Marc Andreessen (64:41)
My experience with like really big technological transformations and of course I kind of live this directly with the Internet and I saw this happen is the really big technological transformations, they take a long time to play out and there's all of these structural implications that just kind of cascade out over time. And then there's kind of this, this, there's this like rush to judgment up front where people kind of say, oh, it's therefore obvious that, you know, xyz, it's therefore obvious that this kind of company is going to be the company of the future. Not that kind. It's obvious that this incumbent's going to be able to adapt and this other one isn't. It's obvious that there's economic opportunity in this kind of startup and not in these others. It's obvious that the Moats are going to be in this area of the technology, but not in this other area. And, and there, and you know what everybody does is they kind of state those things with like just an enormous amount of self assurance where they, they, you know, where they really sound like they have all the answers. And then you know what happens is this, these, these ideas kind of saturate the media, right? Because the, the, the media naturally prices like definitive answers over open questions. Because, you know, you, you want, you know, like when CNBC is like booking guests, they want a guest who's going to come on and say, yes, this is the way it's going to be. X. Not like, you know, I think that's a really good question and let's like debate it from like eight different angles. And what I found is if you look back on those predictions a few years later and you, you can do this, by the way, if you pull up, like, coverage of the Internet, you know, from like, 1993 through, like, 1997, or even through, like, for that matter, even through, like, 2005 or 2010, and you look at, like, the kinds of confidence statements people were making in the first 10 or 15 years. Like, I would say, like, almost all of them were wrong. Generally, like, quite badly wrong. And so I just. I think the process, I think with massive. With if there's going to be a massive amount of technological change, it's going to be like, I don't know, five or six layers of, like, structural change that will play out over time. And again, we've talked about a lot of this, but, like, the implications on, like, what are the definition of products? What are the definitions of companies? What are the definitions of jobs? What are the definitions of industries? How does this play out at the national level? How does this play out at the global level? You know, how does this inter. By the way, how does this intersect with politics? How does this intersect with, you know, unions? How does this intersect with, you know, war? You know, what's China going to do? You know, and so it's just like, there's just. There's. There are just a tremendous number of unknowns. Like, a very, very large number of unknown. And I think it's just, like, really, really dangerous to prejudge these things. And so I'll just give. I'll just give you. And it's just. I'll just run this as a thought experiment. You know, you can see what you think on this. But it's like, you know, like, do. Do AI models? Are AI models themselves, like, defensible? Like, is there a moat on AI models? And on the. On the one hand, you'd be like, wow, it certainly seems like there is or should be, because, like, if something takes, you know, billions of dollars to build and you need, you know, you need this, like, incredible critical mass of, like, compute and data, and there's only a certain number of engineers in the world that know how to do this, are getting paid, like, NBA stars and, you know, and then these companies have to deal with all these, like, crazy, you know, political issues and press issues and reputational stuff and regulatory and legal, like, all of that translates to, like, you know, okay, probably at the end of this, there's going to be two or three companies that are going to end up with, like, you know, 100%, you know, I don't know, whatever, 50, 50 or 30, 30, 30 or 90, 10, 1 or whatever it is, market share, and then they're going to have whatever profitability they have and it's going to be a kind of a classic oligopoly and, or maybe, you know, maybe one company's going to win definitively and it'll be, it'll be a monopoly and that. And by the way, those outcomes have happened in software many times before. And so maybe that, that will be the outcome. You know, the other side of it is, you know, if you had told me three years ago, you know, that in the, you know, kind of Christmas of chatgpt that like within basically a year to year and a half, there would be, you know, five other American companies that would have basically, you know, exactly capable products and then there would be another five companies out of China that would have exactly capable products and then there would additionally be open source. That was basically the same. I would have been like, wow. Like, you know, the thing that seemed like it was blackmagic all of a sudden, you know, has, has become like commoditized really fast, you know, which, which by the way is exactly what happened, right? Like, you know, within, within a year of GPT3 coming out, there were their open source GPT3 is running on a fraction of the hardware, right, that were available for free. And then there were, and then, you know, there were five. You know, now, now you've got, you know, in the game, you know, fully in the game, you've got Google and you've got Anthropic and you've got XAI and you've got Meta and you've got, you know, all these other companies that are, and then Deepseek and you know, Kimmy and all these other Chinese companies. And so even at the level of LLMs or AI models, you can squint and make that argument either way. By the way, same thing. At the level of apps, it's like one school of thought is apps are not a thing because the model's just going to do everything. But another way of looking at it is no, actually adapting the model is the engine into a domain involving human beings where you need to actually have it fit for purpose to be able to function in the medical industry or the legal industry or whatever, or coding, you know, no, you actually need, like the application level is actually going to matter enormously and maybe the LLMs commoditize and maybe the value goes to the apps. And again, you can kind of squint either way on that one. And I know very smart people who are on both sides of that argument. And so my honest answer on this is I think we're in a process of discovery over time, which is, you know, the way I think about this kind of structurally is it's a complex adaptive system. The technology itself, you know, provides one of the inputs. The legal and regulatory process, you know, is another input. Actual individual choices made by entrepreneurs matter a lot. The economics matter a lot. Availability of investor capital varies over time. That matters a lot. And this is a complex system. And so we actually don't know the outcomes on this yet. And we need to basically be. We need to be open to surprises at the structural level of what happens. And of course, as if you see, this is very exciting because it means we're doing this now. We should kind of make bets along every one of these strategies and kind of see and see how this plays out. And I would just say, like, there may be like, one. I don't know, there may be like one particularly brilliant, I don't know, Edge fund manager or something who has this all figured out. But I guess I would say if they exist, I haven't met them yet.
