Lenny's Podcast: Product | Career | Growth
01:05:03 · Marc Andreessen
But obviously as a venture firm, we're betting hard on many of these categories being totally reinvented, and a lot of the best founders are trying to figure out how to do that. So that's kind of AI changing the definition of the product. I think the next layer is actually a lot of what we've already talked about which is AI changing the jobs. And so it's a lot of what we already talked about, but, okay, if I'm a founder of a company and if I have room in my budget for 100 coders, how do I get those coders to be super-empowered AI coders, not the kind of coders I used to have, and if they're super-empowered AI coders, then does that mean, do I still need the 100? Maybe now I only need 10. Or does that mean I still want 100 but now they're doing 10 times more? And so, as you know, a lot of the best founders are working on that right now. And then I think the third shoe to drop hasn't quite dropped yet, but it's kind of the big one which is, all right, the basic idea of having a company, does that change. And again, here you've got this concept of the super-powered individual which is, okay, can you have entire companies where you have basically the founder does everything. Because what the founder's doing is overseeing an army of AI bots. There's kind of this holy grail in our industry that's been running for a long time which is can you have the one-person billion-dollar outcome. We've had a few of those over the years. Bitcoin is probably the most spectacular example with Ethereum right behind it which wasn't quite one person but a very small team. You had Instagram and WhatsApp that had very big outcomes with very small teams. Every once in a while you get one of these things where you just, something hits, and you just have a very small number of people associated with it. But that said, most software companies obviously end up with huge numbers of employees. And so I think the most leading-edge founders are thinking of, okay, how do I reconstitute the actual very definition or idea of having a company and can you have a company that's literally basically just all AI. If you're doing anything in the real world, that's hard, but if you're doing software, that seems like it might be feasible in some cases.
01:07:08 · Marc Andreessen, Lenny Rachitsky
And then there's the ultimate example of that which is can you have like autonomous AI economy stuff happening where you have AI bots on the blockchain or something that are basically out there functioning as a business and making money and just literally where the AI does all the work itself and just issues me dividends. Maybe that's the final outlier result. We have a few founders who are chasing that kind of thing. So I would describe that as kind of the latter that the best founders are on. [01:07:36] S1: Super interesting. This whole idea of a one-person billion-dollar company, I think it depends on your definition of what this is, like an outcome I could see. Running my newsletter as one person with some contractors, there's so many little annoying things that I have to deal with, with just support tickets and issues and bugs. It's hard for me to imagine actually a one-person billion-dollar company, even if AI is handling so much of your support because there's just so many random-edge cases that I'm just... like filling out forms. And so I guess depends on, do you have contractors, does that count, what does it mean to be a one person. But I'm just like, "I can't see that happening." [01:08:12] S0: Yeah. I mean, look, Bitcoin, Satoshi pulled it off. [01:08:16] S1: But the open source community now, does that count? I don't know. [01:08:19] S0: Yeah. [01:08:20] S1: I guess it counts. Okay.
01:08:20 · Lenny Rachitsky, Marc Andreessen
[01:08:20] S1: I guess it counts. Okay. [01:08:21] S0: Yeah, exactly. Right? So yeah, I would say I don't propose to have answers here, but more just like the smartest people I know or many of the smartest people I know are thinking hard about this. [01:08:33] S1: Yeah. What do you think about moats? A big question constantly in AI, the fact that everything's changing, just what's your guys' thesis on moats in AI? Is that even a thing? Do you care?
01:08:45 · Marc Andreessen
[01:08:45] S0: My experience with really big technological transformations, and of course, I kind of lived 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. There's this rush to judgment upfront where people say, "Oh, it's therefore obvious that X, Y, Z. 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." What everybody does is they kind of state those things with just an enormous amount of self-assurance where they really sound like they have all the answers. And then what happens is these ideas kind of saturate the media because the media naturally prizes definitive answers over open questions because it... it's like when CNBC is 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 debate it from eight different angles." What I found is if you look back on those predictions a few years later, and you can do this by the way, if you pull up coverage of the internet from 1993 through 1997, or for that matter even through 2005 or 2010, and you look at the kinds of confidence statements people were making in the first 10 or 15 years, I would say almost all of them were wrong, generally quite badly wrong. And so I think the process, I think 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 structural change that will play out over time. And again, we've talked about a lot of this, but the implications on 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? By the way, how does this intersect with politics? How does this intersect with unions?
01:10:52 · Marc Andreessen
How does this intersect with war? What's China going to do? And so there are just a tremendous number of unknowns, a very, very large number of unknowns, and I think it's just like really, really dangerous to prejudge these things. I'll just run this as a thought experiment and you can see what you think on this, but it's like, are AI models themselves defensible. Is there a moat on AI models? And on the one hand, you'd be like, "Wow, it certainly seems like there is or should be," because if something takes billions of dollars to build and you need this incredible critical mass of computing data and there's only a certain number of engineers in the world that know how to do this and they are getting paid like MBA stars. And then these companies have to deal with all these crazy political issues and press issues and reputational stuff and regulatory and legal. All of that translates to, okay, probably at the end of this, there's going to be two or three companies that are going to end up with like 100%, I don't know, whatever, 50/50 or 30/30/30 or 90/10 and one, or whatever it is, market share and then they're going to have whatever profitability they have and it's going to be kind of a classic oligopoly, or maybe one company's going to win definitively and it'll be a monopoly. And by the way, those outcomes have happened in software many times before. And so maybe that will be the outcome. The other side of it is if you had told me three years ago that in the Christmas of ChatGPT that within basically a year to year and a half there would be five other American companies that would have basically 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, the thing that seemed like it was black magic all of a sudden has become like commoditized really fast," which by the way, is exactly what happened. Within a year of GPT3 coming out, there were there open source GPT3s running on a fraction of the hardware that were available for free. And then there were five. Now you've got, 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 all these other companies that are...
01:12:59 · Marc Andreessen
and then DeepSeek and Kimi 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 kind of 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. No, you actually need the application level's 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. The way I think about this kind of structurally is it's a complex adaptive system. The technology itself provides one of the inputs. The legal and regulatory process 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. This is a complex system, and so we actually don't know the outcomes on this yet. We need to be open to surprises at the structural level of what happens. And of course, as a VC, this is very exciting because it means we're doing this now. We should make bets along every one of these strategies and see how this plays out. I would just say, there may be, I don't know, there may be like one particularly brilliant, I don't know, hedge 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.