the returns so far, I don't know why you would invest in this asset class."
15:06
Uh, but of course every asset class kind of go throughs go goes through that kind of phase and clearly there's a lot of momentum.
15:16
>> And they should be you would expect that biotech would be similarly power law driven, maybe not as extreme, but if you pull out SpaceX, OpenAI, Anthropic from the capital markets.
15:26
>> 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 two years. >> Yeah, we had that.
15:36
Didn't we have a guy on that had sold >> Like three companies. We didn't have money.
15:41
We were deep diving that. >> exit. >> Yep.
15:43
And then he joined another company and sold it for $3 billion like the next day.
15:46
Uh and so that that was >> you have Isomorphic Labs spun out of DeepMind, uh Coinbase, or not Coinbase, but but Brian spun out or like founded New Limit, uh you have Retro BioSciences.
15:59
>> We're going to get Jacob on the show. >> As well.
16:02
Um Altos Labs, uh >> Jeff the Chad from Amazon has this post that he did.
16:09
>> And then uh Anthropic obviously acquired Coefficient Bio >> Yes.
16:14
>> But Jensen and Larry Ellison at Oracle are also doing stuff.
16:16
So, there's a lot of activity.
16:18
It's very fun, and I hope we're going to be able to cover this a lot more in the near future.
16:22
>> point do the uh at what point does like a Pfizer or Johnson and John- Johnson and Johnson start joining the press release economy of just coming out.
16:34
I'm not I'm not saying it'd be a good thing, but coming coming out and saying uh we believe we're, you know, right at the [laughter] >> Got to get it.
16:41
Because there are partnerships all the time that happen, and they're always just like tucked a little bit deeper in the Wall Street Journal because AI is dominating and even private credit takes a front seat to the bio news, but there there's a whole bunch of deal making going on.
16:54
Anyway, there's other deal making going on in fintech.
16:55
We're going to talk about Ramp's raise today, but first I'm going to tell you about Railway.
16:58
Railway is the all-in-one intelligent cloud provider.
17:01
Use your favorite agents to deploy web apps, servers, databases, and more while Railway automatically takes care of scaling, monitoring, and security.
17:08
Um so, Ramp >> What's going on in Rampland?
17:11
>> $44 billion valuation. >> Woah.
17:14
>> Really, really solid traction.
17:14
Just, you know, every 12, 18 months, sometimes much quicker.
17:19
Sometimes they do two rounds in two weeks, but really solid progress.
17:23
They raised $750 million at a $44 billion valuation.
17:27
Last time we grew this fast, we were 1/20 of the size.
17:31
>> Yeah, this is the most This is the most notable thing to me. >> Yeah.
17:35
>> Uh lots of chatter on the timeline around uh you know, other fintech valuations.
17:39
You compare that >> Soft apocalypse stuff.
17:42
Well, yeah, you compare them to like PayPal >> hit you know, Ramp is now worth more than PayPal. >> Okay.
17:47
>> PayPal has 32 billion of revenue. >> Yeah.
17:50
>> Uh but PayPal uh certainly has I would say net you know, probably negative momentum. >> Yeah.
17:57
>> Uh whereas Ramp has incredible momentum.
17:59
And this this is the standout line.
18:01
Uh they were 1/20 the size the last time they were growing this fast.
18:04
And so um yeah, just really, really, really, really impressive execution.
18:08
Uh and incredible opportunity still. >> Yeah.
18:15
Uh So, Eric took to the timeline, posted an essay about the third pillar comparing uh the previous eras of value creation, the two pillars uh people and vendors dating back to 600 BCE.
18:28
If you're not thinking in millennia, what are you doing here?
18:33
Tokens emerge as the third pillar in 2026 AD.
18:35
And he calls it the quadrillion token blind spot.
18:39
Boiled down 500 years of finance.
18:42
And it's really just three questions. Who spent what? Was it worth it?
18:47
What's the bill next month?
18:47
I mean [laughter] Yeah, people get caught up in all these crazy things.
18:52
I mean, you see this is like marketing, I'm sure, and uh and ad buying where people will do all these crazy analyses and ROI ROA ROAS and all this other stuff.
19:02
And like and and it's it's always useful to zoom out and just be like, "Okay, we spent a bunch of money.
19:07
Did the bank balance go up in this company or not?"
19:08
Like >> All all personal and business finance finance at the end eventually comes down to are we making more money than we're spending?
19:17
>> 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.
19:27
And like actually zoom out and try and understand like what is the core value that you're delivering to your customer?
19:33
It is answering that question.
19:37
So, fantastic news over there.
19:38
Let me tell you about the New York Stock Stock Exchange.
19:40
Want to change the world?
19:42
Raise capital at the New York Stock Exchange. You got to do it.
19:44
It's my number one advice for founders these days.
19:49
Um Uh there's some other fundraising news.
19:51
Sabi, the beanie BCI company is getting preempted at 35 million at 500 million post.
19:57
This is a leak from Arthur Rock.
19:59
We'll see where it goes, but >> This is huge for you. >> Why?
20:02
>> Because you are a beanie guy.
20:04
>> I do like beanies, especially when my hair gets long.
20:07
It just keeps it together. Yeah. >> Yeah. >> I like a beanie.
20:09
Uh they're very functional. >> I It's interesting.
20:12
I think that this format >> Mhm.
20:17
>> of course I'm sure they can adapt it to other types of hats, yeah, but this format certainly maybe makes it harder to build momentum in places like California, at least Southern California, Arizona.
20:30
>> big amongst creative directors though. >> Yeah. >> Huge.
20:32
>> Huge huge Silver Lake Silver Lake every >> It's not too hard to change a beanie into a hat cowboy hat.
20:38
Like that's just extra leather around it.
20:41
You could wrap the beanie in the in the cowboy hat.
20:42
You can wear >> Here's what's interesting though. So, Arthur Rock >> Yeah.
20:47
>> Uh usually >> It's pretty dialed. >> Pretty pretty dialed. >> Pretty dialed. >> Uh pretty dialed.
20:52
It's almost like he has inside information.
20:53
[laughter] It's almost like he somehow got >> I mean we've talked about the the game theory of like do does he work at a real like tier one venture capital firm that's Is it like what's the benefit of leaking everything?
21:04
Is he a lawyer that's seeing all the docs turn turn around?
21:08
>> I mean, zero benefit for a lawyer. >> Right?
21:10
>> Um >> Yeah, yeah, yeah.
21:11
The rush of getting likes on the timeline is pretty universal.
21:14
You're a lawyer, you're just like, "Ah, I need a banger." >> Out of fund. >> Okay. >> For sure. >> Yeah.
21:21
>> And uh I don't I don't know anything else. >> Mhm.
21:25
>> Um but uh he's always taking the view that it can be helpful to the founder to build cuz a bunch of people are going to see this. >> Sure.
21:35
>> 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?
21:41
So, it does create momentum.
21:43
Um but uh can certainly be annoying for teams as well.
21:46
Uh this was notable though.
21:48
So, 200 million of LOI from B2B customers. >> Mhm.
21:52
>> And so, very curious what the enterprise play is here. >> I don't know.
22:01
>> But uh we can work on getting a wool >> Does that mean like like through hospital networks or through like the healthcare system?
22:10
Or is it like Mark Zuckerberg wants to go further?
22:13
He wants to track the brainwaves of the employees, not just the massive market. >> your screen.
22:17
>> We're also going to track your brain.
22:19
I I mean, it could go either way cuz like you you you you you you you you you you you you imagine like Neuralink has had a bunch of traction and bunch of amazing.
22:25
I saw uh Nolan, the uh the first patient, P0, uh on Rogan talking about playing COD >> Yeah.
22:32
>> uh with the Neuralink. Amazing.
22:32
Uh and you can imagine that at a certain point like some sort of partnership.
22:37
>> They have multiple hat form factors. >> There we go.
22:39
That's where hats coming. >> We're good.
22:41
I was getting really hung up on the beanie.
22:43
I'm like, there's so many different enterprise or or B2B context here in a you're in a warehouse in Dallas, Texas in the summer. >> Yeah, you don't know.
22:51
Maybe this Maybe this 200 million dollar LOI is from REI or Patagonia.
22:56
You know, you don't know. Who makes beanies? Uh What's the Carhartt?
23:00
Carhartt makes a great beanie. There we go.
23:02
You don't know any of this stuff.
23:03
You're out you're out you're you're you're you're you're you're completely out to lunch with me on the beanie economy. >> Beanie economy. >> Anyway. >> Beanie market map. We'll work on it.
23:12
>> Let me tell you about public. com. public. com.
23:15
Investing for those who take it seriously.
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Stocks, options, bonds, crypto, treasuries, and more.
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All with great customer service.
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>> They just launched a feature today that allows you to connect your favorite chat >> Yes. >> app to public. >> Yes.
23:30
>> And >> More important than ever because with public you're going to be able to go and create the S&P 499 if you don't like SpaceX or the S&P 1 if you love SpaceX.
23:41
You can express your opinion about SpaceX however you want.
23:44
>> help me build an index for one company? >> Yes. >> Sorry.
23:47
>> Index for one company or index for everything but one company. SpaceX is very divisive.
23:52
People are extremely optimistic in certain camps, extremely pessimistic Goldman.
23:57
>> Goldman very optimistic or Goldman expects SpaceX's AI revenue to surge 100 times by 2030. >> Huge. >> Uh big big number.
24:06
I looked at this title and I was thinking like, okay, what's Grok's actual revenue today if you take out >> Yeah. >> X.
24:15
>> What what is their AI revenue today?
24:15
Is it just Grok subscriptions plus Grok Grok tokens?
24:18
Do you include X subscriptions?
24:20
Do you include uh cloud vendor and and neo cloud contracts?
24:26
There's a bunch of different ways to measure it.
24:27
The smaller the number, the easier it is to 100X X but we have seen other AI companies 100X revenues over 2 years, over 3 years, 4 years.
24:34
Like the 100X has become it's not a one-of-one scenario. >> Yes.
24:41
>> It's happened multiple times.
24:41
And so uh we have seen these charts many times.
24:44
And uh if if they execute well, they this is uh entirely possible.
24:47
It is it is extremely >> Other other notable data points from the road show. >> Yeah.
24:54
>> They the forecast anticipate SpaceX making about 360 billion of capital expenditures through 2028.
24:59
Uh Jensen somewhere is pumping. >> Mhm.
25:05
>> Uh very excited about that number. >> Mhm.
25:07
>> Be a new hyper scalar.
25:07
Um and uh anyways, very should be unsurprising but very aggressive. >> Yeah.
25:15
>> And um yeah, the the enterprise story >> I saw the new Nvidia foundation model is also live.
25:22
Uh we'll have to go check it out and look at the model card soon.
25:25
See how it's benchmarking.
25:27
But we got to move on to Benchmark because there's new news in the Benchmark world.
25:32
First, I'm going to tell you about MongoDB.
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25:42
So, >> Moment of silence. >> Moment of silence.
25:45
Why is there a moment of silence?
25:47
>> Uh the the end of an era.
25:47
The last >> they have been very focused for decades.
25:52
>> The last tier one that was a pure venture capital. >> called again?
25:56
Internet boys or something? >> Oh. >> Soft boys?
25:59
There's >> [laughter] >> There's There's some book about them that was very funny. >> Yeah.
26:03
E-boys was the >> E-boys is a hit piece of a book title.
26:08
>> That was a fantastic [laughter] book.
26:09
>> But the subtitle makes up makes up for it.
26:11
And it's a fantastic book.
26:11
And it's a very interesting story where they actually let a a journalist come in and see how they do it and stuff.
26:18
>> story of the six tall men >> Yeah, see see I you clearly he clearly wrote the subtitle and was like I got to take the edge off of this. It's too glazy.
26:25
I got to take I got to take it down a notch.
26:29
And so he uh uh he he threw the E-boys in there.
26:31
But >> Anyways, [laughter] big moves from Benchmark.
26:34
Kate Clark has a scoop in the journal.
26:36
Benchmark has raised 2 billion across two new funds. >> 2 billion.
26:42
>> their first ever dedicated growth fund. >> Hm.
26:45
Did they hire anyone who has experience growth investing?
26:46
Who could possibly do growth investing there?
26:51
Someone who's maybe like at Bond Capital and then Founders Fund then maybe Kleiner like someone with that pedigree.
26:56
>> with that kind of background I think would be fantastic.
26:59
>> for growth equity I think.
27:00
>> Now that you say that though. >> Yeah? >> Ev Williams.
27:03
>> Ev Williams, that's right. >> pick up Ev Williams. >> him up.
27:06
I was almost thinking two steps ahead there.
27:09
>> Are they building their fund strategy now their entire platform strategy around Ev Ev Williams? >> Potentially. Potentially.
27:14
Anyway, uh let me tell you about Shopify.
27:19
Shopify is the first 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.
27:25
And we are very fortunate to be joined by Alex Karp in just a minute.
27:30
He's coming in to uh speak with us at AIP Con here.
27:32
We're going to bring him in in just a minute.
27:36
>> While we wait, Austin-based podcaster Joe Rogan reportedly being considered for 60 Minutes. >> 60 Minutes.
27:44
>> Uh looks like >> have to call it 200 Minutes.
27:45
But shh, cuz he records long podcasts and 60 >> Hundreds. Hundreds.
27:50
It'll just be called Hundreds. >> listening, put us in. Put us in the ring. We're ready to go.
27:56
You need tech correspondent, business correspondent, someone who can just chop it up for 60 minutes.
28:02
We do 60 minutes three times a day. We're ready to go.
28:04
This is going to be light work for us, Barry. I'm ready. I'm ready.
28:08
You can do 60 minutes right now.
28:10
You can do 60 minutes tomorrow. You can do 60 minutes.
28:11
You can do an extra 60 minutes easily.
28:15
When you putting up We're putting up 1,000 minutes a week. It's no problem.
28:18
>> We did consider that at one point early on.
28:20
We thought Should we do basically a morning show? >> Oh, yeah.
28:24
>> Take a 2-hour break and come back. >> Yeah. Yeah. Late night show.
28:26
Yeah, late night show maybe.
28:28
>> I'm still >> We have Alex Karp here with us. >> Welcome to the show. Welcome back.
28:31
Thank you so much for taking the time.
28:33
Uh we're going to have you grab these headset the these headphones, right? Not these.
28:39
You can sit here and sit >> close. Get close. We liked it last time.
28:43
The the three of of were sitting here.
28:46
>> Now, we can we can put the giant up.
28:48
>> Let's put this up here, right here. All set, you can stand. This always works.
28:52
Yeah, yeah, get in here, Klaus. This is good. >> Okay. Uh, how is it going?
28:56
How is EARP Con this time around? What's changed?
29:00
>> Um, well, we've we're in a phase.
29:00
Uh, all each one of these things like marks a time.
29:03
First of all, you guys are even more baller, more successful. >> Thank you.
29:08
>> Some tendies in your pocket.
29:10
>> it might have been in part to you.
29:10
Yeah, we got to say thank you. You blew us up. You're a big star.
29:15
>> looking bigger and stronger [laughter] somehow.
29:16
Hey, are you more attractive in your personal life now randomly?
29:20
>> Well, here's what we're actually focused on, dead hangs.
29:21
So, you came on last time you said your dead hangs around like 5. >> Oh, no.
29:26
It's It's well, it's plateaued in the last couple months at 5:30. >> 5:30.
29:30
>> Okay, so we the thing is like people are going to hear that.
29:33
They're going to think hanging on a bar, how hard could it be? You got to go and do it.
29:37
The audience has to go try to do it. We've started doing it.
29:40
We're still in the >> Under two minutes. >> what?
29:43
Between Yeah, between somewhere around a minute 30, you feel like your tendons are going to rip.
29:48
>> A minute 30, 1:30 dead hang is respectable.
29:49
Two minutes is super elite.
29:54
>> It doesn't feel respectable when you have that 5-minute number.
29:55
You're looking at the time.
29:57
[laughter] >> Strength strength strength matters.
29:58
Now, um, the the the thing is that I don't want to go into rabbit hole on training.
30:04
The single biggest mistake people make is they try to hang every day. You need recovery. It's like anything else.
30:08
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.
30:15
Doesn't have to be super macho.
30:15
And then that's your day.
30:17
So, like just say you can do 1:30.
30:20
>> sets or >> you know, one day a week you do your maximum. >> Max. >> Wow.
30:24
>> So, like let's say you could do two minutes.
30:25
You try to do at least 1:30.
30:27
You fight to get to 1:30, but you don't fight to get to two minutes. >> Got it.
30:30
>> That's your dead hang day.
30:30
And then you can basically around the next day and do whatever you want.
30:33
Don't overdo it, but you could do two times one minute with a long break. >> Mhm.
30:38
>> And then can't you just screw around, do less and less and less?
30:40
Two days before if you do two minute dead hang you do like four times 15 seconds.
30:44
The day before you take off.
30:48
And you do that just keep doing that and you're dead what the mistake people make is they hear my ball or time. >> [laughter] that guy.
30:58
>> I mean the mistake you're making is not doing a course.
31:01
>> [laughter] >> Wait you guys I mean you guys have yet what for you guys you give me like hey call in.
31:08
So um yeah I mean the dead hang is it 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.
31:19
>> What about what about breath hold under water?
31:21
>> 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 >> [laughter] >> I I'm a I'm a dive master I can hold my breath for three minutes.
31:32
>> I'm just saying I'm just yeah so I think it would be like I think you'd be crushing me on that honestly you know but you have like the lung capacity of a >> That's true.
31:38
>> I mean like >> If I'm not moving I'm not using any oxygen.
31:42
>> It's like you're like like you're like a like a like a whale floating out [laughter] there under the ocean waiting to surface.
31:50
So I say I'll tell you the difference uh and it is God they're always minding me out there but like okay when we first met it was like AI maybe real okay.
31:56
Then I would say somehow until about two weeks ago there was like a holy this is real but somehow it's not working but we're not allowed to say it publicly cuz we'll look stupid.
32:09
And then there's a lot of investor hype there's still is like investors printing 10 D's.
32:15
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 on on to that and then so there's a whole value lecture there.
32:29
Uh you have a political situation um where you know people who do not understand basic economics are winning the political argument.
32:37
So, you can you could we could talk about where AI is going. >> here.
32:42
Let's break it let's break it up.
32:43
Let's start with the token maxing thing.
32:45
Let's start with what's what's real.
32:45
Uh how are you actually thinking about What is your What is What is Palantir's philosophy around token consumption?
32:56
>> Well, like we Okay, we have a product that will allow you to be able I mean, internally it's called uh something, but externally, but really we call it the demasturbatory like get off masturbation thing internally.
33:08
It's like people are just like print like sitting there all day kind of like a porn addiction.
33:12
And enterprises are like, "Okay, we knew this We believe this will create value, but we cannot have people just like some people do with their phones."
33:21
just like and just rearranging deck chairs on their personal Titanic.
33:28
Like people are like full on and Yeah, tool-shaped objects, right?
33:34
Tool-shaped objects you're looking at more than you want. You hope no one notices.
33:37
kind of before dinner after dinner.
33:39
Yeah, and every email classified with tags 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.
33:51
So, just to give you a weird analogy No, and I was going to say very very often it's more the opposite where it's about figuring out the right way to do something and then you can use capital to fuel that process, but Let me give you a thing that's too generous for you guys.
34:03
Okay, it it it's taste plus money. >> Okay. >> Yeah.
34:09
>> And there is no the like AI like if you look at like to pick any issue we want to talk about.
34:14
Token mechanism maximum maxing uh what's going on with deploy codes.
34:18
Are other people going to build ontologies?
34:21
Why are Why Why does our political class not understand AI, especially in Europe?
34:26
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?
34:39
At the end of the day, whether it's in the four whether it's the Ukrainians fighting the Israelis, commercial entities, it's there's somebody sitting there who's like, "Okay, but this problem is valuable. This problem isn't."
34:50
And once that value that problem is always has that problem alway almost always but not always has attributes.
34:58
So, there are some problems you could solve with this like, "I want to write a report on GDP growth in China, right?"
35:03
Okay, but if it's a problem that requires a knowledge store.
35:06
Like, "I want to understand the specialized way I underwrite."
35:08
We're going to have a guest here.
35:10
"I want to understand the specialized way I drill for oil and gas that's both legal, ethical, and reduces the cost of production.
35:17
I want to change the the the supply chain of my industry, whether that's military or whether that's building boxes or whether that's cars."
35:26
These things require actual precise, ongoing processes.
35:31
They are enhanced by large language models.
35:32
They are not replaced by large language models.
35:35
And then you get to security issues like the for us like the whole mythos things is just a boon because like, "Yeah, we can take any model, their model, Open AI model, open model, we can identify we can now identify vulnerabilities at like 10 100X.
35:50
Yeah, but then who patches them?"
35:52
How do you patch them on prem?
35:55
How do you patch them on prem so that your specialized knowledge stays on prem?
35:59
Like, if you're any business or Intel service, there's a lot of these things are very similar.
36:04
Like, you're not putting your classified data in a public cloud.
36:06
Same thing if you're like, you have a special way of farming soybeans.
36:11
Well, you're not So, it's like, how do you have how do you So, all these problems are exposed, identified, and then you always have a thing of where's the charisma, which people really underestimate.
36:23
And it's it's not global.
36:25
There's no global charisma now.
36:25
Like, so right now, the large language models are very frontier companies are super charismatic with investors. >> Mhm.
36:33
>> I'll give you some news.
36:33
They're super not uncharismatic with enterprises and the people.
36:38
Like >> Even even with enterprise.
36:41
>> No, no, I mean it's >> Cuz I understand with the people, but >> the enterprise people I have a secret I have a secret like every company has a secret way of selling.
36:46
You know what my secret way of selling is?
36:48
Don't even call it don't don't come talk to us.
36:52
There's a frontier company.
36:52
Go spend two days with >> Mhm.
36:55
>> And if you're lucky after you're done, I'll let you in my door. They're like clamoring.
36:58
They're like they're like hey, I'll take your bad brand which we have a great brand at enterprise.
37:02
But like it it's like it's like secret knowledge cuz the investors love this.
37:07
They're like hey, my stocks are all up. Everything's up.
37:09
I mean Palantir's done very well. We're >> Yeah, yeah.
37:11
>> Like it but it's like and you know, you guys are doing very well, I imagine, right? And it's okay.
37:14
We're we're you know, we can >> But I'll tell you what, you go down the street, you talk to a marine, you talk to a bus driver, you talk to the person who owns the bus driving company. >> Mhm. >> They are not happy.
37:25
They do not like these people.
37:27
They're tired of people token maxing.
37:29
They looks like masturbation at their that's cost them money.
37:34
They they're like and honestly, then you have something we're not allowed to talk about in this country, likeability. >> Mhm.
37:40
>> Like Palantir, we have I think we have like 50 100 million global bans.
37:43
We have like 5 million people that wake up in the morning literally calling me Satan. >> Mhm.
37:49
>> I didn't know I had that kind of >> Mhm. >> warm hand.
37:51
But uh you know, it's like that's what they believe. >> Yeah.
37:55
>> And like and they really believe it. Okay.
37:58
What people are not allowed to really address is like we have fans and enemies. >> Yeah. >> Yeah. >> You're polarizing. >> Yeah.
38:04
We're polarizing which means both sides. >> Yep.
38:06
>> These people have one side. >> Yep.
38:08
>> They're just it is so it's like you and it's like it's a really big >> Social media companies too have the same problem.
38:14
>> Yeah, no >> Everyone uses them, but no one likes them.
38:16
>> Yeah, but but then they also live in a circle and that circle's printing money.
38:20
So it's like you know, when you look in the mirror and you just printed a lot of money, you look pretty fresh.
38:25
>> Is part of it is is part of it that that some element of the technology, let's just say LLMs, is so magical that the companies involved, that the companies that are making and selling frontier intelligence, can be bad at a bunch of other things and still grow? >> Well, no. No, no.
38:41
They are magical at a certain kind of thing, allowing you to write, for example, code.
38:46
Now, that code doesn't can't be used as an knowledge store.
38:50
So, if you look at code in like three different ways, like just using Palantir as a model.
38:53
We have code that's basically infrastructure.
38:55
So, what what are the Ukrainians using?
38:56
What is the Department of War using?
38:57
What do a lot of our enterprises that they're We call that primitives.
39:01
This is basically hard-coded things that that understand the world the way you do.
39:04
It would take millions of technical hours and an understanding of all these enterprises to do it.
39:10
So, it's it's much more like how do you build a steel beam?
39:12
Then you have like code that is written by FDEs.
39:18
Okay, so that's kind of managed.
39:18
It's it The reason why FDEs work, the secret is it's actually managed in something that we as a product.
39:24
So, you're writing to a code base, we're managing that, we're increasing our product.
39:27
It's not just random people writing.
39:28
Then you have, let's call it free code.
39:30
That free code is that's magical.
39:33
Like, you can do it very quickly, it's almost right, it doesn't have to be exact. >> Dashboards, etc.
39:39
>> Dashboards, financial stuff, probability stuff where you you just have to get it you know what >> etc. >> magical.
39:44
By the way, it's magical not only creates it and it's magical porn away on it.
39:50
I don't know people like the porn thing, but it's also addictive.
39:52
It's like, you know it's not good for you, but you know, maybe it'll do damage. >> One more dashboard.
39:57
>> time, it can't hurt that much.
39:57
I know my doctor says it I shouldn't do it, but it's like it's like that, right?
40:01
And you just keep going and like and if you're involved in the that thing, you're also making money. >> Yeah.
40:09
>> And then, last not least, in certain circles, like if you have you want to be a research researcher or you believe, essentially it's a religion.
40:16
So, like, you know, and like one of the thing is very charismatic especially people have never had a religion cuz all of a sudden that hole in your heart that was yearning for I don't know, I would say you know, a established religion, Judaism, Christianity, Islam is like being filled and and all the answers are there.
40:33
But it is very very successful at doing things that a company has to do.
40:40
But it is not actually solving the problem that enterprises are.
40:44
It is now it can solve them. That's the trick.
40:47
It's not It's not binary.
40:47
It's not like you can't say they're not valuable.
40:51
They're totally putting our business on steroids.
40:52
Like without LLMs, nobody would be talking about our anthology, about Apollo managing secure exploits, about our ability to manage an enterprise essentially, turning all these companies into FTEs, these deploy codes.
41:06
We love them cuz now every company wants to deploy code.
41:08
You know how you do that?
41:11
You re-platform on Palantir.
41:11
And like and it actually works.
41:13
It's not somebody with no taste who's never done enterprise, who has no earthly clue how these things work, who's done something else and is like just imagining they know how to do it, right?
41:22
>> part is part of this moment quite entertaining for you because you guys have been working on understanding businesses at a deep fundamental level, creating You guys have effectively been doing the work that it that people are promising AI 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 and being at a point where you don't have to oversell the technology.
41:53
You can sell both things.
41:55
But now there's maybe Here we go. We got it together.
41:57
Um now there's maybe >> Oh, it's the wrong side. That's why. Let's flip it around. There you go. Yeah. There you go. There you go. Living the brand. Living the brand. No, I understand.
42:15
>> of it entertaining to you that it feels like you know, Palantir has always been in some ways um not had competitors because there's nobody with Alex Karp running a company that is does what Palantir does besides Palantir.
42:29
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 that you have.
42:44
>> Well, they're doing two things.
42:44
They're selling They're trying to sell the intelligence part, and they're trying to pretend if you just hire a bunch of people and let them run around their FDs.
42:52
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 and and great to have adoption.
43:04
The pretty ironic thing is half the people adopting now don't even know they're copying.
43:10
But now, the copying thing it helps and hurts.
43:16
Where it hurts is in the beginning it puts clutter in the market. >> Yeah.
43:20
>> Um and there's there's no doubt about it.
43:21
Where it helps, and then we saw this with defense tech honestly.
43:24
So like in defense tech, we were the only people we're the first people despite what is I I love these honestly other podcasters, they're interviewing people who are parroting things I said 20 years ago. They don't know it.
43:36
And it's like, "Oh, that's so insightful."
43:37
It's like, "Yeah, of course it's insightful.
43:39
Karp said it 25 years ago."
43:41
>> [laughter] >> And like but it's but so that kind of that part is super weird.
43:44
But and like but um but it's but what really happens when you see is like it expands the market.
43:52
So like in defense tech, we would not be doing this well in just purely in government unless there weren't 50 companies that were doing similar things because then the people are like, "Okay, first of all >> You view it as like off-balance sheet sales resources where other people are basically doing it themselves.
44:09
>> off Well, now that that's the largely it's They do two things.
44:11
They increase the size of the market because de facto nobody wants to find an underwriting market where there's only one person. >> Sure.
44:20
>> So, like if you're the one person, the percentage of the defense budget you can get is much smaller.
44:24
And two, they set up a comparator.
44:26
It's like, you know, you may not like the freak show. Okay.
44:31
If like but have you noticed the people who are serious buy it?
44:33
And then then it it change and then three it changes the standard.
44:37
Now, what you're seeing now is like that times 100x. >> Yeah.
44:42
>> And it does change like recruiting, retention, and like how you build a company and we're always think you have to think about how to being dyslexic huge advantage there cuz like you don't have a playbook and now that you need you need things to shift and we're doing that.
44:53
Um the [clears throat] the the central thing though that it's just cannot be developed it even if you understood the playbook a lot of these things are like appear like it's like you know, LM code appears like Palantir code but isn't.
45:08
For the flight thing appears like Palantir but isn't.
45:09
Ontology you could theoretically copy parts of it but they're essentially structures that are built deep into organizations that we own.
45:17
And by the way, take you 3 years and then 3 years we're in a completely different world.
45:21
But there is this magical thing called taste.
45:25
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.
45:33
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.
45:41
One of whom is parroting something that sounds weird and that's all they're doing. >> Yep.
45:45
>> And a lot of people very few people can do that.
45:48
And it's the same thing like the enterprises that succeed there is a taste arbiter.
45:53
And at Palantir we have taste arbiter we have taste in every product, taste in every deployment, taste in every casting.
46:00
Who puts the people there?
46:02
How do you put them there?
46:04
How do you organize the thing?
46:04
Our anthology then does that technically.
46:07
How do you manage the whole org with taste? Who should be in charge?
46:09
What data sets should come in?
46:11
What What are What are the ways in which you protect?
46:14
What is What should you push into the public crowd? What should be on prem?
46:18
What What should I mean, leaving aside the law and like wars war ethics, what do you want to protect? What should you protect?
46:25
What should you not protect because quite frankly you want that to be out there so you can get more data.
46:30
All those things are arbited by taste and then you have to have the credibility of having taste.
46:35
That's a real problem for a lot of these places cuz they don't have they they're popular with their friends.
46:39
They don't they really don't understand how unpopular they are in the first [laughter] place.
46:43
They think it's like oh yeah it's like the way I think I have a problem with like professors at Columbia.
46:47
It's like no, it's a real problem.
46:49
Like they think I'm Satan.
46:49
And >> [laughter] >> you know, it's like I I I I think you know, we grew up in the same community.
46:55
Let's talk about Heidegger.
46:55
They're like they don't want to talk about Heidegger.
46:59
So it's like it's like yeah and so that's just a it's a weird thing.
47:01
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 and I'm not saying you have to agree with me politically or anything, there the like partly because dynamic and it very self-inflicted because I I I'll tell you I can't name names.
47:20
I called many of the titans of this world and and like started this 6 months ago like every couple days.
47:29
We're going to be nationalized.
47:31
Like some of them I'm like yeah, we're going to be I mean you know, it's like honestly they're like the batch they they they find me very entertaining.
47:36
Like I don't know I'm not sure like so they call cuz yeah it's like yeah it's like oh yeah this going to be entertaining.
47:40
Yeah so in any case, so I've been telling them for 6 months 6 We're going to be nationalized.
47:48
We're going to be nationalized.
47:48
And they're like why would anyone nationalize?
47:51
Never happened in America.
47:53
It's never Why would anyone nationalize us? We're so likeable.
47:55
We're [laughter] creating so much value.
47:56
We're like, "Okay, I'm not going to debate that. I know how likable I am.
48:00
I'm I'm not going to tell you how likable you are.
48:02
But, I am telling you and you know the momentum on this is on the side of people who nationalize.
48:08
Now, 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.
48:13
These things are not going to Yes, they are going to create opportunities."
48:16
You have to talk openly about how these things are valuable because we have adversaries.
48:20
You can't just say these things All that stuff.
48:22
So, the primary risk, honestly, to Palantir and a lot of these other countries is And then, it's going to be nationalized before nationalized going to be regulated by people who don't understand this.
48:32
And now, they'll tell you in private, "I'm working on this. I'm da da da."
48:35
And this and this lobbyist is like, not going to work.
48:36
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.
48:43
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.
48:48
I'm just saying we should Like, in a world where everything is changing, everything is changing.
48:55
Don't we have to find some communal structure to remember we're American?
48:58
You know, like my You don't like my idea of like we all do a week in the park? Great.
49:02
Come up with some other idea.
49:02
Why we can't have no idea?
49:04
You know, and like And then, they're like, "Well, I'm saying I do not want a draft." Just to be explicit.
49:10
They're like, "Oh, that's pro-war."
49:11
No, honestly, you know what most of our wars are fought because no working class person is making a decision.
49:16
You start making sure everyone is involved in everything.
49:18
I'll see you how few wars we fight.
49:19
It's actually the anti-war position.
49:21
But, any case, disagree with everything.
49:23
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.
49:32
All they're talking about is how much they hate us.
49:33
And those of us who are sensible in the middle, you know, too many of us are chillaxing.
49:39
Like it Oh, like nationalization, it can't happen.
49:41
It's America would never do that.
49:44
Sleepwalking into And you guys have tendies to protect now.
49:46
You guys should be on the front line of this.
49:47
Like you got full to Oh, I'm sorry.
49:49
I have a I have a full on very impressive corporate leader coming on. >> Yes.
49:56
>> So, I got to I got to turn it down. >> question.
49:57
Last question if we have time.
49:59
Uh how are your conversations going with Fortune 500 CEOs around head count planning?
50:06
There's been so many layoffs this last year that people were saying, "Hey, we're getting so much out of AI.
50:11
We're able to, you know, cut back here or there."
50:13
Uh people inside tech often know like these maybe there's just a reduction because there needs to be a reduction. The org got bloated.
50:24
Maybe they do need to fund some AI initiatives. >> business model?
50:27
Like they're getting out competed by somebody.
50:30
>> just doesn't have momentum.
50:30
But how are those conversations going? What does it look like?
50:33
>> Like the the like I talk By the way, I talk to Fortune 500 companies. I talk to unions. I talk to soldiers.
50:37
I talk to fire If you upscale somebody, they're more valuable. >> Sure.
50:44
>> And like all these whether it's people working on batteries, people work 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.
50:53
Like if you run around saying AI allowed you to fire 2/3 of your workforce and you did it because maybe your competitor's kicking your ass. >> Yeah.
51:02
>> That could That is a really like you might as well just go sign up for Bernie Bernie Sanders manifest.
51:07
And part of the thing is they really believe that can't happen.
51:11
So, they're free riding on the fact that it could.
51:12
Like we have And it it just cannot work anymore.
51:15
These things are very, very explosive.
51:16
The American people sense that there is something dangerous here.
51:20
And when people are playing with that fire, it's like it's a they assume the fire won't burn their hands.
51:26
Well, that's not the world we're in.
51:27
That fire is going to consume us.
51:29
And what we see again, the war fighting example is just the most neutral not for everybody.
51:34
But like the soldiers at the bottom have gotten much more valuable.
51:39
Well, and and I don't even just mean the special operators, which obviously they're in a different league.
51:43
But like every the people doing a lot of the operations now are doing our product their high school vocationally trained. You see this everywhere.
51:51
The The modern enterprise is going to have like we have a a true like very very very smart person coming on and it's like you're going to have a very smart executive.
52:03
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.
52:07
But um uh um and uh and then very talented creative people with taste all up and down the stack.
52:16
In any case, I think this is time for me to >> this is time.
52:20
>> We uh >> Thank you so much.
52:22
>> Yeah, great to catch up.
52:22
[clears throat] >> It's always fun.
52:22
First >> want me to stay for 2 minutes or what? Oh, yeah.
52:27
I'm only going to stay look, but just a minute. He's got to be the star.
52:30
>> All right, the other headset is >> Put put it put it in. Put it in your ear.
52:33
Yeah, and I'm just going to I'm going to take off after a minute. >> to put it here.
52:37
And why don't you Here, put that headset on.
52:39
Carl, why don't you introduce our guest?
52:42
>> Microphone on the left.
52:43
>> Well, I let him He's He's a He's one of the smarter people in in business.
52:48
Um has developed um unique ways to underwrite that did not involve firing people.
52:57
And someone and someone I admire. >> Perfect. Thanks, Alex.
53:01
>> Uh with that I'm going to let you guys go.
53:02
Make sure to tell them that the Intelligy powers it.
53:05
>> [laughter] >> Yes, it's everything. >> Always selling hey.
53:09
>> Thanks for coming on the show. It's great to meet you. >> Thank you.
53:11
Yeah, [clears throat] please kick us off with like a bit of a more formal introduction.
53:16
>> It's um Peter Zaffino.
53:16
Um I'm the executive chairman as effective on Monday of of AIG.
53:20
Used to be the >> Congratulations.
53:23
>> uh chairman and CEO um and have, you know, worked with the company for 9 years to help transform it.
53:27
It was in a place where underwriting profitability was challenging, operations were challenging, data was challenging, um capital was challenging.
53:35
Uh so, you know, I had a great team of people with me to transform the company.
53:41
>> 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.
53:49
Like Give us the scope and the scale here.
53:51
Global company with a little bit of a unique footprint.
53:54
We're 50% international, 50% North America.
53:57
But our second largest country after US is Japan. >> Oh.
54:01
>> We have a big business in India.
54:04
And then we have a very big business in in the UK. We do complicated risks.
54:06
So you can think about what's happening in the Middle East now with shipping, marine energy.
54:14
We're heavily involved in that.
54:16
>> So something where there's not an existing futures contract that a company can just go and hedge.
54:20
It's not Oh, I'm going to buy some oil futures cuz I fly planes around and I know I'm going to need diesel fuel in a couple months.
54:28
And so I'm going to hedge that out.
54:28
This is for more complex risks.
54:31
>> It's for more complex risks.
54:31
And you know, you think about the largest, you know, sort of customers in the world.
54:37
Big oil companies, you know, Fortune 500 companies.
54:40
But we also have a personal insurance business which will cover things like accident, health that are distribution to consumers.
54:46
So we have a real balance.
54:50
>> 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.
54:58
It feels like there's a lot of folks with a lot of trust built up over years to understand each other's businesses.
55:05
But then there's probably a lot of other underwriting happening in teams putting together comps and spreadsheets and data.
55:11
And I want to know about the the intersection there.
55:14
It feels like the business is and I don't know if it ever will be just one click checkout for for insurance products for Fortune 500 companies.
55:24
But what what is the interface between the quantitative, the qualitative, the relationship and the data?
55:28
And then how is that changing?
55:31
>> So the quantitative, you have to start at the portfolio level.
55:34
And you want as much data as you possibly can to look at deterministic modeling, probabilistic, and then stochastic.
55:40
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 um ways in which you structure insurance.
55:54
So, for us >> look at You can't look at an individual policy in in isolation.
55:57
You're You're You're managing portfolio risk, risk to the entire firm, and and that's something that's happening probably 24/7, I imagine.
56:07
>> It's hard, and that's what led me to Alex Karp.
56:09
Um you know, it's hard to get the aggregation done in anything that looks like real time. It's usually static.
56:14
It can be 30, 60, 90 days, and your portfolio could change.
56:18
I mean, it's not going to change dramatically, but having the ability to, you know, sort of assess risk and use the quantitative data to make better decisions on a daily basis is the aspiration of the way the company's going.
56:29
>> Yeah, that makes sense.
56:30
Take us back to your first meeting with Karp.
56:32
Curious what the experience was like. So, unique individual. >> Yeah.
56:37
No, I was actually introduced by a board member many years ago.
56:40
And it was really in this pursuit of um not necessarily Foundry or AIP or ontology.
56:49
That's where it led us, but it was more on sort of the quantitative ways in which I was looking at the portfolio.
56:54
Could he help me think through computing, and could he help me think through sort portfolio optimization?
57:00
And I just got more and more uh intrigued.
57:04
I mean, you see the brain.
57:04
I mean, he just thinks about things. Um Yeah. He doesn't hold back.
57:08
I mean, so he's So, I always knew where he stood with with me and with AIG, but just developed a very strong trusting relationship.
57:14
And they're 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 2 years is is too static.
57:29
And so, we actually build our relationship on 90-day goals and that's been incredibly effective.
57:35
>> 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?
57:47
Are you bringing exponentially more data into the platform every year, exponentially more compute resources, teams, number of policies, like what what is the what is the thing that's experiencing a boom right now?
58:02
>> The most important part I believe in terms of business is that you have to have a business solution you're trying to solve.
58:07
So for us it was more data, >> Yeah.
58:10
>> um better data, >> Yeah.
58:12
>> and then reduce cycle time.
58:12
So in other words, like when we get the data that comes in from our distribution partners, how fast can we get it with higher quality data and more data to the underwriter to make decisions? >> Got it.
58:24
>> Um and then how do we actually make the adjustments?
58:27
>> what's what's an example of distribution partner in this context?
58:29
>> would be like a insurance broker or insurance agent or you know, someone who has their clients as a customer. >> effectively? >> Exactly. >> Okay. Yeah, that makes sense. Um what else, Jordy? Do you have something?
58:42
>> Uh where was I going to go?
58:42
The >> I don't want to cut >> Yeah, so so there's been talk about ontology.
58:49
>> Yeah, so we'll we'll we'll we'll get there.
58:50
So so there's been uh we we primarily I mean we at least started covering early-stage startups.
58:54
There's been a debate uh in our kind of little sub industry right now uh around a bunch of new uh insurance-focused startups that are growing incredibly quickly.
59:06
Uh and there's a debate going on as one uh maybe AI makes it more possible to underwrite risk and if you can do that well, grow very quickly.
59:18
Uh the other side, you know, says uh hey, you know, if you're hyper scaling an insurance company, uh maybe that's not maybe you don't want to work with a company that is, you know, going through that hyper Yes. Yes, maybe.
59:34
But yeah, talk 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 with Palantir.
59:50
>> There's never been a time, in my opinion, whether it was, you know, introduction to you fintech and insurtech, how to use algorithms, how to build data lakes and repositories for data.
1:00:01
There's never been a time in in my professional career, so it's 35 years in big companies, >> Yeah.
1:00:08
>> that I've seen the ability to change how an an organization actually runs itself.
1:00:13
And that can come from big companies like Palantir or a 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.
1:00:27
And what what I think is happening, we talked about the sort of data ingestion portion, getting that into a digital workflow, using large language models to extract more data from what comes in, but also helping underwriters make decisions that are, you know, more comprehensive.
1:00:45
You also have the ability in the way in which you service customers to be much better through the use of AI.
1:00:53
I think companies generally, my observations are struggling with the orchestration of how you actually drive agents, people, and data into an organization.
1:01:01
And once that is solved, and it's certainly on on its way, capabilities are there.
1:01:05
Um then you start to think about the entire end-to-end chain being very different. >> Yeah.
1:01:13
>> What I think about Palantir, while they've been such a critical partner, is one is we evolved together, but in that data ingestion, to be able to take structured and unstructured text, all sorts of data and get it into a workflow in a fraction of the time helps us on the things I try to achieve.
1:01:28
It's like we have now data that we probably wouldn't have used before cuz it wasn't good or we couldn't translate it, couldn't get it into the digital workflow.
1:01:37
Um and then we start to build out an ontology.
1:01:40
And I and I really do think it's incredibly important.
1:01:42
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 a binary GenAI, now we're into a genetic AI where it can just do things autonomously for so much longer.
1:01:56
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.
1:02:06
So we've been able to do things with Palantir, I'll use the ontology example again.
1:02:11
We did the full ontology of AIG and then we went to look at an acquisition um called Everest which had about $2 billion as a premium.
1:02:21
We got Palantir in to work with our team.
1:02:22
We could build an ontology of Everest's portfolio on top of ours in 4 days.
1:02:27
Um and quite frankly what we started to learn again about that evolution is that you always relied on data lakes or global data repositories.
1:02:34
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.
1:02:43
All of a sudden these repositories and the central places of getting data and make sure it's scrubbed wasn't as relevant.
1:02:50
So I think we continue to advance that in in the way in which we are looking at our business.
1:02:55
>> I have a lot I have one last question.
1:02:57
Um just on the actual change management, the organizational, how the office feels, what how did you go about actually working with Palantir?
1:03:04
Do you set up your own internal Palantir workforce who sits alongside FDEs?
1:03:11
Do you let Palantir your in and plug in like one person per team that you have set up.
1:03:19
Like was there a best practice?
1:03:22
Did you go with the best practice?
1:03:22
Like what was the actual like experience of deploying the forward deployed engineers?
1:03:28
They get deployed into the organization.
1:03:29
That's got to be a unique situation.
1:03:33
>> First is making sure Alex and then you know, two of the senior executives Ryan and Ted that everybody knows what we're trying to do together. So we start there.
1:03:40
Then we wanted to embed the engineers with our team.
1:03:41
So if we had a business leader that was trying to drive the underwriting output, you'd have you know, technology from AIG.
1:03:47
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.
1:04:08
>> Yeah, that makes total sense. Jordy, anything else? >> Uh, no.
1:04:12
>> Oh, yeah, but I think that we got to get the most important topic.
1:04:14
>> The the last if we do have a second, I don't I don't know. I was uh >> You're good. >> not sure on timing.
1:04:19
What how are you how are you thinking about, you know, workforce planning?
1:04:25
Uh asked uh Karp about this and he said to ask you. >> budgets?
1:04:29
>> Um you know, we we've stayed uh you know, as as you've had this wave wave of AI layoffs, we've been uh over and over and over reminded people that uh if you have a an individual and you give them more capability, you make them more productive, you make them more efficient, uh a thriving business will want to hire more people, right?
1:04:47
Because you can get more out of every individual.
1:04:50
Um and so we've tried to remind people of that over and over and over as, you know, companies that often times are, you know, underperforming or bloated for whatever reason.
1:04:58
Uh but what's your kind of philosophy around uh hiring, headcount planning, uh riffs, all that stuff in this kind of uh new era?
1:05:09
>> We've been focusing on I heard Alex at the tail end and I agree with him.
1:05:10
So we're focusing on growth.
1:05:12
We're focusing on reskilling and actually training our employees to be in a different part of the workflow.
1:05:20
Now you would do this I believe in all of this you have to still have great end-to-end process.
1:05:25
And so things that have been the humans been in LLM trained how to do things like outside of the normal workflow has to you have to get rid of that.
1:05:34
So I think that's just normal business.
1:05:37
But you know our aspiration is not to implement you know AI or anything that we're doing with our partners to eliminate jobs.
1:05:43
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.
1:05:52
>> It's a great optimistic vision. I love it.
1:05:54
Thank you so much for taking the time to come chat with us. Thanks for coming.
1:05:56
Have a great rest of your time.
1:05:59
And up next we have Chad Walquist.
1:06:00
First I'm going to tell you about CrowdStrike.
1:06:02
Your business's AI, their business is securing it.
1:06:04
CrowdStrike secures AI and stops breaches. Welcome to the show. How are you doing Chad? >> Great overcoat. >> That's a new one. >> It's a popular one.
1:06:12
That's an Eliano special. >> It is.
1:06:15
>> Oh yeah, he is the master.
1:06:18
>> Giving us a run for our money. >> Yeah, it's fantastic.
1:06:19
Anyway, kick us off with an introduction of yourself, how you fit into Palantir, a little bit of backstory.
1:06:24
I'm sure we have a ton of questions to run through.
1:06:26
>> First, how often do you guys do these things?
1:06:29
Cuz it feels like this feels like an annual it feels like an annual event. >> Yeah. >> Quarterly.
1:06:34
>> But >> We're going to call every three months now.
1:06:36
>> Karp's Karp talks about you know manipulating time [laughter] warping.
1:06:40
You know a quarter at Palantir is like a day or a year at another company.
1:06:44
So that it kind of makes sense.
1:06:46
>> Yeah, I'm like actually 23.
1:06:47
>> [laughter] >> Yeah, time the time warp is real.
1:06:49
I was we do these quarterly. >> Okay.
1:06:52
>> So I'm I'm a forward deployed architect technically. I do what is needed.
1:06:53
And so doing the needful is kind of the Palantir way is like there's no job below me.
1:06:59
So no matter if I'm out on the edge with customers, I'm talking to executives, explaining the ontology, doing YouTube videos. >> Yeah.
1:07:06
That's all what I'm doing.
1:07:06
So really the the goal is how do we help people decomp problems differently and apply the technology in new ways. >> Can AI do decomp? >> Yes. >> Okay.
1:07:15
Unpack that because that feels like the secret sauce.
1:07:17
That feels like the special thing about Palantir is actually being able to bring someone in who understands an organization.
1:07:21
I think a lot of people see AI tools.
1:07:25
>> A lot of people see AI tools.
1:07:25
No, a lot of people see AI tools and they and they think uh okay, very defined workflow, input output, but now instead of just math that Python can deal with, you can deal with some text and that's great.
1:07:38
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 versus oh, Steve actually does this completely outside of that system and marketing has two two platforms for this thing and engineering has three systems for CAD files and they're all the cloojes that have built up over decades sometimes hundreds of years for some of these organizations.
1:08:06
Like that's what was so special about the forward deployed engineer program, the Palantir model. >> Yep.
1:08:15
>> I'm surprised to hear you say I AI can do it at all.
1:08:17
It feels like the final boss.
1:08:19
>> Well, this is where the the really the Palantir thesis is humans and AI working together. >> Mhm.
1:08:24
>> And so, the way we think about this is modeling our business process and we heard some other people talking about this of modeling my business process into the ontology. >> Mhm.
1:08:31
>> Um because the LLMs doesn't don't necessarily have an a world view or world model view or business in your operations.
1:08:37
The ontology provides that. >> Okay.
1:08:39
>> And so, when we talk about decomp, this is really about actually now I make more data computable as well.
1:08:42
So, we think about LLMs on the agents and >> Yeah. >> interacting with it.
1:08:45
Also, we use LLMs to make more data computable and then model that in the ontology of how things are really working.
1:08:51
And so, what we're actually doing a lot of times now is is building out that world view and then running multiple agents over this actually um being combative towards each other, right?
1:09:02
And so, actually working against each other and having critiques.
1:09:03
And so after you you do that, you can also then give the human human in the loop feedback about this and iterate on this.
1:09:10
And what so we find is that's really a scaling mechanism.
1:09:11
It's like a new power tool, right?
1:09:13
I think you guys were just talking about this the kind of the perspective around jobs and all this stuff.
1:09:18
It's like when you gave carpenters power tools, there weren't less carpenters, there were more. I could do more with it.
1:09:22
It's an empowering thing. >> Yeah.
1:09:24
So, uh how often like I I I I'm interested in the like the pie [clears throat] in the sky Palantir pitch, understand your entire business, run your entire business on Palantir.
1:09:37
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.
1:09:48
Like we have we've had image recognition for a long time.
1:09:50
Let's actually go and implement that and get that into a database, get that into the ontology, get that into Palantir.
1:09:56
So then we can start building on top of it.
1:09:59
And it feels like there might be a tension there.
1:10:01
Obviously, both processes are speeding up, but how do you how do you sort of like keep the project centered around the big goal while still chopping wood on all the things that actually need to happen?
1:10:13
>> Yeah, I think this comes back to the forward deployed piece and like what do we deliver? Outcomes.
1:10:15
And and we work backwards from that rather than hey, I have this data, I'm going to build a data warehouse and then I'll build reports cuz all my data is in one place.
1:10:22
I That's the That's the field of dreams and no one shows up, right?
1:10:24
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.
1:10:30
Now, the one approach that we see a lot is in you know, enterprise software is going to force you into their box, sure. Right?
1:10:35
I You go fit You go fit into this box. Yeah.
1:10:39
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 of hey, 40 ways to do a PO.
1:10:48
Well, maybe it is okay to do 40 ways, but my software can't handle it and it's fragmented, right?
1:10:52
And so there's there's actually a middle ground because, you know, for a long time customization was kind of a four-letter word, right?
1:10:58
You know, no one wanted to do that and I think that's where we think about malleable software.
1:11:02
Actually, how do we help you be more different, not more similar? >> Interesting.
1:11:05
>> And that's so that when we decomp problems thinking about not only the the kind of the quantitative piece but the qualitative piece and the people and process around this.
1:11:13
How do we actually enable those people to do the things that made them special?
1:11:17
>> Is is software getting more malleable?
1:11:19
Because I I I can look at it two ways.
1:11:19
I can look at one, you know, obviously AI agents are incredible at coding.
1:11:26
They can run they they can make changes very very quickly that would take you a day in just a few minutes. >> Yep.
1:11:33
>> At the same time, I see, you know, so many screenshots of people saying, I implemented this feature and the GitHub is plus a million lines of code and at a certain point like the context window is growing as fast as the code generation is growing.
1:11:44
Like there's a I I'm a believer in the answer to bad slop is good slop and more slop, maybe.
1:11:51
But what are you actually seeing on the malleability of software?
1:11:54
Because sometimes the most malleable software in the past has been oh, well, there was a really incredible engineer who figured out this problem and baked it down to a 2,000 line repo. >> Yep.
1:12:07
>> And you can actually just put in your own context window so it becomes more malleable and you can use it as a building block. >> Yep.
1:12:13
>> And [snorts] 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.
1:12:20
>> Well, I think what what's missing is the the malleable enterprise scaffolding that you need. >> Okay.
1:12:25
>> And that's what we think about the ontology and foundry and the platform and then Apollo that allows us to go deploy these changes.
1:12:28
It so it gives us the right amount of structure but the right amount of freedom.
1:12:32
So I think that's the balance we try to find is that malleability malleability in the middle where we can actually scale, we can enable people to do things differently while still creating enterprise, you know, grade, robust, secure, scalable software.
1:12:44
And so it's actually a balance there about how I can enable that engineer that, you know, has been doing that.
1:12:50
Now they can write code much faster.
1:12:53
They can oversee things and that enterprise scaffolding in the middle allows us to actually create the right guardrails, create a safe system of work for them to go develop things in.
1:13:02
Um and then it's also the feedback loop.
1:13:04
So, the other thing that we do with our ontology and our platforms is implicit and explicit feedback from users using it.
1:13:08
So, the OODA loop that I create and really that OODA loop allows our customers to as they're doing workflows, they're giving feedback to agents.
1:13:15
Now, can agents help them do more based on the feedback?
1:13:17
So, both explicit saying, "Hey, that was wrong and that sucked." Or I chose this option.
1:13:22
Now, if you do that enough, agents can start to learn from that.
1:13:24
So, we actually store that in our ontology to allow it to scale.
1:13:27
So, it's really that human-centric process around AI.
1:13:28
AI is not like we shouldn't be thinking about AI from the sake of AI for AI.
1:13:35
It's AI to enable humans to do more. That's the frame.
1:13:38
>> OODA loop, observe, orient, decide, act, right?
1:13:41
Uh I I have a different question, but you can you can go.
1:13:44
>> Uh if you were giving if you had 30 minutes to uh give feedback to the AI labs, what are the kind of key areas, let's say the frontier labs, right? Uh leading models.
1:13:57
What uh what are the kind of key areas that you would be focused on?
1:14:02
>> Yeah, I mean, I think when we think about the enterprise space, you know, we >> One, you're like, "Don't compete with us."
1:14:06
>> No, I I actually like I I think optionality is a good thing.
1:14:08
Like, I am agnostic to where you store your data, where you store how what model you choose, what compute you use.
1:14:13
So, like, we we can allow you to use any of that cuz the last thing that actually drives an outcome is re-platforming, moving to another thing.
1:14:21
>> back to the on-prem culture, the secure cloud culture, ITAR compliance.
1:14:24
Like, this is in the DNA of the company.
1:14:28
>> Yeah, 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."
1:14:35
And we're like, "Well, actually, you've probably been re-platforming for years.
1:14:37
Can we enable what you have to go do these new things?"
1:14:41
So, when we think about like the model companies and it's, you know, how do we ensure that we can get the feedback loops around, you know, tool usage and um, you know, >> Yeah, that's the kind of that's the kind of stuff I was uh wanting to get your point of view on is like I'm sure you're getting into the nitty-gritty with individual models, where where they're spiky, where there's, you know, where there's shortcomings, etc.
1:15:03
>> Yeah, so we we actually just launched I just put a YouTube video out last week on this new tool called Evolve.
1:15:06
We talked about it in the kind of the half-time show where customers are using actually AI to help them understand which model.
1:15:12
So, like maybe >> Oh, interesting.
1:15:14
>> you know, the the the the the the meme around, "Hey, make it exist first and then make it good." >> Yeah.
1:15:19
>> Most of the time I see people building with agents, they're using the latest frontier model. I just got it working. >> Yeah, yeah.
1:15:25
>> Then I then all of a sudden the token maxing and every everything else and you're like, "Oh my gosh, I just blew through my whole budget." >> Yep.
1:15:30
>> So, we built a tool called Evolve that will actually go analyze the logs in production about how these models are operating, what people are doing with them, the architecture over it, um, and actually be able to swap out different models from different providers or, "Hey, actually for most of this workflow, you can use this model that's older and actually without thinking and and test time compute it, it's more deterministic."
1:15:50
>> Or even cached models.
1:15:51
>> Cached models and then or, "Hey, if you actually just have this piece of data in the ontology, then you would all this and 50% of your cost."
1:15:56
And so, you know, some of the customers McCarthy talked about this at our half-time, you know, they they were able to in two days eliminate 60% of their token cost by re-architecting, picking a different model and and prompt tuning.
1:16:08
So, it's a combination of all those the permutations get really hard especially when it's in this probabilistic models. >> Yeah.
1:16:15
>> We've have tools to do this in the deterministic world.
1:16:18
>> Prompt tuning it's a it's a don't make mistakes.
1:16:19
It's okay to make some mistakes.
1:16:21
Yeah, if the mistake is going to cause just a little bit, I'm fine cuz don't make a mistake, that's going to cost me a fortune.
1:16:29
>> Well, there there there's there was some chatter yesterday around uh uh something a model was doing to be more efficient with uh talking and and like this bad. >> Oh, okay man.
1:16:40
>> Yeah, caveman caveman prompting. >> Yeah.
1:16:42
Um, The cave man prompting actually works.
1:16:45
>> How 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 >> What does that mean?
1:17:01
Their product is taking off like chat GPT or >> Well so they've found a way to apply AI in a way that is highly highly effective and gives them an edge. >> Oh, interesting.
1:17:10
>> Uh but >> like the theoretical like Renaissance technology have to transform it. >> Yeah, yeah.
1:17:15
So so X people are very loud, right?
1:17:16
You figure they they're like I just had this Yeah, I just had a product work for 30 hours on this thing. They'll talk about it.
1:17:24
But if you're a Fortune 500 and you figure out how to do something, it's not like you want to like put put your hand up and say like guys like I figured something out, right?
1:17:32
Like secrets are valuable and these advancements and kind of breakthroughs are not going to be uniform.
1:17:39
>> industry will never be the same.
1:17:40
>> Yeah, your direct competitor copies [laughter] you and you're like >> Yeah, and so and so part of part of why, you know, right now the meme is token maxing and that's an obvious going to be an obvious area area of debate.
1:17:52
People are happy to go talk about it, say, you know, CEOs might say, "Hey, let's stop doing this."
1:17:58
Um but there has to be all these other kind of pockets of interesting moments where we won't hear about them until they become kind of like standard operating procedure.
1:18:09
>> Or you see it in the the the >> economic >> earnings in the economics piece, right?
1:18:13
Yeah, yeah, so I yes, unfortunately X is not the real world.
1:18:17
>> [laughter] >> You know, and there there's a lot of grift and noise and you know, podcasting PMing and you know, that kind of stuff that goes on.
1:18:22
But I I I think in the real world, yes, there is the haves and have nots.
1:18:26
I mean, we were just talking about AIG.
1:18:27
Like when you can start to actually do the underwriting and you know, have quotes back in hours or days instead of months on these highly complex enterprise, you know, kind of insurance agreements.
1:18:41
If you don't have that, how are you ever going to compete? >> Yeah.
1:18:43
>> And so, when we think about this of the end of one, right, you know, that those are the companies that we're going after and we see where there are those moments that are not public.
1:18:50
>> Yeah, it's that's such an interesting category because you can imagine AIG, you know, is um you know, working with a potential customer or renewing a policy and that customer is going and talking to all of AIG's competitors. >> Yep.
1:19:04
>> And uh if AIG is able to turn around, you know, a quote or a policy in 24 hours and then it takes another player you know, 2 weeks because it's, you know, complicated. >> and spreadsheets.
1:19:17
>> So many so many teams will just say like, "Hey, we, you know, you know, especially once you have two bids, you can basically say like, okay, this that third, fourth, fifth, we'll kind of wait on those because we have >> Yeah. >> a good option here."
1:19:28
>> Well, it builds trust. >> Yeah.
1:19:29
>> The other piece here, so when you think when you see people operating that with that level of efficiency, you what else can you do?
1:19:34
>> So, I see this whether I'm doing, you know, SAP migrations, the least sexy thing you could talk about, but hey, if I can cut your SAP migration >> Let's give it up for SAP.
1:19:43
>> Yeah, it's like the the least, you know, exciting thing on on paper, but actually, if I if you're spending hundreds of millions of Yeah, you guys get it.
1:19:49
But hundreds of millions of dollars on a migration and we can cut it in half, >> Yeah.
1:19:54
>> that's a massive deal.
1:19:56
>> Uh back on the OODA loop, observe, orient, decide, act.
1:19:59
On the observation side, what is the supply and demand imbalance for dashboards?
1:20:08
Like what and what I mean by that is is when you're working with a company, is there is there more demand for dashboards, more people asking, "Hey, we need a dashboard for this, we need a dashboard for that."
1:20:16
and you have to back people off and say, "I don't know if the dashboard's right for this."
1:20:19
Like you might just want to do a 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."
1:20:30
Like what what what walk me through dashboarding right now because I've always been like sort of like, "Oh, there's too many dashboards.
1:20:36
You build them and then no one looks at them."
1:20:38
>> Yeah, I want to kill all dashboards. Okay.
1:20:39
That's my personal >> [laughter] >> I dashboard I mean, KPIs and dashboards should be a byproduct of operational applications where I'm making decisions.
1:20:48
So, we talk about the Udal loop.
1:20:48
I have to actually act for things to hit the bottom line and be valuable.
1:20:51
>> In the actual application. >> In the application.
1:20:52
So, as I need those things and it's going to inform a better decision. >> Yeah.
1:20:57
>> That's where I want those metrics.
1:20:57
That should be a byproduct.
1:20:58
Not if I go out with the goal of building a dashboard, it's going to be the field of dreams again. No one shows up.
1:21:03
And so, yes, it should be you should You're going to have to build some of those things.
1:21:07
The other side of this also is when you think about a data warehouse, like literally I won't go too deep into this technical riff, but it like, you know, Kimball and dimensional modeling was built in '96 for scaling databases and you're still modeling in the same way in 2026 for your dashboard, your Tableau, whatever those things are.
1:21:24
And like, that's not actually how the world works in rows and columns.
1:21:27
You need complex things to model how the world really works.
1:21:31
And that's what we think about the ontology which means I can reuse it for an operational application, KPIs, agents, all in one single ontology, which it makes it the compound effect where as I add things in, I'm now compounding with each individual decision I'm decid- working with gets better and better and better for the next use cases I connect across my business.
1:21:50
>> Yeah, is there an analogy there to just the deployment of AI tools currently?
1:21:54
I'm I'm I'm just reflecting on the the no sequel boom and I don't know how much strong this was.
1:22:00
This is probably just like an online take, but this idea of like, why would you ever want a relational database?
1:22:04
Why would you ever want a schema?
1:22:05
Dude, don't ever do a migration ever again.
1:22:07
Uh and the future looked like a win-win almost.
1:22:12
Like, I I think Postgres installations probably grew and so did MongoDB and other non-relational databases.
1:22:16
Uh 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?
1:22:41
>> Um I think it's yes and and and a couple different things there is when you think about the real world, it is not just rows and columns.
1:22:47
You can't describe everything with measures and attributes. Yeah.
1:22:50
And so it's actually multimodal.
1:22:52
And so like we think about this in our ontology where you can have one semantic object that actually has a CAD file and an image, a CB model, and tabular stuff in one semantic thing of a plant. >> Yeah.
1:23:03
>> Which means I'm starting to talk in the language of my business.
1:23:04
So being able to have the multimodal representation where in other places, oh, I have to have MongoDB and I have to have a SQL database here and I have to have an S3 bucket here to put all of these different things to store them in ways.
1:23:18
Well, we can do that all in the ontology, vectors, everything else.
1:23:19
So that that's really the the goal around how do I model the real world, how it actually works, and make that transparent so you're not having to figure out which technology to put in a time series thing for sensors on a you know, an oil platform. >> Yep. >> Don't care, right?
1:23:32
And that that's where we want to have the non-differentiated heavy lifting like truly in the platform to remove the friction about getting stuff done.
1:23:40
>> How common is it for a business with more than a hundred million dollars of revenue to have very little understanding of how their business actually works?
1:23:48
Like maybe they Maybe they Maybe they know like the main thing which is like, you know, we make a product and we try to sell it for more than it costs to deliver. >> Yeah.
1:23:58
>> Um but uh but but is is some element of uh how how much can chaos and mystery be reduced effectively today?
1:24:08
Because it feels like we're entering an era like you go back um you know, 50 years and uh the level of like mystery in a large company would have been like is almost inconceivable today, right?
1:24:22
Because you have different time zones, different offices, you know, no email, all that stuff.
1:24:28
And now, like mystery and chaos is probably uh reduced dramatically, but uh still there's companies that that uh maybe maybe before you start working with them, I'm curious what those look like.
1:24:43
>> Yeah, I mean, we work with a lot of a lot of different varieties of companies.
1:24:47
Um you know, I joke that a lot of times, you know, companies make money by accident.
1:24:50
Like they don't actually know what their most profitable product is, and often they're trying to sell the thing that isn't isn't actually the most profitable, and actually not selling the thing that actually is profitable.
1:24:58
And it comes back to how they've modeled the data to aggregate it up to KPIs and other metrics when you actually need to model at the finest grain how your business operates to get a true cost of goods sold, for example, or true cost to serve.
1:25:10
Like you that's very complicated. It's very complex.
1:25:12
So, like we really think about how do I embrace that complexity so that I can truly understand tactically at the edge what how do I do more of the things that are good and less of the bad. It's that simple.
1:25:21
And those get peanut buttered across with KPIs and metrics, and people don't actually know how their business is often.
1:25:28
I can't tell you whether it's a a hundred million-dollar company or a fifty-billion-dollar company how many times I see this that they don't actually understand how they're making money at a fine grain. >> Yeah. >> Uh last question.
1:25:40
Uh is there a world in the future where a company gets created, let's say on Stripe Atlas, and the first account they sign up for other than that is, let's say, a Palantir.
1:25:51
>> Ooh, that's interesting. >> I I would love that.
1:25:53
And so, we do have a Palantir for Builders program.
1:25:54
We have small companies that There's people here that are two-person startups, you know, that are working in their attic in Canada.
1:26:01
I mean, like so it it is a literally um any size company can come work.
1:26:05
There's a free dev tier people can come build.
1:26:09
You actually There's actually a Shopify integration in Palantir.
1:26:11
You can go hook up to your Shopify and pull in Palantir.
1:26:14
There are people doing this.
1:26:14
Now, are we always great at selling it or telling the story? Sure. No.
1:26:17
But but there are companies doing this and I do think there's a day where it's going to be ubiquitous.
1:26:22
Because I also think, you know, there's some some guys here that have, you know, they hey, my my business is dying.
1:26:27
I was, you know, I was down 10% negative margin on on what I was selling.
1:26:32
And through using Palantir, I they watched our YouTube videos and they built it themselves and increased to 9 or 10% positive margin in 3 months. >> That's great.
1:26:41
>> And so like people can go do it.
1:26:41
I think that's the great American story is like how do we enable that and I think we'll get there.
1:26:46
Um it might take a little time. >> I love it.
1:26:48
Well, thank you so much for taking the time.
1:26:50
>> Great to catch up after having me. Great to see you. >> We will talk soon.
1:26:53
Uh our next guest is joining in just 15 minutes.
1:26:56
We're going to go back to the timeline.
1:26:57
First, I'm going to tell you about Figma agents meet the canvas.
1:26:59
Your AI agents can create now create and modify Figma files with design system context.
1:27:08
>> It's so crazy how many companies >> Yeah.
1:27:12
>> are their whole strategy is like we're going to hire guys >> Yeah.
1:27:16
>> and they're going to they're going to do stuff. >> Yeah.
1:27:18
>> He is he is he is the final boss of FDE.
1:27:21
>> What what what what drove the FDE meme?
1:27:23
Was it was it Palantir going public or was it >> was Palantir going parabolic. >> Maybe. Maybe.
1:27:28
Yeah, once once >> Yeah, because it just it just it just blew up.
1:27:33
>> Because before it was like, okay, yeah, successful company, but like no one really knows where the valuation's going.
1:27:37
Now it's like my my uncle just told me that he made a bunch of money and so I paid attention to this.
1:27:45
>> That, but also they had been banging the FDE drum. >> Yeah.
1:27:49
>> Or the and and getting the consulting >> Yeah, but people had earplugs in to the banging of the drum.
1:27:53
And the and the earplugs came out.
1:27:56
>> Yeah, but when you're when they were a 10 to 20 billion dollar company a lot of people could still convince themselves that they were right.
1:28:02
It's just a consulting business.
1:28:06
>> Yeah, exactly, but now it's more >> harder harder to ignore.
1:28:09
We covered this very briefly, um but but very excited for Joe Rogan to be hosting his you know >> It's rumored. This is a rumored leak.
1:28:17
It is not confirmed by any means yet, but >> the sound of it.
1:28:22
>> It would be it's a very different direction.
1:28:24
Uh >> Um This was a good post. I want to bring it up.
1:28:28
Buku Capital says it's really incredible the absolute AI garbage in all caps that people are comfortable sending to their co-workers and bosses.
1:28:36
There's a good chance productivity will actually decrease as AI adoption increases because everyone is busy waiting through AI slop.
1:28:42
I don't think I don't think it'll actually I don't think it'll actually get there, but I have had uh I have had moments over the last month where somebody has sent me you know a deck for their company or materials and I can tell that uh 90% of the work that went into it was on prompting.
1:29:04
And uh I have a very like visceral reaction toward it.
1:29:06
Especially for like early stage companies where uh ideas and the way in which you go about doing things matter so much that uh it's almost like you know painting this initial vision and things like your your go-to-market um product differentiation, why you'll actually win.
1:29:26
Like use AI to make your team slide, that's great, right?
1:29:29
Just taking like a set of facts and making it look good, right?
1:29:33
You're giving somebody a bio, something like that.
1:29:38
Um but I just remember I I I got this deck.
1:29:40
I was clicking through it um and I very uh respectfully said like go and like do this yourself because uh just because you've made something that that looks like a deck >> Yeah.
1:29:57
>> but you didn't do the sort of like fundamental work to actually present this in a way.
1:30:03
If you looked at each slide individually >> Yeah.
1:30:06
>> Your eyes kind of glaze over. >> Yeah.
1:30:08
>> And you and you just sort of like lose focus, stop paying attention to it.
1:30:11
>> it would have been more It would have been more compelling to actually just have a bulleted list of like problem >> I mean, a lot of times you can just say, "Hey, just send me the prompt cuz I can instantiate it in my head.
1:30:19
I can imagine the rest of the paragraphs.
1:30:21
I I have the context window preloaded >> Yeah. >> uh for for myself. Yeah.
1:30:25
Uh we should talk about the new Audi, the Nuvolari. Is this real? Motor 1. This seems real. >> It's real.
1:30:34
>> Uh It's >> big It's the brand's first supercar since the R8.
1:30:37
Twin-turbocharged 4-liter V8 hybrid. 217 mph top speed.
1:30:45
That is 10% faster than a Cayenne Turbo GT.
1:30:48
What is the Cayenne Turbo GT market doing right now? Is it tanking?
1:30:52
Depreciation must be just through the roof on this news because you have a car that's 10% faster.
1:30:55
And so what Every Everyone was going to be rotating out >> I mean, I I I I think they did it.
1:31:00
I I think the Nuvolari >> It's a really cool design.
1:31:03
It's >> Uh feels like somewhat Cybertruck-inspired.
1:31:08
>> Cybertruck-y, futuristic, I don't know.
1:31:10
It just checks the box for like the next supercar for me and uh in a way that the first >> Oh, can't touch the R8? Okay.
1:31:17
Okay, well, it goes 0 to 60 in 2. 6 seconds.
1:31:21
>> Uh well, >> Almost 1,000 horsepower.
1:31:22
Let me tell you about Cisco, critical infrastructure for the AI era, unlock seamless real-time experiences and new value with Cisco.
1:31:31
And our next guest, Sam Berry, is here for the USDA. Welcome to the show.
1:31:34
>> What's going >> How you doing? >> Very good. >> Good to meet you.
1:31:36
Thank you so much for coming on down.
1:31:37
Let's throw this on and just like that. >> Cool. >> On the left side. >> It's good.
1:31:43
>> Introduce yourself a little bit. Tell us about yourself. >> All right.
1:31:45
Yeah, my name is Sam Berry.
1:31:45
I am uh proud to be working at the USDA. >> What do you do there? >> Right now?
1:31:51
I'm the chief >> Nominative determinism.
1:31:52
[laughter] Do you know about nominative determinism? >> No.
1:31:57
>> It's the idea that you you know a person's name could possibly influence or or uh the the but but Berry and working at the Department of Agriculture is like pretty perfect. >> Yeah. No, it's incredible.
1:32:08
Actually, my the Berrys came over here from France in like 1640. >> Woah.
1:32:14
>> So, we've been here for a long time. >> That's crazy.
1:32:16
>> And it was all farmers. >> Yeah. >> There you go. Yeah, yeah, yeah.
1:32:19
>> That was like all farmers up until my grandpa.
1:32:21
Then he became a materials engineer actually and worked on jet engines. >> Okay.
1:32:27
>> And so then his sons became engineers.
1:32:29
My dad became an engineer and then I was an engineer.
1:32:31
So, we're kind of >> Okay.
1:32:32
>> trying to bring the two together. >> go. Back to the USDA. >> Yeah.
1:32:35
>> What wait What is the shape of the USDA?
1:32:37
Like what what is the shape of the organization? Headquarters?
1:32:38
Do you go into the office? Is this you know, US? You think just America? International footprint?
1:32:44
Like do you travel for work?
1:32:46
What what's it like working there?
1:32:48
>> Well, actually, it'd be kind of interesting to ask you what you think like what are the things that you think USDA does?
1:32:53
>> Uh they grade the milk in the states. >> Yeah. Okay.
1:32:56
>> That's what I think about it.
1:32:56
So, I imagine that at some point farmers send the cows to you and you kind of inspect them and say this is a good cow. Is that what happens? >> I don't know.
1:33:06
>> Um there's like inspect there's inspectors.
1:33:08
There's a whole area that does it and like >> like a series of certifications, but but is but what what else is happening?
1:33:15
>> So, all kinds of stuff.
1:33:15
So, do you know that like food stamps? >> Yeah.
1:33:18
>> It's SNAP is inside of USDA.
1:33:19
>> Oh, I didn't know that.
1:33:20
>> I didn't know that either.
1:33:20
I thought I figured it was like AJ just or something, but yeah, it's in USDA.
1:33:25
>> So, that's a hundred billion dollars a year. It's kind of a big deal. >> Yeah.
1:33:28
>> Um so we do we have SNAP that's in the Food Nutrition Service.
1:33:31
Uh Forest Service is inside of USDA. Is that crazy? >> Yeah, yeah.
1:33:36
>> Uh and then F Pack is like what you would really think that USDA it's like the farmer facing like >> Okay.
1:33:43
>> where farm programs are and where they do acreage reporting like the stuff I talked about today. >> Got it.
1:33:47
>> Um and there's rural development >> Okay. >> which is like loans.
1:33:49
It's like a bank basically.
1:33:52
They do loans for all kinds of things. >> Okay.
1:33:54
>> Um actually, in some of the reviews I came in on douche, and uh uh there's like beachfront hotels that are being funded out of RD.
1:34:00
So, there's like a lot of things that need to be cleaned up. >> Okay.
1:34:03
>> Um >> [laughter] >> Yeah, and then there's like food inspection service, and then there's actually a huge scientific arm. >> Yeah.
1:34:11
>> Uh that's inside of >> That makes sense.
1:34:12
Testing things and >> Yeah, like labs.
1:34:15
>> advancing different pesticides.
1:34:17
>> So, things that I mean, I actually become very passionate about it because I certainly didn't have an appreciation for I thought the same thing. It's like grading meat. >> Yeah.
1:34:27
>> Um But, like our we are so uniquely positioned as a country because of the fact that we can feed ourselves. >> Yeah.
1:34:34
>> And like that is not the case for a lot of a lot of countries.
1:34:38
>> Yeah, isn't America basically a net exporter of food, too?
1:34:39
You hear about this in the China debate all the time.
1:34:42
Oh, will they buy XYZ product from us as a retaliation?
1:34:45
And uh yeah, you just don't think about it, but uh >> Yeah, so like China can like minimally feed itself.
1:34:51
Like bare minimum, they could like keep itself alive. >> Yeah.
1:34:54
>> Um but, you know, they're getting like Like we just did a big deal with them to move a bunch of beef over there. >> Yeah.
1:35:01
>> Kind of got some negative press on that, so it's important to know it's uh I forget exactly what it's called, but it's like the parts of the cow that we don't eat here.
1:35:08
So, it's a little misleading to say like the amount that we're sending over there.
1:35:11
>> Also, all these trade deals are like very complex, and there's like six different moving parts.
1:35:14
We get batteries, or they get the chips, and like these are always like, you know, seven-part negotiations.
1:35:20
It's hard to look at anyone in isolation.
1:35:23
>> But, I mean, I think it's a little surprising that like food is actually >> Yeah. >> part of that.
1:35:26
I mean, and then even >> [clears throat] >> in warfare, uh like agriculture and the food supply is usually hit before anything like kinetic even happens, you know?
1:35:35
And then before even the world knows that it's warfare. >> Whoa. >> You know? >> Okay.
1:35:39
>> Uh because you can do that, and you can do things to, you know, >> Yeah.
1:35:43
>> a nation's food supply in the future.
1:35:45
And so, agriculture is like a really big deal. >> Sure. >> Um really important.
1:35:47
So, all this to tie back to I was I wanted to talk about the labs Cuz this is like a whole area inside of USDA, but we do all of these things like invest in figuring out.
1:35:57
So like personally I try to avoid like GMOs and we eat, you know, like we drink raw milk and we get our meat from a local farm.
1:36:04
Um but GMOs are actually really important. >> Yeah.
1:36:06
>> Because if we were hit with some kind of adverse event or something and we needed to create corn that would could survive a drought better, like we have the science and the research to be able to do that. >> Got it.
1:36:17
>> And it's a huge edge that we have like geopolitically. >> Interesting. Yeah.
1:36:22
>> Yeah, talk about over the years I've read so many stories of, you know, this this insect has been detected in, you know, some region of the US and there's speculation on is it, you know, kind of foreign interference, things like that.
1:36:36
Is that is that in USDA domain is trying to help monitor and track and make sure that um >> pests >> Yeah, pests pests like pests are obviously naturally occurring, right?
1:36:47
They can flourish for their own reasons.
1:36:50
Or there can be some some sort of malicious intent as well.
1:36:56
Is that in your guys' >> Yeah, cuz they're not necessarily naturally occurring, right? >> Yeah.
1:36:59
>> 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 >> Yeah, yeah.
1:37:06
>> that's coming up through Mexico. >> Oh, interesting.
1:37:08
>> So our secretary, which by the way, I couldn't say enough good things about Secretary Rollins.
1:37:11
I mean, she's incredible.
1:37:12
Just an actual like genuine good like it's unbelievable what she's able to accomplish.
1:37:17
Uh but new world screw worm is something that's falling in USDA's you know, responsibilities.
1:37:23
And this is like a parasite basically that's coming up through Mexico and it's like a flesh-eating parasite.
1:37:28
So it's like really hardcore. >> Yeah.
1:37:30
>> So we're developing a lab >> I know.
1:37:35
>> No, but you know, I don't think you want to be around it.
1:37:36
But no, it's for like cattle mostly is what it impacts.
1:37:40
And so we're developing a lab and like sterilizing flies.
1:37:42
Which again, like personally, I don't really like any of this stuff.
1:37:46
But it's better to be doing this and be able to protect our nation than like if we let this could just come and flourish in our country and it'd be very detrimental. So I have to go back.
1:37:56
>> And and if we and if it's a necessary, you know, technique that needs to be harnessed, it needs to be harnessed securely.
1:38:02
It needs to be harnessed with you know, the right teams in place to make sure that whatever's rolled out is rolled out effectively and safely, right?
1:38:09
>> Yeah, I mean I think it's just so important, you know, there's uh like tech there's so much farther we can go with technology, but we have so much right now and so many people are just black-pilled, right?
1:38:21
And I think it's important I think you should be like black-pilled on certain things, but you should probably take a lot of pills.
1:38:25
Like you should be red-pilled and black-pilled [laughter] and white-pilled at the same time.
1:38:26
Uh 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.
1:38:36
Like this is the most incredible country on Earth and other countries are advancing though, you know, our edge is like our edge doesn't come >> for free.
1:38:45
>> No, we got to work at it.
1:38:46
>> We got to keep pushing at these things, but when we do this, like when there's a parasite that's you know, coming into our country and we're able to just like use biology to combat it, that's incredible that our country can do that.
1:38:57
>> Talk about these more SMB scale farmers and their approach to technology.
1:39:05
Uh I think a lot of people would be surprised at how much uh how much these individuals at least from from what I've experienced or or happy to lean in to technology.
1:39:12
I met uh a group in Texas that had developed this was years ago, so pre-AI boom, developed their own SAS product to help manage their operations.
1:39:23
Like a a a tool that that they had built by discovering problems that they had uh on their property and I just thought that was um that was really fascinating and and cool at the time because I think Silicon Valley would have maybe some expectation uh that uh that there might be an aversion to that until you get into the more like enterprise grade scale. >> Yeah.
1:39:49
I think it's a really important topic because you're essentially talking about like democratizing access to technology, right? >> Yeah.
1:39:55
>> And certainly with with like AI becoming so much more widely available, that was a big step forward.
1:40:00
But I mean and 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 uh if you're not deploying them in the right way with the right like data boundaries, right?
1:40:14
So uh you know, I think that's something that we're seeing even in our universities.
1:40:18
We do a lot of university research.
1:40:18
And like all the, you know, kids or whatever they the university students, like they're wanting to do experiments with LLMs and do like meat grading like better meat grading cuz that's something that can happen at the farms.
1:40:29
And if you can make that automated, then you know, our ability to produce beef, you know, is greatly impacted.
1:40:35
Uh but there's a major issue in succession planning right now for farms, right?
1:40:42
Like this is a big thing that's happening like the farmer generation is getting very old and kids don't want to go and and run the farm.
1:40:48
>> of them went to big cities and got jobs and white-collar work and stuff.
1:40:51
>> So you know, this is a big thing that is a H-2A.
1:40:53
Yeah, you know, these H-2A visas where like a lot of the farmers are actually still saying like we need the help from you know, we need immigrants to come and help us.
1:41:00
And you know, the best way that we can solve that is through automation.
1:41:03
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.
1:41:12
I don't have an answer for you right now, but in order for us to continue to, you know, remain self-sufficient in providing food.
1:41:18
>> you have a dwindling workforce, increasing the leverage and productivity of the existing workforce allows you to maintain overall aggregate productivity.
1:41:26
This is general technological leverage, so it makes a ton of sense.
1:41:29
>> Do you know anybody that's becoming a farmer?
1:41:31
>> Uh well, we know some folks.
1:41:31
>> Uh well, we know some folks. We've had a number of entrepreneurs on the show who are getting into ag tech and building we've had the founder of the laser weeder that uses a lot of people don't like pesticides but they don't mind if if a pest is zapped with a laser because
1:41:47
that's just heat that's being transferred to the particular plant right there and the tomato plant continues flourishing so he uses just cameras and lasers very cool sort of modern solution to something that people have had a lot of fear around about different pesticides >> a truck fruit picking robotics company. >> Yeah orchard as well
1:42:04
>> Yeah orchard as well but but mostly from tech side usually with some family lineage sort of returning to the roots or 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 farm activity I knew one family that had an avocado farm.
1:42:23
>> I mean it actually it would be super based to be a large scale farmer like more people should do it and maybe you could be the Alex Hormozi of farming. >> Yeah.
1:42:31
>> No for real I mean you can so USDA one of the great things that USDA does is you can get financial assistance like you get big time like big time loans from USDA you have to go through the process and they're actually doing a loan modernization effort right now trying to make that better but like USDA can will fund it for you you got to pay it back but you can like >> get the interest rate super low.
1:42:48
>> low rate it's subsidized yeah.
1:42:49
>> Yeah I mean one of the administrators USDA he would like pull up his phone one day and he's like look it's a planting day for me and it was his John Deere app it's like the most advanced like he had all these tractors going and there's
1:43:01
still people sitting in the tractors but it's to the point where it basically could be fully automated so I mean you 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. >> Talk about data collection I feel like
1:43:13
>> Talk about data collection I feel like data is the lifeblood of you know any decision making any UDA loop anything related to Palantir USDA and I'm wondering about like you mentioned that screwworm you got to track that thing it shows up on some cattle ranchers farm and they're detecting it or they're seeing symptoms.
1:43:33
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.
1:43:41
gov/reportincident or are you pulling things from their filings?
1:43:46
Like how how do you want that to evolve?
1:43:49
I imagine that that with more AI and technology, we're it's only as good as the data that we can actually put into the system.
1:43:57
So just broadly data collection, where is that going these days?
1:44:01
>> Well, if you don't mind instead of screw them, I'd like to focus on SNAP for that question. >> Yeah.
1:44:04
>> So, SNAP is funded by the federal government, but it's administered by the states. >> Okay.
1:44:09
>> So, uh when it comes to So, something that we're doing right now and it was one of the first things that our secretary did like on our first day was she did a data call to all the states that, you know, we want all of your SNAP data to understand how cuz it's our responsibility as the funder of this program to understand [snorts] the integrity like to verify the integrity of the program. >> Yeah.
1:44:26
>> So, we put a request out there, but it has to come from every single state. >> Yeah.
1:44:29
>> 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.
1:44:34
Uh but then there's also a bunch of states that are just not complying, you know, for whatever reason, which it shouldn't be a problem.
1:44:39
I don't understand what the problem is.
1:44:40
Um but the importance of So, that program, that's a hundred billion taxpayer dollars a year.
1:44:46
Like that's pretty substantial.
1:44:47
Um that's an area where we really want to have all angles of the data available so that we can deploy AI and become really smart in detecting fraud.
1:44:57
Like we want to get it to the point where um if somebody's committing SNAP fraud, we should be able to It's like your card, right?
1:45:02
If you if if somebody stole your card and did a transaction that wasn't recognized, like your card's shut off, right?
1:45:07
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.
1:45:13
When there's fraud detected, it's off immediately.
1:45:16
Uh because it's an important program, you know, we want to be able to support people that can't support themselves, but it's uh it's not arguable that there's is massive amount of fraud in there.
1:45:25
I mean, even the uh the organization itself does like an audit every year and they're at like there's 12% improper payments.
1:45:34
Improper payments is kind of a bad word. >> 12 billion a year. >> Yeah, right.
1:45:38
And that's just like kind of based on samples.
1:45:41
>> that could actually be going towards the intent of the program, which is to provide food to people that otherwise would be able to get it.
1:45:51
>> And there's other, you know, you could like grok how the SNAP has been used to fund like international crime organizations and like terrorist groups and everything.
1:45:59
So, it's it's being exploited at a at a huge level.
1:46:00
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.
1:46:11
But, the SNAP challenge is like the biggest or like the the SNAP environment is probably the biggest challenge on the data front. >> Mhm. What's next for you?
1:46:25
You making a career out of this or you going to go be a farmer? >> Hopefully both. >> Okay. >> Yeah.
1:46:29
Yeah, I mean um Yeah, I've got some farmland.
1:46:33
Trying to convert it into farm.
1:46:33
It's like woods right now. But, um uh yeah. >> Where is that? >> Uh in Virginia.
1:46:37
So, actually when I lived in Michigan, we had like a little bit of a farm.
1:46:41
Had some goats and sheep and chi- a bunch of chickens and and ducks.
1:46:46
Um now, you don't ever want to get You don't want to get ducks.
1:46:47
You don't want to get goats.
1:46:49
Um ducks are like really savage, actually. Yeah.
1:46:51
Like a chicken sleeps, you know? >> Yeah.
1:46:54
>> it's got a normal cycle.
1:46:54
Like at night time it goes into the coop and it like sleeps. >> Ducks don't sleep.
1:46:59
>> No, ducks do not sleep.
1:46:59
They like And our house was kind of this like really unique house.
1:47:03
So, the windows were like on the ground and the ducks would come and just stare at us in the window.
1:47:08
>> [laughter] >> No, they're savage.
1:47:09
They just like they sleep for like 10 minutes at a time.
1:47:10
So, they'll just like waddle around and then sleep for 10 minutes.
1:47:13
You have to have the right balance of female and male ducks.
1:47:16
Otherwise, it's like it's really ugly.
1:47:19
>> Yeah, chickens are a lot.
1:47:19
I grew I grew up with chickens and uh most of the time they're they're cool.
1:47:23
My dad would build these sort of like complex contraptions to automate the opening and closing.
1:47:30
>> So, he would use like irrigation to uh on a timer to fill a bucket which would lift there. It would lift it up. Yeah, yeah. >> interesting.
1:47:39
>> Um but but then I still core memories as a kid was waking up.
1:47:43
My dad would yell like, "There's a fox in the coop."
1:47:46
And then we'd be like running out.
1:47:48
We'd be you know >> would be like game on. Yeah, yeah.
1:47:50
Or you get like skunks [clears throat] in there and and uh >> Yeah, we would just ev- everybody would get up and >> [laughter] >> try to go deal with that. >> satisfying.
1:47:59
That's way more satisfying than some software bug.
1:48:02
>> There's so much uh fear and doom and and black pilling uh around data centers.
1:48:09
Uh I wanted to hear from you how your I imagine your your role is to be an advocate for for farmers as well on on on water supplies, things like that.
1:48:21
California went through, you know, probably many many really rough years from a from a water supply um on a water scarcity standpoint.
1:48:31
Thankfully, you know, we've had a lot of rains over the last few years, but how how are you working with farmers or or what is the situation around um the the the kind of like tension between a lot of farmland could also be great land for data centers, right?
1:48:47
And there's been some um uh pretty high-profile stories where farmers either sold their land.
1:48:52
But from your side, you're trying to make sure that we have uh you know, can produce an abundance of food, you know, from a from a national security standpoint.
1:48:59
So, how are you guys thinking about that balance?
1:49:02
>> 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 um train, but it's going to be a couple years it sounds like before they're to that point.
1:49:11
We're actually uh USDA is pursuing a a partnership with at Um and that that part isn't isn't ready yet.
1:49:18
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 cuz we do care about conservation.
1:49:26
Uh, you know, there's 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 uh, putting data centers in space just makes a ton of sense, but that being a couple years out.
1:49:37
So, for today, you know, I'm actually pretty passionate about this cuz in my hometown of uh, Saline, Michigan, it's like small town, mostly farmland, they're putting a data center in there.
1:49:47
And it's like, you know, 30 mi from Detroit and Flint and like all these very industrialized areas.
1:49:55
And so, it's very confusing to me why we wouldn't be putting these data center in their like struggling areas.
1:50:01
Detroit's doing all right, but like Flint, struggling big time.
1:50:04
Uh, like why not put a data center there where there's already the infrastructure, there's like it's already developed land, but instead it's like taking these small townships and and plopping them in the middle.
1:50:14
Um, and the people don't really like it.
1:50:16
Now, the boards seem to like it for some reason, the councils.
1:50:20
So, I don't know what's up with that.
1:50:22
Um, but it doesn't align with what the people want.
1:50:26
>> it creates a a massive amount of tax revenue that can be used to fund a bunch of other programs. >> Yeah.
1:50:31
>> But it's got to actually flow back to the people who are in the town.
1:50:33
And I think that there's like a disconnect there sometimes.
1:50:38
>> I Actually, this is kind of outside, but something that I do think is uh, probably going to happen is, you know, there was this big shift to go to >> the cloud. >> Right?
1:50:45
It's like everybody kind of had their own servers, you know, it's on prem and now we're in the cloud and it's like really you just took you like moved it across the street, right?
1:50:53
Um, 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.
1:51:09
So, 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.
1:51:15
Cuz like me personally, like I want to build like my house is like a like I'll have a kill switch for my Wi-Fi.
1:51:21
And then like we've got the data in the basement.
1:51:24
You got your raw milk supply. You got raw milk.
1:51:27
No, like we're ready to go.
1:51:27
I mean I I was ready to go off the grid before I came and joined the government.
1:51:31
[laughter] This is a much better option.
1:51:32
But um still like I care about my data.
1:51:33
I don't really want to use YouTube music anymore for my music cuz now my recommendations are getting worse and you're like very beholden to that.
1:51:39
It's like I could very easily just >> the music.
1:51:42
>> buy my music and write a simple program to like make my recommendations and it would be way better.
1:51:47
Because there's certain artists that are not getting recommended because they're not, you know, prioritized behind the scenes. >> payola or something.
1:51:53
>> So but not everybody's going to want to manage their own servers, right?
1:51:56
>> Jensen just announced a data center that bolts onto the side of your house. >> Oh, that's sweet.
1:52:00
>> And I mean and and there's more stuff that's coming that way.
1:52:02
I mean people are doing it with the Mac minis.
1:52:03
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.
1:52:12
And I think it will be more of an option.
1:52:14
>> So just to like kind of wrap this up.
1:52:14
So there's this um uh or this like topic.
1:52:19
The uh one of the things that USDA does is we pay 600,000 federal employees.
1:52:21
So like we pay Secret Service. We pay DHS.
1:52:26
We pay it's like it's like a thing inside of USDA. >> Interesting.
1:52:29
>> And so the the payroll system that does that is a mainframe.
1:52:31
And people literally explained it to me like this thing has a personality.
1:52:34
Like you have to [clears throat] like you can't touch it the wrong way.
1:52:37
You have to like it has to have like the right environment to work.
1:52:40
And it like all these things, I mean like a dozen people came to me to tell me these things.
1:52:43
So then I went and visited it.
1:52:45
I was like really excited to, you know, encounter this >> Yeah. Mainframe.
1:52:49
>> And uh it's like a uh 5-year-old brand new like IBM server.
1:52:56
You know, it's just like it's not there's no tapes.
1:52:57
There's not like a team of people working on it. It's like >> Yeah.
1:52:59
It's like, you know, it's like this big.
1:53:01
Um but I I was expecting like a >> [clears throat] >> like a small micro data center or something. >> Yeah, exactly. >> Yeah. No, so totally modern.
1:53:09
>> And it like I like formed this connection with it and I was like, we have had so many conversations about you.
1:53:13
And I just thought that like this is potentially a future where it's like a data center like coffee shop, you know, like people might want their data to be hosted in a place that's like aligned with their views. >> Sure, sure, yeah.
1:53:25
>> You know, cuz 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 cuz I don't need it distributed across the globe. It's like >> Yeah. >> I'm here. >> No, that makes sense. That's interesting. >> Country intelligence. >> Yeah, yeah, yeah. Country intelligence. This is the future. I love it. >> Anyways. Hey, great to meet you. Thanks for having me.
1:53:44
Thanks for doing this work. >> Thank you.
1:53:47
>> And we'll talk to you soon.
1:53:47
We will wrap up the show. >> Yeah.
1:53:50
>> Thank you for tuning in with us today, folks.
1:53:52
We will be back on Monday. >> Yes.
1:53:56
>> And we look forward to it.
1:53:57
>> Some business to do tomorrow, but see you Monday.
1:53:59
Leave us five stars on Apple Podcast and Spotify.
1:54:00
Sign up for the newsletter at tvpnl.
1:54:02
com and have a wonderful weekend. We'll see you later. Goodbye. >> Bye.