I think it is very possible that the first people to live to a thousand are live right now. It still takes on 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 as not a specific product. I think there's going to be a bunch of BCI companies going after different applications or different types of probes.
It feels like we're firmly in the take-off era now, something new has happened on Earth. Welcome back to another episode of How to Build the Future. Today we've got a real treat, Max Hodeck, 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. Thanks for having me.
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? So we finished a big clinical trial last year, which was published in Neuralink, Neuralink, and Medicine in the Fall. So it's a little chip, a tiny little two millimeter by two millimeter 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 the world and then a laser projector that protects an image into the eye. And wherever the laser hits the implant, 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's a huge effect. So we are submitting for approval now. It's not approved on the market yet. Hope to have that later this year. For those watching who have never heard of a brain computer interface, what is it?
And what have people been able to do? What are they able to do now? So the brain is this powerful computer, but it's encased in the skull. It is not magically connected to things. And so it has 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, 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 another to play today.
So if someone has gone blind, you can restore the ability to see. If I'm 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 the thing that people haven't really gotten to as a field as much.
But looking at how does the brain process information? Can you add new brain areas? Are there ways to understand how the brain is like what is going on? Are there uses to build smarter machines or to think about how to treat things like depression and addiction? 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? I think that's playing out in AI right now as well. Right? Like you have computers that have 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 with sort of like a restoration of capability.
And then of course there's like this other thing after that, which is, you know, ASI super intelligence. Do you ever think about what that might be down the road? You know, what is that for ACI? There are many types of ACIs. So it's really is going to be a category like pharma.
It's not one product. It's going to be like the VCI that people get. And there are different modalities that 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 ambient or like a digital at all. So can you like stimulate part of the brain to cause focus or sleep?
And things like that, but not surprising 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 quality ultrasound stuff does require drilling through the skull, but I think that that will be overcome for the implantal BCIs. I mean, this is a very serious brain surgery.
I think that's important to appreciate. So when you think about how do you actually get this in humans and who's going to use it, I mean, these are going to be very disabled populations. You always get risk reward. You start at the most disabled patients. You 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 like you might have seen out there today because the reality is that like a keyboard in mouse is like, great. Like a higher performance like it, you can get like spoken word is like 40 bits per second. You can make people can type in like 20, 20ish bits. And so the 10-bit per second cortical motor decoders 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 produce kind of access richer representations from more of the brain, especially bi-directionally, then you'll start to see like the risk benefit change where like my realness 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 what 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 the capability that you're jealous of. And that will be kind of when you start to see it changing. Talking about how people who maybe never had sight, you know, why was the optic nerve 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 BCIs when you're incredibly young while the brain is still plastic? Like how does all this come together? 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. There actually are some cases of patients that were born blind who it wasn't a loss of the octet 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.
I think this was, the brain could not make sense. And information was totally overwhelming. They would wear eye patches, several of them committed suicide. So there are clear critical periods in early development where if you miss that, something is not going to work. With that said, the brain stays way more plastic throughout life and adulthood than I think is widely appreciated.
That's a relief. 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 flashes proportionally how much that neuron is firing at least almost anywhere in cortex. Within a couple minutes you can learn to control 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 your decoding, what the brain was originally representing in terms of a hand or a representation. But also just if you're getting the signals out of the brain and you're giving the patient feedback for what those signals are doing, then the brain also adapts to you. And so in the first experiment for this, they actually didn't fit anything at all. They just took a couple of neurons or handful of neurons 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 so they fixed the weights and let the brain figure it out, let the brain learn. And the brain is very plastic under feedback and can do this. That powerful moment you have two learning systems that can learn off of one another instead of sort of a fixed one with the statements on the side. Totally.
And the brain really, 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 if you think of it as this energy surface and 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 enormous basin in this energy surface. So from people like you just like during development, you descend into the space 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 a kneel it. So it kind of shrinks the surface a little bit to kind of access these other states. But then when it wears off, you just immediately descend back down into the energy well that the brain fit to. And so even the brain still plastic is in this stable like part of the attractor system so that it doesn't, you don't see the plasticity as much.
But this was selected for and this was absolutely selected for. Yeah. And so there's a tension between their absolute is on 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 especially in how like the modules of the different brain areas end up interconnected past these critical periods. 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, a curious like with the bio hybrid approach? Like, what does it feel like? Yeah.
Is it like having a second screen? Like, you know, is there input or output? I'm very curious. Yeah. So for Prima actually, on the topic of plasticity, in the time that the patients are blind, the brain wants to see, like again, the thing you experience is this world model constructed by the brain.
And that is this generative model that is conjuring your reality. And so when it's not getting input 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 and these patients that you hit it with the laser, they'll say, oh, I see a flash, but then you can do a thing where you'll turn on the laser, you'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 then like I see the flash. And so for the first couple hours of rehab, they kind of just have to like learn to like dissociate the real percepts from the phantom percepts because the brain is like so it is like so turned up the gain, like turned up turned down the noise for that just like getting, learning how to discriminate real information coming from the optic nerve takes a little to rehab. The qualia of prema is normal site. 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 qualia 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. So there's a pair of controlling twins in Canada that it's really like one head with four hemispheres. And what's really interesting is that the two hemispheres of each of the twins's brains are connected normally, but they're not connected with each other except for this one cable connecting the the flammi like from the thalamus to thalamus.
There's this big biological cable that you can see on 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 the depth that I would like love to see it is when they they can see to somebody reach other's eyes, but does the show up is new visual field? Like how is this? How do those get experienced directly?
We already most people have two image modes like you've got your eye open vision, but you also imagination. 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 they can individually have internal monologue, but they also can clearly communicate with this channel because they've done tasks where like they can coordinate without saying anything to do stuff.
And they're conscious of it. And they're conscious of it. And it also don't confuse it for each other. It's not like with 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 this. Like when you look at the cable, are they sending the information in a classical way? Or is this is there like a defective 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 some really interesting things might be possible here, but it's kind of tough to imagine what it would feel like.
Paint the picture for us. You know, you're here. Everything goes really, really well. Where are we in five to 10 years with this technology? I mean, I do think that you can get to close to native acuity, so kind of like your normal 2020 vision work.
We're definitely not there yet, but I see a path to get there and be able to get color and feel in a lot of the field of view to be clear that it's 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, that's that motivating idea behind the company, is you can contrast this like a drug discovery approach to medicine versus a neural engineering approach to medicine. And this is much broader than the retinal processes. We started with that because it's a huge amount of need.
And I think it's the most valuable BCI product on the horizon that I thought was doable now. Humanities isn't very good at drug discovery. Every now and then you find a thing. It's amazing. Like you find a GLP1 or you find like there's every, like there's a handful of drugs that are 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're studying the answers, no. And then it's like where you go from there. You stop blindness getting worse or to reverse or reverse or reverse vision to basically no effect. There's a million dollar per patient gene therapy that has a really very marginal like any benefit to a very small percentage of patients the first place. And with our retinal prosthesis that 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 my chart.
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 like allows like a really fundamental reframing of medicine. And over the next decade, I think like beyond you only will see here, have a balance, have a kill of it per second of motor control. And it's like you. And I think we have cochlear implants.
We have we know how to do motor decoding. The thing we didn't know how to do is restorvision. 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 like this paradigm shift and was possible in healthcare.
Something like this, I remember reading about maybe like 10, 20, 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. It's relatively easy to get flashes of light to cause a patient to kind of see these flashes like these phosphines. There was a company, the 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 like if a patient looked at it, they could say, oh, there's some flashes here, there's some flashes here, it's connected to this and they would show it in the next letter. And it's like, there's some here, there's an H. But it doesn't bring this and assemble together these flashes of light until like a shdalt hole that is an image in the mind's eye. Similarly, when you stimulate cortex, like the back of the head with the visual cordial areas are, you can get these flashes of light and you can even in some cases, a lot of them.
But again, the brain doesn't it's kind of this more psychedelic effect. It 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 like I created a coherent image in the mind's eye of a person. Is there something specific about macular degeneration that causes this to be possible for the set of patients? 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 red inherited diseases like stargarca, it's disease, diabetic retinopathy, you 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 full receptors. And so we think it'll also work for run as pigmentosa for stargarca arts, for these other indications. We're actually just about to start a new clinical trial on an inherited retinal disease, which affects much younger people. And this goes back to the drug discovery versus neural engineering view of the world. 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 than even if you figure this out, it's really hard to do about it. Here, we don't really care why the rods are cone. So we just care that we can get the visual signal back into the computer. I guess I'm just very fascinated by, obviously, as a computer scientist, we spend a lot of time thinking about inputs and signals. And then what I'm hearing is that some of that thinking does actually translate into software, into wetware.
I mean, the brain is a computer. And it's going to get me all that by some corner of the field. But I think that you can take that almost literally. It's a different architecture than a von Duyman architecture electrical computer. But it processes information, it gets information down one of 12 cranial nerves or 31 spinal.
So all of the information flows in our brain goes through a number of cables. The object nerve, we call it cranial nerve two, the visdigal cochlear nerve that carries the carrying balance, cranial nerve eight. There's 31 spinal nerves that carry commands out to the muscles and sensory information in the brain. And you can think of that as like the API of the brain. And if you can get all the signals going down those, then the brain is not magically connected to the environment.
It is whatever spikes are on the cranial and spinal nerves. And in that sense, you've got this well-defined interface to it. Then with the processing, once it gets this information, it's enormously complicated. It constructs everything we experience. I think it's important to appreciate you experience yourself being in the world.
You see the walls and the room and the lights and everything. But that, of course, you're not experiencing directly experiencing a world model fabricated by your brain. One of the interesting things that's come out of progress in artificial intelligence is we're seeing this 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 can tell you 10 years ago, we thought it would go the other way and that the AI people learn a lot from neuroscience. And it's really been the other way around. I'm always curious. You're mentioning the second side, flashes of light. And yet, how did you figure out the API?
I mean, if I was trying to reverse engineer it, I guess I would try to measure the signals. Is it similar with biology? It's difficult to measure the signals. So brain computer interface research and development is limited by your ability to record and stimulate the signals. Neuroscience is comparatively pretty simple.
As soon as you can record, the signals are very quickly figured out what we can talk about neural representations, what they are. Second side is 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 by polar cells, but polar because it's about two ends.
And it connects the rods and cones to 1.5 million optic nerve cells, because the retinal ganglion cells can't be found for like reaches a far distance and connects to somewhere. We stimulate the 100 million by polar cells. Second side stimulated the 1.5 million ganglion cells. 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 isochamera light shines in front of the front. It hits the rods and cones like that. The representation in the rods and cones is a bit not image. It's just like you take the image, you tile it across the rods and cones. That's what it is.
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 first of all, you just can't do it selectively enough. And we don't know the Kodak. We don't have like the not a pattern appropriately. And so you end up getting these flashes of light, who's 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 the critical processing step in the retina that you want 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 we're a little bit different than most medical device or biotechnology companies because they're often bounded around like a specific asset, like a patent or some specific piece of IP that they're going to spin out of the university or maybe something that the founders have worked on. We weren't like that. 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 in biohybrid.
And we had a sense that the most valuable thing that we could build in the near term was a retinal prosthesis. And we thought the time was there. Like the technology was all there that we possible circa 2021. And that was also further from stuff that I had worked on before. And so it felt like a good thing for us to kind of go explore.
I think we took this very first principles approach and you'll 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 evolution actually do. And there's a lot of other nuance there. But in this case, we looked at the retina.
There were kind of reasons and I think that passed that would be much harder. And so the retina, you've got this two by two matrix. You've got a choice. And we have two cells and cones to estimate the bipolar cells for the optic nerve cells and due to it electrically or the technique called optogenetics. And we just went and explored all four quadrants of that.
We very quickly figured out that stimulating the optic nerve cells was very difficult for these reasons. You had this like 1 million degree of freedom calibration that you have to do per patient that can't be done in practice. And so that led us to the bipolar cells, which was before this compression. And so then the question was do you want to simulate 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. Optionic proteins. These are proteins that you can express in neuron to make a neuron that is not normally light sensitive or responsive to light. But the drawback was that the conventional optogenetic proteins take like a bright laser to activate them. And so we were able to do refined 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 that can run 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 simulation. And there was this technology that had been invented at Stanford about a decade ago that a small company in Europe had been developing in the meantime. And we got convinced that that was the right way to go.
We acquired them a few years ago. And this was kind of all from this like birds I view of if you want to store vision in the retina, how would you do that? What are the promising approaches? And are they down and that brought us to here? 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 giving people blind sight is the wildest thing. People watching might be asking themselves like, well, you know, I hear a lot about BBSAS, but you know, how do I actually become something more like you?
I was certainly doing software and my deepest hard skill is software. I had a degree in bio and 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 when 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. Like space is large. The speed of light is low. You don't have any of those constraints in the machine. And so if you could simulate a world, anything was possible there.
But then also if you then 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 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 fairly deep insight. That was not being born out in the world in a way that it seemed like it should be. Some of it is if you can surround that consciousness with the correct inputs.
Yeah, I mean, this also gets into questions of what is consciousness? Like how does the brain create our experience? There's this meme out there that BCI is an artificial intelligence adjacent story. And the goal is to have to merge into machines. And I do think that there's something to that.
But I think the more immediate thing here is that I see BC as really longevity, health care, and so 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 bring computer interfaces to see it for ourselves. And once you've developed that, then I think that you 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 whatever the brain is taking advantage of that are universe supports, then eventually you get super intelligent conscious machines that we can be part of through these ultra-bandwidth connections. It's a very different narrative than how people usually think about BCI today. And we're at the beginning of that. We're at the very beginning of that. The current trial that you have, it's relatively low bandwidth, but it's going to get much higher bandwidth.
And then like anything, you bootstrap with the thing that works, which I think what you have is a clear breakthrough as it is. And then if you look at the PC revolution, for instance, it's like, could you believe that all of this that we have today started with a little blue box in Altair? It still takes some suspension of disbelief because I think the biotech has just been so incremental. There's been big advances, but at the same time, these time constants historically, and 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 it no longer really feels incremental to me.
It feels like we're firmly in the take-off era now, something new has happened on Earth. But I think it's also important to remember that this didn't start in 2019 or in 1989, it started in the late 1800s with the Industrial Revolution just a few years before the Industrial Revolution really kicked off. Life was more or less unchanged in a fundamental sense for several thousand years. And they didn't really even have a concept of progress in many ways. And I don't think there's any way they could have imagined the way that their life would have changed over the course of the first 10, 15 years of the steam engine.
And that is how I feel like looking at the next 15 years right now. I mean, so we have an electrical stimulation right now. And then at the same time, you also do have a biocoupling. It's not purely just electrical. Would you call it a V2 or sort of an X frontier?
So this is a totally different area. You might be able to use a provision. So one of the diseases that premar electrical syndrome 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 BCI technology for that. But that's not what we're doing right now.
There are three elements to our pipeline at science. The first is our work in the retina in blindness, especially with the premar implant. The second is our work in neural interfaces. And the third 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 nature solve this problem?
We often look to nature for inspiration. I lose this way better engineer than we are at least when dealing with biology. But I think the intuition here kind of started from 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 hemispheres, we would say. You experience one integrated moment.
And 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 an ultra high band with brain to brain connection, how would it already make a new cranial nerve? So instead of having an optic nerve or a cycloclear nerve, it wanted to have the internet nerve. How would nature solve this problem?
Is it would grow like a new nerve? It would have a new fiber bundle with the USB port at the end. So the intuition here is like if your brain is a bunch of neurons, what happens if I culture some neurons on your neurons? Do they, when you do that in a lab, 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 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 there's one cell line and probably the single deepest area of IVP 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 like a hypoematogenic stem cells. You don't need a manufactured per patient, which would be really expensive and take much longer.
We've got this hypoematogenic 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 play 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 graph cells that we add. And 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. 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-hybridener interfaces. So earlier you're saying, sort of this, how do we find a USB port? I mean, obviously an 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 jumping to Neuralink, when you were co-founding it, that sort of enters the brain. And then there is no, not necessarily like obvious port. Like, how do you think about that? Where do you attach and how does it work?
And what did you learn from Neuralink that was useful here? Well, I mean, a lot of what I learned from Neuralink was just like, in many ways it was kind of the ultimate start of PhD. And so that was more about how you execute a technically complex company that requires this type of multidisciplinary team and infrastructure. Like I'm very curious from those days, like what was the V1? And then there's a hypothesis and then the outcome.
And then here, like the outcome is very, very awesome with science so far. And obviously, you know, when you think about the brain, I remember being like totally magical to me like, what is, like, how do you even understand what the brain is doing? Like, what languages is speaking? How do we understand what's going on there? That seems like I'm possibly complicated.
The way that I would think about like the brain from this information processing perspective is the brain is full of these things that we call representations. And so you can have a representation of like hand activate. So there's like a geometric object in the brain. Like if you record from some neurons, then when your finger is like held open, and are only firing when it's closed, the other 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 pieces I come to record from, primary motor cortex is a couple of 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 retinol 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? I'm just going to walk through the brain. And each generation of model, the 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 retinol ganglion, a ronter cone, like in these cases, we think of the representations of the incongrete because they correspond to things that are intuitive for us, like colors and 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 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, like, a map of objects or another way right next to it is a map of faces. But even with this map of object space, it's not a representation of general objects, there's like one point, you can think of the 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 a zebra. And as you move around on this like manifold, you get kind of the perceptive, any possible object. And there's millions of neurons that are representing this, like the space of possible objects that the brain could be identifying. It sounds like latent space. It is 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 or even language models, the representations that you get inside them look a lot like the representations you see in the brain fascinating. And so this is like a real hint that the AI people, I mean, that's really good on the right track. Yeah.
I mean, the whole idea, like, there's these things that are stochastic parents who are fine auto-complete, 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. 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. Totally. Exactly. It's like, it's like, the neural activity that you record,
neural activity for the brain. 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. So you have input now, and then you're earlier saying, I mean, a lot of the earlier BCI experiments involved figuring out, like, motor de-coding 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 community is doing that now,
just because like, we know it definitely works. You know, if there's some patient need and it really is just like electronics problem. Like if you can drink the electronics so 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 really is really the first thing that really gets done. There were prior devices that could do the type of motor de-coding, 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 not really that day. 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. BCI couldn't have done this on its own, but Apple and Samsung and others support epic amounts of money on to making these types of electronics exist in the world so that people can use them.
And then it feels like you have a really significant advantage around being a biohybrid. I mean there are all these issues famously about trying to electrically stimulate brain cells for a long period of time. Yeah, I think that there are different products here. I think that on the one hand I think that's where I'm doing it, I think it'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's why we're doing it, but there's definitely trade off there. And I think that BCI we're going to come to see is not a specific product in the way that like pharma is not a product. I think there's going to be a bunch of BCI companies going after different applications or different types of probes will make sense. And I think biohybrid in particular is only really necessary for some of 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. And we'll probably be a little back-loaded relative to some other things in that scalable impact. 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 principle thinking that got you here to this thing that is also pretty groundbreaking. So this is our smallest project.
So there's this field of profusion. You can think of it as they're kind of like hard and lung machines. And I was first clued into the need here about a decade ago when I read an article in a medical journal called the Landsat 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 ECMO circuit ECMO is stuck in for extricorporeal membrane oxygenation. And this answer was for like a heart lung machine.
And in his case his heart was okay but his lungs have failed 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 it took him off the transplant list. And so this article is kind of about the ethical dilemmas of like, what do we do with him? No, but he's alive.
Because he's like video games, he's doing homework, hang 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 half a million dollars on ice-use week. And so there's quotes in this article from the doctor being like, his family and friends drive benefits from his continued survival and how this raised fairness questions because if we like support him for a longer period of time, then we do this for everybody.
And so I saw this and I thought it was a great question that he 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. And so 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, it was often a lot of it was talking about how ECMO shouldn't be used 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 when it creates these dilemmas. And then I went and asked some doctors who was a long time ago, this was almost an academic like, oh well, 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? Yeah, and it needs to be improved.
It needs to be improved definitely. But that wasn't even the response. It was like shouting and throwing things. 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 the thing that was kind of I saw that there was a really important unification here.
It also this the same fundamental type of technology has really transformed organ transplantation. So they call it NMP, normal thermodynamic machine, but it's the same idea. So 20 years ago, if you needed a kidney transplant or liver, if the car crash happened up three in the morning, the surgery happened at four or five in the morning, but now I guess I'll do it 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 systems, they just were like $500,000. They can only be moved by private jet.
One of the big companies in the space, it turns out that they're like 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 17 year old could have brought home as a backpack instead of just what they did in his case, they stopped changing the oxygen filter and we later 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. You can otherwise you have to clean it very carefully. But just overall, there's this huge gap between like clearly like where the scientific breakthroughs were pointing and like what was being done.
Like I think that people don't appreciate is that in many cases, like there's like, you want to be a brain in a valley. It's basically already exists. Like you can keep a like an end life patient alive and I see 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 and like a hospital that connected to the tubes. You need to be able to provide a quality of life. And so you need something that people can like live with. And I think to see this, 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 the problems of medicine here. And so that, like I said, like at science, even though there's several different projects, I really see them as like as one project over the next 10 years.
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 my combinator many years ago, my first stint at YC.
So I got an email one night in early 2016 from Sam, subject on crazy question, be like, Elon starting a brain computer interface company, like who should run it? And I think 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, I came on a back, I'm like, can I like, and, uh, dammit, introduce 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 Neuralink. I also think that he heard my name from enough people that he was talking to at the time. And kind of over the second half of 2016, there was just this group of people that was kind of some degree ever shifting that would meet once a week or so in the evening. And that snowballed into, into Neuralink.
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 at Gontrut UCSF, and then a collaborator there, so it was kind of a very small community.
What was that like initially to talk about the idea of like, you know, connecting a computer to a human being's brain? Elon, he, 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 separate thing from humanity and that needs to emerge somehow, I think that implication was just very clear to him. And so that was a 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 greater intelligence being very dangerous. Like in the beginning, there wasn't like a specific technical idea necessarily, but there was that motivating force. And then the idea was we pulled together like this more discrete group of people that you could find and enough resources to do whatever made sense and eventually got consensus around what you see now as the thin film polymer threads. You're one of the best examples of someone who came from a pure software world and then went into hard tech and now was 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? 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 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 on 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 that didn't usually take undergrads. 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 be a backdoor into this like primate neuroscience group. But then really most of my education in college happened in that lab.
So I grew up programming in my deepest hard skill as software but I've been doing primate brain-computer interface, like closing early coding stuff since 2008. So that was just like you had to be pretty high agency and like persistence in trying to like fall through on that but that only works if you have a sense of where you want to go. So the first is like figure out what you want. What I didn't do was my, so after college I started a company called TransRif Dick that was a robotic cloud laboratory. So the idea was, and it 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.
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 other equipment and then like you need to press these buttons.
It's like what we should build is a central robotic cloud laboratory that exposes APIs that scientists can use to run experiments or the internet. I did that, raised much money when I stepped down as CEO in 2017 to join Neuralink. I mean had millions of dollars in revenue. Like I felt like we got it to kind of an early promising point. And then since the never last decade that promise was not fulfilled.
That was still, that was hard mode. That was like a slog. That era from 2012 to 2016 I strongly identified with Ed Horitz as a struggle. And I think the thing that I should have done earlier is go work for somebody like Elon. Because that just like so dramatically leveled up like my ability to do this and know how the game is played.
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 starting up running a start at this in 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. I think PayPal was like this. But almost always beyond that it's like it's an oral tradition that you pass down from one of 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.
Well science is the next and it sounds like you're assembling. You've already assembled a really accomplished crew of people. And then what we've learned from startups over the years is that when something works like more and more resources and more and more smart people sort of come together and then 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. 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 to innovate on it you need all these different areas and really great people and each of them. But at the same time it's the things that you can do today were unimaginable a few years ago. And I think that I think we have the best team in the field. So I mean next 10, 20 years of science, BCI like I guess where do you see this going and what are you most excited about?
I have this event horizon at 2035 now. 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 it's just like I can't see past it. I think it is very possible that the first people to live to a thousand 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 that not unique like this moments in history have happened all the time before but right now it's a time of exceptional change.
This is going to be really influenced by the technological changes that are happening and the twin plot lines of brain computer interfaces and artificial intelligence. People are beginning to get that artificial intelligence is real is still not priced in. People still don't appreciate it. But they really don't get what's coming in and what's possible for computer interfaces and those are really parallel but very distinct stories. Intelligence is going to be widely available for those that have the agency to employ it.
And I am generally pretty optimistic about that. My PDU is pretty, it's not zero but it's not 50% as well below that. Yeah I don't know if we'll have cured all disease. In fact I definitely wouldn't use that term. I don't know if cured all disease is about 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.
And totally reconfiguring basically that sort of interface between computers and humans. Yeah and humans and each other. A brain computer interface is equivalent to a brain to brain interface in many cases. It's takes you to like totally new territory. Max thank you so much for joining us.
Thanks for building the future and we can't wait to see what you will next. Thanks Gary.