Y Combinator Startup Podcast
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.
00:36:03 · S1
[00:36:03] S1: What doesn't AI replace in scientific research? Where are humans still necessary, if anywhere?
00:36:08 · Max Hodak
[00:36:08] S0: We still definitely need humans. And in scientific research in particular... I mean, it's tough to predict. Like, AI is clearly advancing very rapidly. I do think that you need to think about how to have your company be AI native in the sense that every you want to gather all of the context, all of the stuff happening in your company, and be able to make that available efficiently to agents because those are clearly a big part of the future. So for us in Helix, really everything goes in there, and one of the reasons that we did that was because not just is it powerful to have everything in one database to link together, purchasing to quality to batch records and manufacturing so that we can trace stuff more efficiently, but also so that we could give it all to to agents. And so we found them to be a multiplier for the team, not a replacement. The three biggest areas that AI has had an impact for us so far are... Well, first of all, coding. I mean, that's now basically all this... I've written a lot of code in my life. I don't think I've looked at the source very much the last six months. That is getting really good. Regulations. So if you're doing anything really interesting, you're going to end up regulated, and then you'll probably end up dealing with these things called quality systems. And so a quality system, I think, triggers a lot of scar tissue for people because it's just the quintessential heavy bureaucracy slow everything down. But the idea of quality itself is actually not a problem. The problem is that humans are bad at reading and interpreting these things. And so when we make a product, one of things we have to do is identify all of the standards that might apply. And there's standards for everything. There's standards for how the lithium ion batteries plug into a PCB. There's standards for electrical insulation of the boards. There's standards for shipping label. At some point, you'll have to take your shipping packaging, print a label on it, and put it in a VIBE box and show that the corners of the label don't curl in a way that might cause it to detach. And so you hire regulatory experts to go find all of the standards that might apply, make a list of them, and then have a spreadsheet which is like all of the evidence that you comply with all of the standards. So this thing can take many, many months historically.