The Future Of Brain-Computer Interfaces with Science's Max Hodak

YC alum Max Hodak is the co-founder of Neuralink and founder of Science, a company building brain-computer interfaces that can restore sight. Science has developed a tiny retinal implant that stimulates cells in the eye to help blind patients see again. More than 40 patients have already received the treatment in clinical trials, including one who recently read a full novel for the first time in over a decade. In this episode of How to Build the Future, Max joined Garry to discuss how BCIs work, what it takes to engineer the brain, and why brain-computer interfaces may become one of the most important technologies of the next decade.

• 0:00Speaker 00

  I think it is very possible that the first people to live to 1,000 are alive right now. It still takes some suspension of disbelief because I think biotech has just been so incremental. One of the things that's so exciting about what's happening now is that no longer really feels incremental to me. I think that BCI we're going to come to see is not, is not a specific product. I think there's going to be a bunch of BCI companies going after different applications where different types of probes will make sense. To me, it feels like we're firmly in like the takeoff era now, like something new has happened on earth.

• 0:31Speaker 01

  Welcome back to another episode of How to Build the Future. Today, we've got a real treat. Max Hodak, the co-founder of Neuralink and also founder of Science, one of the most exciting BCI, brain-computer interface companies that we've ever seen. Max, welcome to How to Build the Future.

• 0:51Speaker 00

  Thanks for having me.

• 0:52Speaker 01

  So, Science recently announced more than 40 people have received one of your first BCI treatments, which gives people their site back. What is that? What's happening?

• 1:03Speaker 00

  So, we finished a big clinical trial last year, which was published in the New England Journal of Medicine in the fall. So, it's a little chip, a tiny little 2mm by 2mm silicon chip that's implanted in the back of the eye under the retina. That it's this tiny little array of essentially solar panels. So, the patients wear glasses that have a camera that looks out at the world and then a laser projector that projects an image into the eye. And wherever the laser hits the implant, like the solar panel absorbs the light and then excites the cells directly above it. It's a retinal stimulator and this allows us to bypass the dead rods and cones, like the cells that normally make the eye light sensitive, to get a visual signal back into the retina if they've gone blind because they've lost the rods and cones. And so, yeah, I mean, there's a big clinical trial in Europe across 17 sites and there was a huge effect.

• 1:48Speaker 00

  So, we are submitting for approval now. It's not, it's not approved on the market yet. Hope to have that later this year.

• 1:54Speaker 01

  For those watching who have never heard of a brain-computer interface, what is it and what have people have been able to do? What are they able to do now?

• 2:03Speaker 00

  So, the brain... Is this powerful computer, but it's encased in the skull. Like it is not magically connected to things. And so it has these, these handful of connections to the world. And these give you the senses that you know, and the motor control that you know. But you can kind of ask like, is that to either, do we want to replace these with something else? So for example, like the simulated reality or the matrix use case. Another is restoring lost functionality. So this is, I mean, this is how they're deployed today. So if someone has gone blind, you can restore the ability to see. If they've gone deaf, you can restore the ability to hear. If they're paralyzed, you can restore the ability to move. And then you can think about structural neural engineering. And this is, this is the thing that people haven't really, we haven't gotten to as a field as much.

• 2:47Speaker 00

  But looking at how, how does the brain process information? Can you add new brain areas? Are there ways to understand how the brain is like, what, what is going on? Either to use this to build smarter machines or to think about how to treat things like depression or addiction.

• 3:02Speaker 01

  I'm taken by, to what degree right now it's about sort of taking someone who has a condition or a disease and then bringing them like sort of restoring them to like sort of capability. Right. So I think that's playing out in AI right now as well. Right. Like you had computers that had no ability to do like any sort of pure cognition or like, you know, and, you know, no neurons, and then suddenly a bunch of neurons. And then AGI is sort of like what a human can do. It's sort of like a restoration of capability. And then of course, there's like this other thing after that, which is, you know, ASI, superintelligence. Do you ever think about what that might be down the road?

• 3:46Speaker 01

  You know, what is that for BCI?

• 3:48Speaker 00

  There are many types of BCIs. So it's, there, it really is going to be a category like pharma. It's not, it's not one product. I don't think there's going to be like the VCI that people get. And there are different modalities that will work for different things. So for example, I don't work on ultrasound, but one of the things I think will be possible with ultrasound is like a digital Ambien or like a digital Adderall. So can you like stimulate part of the brain to cause focus or sleep and things like that? Would not surprise If that was possible and that could I could see as being more of a consumer application almost and that won't require brain surgery hopefully right now that the high quality ultrasound stuff does require drilling through the skull but I think that that will be overcome for the implantal BCI is I mean this is a very serious brain surgery I think that's important to

• 4:30Speaker 00

  appreciate so when you think about how do you actually get this into humans and who's going to use it I mean these are going to be very disabled patient populations you always look at risk reward you start at the most disabled patients who get the most benefit for even relatively basic functionality like I don't think that you or I would want to get one of the cortical motor decoders that you might have seen out there today because the reality is that like a keyboard and mouse is like great it is a much higher performance like it you can get like spoken word is like 40 bits per second you can many people can type in the like 20 20 ish bits and so the 10 bit per second cortical motor decode is like not going to make your life better I wouldn't get serious brain surgery for that now as it gets more powerful and as we are able to

• 5:12Speaker 00

  produce kind of access richer representations for more of the brain especially bidirectionally then you will start to see like the risk benefit change where like my review on this is not that I think healthy 30 year olds are going to be getting these soon but eventually many people become patients aging is like the correlate of kind of everything getting worse and so there's some critical age where it kind of crosses over where it makes sense to have something that will restore some functionality that you had and then eventually that will kind of cross the origin and then you'll see people that had something terrible happen to them who now have a capability that you're jealous of and that will be kind of when you start to see it changing

• 5:51Speaker 01

  Talk to me about how people who maybe never had sight you know why was the optic nerve not not actually set up like is that not something that you can do later how does plasticity fit in you know do you have to get BCI is when you're incredibly young while the brain is still plastic like how does all this come together

• 6:10Speaker 00

  Neuroplasticity is really interesting and really misunderstood there are genuine critical periods in early development that if you miss them there are some things that will be very hard to wire up later and there actually are some cases of patients that were born blind who but it wasn't It wasn't a loss of the optic nerve, it wasn't something in the brain, but they had congenital cataracts, so their vision was blurry from birth and they were never able to really form images. Who then had this fixed as adults. And that did not work. Like this was, their brain could not make sense of the information. It was totally overwhelming. They would wear eye patches, several of them committed suicide. So there is, there are clear, critical periods in early development where if you miss that, some things are not going to work.

• 6:51Speaker 00

  With that said, the brain stays way more plastic throughout life and adulthood than I think is widely appreciated.

• 6:58Speaker 01

  That's a relief.

• 6:59Speaker 00

  Yeah. If I put an electrode almost anywhere in your brain and then wake you up during surgery, and I show you a flashing light that is, that flashes proportionally to how much that neuron is firing, at least almost anywhere in cortex. Within a couple of minutes, you can learn to control like that neuron. And so the brain is very plastic under feedback. And this is partly how the cortical motor decoders work. Some of it is you're decoding what the brain was originally representing in terms of like a hand or arm representation. But also just if you're getting these signals out of the brain and you're giving the patient feedback for like what those signals are doing, then the brain also adapts to you. And so in the first experiments for this, they actually didn't fit anything at all. They just took a couple, they took two neurons or a handful of neurons

• 7:40Speaker 00

  and fixed the weights. So it said, when this neuron fires more, we're going to go up the screen. When this neuron fires more, we're going to go down the screen and sideways. They fixed the weights and let the brain figure it out, let the brain learn. And again, the brain is very plastic under feedback and can do this.

• 7:54Speaker 01

  Powerful moment. You have a learn, you know, we have two learning systems that can learn off of one another instead of sort of a fixed one with if statements on this side.

• 8:02Speaker 00

  Totally. Yeah. And the brain really like, if you give the cortex information, it is really good at extracting the meaning. Now in adulthood, I think one of the reasons that you don't see it as being so plastic is because it has already fit well to reality. And so there's like, if you think of it as this like energy surface and like the state of brain states is this like you've got these hills and valleys. So during normal development, typically for most people, there's this like enormous basin in this energy surface. And so for most people, like you, like during development, you descend into this basin and then you're down there and it's stable because you've like fit to reality. And if I show you like weird movies, it's not going to really push you out of that. You can, I think, like one of the theories of what psychedelics do is they kind of add, kind of anneal it. So it kind of shrinks the surface a little bit. So you kind of access these other states. But

• 8:47Speaker 00

  then when it wears off, you just immediately descend back down into the energy well that the brain had fit to. And so even though the brain is still plastic, it is in this stable, like part of the attractor system so that it doesn't, you don't see the plasticity as much.

• 9:04Speaker 01

  This was selected for.

• 9:05Speaker 00

  And this was absolutely selected for. Yeah. And so there's this tension between there absolutely is ongoing plasticity. If there wasn't plasticity, you couldn't learn things. And so like your ability to learn new stuff is like, and have memory, like all memory is brain plasticity in many ways. And so we are constantly experiencing very dramatic plasticity. But there are also clear limits to it, especially in how like the modules of the different brain areas end up interconnected past these critical periods.

• 9:29Speaker 01

  I have like a million questions, honestly. I mean, one of the things that I'm super curious about is like, well, what is the qualia of the person who has Prima? And what is, you know, I'd be curious, like with the bio hybrid approach, like, what does it feel like? And, you know, is it like having a second screen? Like, you know, is there input or output? I'm very curious.

• 9:49Speaker 00

  Yeah. So for Prima, actually, on the topic of plasticity, in the time that the patients are blind, the brain, the brain wants to see, like, again, you, the thing you experience is this world model constructed by the brain. And that is this, is this generative model that is conjuring your reality. And so when it's not getting input from the, from the optic nerve, it's still trying to see things. So it kind of turns up the noise. And so blind patients often report like hallucinations and these like internally generated percepts. When you first turn on the implant in these patients, like you hit it with the laser, they'll, they'll say, oh, I see a flash. But then you can do a thing where you'll, Turn on the laser, they'll see a flash and you'll play a tone. And you do this a couple of times and then you like, don't turn on the laser, but you play the tone. And they're like, I see the flash.

• 10:33Speaker 00

  And so for the first couple of hours of rehab, they kind of just have to like learn to like dissociate the real percept from the phantom percept. Because the brain is like so, it is like so turned up the gain and like turned up, turned down the noise floor. That just like getting, learning how to discriminate real information coming in from the optic nerve takes a little bit of rehab. The equalia of prima is, is normal sight. It's black and white. It's only a, it's a small field of view, but it's, it's vision. The deeper question is like, what is the quilia of like a brain to brain of like an ultra high bandwidth, like a bio hybrid neural interface? And that is just like, I don't like impossible to imagine. Those devices will get built and we're going to find out, but there are some natural case studies.

• 11:17Speaker 00

  So there's a pair of conjoined twins in Canada that it's really like one head with four hemispheres. And what's really interesting is the two hemispheres of each of the twins' brains are connected normally. But they're not connected with each other, except for this one cable connecting the, the thalamus, like from the thalamus to thalamus. There's this big biological cable that you can see on an MRI. And over this, they can share meaningful elements of their conscious experience. And one of the open questions that hasn't really been studied in, in the depth that I would like, love to see it. Is when they, they can see to some degree through each other's eyes, but does this show up as new visual field? Like, how is this, how do those get experience directly?

• 12:00Speaker 00

  Like we already, most people have two image modes. Like you've got your eye open vision, but you also have imagination. Some people are aphantasic and they don't have internal imagery. Most people have kind of two image modes. Do they have three image modes or four image modes? Or if they have internal monologue, they can.

• 12:18Speaker 00

  They seem to each individually have internal monologue, but they also can clearly communicate over this channel because they've done tasks where they can coordinate without saying anything to do stuff.

• 12:28Speaker 01

  And they're conscious of it.

• 12:29Speaker 00

  And they're conscious of it, and also they don't confuse it for each other. It's not like with a schizophrenic where it's like, oh, I'm hearing voices and that they're coming from internally generated me, it's misattributed monologue. That doesn't happen to them, they can tell it apart. But they're experiencing it directly in some way. And so there's a question of is this, like when you look at that cable, are they sending information in the classical way? Or is this, is there like an effective, like phenomenal binding happening over this cable where it's more like the two hemispheres of your brain that are bound together into one moment? And so there's these natural case studies that tell us that some really interesting things might be possible here, but it's kind of tough to imagine what it would feel like.

• 13:07Speaker 01

  Paint the picture for us. You're here, everything goes really, really well. Where are we in five to 10 years with this technology?

• 13:15Speaker 00

  I mean, I do think that you can get to close to native acuity, so kind of like your normal 2020 vision. We're definitely not there yet, but I see a path to get there and be able to get color and fill in a lot of the field of view. To be clear, that is not where we are right now, but in the next 10 years, I think that that's possible. But beyond that, I'd say that our worldview, or my worldview, and the motivating idea behind the company, is you can contrast this, like there's a drug discovery approach to medicine versus a neural engineering approach to medicine. I think this is much broader than the retinal preses. We started with that because it's a huge unmet need, and I think it's the most valuable VCI product on the horizon that I thought was doable now. Humanity isn't very good at drug discovery. Every now and then you kind of find a thing, it's amazing, like you find a GLP-1, or you

• 13:58Speaker 00

  find, like there's a handful of drugs that we were lucky to find. But it's much more common that you spend a decade going down this path, and then at the end you run a study and the answer is no, and then it's like, where do you go from there? There's been a huge amount of work that's gone into finding drugs to stop blindness getting worse, or to reverse and restore vision. To basically no effect. There's a million dollar per patient gene therapy that has really very marginal, if any, benefit to a very small percentage of patients in the first place. And with our retinal prosthesis, what we saw in the trial was we can take a patient who's been unable to see faces for a decade and allow them to read every letter on an eye chart.

• 14:41Speaker 00

  And so not only is the brain the only organ that really in some deep sense matters, we are also just empirically much better at engineering it. And so I think this allows a really fundamental reframing of medicine. And over the next decade, I think beyond, you will see, hear, have balance, have a kilobit per second of motor control, that is like you. And I think we have cochlear implants, we know how to do motor decoding. The thing we didn't know how to do is restore vision. We're working, we are making real progress on that. I think all of this adds up to something that speaks, I think, to the really foundations of this paradigm shift and what's possible in healthcare.

• 15:15Speaker 01

  Something like this, I remember reading about maybe like 10, 20, maybe even 20 years ago, they were able to stimulate the optic nerve with electricity directly, but it was very, very low resolution. And it was so invasive that it could probably only be done in a clinical setting or in a surgical setting.

• 15:31Speaker 00

  It's relatively easy to get flashes of light, to cause a patient to kind of see these flashes. We call these phosphines. There was a company, a decade ago called Second Sight, that had an electrical stimulator that was implanted in the eye. It was a four and a half hour surgery with a titanium box on the side of the eye. It stimulated a different layer of cells than we do. And they were able to get these flashes where if a patient looked at it, they could say, oh, there's some flashes here, there's some flashes here, it's connected, that's an A. And then it would show the next letter. And it's like, there's some here, there's some here, it's an H. But it doesn't, the brain doesn't assemble together these flashes of light into a gestalt hole that is an image in the mind's eye. Similarly, when you stimulate cortex, like the back of the head where the visual cortical areas are, you can get these flashes of light.

• 16:16Speaker 00

  And you can, even in some cases, get a lot of them. But again, the brain doesn't, it's kind of this more psychedelic effect, like this doesn't get assembled together into form vision. And as far as I know, our clinical trial was the first time ever that form vision had been, had created a coherent image in the mind's eye of a person.

• 16:32Speaker 01

  Is there something specific about? Macular degeneration that causes this to be possible for this set of patients?

• 16:39Speaker 00

  So there's a bunch of reasons why people lose rods and cones. There's macular degeneration, there's retinitis pigmentosa, there's some rare inherited diseases like Stargardt's disease, diabetic retinopathy can do it, age-related macular degeneration. It's the most common. So this globally affects 200 million people. The severe form geographic atrophy is a million to a couple million. In that sense, it's a big need. One of the nice things about our device is that it doesn't, we're somewhat agnostic to the reason that you lost the photoreceptors. And so we think it'll also work for retinitis pigmentosa, for Stargardt's, for these other indications. We're actually just about to start a new clinical trial on inherited retinal disease, which affects much younger people.

• 17:23Speaker 00

  And this, again, this goes back to like the drug discovery versus neural engineering view of the world. Like if you want to make a drug, then you care a lot about exactly what molecularly went wrong in the rod or cone. And that is different by disease. Then even if you figure this out, it's really hard to like understand what to do about it. Here, we don't really care why the rods are cones. We just care that we can get the visual signal back into the computer.

• 17:45Speaker 01

  I guess I'm just very fascinated by, you know, obviously as a computer scientist, we spend a lot of time thinking about inputs and signals. And then what I'm hearing is that like some of that thinking does actually translate into, from software into wetware.

• 18:00Speaker 00

  Well, I mean, the brain is a computer. And it's going to, saying that is going to get me yelled at by some corner of the field. But I think, like, I think that you can take that like almost literally. It's a very different architecture than like a von Neumann architecture electrical computer. But it processes information. It gets information down one of 12 cranial nerves or 31 spinal. So all of the information that flows in and out of the brain goes through a small number of cables. The optic nerve, we call it cranial nerve two. The vestibular cochlear nerve that carries hearing balance, cranial nerve eight. There's 31 spinal nerves that carry commands out to the muscles and sensory information to the brain. And you can think of that as like the API of the brain. And if you can like get all the signals going down those, then like that's like the brain is not magically connected to the environment.

• 18:43Speaker 00

  It is, reality is whatever spikes are on the cranial and spinal nerves. And in that sense, you've got this like well-defined interface to it. Then with the processing, once it gets this information is enormously complicated. It constructs everything we experience. I think it's important to appreciate, you experience yourself being in the world, you kind of see the The walls and the room and the lights and everything. But that, of course, you're not experiencing directly. You're experiencing a world model like fabricated by your brain. But one of the interesting things that's come out of progress in artificial intelligence is we're seeing this big unification in neuroscience and AI. I think we're actually learning a lot from AI research, more than I think we thought we would learn from AI research. I mean, I can tell you 10 years ago, we thought it would go the other way and that the AI people would learn a lot from neuroscience, and it's really been the other way around.

• 19:28Speaker 01

  I'm always curious. I mean, you're mentioning Second Sight, sort of flashes of light, and yet here, how did you figure out the API? I mean, if I was trying to reverse engineer it, I guess I would like try to measure the signals. Is it similar with biology?

• 19:45Speaker 00

  It's just it's difficult to measure the signals. So brain-computer interface research and development is limited by your ability to record and stimulate these signals. The neuroscience comparatively is actually pretty simple. As soon as you can record these signals, we've very quickly figured out what we've talked about neural representations, what they are. Second Sight's instructive. So in the retina, there's three layers of cells that matter. There's 150 million rods and cones. This connects to 100 million bipolar cells. Bipolar because they've got two ends. And that connects the rods and cones to 1.5 million optic nerve cells. We call them retinal ganglion cells. Ganglion is like a fancy word for like reaches a far distance and connects to somewhere. We stimulate the 100 million bipolar cells. Second Sight stimulated the 1.5 million ganglion cells.

• 20:27Speaker 00

  And so they were trying to get the signal into the brain past that 100x compression. And the retina was doing a lot of computation there. The eyes of camera, light shines in from the front, it hits the rods and cones like that. The representation in the rods and cones is a bitmap image. It's just like you take the image, you tile it across the rods and cones.

• 20:45Speaker 01

  And that's what it is.

• 20:47Speaker 00

  Now, in the 1.5 million optic nerve cells, it's not like that. Like if you just project an image onto them, you get a bunch of trash. Because at that point, it's already compressed, things like edges, relative motion, a bunch of other, like blobby shapes, color. And so if you stimulate a cell there, you're not going to get just like a pixel, you're going to get like some. Edge, like, direction gradient thing. And when you excite that, you can't do that selectively because we don't, like, first of all, you just can't do it selectively enough. And we don't know, like, the codec. We don't have, like, the, know how to pattern it appropriately. And so you end up getting these flashes of light. It was an empirical discovery of our study that if you excite the bipolar cells with an image, you get an image in the mind's eye because that is clearly the critical processing

• 21:32Speaker 00

  step in the retina that you wanted to preserve. Did you know that that would happen? Or did you have to try different parts? When we started the company, we, I think, were a little bit different than most medical device or biotech companies because they're often founded around, like, a specific asset, like, a patent or some specific piece of IP that they're going to spin out of a university or maybe something that the founders have worked on. We weren't like that. We did, we had a couple ideas at the beginning. We had this, like, neural engineering centric view of healthcare. We had a specific BCI probe idea and biohybrid. And we had a sense that the most valuable thing that we could build in the near term was a retinal prosthetic system. We thought the time was there, like, the technology was all there, that that would be possible circa 2021. And that was also further from stuff that I had worked on before.

• 22:16Speaker 00

  And so it felt like a good thing for us to, to kind of go explore. I think we took this very, like, first principles approach. And you have to be careful with first principles in biology because first principles are not enough in biology. Like, they'll get you very far in many other areas of engineering. But in biology, you also have to understand, like, what did evolution actually do? And there's a lot of other nuance there. But in this case, we, we looked at the retina. There were kind of reasons, intuitions to think that past that would be much harder. And so in the retina, you've got this two by two matrix. You've got a choice of, if you've lost the rods and cones, do you stimulate the bipolar cells or the optic nerve cells? And do you do it electrically or with the technique called optogenetics? And we just went and explored all four quadrants of that. We very quickly figured out that stimulating the, the optic nerve cells was very difficult for these reasons.

• 23:01Speaker 00

  You end up with this, like, 1 million degree of freedom calibration that you have to do per patient that, like, can't be done in, in practice. And so that led us to the bipolar cells, which was before this compression. And so then their question was, do you want to stimulate them electrically or using optogenetics? And we developed both. And so we have a state of the art. Optogenetic gene therapy in house, published a paper last fall on the world's most sensitive optogenetics. Optogenic proteins, these are proteins that you can express in a neuron to make a neuron that is not normally light sensitive, responsive to light.

• 23:31Speaker 01

  Oh wow.

• 23:31Speaker 00

  But the drawback was that the conventional optogenic proteins take like a bright laser to activate them. And so what we were able to do were find optogenetic proteins that are so sensitive that they're sensitive to like indoor office lighting. And so this, you could use in very different ways and then we could target them to the bipolar cells. But that still has like five to seven years of clinical translation away if it ends up working and there's a bunch of pitfalls it could run into along the way. And then we also just surveyed the world to see what was the state of the art for the best out there in electrical stimulation. And there is this technology that had been invented at Stanford about a decade ago that a small company in Europe had been kind of developing in the meantime. And we got convinced that that was the right way to go. And so we acquired them a few years ago.

• 24:16Speaker 00

  And this was kind of all from this like bird's eye view of if you want to restore vision in the retina, kind of how would you do that? What are the promising approaches? Narrow that down and that brought us to here.

• 24:25Speaker 01

  That's insane. That's so cool. I wanted to jump to your start in tech broadly. I mean, did you start in bio and software and engineering? Like, you know, what was your sort of journey into what you're doing now, which is, I mean, giving people blindsight is the wildest thing. People watching might be asking themselves like, well, you know, I hear a lot about B2B SaaS, but you know, how do I actually become something more like you?

• 24:53Speaker 00

  I was certainly doing software and my deepest hard skill is software. I have a degree in biomedical engineering, but I grew up programming. And so I was doing that well before I was doing any biotech stuff. My parents tell me a story about how I sat on the floor of a Barnes and Noble and cried until they bought me a Learn Visual Basic book. I was always interested in the brain. I was definitely inspired by science fiction. The Matrix had a big impact on me, both because the idea of this like world of bits was just so alluring for a bunch of like fundamental reasons. Like I look around at the world, like it's hard to build things. Space is constrained. It's like the earth is small. The resources are intensely contested. The like space is large, the speed of light is low.

• 25:36Speaker 00

  Like you don't have any of those constraints in the machine. And so if you could simulate a world, it kind of anything was possible there. But then. Also, if you then kind of turn that inside out, if you realize that you can build this and that you couldn't tell the difference, then the corollary of that was it must be like the thing that matters is the brain. And if you can engineer the brain and support the brain, then kind of all the rest of it is replaceable. And that just seemed like a kind of a fairly deep insight. That was not being borne out in the world in the way that it seemed like, like it should be.

• 26:09Speaker 01

  Some of it is, um, if you can surround that consciousness with like the correct inputs.

• 26:16Speaker 00

  Yeah, I mean, this also gets into questions of like, what is consciousness? Like the, how does the brain create our experience? There's this meme out there that BCI is an artificial intelligence adjacent story. Um, and that the goal is to, we need to have to merge humans and machines. And I do think that there's something to that. But I think in the more immediate thing here is that I see BCI as really a longevity, like healthcare adjacent story. If the end of the quest of artificial intelligence are super intelligent machines, then I think the end of the BCI quest are actually conscious machines. It might turn out that there's actually no measurement that we can take that will tell us if something is conscious or not, or what it's like. And the only thing that you can actually know on that is your own. And so if that's the case, then to study consciousness, we will need to use brain computer interfaces to like, see it for ourselves.

• 27:01Speaker 00

  And once you've developed that, then I think that you kind of can understand the fundamental physics of what's happening there, whether that's new fundamental physics or it's emergent in some way. But if you can learn how to build, like kind of understand whatever the brain is taking advantage of that our universe supports, then eventually you get super intelligent conscious machines that we can be part of through these, these ultra high bandwidth connections. Uh, I think that's a very different narrative than how people usually think about BCI today.

• 27:27Speaker 01

  I mean, we're at the beginning of that, right?

• 27:28Speaker 00

  Oh yeah, we're at the very beginning of that.

• 27:30Speaker 01

  The current trial that you have, I mean, it's a low, it's relatively low bandwidth, but it's going to get much higher bandwidth. And then, I mean, like anything, you sort of bootstrap with the thing that works, which I think, you know, what, what you have is a clear breakthrough as it is. And then if you look at like the PC revolution, for instance, it's like, could you believe that all of this that we have today started to With like a little blue box like in Altair.

• 27:57Speaker 00

  It still takes some suspension of disbelief because I think biotech has just been so incremental. Like it's been so like there's, there's been big advances, but at the same time, these time constants historically, I mean, you could easily spend 10 years on something that feels very incremental. And I think that one of the things that's so exciting about what's happening now is that no longer really feels incremental to me. To me, it feels like we're firmly in like the takeoff era now, like something new has happened on earth. But I think it's also important to remember that this didn't start in like 2019 or in 1999. This started in the late 1800s with the industrial revolution, just a few years before the industrial revolution really kicked off. I mean, life was more or less unchanged in a fundamental sense for several thousand years. And they didn't really even have like a concept of progress in many ways.

• 28:39Speaker 00

  And I don't think there's any way they could have imagined like the way that their life would have changed over the course of the like first 10, 15 years of the steam engine. And that is how I feel like looking at the next 15 years right now.

• 28:51Speaker 01

  Yeah. I mean, so we have an electrical stimulation right now. And then at the same time, you also do have a biocoupling, like it's not purely just electrical. Would you call it a V2 or like sort of a next frontier?

• 29:04Speaker 00

  So this is a totally different area. I mean, the, you might be able to use it for vision. So one of the diseases that Prima or electrical simulator doesn't treat is glaucoma, which is loss of the optic nerve itself. And so it's possible that you could use our bio hybrid BTR technology for that, but that's not what we're doing right now. There are three elements to our pipeline at, at science. The first is our work in the retina in blindness, especially with the Prima implant. The second is our work in neural interfaces. And the third is, is our work in perfusion with our vessel program. The bio hybrid neural interfaces, the idea here is like, if your brain is a bunch of neurons, like how would, how would nature solve this problem? Like we often look to nature for inspiration. Evolution is a way better engineer than, than we are, at least when dealing with biology. I think the intuition here kind of started from.

• 29:48Speaker 00

  Your brain is composed of two hemispheres and they kind of process different halves of the world separately, but you don't experience two hemispheres or two hemifields, we would say. You experience one integrated moment. And this is, there's a cable that connects the two hemispheres of the brain called the corpus callosum. It's about 200 million fibers. And I was thinking like if nature wanted to build a ultra-high bandwidth brain-to-brain connection, like what would, how did, or if you wanted to make a new cranial nerve, so instead of having an optic nerve or vestibular cochlear nerve, it wanted to have like the internet nerve, like how would nature solve this problem? Is it would grow like a new nerve. It would have a new fiber bundle with a USB port at the end. So the

• 30:30Speaker 00

  intuition here is like, if your brain is a bunch of neurons, what happens if I culture some neurons on your neurons? Do they, like when you do that in a lab, the neurons will typically grow together and wire up and form new biological connections. And so we have an approach to the device where we seed our, the implant with living neurons, these heavily engineered stem cell derived neurons that we've created. Are they related to your own neurons or? No. So really interestingly, this is actually one of the deep areas of research. So we, there's, it's one cell line and the, probably the single deepest area of IP on this is that we've hidden them from the immune system. So we're one of a really small number of companies that have, I think, like pretty convincing, what we call hypoimmunogenic

• 31:14Speaker 00

  stem cells. You don't need to manufacture it per patient, which would be really expensive and take much longer. We've got this hypoimmunogenic, stem cell derived engineered neuron that we load into the device in a dish. And then that kind of gets stuck there. And then you engraft this onto the brain. So we don't, we don't place any wires into the brain. We also don't need to genetically modify the, like your brain. And some of the other ideas out there, for example, using optogenetics or things like ultrasound. This requires using a gene therapy to genetically modify the neurons in your brain, which first of all, that's like a one-way door. And if it goes wrong, that can go really wrong. Whereas here, because we're adding, the only thing that has been edited are the graft cells

• 31:56Speaker 00

  that we add. And if, if those die off, then like you're really not worse off than you were before for the most part. But it comes with the potential of growing throughout the brain, forming biological connections all over the place. And I mean, that's what we've seen in the animal models. That's not in humans yet. But have you seen James Cameron's Avatar movies? Definitely. Like you know the ponytails that the aliens have? That's how I think about it, basically. It's like it's a big new cranial nerve with a connector at the end. I think that's actually the Avatar Q, I think, is like a pretty direct reference for how I think about our bio hybrid neural interfaces.

• 32:31Speaker 01

  So earlier you were saying sort of this, how do we find a USB port? I mean, obviously in Avatar, that's, you know, one of the manifestations in the blue creatures. The optic nerve, in a way, is like a port. And then, you know, jumping to Neuralink, when you were co-founding it, that, you know, sort of enters the brain and then, you know, there is no, not necessarily like an obvious port. Like, how do you think about that? You know, where do you attach and how does it work? And what did you learn from Neuralink that, you know, was useful here?

• 33:03Speaker 00

  Well, I mean, a lot of what I learned from Neuralink was like, just like the, in many ways, it was kind of the ultimate startup PhD. And so that was more about like, how do you execute a technically complex company that requires this type of like multidisciplinary team and infrastructure?

• 33:17Speaker 01

  Like, I'm very curious from those days, like, what was the V1? And then, you know, there's a hypothesis and then, you know, the outcome. And then here, like, the outcome is very, very awesome with science so far. Not done, obviously.

• 33:29Speaker 00

  You know, when you think about the brain, because I remember it being like totally magical to me. Like, what is, like, how do you even understand what the brain's doing? Like, what is, like, what language is it speaking? How do we understand what's going on there that seems like impossibly complicated? The way that I would think about, like, the brain from this information processing perspective is, the brain is full of these, these things that we call representations. And so you can have a representation of like hand activity. So there's like a, like a geometric object in the brain. Like, if you record from some neurons, then when your finger is, is like held open, a neuron will be firing. When it's closed, another neuron will be firing. There's neurons that kind of correspond to every possible state here. And often in primary motor cortex, which is where many of the other BCI companies

• 34:12Speaker 00

  record from, primary motor cortex is a couple synapses, often two synapses from the muscle. So it projects all the way from the top of the head down to the spine. And then there's another synapse from the spine out to the muscle. And so the representation that you get in primary motor cortex is kind of easy to understand because it looks like it directly corresponds to things that we can easily reason about, like hand state and specifically often joint torques. One of the things that I like to do sometimes with the LLMs is like, I'll pick like a neuron to start from, for example, like the retinal ganglion cell. And I'll be like, okay, go forward one synapse. Like, what are all of the cells that we're connected to? I'll pick another one and be like, okay, go forward one synapse. Like, what are all the cells that we're connected to? Just kind of try to walk through the brain.

• 34:55Speaker 00

  And each generation of model, your ability to do this gets better. But one of the things that you see is that when you're close to an input or an output, like a muscle or a cochlear hair cell or a retinal ganglion, a rodder cone, like in these cases, we think of the representations as being concrete because they correspond to things that are intuitive for us, like colors and like image intensities or frequencies of sound or muscle control. But as you go deeper into the brain, it very quickly kind of blows up into these very abstract things. And so there's a part of the brain called infra temporal cortex, where the representation that it has is a map of face, like a map of objects or map, another way right next to it is a map of faces.

• 35:40Speaker 00

  We even have this like map of object space, this neural representation of general objects. There's like one point, you can think of it as like a long list of numbers. And there's some point in that that's like a vase, there's some point that's like the Eiffel Tower, there's some point that's a car, there's some point that's a person, there's some point that's like a zebra. And as you move around in this, on this like manifold, you get kind of the percept of any possible object. And there's millions of neurons there that are representing this.

• 36:12Speaker 00

  Like this space of possible objects that the brain could be identifying.

• 36:15Speaker 01

  Sounds like latent space.

• 36:17Speaker 00

  It is a latent space, exactly. And so there's this huge unification going on between AI and neuroscience. And you know, one of the most interesting things is that when you train AI models, like image models and even language models, the representations that you get inside them look a lot like the representations you see in the brain.

• 36:35Speaker 01

  Fascinating.

• 36:36Speaker 00

  And so this is like a real hint that the AI people.

• 36:38Speaker 01

  I mean, that's really good.

• 36:39Speaker 00

  On the right track. Yeah, no, I mean, the whole idea that like there's these things that are like stochastic parrots or glorified autocompletes. Like these people just don't know what they're talking about. Many people in neuroscience have gone over to AI because they're basically still doing neuroscience, but it's just way easier to do it on the models.

• 36:52Speaker 01

  It sounds like it's very good news for you in that like there is actually some kind of latent space mapping. And then the job of science in terms of being sort of like the API to the brain.

• 37:04Speaker 00

  Totally, exactly.

• 37:05Speaker 01

  It is like entirely possible.

• 37:07Speaker 00

  The neural activity that you, when you record neural activity from the brain, this is just another, this is just another latent. And if you can translate this into another model, then you can do, we think, really cool stuff with that.

• 37:17Speaker 01

  So you have input now and then you earlier saying, I mean, a lot of the earlier BCI experiments involved figuring out like.

• 37:25Speaker 00

  Motor.

• 37:26Speaker 01

  Motor.

• 37:26Speaker 00

  Yeah. So motor decoding is kind of this very classic task and you can do it in any number of ways. But getting like cursor control or keyboard control in a human, that was first done in the late 90s. And so I think a lot of the BCI companies are doing that now just because like we know it definitely works. You know, there's some patient need and it really is just like an electronics problem. Like if you can shrink the electronics so that they're small enough and low power enough so they don't dissipate a lot of heat. So you can close the skin, then that is like a big advance. And that I think is really the first thing that Neuralink has done. There were prior devices that could do that type of motor decoding, but they required a connector coming out through the scalp. And as long as the skin is open, there's a risk that like an infection will climb down that and then you're going to have a really bad day.

• 38:10Speaker 00

  So being able to close the skin is really important, but that was really difficult because it required really efficient electronics that were small enough to fully implant and also were power efficient enough that they wouldn't get hot. And so I think the thing that made this possible. Is what we call the smartphone dividend. Like BCI couldn't have done this on its own, but Apple and Samsung and others have poured epic amounts of money onto making these types of electronics exist in the world so that people like us can use them.

• 38:33Speaker 01

  And then it feels like you have a really significant advantage around being a bio hybrid. I mean, there are all these issues famously about you're sort of trying to electrically stimulate brain cells for a long period of time.

• 38:47Speaker 00

  Yeah, I mean, I think that there are different products here. I think that on the like, on the one hand, I mean, that's why I'm doing it. I think that's a good idea. On the other hand, I think some people look at this and they're like, that is now you have a cell to deal with. You took a device and you added a bunch of biology to it. And I think we have a good handle on that. That's why we're doing it, but there's definitely a trade off there. And I think that BCI we're going to come to see is not, is not a specific product in the way that like pharma is not a product. I think there can be a bunch of BCI companies going after different applications where different types of probes will make sense. And I think bio hybrid in particular is only really necessary for some of like the very highest end things. And on the flip side, it will be harder to deploy for many other important medical needs and important applications along the way.

• 39:33Speaker 00

  And we'll probably be a little backloaded relative to some other things in, in that scalable impact.

• 39:40Speaker 01

  So earlier you're referring to, you know, there's a third part of science, which is vessel. Talk more about that because it feels like you're applying a lot of the first principles thinkings that got you here to this thing that is also like pretty, pretty groundbreaking.

• 39:54Speaker 00

  So this is, this is our smallest project. So there's this field of perfusion. You can think of it as they're kind of like heart and lung machines. And I, I was first clued into the need here about a decade ago when I read an article in, in a medical journal called The Lancet, which was this case study of the 17 year old living in Boston who was waiting for a lung transplant. And while he was waiting for this lung transplant, he was being kept alive on an, on an ECMO circuit. ECMO is a acronym for extracorporeal membrane oxygenation. And this fancy word for like heart lung machine. And in his case, his heart was okay, but his lungs had failed. And so this was keeping him alive. And after a while on the transplant list, he was diagnosed with a complication that made him no longer a priority recipient for donor lungs. And so they took him off the transplant list.

• 40:39Speaker 00

  And so this article is kind of about the ethical dilemmas of like. What do we do with him? But he's alive. Because he's, yeah, he's like playing video games. He's doing homework, hanging out with friends. If we turn off the circuit, he will immediately die. Well, don't do that. On the other hand, he's consuming a half a million dollar a month ICU suite. And so there are these quotes in this article from the doctors being like, his family and friends derive benefits from his continued survival. And how this raised fairness questions because if we like, support him for a longer period of time, then why would we do this for everybody? And so I saw this, I'm like, those are great questions. I need answers to those questions. Because there seemed to be this big gap between what was technically possible and what was economic to deploy for some reason. I mean, that's exactly what being a founder is about. Yeah, so I saw this and I, there's this database of medical literature called PubMed.

• 41:22Speaker 00

  And I realized that if I search PubMed for the phrase ECMO ethical dilemma, there were multiple pages of results. So this was not like a one off. And when I looked at this literature, there was often, a lot of it was talking about how ECMO shouldn't be used as a, as a quote, "bridge to nowhere", and how many doctors were basically trying to discourage families from like even pursuing it in these critical care cases, because it would create this bridge to nowhere. And then like, what do we do? And it creates these dilemmas. And then I went and asked some, some doc, this was a long time ago. This was almost a decade ago now, like, oh, well, like, why don't we consider it as a destination? That's the phrase is like a destination therapy versus a bridge therapy. What if the technology just isn't good enough yet? And it needs to be improved. It needs to be improved, definitely. But that wasn't even the response that I got. The response that I got was just like shouting and throwing things.

• 42:07Speaker 00

  And so I was like, something feels wrong here. But I wasn't really in a position then to pursue it. But this was always a thing that was kind of, I saw that there was a really important unification here. It also, the same fundamental type of technology has really transformed organ transplantation. So there, they call it NMP, normothromic machine perfusion, rather than ECMO, but it's the same idea. So 20 years ago, if you needed a, like a kidney transplant or a liver, if the car crash happened at three in the morning, the surgery would happen at four or five in the morning. But now it gets scheduled for like the afternoon or the next day. And over 75% of liver transplants in the US use this type of perfusion technology now. But like the, the systems that exist for this are like $500,000. They can only be moved by private jet. Like one of the big companies in the space, it turns out that their like

• 42:50Speaker 00

  private jet logistics business is bigger than their medical device business. And it just like, there was just like clearly an engineering that could refine this. And so we looked at this and we thought like, well, what if you could refine this to the point where you could check a kidney as luggage on a United flight to the East Coast? Or what if you could make a thing that that 17 year old could have brought home as a backpack? Instead of just what they did in his case is they stopped changing the oxygenator filter and a week later it caught it and he died. That's what happened. There are other problems here, like being able to close the skin around the brain implant, you also need to make it so that the tubes that connect the blood supply to the circuit, the skin can heal to it, so that's not an infection risk. Otherwise, you have to clean it very carefully. But just overall, there's this huge gap between like clearly like where

• 43:34Speaker 00

  the scientific breakthroughs like were pointing and like what was being done. Like I think, I think that people don't appreciate is that in many cases, like there's like if you want to be a brain in a vat, like this basically already exists. Like you can keep a like an end life patient alive in ICU almost indefinitely. But this is very poor quality of life. And so patients like ask for that to be withdrawn. Like, nobody wants to be basically like a brain in like a hospital bed connected to the tubes. You need to be able to provide a high quality of life. And so you need something that people can like live with. And I think to see this, like if you can get vision, hearing, balance, motor control, the ability to like be out in the world and doing things. I just saw this like very fundamental way to reframe

• 44:16Speaker 00

  the problems of medicine here. And so that, like I said, like at science, even though there's these several different projects, I really see them as like as one project over the next 10 years.

• 44:24Speaker 01

  So, you know, started as an engineer, first principles thinking, which often now is quite associated with Elon Musk. How did Neuralink start? How did you get to know him? And how did all of this sort of come together? Because I first met you when you were doing Y Combinator many years ago, my first stint at YC.

• 44:43Speaker 00

  So I got an email one night in early 2016 from Sam. Subject line, crazy question, be like Elon starting a brain-computer interface company, like who should run it? And I assumed they're talking to a lot of people. And my first reaction was actually, I had some friends at MIT that I thought, I'm like, well, these guys are really smart, you should talk to them. But then like an hour later, I was like, wait a second. And so I emailed him back. I'm like, can I like? And Sam introduced me to Elon and Elon was going around, he'd already had the idea like on his own that he wanted to start a company and he had the name Nerlink. I also think that he heard my name from enough people that he was talking to at the time.

• 45:27Speaker 00

  And kind of over the second half of 2016, there was just this group of people that was kind of some, some degree ever shifting that would meet once a week or so in the evening. And that snowballed into Nerlink. And of the initial group, a bunch of them were people that I knew from Duke. So Tim Hanson, the guy who had originally had the sewing machine idea, he was in the lab that I came from at Duke. He was a grad student, I was an undergrad working for him. And then the professor that he and one of our other friends had gone to UCSF and then a collaborator of theirs. So it was kind of a very small community.

• 46:00Speaker 01

  What was that like initially to talk about the idea of like, you know, connecting a computer to a human being's brain?

• 46:07Speaker 00

  Elon, I mean, he saw what was coming in AI like very, much more clearly than many other people much earlier. And I think the implications of like, if you got a, this, this can't be a separate thing from humanity and that needs to merge somehow. I think that implication was just very clear to him. And so that was the genuine motivating factor of like, how do we make it so that this allows us to upgrade humanity rather than get left behind? I mean, if you look at the natural history of Earth, it's not like this is a totally speculative thing. Humanity has totally dominated the planet and we keep our closest living relatives in glass boxes so they don't go extinct. And so there's a real history here of, of greater intelligence being very dangerous. Like in the beginning, there wasn't like a specific technical idea necessarily, but there was that motivating force.

• 46:50Speaker 00

  And then the idea was we'd pull together like the smartest group of people that he could find and, and enough resources to, to do whatever made sense. And eventually got consensus around what you see now as the, as the thin film polymer threads.

• 47:05Speaker 01

  You're one of the best examples of someone who came from a pure software world and then went into hard tech and now is actually doing real breakthrough type of research and work that is also commercializable. The people watching, they might be on a similar track. Knowing what you know now, like what would you tell to the sort of 2016 version of yourself?

• 47:24Speaker 00

  So I think there's two things. The first is like the thing that I did and then there's the thing I didn't do. The thing that I did, I think was I had, I had a clear sense of what I wanted and then I was very high agency towards that. When I was in college, I knew that I wanted to work in brain computer interfaces. There was a great lab that was doing that work at Duke where I went. And I was pretty persistent in figuring out how to like place myself into that lab. It was in the medical center. They didn't usually take undergrad. They were like, it took me a little while to get in there. I eventually figured out that I could sneak in by taking an independent study in the chemistry department. That would like be a back door into this like primate neuroscience group. But then really most of my education in college happened in that lab. So yeah, I grew up programming in my, my deepest hard skill is software.

• 48:06Speaker 00

  But I, I've been doing primate brain computer interface, like closed neural decoding stuff since 2008. And so that was just like, you had to be pretty high agency and like persistent in trying to like, if like follow through on that, but that only works if you have a sense of where you want to go. And so the first is like, figure out what you want. The thing I didn't do was my, so after college, I started a company called Transcriptic. That was a, it was a robotic cloud laboratory. So the idea was, I mean, I also in college had the experience of working in a synthetic biology group where I needed to go press a button on a device called a plate reader every three hours for three days to take a measurement that I wanted.

• 48:48Speaker 00

  And I was like, in software, like this doesn't, we wouldn't do this. Like this just clearly doesn't make sense. Like we would automate it. This was also the time when AWS was just emerging and cloud computing was becoming a thing. And it seemed very obvious to me that instead of every researcher having their own lab and spending millions of dollars for all their equipment and then like needing to press these buttons, like what we should build is a central robotic cloud laboratory, then expose APIs that scientists can use to run experiments over the internet. I did that, raised a bunch of money when I stepped down as CEO in 20, uh, beginning of 2017 to, to join Neuralink. I mean, it had millions of dollars in revenue. Like I felt like we got it to kind of an early promising point. And then since then, over the last decade, it, that I don't, that promise was not fulfilled. If that was still, that was hard mode. That was like a slog.

• 49:33Speaker 00

  That era from 2012 to like 2016, I strongly identified with Ben Horowitz's essay, The Struggle. And I think the thing that I should have done earlier is go work for somebody like Elon. Cause that just like so dramatically leveled up like my ability to do this and, and know how the game is played. And, um, and I think that often you'll see these really promising kids who are just like, I'm going to do it myself. Like, I don't want to work for anybody else. I'm going to start my own company. I'm going to plow through it. And like, sometimes that works, like who am I to say? But I can tell you that very often startup, like running a startup is an oral tradition. There have been a couple of nucleating times in history where like a really remarkable group of people have kind of figured it out from scratch. Like I think PayPal was like this, but. Almost always beyond that, it's like it's an oral tradition that you get passed down

• 50:16Speaker 00

  from one of like this handful of Silicon Valley cultures that can make a huge difference on the trajectory of your career to get that right when you're 20 versus when you're 26 or 28.

• 50:25Speaker 01

  Well, science is the next and it sounds like you're assembling, you know, you've already assembled a really accomplished crew of people. And then what we've learned from startups over the years is that once something works, like more and more resources, more and more smart people sort of come together and then you know, zooming out, that's what we really hope happens a whole lot more in exactly the spaces that you're in right now. So, you know, science sounds like one of those places to go to right now.

• 50:55Speaker 00

  It's pretty cool. Yeah, I mean, I definitely feel pretty lucky that I get to get to do this because it's such an interdisciplinary problem and the to innovate on it, you need all these different areas and really great people in each of them. But the same time, it's just the the things that you can do today were unimaginable a few years ago. And yeah, I mean, I think that I think we have the best team in the in the fields.

• 51:16Speaker 01

  So, I mean, next 10, 20 years of, you know, science, BCI, like, I guess, where do you see this going and, you know, what are you most excited about?

• 51:26Speaker 00

  I have this like event horizon at 2035 now, like when I was earlier in my life, I always kind of prided myself on the ability to see the future. And that is the next few years, I think I have a sense of, but like by 2035 is just like impossible, there's like, I can't see past it. I think it is very possible that the first people to live to 1,000 are alive right now. And I think it might be many more people than you think. It's not going to be like one or two people on earth today. Earth as a whole is at a not, not unique, like this moments in history, like have happened all the time before, but right now it's a time of exceptional change. This is going to be really, really influenced by the technological changes that are happening. And I the twin plot lines of brain, computer interfaces and artificial intelligence.

• 52:08Speaker 00

  People are beginning to get that artificial intelligence is real, is still not priced in, people still don't appreciate it.

• 52:14Speaker 01

  I agree.

• 52:15Speaker 00

  But they really don't get what's coming in and what's possible with brain, computer interfaces. And those are really parallel, but very distinct stories. Intelligence is going to become widely available for those that have the agency to deploy it. And I am generally pretty optimistic about that. Like I don't, my, my P doom is pretty, it's not zero, but it's, it's not 50% as well below that. Yeah, I don't know if we'll have cured.

• 52:39Speaker 00

  All diseases, in fact, I definitely wouldn't use that term. I wouldn't say, well, I've cured all diseases by 2035. But I think that there will be kind of new lateral options that totally reframe how we think about the human condition on that timescale.

• 52:50Speaker 01

  And totally reconfiguring, basically, that sort of interface between computers and humans.

• 52:56Speaker 00

  Yeah, and humans and each other. If a brain-computer interface is equivalent to a brain-to-brain interface in many cases, this takes you to like totally new territory.

• 53:05Speaker 01

  Max, thank you so much for joining us. Thanks for building the future, and we can't wait to see what you build next.

• 53:10Speaker 00

  Thanks, Gary.