Y Combinator Startup Podcast
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.
00:32:40 · S1, Max Hodak
[00:32:40] S1: Could BCIs or neural interfaces help us figure out what consciousness actually is? How? [00:32:45] S0: Absolutely. So if the end of the artificial intelligence quest is superintelligent machines, I think the end of the BCI quest is conscious machines. The brain is composed of ordinary matter arranged according to the rules of chemistry, only things found on the periodic table. It seems tough to believe that there's some new physics going on in there. And so we're looking for some mapping between the substrate activity and the phenomenal content. Now, if we had a magical BCI that allowed me to see the instant state of every neuron in the brain and drive them, I think we'd figure out consciousness pretty fast. I think this is a practical problem, not a philosophical problem. And... But to prove it... But first of all, that practical problem is real, and we'll have to do this stuff in humans. And to prove any of this, we'll have to do it in humans. I think that it is possible that you could use a BCI to prove it. We have some ideas about how to do these experiments, but they're still some number of years off. Things going into humans now are not designed to study consciousness, but I do think that that is further down this path. [00:33:49] S1: What should I study to contribute to BCIs?
00:33:52 · Max Hodak
[00:33:52] S0: This really depends on your background. Neural interfaces are a very interdisciplinary problem. It uses everything from stem cell biology to materials and microfabrication to software to animal behavior to surgery. So there are many different entry points in it. One of the things that we found is that it's better to have a smaller team that can fit more of the problem in their heads and then compress it together. Contrast this to how academia usually handles interdisciplinary problems, where they'll have an interdisciplinary center that pulls in very deep, verticalized experts who meet at the center. And the problem is that they're all speaking different languages, and so it's often hard to really... Even when they can communicate, typically, end up shipping the interfaces of those departments. Whereas for us, if we can kind of hold the problem in the head of a smaller number of people, we can shift around where the bottlenecks are. Specific example of this is our protein engineering group has been able to develop much more sensitive... Like, much better proteins for some things that we need to do, which has allowed us to relax some electronics requirements. So specifically, we have proteins called opsins. They allow us to make neurons light sensitive so that if we shine light on them, we can fire a neuron. The problem was that you needed to hit a neuron with a lot of light to fire it, which means that you can't have that many light sources because it gets too hot. So we've been able to make the protein more sensitive, which means that we can have more LEDs because each one can be dimmer. And so we've turned this electronics problem into a biology problem that allowed us to relax those constraints. You don't get that as much when you have these interdisciplinary centers where there's one group focused on one thing. There's another group focused on another thing. And so I would say being able to have a broader perspective of more of the problem is really valuable. And then just really as deep and clear an understanding of the system as you can get. Think there's no substitute for being hands on. It doesn't really matter. You want some hard skill to get you in the door software, electronics, mechanical, materials, something. And then from there, would try to learn as much of it as you can.