Invest Like the Best with Patrick O'Shaughnessy
00:19:56 · Eric Vishria
You wanna be able to spin these things up, spin them down, tear them apart over and over again so the iteration speed goes up and what you need on database. And ultimately, I think the cost becomes the arbiter of this. So the cost, and then this zero to infinity scaling and transportability and everything else around that Become like the arbiters of who wins and who doesn't. That's really different than, "Hey, I specced this database for our user thing and I procured a license and I ran it on this kind of hardware and everything else." There have been elements, of course, of these things over time, but I think that just criteria changed, and I think one of the big messages to these SaaS companies A few years ago was, "You have a choice, get to AI or be worth three times revenue. " That was a hard message to hear. You were just like, "Get to AI or three times revenue, those are your choices. " We say three times revenue now, 'cause a lot of these public SaaS companies are trading at six times or whatever, but keep in mind in '21 they were going for thirty times, like everybody, you're like, "Three times, wow. " I've grown four x since then. This isn't the whole multiple compression is a bitch. Yeah. I've grown four x, the multiple has gone down by a factor of six, so I'm worth less even though I've grown four x in a few years and I've gotten to breakeven and all these things. So like, that was the first message. But I think that even became really visceral to me where you're in these meetings and you're like, "Hey, every single day that you are hitting your plan, you are destroying equity value. Think about that. Our whole careers, we learned, you lay out a plan, you execute against it relentlessly and violently, you hit your plan or you exceed your plan, and you keep on building that, and that's how you build equity value. That's what the whole management team's learned, that's what these CEOs learned, like Priya, this is like everything learned, and now you're gonna hear just every day you hit that plan. You're fucking up. You're fucking up. You're destroying value. And the point of saying that to them was to set them free.
00:21:56 · Eric Vishria
Another articulation of this from my friend Ann Lee Skates was just the CEOs who were going through this transitory period and had a business that was at hundreds of millions and they thought, "You know, they're all ready." They were working on their business from eight AM to five PM and then trying to do AI from five to eight in the evenings. And what they needed to be doing, the inverse, and it's so hard to do that because of all of the training and all of the muscle memory and all the inertia and everything that we learned about the hill that we were climbing, like we're all hill climbing in a way, the success model was set the plan, execute the plan, build value, compound value, and it's like, oh no, no, no, stop that. You gotta completely invert. There's a very, very long way to get back to your question of what the profile or what the- Mentality of the winners are right now, but if we look at Brendan from Recore or Lynn from Fireworks or Max or Brett, any of these people. They're so nimble about what the eval is. What are they optimizing against? They're so nimble on all of it. And if you look at every one of those companies, the evolution of the business, it's just-- the business is constantly evolving, and they've done such an excellent job at that. I think that is very, very different than
00:23:10 · Patrick O'Shaughnessy
[00:23:10] S0: the way I was taught. What's your sense of the disorienting nature of model progress? You're one step removed from that as an investor versus as a technical founder with your hands on the metal. Are you behaving differently than you would have three years ago or something because of the pace?
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,