Y Combinator Startup Podcast
00:23:49 · Max Hodak
Because, like, in the beginning, you'll get lots of... Like, there's a bunch of things that are easily advised or easily figured out, but you'll get to a key point years in, and you'll look for advice, and there is nobody to ask. And at that point, you must have a really good sense of the limits and boundaries of your judgment. That is a very eerie feeling, and you have to be able to commit to it regardless. Now, the good news is that in my experience, it's very difficult to actually get stuck. You can get yourself into trouble. And the action space is always larger than it appears. No matter what happens, there's usually when you get I think it's very easy to try and anticipate all kinds of problems that you'll never actually run into. And then you go and do it, and then you get to a point where the system... Like, you run into some real limitation. There's always 100 ideas about how to make it better. This is sometimes phrased as action produces information. This idea is, I think, much deeper than it sounds. So in physics, there's this quantity called action. And so if I throw a ball and it follows a ballistic like a parabolic trajectory, that trajectory is totally set, like when it leaves my hand, unless it gets blown by wind. Some other action is exerted on it. It will follow this ballistic trajectory, which is, in this sense, like kind of an information minimizing trajectory. I can say it just followed. It was ballistic. That totally determines it. If something else happens, you had to spend some energy time to cause that cause that to happen. And so when... Whenever you exert an... Like, action into the universe, that creates information, like, in a like, in a very fundamental sense. And whenever you get stuck, you have to start injecting action, producing entropy. And this produces some fairly counterintuitive effects. I've seen situations where the company is stuck in a deep local minimum, and there's someone who is great in many ways, but it's just the wrong fit for what that company is at the time. And removing them, even though they individually are very strong, unblocks the company and allows it to kind of enter a new phase. When you get stuck, you have to start doing things. And so the thing underneath the object level content of what the product you are building is, you've all these support systems.
00:26:01 · Max Hodak, S1
The company does purchasing and accounting and recruiting and performance reviews and budgeting and safety and quality is the operating system of the company, and that has a huge impact on how far you can take it. So speed is determined by infrastructure. Speed determines success and failure. You just need to put more thought into these... Into getting these foundations right. If you do them right at the beginning, everything else is much easier. If you get them wrong, you'll end up spending $5,000,000 a month and feel like you have very little control over it. And then you're forced into coarser levers and harder decisions. Thank you for coming to my TED Talk. [00:26:38] S1: Do you have advice for people trying to choose between industry and academia, starting a company now versus getting a PhD first?
00:26:46 · Max Hodak
[00:26:46] S0: So it really... It depends on specifically what you're doing. If if your field only exists in basic research, then getting a PhD might be very reasonable. So when things really start to work like if twenty years ago, the best computer scientists were at CMU and Harvard, and fifty years ago, the best rock if you wanted to work on rocket engines, you were at NASA, you were at a university, you were at the University of Maryland or somewhere. And now the best computer scientists are at Google and Apple and OpenAI. And the best rocket scientists are at SpaceX and Blue Origin and others. So when a field really starts to work, industry can just marshal such larger levels of resources and can just move so much faster. And so I think a question has been, why has academia stayed so relevant in the life sciences? And it's just the reality is that it doesn't work that well for most things. Humans just aren't that good at drug discovery. And so if your field is really only in academia, then it can make total sense to get a PhD. But I think, you know, a lot of... It is uncommon that startups don't get the technology to work. It is more common that they can't organize the human organizations to accomplish their goals. And learning, that is also a skill set. The only way to learn it, I think it's an oral tradition. You have to do it. And so if the choice is working at a really high performing company adjacent to where you wanna be versus getting a PhD, I'd probably recommend the company. But it's not an absolute rule, it really depends on the field.
00:28:13 · S1, Max Hodak
[00:28:13] S1: What counts as evidence of exceptional ability to you? [00:28:16] S0: Anything that concretely you can put your finger on that separates that person from their high school class. Like, you have your average high school student, we just want some concrete fact that... Ideally, the best evidence of exceptional ability is our winning legible competitive games. So this could be being a... Like, a chess grandmaster. It could be winning design, build, fly, or Formula SAE competitions. There's a bunch of Silicon Valley deep tech companies that are basically built out of Formula SAE winners from college, people that just spent their college experience building things and racing them and finding out. I think you have to have that type of competitive feedback. It is tough to know if you're exceptional without having some legible competitive game. [00:29:00] S1: How do we hire engineers now? Do we still use lead code, or do we have better ways? If we allow AI use, how do you understand the skills of the applicant?
00:29:09 · Max Hodak, S1
[00:29:09] S0: So we've never... I don't think we've ever really used lead code. Maybe some other people on on the team do it in secret, but I've never asked it. So software, in particular, the rewards to horsepower are so great that it really is just it's a field that attracts really smart people because it gives you this very rapid feedback. Like, you think about there's lot of really smart people in biology, but when you have a biological idea, it can take you many months to find out if it's a good one. In software, if you have an idea, you can often build it in a couple hours, or you can get feedback within days. And so it has this really addictive feedback loop, kind of like high frequency trading that just draws in really smart people. And so we look for, over your life, what signals do we have that you have done something interesting? It's uncommon for someone to get into their mid-20s without there being some thing in their background that they went out and sought out and and did. But it can... But this is, like, such an open ended criteria. It can really be anything. The... We don't More directly to the question, we increasingly don't directly evaluate programming. We evaluate, try to evaluate thinking. So this is design questions. Like, we give you a domain, how do you break it down? Can you understand the decomposition of the problem clearly? It's really measures of, like, can you think clearly rather than can you write code. [00:30:30] S1: What did you take away from your experience at Neuralink?
00:30:33 · Max Hodak
[00:30:33] S0: So the question of, like, should you go get a PhD? I don't have a PhD. I spent five years running a company for my CEO at Neuralink. One of the biggest lessons, I think, is that there are few really generic there's no generic algorithm for how to succeed at a startup. There's no set of five bullet points that can be conveyed that if you just turn the crank, your company will be successful. It is a long series of judgment calls, And so the most important thing is that those filters are tuned really well. And so I think one of the most valuable things for me at Neuralink was I was working with someone who has empirically excellent judgment. We could get into trouble together, and there'd be all... Like, something would happen, and there'd be two possible solutions that would make sense. And I'd go to him and say, like, is it option a or is it option b? He'd look at it and be like, oh, it's definitely option b. The problem would never occur. And having been in those situations where I was trying to make these bets kind of with stakes attached, looking forward in time, not getting feedback until later with that advice was incredibly useful for trend... For fitting those filters. And I don't know that there was really a shortcut, and I think that just hearing the stories when you're not there making the... Like, really thinking about it because there are real stakes and then getting that feedback, that is an essential part of the education of an entrepreneur that I think many people underwrite. I think it is really worth working for a company that has an excellent culture that you respect before jumping right into your own startup. It is relatively uncommon that startup cultures get rediscovered entirely from first principles. Usually, they're passed down as oral traditions because there's a founding team that worked at another company which worked at another company, and so they inherited it. Or in some cases where there's really a breakout, where there's just some market dislocation that it really enables, a team kind of out of nowhere to build it. They'll often get it from the VCs. But it's working with the people that have that judgment so that you can get that reinforcement learning as its long series of facts is really important.