On Tuesday, the New York Times reported

that Meta was looking at downsizing its

AI division as part of a larger

restructuring. Wall Street Journal then

reported Meta has a hiring freeze across

the AI divisions, which is just crazy

because just eight weeks ago, Zuck went

crazy. He was trying to buy Ilia's uh

super intelligence startup. You remember

Ilia declined. So, Zuck poached Daniel

Gross. Meta Aqua hired the scale AI team

and then they agreed to invest 14

billion in that. Sam Waltman claimed

Zuck was making hundred million dollar

offers regularly for open AI talent. I'm

curious, you know, what you think of

that, Sachs? Is the talent war seem to

go insane for a couple of months and now

is pausing. What's your take on all this

I don't know uncertainty boom bust cycle

in such a compressed period of time or

is this just

>> I mean you you are seeing founders

turning down multi-billion dollar

acquisition offers for startups that

hadn't even released a product yet as if

those types of offers grow on trees and

they don't I mean these types of hundred

million or billion dollar job offers

>> they don't come along very often I mean

you have to be at kind of the sweet spot

of a boom cycle and you need a huge

company with tons of money that feels

like it's strategically vulnerable and

is at risk of being left behind. And if

you get a confluence of those factors,

then you can get kind of crazy offers

like that, but they don't come along

very often. I think that what Meta is

doing is probably digesting a little

bit. They've now made a bunch of talent

acquisitions. They've done some aqua

hires at, you know, very expensive aqua

hires and they're probably just

consolidating a little bit. Like I said,

I don't think this is the bust part of

the cycle. I don't think that a bubble

has popped or anything like that. I

actually think that we're still probably

early to the middle of this investment

super cycle. And it's just a healthy

correction in sentiment here that people

are realizing it's going to be a little

bit harder and take more work than just,

oh, the AI is going to figure out how to

improve itself and we get to super

intelligence. That was always a little

bit of a fantasy. uh Mera should have

taken the billion dollar offer from Zach

or Ilia should have taken the 30 billion

Zachs. I mean, these things never

happen. And as you're saying,

>> I mean, I guess it depends how much they

have in the bank account. You know, if

if they're already like billionaires

from whatever they did before, then then

maybe it doesn't matter. But if you were

just starting out, for example, and

didn't have any money in the bank,

that's a pretty hard thing to turn down.

I mean, realistically,

>> yeah,

>> they probably they probably sold a bunch

of OpenAI equity at 300 and 500 billion.

>> They're both OpenAI co-founders, so

>> they seem like they're free rolling, so

they have no incentive to sell.

>> Yeah. Which is fine. It's just that

there's a lot of people who've never

been through a bus cycle before. And if

they think that this is the normal state

of the world, they're going to be sorely

mistaken.

>> By the way, you're right, Sax. It's hard

to build as you know because you did it

a billion dollar company that then exits

for more than a billion. It is hard

>> right where you justify that valuation

based on fundamentals. So right now

we're in a part of the cycle where you

can justify that valuation based on it

strategic value to a multi-t trillion

dollar market cap company. But that only

lasts while those companies are in the

market for strategic acceleration.

>> Yeah. if they're behind, if they're

stuck down,

>> then you have to make your company work

on its own as an actual business

>> and to get to a $30 billion valuation

based on fundamentals, that is

extraordinarily difficult. I mean, that

implies, you know, multiple billions of

revenue, actual revenue.

>> Does somebody want to underwrite 500

billion for OpenAI common shares?

Anybody think that that is a good trade?

>> I'll make the I'll make the bull case.

I'll make the bull case.

>> Good idea, please. Yeah. I think the

simplest way to make the bull case is if

you look at the kager on their mouse and

the conversion from ma to Dows,

you essentially take a small minor

percentage of the Facebook or Google

terminal arpoo and apply it to some

number of ma

3 four years from now. So if I had to

guess, if you take 4 500 million MADOW

growing at I'm guessing, let's just be

conservative 50%

>> 500 million weekly active users right

now.

>> 750. Okay. So then, so then I would

probably put that at like 500 DAO to be

conservative. It's probably doubling

every two years. So 500 goes to a

billion, billion goes to two in four

years. And at 2 billion DAO, they

generate a tenth of Facebook's revenue.

just to be very conservative and you

probably get to a trillion five

valuation then

>> right so it's you could you could triple

up on that bet I mean look open AI has

actual revenue I mean they they have

actual revenue because they have

subscriptions and they appear to have

the dominant position in the consumer

space and it is a replacement for a lot

of people for search which is the most

lucrative franchise on the internet and

this is one of their applications so I

actually think that you can make that

case

you pretty comfortably. But can we could

we go back to a point you were making

before JCAL about in the survey that the

attempts to just apply some sort of

generalized AI model didn't work very

well like 95% of the time it didn't

succeed in these large enterprises but

if they used a more specific vertical

application or vertical model or an SLM

approach which is more a smaller

specialized model then it showed much

greater success. I mean that makes a lot

of sense to me is that in order to drive

business value there's a lot of what I

would call last mile problems right like

LLMs need context and so you have to

first of all connect to all of your

enterprise data sources and you have to

prompt them in a very detailed way in

order to get to a good answer and then

you have to validate that answer to make

sure it's not a hallucination and then

you need to iterate on it and so this

idea that you're just going to have one

super intelligence that just figures all

this stuff out it's just not the way

it's playing out in the real world.

You're seeing again a lot of very

specific business problems that have to

be solved. But I I think that this is a

great thing ultimately for the ecosystem

because it implies that you're going to

get lots of vertical applications and

lots of specialized models that capture

value in lots of different markets. And

that that's actually the way that we're

going to drive this throughout the

economy as opposed to it just being one

foundation model eating all the value.

So I I think this is a very healthy

thing for the ecosystem.

>> Yeah, it makes total sense that the

vertical systems would feel more

deterministic sacks because they're

giving they have a tighter problem set,

a tighter data set to actually come to

an answer whereas the you know

probabilistic you know just asking an

LLM to come up with a business plan for

you. It it just feels like it could be

80% correct, 90% correct as opposed to

99% correct, which the vertical ones are

actually getting very good at getting

the correct answer. Okay, let's talk a

little bit

>> that last 10% is fundamental and that's

where you get all the the last mile

problems and you have to understand the

the industry in order to solve the the

problems. How you kind of go from let's

say 90% accuracy or effectiveness to 99

which is where the business value