Invest Like the Best with Patrick O'Shaughnessy
00:23:27 · Eric Vishria
[00:23:27] S2: Anytime that I'm talking to a founder about a problem in their company or what they're doing or a move they're making or anything else, which is what I spend eighty percent percent of my day doing. I'm very, this is how we used to do it. This is what we would typically do. This would be the typical readout on this old school readout of why this candidate's better than this candidate. Let's reevaluate that in the context of today. Let's reevaluate that in the context of an unstable technology substrate. Let's reevaluate that in the context of a business model that's growing this way versus that way. I've really started to question every assumption and every lesson that I learned before, which of it translates and which of it doesn't. That's a huge difference. I'll give you a really concrete example. Which has been very disruptive inside of these scaling AI companies. So these scaling AI companies, we've had a lot of leaders come from the prior generation with great experience and everything else come to these scaling AI companies and completely flame out. Can you see it across the industry? And so the question is why? These are some of the best leaders from four or five years ago, they had all the lessons, they learned it all, they're excellent, but somehow it's not translating. And there's some impedance mismatch between the AI founders potentially, the needs of the business and what these people are bringing. And I saw it really abruptly with a particular sales leader who we hired, who was like, "Hey, we can't hit any of these things because the way that software sales has been taught forever is a quota capacity model. You have a quota capacity model, each rep does this, in the early days of a company, the quotas are one point two, one point five million, maybe the ISRs are at seven fifty or eight fifty, and then over time it scales up and enterprise gets to two point five million, and that's how all these financial models are built. You start with a quota capacity model, This is what we can do. Boom, boom, boom. Fundamentally, without realizing it, everybody was implementing something that was based on pushing demand, not pulling demand. And for so many of these companies, they're operating here, these customers, and there's a new AI-enabled product that comes along and it's just fucking magic. So these companies are selling magic.
00:25:41 · Eric Vishria, Patrick O'Shaughnessy
Well, it turns out if you're selling magic and you're the first one there, you're gonna sell a lot more than two million. The whole notion of quota capacity model and that being working, it's not that it doesn't matter, it matters kind of, but it's definitely not the first order thing or constraint. So you have these execs come over with this, it's like, "Here's our math, and here's the territory assignment, and here's what we would do, and first you do West Coast, and then you do East Coast, and then you do Central, like all These folks and talking to them is just like, hey, you need to check everything at the door, check it all, which is probably good practice anyway, but check all the baggage, check everything that you learned, and just learn this from first principles. How's it working? What are really the bottlenecks on delivery? What are the bottlenecks on demand? Because it turns out that in a lot of these companies, you have reps doing ten or twenty, [00:26:36] S0: thirty, I saw fifty recently, [00:26:38] S2: it turns out that's different. [00:26:39] S0: What is like the best salesperson that you've seen that's doing it in a de novo way [00:26:43] S2: doing? Honestly, the best salesperson in any of these companies is the founder, and what they're doing is bridging the jagged edge to what the customer's capability is, and that's it. It sounds so simple, It's just so big, just like we were talking about with cloud, I think the biggest mistake everybody made was they just undersized the market, and it turns out the market's really,
00:26:43 · Eric Vishria, Patrick O'Shaughnessy
[00:26:43] S2: doing? Honestly, the best salesperson in any of these companies is the founder, and what they're doing is bridging the jagged edge to what the customer's capability is, and that's it. It sounds so simple, It's just so big, just like we were talking about with cloud, I think the biggest mistake everybody made was they just undersized the market, and it turns out the market's really, [00:27:03] S0: really big, and this market is bigger. It feels like right in this moment in time, actually just this morning, you and I are in this great group chat together where the discussion is. The demand for intelligence seems kind of unlimited, and it seems like the smarter the thing gets, the more demand there is. Maybe the bottleneck is just capital. The world just feels like it needs to take a breath. The RSI concept, if you apply it across technology, doesn't need to breathe. The agents don't get tired, but the world feels kind of like, "Oh, man, it sure would be nice to have three months just to like digest this a little bit and for capital to form and evaluate its prospects. " And the scale is getting so big, does it feel to you like this Or are we just gonna get tapped out of money that can be invested in these things when it seems like we could consume like any amount of money to build any amount of stuff and serve any amount of inference? I'm not a macro economist,
00:27:54 · Eric Vishria
[00:27:54] S2: I am worried about energy. If you think about the models as translating compute into intelligence, quite simply, what do models do? They very effectively translate compute into intelligence. What's the demand for intelligence? Well, it seems like a lot. So then it follows that we will continue to have more and more demand on Compute, I'm saying compute broadly, not chips, not storage, not whatever, but then like what do we need for compute? We need energy, a lot of energy. There's all this topic of distillation and Chinese open source and all these things, but the bigger thing to me is that I think China's bringing on ten times as much energy next year as we are. In the US, if energy is what you need for compute and is the bottleneck, and there's unlimited demand for intelligence, then it stands to reason that if we have a lot less energy Then we will have a lot less intelligence, or a lot less tokens, or a lot more expensive tokens. If we have a lot more expensive tokens, then supply demand, you're gonna end up with less. And that seems very bad. To me, I think the energy bottleneck, however that is, it'll manifest in twenty different ways. Gas turbines go up and down, natural gas goes up and down, and solar, whatever, all these different things, rare earths. That to me is probably more concerning, and if I'm thinking about it from a regulatory perspective or government perspective, and I think the administration is doing some things around this, fostering investment and development of all energy, solar, nuclear, gas, like whatever, do it all, we should do it all, and it'll work itself out. This is one of these things where, yes, one will be relatively better than the other, and I don't know, and I'm not sure enough to predict which one's which, but it'll all work. Speaking of
00:29:39 · Patrick O'Shaughnessy
[00:29:39] S0: compute, I would love to hear the Cere Tell the full version of this. The reason I'm asking about it is, I'm deeply interested in compute, I have big investments in compute, and I'm fascinated by it. It's just like the most magical thing to watch happen. It's mind-boggling when you get close to one of these things, what humans have been able to do on these chips and in these systems. I think you invested in 2016 or thereabouts. I think it was your first foray into like extremely difficult hardware type investments. It's so hard. And now the world is full of opportunities like this Teach me everything you've learned about hardware investing through Storybricks.
00:30:16 · Eric Vishria
[00:30:16] S2: Mostly, it's really hard. It's an amazing example to me about the naivete required. The company came in in twenty sixteen, it was five founders in a deck. I didn't want to go to the pitch, but it's my job, 'cause I'm like, "Why are we gonna go to hardware investment? Like, this is crazy. It's been ten years since we made a semi-investment. " I think basically the team was excellent, and then the kind of first slide was just like, "GPUs actually suck for deep learning, they just happen to be a hundred times better than CPUs. " You have to remember, this is pre-Transformer, OpenAI is this weird research lab at this time, Nvidia was worth like forty billion, not four trillion The TPU hadn't been announced, none of that, so this is early. But the whole idea, as soon as you said it, I was like, "Oh shit, of course! Of course! Like, why? " I had spent the last eighteen months trying to figure out applications of deep learning and looking at the security thing and looking at this medical imaging thing and like all this other stuff, thinking like, "Hey, there must be something here that's gonna be really transformed by this stuff. " Anyways, we go through this whole journey, we end up investing, which was amazing. We In between, just built a lot of conviction that this was a great swing. And I'll tell you what I understood, and I really just understood so little, but basically there are three things that we know how to do to speed up deep learning and hardware. Still till today, increase the number of cores, increase the communication between cores, bring the memory closer to the compute. Those are the three things. That's it, those are the only three dimensions we know in hardware. My articulation of what they said to me, honestly all I understood, was, "Let's just take all three of those things to their logical maximum. You have a wafer scale chip, at that time you would have four hundred fifty thousand cores on it, you'd have something like twenty gig of SRAM on the chip, so you never have to go off chip to get to memory, and because they were all in the same wafer, the communication between cores is maximized. This is the best you could do on that process.