00:07:13 · Matthew Fitzpatrick
[00:07:13] S0: Yeah. I I think the the interesting thing about the AI funding market is it's something like three companies are 30% of the total funding, and then it's about a 100 companies are about 70% of the total funding. So you could argue... You you can debate whether it's about whether or not. I think there's all sorts of different questions about where else one would put money to get... Generate better returns. But I think the main thing I would highlight is it is a a focus on a small number of companies where there is scarcity. Meaning, there's a lot more investors that wanna put money into those 100 companies than is available space. And so I think that's the interesting dynamic of the current AI era is there's not a thousand companies doing this. It's a small number. It's where a lot of the market wants to invest, and so you have a scarcity dynamic. And that's what's causing, you know, whatever valuations we have now. The other thing I'd highlight, though, is it's not a question of... This era of companies is not like, you know, people bring up the .com era. It's not a bunch of eyeball companies. I mean, these companies have hundreds of millions, if not billions of dollars of revenue at a much faster pace than we've really ever seen scaling loss demonstrate before. I think it was like... The number I saw recently, for example, is like this class of... The most recent class of Y Combinator from a year ago has the highest aggregate revenue ever for its cohort a year in. And so you you actually have, like, several different factors at work. You have scarcity in not that many companies that people can invest in, really unique scaling laws from a revenue standpoint in the whole space. And then I do think the macro is... Makes this whole thing complicated as well, which is there are not that many areas right now where the returns are good. You know, areas like real estate that have traditionally been really reliable have been more complicated. And so you have a lot of capital trying to move into this space. And so you can debate what the right fair valuation is, but I think that's the fairly logical set of reasons why we are who we are.
00:09:06 · Guillaume Moubeche
[00:09:06] S1: And from your perspective, because you mentioned the the .com bubble, like, what do you think is gonna be the the impact of AI versus, like, what we had with with the Internet?
00:09:21 · Matthew Fitzpatrick
[00:09:21] S0: So I saw an interesting article that Bloomberg published on Mary Meeker's technology technology predictions twenty years later. And so they basically looked at, like, what she said in 1999 and who she predicted would grow and at what rate. And the interesting thing is... And she was used like this insane optimist. By any measure, she actually was underestimating how transformational the Internet was. Meaning, like, the use... The the amount of ecommerce that developed as a percentage of total retail, the number of users that moved online, even by the wildest, most optimistic prediction you could take at that period, it got... It was more adoption than that. Now the flip side of that is... I know this is kind of the most interesting part of that set of projections was she thought the players that would capture a lot of that growth were like the blockbusters of the world. And so where where I think she was off was that it was not the established players that figure out how to monitor. There are some, like, Walmart, etcetera, that did figure out how to evolve. But, actually, it was a lot of new entrants that kinda sprang up out of, from scratch and figured out how to take advantage of the new market. I think AI will be a very similar... And and by the way, again, that's all the context that it took the Internet, you know, north of ten years to really... Probably north of twenty years to get to real maturity. I think AI would be a very similar paradigm, and almost every new technology has had that sort of a curve. It will take longer. So, you know, I think everyone's been of the view that everything's gonna change in two years. Look. I think enterprise adoption right now, you know, depending on the numbers you look at, it's something like 5% of Jet AI projects make it to production. You know, the interesting stat I heard the other day, like, 98% of inference compute is, like, 37 companies. Most of them are coding apps. So you have, like... We're pretty early in the cycle on enterprise. Hugely advanced to where we are in consumer adoption. So, like, 70% of consumers use this tech at least weekly. So we're there on consumer, but we're we're early in enterprise. And I think enterprise is gonna take north of a decade for real adoption.
00:11:25 · Matthew Fitzpatrick, Guillaume Moubeche
And I think the most interesting question, if you take the Internet as an exact parallel, is if that all happens, is it... Now that old quote of, like, do do the new players get distribution before the existing players build the tech? I think that will be the big question of the next decade is do all the new businesses developed around AI become the new players in the spaces, or do the existing players figure out how to transform? [00:11:49] S1: So I've got two questions for you. The first one, why do you think there's such a gap between enterprise adoption versus, like, consumer adoption? And I'll I'll tell the second one afterwards.
00:12:01 · Matthew Fitzpatrick
[00:12:01] S0: Yeah. I I think it's partly the... As a consumer, when you use a... When you use one of the models, you want it to be broadly right and broadly health. And it actually is always broadly right and always broadly health. So if you ask it a question about, like, where to make a restaurant reservation or, you know, even a new prototype or build for a business you're thinking about, it's... They're unbelievably health. I think the the more complicated dynamic for enterprise is most, call it, particularly Fortune 1,000 use cases, rely on extreme precision. So if you take anything from contact centers to medical coding to credit underwriting, you need 100% accuracy. You need auditability. You need really clear data that that under... That underlies it. And so I think that adoption paradigm is just different. Like, these are probabilistic models that that are not always perfectly correct. They have hallucinations. And so the actual process of taking a model, designing a system around it, quality controlling it, testing it, that's just a newer motion that I think enterprise is gonna take some time to come up the curve on. Like, the phrase often used is it's it's not a it's not a technology gap. It's a capability gap. And so I think this is a motion that folks are... I do think you've seen an acceleration in the market in the last year or so where, like, people are starting to adopt for the first time in material ways. But I think that journey is gonna take real time. I mean, the analogy I'll give, because I think it's probably the best use case of AI kind of in American society, would be if you took underwriting of mortgages, like single family home mortgages. Right? You know, twenty five years ago when you went to go do that, you had, like, literally a local loan officer that made a decision whether you got a credit or not. Right? And so that was like a decision, and one person, random numbers, came back. And now 85 to 90% of that is auto adjudicated by underwriting marks. Right? And so that's been a hugely positive thing for society in that you have much fairer, less biased access to credit. Your your your speed of getting access to credit is much faster, and that's really powered a lot of the the housing market. But that did not happen overnight.
00:14:12 · Matthew Fitzpatrick, Guillaume Moubeche
Like, that took fifteen years, maybe even twenty years for people to actually understand how to redesign the processes, how to redesign the intake forms, how to quality control it, how to show regulators, like, via model risk management, why it was fair, why it was not redlining, and why it was unbiased. And so I think a very similar pattern will exist for Gemini. [00:14:33] S1: And how long do you think... Because you mentioned, you know, like, took about twenty years, you know, for the Internet to reach, let's say, like, maturity stage. We see that things are moving a lot faster with AI. Do you feel like the adoption is limited simply by human nature because, you know, like, changes usually, like, take time? Or do you feel like it's a capability, you know, like, the fact that the way the AI is evolving, like for international, it was really like a capability oriented. We can think of speed. We can build coverage. We can think of network. So, I mean, I'm cursed to to hear, like, how you think about this in terms of speed.