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the World Economic Forum in Davos. It's

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my pleasure to introduce Alex Karp. Um,

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I want to just start off on something
more intimate between he and I. I'm

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pretty proud of what I created at Black

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Rockck, but my total return since I've

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been a CEO has only compounded at 21%.

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Uh, since Alex and Palunteer went

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public, uh, his compounded return is

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73%. So, um,

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congratulations, Alec.

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Uh, but more importantly, um,

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we're in the middle of a profound
technological shift. I think we all are

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hearing about it, reading about it,
feeling it, being a part of it. [snorts]

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And everybody's asking the question, you

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know, what can AI do for me or how can I

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translate it? What can it do for growth?

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What can it do for workers? What can it

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do for countries and national security?

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We are talking about it
that it has the potential

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to grow capacity to modernize industries

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expand opportunity.

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It also will transform how we worked and

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where we work and how we work. And the

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question is are governments prepared for

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that real transformation of a society?

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So we need to make sure that as it's

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deployed it deploys in a way that
empowers people, empowers institutions

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and builds a more resilient global

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economy. Um few leaders

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are truly at this intersection though I

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I I am certainly not that leader but in

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that intersection of technology,

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national security and the real economy

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the way Alex Karp is.

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um as co-founder and CEO of Palunteer,

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uh Alex has closely worked with defense

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and government

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private private organizations to apply

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how AI can be used and and in more of

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the critical areas um and it's really

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important and I must say
my conversations that I've had and with

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Alex over over the last year has really

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enlightened me. So I'm looking forward

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to this Alex. So let me just start off.

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Sovereign states have often been early

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adapters of advanced technology and I

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think we're seeing that really very very

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intimately in the United States.
But from your perspective, how is AI

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supporting decision-making
in defense and security?

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>> Well, first of all, delighted to be
here and with that introduction, maybe I

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should just stop. It's good downhill
from here. Would you like to talk about

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our return some more? And I'll just uh

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I'll just time. I've been as I've been

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public now 26 years. So

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>> So um uh yeah. No, I I think one of the

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things to remember [snorts] uh like the

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the background backdrop of your question

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I I think for is though you have I in

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America and I would say also in Europe

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uh historically

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um uh industrial development and

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military technology were obviously co-

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in is a generalization but more true

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than not um you developed a product for

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the military

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that then was dual functioned and raised

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the standard of living of the country.

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Um and then um and then for lots of

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reasons I'll just leave aside for now
that that's not the way at least

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technology was built until now there's

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all these defense tech startups and

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Palunteer. But um the uh um uh

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it it was that you had this thing where
you were going to create something that

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had to work under the harshest

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conditions that was presumably

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significantly better than anyone else's

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to the point where it would give you a

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more than a slight advantage on a

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battlefield, especially if combined with

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um your way of fighting. And you saw

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this um there was a very famous uh

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social socialist German historian uh who

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was um said well one of the problems
Germany had was the war fighting machine

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was so good that they just said well
we'll decide on the battlefield who's

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right and that that that led to

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obviously lots of disjunctions and real

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problems in the in the American context.

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Um uh and I would say there you have a

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dislocation between what has happened

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recently in America and what
is happening in Europe and what I think so

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I think America and China are are kind
of very successful and I spent most of

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my adult life in Europe and I'm very
pro- European but I think any honest

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assessment is it's not that's not gone

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very well. Um you built things that were

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could be used uh in an adverse

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condition. So rough, dirty, morally gray

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conditions. So how do you change the
morality to fit with how we fight in the

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west is also a big vector. So the morality
is difficult. Um the conditions

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under which you use the technology are
difficult in in in software context. You

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you're [snorts] not you don't have
you're not directly connected to the

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network in many cases. You have
constraints under which you have to

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fight even though that's not the optimal
way to fight. and you have very

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specialized ways of fighting in each

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country. Um, but the the positive side

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of that was that you also were building

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something that could be deployed
that had an obvious value to the average

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citizen, >> right?
Um and um so now you have this

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the AI thing is there's [clears throat]
many things that are really interesting

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about it but if you start with the

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benality that I think um until very very

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recently all kind of adversaries of

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broadly defined the west assumed that

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investments in softwarebased

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uh defense were some kind of crazy thing

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Americans do for marketing.
uh get rich, company blows up, you're on

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[clears throat] your beach in the

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Bahamas, but shareholders are happy and

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it's gone kind of thing. And um uh and

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um what you've seen uh so first of all

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you could say well that's changed but

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Germaine to your question the learning

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process in building how do you build
this for sovereign governments is also a

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learning process for how do they adopt
these technologies. So it's it's not

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just the technology because if you're
building a tank like you know the

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British and then the French and then
the Germans kind of optimized tank

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technology it's easy to see how you
would deploy it but how do you deploy a

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system that's primary value
is organizing parts on the battlefield

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without seeing the parts on the
battlefield and seeing does it work how

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much does it work is it much better than
what we had can we do things we could we

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couldn't do in the past and then there's

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a hidden thing about uh software AI

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that a lot of the value interestingly

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people always assume the value is from

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um where you are to where you should be

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and that's obvious but in most sovereign
nations in the world I mean we deal with

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almost all in one form or another that

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are broadly defined as in a that would

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be you know it it could deal with a

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company that's you know American

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the actual technological

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uh uh rigor of the of the enterprises

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has significant holes in it. So it like

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as you know it's like it's very I'm
dyslexic. It's very dyslexic. There are

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like whole pieces of the enterprise that

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exist on a PowerPoint that when you
go to battle you will find out do not

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exist. And this whatever country you're

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in in this room if you are in the west

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this is a problem you have. Uh a day
of battlefield you will find out this is

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one of the advantages the Ukrainians had.
They essentially started from

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nothing. And so there wasn't you didn't
have to rediscover that your enterprise

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didn't work after you're in fighting.

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And one of the huge advantages America
has is just for you know better or worse

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I believe it or not think mostly for

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worse I was always against

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interventions. I'm not a neocon. Um it's

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uh um uh we had just
all this experience on

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the battlefield so you could see what

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worked and what didn't work. Um, but

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pulling it back to the so the first
thing that sovereign nations really

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struggle with is can I identify which

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tech is better objectively? Can I even
rate it? Can I rate I'm looking at I

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guess say y >> but don't
you need to know where you

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want to go to ask the right question.

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>> Um, you know that's you that's that's

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you actually let me reframe it. Y >>
you have to know where you are to know

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where you want to go. >> Okay.
So like the the point I'm making

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is with this exogesis is one of the most

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important things Palanteer's done on
the battlefield is be able to make up for

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the fact that half your enterprise
doesn't even work on the battlefield. It

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does work in a lab on a powerpoint
only built by country.

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>> It doesn't work because of machinery or

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human or humans. Well, because the

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conditions of a battlefield are rough,
contagious and and like for example like

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the modern if you just take Ukraine
as an example, just to make it empirical,

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you know, as a everyone like reads this

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like, okay, well, how hard could it be

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to move a drone from A to B? Uh, well,

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actually, first of all, you're going
to need to know where you want to put the

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drone. That's going to require
synchronizing all your data. You're

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going to need to do that without
transferring that data to your

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adversary, which means you're going
to have to know every single person who

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touched it. You're going to have
to obuscate it till the final thing. Then

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you're going to want to do it presumably
either within the coordinance of your

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strategy or with your ethics. So where
does it where does it not go? You're

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going to want to be able to correlate.

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Do you want to put the drone on your
asset? No one only two people in Ukraine

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may know that one of the generals is
your asset. You can't tell people that's

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your asset. How do you how do you make
it look like to your should soldiers

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that you actually were taking people out
and you just missed your asset? Um then

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and then the war advances. So
the Russians which are off they're often

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very underestimated for reasons I
don't understand but like they're

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mathematically arguably the best in the
world and things they may not have in

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the beginning they can kind of cobble
together and so they began jamming

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electronics. So now you have
a completely different problem, but your

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presumably your enterprise has to be the
same or developed because now it's not a

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question of going from A to B. It's
a question of going through a completely

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jammed environment where you have no
connectivity while actually collecting

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data while you go through that
environment. And every one of those

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things is like a dynamic challenge.

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>> None of which were foreseen even before

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the Ukrainian. every single battle zone

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in the world has a By the way, the other

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thing is um then people fight

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differently. So like if you look at the
big battlefields, I'm sure some people

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love our work here and some people hate
it. By the way, we we we welcome all

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opinions at Palanteer. Even at inside
Palunteer, we have people who love our

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work and people who are unhappy
with the work. Um uh we welcome all.

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>> That's a spirit of dialogue.

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>> It's a spirit of dialogue with a

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somewhat of a leader. uh and uh um but

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you know if you look at you know how uh

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you know when the Ukrainians it's a

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small team of people and very courageous

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soldiers that are very technical they

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have highly technical people that built

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on top of our product things that we
don't understand and proprietary ways of

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using the product in Israel they
according to rumors use their

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intelligence so most people fight
military to military this was int

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intelligence in Iran from what I can

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tell from the papers. Um and then in

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America you just have massive uh forces

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that no other country has but they had
to be integrated and the integration

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capacity. So every one of those things
is different. Sure. And so the the the

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two-fold role of of of enterprise
software on a battlefield is one to make

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sure all the underlying things actually
works and then two to raise the level to

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a level no one else has in the world.

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>> Let me translate this now. I mean
there's so much technology was created

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from defense whether it was the internet

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uh GPS.

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How do you envision this translating

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from defense and military

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um to corporations to businesses to

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society? Well, first of all, the the

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fact that it's basically a purely raw

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naked environment means that you
actually know the ground truth of what

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could work independent of what
enterprises think can work. And the

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generaliz the think at a at a at a
high level, it's almost one to one

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translatable, but precisely because as

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just to take enterprises in general, not

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all enterprises tend to want to over

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time become like every other enterprise.

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So if you take five, you know,
A enterprise and B enterprise and C

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enterprise, they're in the same market.

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Their tech infrastructure is trying
to make them into the same enterprise. Uh

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they have the same orchart. >> Sure.

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>> They have presumably roughly the same.

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They don't have the same data
infrastructure. And what you learn on

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the battlefield is that and in life
is that that's not particularly valuable.

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What is very valuable is an enterprise
can do something no enterprise in the

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world can do. And so that is the goal
of every single military in intelligence

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service. In fact, all these intelligence
service and militaries have their own

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specialization. And so when we went
to commerce like what we're saying is how

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can we make your insurance
come the way you underwrite?

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How can we make that to your tribal

255
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knowledge about underwriting? How can we
transform this to knowledge everyone has

256
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to a knowledge only you have and with
efficiencies that no one else has? So as

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an example on the battlefield one of the

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most important issues I is how do you
acquire data process it and then put it

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into a framework where it can be
actioned either from an intelligence

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perspective yeah so just obviously what

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does a business do in the end of the day
what is a business doing I mean it is so

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for example especially in anything
underwriting banking hospital intakes

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it's information it's information it's

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sorting the information in a way that

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you have a a distinct advantage over

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other people who are similarly situated

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uh and that that advantage can't be
eviscerated easily and so de facto for

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what you can't what you're doing on the
battlefield with our products without

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getting happy to is like what you're
doing with ontology and foundry in some

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cases on the battlefield but definitely
in like commercial organizations across

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the world but especially in America is
when you approach the underwriter or the

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hospital we power tons and tons
of hospitals right they have an intake

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problem they all have an intake problem.

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They all have a shortage of doctors and
nurses. They are working in a low margin

275
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environment, but every single one has
a different way of processing their

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patients according to what their
specialty is and the kind of patients

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they don't do well with. And how do you
manage that? And so the intake flow and

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into your enterprise in a way that you

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can actually process these things 10 15
times faster than you could before. And

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then the other >> that saves lives then

281
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>> it save what saves
a lot of lives. Um it

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also interestingly because you're

283
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processing the LLM in an ontology you
have a structure it all despite what

284
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people may want to believe it also
bolster civil liberties because now you

285
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can say well I mean just simple
questions was someone processed based on

286
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economic uh considerations or were they
based uh were they processed based on

287
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their background like those things are
impossible to see unless you have like

288
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there's a huge civil liberties
betterment side of this that typically

289
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people don't believe we care about
or but it's actually exactly the opposite.

290
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We do care and you know showing is

291
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caring. It's like we can granularly show
uh why someone came in, why they were

292
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taken, why they were rejected and we can
do it in a way that makes business sense

293
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for the b the business itself. Um and

294
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then it it leads to safety efficiencies

295
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>> and probably brings down costs. Well,

296
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the if you want to do a a shorter kind

297
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of financial version, you basically in

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the past to do what we can do in in
the full light of a public market, you

299
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needed a pri you need to take the company
private. So, and then you would

300
00:16:33,120 --> 00:16:37,357
take out the cost structure and you
that you probably resell it. Well, now you

301
00:16:37,440 --> 00:16:41,117
can take out the cost structure,
make the workers more important. So, the

302
00:16:41,200 --> 00:16:46,397
actual workers, not the fat kind of in
the middle. And uh um and then you can

303
00:16:46,480 --> 00:16:49,917
you can change the way they go to market.
So the >> hospitals you can process this

304
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information faster presumably you'll have

305
00:16:52,560 --> 00:16:57,197
>> much faster and then the nurses
the nurses the one of the the nurses and

306
00:16:57,280 --> 00:17:02,237
doctors are also happier
and yeah >> so you you you

307
00:17:02,320 --> 00:17:05,917
suggested that for companies that rapidly
adopt that they have to best way

308
00:17:06,000 --> 00:17:08,077
to do is go in private then
they could restructure but

309
00:17:08,160 --> 00:17:10,797
>> no no what I'm suggesting
is they don't have to do that anymore

310
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>> they don't have to do that.
So what is the basic um

311
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what is the basic hindrance in the

312
00:17:17,760 --> 00:17:21,677
adaption of of of the of AI? Uh is it

313
00:17:21,760 --> 00:17:26,957
just um legacy systems, legacy issues?

314
00:17:27,040 --> 00:17:29,357
What how do you how do we accelerate the

315
00:17:29,440 --> 00:17:32,797
adoption that it's good for humanity?

316
00:17:32,880 --> 00:17:35,357
>> Well, I mean our adoption accelerates

317
00:17:35,440 --> 00:17:39,917
beyond our capacity. So it it the um I

318
00:17:40,000 --> 00:17:43,757
think if you just buy large language
models off the shelf and try to do any

319
00:17:43,840 --> 00:17:45,117
of this it won't work.
>> It's a commodity.

320
00:17:45,200 --> 00:17:48,077
>> It's a and well also
it's not precise enough.

321
00:17:48,160 --> 00:17:50,717
>> It like you can't
do underwriting with a

322
00:17:50,800 --> 00:17:52,317
you can't you couldn't do any of these

323
00:17:52,400 --> 00:17:56,317
things that are regulated. So moving to

324
00:17:56,400 --> 00:17:59,917
like everybody there's not like the the

325
00:18:00,000 --> 00:18:02,237
problem with adoption at this point is

326
00:18:02,320 --> 00:18:07,117
people have tried things that just
can never work like buying you borrow a

327
00:18:07,200 --> 00:18:11,517
large language model you put it on your
stack and you wonder why you know it's

328
00:18:11,600 --> 00:18:13,837
like not working and so what you're

329
00:18:13,920 --> 00:18:16,717
going to see especially in America is

330
00:18:16,800 --> 00:18:19,357
people trying to do something like what

331
00:18:19,440 --> 00:18:24,077
we do withtology maybe by hand because
once you build the software layer to

332
00:18:24,160 --> 00:18:28,397
orchestrate and manage the lounge
language models in in a language that

333
00:18:28,480 --> 00:18:31,597
your enterprise understands, you
actually can create value. And [snorts]

334
00:18:31,680 --> 00:18:34,877
I don't I I think like there's like
there's a lot of discussions like are we

335
00:18:34,960 --> 00:18:36,957
in an AI bubble? What is the meaning of

336
00:18:37,040 --> 00:18:41,997
the bubble? Well, it's like I I think
what if anything we're just in a lag

337
00:18:42,080 --> 00:18:43,837
where there's a lot of AI. Some of it

338
00:18:43,920 --> 00:18:49,277
works. Again, if you go back to the
battlefield context, like it most pe

339
00:18:49,360 --> 00:18:53,517
everybody in the world assumed this
would not work, but now it does work.

340
00:18:53,600 --> 00:18:58,157
And so now the question isn't does it
work, but how can we get it to work for

341
00:18:58,240 --> 00:19:01,837
my country?
And this is exactly what's happening in

342
00:19:01,920 --> 00:19:06,157
>> companies. It's like oh this company it
worked, mine didn't. What are you doing?

343
00:19:06,240 --> 00:19:10,157
>> Like for example, I mean if you want
to just make it like parochial to us,

344
00:19:10,240 --> 00:19:12,637
Palanteer barely has a salesforce. In

345
00:19:12,720 --> 00:19:17,517
fact, it seems to be getting smaller
every time I go see them. And it's like

346
00:19:17,600 --> 00:19:20,637
and it's simply because you're laughing
because you know it says Palanteerians

347
00:19:20,720 --> 00:19:23,117
here, but it's it's getting smaller and

348
00:19:23,200 --> 00:19:27,117
smaller and smaller. And um and it it's

349
00:19:27,200 --> 00:19:28,397
not because we're trying to save on the

350
00:19:28,480 --> 00:19:31,837
unit economics. It's actually because it

351
00:19:31,920 --> 00:19:36,157
is a low trust environment in AI. People
have tried lots of stuff. A lot of it

352
00:19:36,240 --> 00:19:40,477
hasn't worked. But if you've delivered
stuff that does work, why do you need a

353
00:19:40,560 --> 00:19:43,677
salesforce? like you you
you just it sells itself.

354
00:19:43,760 --> 00:19:46,557
>> Well, you have to say,
"Hey, don't talk to us."

355
00:19:46,640 --> 00:19:48,397
>> And then on that's that's that's that's

356
00:19:48,480 --> 00:19:50,557
on commercial in government

357
00:19:50,640 --> 00:19:54,477
>> at this point.
It's really we don't like

358
00:19:54,560 --> 00:19:58,157
we don't we it's it's it's very hard
to export simply because we have to train

359
00:19:58,240 --> 00:20:00,237
the people and we have limited man.

360
00:20:00,320 --> 00:20:03,997
>> I was going to say that's your limited
bandwidth, the training once somebody

361
00:20:04,080 --> 00:20:06,637
takes on your your software. Well, in

362
00:20:06,720 --> 00:20:11,117
the government contest, every
every country like obviously has the

363
00:20:11,200 --> 00:20:16,157
equivalent of a security clearance,
right? So to use our to build to build

364
00:20:16,240 --> 00:20:20,237
project Maven or Maven into your
architecture, you're going to need

365
00:20:20,320 --> 00:20:25,117
somebody with the highest level
of clearance that also is technical and

366
00:20:25,200 --> 00:20:28,477
most technical people unfortunately
are not going after the highest level of

367
00:20:28,560 --> 00:20:31,997
clearance. So there are very very
few people like that. So the that that

368
00:20:32,080 --> 00:20:36,477
resource is super scarce. Uh and then
they have to be trained and that that

369
00:20:36,560 --> 00:20:40,397
takes that can take a while and and and
then you also have to have like anything

370
00:20:40,480 --> 00:20:44,637
you really have to believe in this and
really think it's important and you know

371
00:20:44,720 --> 00:20:47,037
not everybody fits in that category.

372
00:20:47,120 --> 00:20:51,437
>> How many people need to be trained
to do this? I mean does it in a corporate

373
00:20:51,520 --> 00:20:53,117
level does it have to be from the CEO

374
00:20:53,200 --> 00:20:58,877
down? How does this work as you >>
um well you're talking about like

375
00:20:58,960 --> 00:21:01,837
insurance underwriting?
>> Well like you know Yeah. So insurance

376
00:21:01,920 --> 00:21:05,837
underwriting um I um the the way it

377
00:21:05,920 --> 00:21:08,317
works is the best case scenario you have

378
00:21:08,400 --> 00:21:10,157
the best and worst case scenario. Best

379
00:21:10,240 --> 00:21:15,037
case scenario um CEO is mathematically

380
00:21:15,120 --> 00:21:17,677
inclined >> even though
they they may not know

381
00:21:17,760 --> 00:21:21,517
nothing about product but you can they

382
00:21:21,600 --> 00:21:23,197
can impute a product working by looking

383
00:21:23,280 --> 00:21:28,717
at the math. Um yeah and uh um and uh

384
00:21:28,800 --> 00:21:34,077
and and has you know and then we
probably need to train five or six

385
00:21:34,160 --> 00:21:38,717
people on there. In the beginning we do
all of it and then we transfer that as

386
00:21:38,800 --> 00:21:41,677
much as we can to them or we're part
trying to partner with people who can do

387
00:21:41,760 --> 00:21:46,557
that with us but you need a small number
of people but we still need more than we

388
00:21:46,640 --> 00:21:50,877
have. you you suggested
repeatedly about how

389
00:21:50,960 --> 00:21:53,517
AI could strengthen foundations for an

390
00:21:53,600 --> 00:21:58,877
economy, especially we're seeing that
in the United States. Um, how rapidly can

391
00:21:58,960 --> 00:22:02,157
AI change the growth trajectory? Because

392
00:22:02,240 --> 00:22:04,797
you you you mentioned that earlier about

393
00:22:04,880 --> 00:22:09,197
how it could improve the e economies,

394
00:22:09,280 --> 00:22:11,677
the well-being of companies.

395
00:22:11,760 --> 00:22:15,677
Well, um there in a lot of these things

396
00:22:15,760 --> 00:22:20,557
there's a speed question, but um

397
00:22:20,640 --> 00:22:22,237
I think like with a lot of our

398
00:22:22,320 --> 00:22:25,437
companies, it's like we can take out

399
00:22:25,520 --> 00:22:29,437
in the area we go in up to 80% of your

400
00:22:29,520 --> 00:22:33,197
cost and improve your top line
dramatically. But it really depends on

401
00:22:33,280 --> 00:22:36,717
the use case and what we're doing.
And then there's a speed function which is

402
00:22:36,800 --> 00:22:39,997
we can in the past five years
ago that would take us a year.

403
00:22:40,080 --> 00:22:41,277
>> Sure. Now it could take a week.

404
00:22:41,360 --> 00:22:44,797
>> Week. Yeah. >> Let me
further that question though. I'm

405
00:22:44,880 --> 00:22:48,637
sure it's on the minds of some people
here. Is AI going to create jobs or

406
00:22:48,720 --> 00:22:52,717
destroy jobs overall? >> It Yeah.
I think one of the unfortunate

407
00:22:52,800 --> 00:22:55,597
things of the narrative in the west is

408
00:22:55,680 --> 00:22:59,837
it it will destroy humanity's

409
00:22:59,920 --> 00:23:02,477
jobs of like you know you went to an

410
00:23:02,560 --> 00:23:06,397
elite school and you studied philosophy.

411
00:23:06,480 --> 00:23:10,237
Use myself as an example.
Um, >> I did too.

412
00:23:10,320 --> 00:23:13,117
>> Yeah, you it hopefully you have some

413
00:23:13,200 --> 00:23:17,997
other skill. That one is going
to be hard to market. Uh, and

414
00:23:18,080 --> 00:23:18,797
>> we thought it was hard to market.

415
00:23:18,880 --> 00:23:22,237
>> It was hard to market. Very hard.
Uh, >> it was a good education.

416
00:23:22,320 --> 00:23:25,437
>> A very, very strong education. If
you can get a job, you might keep it. But

417
00:23:25,520 --> 00:23:28,557
the hard That's what I always thought.
I was like, if I finally get a job, I'll

418
00:23:28,640 --> 00:23:30,077
probably keep it and do well, but I'm

419
00:23:30,160 --> 00:23:31,757
not sure who's going to give me my first

420
00:23:31,840 --> 00:23:38,317
job. Um and uh um uh uh but like techn

421
00:23:38,400 --> 00:23:42,157
like technicians if you're
a vocational technician

422
00:23:42,240 --> 00:23:47,277
>> or like like we're building batteries
for a battery company and the people who

423
00:23:47,360 --> 00:23:50,637
are doing it in America are doing
roughly the same job that Japanese

424
00:23:50,720 --> 00:23:53,997
engineers are doing and they went
to high school and now they're very

425
00:23:54,080 --> 00:23:57,037
valuable if not irreplaceable because we

426
00:23:57,120 --> 00:24:02,157
can make them into something different
than what they were very rapidly and

427
00:24:02,240 --> 00:24:05,037
those jobs are going
to become more valuable.

428
00:24:05,120 --> 00:24:07,517
Um, I mean, you know, I not not to

429
00:24:07,600 --> 00:24:10,237
diverge into my usual political screeds,

430
00:24:10,320 --> 00:24:13,277
but it there are will be more than

431
00:24:13,360 --> 00:24:17,517
enough jobs for the citizens of your
nation, especially those with vocational

432
00:24:17,600 --> 00:24:22,717
training. I do think these these trends
really do make it hard to imagine why we

433
00:24:22,800 --> 00:24:26,957
should have large-scale immigration unless
you have a very specialized skill because

434
00:24:27,040 --> 00:24:31,837
>> what about the foundation
for white collar work in

435
00:24:31,920 --> 00:24:35,277
Europe and the United States has been
through the universities but I just

436
00:24:35,360 --> 00:24:37,357
heard you say we're going to need more

437
00:24:37,440 --> 00:24:42,557
vocational men and women and they may
they're going to be but are you also

438
00:24:42,640 --> 00:24:47,197
insinuating we're probably going to need
less white collar? I think like I think

439
00:24:47,280 --> 00:24:49,517
what we need to do is yes, but I I think

440
00:24:49,600 --> 00:24:51,037
we need different ways of testing

441
00:24:51,120 --> 00:24:54,237
aptitude. You know, it's like um you you

442
00:24:54,320 --> 00:24:59,037
know there are a lot of people doing
X that should be doing Y. Like if you

443
00:24:59,120 --> 00:25:01,117
could manage one of our system like just

444
00:25:01,200 --> 00:25:03,917
the person managing our maven system in

445
00:25:04,000 --> 00:25:06,637
the US Army is a former police officer

446
00:25:06,720 --> 00:25:09,517
who I think went to a junior college and

447
00:25:09,600 --> 00:25:12,477
they're doing very very high-end very

448
00:25:12,560 --> 00:25:16,077
complicated targeting globally and that

449
00:25:16,160 --> 00:25:20,397
person actually is irreplaceable and I

450
00:25:20,480 --> 00:25:22,557
think in the past the way we tested for

451
00:25:22,640 --> 00:25:26,637
aptitude uh would not have fully exposed

452
00:25:26,720 --> 00:25:30,957
how irreplaceable that person's talents
are and would they been as talented if

453
00:25:31,040 --> 00:25:31,917
they had not [clears throat] gone to

454
00:25:32,000 --> 00:25:36,157
their college? Yes. Um and but I think

455
00:25:36,240 --> 00:25:39,357
the the a I tend to even inside
Palunteer if you look at inside

456
00:25:39,440 --> 00:25:41,757
Palunteer what am I really doing all

457
00:25:41,840 --> 00:25:44,397
day? I'm want walking around figuring

458
00:25:44,480 --> 00:25:49,357
out what is someone's outlier aptitude
and then I'm putting them on that thing

459
00:25:49,440 --> 00:25:52,957
and trying to get them to stay on that
thing and not on the five other things

460
00:25:53,040 --> 00:25:56,477
they think they're great
at like you know >> keeping their

461
00:25:56,560 --> 00:25:59,837
>> Yeah. It's like well you know everyone
at Palanteer every every engineer at

462
00:25:59,920 --> 00:26:03,117
Palunteer uh it's it's the most wherever

463
00:26:03,200 --> 00:26:07,437
I go in the world like for for as you
know maybe for 18 years everyone thought

464
00:26:07,520 --> 00:26:10,957
we were like a business joke and now
lots of business people want my advice

465
00:26:11,040 --> 00:26:14,157
you know the only people who don't want
my advice at Palanteer about business

466
00:26:14,240 --> 00:26:17,917
are Palunteer engineers they're like hey
Alex I have an idea about how we could

467
00:26:18,000 --> 00:26:22,477
just be in a much better company
and it's it's always like yeah it's like

468
00:26:22,560 --> 00:26:26,637
it's like literally McDonald's
but it's like we should have some titles

469
00:26:26,720 --> 00:26:28,967
and you should stop speaking in public.

470
00:26:29,050 --> 00:26:32,157
[laughter] And uh yeah, and then I mean
there's probably right about speaking in

471
00:26:32,240 --> 00:26:34,637
public sometimes. I certainly admit that.

472
00:26:34,720 --> 00:26:37,597
>> I don't think you uh I don't
think you did anything wrong today.

473
00:26:37,680 --> 00:26:42,637
>> Yeah. So uh yeah, thank you
for that high praise. [laughter]

474
00:26:42,720 --> 00:26:45,277
>> One of the keys to success
is setting the bar very low.

475
00:26:45,360 --> 00:26:48,077
>> Yes. No, I don't believe that's how you

476
00:26:48,160 --> 00:26:53,400
uh operate Palunteer.
Um one last question.

477
00:26:53,680 --> 00:26:56,877
Where where is this where will the curve

478
00:26:56,960 --> 00:27:00,397
of AI go in the utilization uh in the

479
00:27:00,480 --> 00:27:02,797
United States and other developed

480
00:27:02,880 --> 00:27:05,757
economies? But what about the developing

481
00:27:05,840 --> 00:27:08,717
economies? How can they participate in

482
00:27:08,800 --> 00:27:11,517
this? I mean, I read a research report

483
00:27:11,600 --> 00:27:14,317
yesterday that said the application of

484
00:27:14,400 --> 00:27:18,637
AI has been so dominant by societies of

485
00:27:18,720 --> 00:27:20,957
high education or companies of high

486
00:27:21,040 --> 00:27:24,317
education and they're seeing a very big

487
00:27:24,400 --> 00:27:27,997
um divergence that is occurring already

488
00:27:28,080 --> 00:27:32,237
and it's so much based on the application
of education and how that is

489
00:27:32,320 --> 00:27:34,717
being utilized. So, is AI going to

490
00:27:34,800 --> 00:27:38,397
create more um a more a greater

491
00:27:38,480 --> 00:27:41,757
imbalance in our in our
world in terms of growth?

492
00:27:41,840 --> 00:27:43,517
>> Well, I think the obvious first

493
00:27:43,600 --> 00:27:47,117
imbalance is it seems like America and

494
00:27:47,200 --> 00:27:51,277
China understand versions of making
this work and they're different.

495
00:27:51,360 --> 00:27:53,597
>> Yes. >> But they both
work and they work at

496
00:27:53,680 --> 00:27:59,197
scale and I think that is very likely
to accelerate way beyond what most people

497
00:27:59,280 --> 00:28:02,797
believe is possible. like the discount
rate I think not in the short term but

498
00:28:02,880 --> 00:28:05,917
in the long term is way too high on what

499
00:28:06,000 --> 00:28:07,917
will be done and how this will impact

500
00:28:08,000 --> 00:28:10,317
every aspect of our society and I would

501
00:28:10,400 --> 00:28:13,837
say especially on military and then I I

502
00:28:13,920 --> 00:28:17,517
tend to be a realist and that I think
you know you have wide divergences it's

503
00:28:17,600 --> 00:28:21,677
going to be hard to have the kind
of discussions people want to have where

504
00:28:21,760 --> 00:28:26,717
two countries are and and with a maybe
a third following of Russia on the new on

505
00:28:26,800 --> 00:28:29,677
like the because they
they're so good at fighting

506
00:28:29,760 --> 00:28:33,197
Um and then and then I I look I spent

507
00:28:33,280 --> 00:28:35,037
and I'll get to the developing world. I

508
00:28:35,120 --> 00:28:37,757
spent a lot of my life my most important

509
00:28:37,840 --> 00:28:41,597
years and my father's family came from a

510
00:28:41,680 --> 00:28:46,157
part of Germany and I I really care
about Europe and especially the germ

511
00:28:46,240 --> 00:28:48,557
parts of Europe uh where I had many of

512
00:28:48,640 --> 00:28:50,717
my best years. I still fantasize of

513
00:28:50,800 --> 00:28:53,677
going back to grad school for not for

514
00:28:53,760 --> 00:28:57,837
the learning reasons. Um uh and um

515
00:28:57,920 --> 00:28:58,957
>> you're going to have more fun.

516
00:28:59,040 --> 00:29:03,357
>> I had so much fun. Oh, we we won't go
into that. Uh but uh it's like endless.

517
00:29:03,440 --> 00:29:04,957
I sometimes when I'm traveling across

518
00:29:05,040 --> 00:29:06,237
the country, I just think of grad

519
00:29:06,320 --> 00:29:12,717
school. But um uh it's uh uh um but um

520
00:29:12,800 --> 00:29:19,277
um uh I I the the tech adoption in in in

521
00:29:19,360 --> 00:29:22,957
Europe is a serious and very very

522
00:29:23,040 --> 00:29:25,757
structural problem. And what scares me

523
00:29:25,840 --> 00:29:30,477
the most is I haven't seen any political
leader just stand up and say we have a

524
00:29:30,560 --> 00:29:32,557
serious and structural problem that we

525
00:29:32,640 --> 00:29:35,677
are going to fix. So that then you get

526
00:29:35,760 --> 00:29:38,557
to the developing world. I would imagine

527
00:29:38,640 --> 00:29:43,757
it also depends what you mean by the
developing world. I would imagine with

528
00:29:43,840 --> 00:29:47,917
not enough knowledge you're just going
to find pockets that go very well and

529
00:29:48,000 --> 00:29:49,917
pockets that go very poorly. As a

530
00:29:50,000 --> 00:29:52,317
generalization like again if you go back

531
00:29:52,400 --> 00:29:55,437
to this somewhat n unsuccessful salopy I

532
00:29:55,520 --> 00:29:57,837
had about the underlying architecture.

533
00:29:57,920 --> 00:30:00,317
One way to look at at the unfairness of

534
00:30:00,400 --> 00:30:03,357
AI is it pentests meaning it it

535
00:30:03,440 --> 00:30:06,077
loadbears on things. So societies that

536
00:30:06,160 --> 00:30:08,397
can and organizations and companies that

537
00:30:08,480 --> 00:30:13,357
can bear that load have a huge advantage.
The problem is if you can't

538
00:30:13,440 --> 00:30:17,597
if you've been pretending you're bearing
a load you're not it collapses and

539
00:30:17,680 --> 00:30:22,717
that's where you have to start. And so
if you go around and just say okay what

540
00:30:22,800 --> 00:30:24,477
societies and micro cultures are going

541
00:30:24,560 --> 00:30:29,837
to be loadbearing here I think you would
find that parts of the developing world

542
00:30:29,920 --> 00:30:31,197
certain communities in that are going to

543
00:30:31,280 --> 00:30:34,957
do very well you you do need a realistic

544
00:30:35,040 --> 00:30:37,117
assessment of the loadbearing

545
00:30:37,200 --> 00:30:39,997
>> and there there's
a certain honesty that

546
00:30:40,080 --> 00:30:42,397
is painful for all of us in in in this

547
00:30:42,480 --> 00:30:45,677
technology large language models however

548
00:30:45,760 --> 00:30:48,557
implemented in software it you just

549
00:30:48,640 --> 00:30:50,877
cannot not obfuscate what can bear the

550
00:30:50,960 --> 00:30:53,117
load and what can't. And then political

551
00:30:53,200 --> 00:30:55,677
structures are built to do just that.

552
00:30:55,760 --> 00:30:59,997
Like, yeah, I can't fix anything,
but I can give you some line that you

553
00:31:00,080 --> 00:31:03,757
want to hear that's going to make you
not care about how bad your life is and

554
00:31:03,840 --> 00:31:05,517
how much worse it's going to be tomorrow.

555
00:31:05,600 --> 00:31:07,197
>> I can give you that for free.

556
00:31:07,280 --> 00:31:13,037
>> And those that that stuff uh is um

557
00:31:13,120 --> 00:31:15,517
that is harder to get away with in this

558
00:31:15,600 --> 00:31:17,757
culture. And you know, I I still view

559
00:31:17,840 --> 00:31:20,397
myself as a card carrying progressive.

560
00:31:20,480 --> 00:31:24,637
And I think it's the single most
important thing a progressive could do

561
00:31:24,720 --> 00:31:28,477
is go around and say, "Yeah, but the
revolution that's coming is going to

562
00:31:28,560 --> 00:31:32,637
expose the actual market value of what
you're doing, whether we want it or

563
00:31:32,720 --> 00:31:36,077
not." Like it's like I don't even want
to know the market value of some of this

564
00:31:36,160 --> 00:31:40,717
stuff. But it is over and over
a relative rapid period of time. So next

565
00:31:40,800 --> 00:31:45,677
three years you're just going to get
market value honesty in all sorts of

566
00:31:45,760 --> 00:31:47,677
character communities and micro

567
00:31:47,760 --> 00:31:49,517
communities and the best thing you can

568
00:31:49,600 --> 00:31:52,797
do if you are in a community whether

569
00:31:52,880 --> 00:31:54,797
that is a large community like Germany

570
00:31:54,880 --> 00:31:57,677
or a large community larger like America

571
00:31:57,760 --> 00:32:02,397
is and you really care for the people
you're representing is to say yeah but

572
00:32:02,480 --> 00:32:06,397
let's we have to kind
of look closely at what

573
00:32:06,480 --> 00:32:09,357
what load we can bear.

574
00:32:09,440 --> 00:32:14,290
Thank you, Alex. >> Thank
you, everyone. [applause]

575
00:32:16,860 --> 00:32:18,880
[applause]