Invest Like the Best with Patrick O'Shaughnessy
· Henry Ellenbogen
If you have a high risk situation, which we would all agree, Duolingo is and Affirm is, and you're really close to that part of the change, then you need to be compensated for the risk. And so what we would tell people on Duolingo is yeah, like because of this risk, the discount rate has gone up but probably commensurate, the opportunity has also gone up. We can articulate it. Luis has talked about being 20x faster in generating content. We've already seen it. He's publicly said—he developed chess, which is doing incredibly well and my kids really love that product, I mean they're addicted to it—first it was two people for six months, and then he added another four people and he developed a product in nine months. That's the best product he's ever done. And if you ever talked to him, how long would it have taken in the past? He's like, I don't know, I probably need 4-6x as many people and it would've taken them four times as long. And so you could say a startup could do that, but he's doing it. He's doing it out of his platform. That product is well over a million DAUs and is growing at astronomical rates and it's a great underserved end market. It could be a huge business. And so yes, the discount rate has gone up, but this company was purpose built for AI. You actually have a person who studied AI and taught it at Carnegie Mellon and has an organization of A players who are agile in his company, and is humble and constantly learning. A proof point on how it's making him faster than other people. It's driving real value, and obviously that means the probability of it going from a point solution or a couple products to a suite is higher. And so that's what we look for.
· Patrick O'Shaughnessy, Henry Ellenbogen
[00:00:00] S0: Can I stack a couple of the things that you've said that interest me the most into a bigger question. If I take physical Kaizen and digital Kaizen, just to shrink the concepts down. If those were to have a kid, it might be robotics. And so I'm really curious how you approach the potential, with an unknown timeline, that we might get a second wave of—the physical labor economy is way bigger than the digital labor economy—that we may get a second application of Kaizen over the next 40 years. The first one we saw from Mitch and company. How do you think about that opportunity and potential change? [00:00:00] S1: It's something we have really thought hard about. Here's what we have tried to do. First of all, we have tried to get smart by meeting with the entrepreneurs and talking to the companies that we're involved in that we know that are leading this area, just to try to learn. We only recently did this with robotics. So if we started writing down our conclusions and then being humble that it could change every six months in AI, in what you would say is more data businesses or digital businesses. We only started doing this literally in the last month where we documented it for the first time. I'm gonna do something that I don't love to do, which I know our views here are very early and probably deeply wrong, but I'll give you our initial conclusions. Which is in certain use cases, it's pretty clear that already the cost is lower than the equivalent analog process or… [00:00:00] S0: Physical labor process.
· Henry Ellenbogen
[00:00:00] S1: Physical labor process. And yet as we all know, this is the earliest and worst robotics is gonna be. And because machines are iterating with machines and it's being powered by general purpose models, not by specific purpose models, this is riding a curve that is definitely geometric. And so, back to this mental model of Amazon that drives so much of what we think about durability. What Amazon was able to do is ride a cost curve where they were deflating the cost of sending a box out at 3-5% a year for 20 straight years. And the people who did not leverage the right distribution infrastructure, the right investment in robotics at that time—they bought Kiva. The right ML models when they came into play, about how to basically plan inventory and deal with suppliers, actually were probably at a curve that probably inflated at three to 5%, but at best were flat. And that differential in a low margin business, you compound it over five years. Honestly, that's all you need to know. And that's I think what we're starting to get our heads around at Durable, which is in many areas robotics is at parity. Now there's a lot of data that says it's lower cost in certain cases. And the use cases are about to go up, and the cost on the existing use cases probably don't go down at 3-5% at this point in the curve because of the scale brought in, the human capital brought in, the IP bought on. And the fact that you're gonna be able to use these general purpose LLMs to power it, it probably goes down at more like 15 to 20%, but maybe it's Moore’s law and it goes down even faster. Well then we wake up in five years and the people who put themselves on one cost curve, if they compete against the people who put themselves on the other cost curve, those could be power law businesses. What we have thought really hard about is who's gonna go benefit from this curve, where their competition—even if they woke up tomorrow, even if they put the same amount of money into this problem, even if they could hire the same quality of people, which is unlikely. Because they have not invested in the distribution infrastructure or the technological infrastructure to compete on this curve, at minimum, is probably two to three years behind and every day that they wait, they're probably getting further behind.
· Henry Ellenbogen, Patrick O'Shaughnessy
It's apparent to everyone at Durable why our understanding of change has been great for our investments in Duolingo and Affirm and Shopify, but actually I think it's about to be really advantaged, our understanding of people and change, as we go invest in the other 70% of the economy. [00:00:00] S0: If you think about all the businesses you've backed, do you have a favorite kind of competitive advantage or source of competitive advantage? So much about your whole process is: does it have it already, is it on its trajectory to get there? There's different kinds, scale, network effects, et cetera. Are there favorites that you find yourself returning to as the best sources of long-term competitive advantage? [00:00:00] S1: I really love two and they're actually quite different. I love physical real estate. I love Amazon or—we're investors in Carvana, I love their reconditioning centers because at the end of the day... [00:00:00] S0: You can't spin those things up.
· Henry Ellenbogen
[00:00:00] S1: You can't spin those things up, these things that are super messy. You gotta acquire the land, you gotta put in the right place, you gotta build the right network, then you gotta go stand it up with the right CapEx and the right systems and then you have to have the right operating culture. This is really, really hard. If you put your real estate in the wrong place, then your cost of transport's more expensive and this culture you gotta go build there is super hard. So I deeply love these physical world moats that exist. And really, our portfolio has a lot of them in one way or the other. And that's why when you and I talked about robotics, my mind went to distribution. It's like, where can robotics basically take already advantaged businesses and make them more advantaged? The other thing I really believe is these soft things that are incredibly hard. I think about the example of Danaher so much, because what Mitch and Steve have done is stunningly hard to imagine. That for nearly 40 years you've compounded wealth at 20% in something that didn't have deep physical moats or didn't have data network effects like Meta might, or Google have, or the people who believe deeply in OpenAI. Those data network effects are amazing, but the ones where you're really sharp on human capital, you're really sharp on what talent really means, not the sticker of talent. You're really sharp on your operating excellence, the culture of it, the constant improvement of it, the system behind it, like Kaizen. And then you allocate capital against your businesses to really hold them accountable. I think it's amazing. In our portfolio, even though if I were to walk you through the businesses that FirstService is in, which Jay Hennick's chairman of, and what Colliers has where he's CEO of, he's the largest shareholder of both, none of them actually have these super sharp competitive advantages.
· Henry Ellenbogen, Patrick O'Shaughnessy
But yet, if you really have studied Jay and you truly understand his human capital culture and how he basically attracts and hold people accountable, and his ability to basically decentralize incentives so people are aligned in businesses, everything from residential management of condos to roofing and restoration and then how they allocate capital, how they sell things that don't have a path to be great and how they buy businesses, but they do it in a way where it actually aligns incentives in concert with them. It's just super impressive. The other thing is if we're gonna invest in small companies, those companies by the time they form them, tend to be pretty large. We have to be pretty sharp at really understanding other competitive advantages. [00:00:00] S0: You mentioned memos a few times, the memos you write internally. What have you learned makes for a fantastic structure of an investment memo? What works for you?