Patrick Collison on AI & startups
Collison reads Stripe's data as saying this is the best time ever to start a company: new businesses have roughly doubled year over year and are reaching revenue faster, while incumbents now see the status quo as the bigger risk. He expects AI to decentralize rather than concentrate the economy, doubts the labs will capture most of the value, and treats panic about AI ending entrepreneurship as the latest version of a recurring fear. For founders he suggests decorrelated, ambitious bets over lean-startup niche-hunting, and keeping real knowledge in your own head. He cautions that nobody yet knows the parameters of the AI scaling curves, so forecasts deserve humility.
15 points · 3 appearances · 2024–2026
Up next
2015–2026 · changed view
“Something we learned from YC was the importance of focusing on very concrete, easy to explain customer problems. Like it's very easy to hallucinate or to imagine some customer problem that's not actually something viscerally felt by a person who would pay money. And so over the course of, in part working on Octomattic together We encountered this issue of it being really annoying to deal with the movement of money or payments, whatever, on the Internet. And on the one hand, it seemed like an obviously good idea in the sense that nobody liked the existing ways of doing so, and they were broadly extremely unpopular and kind of antiquated and legacy. And yet to, like, fill out this paperwork and go to the bank in person and the paperwork was in Latin and just like, it was all bad. But then the flip side is it just seems kind of ridiculous the two kids would start a financial services business and Fintech didn't exist as a sector at the time. Like, the word literally didn't exist. And so it's just kind of, you know, we felt like the proverbial squirrels in a trench coat trying to masquerade as a sort of a real business or as serious adults, but obviously knowing nothing coming in about the space. And certainly a lot of people we met and pitched or banks or partners or whatever that we talked to, I mean, they didn't literally laugh us out of the room, but you kind of see them looking for the button to call security under the desk and have them haul us out, because it just seems so improbable. So anyway, I think it both seemed like an obviously good idea in that people. People really wanted this, but also a bad idea that nobody took it seriously. But I think the fact that it was ultimately, the fact that it was grounded in such a concrete, actual real user problem saved us.”
2019–2026 · changed view
“Since I was born for example global life expectancy has increased by about six years and infant mortality has fallen by more than 50% again I mentioned the statistic you know the number of people who have left extreme poverty this is incredibly important right and so I think there's a you know we're not the first people to say it but there is a moral imperative to this kind of progress and we shouldn't lose sight of that fact.”
2019–2024
“Part of our hope you know it's not to kind of promote any specific solution any specific I don't know aspect of it but rather that you know even though this is not what's kind of focally central in the headlines today it should be and as we think of what the world is going to look like in 50 years or 100 years it plausibly more than anything else is going to determine the shape of that.”
2022–2024
“In many domains, I don't know. For example I have no facility with or experience with doing things in hardware, which is not a small domain. If you wanted to become a super skilled practitioner there, what's the best career path? I don't know. Maybe it's to drop out and join SpaceX or something. I'm not necessarily endorsing pursuing the most establishment and credential oriented path. People should try to find the gradient of maximal learning in whatever it is they care most about. The question then is what that is. For biology, not that I'm a biologist, but it is very clear that in order to do really good work, there are a lot of "bench" skills one has to acquire and there is a lot of actual specific knowledge. Any kind of life wasn't designed with neat fundamental principles the way that maybe physics was. A lot of it is obviously evolved and contingent and messy and complicated and all the rest. So there is a lot of specific factual stuff to learn. For those two reasons, there are very few successful pure autodidacts in biology. In virtually every case that I'm aware of, at some point, you have to get direct experience in and with a top lab, where you're seeing how people actually do it in practice.”