The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:58:12 · Harry Stebbings, Brendan Foody
[00:58:12] S1: couple where you're like, Wow, that gives me confidence that I can do that too. [00:58:17] S0: Actually, will say this one thing, which is that I remember when I went to Georgetown, I didn't get into Harvard, and I was like, wow, the people at Harvard are probably dramatically smarter than me. And I went to this nonprofit called Prod, where it was a bunch of kids from Harvard and MIT that were all building startups. And they're very smart, don't get me wrong, but I do think that most of us have this very equalizing feeling. That majority of people that accomplish extraordinary things, when you spend more time with them, you realize that they're sort of just a normal person to a significant not all of them, but most of them to a significant extent. And I think that makes you feel like like when I saw Ethan Thornton from Mock raising $70,000,000 as a 19 year old, I'm like, Wait, Ethan is like a chill guy and a good friend, and maybe I could do something like that one day. It just gives you the sense of being able to accomplish so much more.
00:59:15 · Harry Stebbings, Brendan Foody
[00:59:15] S1: It's so interesting you said that, that kind of dispersion effect from seeing your friends achieve. And I think it's one thing that's held Europe back in many ways. You work with some of the largest model providers in the world. How do you feel about Europe's inability to competeprovide leading models to the world? Like when you look at the benchmarks, mean, Mr. Al might make an entry at 72, it's like the Eurovision Song Contest kind of at the bottom. I love Off, I love Mr. Isle, I'm very proud of it as a Eurovision, but shit, we haven't delivered on the model side. Does Europe improve that? Does that matter? [00:59:48] S0: I think that it's gonna be difficult to change because there's just so many strong network effects around talent, right? When we have the best talent, even I knew so many brilliant French researchers that go to work at OpenAI and Droppik and DeepMind, right? Because when we have the best talent at those labs, that's where they all aggregate, and then that compounds to them having more capital, more compute, more impact, etcetera. And so I expect that trend and to be one of the largest, not only economic, but geopolitical advantages that The US has. [01:00:22] S1: So if you're Europe today, do you just go, You know what, sort it, we've lost that model race, but we can still be a dominant energy provider if you're Norway where I'm from. Actually, we do pretty well on Norway providing energy.
01:00:22 · Harry Stebbings, Brendan Foody
[01:00:22] S1: So if you're Europe today, do you just go, You know what, sort it, we've lost that model race, but we can still be a dominant energy provider if you're Norway where I'm from. Actually, we do pretty well on Norway providing energy. [01:00:34] S0: Do we just accept that? I would accept that, yeah. I think that maybe it's worth having some post training capabilities, because there is gonna be value to distillation in some of the work that happens after foundation models are built, and there's definitely gonna be some value in applications, but I don't know if I would lean aggressively into how do we compete head to head with Anthropic. Do you buy the sovereignty argument of we need sovereign models because we don't want our data going to US or China or wherever that is? Maybe in some cases. There is value in localization, and I'll give an example, which is that often oftentimes labs will come to us and say, they need their models, not just to be good at American law, but also to be good at British law or good at French law or whatever the jurisdiction is in the world. I think that that is going to be an important last mile in making the models useful in whatever jurisdiction that they're operating in. That said, the labs are just going to hire 10,000 people in France to teach the models how to be better at French law. I don't think that there's so much that others are gonna be able to do to stop that because the transfer learning capabilities from all of the other domains that they're focusing on are just so powerful. And when
01:00:34 · Brendan Foody, Harry Stebbings
[01:00:34] S0: Do we just accept that? I would accept that, yeah. I think that maybe it's worth having some post training capabilities, because there is gonna be value to distillation in some of the work that happens after foundation models are built, and there's definitely gonna be some value in applications, but I don't know if I would lean aggressively into how do we compete head to head with Anthropic. Do you buy the sovereignty argument of we need sovereign models because we don't want our data going to US or China or wherever that is? Maybe in some cases. There is value in localization, and I'll give an example, which is that often oftentimes labs will come to us and say, they need their models, not just to be good at American law, but also to be good at British law or good at French law or whatever the jurisdiction is in the world. I think that that is going to be an important last mile in making the models useful in whatever jurisdiction that they're operating in. That said, the labs are just going to hire 10,000 people in France to teach the models how to be better at French law. I don't think that there's so much that others are gonna be able to do to stop that because the transfer learning capabilities from all of the other domains that they're focusing on are just so powerful. And when [01:01:49] S1: you say about hiring 10,000 people, the thing that's just astonishing me is the wave of cash. For me, I'm sure OpenAI is the same, but I've seen it specifically with Anthropic. I mean, insane levels of comp. Totally. How do you compete against that?
01:01:49 · Harry Stebbings, Brendan Foody
[01:01:49] S1: you say about hiring 10,000 people, the thing that's just astonishing me is the wave of cash. For me, I'm sure OpenAI is the same, but I've seen it specifically with Anthropic. I mean, insane levels of comp. Totally. How do you compete against that? [01:02:03] S0: It's definitely one of the things that's most top of mind, in particular because markets for people founding companies are so hot, where we've had three employees that have founded companies worth in excess of $100,000,000 [01:02:18] S1: I saw your Tweets where you do the McCormaffia [01:02:20] S0: Tweets, Yeah, exactly. And we're a very young company, right? And I think that it's difficult for a variety of reasons. A lot of people probably don't have a full understanding of just how hard it is to build a company, as you know well, Harry, and how low the probability of success is and how fortunate we were and how lucky we got along the way. And so I think that that's definitely one of the large challenges. Even like there was someone I was hiring the other day and he had an offer for $20,000,000 in cash per year from TBD. And like that's the kind of stuff we run into on a regular basis. TBD? Meta's super intelligence group. 20,000,000 in cash? Per year. Or it's in stock, but liquid. That's hard to compete against. It's hard [01:03:04] S1: to compete against, yeah. Does that change?
01:03:04 · Harry Stebbings, Brendan Foody
[01:03:04] S1: to compete against, yeah. Does that change? [01:03:06] S0: Does that just continue to escalate? It'll probably continue to escalate for a smaller group of people. But I also suspect that as more people gain knowledge of how these labs operate and what the capabilities of how to train a frontier model, that means that there's going to be more supply in the market for people that have that skill set and thus a little bit more reasonable pricing. And so I expect there to be some craziness that continues, but hopefully sort of the ninety ninth percentile at least within the market will bounce itself out. What is the hardest role to hire for today? Researchers. Just because of supply? Because of supply and demand. It's just this market where there's 10 times more demand than there is supply, and that makes it very difficult. We've been building out an incredibly strong research team, like Edward Hu, the first author on Laura, who was previously at OpenAI, is working with us and a bunch of other top researchers, but the market is definitely getting very hot. How much does it cost to hire a high quality AI researcher? Oftentimes it would be in the tens of millions of stock per year, For the really good people, yeah. [01:04:15] S1: I remember when researchers weren't paid very much. This was like ten, fifteen years ago, were like the underpaid but brilliant people in society. Yeah. Now I feel like that's relatively changed.