Max Hodak: How Startups Build Speed

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

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 they give a monkey a joystick and you record the neural activity as it's playing a game, if you make the joystick, say, go sideways when the first four 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 you 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.

This is one of our patients on the cover 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 hexagrids that you see on the implant is essentially a solar cell. So 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 and 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 right of the first possible opportunity.

And 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 next 30 minutes.

We're going to talk about infrastructure and lessons. I mean, this is a startup school. Maybe there's some things that you'll find useful in your company. So Picasso was known for saying that when our 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 amateur's talk strategy, professional's talk logistics. I quote from a guy that's the United States name to tank after. And there are surprisingly few lessons that are really brought 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 the most of my experiences in, not just your 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 VC is a lot of funding software and why they've done so much 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, 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 run the profit, they're not going to be able to hassle over $3,000 perch. They're just going to have a power supply. And so what you realize is not only is this very hard to keep earn under control, but also it's just this is the inappropriate place to exercise spending review. Spending review has to come earlier.

You have some concept of budgeting. It's not really about even just the payment, real 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 $1,500 of powers of $3,000 of power supply trade off. They need to understand the resources that they have so that they can make trade offs within those real resources.

So you set up a procurement system. And now you're highly paid employees. They're spending their days clicking around B to be under price SaaS. And it turns out that from the time that they place an order of power supply, it takes two weeks to arrive. Because you can't actually buy the 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 you can generate purchase orders, so you can generate invoice. And now everyone's upset that things are taking super long to get ordered. And so this actually really requires this is a living organism. People move from academia to startups.

I think one of the reactions that people often have is, 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 spent the order, going back and forth with the vendor to set all of this up is very time consuming. It can easily stretch out.

It takes active management to metrics to cause us to do 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 how does that happen, 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 can figure out your runway. The problem is attribution. Because when you're doing, whether you're working on rockets, or cars, or 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.

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, it breaks. Its attribution is pretty difficult. And so if nobody knows how much an experiment costs, like every time you grow up in your cell line, or every time we make a new probe in the fab, how much does that loop cost?

Nobody knows. Therefore, experiments are free. It doesn't cost dollars. It costs media. 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, again, so it turns out that for every iteration of a wafer that we make, in this case for this protocol costs $40,000. This is like a lot of money.

And you want to raise, so let's say you raise $20 million in a series A. You think you need four years, you need 20 people. And my experience is about half the burn inside count. So let's say 20 people, that's probably three, three and a half. That's like three million 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 your research budget for three or four years. That goes way faster than you think.

But again, your team saws, 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 infrastructure actually determines success or failure in many companies. Another universal experience is hiring.

So hiring also really separates the success from failures. Startups usually don't come out of nowhere. The best company in my experience come from what might be characterized as scenes. There's a moment that enables a new company to be born. And there's a bunch that comes to you that really creates this unique nucleation for the new company.

And once that moment is passed, because some company has executed on it or time has 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 higher from the network that produces the startup. There's usually some extended scene that the thing came out of.

There's a bunch of co-founders that come together crystallize out of that. But then there's an extended community. That should really be the target of your initial marketing. These are the people that already speak your language, are you 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 higher in different ways. There's different processes that makes sense of different founders. This is the thing that really is going to match 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's 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. The first thing that we do with applications, you'll review them, is 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 sub-opimal outcome. And so for us, again, we built a lot of software to do this. There are four steps to our process. The first is that we built a software interface for users to apply, for applicants to apply online, where we can capture some structured information from them up front, 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 it's allowed us to do something that we couldn't find in any of the commercial ATSs. 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 applicants' resumes 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, no, strong no.

And so when a person applies, the system picks out seven or eight current employees that it thinks looks 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 of 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 is 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.

And so in the beginning, as a 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 still want to, 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 24, 48 hours and not bottleneck that on any smaller 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 really great diplomatic incidents? Do you meet a basic hurdle for technical competence and demonstrated ability to learn things? And do you argue 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 scoreable to two or three numbers that we can put on a plot.

So that when we get responsive to homeworks, we can plot them all. And it's very obvious when someone has really beaten the pre-adofrontier. And we otherwise don't care whatever AI models they use. Like 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 practical tests.

But ideally, we would have an AI-resistant take-home for each of these. And Thoropic had a really interesting take on the AI-resistant homework, where they've had a couple of tasks where there's like the GPU kernel optimization, like 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 on its 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, from there, reasonably high, at least 25% conversion to an offer. These other ways, 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 there's four steps to this.

Initial voting, the phone screen, homework, and a full interview. And this is, as far as 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. I don't think there's a smaller number of steps that we could use. So this has become a process.

So you're hiring people. They're coming to work. They're starting your recurring payroll. But how do you know that you're good at this? Eventually, you'll get feedback from the market on how good you are hiring, because the company will work or won't.

Your team will be capable of accomplishing the stuff that you've set out. And they'll help you, of course, go 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 it refered you going in a few years of course direction.

A conventional answer to this is a 360 review process. So once a year, you set out a lot of forms. You gather 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 like 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 hiring people is hired.

And so people drag their feet on it. This is kind of reinforcing things you already knew. And it only happens once a year. Maybe twice a year he 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 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 developed, which I've now used for the last six or seven years, is every few, every couple weeks, every four to six weeks, it's not that often. People are on the company getting pinged with a question through the software through Helix. And it's a form, but 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 was 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. And 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 got out of that, if you see additional peaks, for example, this is included.

There could be voting clicks that need further investigation. But as a whole, 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 in really around the company. So this is, and it also totally gets rid of that traumatic, super heavy, once the area are driven performance will be processed. So the point of this talk is not that you should use this 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 talk with more specific to how to actually get this to work well.

But the real thing with 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 two different approaches to solve a problem, if there's one that allows you to compound in a shorter amount of time than the other, even if the other approach has significant redeeming characteristics, you should really consider going with this shorter iteration cycle because the compounding effect is just so dramatic.

And so speed determines success and failure, and speed is a term by infrastructure. This is driven by really boring sounding things like how well you're purchasing and recruiting and spending processes work. This is as important as how well you understand the object level technical content of the thing that you're building. I see companies founded by 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 thousands of thousands of people who are feet of physical infrastructure, you haven't built a system to manage that, it becomes unwieldy. And then you can't connect strategy to execution. So we heavily lean towards things that have shorter iteration cycles, kind of setter as all else equal. But that's not a blanket role. There are no blanket rules and starts.

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 judgments. As a 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, let's say there's somebody sitting next to you and you cheat on the test by looking over at them, you're great all else equal. You're great 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 makes 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.

You'll get to a point four or five years into the company when there's hundreds of millions of dollars online and there's some really high-stakes decision and only you can make it. And then you'll look around for advice. Because again, you'll get lots of it. There's a bunch of things that are easily advised, you'll get to a key point, years in, and you'll look for advice. And there's 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. 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, no matter what happens, there's usually, 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, you run into some real limitation. There's always a hundred 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 list, like a parabolic trajectory, that trajectory is totally set when it leaves my hand, unless it's blown by wind. Some other action is exerted on it. It will follow this ballistic trajectory, which is in the sense of 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 like ejecting action producing entropy.

And this produces some fairly counterintuitive effects. Like 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 of content of what the product you are building is, you've got this, you've got all these support systems. Like how 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 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 got 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 course or 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 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 who were at NASA or at University or 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 the question has been why has academia states relevant to the life sciences? And the reality is that it doesn't work that well for most things. The 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 starts to 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. So the only way to learn it, I think it's no real tradition to 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 role. 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 if you have your average high school students, we just want some concrete fact that is that, I mean, ideally the best evidence of exceptional ability is our winning legible competitive games.

So this could be like a chess grandmaster. It could be winning design bill and fly or formula SE competitions. There's a bunch of Silicon Valley Deep Talk companies that are basically built out of formula SE winners from college. People like just another college experience building things and racing them and finding out. You have to have that type of competitive feedback.

It's tough to know if you're exceptional without having some legible competitive game. How do we hire engineers now? Do we still use leak code or do we have better ways? If we allow AI use, how do you understand the skills of the applicant? So I don't think we've ever really used leak code.

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. If you think about, there's a lot of really smart people and 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.

You can get feedback within days. 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 having, without there being something 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 don't directly evaluate programming. We evaluate, try to evaluate thinking. So this is designed 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 like, can you think clearly rather than can you write code? What did you take away from your experience at Neuralink? 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. 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. It is a long series of judgment calls. And so the most important thing is that those filters are tuned really well.

And so the, I think the most, 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, 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 to be back 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 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, 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 out another company which worked another company and inherited it.

Or in some cases where there's really a breakout where there's just some market dislocations that really enables a team kind of out of know where to build. 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 long series of facts is really important. Could BCIs or neural interfaces help us figure out what consciousness actually is, how? Absolutely.

So at 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 like 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 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, the 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 this experiments. But they are still a 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.

What should I study to contribute to BCIs? 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 are kind of meet at the center. And the problem is they're also speaking different languages. And so it's often hard to really, even when they can't communicate, typically you end up shipping the interfaces of those departments. Whereas for us, if we can hold a problem in the head of a smaller number of people, we can shift around where the bottlenecks are.

A specific example of this is our protein engineering group has been able to develop much more sensitive, 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.

These get too hot. So we've been able to make the protein more sensitive, which means that we 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 and one thing, there's another group focused and other 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 the as deep and clear understanding of the systems you can get. 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, I would try to learn as much of it as you can.

What does an 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. 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 the context, all the stuff happening in your company, and be able to make that available efficiently to agents, because those are clearly a part of the future. So for us, and 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 to link together, purchasing to quality, to match records, and manufacturing so that we can trace stuff more efficiently, but also so that we can 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, well, 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 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 quality systems, I think, triggers a lot of scar tissue for people. It's just that 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, those 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 Vibox, and show it the corners of the label don't curl in a way that might cause it to be attacked. 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 the standards. So you have this thing can take many, many months historically. 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, I mean, 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 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 that suite. 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.

YC, famously, has a lot of internal software that really makes YC work. Facebook also very famously invested heavily in internal tools and now has, I think it's 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&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 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's not. And this is the thing that has changed with agents. The fact that you can buy 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 greenhouse.

Greenhouse required us to have a small number of people as a bottleneck at that first funnel stage. And so, we were able to explore voting mechanisms, fairly detailed voting mechanisms. That can make smart inferences about who would know about it, know about it, and applicant. Things that you can't really do with 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, well, atrophy. And there's things that make sense for different teams and founders and the way 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 BCIs and artificial intelligence adjacent story. And there's some of that.

Like, if AI is building super intelligent machines, and BCIs are building conscious machines, and are building brain-to-brain connections, so that the boundaries between those things become less meaningful. Like 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 BCIs 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 company is, or any of these past health care companies are not interested in carers. I think that that is what all of them want. It's just that's been beyond our capabilities. And in neural engineering, the people here at BCI think they think of motor decoding, like I put some electrodes in motor cortex, and now they can control 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 effects sizes that you just don't really see in medicine. Like, if you have a patient on a dopaminergic drug for Parkinson's, it works for some period of time.

It's a relatively small effect after a little while. You try to deep brain simulator and patient goes from not being able to hold a cup of water to be more recursive in like 10 seconds. You try to, like, if you want to talk about strong patient testimonials, you should see a newborn having a cochlear implant turned on. Like, 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 humanities capabilities, but you get these results very, like, pretty readily that, again, are just, like, you can get an ingredient, you can get them reliably, and there's large effects. And so I see this as a way to extend and improve the life of everybody.

I mean, there's, the brain is the thing that makes you you. It's the only thing that in principle you can transplant. You get a new heart or a new liver. You cannot even in principle get a new brain. I mean, the brain is usually not the thing that fails.

So if you can deal with the brain directly, I think this is more of a radical longevity story than it is in AI one for the moment, although all of these things will come together over some period of time. For your eigen review performance system, how do you prevent employees from including on their votes or downloading somebody on purpose? So as I mentioned, there's some tricks. So, like, for example, applying a Markov chain monitor to dropout allows us to detect things like loading quicks, because now, instead of seeing one PQC two peaks, that is a clue to look in. I'll look into that.

It's designed to be tolerant of these things. I think it is really fairly transparent. It's also not a only signal. It's one of several. If anybody is interested in this, send me an email and I will share a document with a specific tricks, but I have one to say a little more about how you were to deploy it first.

Some of this is trade craft. When you're building something as long horizon as neurotech, how do you figure out how much run way 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, like especially more experienced ones, pitching VCs for what they think is reasonable to ask for rather than what they need to run the experiment. You're raising some amount of money to go find out some answer. The answer to that might be no.

You're not going to 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 refunding with $50 million or $0 million, but not $5 million. You won't run the experiment. It'll be a 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 is going to take actually run the experiment, which is not the whole company. That is, what is your next value inflection? What you should, no matter how ambitious and open-ended of your plan is you should have some sense. 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 out of 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 like raise that or not. Beyond that, it's the always discovering 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 of like that, you won't be on a bridge to nowhere or that it will work on the funding that you have. Like 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, as I think like very open-ended Deep Tech Company with a very long roadmap, which is true, we're also relentlessly focused on revenue at this point.

We are trying to get sustainability. It feels like, I mean, kind of the company is kind of constantly dying slowly of this money cancer that we can beat into remission every couple of years with fundraising. But then it like eventually comes back and I like want that feeling to be over. And so no matter like 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 roadmap, not valued on your probability of dying.

And it really opens up another side 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. I mean, I think if other than speed being the basis 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 like a piece of instruction that are like, oh, I figured it out. And that doesn't exist. Like every one of these things is different. When you get to that moment in history, you're doing something new.

I mean, we can take, we can reflect for a second 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, it's like make your society better, and you raised this to the people of capital, the reaction was like, you should pay attention to harvest. The fact that it is not widely available, it's not universally available, but it's not like 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 interesting, 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.

And that shouldn't feel normal. This is given to push the frontier out. When you're on the frontier, you're like, you're figuring it out as you go. Like 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 involving getting BCIs to work? So in BCIs, we often feel very limited by power and thermal constraints on the implants. And so this creates the strong pressure to the implants 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 if you, and this can won't like fully heal around it, if you have any connectors to scalp, you're constantly at risk of a bacteria crawling down that and the brain into the patient's never 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 a 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 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 the 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 gonna be getting attacked by the body and it's not regenerating itself. And so you need a material that is gonna survive that, for an extended period of time.

The classic example of this is the laser welded titanium can, which if you see like a pacemaker or a brain simulator that has been titanium box, obviously we can't put a big titanium box in the eye. Interestingly, one of the earlier retinal prostheses before us, 10 years ago, was a device that did have a titanium box that they attached to the eyeball. So they had a four and a half hour surgery that had a little belt that went around the eyeball with a titanium box on the side of the eye with a battery and a little PCB. Like this didn't work, this was not good enough. They needed to get rid of that somehow.

In our case, we saw this with the laser projection to repair 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 harmful to the body and is resistant to all the things, like all of the ways the body will try and kill it. But that material science is a very open-ended field. If you're interested in material science, that is a thing that we need progress in. How did you approach interaction with the medical field to build your retinal implant?

I mean, businesses are just like a fancy word for talking to people and doing things. Like you send them, you send them emails. I mean, this is the, so for our retinal implant, it was originally invented by Professor at Stanford almost 15 years ago, I think. It was licensed to a European company that we were tracking. Only 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 like, what is the most valuable thing that we can do that would be likely to work in the near future, that would have a big impact to patients and allow us to be the foundation for a type of scalable medical device company that we wanted to build. And we came to the conclusion that restored vision to the blind by seemingly in the retina was the thing. In that there's, you kind of have a choice of two types of cells in the retina that you can simulate. You can go by polar cells or the optic nerve.

And you can do that electrically or you can do that optically. We explore all four quadrants of that. We developed internally a state of the archen therapy that optically stimulated one of those cells. And we identified this French company as being a state of the art and electrical simulation. And so we, I mean, it's a small community.

You can meet people, you can talk to them. We eventually made sense for us to acquire them. We ended up with licensed technology and we work with surgeons and doctors all the time. You, like if there's a new surgery that you want to figure out, I mean typically, this is best going through networks so that people are more likely to respond to your email. We call email surgeons all the time saying, like, hey, we have a weird surgery to develop.

You want to be a consultant and people are applying. There, 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 20% of that company is what you might characterize as committed Martian colonists. And 80% are serious engineers. I think that those people are lunatics and they just want to work on the highest performance of the analogs 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 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 over the window, while simultaneously running clinical trials in six countries, now with an approved medical device in Europe and clinical trial results in new and literal 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, 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 realized almost 30 years ago that if you could engineer, like if you could alter the brain, you could alter reality. Like this was one of the biggest missions of the next 34 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 gone into AI instead of UCI.

But somebody has to do it. And I think it's important to, like biotech is hard. It's like it's a much harder path than many other things that you can do. But when you're successful, it has an impact on like 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 have vibes in different cities and like the vibe in Cambridge, Massachusetts, you should be smarter or the vibe in New York as you should be wealthier.

The vibe in San Francisco as you should be more powerful, especially with the rise of things like artificial intelligence and people realize that this is just about money. And 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 product, 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 only this is about power.

And there are any 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 big impacts of all the things that are being worked on by the people in this room. And biotech 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 Elex is contrarian. Like I think that if you raise a series A and then you tell your investors that you're gonna vibe code a purchasing system, I think that any reasonable board is gonna ask you what you're thinking. And that, we're able to do that because I never got those questions because we don't like control the company.

But that's one narrow example, I guess. All right, thank you.