Loading summary
Transcript1 lines
- [00:00]
A
Foreign. My name is Max Hodak. I'm the CEO of a company called Science. We're going to talk a little bit about infrastructure at startups. So I've spent most of my life working on brain computer interfaces. This is almost 20 years ago now. I started my career as an undergrad working in a lab at Duke. This is from our very first Society for Neuroscience conference. The experiment I was working on back then was if you put electrodes in the brain of a monkey and then give a monkey a joystick and you record the neural activity as it's playing a game. If you make the joystick say, go, the cursor go sideways when you push forward on the joystick, which does the brain do? Does the brain represent the joystick or the screen or something else? It turns out that there are neurons that do both. At our company, Science, our main product is a retinal prosthesis. It's a chip that's implanted under the retina in the back of the eye to restore vision to patients that have gone blind due to loss of the rods and cones in their eye. On the left, this is one of our patients on the COVID of Time last November. On the right, you can see there's a picture of the implant with the glasses. So every little one of those hex grids that you see on the implant is essentially a solar cell. So when this is implanted under the retina, the patient wears glasses that have a camera that sees the world and a laser projector that projects onto the implant. And when it projects the image in infrared, wherever the light is absorbed on the implant, it creates a little electric field to excite the retina, thereby directly bypassing the dead rods and cones to stimulate this visual signal back into the retina. The first possible opportunity. This is a pretty cool product. It finished major clinical trials last year. It's been in three clinical trials now. It's covered in the BBC. Last fall, one of our patients finished a 300 page novel with the device and mailed us the book. But I'm not going to talk about this work for the most part. For the next 30 minutes, we're going to talk about infrastructure and lessons. I mean, this is startup school. Maybe there's some things that you'll find useful in your company. So Picasso was noted for saying that when art critics get together, they talk about form and structure and meaning. And when artists get together, they talk about where to buy cheap turpentine. This is also often phrased as amateurs talk strategy, professionals talk logistics. A Quote from a guy that the United States named a tank after. And so there are surprisingly few lessons that are really broad across companies. Typically, the experience of running a startup is you're just looking at kind of a continual stream of facts that hit your desk every day, and you're trying to make the best local decision that you can for those facts. And if it looks inconsistent over weeks, that's usually the way to go. But there are a couple topics that keep kind of very repeatedly coming up that are kind of universal experiences, at least for deep tech companies, which is the thing that I kind of know most of my experiences in, not just pure software. And so there are things that keep coming up, like buying things. So your first reaction might be that if you do software, you don't need to buy things. It will be me alone in an empty room with some computers writing software, and this is going to be how we build the company. And if this is you, yes, you have figured out a reason why VCs love funding software and why they've done so much of it for the last 25 years. But if you do anything other than pure software, you'll be buying many, many thousands of things. This is us about, I think, six months into the company, and it's a little tough to make out. There are a lot of computers. There's also a bunch of microscopes and other electronics and 3D printers and resin and PCBs. You're buying things really continuously. And so it might sound really obvious, like a really basic question, like, you surely just buy things. So for you as the founder, you can use a credit card. Credit cards work great. You can buy lots of things with credit cards. You can also send a wire transfer. The question is, how does your 17th employee buy things? Do they have a credit card? Let's say you hand out credit cards to all of your employees and tell them to buy what they need. So you start getting messages like this, And you think, like, you know, I care about Burn. We have to spend efficiently. I'm going to approve all the purchases as they happen. And you get a message like this, and then you think, $3,000 sounds like a lot for a power supply. Do we need a $3,000 power supply? What if we get one from an auction? Like, in three days there's an auction. Maybe we'll get it for half off. We can get it in two weeks. But then you also remember that you've hired some very highly paid and talented employees. And are you saying they can't get the tools that they need that. You're spending $100,000 a week. If you wait a week to get a power supply half off, you have certainly dwarfed any possible benefit from getting it. And then, you know, and if they were at anthropic, they're not going to be getting hassled over a $3,000 purchase. They're just going to have a power supply. And so what you realize is not only is this very hard to keep burn under control, but also it just this is the inappropriate place to exercise spending review. Spending review has to come earlier. You have to have some concept of budgeting. It's not really about even just the payment rail of buying a thing. It's how do you understand the bucket of money that you have? And I don't want to be making the $1500 power supply versus $3000 power supply trade off. They need to understand the resources that they have so that they can make trade offs within those available resources. So you set up a procurement system and now your highly paid employees are spending their days clicking around B2B Enterprise SaaS and it turns out that from the time that they place an order for a power supply, it takes two weeks to arrive because you can't actually buy that with a credit card. You have to set up an account with the vendor and deal with insurance and certification paperwork and get an account set up. And they have to generate a quote so that you can generate a purchase order so that you can generate an invoice. And now everyone's upset that things are taking super long to get ordered. And so this actually really requires like a, like this is a living organism. Like when people move from academia to startups. I think one of the reactions that people often have is like, why are there people whose job there is to purchase things? Surely I can just buy things. But absolutely there are people whose job is to buy things from the time that you submit the order. Going back and forth with the vendor to set all of this up is very time consuming and it can easily stretch out. It takes like active management to metrics to cause these things to go fast. I think part of why when we think about this we have a reputation. I think of often being very quick and people are unsure like how does that happen? It is mostly not that we are smarter, it is infrastructure like this that is how speed is built. So now people can buy things. At least you can keep overall burn under control. Now you know that you're not going to exceed some large amount of spending every month and then you realize that that wasn't really the problem. You could figure out your Runway. The problem is attribution is when you're doing, whether you're working on rockets or cars or drugs or brain computer interfaces or anything that involves dealing with the real world, you realize that one of your other problems is that you're buying stuff in bulk. Like, we buy gases from argon to silane to nitrogen to resins to media. And then we buy these things in bulk, and we part them out to lots of different experiments. Now, when you do this, attribution is pretty difficult. And so if nobody knows how much an experiment costs every time you grow up a new cell line or every time we make a new probe in the fabric, how much does that loop cost? Nobody knows. Therefore, experiments are free. It doesn't cost dollars. It costs media. And media comes from the fridge. And we want to know, how do we price a thing that we make? Like, we make a bunch of things in volume in the foundry. We want to know, what can we sell that for? That requires all of these spreadsheets to get an estimate of the pricing. And there's opinions in here like, these are not all facts, like, how much do you include rent? How much do you include depreciation of the tools? This comes with opinions about your future volume. All of this is required to understand not just what you should charge, but also what you're spending and what your Runway is. And so to deal with this, we've built a huge amount of internal software at the company for managing this. One of the first things that we did is almost everything that you can do in the company is a button somewhere in the software. We call it Helix, including stuff like purchasing. But because this extends all the way through to manufacturing, where we have every step that happens in the lab, in the database, we can correlate all of this through and get this information and these like. And so it turns out that for every iteration of a wafer that we make, in this case for this protocol, it cost $40,000. This is like a lot of money. And you might have raised. Let's say you raised $20 million in a Series A, you think you need four years, you need 20 people. In my experience, about half the burn is headcount. So that's. So let's say 20 people. That's probably three, three and a half. That's like three million a year in revenue. That's half of your burn. You need 20,000 square feet, about $4 a square foot. That's another 750,000 to a million a year. So now suddenly you've got really, it's a $3 million a year research budget for three or four years. That goes way faster than you think. But again, your team just saw that you raised a larger amount of money than they've ever seen in their lives and they think that the $3,000 power supplies are free. This is pretty important. This is a thing that actually, this infrastructure actually determines success or failure in many companies. Another universal experience is hiring. So hiring also, I think really separates the successes from the failures. Startups usually don't come out of nowhere. I think the best companies in my experience come from what might be characterized as scenes. There's a moment that enables a new company to be born and, and there's a bunch that comes together that really creates this unique nucleation for the new company. And once that moment is passed, because some company has executed on it or just the time is gone, it's kind of tough to get back. And so the best hiring comes from within your network. People that you've worked with before, you know, are good. And the extended version of that is to hire from the network that produced the startup. There's usually some extended scene that the thing came out of. There's a bunch of co founders that come together out of that, crystallize out of that. But then there's an extended community and that should really be the target of your initial marketing. These are the people that already speak your language, are already familiar with it, but there's never enough of them to really fill an entire company. You have to hire from the general public. So there's different companies hire in different ways. There's different processes that make sense to different founders. This is the thing that really is going to be matched to who the founders are and how they view the world. And there's no one right answer. But this is a thing where you need a really defined process. There is no right answer. But a wrong answer for sure is not having something that you do very religiously as a company. And this is an area where reality has a surprising amount of detail. It seems really straightforward, like, oh, you'll have a job board, you'll get applications, you'll review them. This very quickly becomes a huge, huge drag on the rest of your team. You can easily spend almost all of your time recruiting if you're not doing it efficiently to get to a suboptimal outcome. And so for us, again, we've built a lot of software to do this. There are four steps to our process. The first is that we've built a software interface for users to apply for applicants to apply online, where we can capture some structured information from them upfront, including the ability to apply to multiple jobs in parallel. So we originally used a commercial applicant tracking system. We've moved this to our internal tools. And one of the reasons we did that is because this allowed us to do something that we couldn't find in any of the commercial ats. So the first step of our process when users apply is it goes to company wide voting. This is a heavily redacted version of the internal interface, but hopefully you can make out the idea of what's going on here. So the applicant's resume is in the middle. We collect a little bit of other structured information. But the most important thing is on the far right, you see this, there's a question like how would you vote for this candidate? Are they known? Good. Strong yes. Yes. No. Strong no. And so when a person applies, the system picks out seven or eight current employees that it thinks look something like their backgrounds and it pings them all for votes. And so we can distribute the voting across a lot of the company for this initial review, which is essential because if you're doing anything cool, by the time you get a couple years into it, that top of Funnel is overwhelming. And if you place any small group of employees or any one person in the way as a bottleneck on this, they will absolutely bottleneck the whole rest of the organization. And it's also, you want to, I think, average over judgment. I think there's different people that are better or worse at hiring and have different perspectives on what you're looking for at that stage. So in the beginning, as the founder, you can meet with everybody. That will take you quite far. You should definitely interview everybody for quite a while. But even beyond that, you want ways to average over the judgment of the rest of your team. And voting mechanisms are usually a really good way to do that. So these are our actual statistics over the last couple years. So 17% of the top of Funnel applications that we get go to a phone screen. Again, that first initial app voting stage is drawn from a company wide pool so that we can get the voting done quickly, within usually 20, 40, 48 hours, and not bottleneck that on any small group of people. The phone screen is again drawn from a company wide pool of people. This is not team specific. This is a company wide bar, really looking for three things. Judgment, horsepower, and agency. If we throw you into a complex, vaguely defined situation, will you tend to make good decisions or will you create diplomatic incidents? Do you meet just a basic hurdle for technical competence and demonstrated ability to learn things? And are you effective at causing the world to look like you wish it were? How does your life look or not like whatever ambitions you had and do you have specific ambitions for your life? And so this we can distribute over the entire company and then half of those tend to go to homework. Ideally we'd be using entirely AI resistant homeworks now. So our favorite types of homeworks are things that don't saturate, have a very high ceiling and are naturally scorable to two or three numbers that we can put on a plot so that when we get responses to homeworks we can plot them all and then it's very obvious when someone has really beaten the Pareto frontier and we otherwise don't care. Whatever AI models they use that can make you better in cases where that's not possible for homework. Right now we're doing increasing number of technical phone calls or on site practical tests. But ideally we would have an AI resistant take home for each of these. Anthropic had a really interesting take on the AI resistant homework where they've had a couple tasks where it's like GPU kernel optimization. What is the minimum number of cycles you can get it down to? And this is naturally adjusting the hurdle for a while was, I think it was Sonnet's performance. If you could beat that then you could get an interview. I think that there's a bunch of ways to construct AI resistant homeworks and then by the time you get to the interview it is important that you have a from there reasonably high like at least 25% conversion to an offer because otherwise it is just you're going to waste too much of your time doing on sites for employees that don't convert. You can't get that down. And so this is, there's four steps to this. Initial voting, the phone screen, homework and a full interview. And this is as far as like from what my experience, this is the minimum set of information that we need to make a full decision. And I don't think that there's a more efficient way to elicit this. Like I don't think there's a smaller number of steps that we could use. So this has become our process. So you're hiring people, they're coming into work, they're starting, they're incurring payroll. But how do you know that you're good at this? Like eventually you'll get feedback from the market on how good you are at hiring because the company will work or won't like. Your team will be capable of accomplishing the stuff that you've set out, and they'll help you course correct through that. But this is a very, very long feedback and it's a very poorly behaved loss function. And so it's kind of your job as management to design synthetic gradients that allow you to find out earlier and along the way how recruiting is going. And if you need a course correction. A conventional answer to this is the 360 review process. So once a year, you send out a lot of forms, you gather up a bunch of feedback around each employee, you set up a bunch of meetings with HR and with the various managers, and you can do the conventional performance review cycle, which, based on my experience, is this is a very disruptive process that doesn't tend to surface issues that you don't already know about, but haven't acted on because you knew that thing was there. But firing people is hard, and so people drag their feet on it. And this is kind of reinforcing things you already knew. And it only happens once a year, maybe twice a year, if you split up the company into cohorts. But I think really what would be nice to have is a signal that gives you this kind of natural feedback from across the company about who's good and who isn't and what's working and what's not in a way that is largely unbiased and is more continuous. Imagine if you could get feedback kind of every few weeks on where there are issues and where things are going well. And so the process that I had developed, which I've now used for the last six or seven years, is every couple weeks, every four to six weeks. It's not that that often people around the company get pinged with a question through the software, through Helix, and there's a form, but really there's only one question that really matters, which is knowing how this person turned out. Would you vote again today for their hire? It's the same questions we use on the initial voting. And so you'll get a prompt to say, like, this person you work with, like, how would you vote for their hire today? And then what we can do is we construct a graph over the company of all of the feedback. And so the basic intuition is that your vote should be weighted more highly if everybody else has rated you highly. And the astute may notice that this looks a lot like the original Google algorithm, PageRank, which is an idea called Eigenvector Centrality where you can create a weight over the graph by looking at how the graph points together. This is a little bit different than actually literally eigenvector centrality, but it's very similar. And so we call this technique eigenreviews. And I've become convinced that this is more or less the right way to do performance reviews. There's some other tricks that you have to apply to get this to work really well. For example, we apply dropout, where we'll run 1,000 iterations, where we'll randomly remove some percentage of the edges each iteration. And then when you look at the distribution of scores that you get out of that, if you see additional peaks, for example, this is a clue that there could be voting cliques that need further investigation. But as a whole this is it distributes the judgment across the company, updates more or less continuously with about a month lag and gives you just way better insight into what's going on really around the company. And it also totally gets rid of that kind of traumatic, super heavy, once a year HR driven performance review process. So the point of this talk is not that you should use this in particular, although you should consider it. And if you actually roll this out at your company, you can email me and I'll send you a doc with more specific tricks on how that actually got this to work well. But the real theme of the talk is that rate of iteration separates success from failure. And if you can get a fast iteration loop that really overcomes many other things you're going to run into, and this effect is so severe, if you can learn one thing every week and there's a competitor that's learning a thing every month, they will never matter. Overwhelmingly, if you're looking at different ways, like two different approaches to solve a problem, if there's one that allows you to compound in a much shorter amount of time than the other, even if the other approach has significant redeeming characteristics, you should really consider going with the shorter iteration cycle because the compounding effect is just so dramatic. So speed determines success and failure, and speed is determined by infrastructure. This is driven by really boring sounding things like how well do your purchasing and recruiting and spending processes work. This is as important as how well do you understand the object level technical content of the thing that you're building. I see companies founded by just like stellar pedigree scientists and engineers all the time that die on the vine because this execution is tough to follow through when your job is to organize. It's uncommon that these deep tech companies fail because the technology doesn't work, they fail. Because once you end up with this organization of hundreds of people and hundreds of thousands of square feet of physical infrastructure, you haven't built the systems to manage that and becomes unwieldy. And then you can't make like, you can't connect strategy to execution. So we heavily lean towards things that have shorter iteration cycles, kind of cetera, like all else equal. But that's not a blanket rule. Like there are no blanket rules in startups. You're looking at each new fact pattern that comes in as its own unique thing and then making decisions that make sense to you. And one of the harder lessons, as a startup founder, one of the harder things I think to really deal with is the fact that you cannot delegate your judgment. As the CEO, you must always make decisions that make sense to you, no matter how much momentum or inertia the alternatives seem to have. So in school, if you're, let's say there's like somebody sitting next to you and you cheat on the test by looking over at them, your grade will like all else equal. Your grade will be dragged towards the average of the class that is not good enough. To succeed in startups, you have to do things that are at the long tail. The successful companies are the exceptions. By becoming an average that is not good enough. And so in order to succeed, your judgment has to be differentiatedly good. Now the reality might be that you don't know if your judgment is good yet. And so one way or another you will have to find out. And that means making decisions that make sense to you. Even when you are totally alone in that realization. That is the only way to get to the really big outcomes. Now it's not that often that everyone else will think one thing and you'll be like, you're all totally wrong. But it is a really eerie feeling. Like you'll get to a point four or five years into the company when there's hundreds of millions of dollars on the line and there's some really high stakes decision and only you can make it. And then you will look around for advice because in the beginning you'll get lots of it. 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. You can kind of no matter what happens, there's usually like, when you get like. 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, 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 to happen. And so whenever you exert action into the universe, that creates information in a very fundamental sense. And whenever you get stuck, you have to start ejecting 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 needs 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're, 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 got this. You have all these support systems kind of the company like how the company does purchasing and accounting and recruiting and performance, reason 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 need to put more thought 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 million 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. Do you have advice for people trying to choose between industry and academia? Starting a company now versus getting a PhD first. So it really, it depends on specifically what you're doing. 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 20 years ago the best computer scientists were at CMU and Harvard and 50 years ago the best rock like if you wanted to work on rocket engines, you were at NASA, you were at a university, you're at University of Maryland or somewhere and now they're at, 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 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. Like 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 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 want to be versus getting a PhD, I'd probably recommend the company. But it's not an absolute rule and it really depends on the field. What counts as evidence of exceptional ability to you? Anything that concretely you can put your finger on that separates that person from their high school class. Like what is if you have your average high school student. We just want some concrete fact that I mean ideally the best evidence of exceptional ability is are winning at legible competitive games. So this could be being 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. How do we hire engineers now? Do we still use Leetcode or do we have better ways? If we allow AI use. How do you understand the skills of the applicant? So we've never, I don't think we've ever really used leetcode. Maybe some other people 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 if you think about there's a 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, and so we look for kind of over your life, what signals do we have that you have done something interesting? Like it's uncommon for someone to get into their mid-20s without having, without there being some thing in their background that they went out and sought out and did. But this is like such an open ended criteria. It can really be anything. We don't, we increasingly more directly to the question. We increasingly don't directly evaluate programming, we evaluate, try to evaluate thinking. So this is design questions like if 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 can you think clearly rather than can you write code? What did you take away from your experience at Neuralink? So the question of should you go get a PhD? I don't have a PhD. I spent five years running a company for my CEO at Neuralink that was, I mean there's 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 like set of like five bullet points that can be conveyed that if you just like turn the crank your company will be successful 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. Like we could get into trouble together and there'd be all, 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 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 underrate. 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 free, like you can get that reinforcement. Learning as its long series of facts is really important. Could BCIs or neural interfaces help us figure out what consciousness actually is? How? Absolutely so if the end of the artificial intelligence quest is super intelligent 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, 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 it is possible that you could use a BCI to prove it. We have some ideas about how to do those experiments, but there's still some number of years off. Right? Right. The things going into humans now are not designed to study consciousness, but I do think that that is further down this path. What should I study to contribute to BCIs. The. This really depends on your background. Neural interfaces are a very interdisciplinary problem. It really 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 kind of meet at the center. And the problem is that they're all speaking different languages. And so it's often hard to really like, even when they can communicate. Typically you 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 if we have. 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. 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 like 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. I think there's no substitute for being hands on. It doesn't really matter. Like you want some hard skill to get you in the door. Software, electronics, mechanical materials, something. And then from there, I would try to learn as much of it as you can. What doesn't AI replace in scientific research? Where are humans still necessary, if anywhere? 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 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 we. Not just is it powerful to have everything, 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 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, first of all, coding. I mean, that's like, 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 this is. 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 the 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 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 you have this. This thing can take many, many months. Historically, AI has totally transformed it. I mean, 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 kind of over. I mean, there is. We definitely need to deregulate some things. But I think that the combination of AI and regulation is. 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. Why build your own infrastructure platforms rather than just buying them? I mean, you can't really buy these things. There are ERP systems out there, but there's no company that loves their ERP system. I don't know if there's anyone who's really like, I want to 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, 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. I think this is the 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, so like we previously had used, we used Greenhouse. Greenhouse required us to have a small number of people as a bottleneck at that, at that first funnel stage, placing that with software, we were able to explore voting mechanisms and fairly detailed voting mechanisms that can 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, it can be very, very useful. What changes should we expect in the world as BCIs start to work and get widely adopted? Do intelligence differences no longer matter? So there's this meme that BCI is an artificial intelligence adjacent story. And there's some of that eventually, if AI is building super intelligent machines and BCI labs are building conscious machines and we're building brain to brain connections so that the boundaries between those things become less meaningful, at some point you want a super intelligent conscious machine that we can participate in, but that actually feels further away to me. I think in the near term BCI is really a longevity story. And I view longevity as really just health care, I mean just biotech. It's just that it hasn't like. I think it is not right to say that the pharma companies or any of these, like past health care companies are not interested in cures. I think that that is what all of them want. It's just that that's been beyond our capabilities. And in neural engineering, the people here, BCI think they think of motor decoding. Like I put some electrodes in motor cortex and now they can control it like a video game. I think neural engineering is much broader than that. We include our retinal prosthesis in that, we include cochlear implants in that. And this I think is a contrarian take on all of health care. It gives you these effect sizes that you just don't really see in medicine. If you have a patient on a dopaminergic drug for Parkinson's that works for some period of time, but it's a relatively small effect. After a little while, you turn on a deep brain stimulator and a patient goes from not being able to hold a cup of water to being able to write cursive in 10 seconds, you try. If you want to talk about strong patient testimonials, you should see a newborn having their cochlear implant turned on. These are just. When you deal directly with the brain as a computer, not only do you not have to solve some of these really hard biology problems that are just beyond humanity's capabilities, but you get these results pretty readily that again are just. You can get an engineering gradient, you can get them more reliably and they're just large effects. And so I see this as a way to extend and improve the life of everybody. I mean, the brain is the thing that makes you you. It's the only thing that in principle you can't transplant. You can Get a new heart or a new liver, you cannot even in principle get a new brain. And the brain is usually not the thing that fails. And so if you can deal with the brain directly, I think this is more of a radical longevity story than it is an AI one for the moment. Although all of these things will come together over some period of time. For your eigenreview performance system, how do you prevent employees from colluding on their votes or downvoting somebody on purpose? So as I mentioned, there are some tricks. So for example, applying Markov chain Monte Carlo dropout allows us to detect things like voting clicks because now instead of seeing one peak, you'll see two peaks. That is a clue to look in, look into that. I mean it's designed to be tolerant of these things. I think it is really fairly transparent. It's also not our only signal. It's one of several. If anybody is interested in this, send me an email and I will share a document with the specific tricks. But I want to understand a little more about how you were going to deploy it first. Some of this is tradecraft. When you're building something as long horizon as neurotech, how do you figure out how much Runway you actually need to keep the company alive? And how do you get investors to fund that much? So sometimes, I mean you often see founders, especially more inexperienced ones, pitching VCs for what they think is reasonable to ask for rather than what they need to run the experiments. You're raising some amount of money to go find out some answer. The answer to that might be no. The investors understand this depending on what business you're in. But you have to actually run the experiment. And one of the things, there are definitely some ideas that are worth funding with $50 million or $0 but not $5 million, you won't run the experiment. It'll be really frustrating experience. You'll get an ambiguous outcome. And so my first piece of advice is like you should figure out what you think it's going to take to actually run the experiment, which is not the whole company, that is what is your next value inflection point. You should, no matter how ambitious and open ended of your plan is, you should have some sense of like what is your next key value inflection point? What are the experiments that need to go into that, price that out and then raise twice the money. So I mean there's some amount of waste. I think if you can get waste down to 20 or 30%, that's pretty good. And anyway the advice is figure out what it costs to actually run the experiment, raise twice that and raise that or not. Beyond that, you'll always discover new things. There's usually some path through the mess. But you're also, when you start the company, you're not going to get a guarantee that you won't be on a bridge to nowhere or that it will work on the funding that you have. You're going to have to get in there and figure it out halfway through. I think that people should push for profitability sooner than they often think that they need to for us. I mean, even though we are seen as this, I think like very open ended, deep tech company with a very long roadmap, which is true. We are also relentlessly focused on revenue at this point we are trying to get to sustainability. It feels like, I mean the company is kind of constantly dying slowly of this money cancer that we can beat into remission every couple years with the fundraising. But then it like eventually comes back and I want that feeling to be over. And so no matter how big of a problem or big of a vision it feels you do need to think about how do you get to revenue so that not just you can do it forever, but then you'll be valued on your long term roadmap, not valued on your probability of dying. And it really opens up another set of investors that wouldn't be relevant otherwise. What is the best piece of advice you've received? I don't know. I've acquired way too much brain damage over the last 20 years to have a memory capable of picking that out. It. I mean, I think if other than speed being the basis of success and infrastructure determines your speed, it is important to appreciate that there are no general principles. I think people are looking for shortcuts, people are looking for a pithy set of instructions that are like, oh, I figured it out and that doesn't exist. Every one of these things is different. When you get to that moment in history, you're doing something new. We can reflect for a second on how crazy it is that this is possible. Like for the vast majority of human history. If you were a smart 20 year old that had an idea to make your society better and you raised this to the people with capital, the reaction was like, you should pay attention to the harvest. The fact that it is not widely available, it's not universally available, but it's now widely available that if you're a really smart 20 year old, you can come to San Francisco and make the case. And if it's an interesting idea you'll get millions of dollars to find out, like, this is not the case for most of the world today, and it's certainly not the case for most of history anywhere. But that shouldn't feel normal. This is given to push the frontier out. And when you're on the frontier, you're figuring it out as you go. That is the job. And so I would try to rely less on things that feel like startup advice and more on how good is your judgment, how well is that refined in your domain, and remembering that you have to think for yourself. What are some of the hardest remaining engineering challenges involved in getting BCIs to work? So, in BCIs, we often feel very limited by power and thermal constraints on the implants. And so this creates a strong pressure to implant as little as possible and do the rest off the body. You can't pass a wire through the skin because the skin is a very important immune barrier and the skin won't fully heal around it. If you have any connector through the scalp, you're constantly at risk of a bacteria crawling down that and into the brain. And then the patient's gonna have a really bad time. And so you really have to be able to close the skin. That requires you to have implanted like a radio or transceiver of some sort and getting the power on that down. Like there's a frontier at low power electronics, which is really important. More. As I mentioned earlier, a lot of this is now becoming increasingly biology as our biological engineering capabilities increase. But then on those implants, ironically, one of the harder kind of more open problems is what we call packaging. Our colleagues in Europe call it tropicalization. This is your ability to keep your device in and the body out of an implant that you put in the body. So there are no truly passive surfaces anywhere in the body. Even bone is constantly getting remolded. And so if I put a device in, it's going to be getting attacked by the body and it's not regenerating itself. And so you need a material that is going to survive that for an extended period of time. The classic example of this is the laser welded titanium can, which if you've seen like a pacemaker or deep brain stimulator, they've got this big titanium box. Obviously we can't put a big titanium box in the eye. Interestingly, one of the earlier retinal prostheses before, before us, 10 years ago, was a device that did have a titanium box that they attached to the eyeball. So it was a four and a half hour surgery. They had a little belt that went around the eyeball with a little titanium box on the side of the eye with a battery and a little pcb. This didn't work. This was not good enough. They needed to get rid of that somehow. In our case we've solved this with the laser projection trick where we power it wirelessly. But having this next generation packaging some type of conformal coating that we can use to protect the implant that is not degraded by the body, is also not harmful to the body and is resistant to all the things like all of the ways the body will try and kill it. That material science is a very open ended field. And if you're interested in material science, that is a thing that we need progress in. How did you approach interacting with the medical field to build your retinal implant? The. I mean business is just like a fancy word for talking to people and doing things like you send, you talk to them, like you send them emails. I mean this is the. So for our retinal implant, it was originally invented by a professor at Stanford almost 15 years ago. I think it was licensed to a European company that we were tracking. Let me back up a second. So when we started the company, I came from Neuralink, four of my five co founders came from Neuralink. We kind of took a look around the world in early 2021 and asked what is the most valuable thing that we can do that would be likely to work in the near future, that would may have a big impact to patients and allow us to be the foundation for the type of scalable medical device company that we wanted to build. And we came to the conclusion that restoring vision to the blind by stimulating the retina was the thing in that you have a choice of two types of cells in the retina that you can stimulate. These things called bipolar cells or the optic nerve. And you could do that electrically or you could do that optically. We explored all four quadrants of that. We developed internally a state of the art gene therapy that optically stimulated one of those cells. And and we identified this French company as being the state of the art and electrical stimulation. And so we, I mean it's a small community. You can meet people, you can talk to them. It eventually made sense for us to acquire them. We ended up with license the technology and we, and we work with surgeons and doctors all the time. Like if there's a new surgery that you want to figure out, I mean typically this makes, this is best going through networks so that people are more likely to respond to your Email. But we cold email surgeons all the time saying like, hey, we have a weird surgery to develop. Do you want to be a consultant? And people reply. Before I get to the next question, there is a real cultural thing here. So in my time hanging out around the periphery of SpaceX, I observed that like at least circa seven or eight years ago, probably like 20% of that company is what you might characterize as committed Martian colonists, and 80% are serious engineers that think that those people are lunatics and they just want to work on the highest performance methalox engines in the world. And you need both of those cultures to be really successful long term. And that's especially tricky in medicine, right, because that's a very, very conservative, arguably very authoritarian culture for the most part. And similarly, at our company we have, I'd say 30%. I mean, it's an overtly transhumanist mission. And then 70%, like serious clinicians and scientists and researchers and people who think that those guys are crazy, but we're going to build some really valuable medical devices for critical unmet needs in the process. I think one of the things that makes Science the company very special is that it has both of those cultures and is able to integrate them and we're able to simultaneously do some really cool research that I think is really at the edge of the Overton window while simultaneously running clinical trials in six countries. Now with an approved medical device in Europe and clinical trial results in the New England Journal of Medicine, you have to be able to navigate both of those things, I think, to really reshape the future. Has biotech gotten easier to break into? For earlier stage founders, Biotech remains capital intensive. And so that is like, I don't know that I'd recommend biotech if you have a choice of other stuff to do. I think for me this was, I got like, I realized almost 30 years ago that if you could engineer, like, if you could alter the brain, you could alter reality. And like this was one of the biggest missions of, of the next 30, 40 years was building these things. And so for me, I think it's like every now and then I think that my life would be way easier if I just got into AI instead of bci. But somebody has to do it. And I think it's important to like, biotech is hard, it is a much harder path than many other things that you can do. But when you're successful, it has an impact on really what you see elsewhere that I think increasingly there's. I mean, it was Paul Graham that wrote a long time ago that you get vibes in different cities. And the vibe in Cambridge, Massachusetts is you should be smarter. Or the vibe in New York is you should be wealthier. The vibe in San Francisco is you should be more powerful. Especially with the rise of things like artificial intelligence, I think people realize that this isn't just about money. And I think for many of the most effective startup founders, it's not about the money, it's about changing. Like there's some way in which you want the world to be different. And it just turns out that for that project, the for profit company is an incredibly powerful way to marshal the resources required to cause the world to be different in that way. And this is not about money, this is about power. And there are many different types of power. There's economic power, there's military power, but the power to heal the sick is like a very dramatic one. And when you get that, not only is that, is that a real force to reshape the world. It's one that can be shared very readily. Like, you can't share military power, economic power, but you can share the power of restoring sight to the blind or of giving life to the cancer patients. And I think that the world is getting more complicated and there's, there's like, there's big impacts of all the things that are being worked on by the people in this room. And biotech is, is hard. It's very capital intensive. It's a long road. When you start a company in this space, you're committing to a decade of your life that you will never get back, no matter how it turns out. But the results of that, when it works, the impact that this has on patients and their families is really unlike really any other sector. So the last question, what's a popular belief in tech that you think is wrong? And I don't even know what the popular beliefs in tech are now. Well, I mean, okay, even the whole basis of building Helix is contrarian. Like, I think that if you raise a Series A and then you tell your investors that you're going to vibe code a purchasing system, I think that any reasonable board is going to like ask you what you're thinking and that we were able to do that because I never got those questions, because we don't, because I control the company. But that's one narrow example, I guess. All right, thank you.