Y Combinator Startup Podcast
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.
00:38:21 · Max Hodak, S1
AI has totally transformed it. We can very quickly look up all the standards. We can very quickly generate the evidence tables. And I think that to the degree that there's of over we definitely need to deregulate some things, but I think that the combination of AI and regulation is a better fit than people think, and you can use it to smooth a lot of stuff where the regulations are written in blood and largely good ideas. It's just hard for humans to do it. [00:38:47] S1: Why build your own infrastructure platforms rather than just buying them?
00:38:51 · Max Hodak
[00:38:51] S0: I mean, you can't really buy these things. There's no... There are ERP systems out there, but there's no company that, like, loves their ERP system. Like, I don't know if there's anyone who's really like, I wanna spend more time in NetSuite. And on the contrary, there are a bunch of examples now of companies that grow up around a piece of software that's really fit just for them. Like, YC famously has a lot of internal software that I think really makes YC work. Facebook also very famously invested heavily in internal tools and now has... Like, I think, gets a lot of efficiency from that. SpaceX and Tesla internally have a pretty giant piece of software called Warp Speed that runs a lot of their manufacturing and R and D processes. And so when one company grows up around a harness fit to it, it can be very powerful. It is powerful in a way that the software that you can buy isn't. But this requires you to really look into the future because certainly, especially at the seed stage, this is not the thing that you would think you should be focusing on. And historically, it has not been. And this is a thing that has changed with agents. The fact that you can vibe code this now makes it a reasonable thing to think about. Historically, software has been so expensive, you would have had to buy it, and that's what everybody did for a long time. That was, I think, a worse world, and that world has changed, and so now there are better options available. But like I said, it's like we previously had used... We used Greenhouse. Greenhouse required us to have a small number of people as a bottleneck at first funnel stage. Placing that with software, we were able to explore voting mechanisms and fairly detailed voting mechanisms that make smart inferences about who would know about an applicant, things that you can't really do with the commercial software. And so for a lot of these processes, you should think about how you want it to work for you. These are human organizations, these human processes that have to be staffed, and if they aren't done routinely, will atrophy. And there's things that make sense for different teams and founders in the way that they view the world and think about it. It really is all very different. But if you build a thing that works for you and then you bake that into the company so when you put something there, it stays there.