Invest Like the Best with Patrick O'Shaughnessy
00:47:02 · Gavin Baker
[00:47:02] S2: not last month, everything but AI was vertical. And I do think open source getting closer to the frontier and companies like Fireworks making it really easy to customize a model such that you can get, in some cases, better than frontier performance for, uh, meaningfully lower cost. That is a godsend for the software industry, and it's also a godsend for all these AI natives. You know, it's like our friend Vishya, I think he said two years ago, "I've never seen more companies go from being founded to like fifty million dollars a year in revenue and generating cash flow in like whatever it is, nine months, and it's hard to know if any of them are durable because a lot of people would dismiss them as ChatGPT wrappers." Well, now with open source, you've generated some data that's unique to your use case. Whatever your vertical you're going after as a wrapper is, Fireworks did come out with a really cool product called Nexus, and if you're using Cloud Code, OpenAI Codex, Grok Build, it is literally three lines of code, like twenty words, and Fireworks ingest your data, they can RL a model, and then there's a router that sends the query, and they've had amazing results, and this is kind of the solution for every AI-native, that's why you saw Harvey before it was acquired, Cursor leads so heavily into this, Harvey, Lagora, all of them, because if you can go from just using one, two, or three frontier models to using those frontier models for whatever it is, thirty to sixty percent of your token consumption, and then use your own RL model, all of a sudden you're not a wrapper, you're way more defensible.
00:48:50 · Patrick O'Shaughnessy
[00:48:50] S0: I was so interested by that Cursor thing that came out, I think it was Cursor, where it's sort of like AI is speedrunning like what we've learned amongst humans, which is you could use the frontier model to plan and then farm out tasks to the dumber models, and it's fifteen times more efficient or whatever the
00:49:05 · Gavin Baker
[00:49:05] S2: metric was. And it may be, and this is like super ironic, lower margin open source tokens that are just a little bit behind the frontier We have friends who believe that once a frontier model hits RSI, it will actually have a dramatically lower cost to serve at every level of intelligence by kind of distilling this, and then there's no place for open source, and I would say that's like a anthropic, OpenAI, Grok Maximalist view, we shouldn't dismiss anything, anything is possible. We wanna be very humble, I particularly wanna be humble after the month I've had, um, but that doesn't seem that likely to me. One, because there are so many of these AI natives that have actually generated a decent amount of domain-specific proprietary data. And before open source had this moment, these inference clouds and these routers really developed, you kinda didn't have a choice, like whatever the terms of service were, you accepted them. But if you can now get off that treadmill, that gives you a degree of independence, maybe durability, safety. But going back to your point, it may be that these cheaper tokens massively inflate the value of the most cutting-edge frontier tokens, because if today, if you have-- I'm gonna make this up. One twenty IQ open source models, and they're really cheap to run, doesn't that make a one sixty IQ model that can orchestrate them more valuable? We talked last time about how I've been really surprised that so much of the economic returns have accrued to the frontier, that is changing with what we're seeing with these inference clouds, together, modal, base ten, they're all working in a very cash efficient way. What's shocking about those business models? Is they're growing almost as fast as the frontier labs in the early days, but burning very little cash. It's pretty extraordinary to go back to silly SaaS metrics like the, you know, the rule of forty perspective, like, "These are crazy numbers."
00:51:20 · Patrick O'Shaughnessy, Gavin Baker
[00:51:20] S0: Do you think there's a lot of instruction in just like the distribution of pay inside of an organization? Like, the CEO makes X times more than the median person at a company, and maybe that's Frontier tokens versus, you know, Open Source tokens, something simple like... Absolutely. Yeah. Something simple like- [00:51:33] S2: Yeah, it may be that what we discussed last time, where, you know, Frontier tokens, like the pie is growing really, really fast, they may continue to capture the overwhelming majority of economic value, but not all of it the way they have been, and Open Source tokens might be the majority Back, that's great for infrastructure demand because a token is a token and it takes the same amount of flops, watts, space, [00:51:59] S0: cooling to make. What's the worst thing that could happen in AI? Is it regulatory? I think regulatory
00:52:06 · Gavin Baker
[00:52:06] S2: has to be the biggest risk. It's the most obvious risk. And so that was kind of one reason I was excited to be here this week. I wanna be scared, you know? I like, I don't wanna feel like a lunatic watching these stocks get cheaper, thinking the expected forward returns are going up, while it feels like the on the ground fundamentals have pretty materially improved in July relative to even June, but I still come away thinking like, regulation, it just has to be the biggest risk, like you just can't ignore New York Making a data center moratorium. We're living in this weird post-factual, post-logical political world, and, I mean, I think the AI industry, it has done a terrible job of PR, and I do think they're at least realizes that now. Yeah. Maybe if not fixed it, it realizes it. Yeah, but like the political narrative, I think amongst a lot of ordinary Americans, is like, "Data centers, they're gonna raise your electricity prices, they're gonna take all your water, and then they're gonna take your job." The reality is, given the deals that are being cut now, when a data center goes in, electricity prices actually generally go down for everyone around there, 'cause behind the meter deals, this is that like data center pledge that Trump asked people to sign, generally the data center developer, used to be they just had to get the police department and the fire departments like new trucks and new cars and new body armor or whatever. Now it's like, we're gonna build you a hospital, a school, a new police station, and a fire station. We're gonna lower your power bills. How does that sound? And by the way, the jobs are ongoing because it turns out that you kind of need these plumbers, electricians, HVAC contractors, data centers are in a lot of ways the best thing to happen for blue collar wages in my lifetime, and yet you have the Democrats who ostensibly represent the blue collar workers taking those jobs away. It's just kind of wild how, what is the phrase, like a lie could go around the world faster than
00:54:10 · Patrick O'Shaughnessy, Gavin Baker
[00:54:10] S0: truth gets out of bed, yeah. Yeah, [00:54:11] S2: faster than truth gets out of bed. But an author made a mistake in a book, and overestimated the amount of water usage in data centers by ten thousand x. Not a little bit, like not one order of magnitude, not two orders of magnitude, not three. She's admitted that mistake many times, I was completely wrong. It's like been super debunked. [00:54:32] S0: It's like the Popeye effect. You ever hear that example? No. You know, Popeye eats spinach. The reason was same deal, and in an academic book, they placed the decimal two things wrong. So spinach doesn't have more iron than everything else. It was just this one source, and then that propagated through people still say it