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On Tuesday, the New York Times reported that Meta was looking at downsizing its AI division as part of a larger
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restructuring. Wall Street Journal then reported Meta has a hiring freeze across
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the AI divisions, which is just crazy because just eight weeks ago, Zuck went crazy. He was trying to buy Ilia's uh
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super intelligence startup. You remember Ilia declined. So, Zuck poached Daniel Gross. Meta Aqua hired the scale AI team
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and then they agreed to invest 14 billion in that. Sam Waltman claimed Zuck was making hundred million dollar
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offers regularly for open AI talent. I'm curious, you know, what you think of that, Sachs? Is the talent war seem to
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go insane for a couple of months and now is pausing. What's your take on all this
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I don't know uncertainty boom bust cycle in such a compressed period of time or is this just
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I mean you you are seeing founders turning down multi-billion dollar acquisition offers for startups that
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hadn't even released a product yet as if those types of offers grow on trees and
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they don't I mean these types of hundred million or billion dollar job offers they don't come along very often I mean
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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
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like it's strategically vulnerable and is at risk of being left behind. And if you get a confluence of those factors,
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then you can get kind of crazy offers like that, but they don't come along very often. I think that what Meta is
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doing is probably digesting a little bit. They've now made a bunch of talent acquisitions. They've done some aqua
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hires at, you know, very expensive aqua hires and they're probably just consolidating a little bit. Like I said,
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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
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actually think that we're still probably early to the middle of this investment super cycle. And it's just a healthy
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correction in sentiment here that people are realizing it's going to be a little
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bit harder and take more work than just, oh, the AI is going to figure out how to
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improve itself and we get to super intelligence. That was always a little bit of a fantasy. uh Mera should have
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taken the billion dollar offer from Zach or Ilia should have taken the 30 billion
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Zachs. I mean, these things never happen. And as you're saying, I mean, I guess it depends how much they
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have in the bank account. You know, if if they're already like billionaires from whatever they did before, then then
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maybe it doesn't matter. But if you were just starting out, for example, and didn't have any money in the bank,
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that's a pretty hard thing to turn down. I mean, realistically, yeah, they probably they probably sold a bunch
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of OpenAI equity at 300 and 500 billion. They're both OpenAI co-founders, so they seem like they're free rolling, so
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they have no incentive to sell. Yeah. Which is fine. It's just that there's a lot of people who've never
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been through a bus cycle before. And if they think that this is the normal state
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of the world, they're going to be sorely mistaken. By the way, you're right, Sax. It's hard
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to build as you know because you did it a billion dollar company that then exits
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for more than a billion. It is hard right where you justify that valuation based on fundamentals. So right now
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we're in a part of the cycle where you can justify that valuation based on it strategic value to a multi-t trillion
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dollar market cap company. But that only lasts while those companies are in the market for strategic acceleration.
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Yeah. if they're behind, if they're stuck down, then you have to make your company work
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on its own as an actual business and to get to a $30 billion valuation based on fundamentals, that is
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extraordinarily difficult. I mean, that implies, you know, multiple billions of revenue, actual revenue.
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Does somebody want to underwrite 500 billion for OpenAI common shares? Anybody think that that is a good trade?
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I'll make the I'll make the bull case. I'll make the bull case. Good idea, please. Yeah. I think the
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simplest way to make the bull case is if you look at the kager on their mouse and
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the conversion from ma to Dows, you essentially take a small minor percentage of the Facebook or Google
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terminal arpoo and apply it to some number of ma 3 four years from now. So if I had to
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guess, if you take 4 500 million MADOW growing at I'm guessing, let's just be conservative 50%
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500 million weekly active users right now. 750. Okay. So then, so then I would probably put that at like 500 DAO to be
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conservative. It's probably doubling every two years. So 500 goes to a billion, billion goes to two in four
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years. And at 2 billion DAO, they generate a tenth of Facebook's revenue. just to be very conservative and you
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probably get to a trillion five valuation then right so it's you could you could triple
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up on that bet I mean look open AI has actual revenue I mean they they have actual revenue because they have
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subscriptions and they appear to have the dominant position in the consumer space and it is a replacement for a lot
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of people for search which is the most lucrative franchise on the internet and this is one of their applications so I
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actually think that you can make that case you pretty comfortably. But can we could
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we go back to a point you were making before JCAL about in the survey that the attempts to just apply some sort of
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generalized AI model didn't work very well like 95% of the time it didn't succeed in these large enterprises but
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if they used a more specific vertical application or vertical model or an SLM approach which is more a smaller
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specialized model then it showed much greater success. I mean that makes a lot of sense to me is that in order to drive
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business value there's a lot of what I would call last mile problems right like LLMs need context and so you have to
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first of all connect to all of your enterprise data sources and you have to prompt them in a very detailed way in
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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
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you need to iterate on it and so this idea that you're just going to have one super intelligence that just figures all
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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
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specific business problems that have to be solved. But I I think that this is a great thing ultimately for the ecosystem
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because it implies that you're going to get lots of vertical applications and lots of specialized models that capture
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value in lots of different markets. And that that's actually the way that we're going to drive this throughout the
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economy as opposed to it just being one foundation model eating all the value. So I I think this is a very healthy
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thing for the ecosystem. Yeah, it makes total sense that the vertical systems would feel more
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deterministic sacks because they're giving they have a tighter problem set, a tighter data set to actually come to
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an answer whereas the you know probabilistic you know just asking an LLM to come up with a business plan for
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you. It it just feels like it could be 80% correct, 90% correct as opposed to 99% correct, which the vertical ones are
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actually getting very good at getting the correct answer. Okay, let's talk a little bit
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that last 10% is fundamental and that's where you get all the the last mile problems and you have to understand the
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the industry in order to solve the the problems. How you kind of go from let's say 90% accuracy or effectiveness to 99
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which is where the business value