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.
00:15:12 · Matthew Fitzpatrick
[00:15:12] S0: I... Well, one thing I'll say is I think it will be uneven in its in its proliferation. So I think you will see certain areas of society that will proliferate much faster. You know? I think I think contact centers, for example, is one that it might... It's definitely not there yet, and there's been a fair amount of bumpiness, but I could see that making huge inroads the next couple of years. I think media is another one where, you know, everything video creation, I think you're gonna see really rapid adoption of of that as an example. I think there are other areas like financial services that will that will definitely take longer. Particularly, any regulated industry is gonna take longer. And I I think the... And I think, by the that's different than the Internet because the Internet is a broad based technology. It's a network. In in many of these cases, it... Like, you could be super advanced, and I could not be advanced at all, and that we could both be using the same technology. So the adoption paradigm here is more individual to individual institutions, individual companies, individual sectors. And so that means, I think, in some cases, it will advance faster, the Internet business presence. In some case, will advance slower. But I do think it's going to take longer than people expect even though it is... Even though you see hyperadoption in the... At the individual level. Like, there... There's a study I really like by the NBER, like National Bureau of Economics, and they basically look at individual productivity use cases versus company level. And it's like the average... They look at, like, 60,000 employees or something like that. And the average individual improves in productivity, like, 15 to 20. But the average company improves in productivity, I think, is, like, one to 3%. And I think that is really the paradigm right now is, like, people using all these tools, and they're helpful, but they don't really redesign the way the work is done. And I do think that takes time. Like, there's an analogy that, like, the automobile took forty years for mass adoption, and it actually took the redesigning of the factory to get that. Like, you actually had to redesign factories around the assembly line to bring costs down the automobile to use them.
00:17:15 · Matthew Fitzpatrick
And I think there's a similar analogy here, which is we're not really changing the nature of the work in the factory. We're just having everyone use the tools, and I think that will take time to play through. So we're five years into the journey now. Everyone is using a lot of the technology, but I think the nature of work will take another... You know? We're we're still, I think, five to ten years away from material, like, than the amount of use cases being shaped. I will say, though, this is where digitally native companies are different than enterprise. And I I think that's... If you are a company founded in the last ten years, you... Or, sorry, last two years, anchored around all the cool... All the digitally native kind of tooling, coding apps, etcetera. I think the acceleration of those businesses has been vertical. I think it's very different if you were a ten year or twenty or thirty year old company reengineering yourself around that tech. And I I I think that's... That is a much harder thing to do, and that's most of the scaled companies in the world.