00:32:04 · Ev Randle, Molly O'Shea
[00:32:04] S0: Yeah. Yeah. It's I mean, it's it's it's the $1,000,000,000,000 question or the multi trillion dollar question if you kind of combine SpaceX AI, OpenAI, Anthropic all into that equation, and I think it's left, like it depends on what the next twelve to many years look like, and what I mean by that is like, do you believe that we're on a path to recursive self improvement and this future of a bunch of geniuses in a data center, whereas if you do have a bunch of geniuses in a data center, if the frontier goes that high, then you legitimately do have probably a lot of pricing power and a lot of ability to continue to grow and continue to monetize and probably even reaccelerate growth if you're a frontier lab. On the other hand, if at any point it seems like capabilities are actually hitting an absolute ceiling and distillation continues as it has historically and the open source actually gets 95% as good as wherever the ceiling of capabilities tops out, that's a really scary situation for the Frontier Labs. I don't think it's like a death nail for them because, again, most of the users of ChatGPT would use ChatGPT whether or not it was a GPT model in there or not. Like they like the product. Most of them don't even know, they wouldn't be able to tell you if the model's different. In our little bubble in San Francisco, people would be able to tell you, but like the vast majority of the 900,000,000 weekly active users were They [00:33:31] S1: no idea what 5.5 is.
00:33:31 · Molly O'Shea, Ev Randle
[00:33:31] S1: no idea what 5.5 is. [00:33:33] S0: No idea, and like they wouldn't be able to tell you, if you swapped it out for like 5.2 or something, they wouldn't be able to tell you, and so I think they're, it wouldn't, like they're not, like that scenario wouldn't kill them because again, people like the products, and they've done, both OpenAI and Topic have done a tremendous job of building amazing products on top of their models, but it would certainly be a very, very different situation because you'd have a much, much greater impact from open source depressing their ability to have pricing power and charge a premium for the tokens that they're producing. And it's like, I mean, open source inference also costs money. It's not like open source means free. Someone still has to run the GPUs, someone still has to build the data centers, someone has to still operate the data centers. It's more just about how much premium margin that the Frontier Labs can create, and I don't think we'll know until it's very clear whether or not we actually have RSI and there's going to be geniuses or god in a data center or whatever you wanna call it, at which case we'll have different problems to be thinking about, or if we do end up at some point topping out in capabilities sometime in the next few years and distillation continues, I think it's much harder to garner a premium margin if you're a frontier model company. It becomes much harder. [00:34:44] S1: Given the scale and how fast things are running, another common question is how do we fund this? [00:34:50] S0: Mhmm.
00:34:50 · Ev Randle, Molly O'Shea
[00:34:50] S0: Mhmm. [00:34:50] S1: And there's different types of funding that's happening. I mean, I think we're like literally across from General Catalyst and they have their CVF fund and like, there's people who are going after debt for tokens and trying to create the right mix. The kind of like, like, the undercurrent of all of this is companies are raising money faster than ever before. One in one of the questions in our, like, conversation before is like, do we talk about the bubble? I don't think the bubble is very interesting. I'm, like, most interested in, like, what are the biggest concerns right now with that? Because reading through the cracks of all of it, right, it's like what I think we all know when companies are running off the tracks. [00:35:31] S0: Mhmm. [00:35:31] S1: And, when they're overfunded to an extent, but then again, like there are, it's a weird time. Like there are total hail Marys where these companies are outperforming what you thought they were doing. So And they raise another round. And then six months after that, they raise another round and then they just keep on getting more and more capital under them. But okay, so to this point, the funding ecosystem has changed dramatically. Like the playbook is out the door. And so with a firm like Benchmark, who is just fundamentally on the early stages, how do you capture that value at the early stages and continue that?
00:36:04 · Ev Randle
[00:36:04] S0: Yeah, I think, I mean, it's something that we talk about a lot internally, because you're right that even the funding market, it's not just like, I think the one that everyone talks about, the change in the funding market and the venture growth asset class, the trend that everyone talks about is companies staying private for longer. That's been the prevailing trend that everyone talks about is that have, mean name your company, I mean obviously some of these companies are now going public like SpaceX, but before it was like SpaceX is never going public and Stripe is never going public and Databricks is never, if this was 2005, all these companies would've been public for years, and now there's just so much capital available for them in the private markets via these venture growth platforms that they just stay private essentially indefinitely. And I think a more recent change is that, as you're mentioning, AI is just so so different because depending on what you wanna do, oftentimes there are massive like day one costs. Like if you wanna create a NeoLab, and you have some incredible research direction that you wanna pursue, but you need $2,000,000,000 of compute to see whether it's gonna work or not, That's very, very different than Airbnb raising the 500 ks or whatever they raised at YC in their seed to see if Airbnb would have any product market pit. It's just a completely different equation and type of funding mechanism and just, like it's a completely different capital market. Also, think what you're seeing is now because of the staying private longer thing, you also have these crazy situations where oftentimes you almost like see a rebirth of a company, where oftentimes people think like oh well when it's a growth stage, it's like early stage you get high multiples on money, but you know it's higher risk, and then late stage you get like, you know you can get like a two to three X, but it's much lower risk. And I think the crazy thing is that because these markets are so big in AI and elsewhere, and because these companies are staying private for much longer, you actually have these situations where a late stage company can have much higher upside than like a Series C, which is super weird.
00:38:39 · Molly O'Shea
[00:38:39] S1: So
00:38:39 · Ev Randle
[00:38:39] S0: weird. It's so weird, it's like my first investment when I was at Kleiner Perkins was SpaceX, and it was at an over $100,000,000,000 valuation. Talking with peers and friends at the time, it's like man, you gunning for like a two and a half X? Like how much upside could there possibly be if you're getting in at a triple digit billion entry valuation? And I think the thing that ended up being true for SpaceX was like, well actually you had this thing called Starlink that had started a couple years before that and had really started to scale, and that was like a rebirth of the company. And if you look at the P and L today and in the S1, the vast majority of the business is their consumer broadband business, and B2B broadband business via Starlink, it's not even the launch business. And so I think the two things that we grapple with and think about a lot is like how do we continue to be the best venture capital firm that partners with entrepreneurs very early in their journeys and is their most meaningful partner when you have a much, much more capital intensive early life cycle for some of these AI businesses, and when you have these businesses that actually sort of go through these transformational moments at later stages where it feels like actually even though they're fifteen years in, they're still only 1% done with their journey. So I don't have a great answer for that. I think we're extremely flexible in what we do. Like we don't say that we just do seed or we don't say we just do series A. Like we partner with the very best entrepreneurs in the world when we think that we can deliver unbelievable upside for our LPs and be the most meaningful partner to them. Those are the kind of the constraints that we think about, and I think the trends that we're seeing both in AI and in capital markets writ large definitely expand our thinking in terms of like, what actual rounds and what type of situations are applicable to a firm like Benchmark.