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You're surrounded by journals.
Hold your position.

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Overnight success.

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Double leg,

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>> right?

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That's misinformation.

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>> Clearing order inbound.

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>> Let's just roll.

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>> We are surrounded
by journals. Hold your

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position. >> Come, get up.

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>> Trust the experts.

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Five.

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We are

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founder.

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>> Five good.

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>> I see multiple journalists
on the horizon. Stand by.

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UAV online. [music]

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>> Blaze.

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Double blaze.

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Triple blaze.

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Double kill.

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Cook is

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team deathmatch.

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We are experts.

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Triple blades.

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Let's just roll. Right.

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Mark clearing order inbound.

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Get up.

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We are surrounded by journalists.
Hold your position.

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>> Strike one.

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>> Strike two. Activate

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golden retriever mode. [music]

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>> Mark clearing order inbound.

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>> Five good.

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I see multiple journalists
on the horizon. Stand by.

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founder.

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>> You're watching >>
You're watching TBPN.

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>> Today is Thursday, June 4th, 2026. We

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are live from Palunteer AIPCON. Uh
with the Temple of Technology is back in

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Fortress of Finance. We will return to it,
but it is also a state of mind. Um

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00:04:44,160 --> 00:04:47,677
that's right. We are also sponsored
by RAMP. Time is money. Save both. Easy to

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use corporate cards, bill pay,
accounting, and a whole lot more all in

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one place. Big news from RAMP today.

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Massive fundraising a little bit.
But first, we got to talk

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>> Oh, is it still going?
I like it. The Ramp song's back.

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>> This was This was early days. We really
talked about RAM so much. Turned it into

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a song. Anyway, uh the topic of

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conversation in DC has uh it's still in

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AI world, but instead of talking about
uh approving models before they're

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released today, it's about the bio threat.
Brandon Guerell wrote in the

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TBPN newsletter today, the great houses
of AI have united behind the bio threat.

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There's actually a lot more to that
because it was a big long list of

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signitories from AI, but also from the bio
world and biotech and even startups.

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We've seen former guests of the show
sign on. I'm excited to bring some of

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those folks back on the show in the
coming weeks and hear more about this

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because I have this belief that as AI

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advanced, we got cyber because it was such
a tight feedback loop, such a tight

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verifiable reward. Reinforcement
learning works really well in that

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context. Bio has some
similar characteristics.

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>> And it was a very tangible Y2K style

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moment. Exactly. Where there was it
was a let's just say powerful business

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strategy. >> Yeah. Is it Yeah. It was like
is it over? You start thinking about the

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consequence of this and you don't need
to get to AGI super intelligence god.

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You can just have a really powerful tool
that creates a new problem and that uh

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creates full employment for Nicashura
over at Palo Alto Networks who we had a

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chance to talk to yesterday and uh he's
been very fortunate in implementing the

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solutions to the cyber security threats
posed by new AI systems some of the new

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AI capabilities they're rolling out. Um,

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but bio might be next. And so it's

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exciting to see that uh the great houses

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of AI are uniting behind the bio threats.
So let's take you through this.

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First I'm going to tell you about
console.com. Console builds AI agents

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that automate 70% of IT HR and finance
support giving employees instant

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resolution for access requests
and password resets. So uh in 1981 a group

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of researchers published the primary
structure of the polio virus genome in

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the journal Nature. So they're basically

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open sourcing the sequence for making

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polio which just a few years earlier

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polio I think was on the decline by 1981
but uh a very very problematic virus.

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It's an RNA virus meaning that its

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nucleiobases or building blocks are ACGU

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if you're familiar with uh RNA adsine

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cytosine guanine and uricil. Uh put more

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plainly, thanks Brandon Guerell, uh he
says when the researchers published the

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primary structure of the polio virus,
they gave the world the literal sequence

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of polio virus building blocks in order
from start to finish. By the mid- 20th

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century, before mass vaccination,
polio was paralyzing and killing more than

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half a million people per year
worldwide. So you have this pretty

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deadly virus killing more than half
a million people per year worldwide. And

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you have just open sourced it.
What happens? So in 2002, researchers

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synthesized infectious polio virus from
its publicly available sequence data. So

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they didn't actually need any of the
polio virus RNA to start. They didn't

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need it on hand. They didn't need to.

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It's not like they took a little sample
and they just cloned it up and made it

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bigger. They they just took the data
and they made the actual virus. So this is

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the this is the shape of the threat.
If there's a new uh if there's a new virus

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or an existing virus or a forgotten
about virus and you have the code to it,

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you can potentially print that RNA and
then have the virus in your hands. Even

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if you don't have a sample, you weren't
able to kill a sample. So instead, these

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researchers in 2002, they were able
to take the published sequence, chemically

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synthesize short DNA fragments, assemble
them into full length a full length DNA

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copy of the polio virus genome, and then
use the DNA to make the viral RNA to

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fully recover the infectious virus.
So in 2005, researchers used these same

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technologies to reconstruct the Spanish

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flu, a virus in 1918 that killed 675,000

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Americans and had a 2 to 3% mortality
rate among those infected. Very, very

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dangerous stuff. So basically, these two
reconstructed viruses showed that having

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a physical virus on hand was no longer
necessary as source material to create

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viruses. All you needed was the
blueprints. as long as you have the code

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literally just like texted
in a text file a bunch of ATGU

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uh you can go and make this as long
as you have the equipment on hand but that

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is getting democratized as well
and that's what this uh AI letter is all

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about. So that's a situation that we're
still in today except now that we have

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AI there are easier ways to potentially

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reconstruct DNA sequences that could
create new viruses. So yesterday, Demis

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Hassabis, Sam Alman, Dario Amade, Alex
Wang, and dozens of other high-profile

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leaders across AI, tech policy, nucleic
acid synthesis, and biotech signed an

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open letter called in support
of mandatory nucleic acid synthesis,

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screening, and recordkeeping. You might
have seen it on the timeline. And at

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first glance, Brandon here assumed, and I
assumed the same thing, assumed it was

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another press release from a Frontier
Lab claiming it had just discovered new

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capabilities in one of its internal
models that would ultimately lead to

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cata catastrophe. A lot of this like
doom fear-based marketing has been

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happening. So, that was sort of the
natural reaction. Uh, and that's what

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Brandon >> Some people's
reaction would be, were we

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not doing recordkeeping here already?

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>> That's a great question and Brandon
actually did answer that. But, it's not

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just a PR stunt and it's not a new
capability. They're not saying that the

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models can just create a novel virus,

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you know, one shot like that that that
that is solved yet. Uh it's not there,

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but they see it as something that's
coming down the pipe. And this letter is

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not this dangerous new capability. It's
more asking the US government to force

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nucleic acid synthesis companies to

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screen orders for sequences of concern.

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So, hey, somebody just ordered this.

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Looks a lot like a virus. Like, what are
we doing here? uh you said that you were

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trying to treat cancer or you said that
you were, you know, trying to make a new

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peptide and all of a sudden you're
asking for polio virus or something that

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looks like polio virus. Like let's let's
dig into this. Uh that's where they're

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going with that. And so uh they also
need to verify the legit the legitimacy

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of the customer and to keep a record of
what they're sending and to whom. That's

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a crazy one that I'm sure you're like,
wait, they weren't keeping records. They

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were a little bit. Uh he gets into this.

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So he says the reason the letter
is coming out now is that the threat of

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nucleic acid synthesis sequences
sequencing getting into the wrong hands

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has been enhanced by AI. So anyone with
an AI tool in the future could in theory

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if the models don't have safeguards
on them could uh synthes could create a

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sequence that then they go to a nucleic
acid sequence company get printed send

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it to them mix it up boom they got
a virus not good. So uh most of the global

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nucleic acid synthesis industry has
already signed up to do some of this.

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They did they started this in 2009 with

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what's called the international gene

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synthesis consortium and roughly 80% of

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commercial synthesis capacity worldwide

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is on is is on board but membership >>
20% [laughter] still just hanging out

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we're good >> 80%
of nuclear weapons are safely

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stored. Don't ask about the other 20%.

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That's kind of what this letter is

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getting at because 80% it was a good

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first effort 2009 it's been 16 17 years.

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Uh we haven't had >> Yeah.
But there's a new reason to go

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further. Let's get that last 20%.
That's what they're asking for. So um

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>> me membership is not a strong guarantee
that they're actually screening or

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keeping records of their customers
because it's voluntary. The 80% number

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is also self-reported, for example,
and a bunch of other factors contribute to

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the relative climsiness of the agreement.

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>> So, it's not >> you can
opt into this program by just

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saying that you're opting into it,
but then even the reporting once you're

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opted in is voluntary. So I I think the
way this works is the international gene

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synthesis consortium is probably a

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nonprofit NGO you know non-governmental

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organization and every all the companies

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uh they they volunteer 80% of commercial

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synthesis volume has opted into this and

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then this organization the international
gene synthesis consortium they say hey

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we've looked at the market and we're
covering about 80% has opted into this

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we're we're on board with 80%
and and the government isn't coming in and

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checking the records. They're not
actually saying, "Okay, well, we we have

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a different number because we're
the government and you have your this

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number. Let's verify this number." It's
self-reported by that organization, but

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there's no reason not to trust that
organization necessarily. Um, so what

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else? >> Bunch of other
factors contribute to the

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relative flimsiness of this agreement.

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Uh, HHS also has guidance in place
around the issue, but again, it's

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voluntary, meaning that the possibility
of bad actors getting their hands on

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dangerous nucleic acid sequences,
at least from American companies, still

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cannot be ruled out. Overall, it's good

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to see industry leaders signing this

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letter and doubly refreshing uh that the

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letter is not yet another warning
of apocalyptic AI doom, which I think the

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public has unfortunately be come
to expect from announcements like this.

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Hopefully, the relevant legislators
are paying attention and can make this

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happen in short order. So, I thought
that was a good uh a good breakdown and

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I agree with a lot of that. Um Andrew
Curran also has some uh deep dive on

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this with some more uh of the signitories.
He shares screenshots of

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all of these and it really is everyone.

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Uh yeah, Y Combinator,

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DeepMind, Microsoft, Interconnect AI,

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Harvard, uh tons of stuff. And then over

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in uh in the nucleic acid synthesis

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industry, you have Twist Bioscience, uh

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ANZA, Emerald Cloud Lab, and Kathleen
McMahon from Boss is on here, former

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00:14:09,840 --> 00:14:14,797
guest of the show. >> Um so,
so uh good news, but uh obviously

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and just a early step. This is just an
open letter to the government saying hey

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we think you should we want to support
this we think that the government should

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start thinking about this.
Uh the other news in the bio world,

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>> yeah, I mean the news is just that

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there's incredible momentum in biotech,

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early stage biotech. Uh after >> momentum,
but not like volume, not scale

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yet because you're looking at $3

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trillion IPOs going out this year

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potentially. So much news in AI, microns

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out of trillion. Every chip stock is,
you know, in the hundreds of billions,

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trillions. This is much smaller, but >>
but it's notable because biotech had

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been uh left for dead
in some ways. We had a

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biotech investor on >> probably
14 months ago at this point who

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said I don't even know I mean just
looking at the returns so far I don't

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know why you would invest in this asset

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00:15:08,400 --> 00:15:13,357
class. Uh but of course every asset
class kind of go throughs go uh goes

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through that kind of phase
and clearly there's a lot of momentum

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>> and they should be you would expect
that biotech would be similarly power law

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driven maybe not as extreme but if you
pull out SpaceX open AI anthropic from

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capital >> yes but I feel like the biotech

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community has a little bit more of like

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a culture of like base hits doubles
triples where they flip companies pretty

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pretty frequently in the >> Yeah we had
that didn't we have a guy on that had sold

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like three companies. We didn't have
when we were billion dollar exits.

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>> Yep. And then he joined another company
and sold it for three billion like the

228
00:15:46,320 --> 00:15:47,837
next day. Uh and so

229
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>> so anyways, you have Isomorphic Labs

230
00:15:49,840 --> 00:15:53,677
spun out of uh Deep Mind uh Coinbase or

231
00:15:53,760 --> 00:15:58,637
not Coinbase, but but Brian spun out
or like founded New Limit. Uh you have

232
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Retro >> a new round. We're gonna get
Jacob on the show >> as well for that.

233
00:16:02,480 --> 00:16:05,197
>> Um Altos Labs uh

234
00:16:05,280 --> 00:16:08,477
>> from Jeff the Chad from Amazon. funny

235
00:16:08,560 --> 00:16:13,037
>> as this post puts it >> uh
Anthropic obviously acquired

236
00:16:13,120 --> 00:16:16,637
coefficient bio as well >>
but Jensen and Larry Ellison at Oracle

237
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are also doing stuff so there's a lot
of activity it's very fun and I hope we're

238
00:16:20,400 --> 00:16:22,077
going to be able to cover
this a lot more in the near

239
00:16:22,160 --> 00:16:26,557
>> future at what point do the

240
00:16:26,640 --> 00:16:28,797
uh at what point does like a Fizer or

241
00:16:28,880 --> 00:16:34,317
Johnson and John Johnson and Johnson
start joining the press release economy

242
00:16:34,400 --> 00:16:37,517
of just coming I'm not I'm not saying
it' be a good thing but coming coming

243
00:16:37,600 --> 00:16:42,317
out and saying, uh, we believe we're,
you know, right at [laughter] the

244
00:16:42,400 --> 00:16:45,837
because there are partnerships all the
time that happen and they're always just

245
00:16:45,920 --> 00:16:48,877
like tucked a little bit deeper
in the Wall Street Journal because AI is

246
00:16:48,960 --> 00:16:52,477
dominating and even private credit
takes the front seat to the bio news, but

247
00:16:52,560 --> 00:16:55,117
there there's a whole bunch of deal
making going on. Anyway, there's other

248
00:16:55,200 --> 00:16:58,317
deal making going on in fintech. We're
going to talk about ramps raised today.

249
00:16:58,400 --> 00:17:01,117
But first, I'm going to tell you about
railway. Railway is the all-in-one

250
00:17:01,200 --> 00:17:04,157
intelligent cloud provider.
Use your favorite agent to deploy web app

251
00:17:04,240 --> 00:17:07,197
servers, databases, and more.
while railway automatically takes care of

252
00:17:07,280 --> 00:17:11,917
scaling, monitoring and security. Um,
so ramp, >> what's going on in ramp land?

253
00:17:12,000 --> 00:17:14,157
>> 44 billion valuation.

254
00:17:14,240 --> 00:17:19,277
>> Really, really solid traction just, you
know, every 12 18 months, sometimes much

255
00:17:19,360 --> 00:17:23,597
quicker. Sometimes they do two rounds
in two weeks, but uh uh really solid

256
00:17:23,680 --> 00:17:26,237
progress. They raised $750 million at a

257
00:17:26,320 --> 00:17:32,397
$44 billion valuation. Last time we grew
this fast, we were 120th of the size. So

258
00:17:32,480 --> 00:17:35,517
accelerating. >> This is the
most notable thing to me. uh

259
00:17:35,600 --> 00:17:39,117
lots of chatter on the timeline around

260
00:17:39,200 --> 00:17:41,757
uh you know other fintech valuations.

261
00:17:41,840 --> 00:17:44,317
You compare them >> apocalypse.
Well, yeah. You compare them

262
00:17:44,400 --> 00:17:47,437
to you know ramp is now
worth more than PayPal.

263
00:17:47,520 --> 00:17:49,677
>> Okay. >> PayPal has
32 billion of revenue.

264
00:17:49,760 --> 00:17:52,157
>> Yeah. >> Uh but PayPal

265
00:17:52,240 --> 00:17:55,677
>> uh certainly has I
would say ne you know

266
00:17:55,760 --> 00:17:57,517
probably negative momentum. Yeah.

267
00:17:57,600 --> 00:18:01,997
>> Uh whereas RAMP has incredible momentum
and this this is the standout line. Uh

268
00:18:02,080 --> 00:18:06,637
they were 120th the size the last
time they were growing this fast and so

269
00:18:06,720 --> 00:18:10,317
>> um yeah just really really
really impressive execution. Yeah.

270
00:18:10,400 --> 00:18:12,477
>> Uh and

271
00:18:12,560 --> 00:18:14,637
incredible opportunity still.

272
00:18:14,720 --> 00:18:18,397
>> Yeah. Uh so Eric
uh took to the timeline

273
00:18:18,480 --> 00:18:21,117
posted an essay about the third pillar

274
00:18:21,200 --> 00:18:24,717
comparing uh the previous eras of value

275
00:18:24,800 --> 00:18:27,357
creation. The two pillars uh people and

276
00:18:27,440 --> 00:18:30,877
vendors dating back to 600 BCE. If

277
00:18:30,960 --> 00:18:32,477
you're not thinking in millennia, what

278
00:18:32,560 --> 00:18:35,037
are you doing here? Tokens emerged as

279
00:18:35,120 --> 00:18:37,997
the third pillar in 2026 AD. And he

280
00:18:38,080 --> 00:18:40,397
calls it the quadrillion token blind

281
00:18:40,480 --> 00:18:43,277
spot. Boil down 500 years of finance.

282
00:18:43,360 --> 00:18:45,437
And it's really just three questions.

283
00:18:45,520 --> 00:18:48,477
Who spent what? Was it worth it? What's

284
00:18:48,560 --> 00:18:53,357
the bill next month? I [laughter] mean,
people get caught up in all these crazy

285
00:18:53,440 --> 00:18:56,477
things. I mean, you see this in like
marketing, I'm sure, and uh and ad

286
00:18:56,560 --> 00:18:58,477
buying where people will do all these

287
00:18:58,560 --> 00:19:02,637
crazy analyses and ROI ROAS and all this

288
00:19:02,720 --> 00:19:06,557
other stuff and like and it's always
useful to zoom out and just be like,

289
00:19:06,640 --> 00:19:10,157
okay, we spent a bunch of money. Did the
bank balance go up in this company or not?

290
00:19:10,240 --> 00:19:12,557
>> All all personal and business finance

291
00:19:12,640 --> 00:19:17,197
finance at the end eventually comes down
to are we making more money than we're

292
00:19:17,280 --> 00:19:20,077
spending? >> Yeah.
And I think uh yeah, Eric is is

293
00:19:20,160 --> 00:19:24,637
right to dive super deep into like
token optimization and thinking about the

294
00:19:24,720 --> 00:19:27,757
tools that they're building, but then
at the same time like not not don't get

295
00:19:27,840 --> 00:19:31,837
lost in the sauce and like actually zoom
out and try and understand like what is

296
00:19:31,920 --> 00:19:33,837
the core value that you're delivering to

297
00:19:33,920 --> 00:19:38,957
your customer. Uh it is answering
that question. Um so fantastic news over

298
00:19:39,040 --> 00:19:41,917
there. Let me tell you about the New
York Stock Exchange. Want to change the

299
00:19:42,000 --> 00:19:46,397
world? Raise capital at the New York
Stock Exchange. You got to do it. It's

300
00:19:46,480 --> 00:19:48,317
my number one advice for founders these

301
00:19:48,400 --> 00:19:50,797
days. Um, uh, there's some other

302
00:19:50,880 --> 00:19:53,677
fundraising news. Sabi, the Beanie BCI

303
00:19:53,760 --> 00:19:58,637
company is getting preempted at 35
million at 500 million post. Uh, this is

304
00:19:58,720 --> 00:20:01,117
a leak from R for Rock.
We'll see where it goes.

305
00:20:01,200 --> 00:20:02,637
>> This is huge for you. >> Why?

306
00:20:02,720 --> 00:20:04,077
>> Because you are a beanie guy.

307
00:20:04,160 --> 00:20:07,597
>> I do like beanies.
>> A beanie in the morning.

308
00:20:07,680 --> 00:20:09,357
>> Keeps it together. Yeah, I like a

309
00:20:09,440 --> 00:20:11,277
beanie. Uh very very

310
00:20:11,360 --> 00:20:16,797
>> I it's interesting.
I think that uh this format

311
00:20:16,880 --> 00:20:20,557
>> Mhm. >> of course I'm sure they
can adapt it to other types of hats.

312
00:20:20,640 --> 00:20:23,117
>> Yeah. >> But this format certainly

313
00:20:23,200 --> 00:20:25,437
>> maybe makes it harder to build momentum

314
00:20:25,520 --> 00:20:30,157
in places like California,
at least Southern California, Arizona.

315
00:20:30,240 --> 00:20:31,837
>> Be big amongst
creative directors though.

316
00:20:31,920 --> 00:20:35,197
>> Yeah. >> Huge. >> Huge.
Huge potentially. Silver Lake.

317
00:20:35,280 --> 00:20:38,797
Silver Lake. Yeah. Every >> It's
not too hard to change a beanie

318
00:20:38,880 --> 00:20:42,717
into a hat. cowboy hat. Like that's just
extra leather around it. You could wrap

319
00:20:42,800 --> 00:20:44,717
the beanie in the in the
cowboy hat. You can wear

320
00:20:44,800 --> 00:20:46,877
>> Here's what's interesting
though. So R for Rock.

321
00:20:46,960 --> 00:20:50,957
>> Yeah. >> Uh usually
>> it's pretty dialed.

322
00:20:51,040 --> 00:20:52,397
>> Pretty dialed. >> Pretty dile.

323
00:20:52,480 --> 00:20:55,437
>> Uh pretty dial. It's almost like he
has inside information. [laughter] It's

324
00:20:55,520 --> 00:20:58,477
almost like he somehow got >> Yeah.
But I mean, we've talked about the

325
00:20:58,560 --> 00:21:02,637
game theory of like do does he work at
a real like tier one venture capital firm

326
00:21:02,720 --> 00:21:06,477
that's is like what's the benefit
of leaking everything? Is he a lawyer

327
00:21:06,560 --> 00:21:08,957
that's seeing all the docs turn around?

328
00:21:09,040 --> 00:21:10,797
Zero benefit for a lawyer, >> right?

329
00:21:10,880 --> 00:21:15,837
>> Um the rush of getting likes on the
timeline is pretty universal. You're a

330
00:21:15,920 --> 00:21:18,477
lawyer, you're just like,
"Ah, I need a banger."

331
00:21:18,560 --> 00:21:20,317
>> Out of fund. Okay. >> For sure.

332
00:21:20,400 --> 00:21:24,397
>> Yeah. >> And uh I
don't I don't know anything

333
00:21:24,480 --> 00:21:25,277
else.

334
00:21:25,360 --> 00:21:28,477
>> Um but uh he's always taken the view

335
00:21:28,560 --> 00:21:33,837
that it can be helpful to the founder
to build because a bunch of people are

336
00:21:33,920 --> 00:21:37,757
going to see this. >> Sure.
that that this didn't sort of land

337
00:21:37,840 --> 00:21:41,277
in their deal flow or land on their desk
and they're going to reach out, right?

338
00:21:41,360 --> 00:21:43,997
So, it does create momentum. Um,

339
00:21:44,080 --> 00:21:48,397
>> but uh can certainly be annoying
for teams as well. Uh, this was notable

340
00:21:48,480 --> 00:21:50,877
though. So, 200 million of of LOI from

341
00:21:50,960 --> 00:21:52,797
B2B customers

342
00:21:52,880 --> 00:21:57,597
>> and so very curious what the enterprise

343
00:21:57,680 --> 00:22:01,677
>> play is here. I don't know

344
00:22:01,760 --> 00:22:06,477
>> but uh we can work on getting a whole
>> Does that mean like uh like through

345
00:22:06,560 --> 00:22:08,557
hospital networks or through like the

346
00:22:08,640 --> 00:22:13,677
health care system or is it like Mark
Zuckerberg wants to go further? He wants

347
00:22:13,760 --> 00:22:17,677
to track the brain waves of the
employees not just track your screen.

348
00:22:17,760 --> 00:22:21,517
>> We're also going to track your brain.
I I mean it could go either way because

349
00:22:21,600 --> 00:22:23,837
you you imagine like Neurolink has had a

350
00:22:23,920 --> 00:22:25,837
bunch of traction and bunch of amazing I

351
00:22:25,920 --> 00:22:29,597
saw uh Nolan the uh the first patient P0

352
00:22:29,680 --> 00:22:34,797
uh on Rogan talking about playing COD
with the Neurolink. Amazing. Uh and you

353
00:22:34,880 --> 00:22:37,837
can imagine that at a certain
point like some sort of partnership.

354
00:22:37,920 --> 00:22:39,757
>> They have multiple hat form factors.

355
00:22:39,840 --> 00:22:43,197
>> There we go. Hats coming. >> We're
good. I was getting really hung up

356
00:22:43,280 --> 00:22:47,357
on the beanie. I'm like there's so many
different enterprise or or B2B contexts.

357
00:22:47,440 --> 00:22:51,437
You're in a You're in a warehouse
in Dallas, Texas in the summer.

358
00:22:51,520 --> 00:22:56,397
>> Yeah. You don't know. Maybe this maybe
this $200 million LOI is from REI or

359
00:22:56,480 --> 00:23:00,797
Patagonia. You know, you don't know who
makes beanies. Uh what's the Carheart?

360
00:23:00,880 --> 00:23:02,477
Carheart makes a great beanie.

361
00:23:02,560 --> 00:23:04,717
>> Go. You don't know any of this stuff.

362
00:23:04,800 --> 00:23:09,197
You're you're completely out to lunch.
I went through the beanie economy.

363
00:23:09,280 --> 00:23:12,317
>> Beanie economy. Anyway,
beanie market map. Work on it.

364
00:23:12,400 --> 00:23:15,757
>> Let me tell you about public.com.

365
00:23:15,840 --> 00:23:18,877
Public.com. investing for those who take
it seriously, stocks, options, bonds,

366
00:23:18,960 --> 00:23:22,397
crypto, treasuries, and more.
All with great customer service.

367
00:23:22,480 --> 00:23:23,997
>> They just launched a feature today that

368
00:23:24,080 --> 00:23:29,917
allows you to connect uh
your favorite chat app to public.

369
00:23:30,000 --> 00:23:32,957
>> Yes. >> And more
important than ever, because

370
00:23:33,040 --> 00:23:34,717
with public, you're going to be able to

371
00:23:34,800 --> 00:23:38,557
go and create the S&P 499 if you don't

372
00:23:38,640 --> 00:23:42,877
like SpaceX or the S&P 1 if you love
SpaceX. You can express your opinion

373
00:23:42,960 --> 00:23:45,917
about SpaceX however you want. Can you
please help me build an index for one

374
00:23:46,000 --> 00:23:49,677
company? >> Yes, index
for one company or index for

375
00:23:49,760 --> 00:23:52,237
everything but one company. Uh SpaceX is

376
00:23:52,320 --> 00:23:56,717
very uh divisive. People are extremely
optimistic in certain camps. Extremely

377
00:23:56,800 --> 00:24:00,797
pessimistic. >> Very optimistic.
Uh Goldman expects

378
00:24:00,880 --> 00:24:05,437
SpaceX's AI revenue to surge
a hundred times by 2030.

379
00:24:05,520 --> 00:24:09,197
>> Huge. >> Uh big big number.
I looked at this

380
00:24:09,280 --> 00:24:13,117
title and I was thinking like, okay,
what's Grock's actual revenue today? If

381
00:24:13,200 --> 00:24:16,717
you take out >> Yeah. X, >> what
what is their AI revenue today? Is

382
00:24:16,800 --> 00:24:18,717
it just Grock subscriptions plus GR

383
00:24:18,800 --> 00:24:20,557
Grock tokens? Do you include X

384
00:24:20,640 --> 00:24:26,557
subscriptions? Do you include uh cloud
vendor and and neo cloud contracts?

385
00:24:26,640 --> 00:24:29,197
There's a bunch of different ways
to measure it. The smaller the number, the

386
00:24:29,280 --> 00:24:32,157
easier it is to 100x. But we have seen

387
00:24:32,240 --> 00:24:34,557
other AI companies 100x revenues over

388
00:24:34,640 --> 00:24:36,397
two years, over three years, four years.

389
00:24:36,480 --> 00:24:41,037
Like the 100x has become,
it's not a one-of-a-one scenario.

390
00:24:41,120 --> 00:24:43,037
>> Yeah. >> It's happened
multiple times. And so uh

391
00:24:43,120 --> 00:24:45,197
we have seen these charts many times and

392
00:24:45,280 --> 00:24:48,637
uh if if they execute well there this is

393
00:24:48,720 --> 00:24:51,277
entirely possible. It is it is extremely

394
00:24:51,360 --> 00:24:54,317
>> other other notable data
points from the road show.

395
00:24:54,400 --> 00:24:57,357
>> Yeah.
>> They the forecast anticipate SpaceX

396
00:24:57,440 --> 00:24:59,677
making about 360 billion of capital

397
00:24:59,760 --> 00:25:03,277
expenditures through 2028. Uh Jensen

398
00:25:03,360 --> 00:25:06,157
somewhere fist pumping very excited

399
00:25:06,240 --> 00:25:09,597
about that number be a new hyperscaler.

400
00:25:09,680 --> 00:25:12,237
Um and uh anyways very

401
00:25:12,320 --> 00:25:14,717
>> should be unsurprising but very

402
00:25:14,800 --> 00:25:17,837
>> aggressive. Yeah. And um yeah the

403
00:25:17,920 --> 00:25:20,637
enterprise story is also live too. I saw

404
00:25:20,720 --> 00:25:23,117
the new Nvidia foundation model is also

405
00:25:23,200 --> 00:25:25,277
live. Uh we'll have to go check it out

406
00:25:25,360 --> 00:25:27,677
and look at the model card soon. See how

407
00:25:27,760 --> 00:25:32,637
it's benchmarking. But we got to move on
to benchmark because there's new news in

408
00:25:32,720 --> 00:25:36,317
the benchmark world. First I'm going
to tell you about MongoDB. What's the only

409
00:25:36,400 --> 00:25:40,397
thing faster than the AI market?
your business on MongoDB. Don't just build

410
00:25:40,480 --> 00:25:44,397
AI, own the data platform
that powers it. So,

411
00:25:44,480 --> 00:25:47,517
>> moment of silence.
>> Moment of silence. Why is that sense?

412
00:25:47,600 --> 00:25:50,637
>> Uh the the end of an era, the last
>> I guess I guess they have been very

413
00:25:50,720 --> 00:25:55,997
focused for decades. >> The last tier
one that was a pure venture capital.

414
00:25:56,080 --> 00:25:59,117
>> What did they get called again?
Internet boys or something.

415
00:25:59,200 --> 00:26:02,717
>> Oh, >> soft boys. [laughter] There was
there some book about them that was very

416
00:26:02,800 --> 00:26:08,397
funny. But e boys is a hit
piece of a book book title.

417
00:26:08,480 --> 00:26:11,197
>> It's a [laughter] fantastic book,
>> but the subtitle makes up makes up for

418
00:26:11,280 --> 00:26:13,997
it. And it's a fantastic book and it's
a very interesting story where they

419
00:26:14,080 --> 00:26:16,397
actually let a journalist come in and

420
00:26:16,480 --> 00:26:19,597
see how they true story of the six tall

421
00:26:19,680 --> 00:26:24,077
men. He clear he clearly
wrote the subtitle

422
00:26:24,160 --> 00:26:25,837
and was like, I got to take the edge off

423
00:26:25,920 --> 00:26:27,997
of this. It's too glazy. I got to I got

424
00:26:28,080 --> 00:26:33,517
to take it down a notch. And so he uh uh
he he threw the e boys in there. But uh

425
00:26:33,600 --> 00:26:36,877
>> anyways, [laughter] big moves from
Benchmark. Tate Clark has a scoop in the

426
00:26:36,960 --> 00:26:40,717
journal. Benchmark has raised >>
two billion across two new funds.

427
00:26:40,800 --> 00:26:45,357
>> Wow. >> And uh most notably
their first ever dedicated growth fund.

428
00:26:45,440 --> 00:26:47,437
>> H do they hire
anyone who has experience

429
00:26:47,520 --> 00:26:51,677
growth investing? Who could possibly
do growth investing there? Someone who's

430
00:26:51,760 --> 00:26:55,837
maybe like a bond capital and then
founders fund then maybe Kleiner. Like

431
00:26:55,920 --> 00:26:58,797
someone with that. Somebody with that kind
of background I think would be fantastic.

432
00:26:58,880 --> 00:27:00,957
>> Pretty good for growth investing.

433
00:27:01,040 --> 00:27:02,557
>> Now that you say that though.

434
00:27:02,640 --> 00:27:05,597
>> Yeah. >> Ev Randall.
>> Ev Randall. That's right.

435
00:27:05,680 --> 00:27:07,917
>> They did pick up Ed.
>> They did pick him up. They're almost

436
00:27:08,000 --> 00:27:09,917
thinking two steps ahead there.

437
00:27:10,000 --> 00:27:13,517
>> Are they building their fund strategy
now? Their entire platform strategy

438
00:27:13,600 --> 00:27:17,597
around Ed Randall >> potentially.
Potentially. Anyway, uh let

439
00:27:17,680 --> 00:27:20,797
me tell you about Shopify. Shopify
is the commerce platform that grows with

440
00:27:20,880 --> 00:27:23,997
your business, lets you sell in seconds,
online, in store, on mobile, on social,

441
00:27:24,080 --> 00:27:26,077
on marketplaces, and now with AI agents.

442
00:27:26,160 --> 00:27:27,757
And we are very fortunate to be joined

443
00:27:27,840 --> 00:27:30,797
by Alex Karp in just a minute. He's

444
00:27:30,880 --> 00:27:35,837
coming in to speak with us at AIP Con
here. We're going to bring him in in

445
00:27:35,920 --> 00:27:39,357
just a minute. >> While we
wait, Austin based podcaster

446
00:27:39,440 --> 00:27:43,277
Joe Rogan reportedly being
considered for >> 60 Minutes.

447
00:27:43,360 --> 00:27:48,077
>> 60 Minutes. Uh, looks like >>
they're gonna have to call it 200 minutes

448
00:27:48,160 --> 00:27:51,037
because he records long podcasts
in 60 minutes isn't enough for him.

449
00:27:51,120 --> 00:27:55,117
>> Hundreds. It'll just be called >>
Barry, if you're listening, put us in.

450
00:27:55,200 --> 00:27:57,197
Put us in the ring. We're ready to go.

451
00:27:57,280 --> 00:28:00,317
You need tech, business correspondent,

452
00:28:00,400 --> 00:28:04,637
someone who can just chop it up for 60
minutes. We do 60 minutes three times a

453
00:28:04,720 --> 00:28:08,637
day. We're ready to go. This is going
to be light work for us. Barry,

454
00:28:08,720 --> 00:28:11,597
>> I'm ready. I'm ready. You could do
60 minutes right now. You could do 60

455
00:28:11,680 --> 00:28:15,357
minutes tomorrow. You could do 60 minutes.
You could do an extra 60 minutes easily

456
00:28:15,440 --> 00:28:18,157
>> when you're putting up. We're putting
up a thousand minutes a week. It's no

457
00:28:18,240 --> 00:28:20,477
problem. >> We did consider
that at one point early

458
00:28:20,560 --> 00:28:23,917
on. We thought, should we
do basically a morning show?

459
00:28:24,000 --> 00:28:26,877
>> Oh, yeah. >> Take a two-hour break
and come back and do another show.

460
00:28:26,960 --> 00:28:30,717
>> Late night show. Yeah, late night show,
maybe. Anyway, we have Alex Garp here

461
00:28:30,800 --> 00:28:33,677
with us. >> Welcome to the show.
Welcome back. Thank

462
00:28:33,760 --> 00:28:37,117
you so much for taking the time.
Uh we're going to have you grab these

463
00:28:37,200 --> 00:28:40,157
headset. These headphones,
right? Not these.

464
00:28:40,240 --> 00:28:45,197
>> You can sit close. Get in here. We got
>> We liked it last time. The three of us

465
00:28:45,280 --> 00:28:48,397
were sitting here. >> No,
we can. We can put the giant.

466
00:28:48,480 --> 00:28:50,317
>> Let's put this up here.
Right here. I'll

467
00:28:50,400 --> 00:28:52,237
sit. You can stand. This always works.

468
00:28:52,320 --> 00:28:53,837
Makes me feel Yeah. Get in here close.

469
00:28:53,920 --> 00:28:57,757
This is good. Feel good. Okay. Feel good.
Uh, how is it going? How is AIPCON

470
00:28:57,840 --> 00:29:00,077
this time around? What's changed?

471
00:29:00,160 --> 00:29:04,477
>> Um, well, we we're in a phase. All each
one of these things like marks a time.

472
00:29:04,560 --> 00:29:08,157
First of all, you guys are even
more baller, more successful.

473
00:29:08,240 --> 00:29:09,837
>> Thank you.
>> Some tendies in your pocket.

474
00:29:09,920 --> 00:29:12,797
>> I think it might have been been part
two. We got to say thank you. You know,

475
00:29:12,880 --> 00:29:16,647
it's like blew us up. You're looking
you're looking bigger and stronger.

476
00:29:16,730 --> 00:29:19,757
[laughter] So, hey, are you more
attractive in your personal life now?

477
00:29:19,840 --> 00:29:23,437
Randomly. >> Well, here's what we're
actually focused on. Dead hangs.

478
00:29:23,520 --> 00:29:26,557
>> So, you came on last time.
You said your dead hangs around like five

479
00:29:26,640 --> 00:29:30,157
>> Oh, no. It's It's Well, it's plateaued
in the last couple months at 5:30.

480
00:29:30,240 --> 00:29:34,717
>> 5:30. Okay. So, we The thing is like
people are going to hear that. They're

481
00:29:34,800 --> 00:29:38,237
going to think hanging on a bar.
How hard could it be? You got to go and do

482
00:29:38,320 --> 00:29:39,837
it. The audience has to go try to do it.

483
00:29:39,920 --> 00:29:42,717
We've started doing it. We're still in the

484
00:29:42,800 --> 00:29:47,197
>> We're still between Yeah. between
and somewhere around a minute 30. You feel

485
00:29:47,280 --> 00:29:48,877
like your tendons are going to rip.

486
00:29:48,960 --> 00:29:52,237
>> 300 dead hang is respectable.

487
00:29:52,320 --> 00:29:54,237
>> Okay. >> 2 minutes is super elite.

488
00:29:54,320 --> 00:29:57,597
>> It does feel respectable when you have
the five minute number. You're looking

489
00:29:57,680 --> 00:30:01,117
at the [laughter] >> think
strength matters. No. Um the the

490
00:30:01,200 --> 00:30:04,557
the thing is I don't want to go into
rabbit hole in training. The single

491
00:30:04,640 --> 00:30:08,477
biggest mistake people make is they try
to hang every day. You need recovery.

492
00:30:08,560 --> 00:30:12,797
It's like anything else. So if you want
to mimic and get progress, you just do

493
00:30:12,880 --> 00:30:16,077
what I do, which is once a week you hang
as long as you can. Doesn't have to be

494
00:30:16,160 --> 00:30:18,477
super macho. And then that's your day.

495
00:30:18,560 --> 00:30:21,117
So like just say you can do
130, >> but multiple sets or

496
00:30:21,200 --> 00:30:23,677
>> No, no, no. One day a week
you do your maximum >> max.

497
00:30:23,760 --> 00:30:26,237
>> Wow. >> So like let's say
you could do two minutes. Okay.

498
00:30:26,320 --> 00:30:29,437
>> You try to do at least 1:30. You fight
to get to 1:30, but you don't fight to

499
00:30:29,520 --> 00:30:31,997
get to two minutes. Got it.
>> That's your dead hang day. And then you

500
00:30:32,080 --> 00:30:33,597
can basically around the next day.

501
00:30:33,680 --> 00:30:37,437
Do whatever you want. Don't overdo it,
but you could do two times one minute

502
00:30:37,520 --> 00:30:40,637
with a long break. >> Mhm.
And then can't you just go around

503
00:30:40,720 --> 00:30:44,557
do less and less and less two days
before if you two-minute dead hang you

504
00:30:44,640 --> 00:30:48,877
do like four times 15 seconds
the day before you take off

505
00:30:48,960 --> 00:30:52,877
>> and you do that just keep doing that
and you're dead. What the mistake people

506
00:30:52,960 --> 00:30:54,897
make is they hear my ball or time.

507
00:30:54,980 --> 00:30:59,277
[laughter] that guy.
I mean the mistake you're

508
00:30:59,360 --> 00:31:02,397
making is not doing a course. This could
be a whole new [laughter] revenue line.

509
00:31:02,480 --> 00:31:05,517
This could be a whole new revenue line
of >> pound. You guys have it more for you

510
00:31:05,600 --> 00:31:09,357
guys. be like, "Hey, call in." So, um,

511
00:31:09,440 --> 00:31:13,037
yeah, I mean, the dead hang is a it's
like and also some of it's just genetic.

512
00:31:13,120 --> 00:31:17,997
Like my other metrics are elite,
but that this is somehow alien territory.

513
00:31:18,080 --> 00:31:21,517
>> God-given gift. >> What about
uh what about breathold underwater?

514
00:31:21,600 --> 00:31:25,197
>> I don't do that. And I'm not sure I
have like I grew up swimming. I think I'm

515
00:31:25,280 --> 00:31:29,197
weaker at that. Like I bet you I'd
be in your guys range. [laughter]

516
00:31:29,280 --> 00:31:32,237
>> I I'm a dive master.
I can hold my breath for three minutes.

517
00:31:32,320 --> 00:31:35,277
>> And I'm just Yeah. I'm just Yeah.
So, I think you would be I think you'd be

518
00:31:35,360 --> 00:31:38,317
crushing me on that, honestly. But you
have like the lung capacity of a whale.

519
00:31:38,400 --> 00:31:40,637
That's true. I mean, like you got like

520
00:31:40,720 --> 00:31:45,437
I'm not moving. I'm not using. It's like
you're like like you're like a like a

521
00:31:45,520 --> 00:31:50,237
like a whale floating out [laughter] there
under the ocean waiting to surface. True.

522
00:31:50,320 --> 00:31:54,237
>> So, no, I say I'll tell you
the difference. Uh, God, they're always

523
00:31:54,320 --> 00:31:56,157
minding me out there. But like, okay,

524
00:31:56,240 --> 00:31:59,037
when we first met, it was like AI may be

525
00:31:59,120 --> 00:32:02,477
real. Okay. Then I would say somehow

526
00:32:02,560 --> 00:32:07,117
until about two weeks ago there
was like a holy this is real but

527
00:32:07,200 --> 00:32:09,997
>> somehow it's not working but we're not
allowed to say it publicly because we'll

528
00:32:10,080 --> 00:32:12,957
look stupid. Yeah. >> And then
there's a lot of investor hype.

529
00:32:13,040 --> 00:32:16,717
Yep. >> Um there still is like investors
printing attendees. So you have the

530
00:32:16,800 --> 00:32:20,637
investors on one side.
I think people realize it's real but

531
00:32:20,720 --> 00:32:24,477
>> you know it's like you know you
have the whole token maxing. Yep.

532
00:32:24,560 --> 00:32:26,797
>> And people are on to that and then so

533
00:32:26,880 --> 00:32:28,957
there's a whole value lecture there. Y

534
00:32:29,040 --> 00:32:33,197
>> um uh you have a political situation

535
00:32:33,280 --> 00:32:35,677
um where you know people who do not

536
00:32:35,760 --> 00:32:40,237
understand basic economics are winning
the political argument. So you can you

537
00:32:40,320 --> 00:32:43,437
could we could talk about where >>
there's a lot here. Let's break it let's

538
00:32:43,520 --> 00:32:46,797
break it up. Let's start with the token
maxing thing. Let's start with what's

539
00:32:46,880 --> 00:32:51,837
what's real. Uh how are you
actually thinking about deploying AI?

540
00:32:51,920 --> 00:32:55,677
>> First of all, what what is your what
is what is Palanteer's philosophy around

541
00:32:55,760 --> 00:32:58,797
token consumption? >> Well,
like we Okay. We have a product

542
00:32:58,880 --> 00:33:03,197
that will allow you to ba I mean
internally it's called something but

543
00:33:03,280 --> 00:33:08,157
externally but really we call it
the demastatory like get off masturbation

544
00:33:08,240 --> 00:33:12,477
thing internally. Sure. It's like people
are just like print like sitting there

545
00:33:12,560 --> 00:33:14,717
all day kind of like a porn addiction

546
00:33:14,800 --> 00:33:17,357
and enterprises are like okay we knew

547
00:33:17,440 --> 00:33:21,837
this we believe this will create value
but we cannot have people just like some

548
00:33:21,920 --> 00:33:24,237
people >> checking
the weather with it just like

549
00:33:24,320 --> 00:33:27,837
and just re rearranging deck
chairs on their personal Titanic

550
00:33:27,920 --> 00:33:30,957
>> literally like porn
like people are like full on

551
00:33:31,040 --> 00:33:34,957
>> it feels it feels so >> yeah
toolshaped objects tool-shaped

552
00:33:35,040 --> 00:33:38,477
objects you're looking at more than you
want you hope no one notices you're kind

553
00:33:38,560 --> 00:33:42,397
of It feels productive to have
every email classified with

554
00:33:42,480 --> 00:33:47,037
>> here's what it comes down to like
business problems can never be I mean

555
00:33:47,120 --> 00:33:50,957
sometimes they can be solved purely
with money and just spending more but very

556
00:33:51,040 --> 00:33:53,917
often >> actually I think it's the
opposite so just to give you a weird an

557
00:33:54,000 --> 00:33:56,957
>> no and I was going to say very very
often it's more the opposite where it's

558
00:33:57,040 --> 00:34:00,717
about figuring out the right way to do
something and then you can use capital

559
00:34:00,800 --> 00:34:04,317
to fuel that process but >> let
me give you a thing that's too

560
00:34:04,400 --> 00:34:09,197
generous for you guys >> okay
it it it's taste plus money.

561
00:34:09,280 --> 00:34:13,197
>> Okay. Yeah. >> And there
is no like AI like if you look

562
00:34:13,280 --> 00:34:14,477
at like pick any issue you want to talk

563
00:34:14,560 --> 00:34:18,157
about token mechanism maxim maxing uh

564
00:34:18,240 --> 00:34:20,477
what's going on with deploy codes are

565
00:34:20,560 --> 00:34:22,877
other people going to build ontologies

566
00:34:22,960 --> 00:34:25,597
why are why why does our political class

567
00:34:25,680 --> 00:34:30,237
not understand AI especially in Europe
it's like yes because all these things

568
00:34:30,320 --> 00:34:32,317
can be scaled in a very valuable but

569
00:34:32,400 --> 00:34:35,357
largely going to commodified way but you

570
00:34:35,440 --> 00:34:39,357
can't scale the taste of like what is
the business problem you want to have to

571
00:34:39,440 --> 00:34:43,277
solve and need to solve at the end
of the day whether it's in the for whether

572
00:34:43,360 --> 00:34:47,277
it's the Ukrainians fighting
the Israelis commercial entities it's

573
00:34:47,360 --> 00:34:50,877
there's somebody sitting there who's
like okay but this problem is valuable

574
00:34:50,960 --> 00:34:53,277
this problem isn't and once that value

575
00:34:53,360 --> 00:34:58,237
that problem is always has that problem
>> always almost always but not always has

576
00:34:58,320 --> 00:35:01,117
attributes so there are some problems
you could solve with this like I want to

577
00:35:01,200 --> 00:35:05,437
write a report on GDP growth in China
right okay but if it's a problem that

578
00:35:05,520 --> 00:35:09,757
requires a knowledge store like I want
to understand the specialized way I

579
00:35:09,840 --> 00:35:12,637
underwrite. We're going to have
a guest here. I want to understand the

580
00:35:12,720 --> 00:35:17,037
specialized way I drill for oil and gas
that's both legal, ethical, and reduces

581
00:35:17,120 --> 00:35:19,357
the cost of production. I want to change

582
00:35:19,440 --> 00:35:24,077
the the the supply chain of my industry,
whether that's military or whether

583
00:35:24,160 --> 00:35:25,597
that's building boxes or whether that's

584
00:35:25,680 --> 00:35:28,477
cars. These things require actual

585
00:35:28,560 --> 00:35:34,077
precise ongoing processes. They are
enhanced by large language models. They

586
00:35:34,160 --> 00:35:37,037
are not replaced by large language models.
And then you get to security

587
00:35:37,120 --> 00:35:39,277
issues like but for us like the whole

588
00:35:39,360 --> 00:35:41,117
myth of things is just a boon because

589
00:35:41,200 --> 00:35:44,077
like yeah we could take any model their

590
00:35:44,160 --> 00:35:46,397
model open AI model sure

591
00:35:46,480 --> 00:35:49,277
>> open model we can identify we can now

592
00:35:49,360 --> 00:35:51,597
identify uh vulnerabilities at like 10

593
00:35:51,680 --> 00:35:54,957
100x yeah but then who patches them

594
00:35:55,040 --> 00:35:58,877
>> how do you patch them on prem how
do you patch them on prem so that your

595
00:35:58,960 --> 00:36:02,957
specialized knowledge stays on prem like
if you're any business or Intel service

596
00:36:03,040 --> 00:36:06,717
a lot of these things are very similar
like you're not putting your classified

597
00:36:06,800 --> 00:36:11,117
data in a public cloud. Same thing if
you're like you have a special way of

598
00:36:11,200 --> 00:36:14,317
farming soybeans. >> Yeah. >> Well,
you're not. So, it's like how do

599
00:36:14,400 --> 00:36:19,837
you have how do you so all these
problems are exposed identified and then

600
00:36:19,920 --> 00:36:23,197
>> you always have a thing of where's
the charisma which people really

601
00:36:23,280 --> 00:36:25,437
underestimate and it's it's not global.

602
00:36:25,520 --> 00:36:29,197
There's no global charisma now. Like so
right now the large language models are

603
00:36:29,280 --> 00:36:33,517
very frontier companies are super
charismatic with investors. Mhm.

604
00:36:33,600 --> 00:36:38,397
>> I'll give you some news. They're >>
super not charismatic with enterprises

605
00:36:38,480 --> 00:36:41,277
and the people like in a way.
>> Even with enterprise.

606
00:36:41,360 --> 00:36:43,197
>> No, no. I mean, it's >> because
I understand what the people No,

607
00:36:43,280 --> 00:36:46,477
no. The enterprise people. I have
a secret. I have a secret like every

608
00:36:46,560 --> 00:36:49,917
company has a secret way of selling.
You know what my secret way of selling is?

609
00:36:50,000 --> 00:36:54,317
Don't even call. Don't Don't come talk
to us. There's a frontier company. Go

610
00:36:54,400 --> 00:36:57,917
spend two days with them. And if you're
lucky, after you're done, I'll let you

611
00:36:58,000 --> 00:36:59,917
in my door. They're like clamoring.

612
00:37:00,000 --> 00:37:03,357
They're like They're like, "Hey,
I'll take your bad brand." We have a great

613
00:37:03,440 --> 00:37:06,797
brand in enterprise, but like it it's
like it's like secret knowledge because

614
00:37:06,880 --> 00:37:09,517
the investors love this. They're like,
"Hey, my stocks are all up. Everything's

615
00:37:09,600 --> 00:37:11,037
up. I mean, Palunteer's done very well.

616
00:37:11,120 --> 00:37:14,557
We're like, but it's like and you know,
you guys are doing very well, I

617
00:37:14,640 --> 00:37:17,917
imagine." Right? And it's okay. We're
we're you know, we can But I'll tell you

618
00:37:18,000 --> 00:37:21,997
what, you go down the street, you talk
to a marine, you talk to a bus driver,

619
00:37:22,080 --> 00:37:25,997
you talk to the person who
owns the bus driving company,

620
00:37:26,080 --> 00:37:29,837
>> they are not happy. They do not like
these people. They're tired of people

621
00:37:29,920 --> 00:37:32,477
token maxing. Yeah. That it looks like

622
00:37:32,560 --> 00:37:37,677
masturbation at their that's cost them
money. They they're like And honestly,

623
00:37:37,760 --> 00:37:39,837
then you have something we're not
allowed to talk about in this country.

624
00:37:39,920 --> 00:37:43,037
Likability. >> Like a palunteer.
We have I think we

625
00:37:43,120 --> 00:37:46,637
have like 50 100 million global fans.
We have like 5 million people that wake up

626
00:37:46,720 --> 00:37:49,517
in the morning literally calling me Satan.

627
00:37:49,600 --> 00:37:53,997
>> I didn't know I had that kind of >>
warm hand. But uh you know, it's like

628
00:37:54,080 --> 00:37:55,517
that's what they believe. >> Yeah.

629
00:37:55,600 --> 00:37:57,437
>> And like and they really believe it.

630
00:37:57,520 --> 00:38:02,157
Okay. What people are not allowed
to really address is like we have fans and

631
00:38:02,240 --> 00:38:04,317
enemies. >> Yeah. >> Yeah.
These people polarizing.

632
00:38:04,400 --> 00:38:07,597
>> Yeah. We're polarizing which means both
sides. Yep. These people have one side.

633
00:38:07,680 --> 00:38:12,477
>> Yep. >> They're just It is. So it's
like you and it's like it's a really big

634
00:38:12,560 --> 00:38:14,157
>> social media companies
too have the same problem.

635
00:38:14,240 --> 00:38:16,237
>> Yeah. >> Everyone uses
them but no one likes them.

636
00:38:16,320 --> 00:38:19,757
>> Yeah. But but then they also live in
a circle and that circle's printing money.

637
00:38:19,840 --> 00:38:21,677
Yep. >> So it's like
you know when you look in

638
00:38:21,760 --> 00:38:23,917
the mirror and you just
printed a lot of money,

639
00:38:24,000 --> 00:38:26,237
>> you look pretty fresh. Is part of it is

640
00:38:26,320 --> 00:38:28,637
is part of it that that some element of

641
00:38:28,720 --> 00:38:32,797
the technology let's just say LLMs is so

642
00:38:32,880 --> 00:38:37,757
magical that the companies involved
that the companies that are making and

643
00:38:37,840 --> 00:38:41,037
selling frontier intelligence can be
bad at a bunch of other things and still

644
00:38:41,120 --> 00:38:44,877
grow >> well no no they
are magical at a certain

645
00:38:44,960 --> 00:38:49,357
kind of thing allowing you to write for
example code now that code doesn't can't

646
00:38:49,440 --> 00:38:52,637
be used as a knowledge store so if you
look at code in like three different

647
00:38:52,720 --> 00:38:55,437
ways like just using palent as a
model we have code That's basically

648
00:38:55,520 --> 00:38:58,077
infrastructure. So what what are the
Ukrainians using? What is the department

649
00:38:58,160 --> 00:39:01,917
of war using? What do a lot of our
enterprises? We call that primitives.

650
00:39:02,000 --> 00:39:05,597
It's basically hard-coded things that
that understand the world low way do you

651
00:39:05,680 --> 00:39:10,157
do? It would take millions of technical
hours and an understanding of all these

652
00:39:10,240 --> 00:39:13,997
enterprises to do it. So it's it's much
more like how do you build a steel beam?

653
00:39:14,080 --> 00:39:18,317
Then you have like code
that is written by FDAs.

654
00:39:18,400 --> 00:39:22,877
>> Okay. So that's kind of managed. It's
the reason why FDAs work. The secret is

655
00:39:22,960 --> 00:39:25,997
it's actually managed on something that
we as a product. So you're writing to a

656
00:39:26,080 --> 00:39:28,717
codebase. We're managing that. We're
increasing our product. It's not just

657
00:39:28,800 --> 00:39:32,397
random people write. Then you
have let's call it free code.

658
00:39:32,480 --> 00:39:36,797
>> That free code is that's magical. Like
you can do it very quickly. It's almost

659
00:39:36,880 --> 00:39:38,317
right. It doesn't have to be exact.

660
00:39:38,400 --> 00:39:41,517
>> Dashboards, >> dashboards,
financial stuff,

661
00:39:41,600 --> 00:39:44,637
>> probabilistic stuff where you
just have to get one off analysis.

662
00:39:44,720 --> 00:39:47,917
>> It was magical. >> By the way,
it's magical. It not only

663
00:39:48,000 --> 00:39:51,837
creates and it's magical in a way.
I know people don't like the porn thing,

664
00:39:51,920 --> 00:39:55,037
but it's also addicting. It's like,
you know, it's not good for you, but you

665
00:39:55,120 --> 00:39:57,197
know, it may lead to damage.
>> One more dashboard,

666
00:39:57,280 --> 00:39:59,597
>> one more time. It can't hurt that much.

667
00:39:59,680 --> 00:40:03,677
I know my doctor says it I shouldn't do
it, but it's like it's like that, right?

668
00:40:03,760 --> 00:40:07,277
And you just keep go and like and if
you're involved in the that thing,

669
00:40:07,360 --> 00:40:09,117
you're also making money. >> Yeah.

670
00:40:09,200 --> 00:40:10,957
>> And then last not least in certain

671
00:40:11,040 --> 00:40:13,597
circles like if you have you want to be

672
00:40:13,680 --> 00:40:18,557
a Russ researcher or you believe
essentially it's a religion. So like you

673
00:40:18,640 --> 00:40:21,517
know and like one of the things is very
charismatic especially to people who've

674
00:40:21,600 --> 00:40:24,637
never had a religion because all of a
sudden that hole in your heart that was

675
00:40:24,720 --> 00:40:26,877
yearning for I don't know I would say

676
00:40:26,960 --> 00:40:32,957
you know a a established religion Judaism,
Christianity, Islam is like being filled.

677
00:40:33,040 --> 00:40:35,517
>> Yeah. >> And and all
the answers are there but it

678
00:40:35,600 --> 00:40:38,397
it's very very successful at doing

679
00:40:38,480 --> 00:40:40,717
things that a company has to do.

680
00:40:40,800 --> 00:40:44,237
>> But it is not actually solving the

681
00:40:44,320 --> 00:40:47,837
problem that enterprises are. It is now
it can solve them. That's the trick.

682
00:40:47,920 --> 00:40:51,197
It's not It's not binary. It's not
like you can't say they're not valuing.

683
00:40:51,280 --> 00:40:53,037
They're totally putting our business on

684
00:40:53,120 --> 00:40:56,557
steroids. Like without LM, nobody would

685
00:40:56,640 --> 00:40:58,957
be talking about our ontology, about

686
00:40:59,040 --> 00:41:03,677
Apollo managing SEC exploits, about
our ability to manage an enterprise

687
00:41:03,760 --> 00:41:07,917
essentially as turning all these
companies into FDES. These deploy codes,

688
00:41:08,000 --> 00:41:10,557
we love them because now every company
wants a deploy code. You know how you do

689
00:41:10,640 --> 00:41:13,597
that? >> You replplatform
on Palunteer and like

690
00:41:13,680 --> 00:41:16,317
and it actually works. It's not
somebody with no taste, who's never done

691
00:41:16,400 --> 00:41:19,357
enterprise, who has no earthly clue how
these things work, who's done something

692
00:41:19,440 --> 00:41:22,237
else, and is like just imagining
they know how to do it, right?

693
00:41:22,320 --> 00:41:25,117
>> Yeah. Is part is part of this moment

694
00:41:25,200 --> 00:41:28,157
quite entertaining for you because

695
00:41:28,240 --> 00:41:30,477
you guys have been working on

696
00:41:30,560 --> 00:41:32,717
understanding businesses at a deep

697
00:41:32,800 --> 00:41:35,917
fundamental level creating

698
00:41:36,000 --> 00:41:37,997
you guys have effectively been doing the

699
00:41:38,080 --> 00:41:40,637
work that it that people are promising

700
00:41:40,720 --> 00:41:43,997
AI can do for 20 years now but actually

701
00:41:44,080 --> 00:41:46,717
doing it finding all the really rough

702
00:41:46,800 --> 00:41:52,317
finding all the really rough edges
and uh and not and and being at a point

703
00:41:52,400 --> 00:41:55,277
where you don't have to oversell
the technology. You can sell both things.

704
00:41:55,360 --> 00:42:00,397
But now there's maybe >> Here we go.
We got it together. Um, now

705
00:42:00,480 --> 00:42:03,677
there's maybe >> Oh,
it's the wrong side. That's why.

706
00:42:03,760 --> 00:42:07,837
Flip it around. >> The dyslexic.

707
00:42:07,920 --> 00:42:10,317
>> There you go. There you go.
Living the brand. Living the brand.

708
00:42:10,400 --> 00:42:12,637
>> We got that. I don't know.
All that stuff wasn't recording.

709
00:42:12,720 --> 00:42:17,117
>> I I trailed off. But uh but is part
of it entertaining to you that it feels

710
00:42:17,200 --> 00:42:18,957
like

711
00:42:19,040 --> 00:42:21,677
>> you know Palunteer has always been in

712
00:42:21,760 --> 00:42:24,637
some ways um not had competitors because

713
00:42:24,720 --> 00:42:27,437
there's nobody with Alex Karp running a

714
00:42:27,520 --> 00:42:29,517
company that is does what Palanteer does

715
00:42:29,600 --> 00:42:31,837
besides Palanteer. But at the same time,

716
00:42:31,920 --> 00:42:34,557
there's been tens of billions of dollars

717
00:42:34,640 --> 00:42:36,957
deployed now to effectively do what

718
00:42:37,040 --> 00:42:40,077
Palanteer does, but just selling the

719
00:42:40,160 --> 00:42:44,717
intelligence part, not selling all the
underlying kind of infrastructure that you

720
00:42:44,800 --> 00:42:47,357
>> Well, they're doing two things.
They're selling they're trying to sell the

721
00:42:47,440 --> 00:42:51,277
intelligence part and they're trying
to pretend if you just hire a bunch of

722
00:42:51,360 --> 00:42:52,397
people and let them run around their

723
00:42:52,480 --> 00:42:55,517
FDS. Now, the the very cool thing is

724
00:42:55,600 --> 00:42:58,717
when you've been in your basement doing

725
00:42:58,800 --> 00:43:03,277
your thing and everyone kind of views
it as the freak show, it it's really

726
00:43:03,360 --> 00:43:05,277
interesting and and and great to have

727
00:43:05,360 --> 00:43:08,637
adoption. The pretty ironic thing is

728
00:43:08,720 --> 00:43:10,637
half the people adopting now don't even

729
00:43:10,720 --> 00:43:12,797
know they're copying. But now the

730
00:43:12,880 --> 00:43:16,477
copying thing helps and hurts.

731
00:43:16,560 --> 00:43:19,757
Where it hurts is in the beginning
it puts clutter in the market.

732
00:43:19,840 --> 00:43:21,757
>> Yeah. >> Um and there's
there's no doubt about

733
00:43:21,840 --> 00:43:26,637
it. where it helps and then we saw this
with defense tech honestly. So like in

734
00:43:26,720 --> 00:43:28,397
defense tech, we were the only people.

735
00:43:28,480 --> 00:43:32,797
We were the first people despite what
it's I I love these honestly other

736
00:43:32,880 --> 00:43:36,157
podcasters. They're interviewing people
who are paring things I said 20 years

737
00:43:36,240 --> 00:43:39,437
ago. They don't know it and it's like oh
that's so insightful. It's like yeah of

738
00:43:39,520 --> 00:43:43,037
course it's insightful. Karp said it
25 years ago. Uh [laughter] and like but

739
00:43:43,120 --> 00:43:45,197
it's but so that kind of that part is

740
00:43:45,280 --> 00:43:48,957
super weird but uh and like but um but

741
00:43:49,040 --> 00:43:53,517
it's but what really happens when we see
is like it expands the market. So like

742
00:43:53,600 --> 00:43:57,837
in defense tech we would not be doing
this well in just purely in government

743
00:43:57,920 --> 00:44:02,557
unless there weren't 50 companies that
were doing similar things because then

744
00:44:02,640 --> 00:44:06,477
the people are like okay first of all
>> you view it as like offbalance sheet

745
00:44:06,560 --> 00:44:09,117
sales resources where
other people are basically

746
00:44:09,200 --> 00:44:14,157
>> well it's off well no that that's
the large it's they do two things they

747
00:44:14,240 --> 00:44:17,837
increase the size of the market
because de facto nobody wants to find it

748
00:44:17,920 --> 00:44:20,237
underwrite a market where
there's only one person >> sure

749
00:44:20,320 --> 00:44:24,157
>> so like if you're the one person the
percentage of the defense budget you can

750
00:44:24,240 --> 00:44:26,797
get is much smaller and two they set up

751
00:44:26,880 --> 00:44:31,997
a comparator. It's like you know you may
not like the freak show. Okay, if like

752
00:44:32,080 --> 00:44:36,077
but have you noticed the people who are
serious buy it and then then it changed

753
00:44:36,160 --> 00:44:37,757
and then three it changes the standard.

754
00:44:37,840 --> 00:44:41,677
Now what you're seeing now
is like that times 100x.

755
00:44:41,760 --> 00:44:43,917
>> Yeah. And it does
change like recruiting

756
00:44:44,000 --> 00:44:47,997
retention and like how you build
a company and we're we're always think you

757
00:44:48,080 --> 00:44:50,397
have to think about how to being
dyslexic huge advantage there because

758
00:44:50,480 --> 00:44:53,517
like you don't have a playbook and now
that need you need things to shift and

759
00:44:53,600 --> 00:44:55,757
we're doing that. Um [clears throat] the

760
00:44:55,840 --> 00:44:58,477
the the central thing though that is

761
00:44:58,560 --> 00:45:03,277
just cannot be developed even if you
understood the playbook. A lot of these

762
00:45:03,360 --> 00:45:07,197
things are like appear like it's like
you know LM code appears like pounder

763
00:45:07,280 --> 00:45:11,117
code but isn't for a deployed thing
appears like pounder it isn't ontology.

764
00:45:11,200 --> 00:45:15,117
You could theoretically copy parts of it,
but they're essentially structures

765
00:45:15,200 --> 00:45:19,357
that are built deep into organizations
that we own. And by the way, take you

766
00:45:19,440 --> 00:45:23,277
three years. And in three years,
we're in a completely different world. But

767
00:45:23,360 --> 00:45:27,757
there is this magical thing called taste.
Like in the end of the day, the

768
00:45:27,840 --> 00:45:31,277
reason why you guys have done so well,
it's of course there's aptitude and

769
00:45:31,360 --> 00:45:35,517
diligence and showing up and all those
things. Yeah. But you have to be able to

770
00:45:35,600 --> 00:45:39,597
differentiate between two people who
are in business, one of whom is saying

771
00:45:39,680 --> 00:45:43,517
something that sounds weird, that is
insightful, one of whom is pariting

772
00:45:43,600 --> 00:45:47,357
something that sounds weird and that's
all they're doing. And a lot of people,

773
00:45:47,440 --> 00:45:49,677
very few people can do that. And you

774
00:45:49,760 --> 00:45:51,917
have the same thing like the enterprises

775
00:45:52,000 --> 00:45:55,117
that succeed, there is a taste arbiter.

776
00:45:55,200 --> 00:45:59,757
And at Palunteer, we have taste art.
We have taste in every product, taste in

777
00:45:59,840 --> 00:46:03,677
every deployment, taste in every casting.
Who puts the people there? How

778
00:46:03,760 --> 00:46:06,717
do you put them there? How do you
organize the thing? Our ontology then

779
00:46:06,800 --> 00:46:10,397
does that technically. How do you manage
the whole org with taste? Who should be

780
00:46:10,480 --> 00:46:12,397
in charge? What data set should come in?

781
00:46:12,480 --> 00:46:15,677
What what are what are the ways in which
you protect? What is what should you

782
00:46:15,760 --> 00:46:19,677
push into the public crowd? What should
be on prem? What what should I mean

783
00:46:19,760 --> 00:46:24,477
leaving aside the law and like wars
or ethics? What do you want to protect?

784
00:46:24,560 --> 00:46:27,917
What should you protect? What should you
not protect? Because quite frankly, you

785
00:46:28,000 --> 00:46:31,597
want that to be out there so you can get
more data. All those things are arbited

786
00:46:31,680 --> 00:46:36,237
by taste. And then you have to have
the credibility of having taste. That's a

787
00:46:36,320 --> 00:46:39,197
real problem for a lot of these places
because they don't have they they're

788
00:46:39,280 --> 00:46:41,837
popular with their friends. They
don't they really don't understand how

789
00:46:41,920 --> 00:46:43,347
unpopular they are in enterprise.

790
00:46:43,430 --> 00:46:46,077
[laughter] Like they think it's like
oh yeah like the way I think I have a

791
00:46:46,160 --> 00:46:50,077
problem with like professors at Colombia.
It's like no it's a real

792
00:46:50,160 --> 00:46:52,637
problem. Like they think
I'm Satan and >> [laughter]

793
00:46:52,720 --> 00:46:55,997
>> uh you know it's like I I I think you
know we grew up in the same community.

794
00:46:56,080 --> 00:46:59,117
Let's talk about Haidiger. They're like
they don't want to talk about Haidiger.

795
00:46:59,200 --> 00:47:03,117
>> So it's like it's like Yeah. And so
that's just a it's a weird thing. It's

796
00:47:03,200 --> 00:47:05,117
going to be a super The one thing I

797
00:47:05,200 --> 00:47:08,317
would say for anyone listening, if

798
00:47:08,400 --> 00:47:12,317
you're listening to this and you're
chillaxing and not active, I'm not

799
00:47:12,400 --> 00:47:14,237
saying you have to agree
with me politically or anything.

800
00:47:14,320 --> 00:47:17,037
>> Yeah. >> There the like
partly because of this

801
00:47:17,120 --> 00:47:21,997
dynamic and very self-inflicted because
I I I tell you I can't name names. I

802
00:47:22,080 --> 00:47:26,157
called many of the titans
of this world and and

803
00:47:26,240 --> 00:47:29,037
like started this six months
ago like every couple days

804
00:47:29,120 --> 00:47:31,517
>> were going to be
national every couple days.

805
00:47:31,600 --> 00:47:33,197
>> Like some of them
I'm like, "Yeah, we're

806
00:47:33,280 --> 00:47:35,517
going to be I mean, you know, it's like
honestly they're like the bat." They

807
00:47:35,600 --> 00:47:38,957
they find me very entertaining. Like
I'm not sure. Like so they call cuz Yeah.

808
00:47:39,040 --> 00:47:41,277
It's like it's like, "Oh yeah, this
going to be entertaining. You're going

809
00:47:41,360 --> 00:47:44,077
to pick up." >> Yeah.
So any case, so I've been telling

810
00:47:44,160 --> 00:47:47,517
them for 6 months, six.
We're going to be nationalized.

811
00:47:47,600 --> 00:47:50,477
>> Yeah. You talked about >>
we're going to be nationalized. And

812
00:47:50,560 --> 00:47:53,597
they're like, "Why would anyone
nationalize? Never happened in America.

813
00:47:53,680 --> 00:47:56,957
It's never Why would anyone nationalize
us? We're so likable. [laughter] We're

814
00:47:57,040 --> 00:48:00,557
creating so much value." like okay
I'm not going to debate that I know how

815
00:48:00,640 --> 00:48:02,397
likable I am I'm not going to tell you

816
00:48:02,480 --> 00:48:04,877
how likable you are but I am telling you

817
00:48:04,960 --> 00:48:09,437
and you know the momentum on this is on
the side of people in national we don't

818
00:48:09,520 --> 00:48:12,877
get our act together and figure out ways
we can say hey look there are problems

819
00:48:12,960 --> 00:48:15,997
here we're going to deal with these
things are not going to yes they are

820
00:48:16,080 --> 00:48:19,357
going to create opportunities you have
to talk openly about how these things

821
00:48:19,440 --> 00:48:23,037
are valuable because we have adversaries
you can't just say these all that stuff

822
00:48:23,120 --> 00:48:26,637
so the primary risk honestly
to Palenteer and a lot of these other

823
00:48:26,720 --> 00:48:30,877
countries is and And it's going to be
nationalized before national it's going

824
00:48:30,960 --> 00:48:33,997
to be regulated by people who don't
understand this. And now they'll tell

825
00:48:34,080 --> 00:48:37,997
you in private I'm working on this. I'm
d this lobbyist. It's like not going to

826
00:48:38,080 --> 00:48:41,837
work. So like that's something like if
you're listening to this and you're like

827
00:48:41,920 --> 00:48:45,677
look you know you don't have to agree
with me on all my proclamations. I got a

828
00:48:45,760 --> 00:48:48,557
lot of by the way there's some people
who think I'm saying we should have a

829
00:48:48,640 --> 00:48:51,997
draft. Too lazy to read. I'm just
saying we should like in a world where

830
00:48:52,080 --> 00:48:56,637
everything is changing. Everything
is changing. Don't we have to find some

831
00:48:56,720 --> 00:48:59,837
communal structure to remember we're
American? You don't like my you don't

832
00:48:59,920 --> 00:49:04,077
like my idea of like we all do a week in
the park? Great. Come up with some other

833
00:49:04,160 --> 00:49:08,237
idea. We can have no idea, you know,
and like and then they're like, "Well, I'm

834
00:49:08,320 --> 00:49:11,677
saying I I do not want a draft, just
to be explicit." They're like, "Oh, that's

835
00:49:11,760 --> 00:49:13,117
pro-war." No, honestly, you know what?

836
00:49:13,200 --> 00:49:16,237
Most of our wars are fought because
no working-class person is making a

837
00:49:16,320 --> 00:49:19,677
decision. You start making sure everyone
is involved in everything. I'll see you

838
00:49:19,760 --> 00:49:23,437
how few wars we fight. It's actually
the anti-war position. But any case,

839
00:49:23,520 --> 00:49:25,677
disagree with everything. If you're we

840
00:49:25,760 --> 00:49:30,877
have on the right and on the left people
uh people who have no earthly clue what

841
00:49:30,960 --> 00:49:32,477
they're talking about right and left.

842
00:49:32,560 --> 00:49:35,837
>> All they're talking about is how
much they hate us and those of us who are

843
00:49:35,920 --> 00:49:39,437
sensible in the middle. >> You
know too many of us are chill waxing

844
00:49:39,520 --> 00:49:44,237
like it oh like nationalization it can't
happen. America would never do that.

845
00:49:44,320 --> 00:49:47,997
>> Sleepwalking into and you guys have
tendies to protect now. You guys should

846
00:49:48,080 --> 00:49:51,917
be on the front line of this. Like you
got full Oh, I'm sorry. Hey, I have a I

847
00:49:52,000 --> 00:49:56,877
have a full on very impressive uh
corporate leader coming on. So, I got I

848
00:49:56,960 --> 00:50:00,557
got to turn it down. >> Last question.
Last question if we have time. Uh

849
00:50:00,640 --> 00:50:03,357
>> how are your conversations going with

850
00:50:03,440 --> 00:50:06,637
Fortune 500 CEOs around headcount

851
00:50:06,720 --> 00:50:10,397
planning? There's been so many layoffs
this last year that people were saying,

852
00:50:10,480 --> 00:50:12,077
hey, we're getting so much out of AI.

853
00:50:12,160 --> 00:50:13,997
We're able to, you know, cut back here

854
00:50:14,080 --> 00:50:18,157
or there. uh people inside tech often

855
00:50:18,240 --> 00:50:21,037
know like these maybe there's just a

856
00:50:21,120 --> 00:50:25,757
reduction because there needs to be
a reduction or got bloated maybe they do

857
00:50:25,840 --> 00:50:30,637
need to fund some AI >> declining business
model yeah the business just doesn't have

858
00:50:30,720 --> 00:50:33,677
momentum but how are those conversations
going what does it look like

859
00:50:33,760 --> 00:50:35,597
>> like the the like
I by the way I talk to

860
00:50:35,680 --> 00:50:37,837
fortune 500 companies I talk to unions I

861
00:50:37,920 --> 00:50:40,477
talk to soldiers I talk to fire it

862
00:50:40,560 --> 00:50:45,757
>> if you upscale somebody >> they're
more valuable And like all these

863
00:50:45,840 --> 00:50:49,277
whether it's people working on batteries,
people driving trucks, people

864
00:50:49,360 --> 00:50:53,117
corporate leaders and again this is where
I think we have to be very careful

865
00:50:53,200 --> 00:50:57,037
to be more disciplined on the corporate
side. Like if you run around saying AI

866
00:50:57,120 --> 00:51:00,797
allowed you to fire twothirds of your
workforce and you did it because maybe

867
00:51:00,880 --> 00:51:02,477
your competitor's kicking your ass.

868
00:51:02,560 --> 00:51:05,197
Yeah, >> that could
that is a really like you

869
00:51:05,280 --> 00:51:09,357
might as well just go sign up for Bernie
Bernie Sanders manifest. And part of the

870
00:51:09,440 --> 00:51:13,037
thing is they really believe that can't
happen. So they're free riding on the

871
00:51:13,120 --> 00:51:16,237
fact that it could >> like we
have and it just cannot work

872
00:51:16,320 --> 00:51:20,157
anymore. These things are very very
explosive. The American people sense

873
00:51:20,240 --> 00:51:22,077
that there is something dangerous here.

874
00:51:22,160 --> 00:51:25,917
And when people are playing with that
fire, it's like it's a they assume the

875
00:51:26,000 --> 00:51:29,117
fire won't burn their hands. That's not
the world we're in. That fire is going

876
00:51:29,200 --> 00:51:33,917
to consume us. And what we see, again,
the war fighting example is just the

877
00:51:34,000 --> 00:51:38,477
most neutral, not for everybody,
but like the soldiers at the bottom have

878
00:51:38,560 --> 00:51:41,357
gotten much more valuable.
And and I don't even just mean the special

879
00:51:41,440 --> 00:51:45,197
operators, which obviously they're
in a different league, but like every the

880
00:51:45,280 --> 00:51:48,237
people doing a lot of the operations
now are doing our product. They're high

881
00:51:48,320 --> 00:51:52,797
school vocationally trained. You see
this everywhere. The the the modern

882
00:51:52,880 --> 00:51:54,877
enterprise is going to have like we have

883
00:51:54,960 --> 00:51:59,677
a a true like very very very smart uh

884
00:51:59,760 --> 00:52:03,437
person coming on and it's like you're
going to have a very smart executive.

885
00:52:03,520 --> 00:52:05,597
He's much better at hiding it than I

886
00:52:05,680 --> 00:52:07,197
would be if I were him. But that's you

887
00:52:07,280 --> 00:52:11,117
can talk to him about that. But um uh um

888
00:52:11,200 --> 00:52:15,197
and uh and then very talented, creative

889
00:52:15,280 --> 00:52:19,117
people with taste all up and down
the stack. Any case, I think this is time

890
00:52:19,200 --> 00:52:20,477
for me to >> I think this is time.

891
00:52:20,560 --> 00:52:22,157
>> We uh >> Thank you so much.

892
00:52:22,240 --> 00:52:24,397
>> Great to catch up.
[clears throat] >> Always fun first.

893
00:52:24,480 --> 00:52:26,957
>> Oh, they want me to stay
for two minutes or what?

894
00:52:27,040 --> 00:52:30,477
>> Oh, I'm only going to stay. Look, but
just a minute. He's got to be the star.

895
00:52:30,560 --> 00:52:33,357
>> The other headset.
>> Put it Put him Put you go in here.

896
00:52:33,440 --> 00:52:36,077
>> Yeah. And I'm just going to I'm going
to take off after a minute. We're going to

897
00:52:36,160 --> 00:52:39,917
put here here and why don't
you here put that headset on

898
00:52:40,000 --> 00:52:43,277
>> car. Why don't you introduce
our guest >> microphone on the left?

899
00:52:43,360 --> 00:52:46,557
>> Well, I you know let he's he's

900
00:52:46,640 --> 00:52:49,757
>> one of the smarter
people in in business

901
00:52:49,840 --> 00:52:52,717
um has developed

902
00:52:52,800 --> 00:52:55,517
um unique ways to underwrite that did

903
00:52:55,600 --> 00:53:00,557
not involve firing people >>
and someone and someone I admire.

904
00:53:00,640 --> 00:53:03,437
>> Thanks, Alex. >> With that,
I'm going to let you guys go.

905
00:53:03,520 --> 00:53:07,597
Make sure to tell them that the anttology
powers are [laughter] >> it's everything.

906
00:53:07,680 --> 00:53:09,277
>> Always selling. Hey, >> fantastic.

907
00:53:09,360 --> 00:53:11,197
>> Thanks for coming on the show.
It's great to meet you. Pleasure.

908
00:53:11,280 --> 00:53:15,517
>> Uh yeah, [clears throat] please uh kick
us off with like a bit of a more formal

909
00:53:15,600 --> 00:53:19,357
introduction. >> Yes. So,
I'm Peter Zapino. Um I'm the

910
00:53:19,440 --> 00:53:23,757
executive chairman as effective
on Monday of of AIG. I used to be the

911
00:53:23,840 --> 00:53:27,917
>> chairman and CEO. Um and have you know
worked with the company for nine years

912
00:53:28,000 --> 00:53:31,757
to help transform it. It was in a place
where underwriting profitability was

913
00:53:31,840 --> 00:53:35,917
challenging, operations were challenging,
data was challenging. Um,

914
00:53:36,000 --> 00:53:40,157
capital was challenging. Uh,
so you know had a great team of people

915
00:53:40,240 --> 00:53:41,757
>> with me to transform the company.

916
00:53:41,840 --> 00:53:45,117
>> So give us the give us the shape
of the business in terms of the different

917
00:53:45,200 --> 00:53:49,517
business lines, the different products,
the international footprint, the

918
00:53:49,600 --> 00:53:52,877
workforce like what give us
the scope and the scale here.

919
00:53:52,960 --> 00:53:55,917
>> Global company with a little
bit of a unique footprint. We're 50%

920
00:53:56,000 --> 00:54:01,357
international, 50% North America, but our
second largest country after US is Japan.

921
00:54:01,440 --> 00:54:03,437
>> Oh. >> Um, we have
a big business in India.

922
00:54:03,520 --> 00:54:06,397
>> Okay. >> Um, and then
we have a very big business

923
00:54:06,480 --> 00:54:10,157
in in the UK. Uh, we do complicated risks.

924
00:54:10,240 --> 00:54:13,677
>> So, you could think about what's
happening uh in the Middle East now with

925
00:54:13,760 --> 00:54:16,957
shipping, marine, energy.
We're heavily involved in that.

926
00:54:17,040 --> 00:54:20,317
>> So, something where there's not an
existing futures contract that a company

927
00:54:20,400 --> 00:54:24,557
can just go and hedge. It's not, oh, I'm
going to buy some oil futures because I

928
00:54:24,640 --> 00:54:28,397
I I fly planes around and I know I'm
going to need diesel fuel in a couple

929
00:54:28,480 --> 00:54:31,917
months and so I'm going to hedge that out.
This is for more complex risks.

930
00:54:32,000 --> 00:54:35,437
>> It's for more complex risks and you
know you think about the largest you know

931
00:54:35,520 --> 00:54:37,677
sort of customers uh in the world and

932
00:54:37,760 --> 00:54:42,477
you know big oil companies um you know
Fortune 500 companies but we also have a

933
00:54:42,560 --> 00:54:46,557
>> personal insurance business which
will cover things like accident health

934
00:54:46,640 --> 00:54:48,957
>> uh that are distribution to consumers.

935
00:54:49,040 --> 00:54:52,557
Uh so we have a real balance. Part
of that feels like if you're talking about

936
00:54:52,640 --> 00:54:57,037
insuring a Fortune 500 company against
a geopolitical risk, uh that feels like a

937
00:54:57,120 --> 00:54:58,557
meeting that takes place in a boardroom.

938
00:54:58,640 --> 00:55:02,877
It feels like there's a lot of folks
with a lot of trust built up over years

939
00:55:02,960 --> 00:55:05,357
to understand each other's businesses.

940
00:55:05,440 --> 00:55:09,037
Uh but then there's probably a lot
of other underwriting happening and teams

941
00:55:09,120 --> 00:55:13,197
uh putting together comps
and spreadsheets and data. And I want to

942
00:55:13,280 --> 00:55:15,197
know about the the intersection there.

943
00:55:15,280 --> 00:55:19,277
It feels like the business is and I
don't know if it ever will be just

944
00:55:19,360 --> 00:55:21,997
oneclick checkout for for insurance

945
00:55:22,080 --> 00:55:26,557
products for Fortune 500 companies.
Uh but what what is the interface between

946
00:55:26,640 --> 00:55:30,237
the quantitative the qualitative
the relationship and the data and then how

947
00:55:30,320 --> 00:55:32,717
is that changing? >> So
the quantitative you have to start at

948
00:55:32,800 --> 00:55:36,077
the portfolio level. Okay.
>> Um and you want as much data as you

949
00:55:36,160 --> 00:55:40,077
possibly can to look at deterministic
y modeling probabilistic and then

950
00:55:40,160 --> 00:55:44,317
stochastic. I think once you understand
like your mean and you understand the

951
00:55:44,400 --> 00:55:47,277
standard deviation around that then you
have to apply it to you know sort of the

952
00:55:47,360 --> 00:55:51,597
widgets which is each policy >>
throughout you know the globe as well as

953
00:55:51,680 --> 00:55:55,117
um ways in which you structure
yeah insurance. So for us

954
00:55:55,200 --> 00:56:00,237
>> you can't look at you can't look at an
individual policy uh in in isolation you

955
00:56:00,320 --> 00:56:04,557
you're you're managing portfolio risk
risk to the entire firm and and that's

956
00:56:04,640 --> 00:56:07,357
something that's happening
probably 247 I imagine

957
00:56:07,440 --> 00:56:09,517
>> it's hard and that's
what led me to Alex

958
00:56:09,600 --> 00:56:13,437
Karp. Um, you know, it's hard to get the
aggregation done in anything that looks

959
00:56:13,520 --> 00:56:17,917
like real time. It's usually static.
It can be 30, 60, 90 days.

960
00:56:18,000 --> 00:56:19,277
>> And your portfolio
could change. I mean,

961
00:56:19,360 --> 00:56:23,117
it's not going to change dramatically,
>> but having the ability to, you know,

962
00:56:23,200 --> 00:56:26,317
sort of assess risk and use
the quantitative data to make better

963
00:56:26,400 --> 00:56:29,677
decisions on a daily basis is the
aspiration of the way the company's going.

964
00:56:29,760 --> 00:56:32,397
>> Yeah. >> Take us back
to your first meeting with

965
00:56:32,480 --> 00:56:35,917
Karp. Curious uh what
the experience was like.

966
00:56:36,000 --> 00:56:37,757
>> It's a unique individual. >> Call you.

967
00:56:37,840 --> 00:56:40,237
>> Yeah. No. Um I was actually introduced

968
00:56:40,320 --> 00:56:43,517
by a board member many years ago and it

969
00:56:43,600 --> 00:56:46,557
was really in this pursuit of um not

970
00:56:46,640 --> 00:56:51,757
necessarily foundry or AIP or ontology
that's where it led us but it was more

971
00:56:51,840 --> 00:56:55,837
on sort of the quantitative ways in which
I was looking at the portfolio and

972
00:56:55,920 --> 00:56:59,357
could he help me think through computing
>> and could he help me think through sort

973
00:56:59,440 --> 00:57:04,877
of portfolio optimization and I just got
more and more uh intrigued. I mean, you

974
00:57:04,960 --> 00:57:07,917
see the brain. I mean, he just
thinks about things. Um, yeah,

975
00:57:08,000 --> 00:57:12,077
>> he doesn't hold back. I mean, so he's
so I always knew where he stood with uh

976
00:57:12,160 --> 00:57:14,317
with me and with AIG, but just developed

977
00:57:14,400 --> 00:57:17,037
a very strong trusting relationship

978
00:57:17,120 --> 00:57:19,597
>> and there's such a tremendous partner

979
00:57:19,680 --> 00:57:24,477
that we're able to iterate with them
almost like no other company because we

980
00:57:24,560 --> 00:57:28,157
do things in 90-day increments >>
because going out like a year or two

981
00:57:28,240 --> 00:57:31,677
years is is too static.
And so, we actually build

982
00:57:31,760 --> 00:57:33,837
>> our relationship on 90-day goals. Okay.

983
00:57:33,920 --> 00:57:35,597
And that's been incredibly effective.

984
00:57:35,680 --> 00:57:39,997
>> What is uh you know a lot of the AI
companies talking about scaling laws,

985
00:57:40,080 --> 00:57:42,397
exponential growth in token production

986
00:57:42,480 --> 00:57:47,117
or even revenue in many cases,
but what's growing exponentially in your

987
00:57:47,200 --> 00:57:49,277
business? Are you bringing exponentially

988
00:57:49,360 --> 00:57:52,317
more data into the platform every year?

989
00:57:52,400 --> 00:57:55,837
Exponentially more compute resources,

990
00:57:55,920 --> 00:57:58,317
teams, number of policies like what what

991
00:57:58,400 --> 00:58:02,877
is the what is the thing that's
experiencing a boom right now? most

992
00:58:02,960 --> 00:58:06,637
important part I believe in terms
of business is that you have to have a

993
00:58:06,720 --> 00:58:10,557
business solution you're trying to solve
for. So for us it was >> more data.

994
00:58:10,640 --> 00:58:12,877
>> Yeah. >> Um better data. >> Yeah.

995
00:58:12,960 --> 00:58:16,237
>> And then reduce cycle time. So in other
words like when we get the data that

996
00:58:16,320 --> 00:58:21,117
comes in from our distribution partners,
how fast can we get it with higher

997
00:58:21,200 --> 00:58:24,477
quality data and more data
to the underwriter to make decisions?

998
00:58:24,560 --> 00:58:27,117
>> Got it. >> Um and then
how do we actually make

999
00:58:27,200 --> 00:58:29,917
>> what's an example of distribution
partner in this context? So it would be

1000
00:58:30,000 --> 00:58:32,157
like a insurance broker or insurance

1001
00:58:32,240 --> 00:58:35,757
agent um or you know someone who has

1002
00:58:35,840 --> 00:58:37,837
>> their client
is the product effectively.

1003
00:58:37,920 --> 00:58:42,077
Exactly. Okay. Yes. Yeah, that makes
sense. Um what else? Jordy, do you have

1004
00:58:42,160 --> 00:58:47,277
something? >> Uh where
was I going to go? The

1005
00:58:47,360 --> 00:58:51,037
>> Alex ontologies. We'll get there. So So

1006
00:58:51,120 --> 00:58:53,677
there's been uh we we primarily I mean

1007
00:58:53,760 --> 00:58:56,077
we at least started covering early stage

1008
00:58:56,160 --> 00:58:58,397
startups. There's been a debate uh in

1009
00:58:58,480 --> 00:59:01,677
our kind of little subindustry right now

1010
00:59:01,760 --> 00:59:07,037
uh around a bunch of new uh insurance
focused startups that are growing

1011
00:59:07,120 --> 00:59:08,397
incredibly quickly.

1012
00:59:08,480 --> 00:59:10,957
>> Uh and there's a debate going on is one

1013
00:59:11,040 --> 00:59:14,397
uh maybe AI makes it more possible to

1014
00:59:14,480 --> 00:59:17,197
underwrite risk and if you can do that

1015
00:59:17,280 --> 00:59:20,157
well grow very quickly. Uh the other

1016
00:59:20,240 --> 00:59:23,037
side, you know, says uh hey, you know,

1017
00:59:23,120 --> 00:59:28,077
if you're hypers scaling an insurance
company, uh maybe that's not maybe you

1018
00:59:28,160 --> 00:59:31,117
don't want to work with a company that is,
you know, going through that hyper

1019
00:59:31,200 --> 00:59:34,397
>> the iron law of the
universe goes up fast.

1020
00:59:34,480 --> 00:59:36,877
>> But yeah, talk talk about um talk about

1021
00:59:36,960 --> 00:59:41,757
what AI has actually enabled, where
you're excited about it, where it's

1022
00:59:41,840 --> 00:59:45,837
failing broadly, maybe where it's
overhyped, and you can I guess tie that

1023
00:59:45,920 --> 00:59:50,157
into uh everything you
built with Palunteer.

1024
00:59:50,240 --> 00:59:52,317
There's never been a time in my opinion

1025
00:59:52,400 --> 00:59:55,117
whether it was you know introduction to

1026
00:59:55,200 --> 00:59:58,557
fintech and sh how to use algorithms how

1027
00:59:58,640 --> 01:00:01,197
to build data lakes and repositories for

1028
01:00:01,280 --> 01:00:06,797
data there's never been a time in in my
professional career so it's 35 years in

1029
01:00:06,880 --> 01:00:11,197
big companies yeah >> that I've
seen the ability to change how

1030
01:00:11,280 --> 01:00:13,597
an an organization actually runs itself

1031
01:00:13,680 --> 01:00:15,837
and that can come from big companies

1032
01:00:15,920 --> 01:00:19,277
like Palunteer or Google or it could

1033
01:00:19,360 --> 01:00:21,437
come from uh you know companies that are

1034
01:00:21,520 --> 01:00:24,557
being funded by venture and have a very

1035
01:00:24,640 --> 01:00:28,957
specific niche that can be you know
additive to the organization. And what

1036
01:00:29,040 --> 01:00:30,957
what I think is happening we talked

1037
01:00:31,040 --> 01:00:33,677
about the sort of data ingestion portion

1038
01:00:33,760 --> 01:00:36,077
getting that into a digital workflow

1039
01:00:36,160 --> 01:00:38,317
using large language models to extract

1040
01:00:38,400 --> 01:00:42,077
more data from what comes in but also uh

1041
01:00:42,160 --> 01:00:43,997
helping underwriters make decisions that

1042
01:00:44,080 --> 01:00:47,117
are you know more comprehensive. You

1043
01:00:47,200 --> 01:00:49,917
also have the ability in the way in

1044
01:00:50,000 --> 01:00:52,717
which you service customers to be much

1045
01:00:52,800 --> 01:00:57,517
better through the use of AI. I think
companies generally uh my observations

1046
01:00:57,600 --> 01:01:01,277
are struggling with the orchestration
of how you actually drive agents, people

1047
01:01:01,360 --> 01:01:03,837
and data into an organization and once

1048
01:01:03,920 --> 01:01:09,037
that is solved and it's certainly on it
on its way capabilities are there um

1049
01:01:09,120 --> 01:01:12,877
then you start to think about the entire
endtoend chain being very different.

1050
01:01:12,960 --> 01:01:15,357
>> Yeah. What I think
about Palunteer while

1051
01:01:15,440 --> 01:01:20,637
they've been such a critical partner
is one as we evolved together but in that

1052
01:01:20,720 --> 01:01:25,197
data ingestion to be able to take
structured unstructured text all sorts

1053
01:01:25,280 --> 01:01:28,397
of data and get into a workflow in a
fraction of the time helps us on the

1054
01:01:28,480 --> 01:01:31,837
things I try to achieve is like we have
now data that we probably wouldn't have

1055
01:01:31,920 --> 01:01:35,437
used before because it wasn't good or we
couldn't translate it couldn't get it

1056
01:01:35,520 --> 01:01:38,237
into the digital workflow. Um, and then

1057
01:01:38,320 --> 01:01:43,117
we start to build out an ontology. And I,
and I really do think it's incredibly

1058
01:01:43,200 --> 01:01:47,117
important. If there's one thing I look
at for our organization, certainly the

1059
01:01:47,200 --> 01:01:49,837
advancements of LLM's, their ability to

1060
01:01:49,920 --> 01:01:54,237
do things more autonomously now where
we started with the binary gen AI, now

1061
01:01:54,320 --> 01:01:57,437
we're into a Gentic AI where we can
just do things autonomously for so much

1062
01:01:57,520 --> 01:02:00,397
longer. without the ontology of actually

1063
01:02:00,480 --> 01:02:05,117
building like what the sort of digital
twin of your business looks like where

1064
01:02:05,200 --> 01:02:08,317
you take it and how you evolve it
becomes very challenging. So we've been

1065
01:02:08,400 --> 01:02:11,277
able to do things with Palunteer. I'll

1066
01:02:11,360 --> 01:02:15,997
use the ontology example again. We did
the full ontology of AIG and then we

1067
01:02:16,080 --> 01:02:20,317
went to look at an acquisition um called
Everest which had about $2 billion of

1068
01:02:20,400 --> 01:02:22,637
premium. >> We got
Palunteering to work with our

1069
01:02:22,720 --> 01:02:24,237
team. We could build an ontology of

1070
01:02:24,320 --> 01:02:29,837
Everest's portfolio on top of ours
in 4 days. Um and quite frankly what we

1071
01:02:29,920 --> 01:02:33,917
started to learn again about that
evolution is that you always relied on

1072
01:02:34,000 --> 01:02:36,637
data lakes or global data repositories.

1073
01:02:36,720 --> 01:02:39,357
What we found is that we could get you

1074
01:02:39,440 --> 01:02:44,557
know sort of foundry and start to build
out this ontology with going to the

1075
01:02:44,640 --> 01:02:47,917
admin platforms. All of a sudden these
repositories and the central places of

1076
01:02:48,000 --> 01:02:51,197
getting data and make sure it's scrubbed
wasn't as relevant. So I think we

1077
01:02:51,280 --> 01:02:55,357
continue to advance that >> in in
the way in which we are looking at

1078
01:02:55,440 --> 01:02:59,597
our business. I have a la I have one
last question. Um just on the actual

1079
01:02:59,680 --> 01:03:04,157
change management, the organization like
how the office feels. What how did you

1080
01:03:04,240 --> 01:03:06,157
go about actually working with

1081
01:03:06,240 --> 01:03:08,797
Palunteer? Do you set up your own

1082
01:03:08,880 --> 01:03:12,717
internal Palunteer workforce who sits

1083
01:03:12,800 --> 01:03:15,357
alongside FDES? Do you let Palunteer

1084
01:03:15,440 --> 01:03:18,477
come in and plug in like one person per

1085
01:03:18,560 --> 01:03:22,717
team that you have set up? Like
was there a best practice? Did you go with

1086
01:03:22,800 --> 01:03:27,437
the best practice? like what was the
actual like experience of deploying the

1087
01:03:27,520 --> 01:03:30,237
forward deployed engineers they get
deployed into the organization that's

1088
01:03:30,320 --> 01:03:33,917
got to be uh a unique situation

1089
01:03:34,000 --> 01:03:37,757
>> first is making sure Alex and then
you know two of the senior executives Ryan

1090
01:03:37,840 --> 01:03:40,797
and Ted that everybody knows what we're
trying to do together so we start there

1091
01:03:40,880 --> 01:03:43,997
>> then we wanted to embed the engineers
with our team so if we had a business

1092
01:03:44,080 --> 01:03:47,837
leader that was trying to drive the
underwriting output you'd have you know

1093
01:03:47,920 --> 01:03:51,837
technology from AIG you would have some
of the change management but you'd have

1094
01:03:51,920 --> 01:03:56,237
the engineers sitting there with our teams
throughout the entire process because

1095
01:03:56,320 --> 01:03:59,597
>> the iteration is really important
in terms of translating what you're trying

1096
01:03:59,680 --> 01:04:03,437
to achieve from the business side and
the engineers actually helping us think

1097
01:04:03,520 --> 01:04:06,397
through the application of some
of the LLMs or ways in which we could

1098
01:04:06,480 --> 01:04:08,157
circumvent some of the
things that we were doing.

1099
01:04:08,240 --> 01:04:10,717
>> Yeah, that makes a ton of sense.
Jordan, anything else?

1100
01:04:10,800 --> 01:04:14,797
>> Uh no. Well, insurance has
to be the most important topic.

1101
01:04:14,880 --> 01:04:17,517
>> No, the last if we do have a second I I

1102
01:04:17,600 --> 01:04:21,997
was uh uh >> not sure on timing.
what how are you how

1103
01:04:22,080 --> 01:04:26,877
are you thinking about you know
workforce planning uh asked uh Karp

1104
01:04:26,960 --> 01:04:30,477
about this and he said to ask >>
token budgets um you know we we've

1105
01:04:30,560 --> 01:04:32,957
stayed uh you know as as you've had this

1106
01:04:33,040 --> 01:04:37,277
wave of AI layoffs we've been uh over

1107
01:04:37,360 --> 01:04:42,237
and over and over reminded people that
uh if you have a an individual and you

1108
01:04:42,320 --> 01:04:45,437
give them more capability you make
them more productive you make them more

1109
01:04:45,520 --> 01:04:49,357
efficient a a thriving business will
want to hire more people right because

1110
01:04:49,440 --> 01:04:53,197
you get more individual
and so we've tried to remind

1111
01:04:53,280 --> 01:04:57,197
people of that over and over and over
as you know companies that often times are

1112
01:04:57,280 --> 01:04:59,117
you know underperforming or bloated for

1113
01:04:59,200 --> 01:05:01,437
whatever reason but what's your kind of

1114
01:05:01,520 --> 01:05:04,557
philosophy around uh hiring headcount

1115
01:05:04,640 --> 01:05:07,117
planning uh riffs all that stuff in this

1116
01:05:07,200 --> 01:05:11,677
kind of uh new era we've been focusing
on I heard Alex at the tail end and I

1117
01:05:11,760 --> 01:05:13,837
agree with him so we're focusing on

1118
01:05:13,920 --> 01:05:16,557
growth uh we're focusing on reskilling

1119
01:05:16,640 --> 01:05:21,357
and actually training um our employees
to be in different part of the workflow.

1120
01:05:21,440 --> 01:05:25,837
Now >> you would do this I believe in all
of this you have to still have great

1121
01:05:25,920 --> 01:05:29,997
endto-end process and so things that have
been the humans been an LLM trained

1122
01:05:30,080 --> 01:05:34,317
how to do things like outside of the
normal workflow has to you have to get

1123
01:05:34,400 --> 01:05:36,877
rid of that. I mean so I think
that's just normal business. Yeah.

1124
01:05:36,960 --> 01:05:41,677
>> Um but you know our aspiration is not
to implement you know AI or anything that

1125
01:05:41,760 --> 01:05:44,877
we're doing with our partners
to eliminate jobs. I mean, it's about

1126
01:05:44,960 --> 01:05:49,277
growth, reskilling, and finding ways
in different markets to have exponential

1127
01:05:49,360 --> 01:05:52,637
growth and opportunity and having a lot
more insight in the business that we run.

1128
01:05:52,720 --> 01:05:54,317
>> That's a great
optimistic vision. I love

1129
01:05:54,400 --> 01:05:56,877
it. Thank you so much for taking time
to come with us. Thanks for coming. Great

1130
01:05:56,960 --> 01:05:58,317
to be with you.
>> Have a great rest of your time.

1131
01:05:58,400 --> 01:06:02,237
>> Thanks. >> And up next, next >> we
have Chad Walquist. First, I'm going

1132
01:06:02,320 --> 01:06:04,957
to tell you about Crowd Strike.
Your business is AI. Their business is

1133
01:06:05,040 --> 01:06:09,677
securing it. Crowd Strike secures AI and
stops breaches. Welcome to the show. How

1134
01:06:09,760 --> 01:06:12,077
are you doing, Chad? >> Great overcoat.

1135
01:06:12,160 --> 01:06:15,357
That's a new one.
That's an Eleaniano special. It is.

1136
01:06:15,440 --> 01:06:18,637
>> Oh, yeah. He is the master.

1137
01:06:18,720 --> 01:06:19,757
>> Giving us a run for our money.

1138
01:06:19,840 --> 01:06:22,957
>> Yeah, it's fantastic. Uh, anyway, kick
us off with an introduction on yourself,

1139
01:06:23,040 --> 01:06:25,917
how you fit into Palunteer, a little bit
of backstory. I'm sure we have a ton of

1140
01:06:26,000 --> 01:06:29,117
questions to run through. >> Uh,
first, how often do you guys do

1141
01:06:29,200 --> 01:06:32,477
these things? Because it feels like
this feels like an annual It feels like an

1142
01:06:32,560 --> 01:06:36,157
annual event. >> Yeah, >> but we're
getting the call every month.

1143
01:06:36,240 --> 01:06:40,157
Karp carp karp talks about you know
manipulating time [laughter] working you

1144
01:06:40,240 --> 01:06:45,357
know a quarter at palunteer is like
a day or a year at an exact so that it

1145
01:06:45,440 --> 01:06:49,037
kind of makes sense.
>> Yeah I'm like actually 23. [laughter]

1146
01:06:49,120 --> 01:06:53,117
>> Yeah time the time warp is real. So
we do these quarterly. So I'm I'm a ford

1147
01:06:53,200 --> 01:06:54,717
deployed architect technically. Yeah.

1148
01:06:54,800 --> 01:06:58,477
>> I do what is needed and so doing the
needful is kind of the palunteer way is

1149
01:06:58,560 --> 01:07:01,677
like there's no job below me. And so
no matter if I'm out on the edge with

1150
01:07:01,760 --> 01:07:05,597
customers, I'm talking to executives,
explaining the ontology, doing YouTube

1151
01:07:05,680 --> 01:07:08,157
videos. >> That's all what
I'm doing. So really the

1152
01:07:08,240 --> 01:07:10,957
goal is how do we help people decomp
problems differently and apply the

1153
01:07:11,040 --> 01:07:14,237
technology new ways. >> Can AI do decomp?

1154
01:07:14,320 --> 01:07:17,037
>> Yes. >> Okay.
Unpack that because that feels

1155
01:07:17,120 --> 01:07:19,997
like the secret sauce. That feels like
the special thing about Palunteer is

1156
01:07:20,080 --> 01:07:23,517
actually being able to bring someone in
who understands an organization. I think

1157
01:07:23,600 --> 01:07:27,757
a lot of people see AI tools. A lot of
people see AI tools. No, a lot of people

1158
01:07:27,840 --> 01:07:30,157
see AI tools and they and they think uh

1159
01:07:30,240 --> 01:07:33,277
okay very defined workflow input output

1160
01:07:33,360 --> 01:07:35,997
but now instead of just math that Python

1161
01:07:36,080 --> 01:07:37,357
can deal with you can deal with some

1162
01:07:37,440 --> 01:07:41,357
text and that's great but demp to me has

1163
01:07:41,440 --> 01:07:44,477
always felt less like let's go into your

1164
01:07:44,560 --> 01:07:47,037
HR system and understand the basic job

1165
01:07:47,120 --> 01:07:49,197
description and like oh someone uploaded

1166
01:07:49,280 --> 01:07:52,717
this resume versus oh Steve actually

1167
01:07:52,800 --> 01:07:54,957
does this completely outside of that

1168
01:07:55,040 --> 01:07:57,277
system and marketing has two two

1169
01:07:57,360 --> 01:07:59,997
platforms for this thing and engineering

1170
01:08:00,080 --> 01:08:04,557
has three systems for CAD files and the
all the clues that have built up over

1171
01:08:04,640 --> 01:08:08,797
decades sometimes hundreds of years
for some of these organizations like you

1172
01:08:08,880 --> 01:08:13,517
that's what was so special about
the forward deployed engineer program the

1173
01:08:13,600 --> 01:08:17,357
Palunteer model y >> I'm
surprised to hear you say I AI can

1174
01:08:17,440 --> 01:08:21,597
do it at all it feels like the final boss
>> well this is where the the really the

1175
01:08:21,680 --> 01:08:24,957
palunteer thesis is humans
and AI working together

1176
01:08:25,040 --> 01:08:27,997
>> and so the way we thing about this
is modeling our business process and we

1177
01:08:28,080 --> 01:08:30,717
heard some other people talking about
this of modeling my business process and

1178
01:08:30,800 --> 01:08:35,837
the ontology um because the LMS don't
necessarily have an a world view or

1179
01:08:35,920 --> 01:08:39,597
world model of your business and your
operations the ontology provides that okay

1180
01:08:39,680 --> 01:08:42,797
>> and so when we talk about demp this
is really about actually now I make more

1181
01:08:42,880 --> 01:08:45,837
data computable as well so we
think about LM on the agents and I'm

1182
01:08:45,920 --> 01:08:50,077
interacting also we use LM to make more
data computable and then model that in

1183
01:08:50,160 --> 01:08:53,357
the ontology of how things are really
working and so what we're actually doing

1184
01:08:53,440 --> 01:08:55,197
a lot of times now is is building out

1185
01:08:55,280 --> 01:08:57,197
that world view and then running

1186
01:08:57,280 --> 01:09:02,237
multiple agents over this actually um
being combative towards each other right

1187
01:09:02,320 --> 01:09:06,157
and so actually working against each
other and having critiques and so after

1188
01:09:06,240 --> 01:09:09,437
you do that you can also then give
the human human in the loop feedback about

1189
01:09:09,520 --> 01:09:12,157
this and iterate on this and so what
we find is that's really a scaling

1190
01:09:12,240 --> 01:09:15,197
mechanism it's like a new power tool
right I think you guys were just talking

1191
01:09:15,280 --> 01:09:18,957
about this the kind of the perspective
around jobs and all this stuff it's like

1192
01:09:19,040 --> 01:09:22,157
when you gave carpenters power tools
there weren't less carpenters there were

1193
01:09:22,240 --> 01:09:24,797
more I could do more with it
right it's an empowering thing

1194
01:09:24,880 --> 01:09:28,957
>> yeah So, uh, how often like I I I I'm

1195
01:09:29,040 --> 01:09:33,037
interested in the, uh, like the pie
[clears throat] in the sky, Palunteer

1196
01:09:33,120 --> 01:09:35,837
pitch, understand your entire business,

1197
01:09:35,920 --> 01:09:38,797
run your entire business on Palunteer.

1198
01:09:38,880 --> 01:09:43,037
And then some of the nitty-gritty where
sometimes like the lowhanging fruit is

1199
01:09:43,120 --> 01:09:47,677
like, wait, there's a like there's
someone's job to just like take a form

1200
01:09:47,760 --> 01:09:51,757
and type it into a sheet. Like, we have
we've had image recognition for a long

1201
01:09:51,840 --> 01:09:56,077
time. let's actually go and implement
that and get that into a database, get

1202
01:09:56,160 --> 01:09:59,197
that into the ontology, get that into
Palunteer so then we can start building

1203
01:09:59,280 --> 01:10:03,357
on top of it. And it feels like there
might be a tension there. Obviously,

1204
01:10:03,440 --> 01:10:05,437
both processes are speeding up, but h

1205
01:10:05,520 --> 01:10:10,477
how do you how do you sort of like keep
the project centered around the big goal

1206
01:10:10,560 --> 01:10:13,437
while still chopping wood on all
the things that actually need to happen?

1207
01:10:13,520 --> 01:10:14,557
>> Yeah. And I think
this comes back to the

1208
01:10:14,640 --> 01:10:17,597
forward deployed piece and like what
do we deliver outcomes and and we work

1209
01:10:17,680 --> 01:10:19,837
backwards from that rather than hey I
have this data I'm going to build a data

1210
01:10:19,920 --> 01:10:22,557
warehouse and then I'll build reports
because all my data is in one place

1211
01:10:22,640 --> 01:10:25,357
that's the that's the field
of dreams and no one shows up

1212
01:10:25,440 --> 01:10:28,957
>> right and so really when we demp things
and work backwards from that you know

1213
01:10:29,040 --> 01:10:32,717
the simple things like the form filling
out there's a lot of that now the one

1214
01:10:32,800 --> 01:10:35,757
approach that we see a lot is you know
enterprise software is going to force

1215
01:10:35,840 --> 01:10:39,757
you into their box. >> Sure. >> Right.
You go fit you go fit into this box.

1216
01:10:39,840 --> 01:10:43,517
>> Yeah. Well, then you know, okay, did
I take away the special sauce which was my

1217
01:10:43,600 --> 01:10:47,037
company because people were doing these
all these kind of amalgamations. Hey, 40

1218
01:10:47,120 --> 01:10:50,637
ways to do a PO. >> Yep. >> Well,
maybe it is okay to do 40 ways,

1219
01:10:50,720 --> 01:10:53,437
but my software can't handle it and it's
fragmented, right? And so there's

1220
01:10:53,520 --> 01:10:56,477
there's actually a middle ground
because, you know, for a long time

1221
01:10:56,560 --> 01:11:00,077
>> customization was kind of a four-letter
word, right? No, no one wanted to do

1222
01:11:00,160 --> 01:11:02,797
that. And I think that's where we think
about malleable software actually. How

1223
01:11:02,880 --> 01:11:04,877
do we help you be more
different, not more similar?

1224
01:11:04,960 --> 01:11:07,997
>> Interesting. And that's so that when
we decomp problems thinking about not only

1225
01:11:08,080 --> 01:11:12,637
the the kind of the uh quantitative
piece but the qualitative piece and the

1226
01:11:12,720 --> 01:11:16,477
people and process around this how do
we enable those people to do the things

1227
01:11:16,560 --> 01:11:19,437
that made them special?
>> Is is software getting more malleable

1228
01:11:19,520 --> 01:11:21,757
because I I I can look at it two ways. I

1229
01:11:21,840 --> 01:11:25,437
can look at uh one you know obviously AI

1230
01:11:25,520 --> 01:11:30,157
agents are incredible at coding. They
can run they they can make changes very

1231
01:11:30,240 --> 01:11:33,037
very quickly that would take you
a day in just a few minutes. Y

1232
01:11:33,120 --> 01:11:36,717
>> at the same time I see you know
so many screenshots of people saying I

1233
01:11:36,800 --> 01:11:41,037
implemented this feature and the GitHub
is plus a million lines of code and at a

1234
01:11:41,120 --> 01:11:43,517
certain point like the context window is

1235
01:11:43,600 --> 01:11:45,757
growing as fast as the code generation's

1236
01:11:45,840 --> 01:11:48,637
growing like there's a I I'm a believer

1237
01:11:48,720 --> 01:11:51,037
in the answer to bad slop is good slop

1238
01:11:51,120 --> 01:11:55,917
and more slop maybe but what are you
actually seeing on the malleability of

1239
01:11:56,000 --> 01:11:59,677
software because sometimes the most
malleable software in the past has been

1240
01:11:59,760 --> 01:12:01,677
oh well there was a really incredible

1241
01:12:01,760 --> 01:12:03,677
engineer who figured out this problem

1242
01:12:03,760 --> 01:12:07,037
and baked it down to a 2,000line repo

1243
01:12:07,120 --> 01:12:10,717
and you can actually just put it in your
own context window so it becomes more

1244
01:12:10,800 --> 01:12:13,997
malleable and you can use it as a
building block. Yep. And [snorts] that

1245
01:12:14,080 --> 01:12:18,157
feels like that's going away and I want
to make sure that we've that we're ready

1246
01:12:18,240 --> 01:12:20,557
for when it goes away
and it remains malleable.

1247
01:12:20,640 --> 01:12:24,077
>> Well, I think what what's missing is
the the malleable enterprise scaffolding

1248
01:12:24,160 --> 01:12:27,117
that you and that's what we think
about the ontology and foundry and the

1249
01:12:27,200 --> 01:12:30,957
platform and then Apollo that allows us
to go deploy these changes. So it gives

1250
01:12:31,040 --> 01:12:33,517
us the right amount of structure but
the right amount of freedom. So I think

1251
01:12:33,600 --> 01:12:37,357
that's the balance we try to find
is that malle malleability in the middle

1252
01:12:37,440 --> 01:12:41,197
where we can actually scale. We can
enable people to do things differently

1253
01:12:41,280 --> 01:12:46,237
while still creating enterprise you know
grade robust secure scalable software.

1254
01:12:46,320 --> 01:12:48,797
And so it's actually
a balance there about how

1255
01:12:48,880 --> 01:12:50,797
>> I can enable that
engineer that you know

1256
01:12:50,880 --> 01:12:54,877
has been doing that now they can write
code much faster. they can oversee

1257
01:12:54,960 --> 01:12:58,477
things and that enterprise scaffolding
in the middle allows us to actually

1258
01:12:58,560 --> 01:13:01,757
create the right guard rails create
a safe system of work for them to go

1259
01:13:01,840 --> 01:13:05,037
develop things in. Um and then it's also
the feedback loop. So the other thing

1260
01:13:05,120 --> 01:13:08,397
that we do with our ontology and our
platforms is implicit and explicit

1261
01:13:08,480 --> 01:13:12,637
feedback from users using it. So the udal
loop that I create and really that

1262
01:13:12,720 --> 01:13:15,517
udal loop allows our customers
as they're doing workflows they're giving

1263
01:13:15,600 --> 01:13:19,197
feedback to agents. Now can agents help
them do more based on the feedback. So

1264
01:13:19,280 --> 01:13:22,077
both explicitly saying hey that was
wrong that sucked or I chose this

1265
01:13:22,160 --> 01:13:25,997
option. Now if you do that enough agents
can start to learn from that. So we

1266
01:13:26,080 --> 01:13:29,197
actually store that in our ontology
to allow it to scale. So it's really that

1267
01:13:29,280 --> 01:13:32,637
human centric process around AI. AI
is not like we shouldn't be thinking about

1268
01:13:32,720 --> 01:13:37,837
AI from the sake of AI for AI.
It's AI to enable humans to do more. Yeah,

1269
01:13:37,920 --> 01:13:41,757
>> that's the frame. >> Udaloo,
observe, orient, decide, act,

1270
01:13:41,840 --> 01:13:45,037
right? Uh I have a different
question, but you can you can go.

1271
01:13:45,120 --> 01:13:48,157
>> Uh if you were giving if you had 30

1272
01:13:48,240 --> 01:13:49,037
minutes

1273
01:13:49,120 --> 01:13:53,357
>> to uh give feedback
to the AI labs, what

1274
01:13:53,440 --> 01:13:55,517
are the kind of key areas? Let's say the

1275
01:13:55,600 --> 01:13:58,477
frontier labs, right? Uh leading models.

1276
01:13:58,560 --> 01:14:02,557
What uh what are the kind of key
areas that you would be focused on?

1277
01:14:02,640 --> 01:14:05,117
>> Yeah, I mean I think when we think
about the enterprise space, you know, we

1278
01:14:05,200 --> 01:14:06,797
>> one you're like don't compete with us.

1279
01:14:06,880 --> 01:14:10,397
No, I actually like I I think
optionality is a good thing. Like I am

1280
01:14:10,480 --> 01:14:13,757
agnostic to where you store your data,
where you store, how what model you

1281
01:14:13,840 --> 01:14:17,197
choose, what compute you use. So like
we we can allow you to use any of that

1282
01:14:17,280 --> 01:14:20,637
because the last thing that actually
drives an outcome is replatforming,

1283
01:14:20,720 --> 01:14:23,357
moving to another >> and that
goes back to the onrem culture,

1284
01:14:23,440 --> 01:14:28,077
the secure cloud culture, ITAR
compliance, like this is in the DNA of

1285
01:14:28,160 --> 01:14:30,477
the company. >> Yeah.
And so how do we actually enable

1286
01:14:30,560 --> 01:14:33,917
people where they are instead
of the focus on oh if you replplatform

1287
01:14:34,000 --> 01:14:37,197
everything to Palanteer everything
will be great. like well actually you've

1288
01:14:37,280 --> 01:14:39,677
probably been replplatforming for years.

1289
01:14:39,760 --> 01:14:42,637
Can we enable what you have to go do
these new things? So when we think about

1290
01:14:42,720 --> 01:14:45,597
like the model companies and it's you
know how do we ensure that we can give

1291
01:14:45,680 --> 01:14:49,277
the feedback loops around you
know tool usage and um you know

1292
01:14:49,360 --> 01:14:53,117
>> yeah that's the kind of that's the kind
of stuff I was uh wanting to get your

1293
01:14:53,200 --> 01:14:57,117
point of view on is like are I'm sure
you're getting into the nitty-gritty

1294
01:14:57,200 --> 01:15:01,597
>> with individual models where where
they're spiky where there's you know

1295
01:15:01,680 --> 01:15:03,197
where there's shortcomings etc.

1296
01:15:03,280 --> 01:15:06,077
>> Yeah. So we we actually just launched
I just put a YouTube video out last week

1297
01:15:06,160 --> 01:15:08,477
on this new tool called Evolve.
We talked about it in the kind of the

1298
01:15:08,560 --> 01:15:12,717
halftime show where customers are using
actually AI to help them understand

1299
01:15:12,800 --> 01:15:17,757
which model. So like maybe you know
the the the the meme around hey make it

1300
01:15:17,840 --> 01:15:19,277
exist first and then make it good.

1301
01:15:19,360 --> 01:15:21,597
>> Yeah. >> Most of the time
I see people a building

1302
01:15:21,680 --> 01:15:25,037
with agents they're using the latest
frontier model. I just got it working

1303
01:15:25,120 --> 01:15:27,757
and then then all of a sudden the token
maxing and everyone everything else and

1304
01:15:27,840 --> 01:15:30,397
you're like oh my gosh I just blew
through my whole budget. Yep. So we

1305
01:15:30,480 --> 01:15:33,917
built a tool called evolve that will
actually go analyze the logs in

1306
01:15:34,000 --> 01:15:37,037
production about how these models are
operating, what people are doing with

1307
01:15:37,120 --> 01:15:41,357
them, the architecture over it, um,
and actually be able to swap out different

1308
01:15:41,440 --> 01:15:45,117
models from different providers or hey
actually for most of this workflow you

1309
01:15:45,200 --> 01:15:48,717
can use this model that's older
and actually without thinking and and test

1310
01:15:48,800 --> 01:15:51,917
time compute it. It's more
deterministic >> or even cached models

1311
01:15:52,000 --> 01:15:54,877
>> cache models and then or hey if you
actually just have this piece of data in

1312
01:15:54,960 --> 01:15:58,717
the ontology then you would eliminate
all this and 50% of your cost. Yep. And

1313
01:15:58,800 --> 01:16:01,117
so you know some of the customers
McCarthy talked about this at our

1314
01:16:01,200 --> 01:16:05,437
halftime you know they they were able to
in two days eliminate 60% of their token

1315
01:16:05,520 --> 01:16:09,997
cost by rearchitecting picking
a different model and in prompt tuning. So

1316
01:16:10,080 --> 01:16:12,877
it's the combination of all those the
permutations get really hard especially

1317
01:16:12,960 --> 01:16:17,197
when it's in this probabistic models
we've have tools to do this in the

1318
01:16:17,280 --> 01:16:19,757
deterministic world >> prompt
tuning it's a instead of don't

1319
01:16:19,840 --> 01:16:23,517
make mistakes it's okay
to make some mistakes

1320
01:16:23,600 --> 01:16:27,357
if the mistake is going to cost just
a little bit I'm fine because don't make

1321
01:16:27,440 --> 01:16:30,237
mistakes that's going to cost me
a fortune. Well, there there were there's

1322
01:16:30,320 --> 01:16:32,477
there was some chatter yesterday around

1323
01:16:32,560 --> 01:16:38,317
uh uh something a model was doing to be
more efficient was uh talking in in like

1324
01:16:38,400 --> 01:16:42,637
this bad. >> Oh. Oh.
Kan caveman caveman prompting.

1325
01:16:42,720 --> 01:16:44,397
Um >> the caveman prompt method actually

1326
01:16:44,480 --> 01:16:47,597
works. What how how often are you

1327
01:16:47,680 --> 01:16:52,397
working with a company that is having
call it like a mini chat GBT moment

1328
01:16:52,480 --> 01:16:56,157
within their enterprise and then they're
just like let's not tell anyone about

1329
01:16:56,240 --> 01:17:01,117
this because I imagine like there's
all these there's clearly places where

1330
01:17:01,200 --> 01:17:03,997
>> what does that mean their product
is taking off like Chad GBT or

1331
01:17:04,080 --> 01:17:08,397
>> well so they've found a way to apply AI
in a way that is highly highly effective

1332
01:17:08,480 --> 01:17:11,117
and gives them an edge >>
oh interesting >> uh but

1333
01:17:11,200 --> 01:17:14,717
>> yeah like like
the theoretical like technology

1334
01:17:14,800 --> 01:17:18,717
transform. So, so X people are very
loud, right? Figure uh they they're

1335
01:17:18,800 --> 01:17:20,717
like, I just had this >>
done using everything.

1336
01:17:20,800 --> 01:17:24,957
>> Yeah, I just had a product work for 30
hours on this thing. They'll talk about

1337
01:17:25,040 --> 01:17:29,037
it. But if you're a Fortune 500 and you
figure out how to do something, it's not

1338
01:17:29,120 --> 01:17:32,237
like you want to like put put your hand
up and say like, I figured something

1339
01:17:32,320 --> 01:17:37,277
out, right? Like secrets are valuable
and >> these advancements and kind of

1340
01:17:37,360 --> 01:17:40,077
breakthroughs are not going to be uniform.
the airline industry will never

1341
01:17:40,160 --> 01:17:43,277
be the same and then your direct
competitor copies [laughter] you and

1342
01:17:43,360 --> 01:17:47,277
you're like, >> "Yeah." >> And so,
and so part of part of why, you

1343
01:17:47,360 --> 01:17:49,117
know, right now the meme is token

1344
01:17:49,200 --> 01:17:52,237
maxing. Um, and that's an obvious going

1345
01:17:52,320 --> 01:17:54,477
to be an obvious era area of debate.

1346
01:17:54,560 --> 01:17:58,077
People are happy to go talk about it,
say, you know, CEOs might say, "Hey,

1347
01:17:58,160 --> 01:18:02,077
let's stop doing this." Um, but there
has to be all these other kind of

1348
01:18:02,160 --> 01:18:07,037
pockets of interesting moments where we
won't hear about them until they become

1349
01:18:07,120 --> 01:18:09,117
kind of like standard operating procedures

1350
01:18:09,200 --> 01:18:11,997
>> or you see it
in the the the uh earnings

1351
01:18:12,080 --> 01:18:13,277
and the economics piece, right? Yeah.

1352
01:18:13,360 --> 01:18:18,397
Yeah. So, I Yes. Unfortunately,
X is not the real world, [laughter]

1353
01:18:18,480 --> 01:18:20,957
>> you know, and there there's a lot
of grift and noise and, you know,

1354
01:18:21,040 --> 01:18:23,917
podcasting, PMing and, you know,
that kind of stuff that goes on,

1355
01:18:24,000 --> 01:18:27,357
>> but I I I think in the real world,
yes, there is the halves and have nots. I

1356
01:18:27,440 --> 01:18:31,677
mean, we were just talking about AIG,
like when you can start to actually do

1357
01:18:31,760 --> 01:18:35,917
the underwriting and, you know, have
quotes back in hours or days instead of

1358
01:18:36,000 --> 01:18:41,037
months on these highly complex enterprise,
you know, kind of insurance agreements.

1359
01:18:41,120 --> 01:18:43,677
>> If you don't have that,
how are you ever going to compete? Yeah.

1360
01:18:43,760 --> 01:18:46,957
>> And so when we think about this of the
N of one, right, you know, that those are

1361
01:18:47,040 --> 01:18:49,277
the companies that we're going after
and we see where there are those moments

1362
01:18:49,360 --> 01:18:51,517
that are not public. It's
the competitive advantage. interesting

1363
01:18:51,600 --> 01:18:54,397
category because you can imagine AIG,

1364
01:18:54,480 --> 01:18:57,597
you know, is um, you know, working with

1365
01:18:57,680 --> 01:19:01,757
a potential customer or renewing
a policy and that customer is going and

1366
01:19:01,840 --> 01:19:03,837
talking to all of AIG's competitors.

1367
01:19:03,920 --> 01:19:04,317
Yep.

1368
01:19:04,400 --> 01:19:08,077
>> And uh, if AIG is able to turn around,

1369
01:19:08,160 --> 01:19:11,437
>> you know, a quote or a policy in 24

1370
01:19:11,520 --> 01:19:14,237
hours and then it takes another player,

1371
01:19:14,320 --> 01:19:18,637
>> you know, two weeks because it's, you
know, complicated. So many so many teams

1372
01:19:18,720 --> 01:19:22,157
will just say like hey we you know you
know especially once you have two bids

1373
01:19:22,240 --> 01:19:25,837
you can basically say like okay this
that third fourth fifth we'll kind of

1374
01:19:25,920 --> 01:19:28,637
wait on those because we
have a good option here

1375
01:19:28,720 --> 01:19:31,197
>> well it builds trust the other piece
here so when you think when you see

1376
01:19:31,280 --> 01:19:35,277
people operating that with that level of
efficiency what else can you do so I see

1377
01:19:35,360 --> 01:19:38,717
this whether I'm doing you know SAP
migrations the least sexy thing you

1378
01:19:38,800 --> 01:19:43,597
could talk about but hey if I can cut
your SAP migration >> let's give it up for

1379
01:19:43,680 --> 01:19:47,357
>> yeah it's like the the least you know
exciting thing on on paper but Actually,

1380
01:19:47,440 --> 01:19:50,797
if if you're spending hundreds
of millions of Yeah, you guys get it. But

1381
01:19:50,880 --> 01:19:54,077
hundreds of millions of dollars on a
migration and we can cut it in half.

1382
01:19:54,160 --> 01:19:56,237
>> Yeah, >> that's a massive deal.

1383
01:19:56,320 --> 01:19:59,197
>> Uh, back on the udaloop, observe,

1384
01:19:59,280 --> 01:20:02,477
orient, decide, act on the observation

1385
01:20:02,560 --> 01:20:05,197
side, what is the supply and demand

1386
01:20:05,280 --> 01:20:08,157
imbalance for dashboards?

1387
01:20:08,240 --> 01:20:12,397
Like, and what I mean by that is is when
you're working with a company, is there

1388
01:20:12,480 --> 01:20:14,477
is there more demand for dashboards?

1389
01:20:14,560 --> 01:20:16,877
more people asking, hey, we need
a dashboard for this, we need a dashboard

1390
01:20:16,960 --> 01:20:19,917
for that. And you have to back people
off and say, I don't know if the

1391
01:20:20,000 --> 01:20:21,357
dashboard's right for this. Like, you

1392
01:20:21,440 --> 01:20:23,277
might just want to do an ad hoc analysis

1393
01:20:23,360 --> 01:20:27,357
or actually go and see. Uh versus you're

1394
01:20:27,440 --> 01:20:30,557
seeing so much opportunity that you're
like, okay, we want to push dashboards

1395
01:20:30,640 --> 01:20:34,317
out everywhere. Like what? Walk me
through dashboarding right now because

1396
01:20:34,400 --> 01:20:37,517
I've always been like sort of like, oh,
the too many dashboards. You build them

1397
01:20:37,600 --> 01:20:38,637
and then no one looks at them.

1398
01:20:38,720 --> 01:20:39,997
>> Yeah, I want to kill all dashboards.

1399
01:20:40,080 --> 01:20:44,957
>> Okay, that's my perspective. [laughter]
dash I mean KPIs and dashboards should

1400
01:20:45,040 --> 01:20:48,317
be a byproduct of operational
applications where I'm making decisions.

1401
01:20:48,400 --> 01:20:51,117
So we talk about the udaloop I have
to actually act for things to hit the

1402
01:20:51,200 --> 01:20:52,877
bottom line be valuable >>
in the actual application

1403
01:20:52,960 --> 01:20:56,557
>> in the application. So as I need those
things and it's going to inform a better

1404
01:20:56,640 --> 01:20:58,877
decision. Yeah, >> that's
where I want those metrics. That

1405
01:20:58,960 --> 01:21:01,997
should be a byproduct. Not if I go out
with the goal of building a dashboard,

1406
01:21:02,080 --> 01:21:04,877
it's going to be the field
of dreams again. No one shows up.

1407
01:21:04,960 --> 01:21:07,917
>> And so yes, it should be you you're
going to have to build some of those

1408
01:21:08,000 --> 01:21:11,197
things. The other side of this also
is when you think about a data warehouse

1409
01:21:11,280 --> 01:21:14,717
like literally I won't go too deep into
this technical riff but like you know

1410
01:21:14,800 --> 01:21:19,037
Kimble and dimensional modeling
was built in 96 for scaling databases

1411
01:21:19,120 --> 01:21:22,477
and you're still modeling
in the same way in 2026

1412
01:21:22,560 --> 01:21:24,717
>> for your dashboard
your Tableau whatever

1413
01:21:24,800 --> 01:21:28,637
those things are and like that's not
actually how the world works in rows and

1414
01:21:28,720 --> 01:21:31,677
columns you need complex things to model
how the world really works and that's

1415
01:21:31,760 --> 01:21:35,117
what we think about the ontology which
means I can reuse it for an operational

1416
01:21:35,200 --> 01:21:37,997
application KPIs agents all on one

1417
01:21:38,080 --> 01:21:42,477
single ontology which it makes it the
compound effect where as I add things in

1418
01:21:42,560 --> 01:21:47,357
I'm now compounding with each individual
decision I'm design working with gets

1419
01:21:47,440 --> 01:21:50,717
better and better and better for the next
use cases I connect across my business.

1420
01:21:50,800 --> 01:21:53,277
>> Yeah. Is there an analogy there to just

1421
01:21:53,360 --> 01:21:55,437
the deployment of AI tools currently?

1422
01:21:55,520 --> 01:22:00,877
I'm I'm just reflecting on the the NoSQL
boom and I don't know how strong this

1423
01:22:00,960 --> 01:22:03,677
was. This is probably just like
an online take but this idea of like why

1424
01:22:03,760 --> 01:22:06,477
would you ever want a relational
database? Why would you ever want a

1425
01:22:06,560 --> 01:22:08,317
schema? Don't never do a migration ever

1426
01:22:08,400 --> 01:22:11,837
again. Uh and the future looked like a

1427
01:22:11,920 --> 01:22:15,997
win-win almost like I I think Postgress
installations probably grew and so did

1428
01:22:16,080 --> 01:22:19,997
MongoDB and other non-reational databases.
uh and people use Reddus for

1429
01:22:20,080 --> 01:22:22,957
things and they use all sorts
of different tools and we built and we

1430
01:22:23,040 --> 01:22:25,997
stood on the shoulders of giants and we
got more giants and then you know that

1431
01:22:26,080 --> 01:22:27,357
means full employment for you obviously

1432
01:22:27,440 --> 01:22:30,717
but uh but I'm wondering like as like

1433
01:22:30,800 --> 01:22:34,717
are you seeing uh glimmers of of the AI

1434
01:22:34,800 --> 01:22:37,197
tools eating into different pieces of

1435
01:22:37,280 --> 01:22:39,197
the technical stacks or is it all like

1436
01:22:39,280 --> 01:22:41,917
yes and across the enterprises? Um I

1437
01:22:42,000 --> 01:22:46,237
think it's yes and and in in a couple
different things there is when you think

1438
01:22:46,320 --> 01:22:49,517
about the real world it is not just
rows and columns you can't describe

1439
01:22:49,600 --> 01:22:53,357
everything with measures and attributes
>> and so it's actually multimodal and so

1440
01:22:53,440 --> 01:22:55,997
like we think about this in our ontology
where you can have one semantic object

1441
01:22:56,080 --> 01:23:00,717
that actually has a CAD file and an
image a CB model and tabular stuff in

1442
01:23:00,800 --> 01:23:02,477
one semantic thing of a plant.

1443
01:23:02,560 --> 01:23:04,877
>> Yeah. >> Which means
I'm starting to talk in the

1444
01:23:04,960 --> 01:23:08,957
language of my business. So being able
to have the multimodal representation

1445
01:23:09,040 --> 01:23:12,637
where in other places oh I have
to have MongoDB and I have to have a SQL

1446
01:23:12,720 --> 01:23:15,837
database here and I have to have
an S3 bucket here to put all of these

1447
01:23:15,920 --> 01:23:20,157
different things to store them in ways
well we can do that all in the ontology

1448
01:23:20,240 --> 01:23:23,437
vectors everything else so that that's
really the the goal around how do I

1449
01:23:23,520 --> 01:23:27,357
model the real world how it actually
works and make that transparent so

1450
01:23:27,440 --> 01:23:30,077
you're not having to figure out which
technology to put in a time series thing

1451
01:23:30,160 --> 01:23:34,797
for sensors on a you know oil platform
>> don't care right and that that's where

1452
01:23:34,880 --> 01:23:38,317
we want to have the non- differentiated
heavy lifting like truly in the platform

1453
01:23:38,400 --> 01:23:40,957
to remove the friction
about getting stuff done.

1454
01:23:41,040 --> 01:23:42,717
>> How common is it for a business with

1455
01:23:42,800 --> 01:23:45,437
more than a hund00 million of revenue to

1456
01:23:45,520 --> 01:23:50,397
have very little understanding of how
their business actually works? Like

1457
01:23:50,480 --> 01:23:53,517
maybe they own maybe they own maybe they
know like the main thing which is like

1458
01:23:53,600 --> 01:23:56,397
you know >> we make a product
and try to sell it for

1459
01:23:56,480 --> 01:23:59,037
more than it costs to deliver. Yeah. Um

1460
01:23:59,120 --> 01:24:05,037
but uh but but is is some element of uh

1461
01:24:05,120 --> 01:24:08,477
how how much can chaos and mystery be

1462
01:24:08,560 --> 01:24:12,317
reduced effectively today? Because it
feels like we're entering an era like

1463
01:24:12,400 --> 01:24:16,637
>> you go back um >> you know 50 years and

1464
01:24:16,720 --> 01:24:20,157
>> uh >> the level
of like mystery in a large

1465
01:24:20,240 --> 01:24:23,997
company would have been like is almost
unconceivable today, right? because you

1466
01:24:24,080 --> 01:24:26,317
have different time zones, different

1467
01:24:26,400 --> 01:24:31,997
offices, you know, no email, all that
stuff. And now like mystery and chaos is

1468
01:24:32,080 --> 01:24:33,037
probably

1469
01:24:33,120 --> 01:24:36,557
>> uh reduced dramatically, but uh still

1470
01:24:36,640 --> 01:24:39,197
there's companies that that uh maybe

1471
01:24:39,280 --> 01:24:43,597
maybe before you start working with them,
I'm curious what those look like.

1472
01:24:43,680 --> 01:24:45,517
>> Yeah, I mean we
work with a lot of a lot

1473
01:24:45,600 --> 01:24:49,357
of different varieties of companies.
Um you know, I joke that a lot of times,

1474
01:24:49,440 --> 01:24:52,797
you know, companies make money
by accident. like they don't actually know

1475
01:24:52,880 --> 01:24:55,597
what their most profitable product
is and often they're trying to sell the

1476
01:24:55,680 --> 01:24:58,477
thing that isn't isn't actually the most
profitable and actually not selling the

1477
01:24:58,560 --> 01:25:01,837
thing that actually is profitable and it
comes back to how they've modeled their

1478
01:25:01,920 --> 01:25:04,877
data is to aggregate it up to KPIs and
other metrics when you actually need to

1479
01:25:04,960 --> 01:25:07,597
model at the finest grade how your
business operates to get a true cost of

1480
01:25:07,680 --> 01:25:12,557
goods sold for example or true cost
to serve like you that's very complicated

1481
01:25:12,640 --> 01:25:15,757
it's very complex so like we really
think about how do I embrace that

1482
01:25:15,840 --> 01:25:19,757
complexity so that I can truly
understand tactically at the edge how do

1483
01:25:19,840 --> 01:25:23,197
I do more of the things that are good
and less of the bad. It's that simple.

1484
01:25:23,280 --> 01:25:26,797
And those get peanut buttered across
with KPIs and metrics. And people don't

1485
01:25:26,880 --> 01:25:29,757
actually know how their businesses are
operating. I can't tell you whether it's

1486
01:25:29,840 --> 01:25:33,597
a hundred million dollar company or a $50
billion company how many times I see

1487
01:25:33,680 --> 01:25:37,677
this that they don't actually understand
how they're making money at a fine grain.

1488
01:25:37,760 --> 01:25:42,077
>> Yeah. >> Uh last question. Uh,

1489
01:25:42,160 --> 01:25:46,157
is there a world in the future where
a company gets created, let's say on

1490
01:25:46,240 --> 01:25:49,597
Stripe Atlas, and the first
account they sign up for

1491
01:25:49,680 --> 01:25:52,317
>> other than that is,
let's say, a Palunteer.

1492
01:25:52,400 --> 01:25:54,957
>> Yes, that's interesting.
>> I would love that. And so, we do have a

1493
01:25:55,040 --> 01:25:57,757
Palanteer for builders program. We have
small companies that there's people here

1494
01:25:57,840 --> 01:26:01,037
that are two person startups, you know,

1495
01:26:01,120 --> 01:26:04,797
that are working in their attict
in Canada. I mean, like, so it it is

1496
01:26:04,880 --> 01:26:08,557
literally um any size company. Come come
work. There's a free dev tier people can

1497
01:26:08,640 --> 01:26:11,757
come build. You actually there's
actually a Shopify integration in

1498
01:26:11,840 --> 01:26:14,557
Palunteer. You can go hook up to your
Shopify and pull in Palanteer. Yeah,

1499
01:26:14,640 --> 01:26:17,677
there are people doing this. Now, are
we always great at selling it or telling

1500
01:26:17,760 --> 01:26:20,877
the story? Sure. No.
>> But but there are companies doing this

1501
01:26:20,960 --> 01:26:23,917
and I do think there's a day where it's
going to be ubiquitous because I also

1502
01:26:24,000 --> 01:26:27,037
think, you know, there's some some guys
here that have, you know, they hey, my

1503
01:26:27,120 --> 01:26:29,357
my business is dying. I was, you know, I

1504
01:26:29,440 --> 01:26:34,157
was down 10% negative margin on on
what I was selling and through using

1505
01:26:34,240 --> 01:26:37,917
Palanteer. I they watched our YouTube
videos and they built it themselves and

1506
01:26:38,000 --> 01:26:40,797
increase to a 9 or 10%
positive margin in three months.

1507
01:26:40,880 --> 01:26:43,517
>> That's great. >> And so like
people can go do it. I think

1508
01:26:43,600 --> 01:26:46,797
that's the great American story is like
how do we enable that and I think we'll

1509
01:26:46,880 --> 01:26:48,477
get there. Um it might take a little time.

1510
01:26:48,560 --> 01:26:52,397
>> I love it. Well, thank you so much
for taking the time. Great to see you. We

1511
01:26:52,480 --> 01:26:57,197
will talk soon. Uh our next guest
is joining in just 15 minutes. We're going

1512
01:26:57,280 --> 01:27:01,037
to go back to the timeline. First I'm
going to tell you about Figma agents

1513
01:27:01,120 --> 01:27:03,677
meet the canvas. So your AI agents can

1514
01:27:03,760 --> 01:27:07,037
now create and modify Figma files with

1515
01:27:07,120 --> 01:27:11,997
design system context.
>> It's so crazy how many companies

1516
01:27:12,080 --> 01:27:14,477
>> Yeah. >> are their whole
strategy is like we're

1517
01:27:14,560 --> 01:27:16,157
going to hire guys like Chad. Yeah.

1518
01:27:16,240 --> 01:27:18,317
>> And they're gonna
they're going to do stuff.

1519
01:27:18,400 --> 01:27:20,397
>> He is he is he is the final boss of

1520
01:27:20,480 --> 01:27:25,597
>> FD meme. What what what drove the FTE
meme? Was it was it Palunteer going

1521
01:27:25,680 --> 01:27:29,037
public or was it >> I think it
was Palunteer going parabolic?

1522
01:27:29,120 --> 01:27:31,037
>> Maybe. Maybe. Yeah. Once once.

1523
01:27:31,120 --> 01:27:35,517
>> Yeah. because it just it just because
before it was like okay yeah successful

1524
01:27:35,600 --> 01:27:38,317
company but like no one really knows
where the valuation's going. Now it's

1525
01:27:38,400 --> 01:27:43,917
like my my uncle just told me that he
made a bunch of money and so I got pay

1526
01:27:44,000 --> 01:27:47,837
attention >> well that but
also they had been banging

1527
01:27:47,920 --> 01:27:52,317
the FTE drum >> or the
and getting the consulting

1528
01:27:52,400 --> 01:27:55,277
>> but people had earplugs in to the
banging of the drum and the and the

1529
01:27:55,360 --> 01:27:58,477
earplugs came out. >> Yeah.
But when you're when they were a

1530
01:27:58,560 --> 01:28:01,597
10 to20 billion dollar company, a lot of

1531
01:28:01,680 --> 01:28:05,757
people could still convince themselves
that they were right. It's just a

1532
01:28:05,840 --> 01:28:07,837
consulting business. >> Yeah.
Yeah. Yeah. Exactly. But

1533
01:28:07,920 --> 01:28:10,397
>> and that gets harder harder to ignore.

1534
01:28:10,480 --> 01:28:13,357
We covered this very briefly. Um but uh

1535
01:28:13,440 --> 01:28:17,597
but yeah, very excited for Joe
Rogan to be hosting his uh you know,

1536
01:28:17,680 --> 01:28:19,437
>> this is rumored.
This is a rumored leak.

1537
01:28:19,520 --> 01:28:22,877
It is not confirmed by any means yet,
but >> but I like the sound of it.

1538
01:28:22,960 --> 01:28:25,917
>> It would be it's a very
different direction. Uh

1539
01:28:26,000 --> 01:28:30,557
>> um this was a good post. I wanted
to bring it up. Buco Capital says, "It's

1540
01:28:30,640 --> 01:28:33,997
really incredible the absolute AI
garbage in all caps that people are

1541
01:28:34,080 --> 01:28:37,197
comfortable sending to their co-workers
and bosses. There's a good chance

1542
01:28:37,280 --> 01:28:40,877
productivity will actually decrease
as AI adoption increases because everyone

1543
01:28:40,960 --> 01:28:44,157
is busy waiting through uh AI slop. I

1544
01:28:44,240 --> 01:28:46,477
don't think I don't think it'll actually

1545
01:28:46,560 --> 01:28:48,477
I don't think it'll actually get there.

1546
01:28:48,560 --> 01:28:53,357
But I have had uh I have had moments
over the last month where somebody has

1547
01:28:53,440 --> 01:28:55,437
sent me, you know, a deck for their

1548
01:28:55,520 --> 01:28:59,117
company or materials and I can tell that

1549
01:28:59,200 --> 01:29:02,557
>> uh 90% of the work
that went into it was

1550
01:29:02,640 --> 01:29:07,037
on prompting. >> Yeah.
And uh I have a very like visceral

1551
01:29:07,120 --> 01:29:08,717
reaction toward it especially for like

1552
01:29:08,800 --> 01:29:12,717
early stage companies where uh ideas

1553
01:29:12,800 --> 01:29:14,477
and the way in which you go about doing

1554
01:29:14,560 --> 01:29:19,357
things matter so much that uh it's

1555
01:29:19,440 --> 01:29:23,117
almost like you know painting this initial
vision and things like your your

1556
01:29:23,200 --> 01:29:26,237
go to market um product differentiation

1557
01:29:26,320 --> 01:29:31,197
why you'll actually win like use AI
to make your team slide that's great right

1558
01:29:31,280 --> 01:29:35,037
just taking like a set of facts
and making it look good, right? You're

1559
01:29:35,120 --> 01:29:39,117
giving somebody a bio,
something like that. Um,

1560
01:29:39,200 --> 01:29:41,757
>> but I just remember uh I I I got this

1561
01:29:41,840 --> 01:29:44,957
deck. I was clicking through it. Um, and

1562
01:29:45,040 --> 01:29:49,517
I very uh respectfully said like go and

1563
01:29:49,600 --> 01:29:55,277
like do this yourself because uh just
because you've made something that that

1564
01:29:55,360 --> 01:29:59,037
looks like a deck. >> Yeah.
But you didn't do the sort of like

1565
01:29:59,120 --> 01:30:04,477
fundamental work to actually present
this in a way uh if you looked at each

1566
01:30:04,560 --> 01:30:07,997
slide individually. >> Yeah.
Your eyes kind of glaze over.

1567
01:30:08,080 --> 01:30:10,877
>> Yeah. >> And and you just sort
of like lose focus, stop paying attention.

1568
01:30:10,960 --> 01:30:13,277
>> Yeah. It's like it would have been
more it would have been more compelling to

1569
01:30:13,360 --> 01:30:16,557
actually just have a bulleted
list of like problem.

1570
01:30:16,640 --> 01:30:20,077
>> I mean a lot of times you can just send
me the prompt because I can instantiate

1571
01:30:20,160 --> 01:30:23,917
in my head. I can imagine the rest
of the paragraphs. I I have the context

1572
01:30:24,000 --> 01:30:26,557
window preloaded. Yeah. uh for for

1573
01:30:26,640 --> 01:30:28,877
myself. Yeah. Uh we should talk about

1574
01:30:28,960 --> 01:30:33,757
the new Audi, the Nuvo Lari. Is this real?
Motor one. This seems real. It's

1575
01:30:33,840 --> 01:30:36,317
real. >> Uh it's big deal.
It's the It's the

1576
01:30:36,400 --> 01:30:38,717
brand's first supercar since the R8.

1577
01:30:38,800 --> 01:30:43,517
Twinturbocharged 4 liter V8 hybrid. 217

1578
01:30:43,600 --> 01:30:47,517
mph top speed. That is 10% faster than a

1579
01:30:47,600 --> 01:30:51,597
Cayenne Turbo GT. What is the Cayenne
Turbo GT market doing right now? Is it

1580
01:30:51,680 --> 01:30:55,357
tanking? Depreciation must be just
through the roof on this news because

1581
01:30:55,440 --> 01:30:59,997
you have a car that's 10% faster and so
why every everyone is gonna be rotating

1582
01:31:00,080 --> 01:31:05,357
out. >> I mean I think they did I I think
the new Ari design. It's a really cool

1583
01:31:05,440 --> 01:31:07,597
design. >> Feels like
somewhat cybert truck.

1584
01:31:07,680 --> 01:31:10,317
>> Cyber punky futuristic.
I don't know. It

1585
01:31:10,400 --> 01:31:15,197
just checks the box for like the next
supercar for me. And uh in a way that the

1586
01:31:15,280 --> 01:31:17,037
>> Ben says it can't touch the R8.

1587
01:31:17,120 --> 01:31:19,677
>> Oh, can't touch the R8. Okay. Okay.

1588
01:31:19,760 --> 01:31:22,237
Well, it goes 0 to 60
in 2.6 seconds. Uh well

1589
01:31:22,320 --> 01:31:24,477
>> almost 1,000 horsepower.
Let me tell you

1590
01:31:24,560 --> 01:31:28,797
about Cisco. Critical infrastructure
for the AI era. Unlock seamless realtime

1591
01:31:28,880 --> 01:31:31,037
experiences and new value with Cisco.

1592
01:31:31,120 --> 01:31:34,717
And our next guest Sam Barry is here
from the USDA. Welcome to the show.

1593
01:31:34,800 --> 01:31:35,997
>> How are you doing? >> Very good.

1594
01:31:36,080 --> 01:31:39,837
>> Good to meet you. Thank you so much
for coming on down. Let's throw this on.

1595
01:31:39,920 --> 01:31:42,397
>> And just like that.
>> On the left side.

1596
01:31:42,480 --> 01:31:45,597
>> So >> good. >> Introduce yourself
a little. Tell us about yourself.

1597
01:31:45,680 --> 01:31:50,477
>> All right. Yeah. My name is Sam Barry.
I am uh proud to be working at the USDA.

1598
01:31:50,560 --> 01:31:52,477
Would you do that right now?
I'm the chief.

1599
01:31:52,560 --> 01:31:56,717
>> Nominative determinism. [laughter]
Do you know about nominative determinism?

1600
01:31:56,800 --> 01:31:59,517
>> No. >> It's the idea
that you you know a

1601
01:31:59,600 --> 01:32:01,837
person's name could possibly influence

1602
01:32:01,920 --> 01:32:05,677
or or uh the but but Barry and working

1603
01:32:05,760 --> 01:32:09,197
at the Department of Agriculture is like
pretty perfect. No, it's incredible.

1604
01:32:09,280 --> 01:32:14,317
Actually, my uh the berries came over
here from France in um >> like 1640.

1605
01:32:14,400 --> 01:32:16,317
>> Wow. >> So, we've
been here for a long time.

1606
01:32:16,400 --> 01:32:18,157
>> That's crazy.
>> And uh it was all farmers.

1607
01:32:18,240 --> 01:32:21,597
>> Yeah. >> Yeah.
I was like all farmers until my

1608
01:32:21,680 --> 01:32:26,797
grandpa then he became a materials
engineer actually worked on jet engines.

1609
01:32:26,880 --> 01:32:30,077
>> Okay. >> And so then
his sons became engineers.

1610
01:32:30,160 --> 01:32:32,797
My dad became an engineer. Then I
was an engineer. So we're kind of >> okay

1611
01:32:32,880 --> 01:32:33,837
>> trying to bring the two together.

1612
01:32:33,920 --> 01:32:35,197
>> Now you're going back to the USDA.

1613
01:32:35,280 --> 01:32:38,717
>> Yeah. >> What what is the shape of the
USDA? Like what what is the shape of the

1614
01:32:38,800 --> 01:32:43,597
organization headquarters? Do you go
to the office? Is this you know US? You

1615
01:32:43,680 --> 01:32:47,197
think just America international
footprint? Like do you travel for work?

1616
01:32:47,280 --> 01:32:49,917
What's it like working there? Well,
actually, it'd be kind of interesting to

1617
01:32:50,000 --> 01:32:53,437
ask you what you think, like what are
the things that you think USDA does?

1618
01:32:53,520 --> 01:32:55,997
>> Uh, they grade the milk in the states.

1619
01:32:56,080 --> 01:32:57,357
That's what I think about it. So, I

1620
01:32:57,440 --> 01:33:00,157
imagine that at some point farmers send

1621
01:33:00,240 --> 01:33:04,877
the cows to you and you kind of inspect
them and say, "This is a good cow." Is

1622
01:33:04,960 --> 01:33:06,477
that what happens? >> I don't know.

1623
01:33:06,560 --> 01:33:10,397
>> Um, there's like inspectors.
There's a whole area that does that.

1624
01:33:10,480 --> 01:33:12,077
>> There's like a series
of certifications,

1625
01:33:12,160 --> 01:33:15,437
but but but is but what else is happening?

1626
01:33:15,520 --> 01:33:17,997
>> So, all kinds of stuff.
Do you know that like food stamps?

1627
01:33:18,080 --> 01:33:19,917
>> Yeah, >> SNAP is inside of USDA.

1628
01:33:20,000 --> 01:33:21,437
>> Oh, I didn't know that.
>> I didn't know that either. I thought I

1629
01:33:21,520 --> 01:33:25,517
figured it was in like HHS or something,
but uh yeah, it's in USDA.

1630
01:33:25,600 --> 01:33:28,157
>> So, that's hundred billion dollars
a year. It's kind of a big deal.

1631
01:33:28,240 --> 01:33:31,437
>> Yeah. >> Um so, we do
we have SNAP that's in the

1632
01:33:31,520 --> 01:33:35,437
food nutrition service.
>> Uh Forest Service is inside of USDA.

1633
01:33:35,520 --> 01:33:38,717
Like crazy. >> Yeah. Yeah.
Uh, and then FPAC is like

1634
01:33:38,800 --> 01:33:43,037
what you would really think that USDA
it's like the farmer facing like okay

1635
01:33:43,120 --> 01:33:46,077
>> where farm programs are where they
do acreage reporting like the stuff I

1636
01:33:46,160 --> 01:33:47,197
talked about today. >> Got it.

1637
01:33:47,280 --> 01:33:49,037
>> Um, then there's rural development.

1638
01:33:49,120 --> 01:33:53,757
>> Okay. >> Which is like loans like
a bank basically do loans for all kinds of

1639
01:33:53,840 --> 01:33:59,117
things. >> Okay. >> Um, actually in some
of the reviews I came in on Doge and uh

1640
01:33:59,200 --> 01:34:02,077
>> uh there's like beachfront hotels that
are being funded out of RD. So there's

1641
01:34:02,160 --> 01:34:03,517
like a lot of things
that need to be cleaned up.

1642
01:34:03,600 --> 01:34:06,317
>> Okay. >> Um, [laughter] >> yeah.
And then there's like food

1643
01:34:06,400 --> 01:34:10,957
inspection service. And then there's
actually a huge scientific arm.

1644
01:34:11,040 --> 01:34:14,797
>> Yeah. >> That's inside of >>
That makes sense. Testing things and

1645
01:34:14,880 --> 01:34:17,597
>> Yeah. Like labs,
>> advancing different pesticides.

1646
01:34:17,680 --> 01:34:21,917
>> So things that I mean actually
become very passionate about it because I

1647
01:34:22,000 --> 01:34:24,717
certainly didn't have an appreciation
for I thought the same thing. It's like

1648
01:34:24,800 --> 01:34:28,157
grading meat. Yeah. You know, >> um

1649
01:34:28,240 --> 01:34:32,877
>> but like our we are so uniquely
positioned as a country because of the

1650
01:34:32,960 --> 01:34:34,157
fact that we can feed ourselves.

1651
01:34:34,240 --> 01:34:38,237
>> Yeah. Yeah. >> And like that is not
the case for a lot of a lot of countries.

1652
01:34:38,320 --> 01:34:41,677
>> Isn't America basically a net exporter
of food too? You hear about this in the

1653
01:34:41,760 --> 01:34:46,717
China debate all the time. Will they
buy XYZ product from us as a retaliation?

1654
01:34:46,800 --> 01:34:49,757
And uh yeah, you just
don't think about it. But uh

1655
01:34:49,840 --> 01:34:53,437
>> yeah. So like China can like minimally
feed itself like bare minimum it could

1656
01:34:53,520 --> 01:34:57,357
like keep itself alive.
>> Um but you know they're getting like

1657
01:34:57,440 --> 01:35:01,037
like we just did a big deal with them
to move a bunch of beef over there.

1658
01:35:01,120 --> 01:35:03,437
>> Yeah. >> And kind
of got some negative press

1659
01:35:03,520 --> 01:35:06,397
>> on that. So, it's important to know
it's uh I forget exactly what it's called,

1660
01:35:06,480 --> 01:35:09,837
but it's like the parts of the cow
that we don't eat here. So, it's a little

1661
01:35:09,920 --> 01:35:11,917
misleading to say like the amount
that we're sending over there.

1662
01:35:12,000 --> 01:35:14,717
>> Also, all these trade deals are
like very complex and there's like six

1663
01:35:14,800 --> 01:35:19,757
different moving parts. We get batteries
or they get the chips and like these are

1664
01:35:19,840 --> 01:35:22,237
always like, you know, seven part
negotiations. It's hard to look at

1665
01:35:22,320 --> 01:35:24,397
anyone in isolation. >> But,
I mean, I think it's a little

1666
01:35:24,480 --> 01:35:27,957
surprising that aggro like food
is actually part of that. I mean and then

1667
01:35:28,040 --> 01:35:33,357
[clears throat] in warfare uh like
agriculture the food supply is usually

1668
01:35:33,440 --> 01:35:36,637
hit before anything like kinetic even
happens you know and then before even

1669
01:35:36,720 --> 01:35:39,277
the world knows that it's
warfare >> you know okay

1670
01:35:39,360 --> 01:35:44,157
>> uh because you can do that and you can
do things to you know impact a nation's

1671
01:35:44,240 --> 01:35:47,197
food supply in the future and so
agriculture is like a really big deal

1672
01:35:47,280 --> 01:35:49,757
>> sure >> um really
important so all this to tie

1673
01:35:49,840 --> 01:35:53,357
back to I wanted to talk about the labs
because this is like a whole area inside

1674
01:35:53,440 --> 01:35:58,237
of uh USDA but uh we do all of these
these things like invest in figuring

1675
01:35:58,320 --> 01:36:01,517
out. So like personally I try and avoid
like GMOs and we eat, you know, like we

1676
01:36:01,600 --> 01:36:04,717
drink raw milk and we get
our meat from a local farm.

1677
01:36:04,800 --> 01:36:06,877
>> Um but GMOs are actually
really important. Yeah.

1678
01:36:06,960 --> 01:36:09,277
>> Because if we were
hit with some kind of

1679
01:36:09,360 --> 01:36:13,277
adverse event or something and we needed
to create corn that would could survive

1680
01:36:13,360 --> 01:36:17,037
a drought better like we have >>
the science and the research to be able

1681
01:36:17,120 --> 01:36:21,197
to do that. Got it. >> And it's a huge
edge that we have >> like geopolitically.

1682
01:36:21,280 --> 01:36:25,437
>> Interesting. Yeah. >> Yeah.
talk about uh over the years I've

1683
01:36:25,520 --> 01:36:27,997
read so many stories of you know this uh

1684
01:36:28,080 --> 01:36:32,557
this insect has been detected in you
know some region of the US and there's

1685
01:36:32,640 --> 01:36:34,957
speculation on is it um you know kind of

1686
01:36:35,040 --> 01:36:37,037
foreign interference things like that is

1687
01:36:37,120 --> 01:36:42,157
that is that in USDA domain is trying
to help monitor and track and make sure

1688
01:36:42,240 --> 01:36:46,157
that >> um pests yeah
pests pests like pests are

1689
01:36:46,240 --> 01:36:48,397
obviously naturally occurring right they

1690
01:36:48,480 --> 01:36:50,877
flourish for their own reasons or there

1691
01:36:50,960 --> 01:36:54,877
can be um some uh some sort of malicious

1692
01:36:54,960 --> 01:36:58,477
intent as well. Is that your guys' >>
Yeah, cuz they're not ne necessarily

1693
01:36:58,560 --> 01:37:01,357
naturally occurring, right?
>> And uh so one that we have going on

1694
01:37:01,440 --> 01:37:04,637
right now, and I'm not saying this one's
not naturally occurring, but the uh new

1695
01:37:04,720 --> 01:37:09,437
world screw screw worm that's coming up
through Mexico. So our secretary, which

1696
01:37:09,520 --> 01:37:12,317
by the way, I couldn't say enough good
things about Secretary Rollins. I mean,

1697
01:37:12,400 --> 01:37:14,237
she's incredible. Just an actual like

1698
01:37:14,320 --> 01:37:19,517
genuine good and like it's unbelievable
what she's able to accomplish. Uh, but

1699
01:37:19,600 --> 01:37:23,757
New World's screworm is something
that's falling in USDA's uh, you know,

1700
01:37:23,840 --> 01:37:27,117
responsibilities. And this is like
a parasite basically that's coming up

1701
01:37:27,200 --> 01:37:29,757
through Mexico and it's like a flesh
eating parasite. So it's like really

1702
01:37:29,840 --> 01:37:34,157
hardcore. Yeah. >> So we're
developing a lab >> flesh.

1703
01:37:34,240 --> 01:37:37,517
No sorts. No, but you know, I don't
think you want to be around it. But no,

1704
01:37:37,600 --> 01:37:42,077
it's for like cattle mostly is what it
impacts. Um, and so we're developing a

1705
01:37:42,160 --> 01:37:45,677
lab and like sterilizing flies, which
again, like personally, I don't really

1706
01:37:45,760 --> 01:37:49,677
like any of this stuff, but it's better
to be doing this and be able to protect

1707
01:37:49,760 --> 01:37:53,117
our nation than like if we let this just
come and flourish in our country, I

1708
01:37:53,200 --> 01:37:54,397
mean, it' be very detrimental.

1709
01:37:54,480 --> 01:37:57,437
>> Yeah. >> So, I'll go
back and and if and if it's

1710
01:37:57,520 --> 01:38:01,677
a necessary, you know, technique that
needs to be harnessed, it needs to be

1711
01:38:01,760 --> 01:38:05,037
harnessed securely and it needs to be
harnessed with uh, you know, the right

1712
01:38:05,120 --> 01:38:07,837
teams in place to make sure that
whatever's rolled out is rolled out

1713
01:38:07,920 --> 01:38:09,437
effectively and safely, right?

1714
01:38:09,520 --> 01:38:13,277
>> Yeah. I mean, I think it's just so
important. You know, there's uh like

1715
01:38:13,360 --> 01:38:17,437
techn there's so much farther we can
go with technology, but we have so much

1716
01:38:17,520 --> 01:38:20,717
right now. >> And uh so
many people are just black

1717
01:38:20,800 --> 01:38:22,797
pill, right? And I think it's important.

1718
01:38:22,880 --> 01:38:25,117
I think you should be like black pill
on certain things, but you should probably

1719
01:38:25,200 --> 01:38:27,117
take a lot of pills. Like you should be
red pill and black pill [laughter] and

1720
01:38:27,200 --> 01:38:30,237
white pill at the same time.
>> Uh because take

1721
01:38:30,320 --> 01:38:34,077
>> like we have a long way
to go and when we're just like

1722
01:38:34,160 --> 01:38:37,117
>> sitting feeling sorry for ourselves,
like it's not a good position to be in.

1723
01:38:37,200 --> 01:38:40,237
This is the most
incredible country on earth

1724
01:38:40,320 --> 01:38:44,317
>> and other countries are advancing
though. You know, our edge is like our

1725
01:38:44,400 --> 01:38:46,237
edge doesn't come for free.
>> No, we got to work at it.

1726
01:38:46,320 --> 01:38:47,837
>> We got to keep pushing at these things.

1727
01:38:47,920 --> 01:38:51,437
But when we do this, like when there's
a parasite that's, you know, coming into

1728
01:38:51,520 --> 01:38:54,637
our country and we're able
to just like use biology

1729
01:38:54,720 --> 01:38:56,877
>> to combat it. Like that's incredible

1730
01:38:56,960 --> 01:38:58,077
that our country can do that.

1731
01:38:58,160 --> 01:39:02,317
>> Talk about uh these more SMB scale

1732
01:39:02,400 --> 01:39:04,397
farmers and their approach to

1733
01:39:04,480 --> 01:39:06,957
technology. Uh I think a lot of people

1734
01:39:07,040 --> 01:39:11,517
would be surprised at how much uh how
much these individuals at least from

1735
01:39:11,600 --> 01:39:13,357
from what I've experienced are happy to

1736
01:39:13,440 --> 01:39:16,077
lean into technology. I met uh a group

1737
01:39:16,160 --> 01:39:19,117
in Texas that had developed this was

1738
01:39:19,200 --> 01:39:21,837
years ago so pre-ai boom developed their

1739
01:39:21,920 --> 01:39:24,077
own SAS product to help manage their

1740
01:39:24,160 --> 01:39:26,957
operations like a a a tool that that

1741
01:39:27,040 --> 01:39:29,197
they had built by discovering problems

1742
01:39:29,280 --> 01:39:33,037
that they had uh on their property. And

1743
01:39:33,120 --> 01:39:37,117
I just thought that was um that was
really fascinating and and cool at the

1744
01:39:37,200 --> 01:39:38,397
time because I think Silicon Valley

1745
01:39:38,480 --> 01:39:41,517
would have maybe some expectation uh

1746
01:39:41,600 --> 01:39:46,717
that uh that there might be an aversion
to that until you get into the more like

1747
01:39:46,800 --> 01:39:51,037
enterprisegrade uh scale. >> Yeah,
I think it's a really important

1748
01:39:51,120 --> 01:39:54,077
topic because you're essentially talking
about like democratizing access to

1749
01:39:54,160 --> 01:39:56,797
technology, right? And certainly with uh

1750
01:39:56,880 --> 01:40:01,357
with like AI becoming so much more
widely available, uh that was a big step

1751
01:40:01,440 --> 01:40:04,797
forward. But I mean, and this is a big
point that's being hit on at this

1752
01:40:04,880 --> 01:40:06,717
conference and what Palanteer is really

1753
01:40:06,800 --> 01:40:12,237
focusing on is those LLMs become
useless if they're not uh if you're not

1754
01:40:12,320 --> 01:40:16,077
deploying them in the right way with the
right like data boundaries, right? So,

1755
01:40:16,160 --> 01:40:18,477
um you know, I think that's something
that we're seeing even in our

1756
01:40:18,560 --> 01:40:22,877
universities. We do a lot of university
research and uh like all the you know

1757
01:40:22,960 --> 01:40:26,157
kids or whatever the the university
students like they're wanting to do

1758
01:40:26,240 --> 01:40:29,117
experiments with LLMs and do like meat
grading like better meat grading because

1759
01:40:29,200 --> 01:40:32,317
that's something that can happen
at the farms and uh if you can make that

1760
01:40:32,400 --> 01:40:34,477
automated then you know our ability to

1761
01:40:34,560 --> 01:40:36,957
produce beef uh you know is is greatly

1762
01:40:37,040 --> 01:40:41,597
impacted. Um, but there's a major issue
in secession planning right now for

1763
01:40:41,680 --> 01:40:44,877
farms, right? Like this is a big
thing that's happening. Like the farmer

1764
01:40:44,960 --> 01:40:48,477
generation is getting very old and kids
don't want to go and and run the farm.

1765
01:40:48,560 --> 01:40:51,997
>> A lot of them went to big cities
and jobs and white color work and stuff.

1766
01:40:52,080 --> 01:40:56,237
>> So, uh, you know, this is a big thing
that is a H2A. Yeah. You know, these H2A

1767
01:40:56,320 --> 01:40:58,717
visas where like a lot of the farmers
are actually still saying like we need

1768
01:40:58,800 --> 01:41:01,597
the help from, you know,
we need immigrants to come and help us

1769
01:41:01,680 --> 01:41:04,797
>> and, uh, you know, the best way that we
can solve that is through automation.

1770
01:41:04,880 --> 01:41:07,997
So, I think that >> that's
something I would love to see

1771
01:41:08,080 --> 01:41:12,157
USDA do more of or, you know,
uh >> it's something that needs to be

1772
01:41:12,240 --> 01:41:14,957
answered. I don't have an answer for you
right now. Yeah. But in order for us to

1773
01:41:15,040 --> 01:41:18,397
continue to, you know, remain
self-sufficient and providing food.

1774
01:41:18,480 --> 01:41:21,117
>> Yeah. Whenever you have a dwindling
workforce, increasing the leverage and

1775
01:41:21,200 --> 01:41:25,677
productivity of the existing workforce
allows you to maintain overall aggregate

1776
01:41:25,760 --> 01:41:29,117
productivity. This is Yeah. general
technological leverage. So, it makes a

1777
01:41:29,200 --> 01:41:31,517
ton of sense. >> Do you know
anybody that's becoming a farmer?

1778
01:41:31,600 --> 01:41:33,357
>> Uh, well, we know some folks. We've had

1779
01:41:33,440 --> 01:41:35,277
a number of uh entrepreneurs on the show

1780
01:41:35,360 --> 01:41:38,797
who are getting into a tech building. Uh

1781
01:41:38,880 --> 01:41:43,037
we've had the founder of the laser
weeder that uh uses uh a lot of people

1782
01:41:43,120 --> 01:41:46,877
don't like pesticides, but they don't
mind if uh if a pest is zapped with a

1783
01:41:46,960 --> 01:41:51,117
laser because that's just heat that's
being transferred to the particular

1784
01:41:51,200 --> 01:41:54,477
plant right there and the tomato plant
continues flourishing. So it uses just

1785
01:41:54,560 --> 01:41:59,117
cameras and lasers. Very cool sort of
modern solution to something that people

1786
01:41:59,200 --> 01:42:02,797
have had a lot of beer around about
around different pesticides. fruit

1787
01:42:02,880 --> 01:42:07,277
picking robotics company. >> Yeah,
Orchard as well. Uh but but mostly

1788
01:42:07,360 --> 01:42:11,677
from tech side, usually with some family
lineage uh sort of returning to the

1789
01:42:11,760 --> 01:42:16,237
roots or or tapping into their networks
uh to go back, but uh I mean truthfully,

1790
01:42:16,320 --> 01:42:19,757
I don't know that many people that I
grew up with. I mean, I grew up in LA,

1791
01:42:19,840 --> 01:42:23,517
so not much farming activity. I know
one family that had an avocado farm.

1792
01:42:23,600 --> 01:42:26,557
>> I mean, it actually it would be super
based to be a large scale farmer. Like

1793
01:42:26,640 --> 01:42:29,437
more people should do it. And um >>
we need a maybe you could be the Alex

1794
01:42:29,520 --> 01:42:32,637
Hormosi of farming. >> Yeah.
No, for real. I mean, you can. So,

1795
01:42:32,720 --> 01:42:35,437
USDA, one of the great things that
USDA does is you can get financial

1796
01:42:35,520 --> 01:42:39,037
assistance. Like, you get big time like
big time loans from USDA. You have to go

1797
01:42:39,120 --> 01:42:41,917
through the process and they're actually
doing a loan modernization effort right

1798
01:42:42,000 --> 01:42:45,277
now to try and make that better. But >>
like USDA, we'll fund it for you. You

1799
01:42:45,360 --> 01:42:49,517
got to pay it back, but you can like get
the interest rates low rate subsidized.

1800
01:42:49,600 --> 01:42:53,517
Yeah. >> Yeah. I mean, one of our uh
administrators at USDA, he like pulled

1801
01:42:53,600 --> 01:42:56,157
up his phone one day and he's like,
"Look, it's a planting day for me." And

1802
01:42:56,240 --> 01:42:59,517
it was his John Deere app. It's like
the most advanced, you know, like

1803
01:42:59,600 --> 01:43:02,637
>> uh he had all these tractors going
and there's still people sitting in the

1804
01:43:02,720 --> 01:43:05,837
tractors, but it's to the point where
it basically could be fully automated.

1805
01:43:05,920 --> 01:43:08,957
>> So, I mean, you can get yourself
a couple thousand acres and just start,

1806
01:43:09,040 --> 01:43:13,197
you know, growing corn or wheat or cotton
like cotton and then, you know, whatever.

1807
01:43:13,280 --> 01:43:17,677
>> Uh talk about data collection. I feel
like data is the lifeblood of, uh, you

1808
01:43:17,760 --> 01:43:21,597
know, any decision- making, any udaloop,
anything related to Palunteer, USDA. And

1809
01:43:21,680 --> 01:43:23,037
I'm wondering about like you mentioned

1810
01:43:23,120 --> 01:43:25,757
that screw worm. You got to track that

1811
01:43:25,840 --> 01:43:28,637
thing. it shows up on some cattle

1812
01:43:28,720 --> 01:43:32,397
rancher's farm and they're detecting it

1813
01:43:32,480 --> 01:43:35,037
or they're seeing symptoms, maybe they

1814
01:43:35,120 --> 01:43:39,437
know roughly what percentage of the herd
is affected, but how do they actually

1815
01:43:39,520 --> 01:43:40,797
get that information to you? Are they

1816
01:43:40,880 --> 01:43:43,757
going to usda.govreport

1817
01:43:43,840 --> 01:43:46,637
incident or are you pulling things from

1818
01:43:46,720 --> 01:43:49,197
their filings? Like h how how do you

1819
01:43:49,280 --> 01:43:51,997
want that to evolve? I imagine that that

1820
01:43:52,080 --> 01:43:56,877
with more AI and technology we're it's
only as good as the data that we can

1821
01:43:56,960 --> 01:44:00,477
actually put into the system. So just
broadly data collection where is that

1822
01:44:00,560 --> 01:44:02,637
going these days? >> Well if
you don't mind instead of screw

1823
01:44:02,720 --> 01:44:04,797
I'd like to focus
on SNAP for that question.

1824
01:44:04,880 --> 01:44:08,957
>> So SNAP is funded by the federal
government but it's administered by the

1825
01:44:09,040 --> 01:44:12,797
states. >> Okay. So uh when it comes to so
something that we're doing right now and

1826
01:44:12,880 --> 01:44:15,597
it was one of the first things that our
secretary did like on our first day was

1827
01:44:15,680 --> 01:44:18,557
she did a data call to all the states
that you know we want all of your snap

1828
01:44:18,640 --> 01:44:21,917
data to understand how because it's
our responsibility as the funer of this

1829
01:44:22,000 --> 01:44:25,357
program >> to understand [snorts]
the integ like to verify the integrity

1830
01:44:25,440 --> 01:44:27,997
of the program. >> So we put
a request out there but it has

1831
01:44:28,080 --> 01:44:29,117
to come from every single state.

1832
01:44:29,200 --> 01:44:31,277
>> Yeah. >> And a lot
of the state programs they're

1833
01:44:31,360 --> 01:44:34,157
not technical or they've got contractors
that you know it's just a difficult

1834
01:44:34,240 --> 01:44:37,037
thing to get us the data. Uh but then
there's also a bunch of states that are

1835
01:44:37,120 --> 01:44:39,597
just not complying, you know, for whatever
reason, which it shouldn't be a

1836
01:44:39,680 --> 01:44:41,277
problem. I don't understand
what the problem is.

1837
01:44:41,360 --> 01:44:46,557
>> Um >> but the importance of so that
program that's a hundred billion taxpayer

1838
01:44:46,640 --> 01:44:50,237
dollars a year. Like that's pretty
substantial. Um that's an area where we

1839
01:44:50,320 --> 01:44:52,717
really want to have all angles of the

1840
01:44:52,800 --> 01:44:57,277
data available so that we can deploy
AI and become really smart in detecting

1841
01:44:57,360 --> 01:45:00,957
fraud. Like we want to get it to the
point where um if somebody's committing

1842
01:45:01,040 --> 01:45:04,477
snap fraud, we should be able it's like
your card, right? If you if if somebody

1843
01:45:04,560 --> 01:45:07,837
stole your card and did a transaction
that wasn't recognized, like your card's

1844
01:45:07,920 --> 01:45:10,957
shut off. Yep. Right. Yep. >> So,
we want to get to the point where

1845
01:45:11,040 --> 01:45:14,317
we're very intelligent and we're
confident enough in the system that we

1846
01:45:14,400 --> 01:45:17,837
can do that. When there's fraud
detected, it's off immediately.

1847
01:45:17,920 --> 01:45:19,357
>> Uh because it's an important program.

1848
01:45:19,440 --> 01:45:22,637
You know, we want to be able to support
people that can't support themselves,

1849
01:45:22,720 --> 01:45:26,477
but it's uh it's not arguable that
there's a massive amount of fraud in

1850
01:45:26,560 --> 01:45:30,637
there. I mean, even the uh
the organization itself does like an audit

1851
01:45:30,720 --> 01:45:35,357
every year and they're at like there's
12% improper payments. Um improper

1852
01:45:35,440 --> 01:45:36,477
payments is kind of a bad word.

1853
01:45:36,560 --> 01:45:40,397
>> So 12 12 billion a year. >> Yeah.
Right. And that's just like kind

1854
01:45:40,480 --> 01:45:42,957
of based on >> that's
money that that's that could

1855
01:45:43,040 --> 01:45:45,277
actually be going towards the intent of

1856
01:45:45,360 --> 01:45:50,877
the program, which is to provide >>
food to people that otherwise wouldn't

1857
01:45:50,960 --> 01:45:53,837
be able to get it. And there's other,
you know, you could like grock how SNAP

1858
01:45:53,920 --> 01:45:58,157
has been used to fund uh like
international crime organizations and

1859
01:45:58,240 --> 01:46:02,397
like terrorist groups and everything. So
it's it's being exploited at a at a huge

1860
01:46:02,480 --> 01:46:05,677
level. And uh I mean it's something
that our secretary has prioritized, but

1861
01:46:05,760 --> 01:46:08,157
that's probably our biggest fpack. What

1862
01:46:08,240 --> 01:46:10,557
I talked about today is like our most

1863
01:46:10,640 --> 01:46:14,637
complex system of data, but the SNAP

1864
01:46:14,720 --> 01:46:19,357
challenge is uh like the biggest or like
this the snap environment is probably

1865
01:46:19,440 --> 01:46:20,957
the biggest challenge on the data front.

1866
01:46:21,040 --> 01:46:26,637
Mhm. >> What's next for you? You making a
career out of this or you going to go be a

1867
01:46:26,720 --> 01:46:31,037
farmer? >> Hopefully both.
Okay. Yeah. Yeah. I mean, um

1868
01:46:31,120 --> 01:46:35,037
>> yeah, I've got some farmland. Um trying
to convert it in It's like woods right

1869
01:46:35,120 --> 01:46:37,917
now, but um >> uh where's that?

1870
01:46:38,000 --> 01:46:39,997
>> Uh in Virginia,
so actually when I lived

1871
01:46:40,080 --> 01:46:43,837
in Michigan, we had like a little bit
of a farm. We had some goats and sheep and

1872
01:46:43,920 --> 01:46:47,597
a bunch of chickens and and ducks.
Um you don't ever want to get you don't

1873
01:46:47,680 --> 01:46:51,437
want to get ducks. You don't want to get
goats. Um, ducks are like really savage

1874
01:46:51,520 --> 01:46:55,437
actually. Yeah, like a chicken sleeps,
you know? So, like it's got a normal

1875
01:46:55,520 --> 01:46:58,157
cycle like at night time it goes
into the coupe and it like sleeps.

1876
01:46:58,240 --> 01:47:01,677
>> Ducks don't sleep. >> No,
ducks do not sleep. No, they like in

1877
01:47:01,760 --> 01:47:05,117
our house was kind of this like really
unique house. So, the windows were like

1878
01:47:05,200 --> 01:47:08,817
on the ground and the ducks would come
and just stare at us in the window.

1879
01:47:08,900 --> 01:47:11,837
[laughter] No, they're savage. They just
like they sleep for like 10 minutes at a

1880
01:47:11,920 --> 01:47:14,717
time. So, they'll just like waddle
around and then sleep for 10 minutes.

1881
01:47:14,800 --> 01:47:17,997
You have to have the right balance
of female and male ducks. Otherwise, it's

1882
01:47:18,080 --> 01:47:21,117
like really ugly. >> Yeah,
chickens are a lot. I grew I grew

1883
01:47:21,200 --> 01:47:25,677
up with chickens and uh most of the time
they're they're cool. My dad would build

1884
01:47:25,760 --> 01:47:27,997
>> these sort of like complex contraptions

1885
01:47:28,080 --> 01:47:30,957
to automate the opening enclosure. So,

1886
01:47:31,040 --> 01:47:33,837
he would use like irrigation to

1887
01:47:33,920 --> 01:47:36,237
>> uh on a timer to fill a bucket which

1888
01:47:36,320 --> 01:47:38,557
would lift there would lift it up. Yeah.

1889
01:47:38,640 --> 01:47:41,117
>> Okay. Interesting. Um but but then I

1890
01:47:41,200 --> 01:47:44,317
still core memories as a kid was waking

1891
01:47:44,400 --> 01:47:48,717
up my dad would yell like there's
a fox in the coupe and then we'd be like

1892
01:47:48,800 --> 01:47:52,637
running out. We'd be it would be like
game on. Yeah. Yeah. Or you get like

1893
01:47:52,720 --> 01:47:54,957
skunks [clears throat]
in there and coyotes maybe.

1894
01:47:55,040 --> 01:47:58,717
>> Yeah. We would just everybody
would get up and [laughter] try to go

1895
01:47:58,800 --> 01:48:01,197
>> deal with be satisfying.
That's way more

1896
01:48:01,280 --> 01:48:02,877
satisfying than some software bug.

1897
01:48:02,960 --> 01:48:06,157
there's so much uh fear and doom and and

1898
01:48:06,240 --> 01:48:09,837
blackpilling uh around data centers. Uh

1899
01:48:09,920 --> 01:48:13,997
I wanted to hear from you how you're I

1900
01:48:14,080 --> 01:48:16,077
imagine your your role is to be an

1901
01:48:16,160 --> 01:48:19,597
advocate for for farmers as well on on

1902
01:48:19,680 --> 01:48:22,477
on water supplies, things like that.

1903
01:48:22,560 --> 01:48:25,757
California went through,
you know, probably

1904
01:48:25,840 --> 01:48:28,157
>> many many really
rough years from a from

1905
01:48:28,240 --> 01:48:32,957
a water supply um and a water scarcity
standpoint. Thankfully, you know, had a

1906
01:48:33,040 --> 01:48:34,957
lot of rains over the last few years,

1907
01:48:35,040 --> 01:48:38,157
but how how are you working with farmers

1908
01:48:38,240 --> 01:48:41,357
or or what is the situation around um

1909
01:48:41,440 --> 01:48:43,997
the the the kind of like tension between

1910
01:48:44,080 --> 01:48:46,397
a lot of farmland could also be great

1911
01:48:46,480 --> 01:48:51,597
land for data centers, right?
And there's been some um uh pretty

1912
01:48:51,680 --> 01:48:54,957
high-profile stories where farmers
either sold their land, but from your

1913
01:48:55,040 --> 01:48:58,237
side, you're trying to make sure that we
have, you know, can produce an abundance

1914
01:48:58,320 --> 01:49:01,197
of food, you know, from a from a national
security standpoint. or how are

1915
01:49:01,280 --> 01:49:02,557
you guys thinking about that balance?

1916
01:49:02,640 --> 01:49:05,357
>> Yeah, I mean I think the best solution
is putting the data centers in space,

1917
01:49:05,440 --> 01:49:09,117
you know, like which is totally led
by Elon and people are jumping on that

1918
01:49:09,200 --> 01:49:11,837
train, but it's going to be a couple
years it sounds like before they're to

1919
01:49:11,920 --> 01:49:16,077
that point. We're actually uh USDA
is pursuing a partnership with SpaceX.

1920
01:49:16,160 --> 01:49:20,077
>> Um and that that part isn't isn't
ready yet. We don't really have a need for

1921
01:49:20,160 --> 01:49:23,357
that, but it's there's a partnership
on the technical side, but there's also

1922
01:49:23,440 --> 01:49:26,797
just on the like conceptual side of the
fact that like we're align because we do

1923
01:49:26,880 --> 01:49:30,557
care about conservation. um you know
there was a lot of green stuff that was

1924
01:49:30,640 --> 01:49:33,437
like you know sure not stuff that we
care about but we do care about

1925
01:49:33,520 --> 01:49:36,957
conserving our land and uh putting data
centers in space just makes a ton of

1926
01:49:37,040 --> 01:49:40,237
sense but that being a couple years
out so for today you know I'm actually

1927
01:49:40,320 --> 01:49:44,077
pretty passionate about this because
in my hometown of Selen Michigan it's like

1928
01:49:44,160 --> 01:49:46,797
small town mostly farmland they're

1929
01:49:46,880 --> 01:49:48,717
putting a data center in there

1930
01:49:48,800 --> 01:49:51,437
>> and it's like you know 30 miles from

1931
01:49:51,520 --> 01:49:54,157
Detroit and Flint and like all these

1932
01:49:54,240 --> 01:49:59,037
very industrialized areas and so it's
very confusing to me why we wouldn't be

1933
01:49:59,120 --> 01:50:02,397
putting these data center and they're
like struggling areas. Detroit's doing

1934
01:50:02,480 --> 01:50:04,637
all right, but like
Flint struggling big time.

1935
01:50:04,720 --> 01:50:08,317
>> Uh like why not put a data center there
where there's already the infrastructure

1936
01:50:08,400 --> 01:50:11,357
there's like >> it's already
developed land, but instead

1937
01:50:11,440 --> 01:50:13,757
it's like taking these small townships

1938
01:50:13,840 --> 01:50:18,397
and and plopping them in the middle. Um
and the people don't really like it. Now

1939
01:50:18,480 --> 01:50:22,237
the boards seem to like it for some
reason, the councils. So I don't know

1940
01:50:22,320 --> 01:50:24,077
what's up with that. Um but it doesn't

1941
01:50:24,160 --> 01:50:28,237
align. It creates it creates a massive

1942
01:50:28,320 --> 01:50:32,077
amount of tax revenue that can be used
to fund a bunch of other programs that

1943
01:50:32,160 --> 01:50:36,157
>> but it's got to actually flow back
to the people who are in the town and I

1944
01:50:36,240 --> 01:50:39,837
think that there's like a disconnect
there sometimes. I actually this is kind

1945
01:50:39,920 --> 01:50:43,597
of outside but something that I do think
is uh probably going to happen is you

1946
01:50:43,680 --> 01:50:47,117
know there was this big shift to go to
the cloud right it's like everybody kind

1947
01:50:47,200 --> 01:50:50,157
of had their own servers you know it's
on prem and now we're in the cloud and

1948
01:50:50,240 --> 01:50:53,997
it's like really you just took you
like moved it across the street right

1949
01:50:54,080 --> 01:50:56,077
>> um and now that people
are becoming more

1950
01:50:56,160 --> 01:51:00,317
aware of like what that means and when
it's like oh my data is in AWS or you

1951
01:51:00,400 --> 01:51:03,357
know it's like and maybe this is a global
company and how much can I really

1952
01:51:03,440 --> 01:51:07,917
trust this company that there's going
to be a shift back to caring actually

1953
01:51:08,000 --> 01:51:11,037
actually caring about where your data is
living. Interesting. So I think a good a

1954
01:51:11,120 --> 01:51:14,877
b business opportunity would be I I
think there's a world where there's a

1955
01:51:14,960 --> 01:51:18,397
culture that comes up around data
centers because like me personally like

1956
01:51:18,480 --> 01:51:22,877
I want to build like my house is like
uh like I'll have a kill switch for my

1957
01:51:22,960 --> 01:51:25,517
Wi-Fi and then like we've
got the data in the basement.

1958
01:51:25,600 --> 01:51:27,037
>> You got your raw milk supply.

1959
01:51:27,120 --> 01:51:30,157
>> Got raw milk. No, like we're ready to
go. I mean we're I I was ready to go off

1960
01:51:30,240 --> 01:51:32,317
the grid before I came and joined
the government. [laughter] This is a much

1961
01:51:32,400 --> 01:51:35,597
better option. But um still like I care
about my data. I don't really want to

1962
01:51:35,680 --> 01:51:38,797
use YouTube music anymore for my music
cuz now my recommendations are getting

1963
01:51:38,880 --> 01:51:42,157
worse and you're like very beholden to
that. It's like I could very easily just

1964
01:51:42,240 --> 01:51:45,197
>> have the music >> buy my music
and write a simple program

1965
01:51:45,280 --> 01:51:48,317
to like make my recommendations and it
would be way better because there's

1966
01:51:48,400 --> 01:51:51,357
certain artists that are not getting
recommended because they're not, you

1967
01:51:51,440 --> 01:51:53,597
know, prioritized behind
the scenes >> or something.

1968
01:51:53,680 --> 01:51:56,797
>> So, but not everybody's going to want
to manage their own servers, right?

1969
01:51:56,880 --> 01:51:59,837
>> Jensen just announced a data center
that bolts onto the side of your house.

1970
01:51:59,920 --> 01:52:02,477
>> Oh, that's sweet.
>> And uh I mean and there's more stuff

1971
01:52:02,560 --> 01:52:05,437
that's coming that way. I mean,
people are doing with the Mac minis. Can't

1972
01:52:05,520 --> 01:52:07,277
really do the Frontier AI on the Mac

1973
01:52:07,360 --> 01:52:09,517
Mini just now, but in a few years, you

1974
01:52:09,600 --> 01:52:14,477
know, the DJX uh desktops, like it's all
coming and I think it will be more of an

1975
01:52:14,560 --> 01:52:16,637
option. >> So, just to like
kind of wrap this up,

1976
01:52:16,720 --> 01:52:18,957
so there's this um uh or this like

1977
01:52:19,040 --> 01:52:23,917
topic, the uh one of the things
that USDA does is we pay 600,000 federal

1978
01:52:24,000 --> 01:52:26,957
employees. So, like we pay Secret
Service, we pay DHS, we pay it's like

1979
01:52:27,040 --> 01:52:28,557
it's like a thing inside of USDA.

1980
01:52:28,640 --> 01:52:31,357
>> Interesting. >> And so
the the payroll system that does

1981
01:52:31,440 --> 01:52:34,797
that is a mainframe. Okay. And people
literally explained it to me like this

1982
01:52:34,880 --> 01:52:37,597
thing has a personality. Like you have
[clears throat] to like you can't touch

1983
01:52:37,680 --> 01:52:40,477
it the wrong way. You have to has
to have like the right environment to work

1984
01:52:40,560 --> 01:52:43,757
and it like all these things. I mean
like a dozen people came to me and told

1985
01:52:43,840 --> 01:52:47,277
me all these things. So then I went and
visited it. I was like really excited to

1986
01:52:47,360 --> 01:52:49,597
you know encounter this being mainframe

1987
01:52:49,680 --> 01:52:51,677
>> and uh it's like a

1988
01:52:51,760 --> 01:52:57,197
>> uh five-year-old brand new like IBM
server you know it's just like it's not

1989
01:52:57,280 --> 01:53:00,477
there's no tapes. There's not like
a team of people underwhelmed. Yeah. It's

1990
01:53:00,560 --> 01:53:02,477
like you know it's like this big. Okay.

1991
01:53:02,560 --> 01:53:06,157
>> Um, but I was expecting like
a [clears throat] like a small micro data

1992
01:53:06,240 --> 01:53:10,317
center or something. Exactly. Yeah. So
totally modern and it like I like formed

1993
01:53:10,400 --> 01:53:14,237
this connection with it and I was like we
have had so many conversations about you

1994
01:53:14,320 --> 01:53:17,917
>> and I just thought that like this is
potentially a future where it's like a

1995
01:53:18,000 --> 01:53:22,077
data center a coffee shop you know
like people might want their data to be

1996
01:53:22,160 --> 01:53:24,477
hosted in a place that's
like aligned with their views.

1997
01:53:24,560 --> 01:53:25,997
>> Sure. Sure. Yeah.
You know, because it's

1998
01:53:26,080 --> 01:53:29,757
like I can trust like I don't want this
in my house, but I can like trust this

1999
01:53:29,840 --> 01:53:33,197
like cool company, local company that my
my data lives there because I don't need

2000
01:53:33,280 --> 01:53:35,837
it distributed across the globe.
It's like >> I'm here.

2001
01:53:35,920 --> 01:53:37,197
>> No, that makes sense.
That's interesting.

2002
01:53:37,280 --> 01:53:39,437
>> Country intelligence.
>> Yeah. Yeah. Yeah.

2003
01:53:39,520 --> 01:53:41,597
>> Intelligence.
This is the future. I love

2004
01:53:41,680 --> 01:53:43,517
it. Anyway, thank you so
much for coming on the show.

2005
01:53:43,600 --> 01:53:45,837
>> Great to meet you
for Thanks for doing this work.

2006
01:53:45,920 --> 01:53:48,477
>> Have a great rest of your day.
Talk to you soon.

2007
01:53:48,560 --> 01:53:50,717
>> We will wrap up the show. Yeah.

2008
01:53:50,800 --> 01:53:55,757
>> Thank you for tuning in with us today,
folks. We will be back on Monday.

2009
01:53:55,840 --> 01:53:57,757
>> Yes. >> And we look forward to it.

2010
01:53:57,840 --> 01:54:01,037
>> Some business to do tomorrow, but see
you Monday. Leave us five stars on Apple

2011
01:54:01,120 --> 01:54:04,477
Podcast and Spotify.
Sign up for the newsletpn.com

2012
01:54:04,560 --> 01:54:10,120
and have a wonderful weekend.
We'll see you. Goodbye. Goodbye.