<![CDATA[Juan Benet Podcast]]>https://www.juanbenetpodcast.comhttps://substackcdn.com/image/fetch/$s_!r7Do!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Feed750ea-6e19-49e5-be93-5e80de72806b_1280x1280.pngJuan Benet Podcasthttps://www.juanbenetpodcast.comSubstackWed, 01 Jul 2026 01:00:30 GMT<![CDATA[The Power of a Single Neuron and Simulating the Brain | Dr. Konrad Kording]]>https://www.juanbenetpodcast.com/p/the-power-of-a-single-neuron-andhttps://www.juanbenetpodcast.com/p/the-power-of-a-single-neuron-andThu, 25 Jun 2026 18:19:52 GMTNew episode with Dr. Konrad Kording, professor of bioengineering and neuroscience at the University of Pennsylvania and co-director of CIFAR’s Learning in Machines & Brains program. Konrad works at the intersection of causality, machine learning, and neuroscience, building rigorous methods for causal reasoning when experiments aren’t possible — and challenging how researchers interpret neural data and build AI.

Konrad argues the most promising path to understanding how the brain works is to read the brain’s wiring directly, down to the molecular detail of each connection, and to build compilers and simulations to understand the brain’s computation directly.

In this episode we go deep into how neurons work, how neurons wire together, and how organic and artificial neural networks differ. We discuss why organic neurons are doing much more; how a model of a single organic neuron can solve MNIST — computing more like a 3-layer artificial neural network; how the brain might learn by solving credit assignment with only local signals; how to approximate backprop without a global algorithm; why AI and humans are intelligent along different dimensions; why Konrad isn’t very worried about AI replacing us; economic models of intelligence and physical work; and much more.

Konrad is a brilliant, contrarian thinker who explains complex concepts very intuitively. It is a solid computational neuroscience primer. I hope you enjoy this conversation as much as I did!

Other links to this episode and references below.

Topics covered:

Podcast Links

Links From the Podcast Episode

Guest + Organizations

Papers directly from Konrad Kording’s lab:

Referenced external papers:

Books & Media:

Juan & Protocol Labs

]]><![CDATA[Reading the Brain Without Opening the Skull | Tom Oxley, Synchron]]>https://www.juanbenetpodcast.com/p/reading-the-brain-without-openinghttps://www.juanbenetpodcast.com/p/reading-the-brain-without-openingThu, 11 Jun 2026 18:44:38 GMTNew episode with Dr. Tom Oxley, co-founder and CEO of Synchron. Synchron has built a BCI called the Stentrode, which reaches the motor cortex via a blood vessel — leveraging the approach of cardiovascular stents, without having to open the skull at all!

15 million people live with motor impairment. The Stentrode lets people operate their phones and computers through thought, and could restore independence to people who’ve lost the ability to control their devices. Tom sees BCIs as a major technological leap that will help human flourishing, by enabling better communication, decoding and conveying emotions, and enabling us to leverage the great capabilities of our computing infrastructure.

This was a great, wide-ranging conversation. We discuss the origins of Synchron, the endovascular approach and its benefits, their next-gen system designed for high-channel-count recordings across distributed brain regions, the longer-term possibilities of helping people communicate better, how Tom developed as a founder and how he leads the company, how BCIs could unlock powerful mental states similar to psychedelics and meditation, how neurotech will transform humanity in the 2030s and 2040s, and why Tom thinks the US will lose the BCI race to China unless the US greatly accelerates. Hope you enjoy!

Links to this episode and references below.

Topics Covered

  • 00:00:00 Introduction

  • 00:03:18 Carl Jung, stroke surgery, and the road to BCI

  • 00:06:58 The endovascular approach: reaching the brain without surgery

  • 00:10:37 Getting Synchron off the ground

  • 00:17:23 Reading the brain: channels, signal, and noise

  • 00:36:41 The numbers: 15M patients, FDA, and Medicare

  • 00:43:17 Cognitive AI: foundation models, the data economy, and the 2040s

  • 00:52:55 Consciousness, psychedelics, and the extended self

  • 01:02:51 Agency, addiction, and geopolitics

  • 01:06:51 The optimistic vision: unlocking the subconscious

  • 01:10:49 Building a company: 696 no’s

  • 01:21:30 Losing the BCI lead to China

Links From the Podcast Episode

Guest + Organizations

Research Papers + Technical References

References Mentioned in Conversation

Books + Media

Links

Disclaimer⁠

]]><![CDATA[Jacques Carolan — The Mission to Get Breakthrough Brain Treatments to Everyone]]>https://www.juanbenetpodcast.com/p/jacques-carolan-the-mission-to-gethttps://www.juanbenetpodcast.com/p/jacques-carolan-the-mission-to-getWed, 27 May 2026 20:28:31 GMTEpisode 3 of my new podcast features Dr. Jacques Carolan, a founding Program Director at ARIA, the UK’s Advanced Research and Invention Agency. He directs two neurotech programs aimed at one of the most important opportunity spaces: developing tools and systems to interface, at scale, with the human brain.

One program is built on the idea that brain disorders are circuit problems, and funds tools to target those circuits with molecular precision across the whole brain. The other aims to deliver high-performance neurotech to the brain non-invasively or at most in a 30-minute outpatient procedure.

We dig into the engineering and biology behind both programs, potential scaling unlocks for the field, how ARIA programs drive breakthroughs, Jacques background, the role of media in shaping the future, and much more. I hope you enjoy the conversation!

Other links to this episode and references below.

Topics covered

  • 00:00:00 Introduction

  • 00:01:22 Why 20 years of neurotech breakthroughs haven’t reached patients

  • 00:04:08 The two variables that determine whether any medical technology gets adopted

  • 00:09:17 Brain disorders cost the UK £100B/year and we’re barely treating them

  • 00:16:15 Using stem cells and gene therapy to build better brain interfaces

  • 00:21:40 Self-regulating gene therapy that helps the brain quiet its own seizures

  • 00:24:03 The non-technical reasons transformative neurotech fail to reach patients

  • 00:31:34 Watching a 30-second brain ablation stop severe tremors

  • 00:38:11 The case for delivering brain implants and therapies without opening the skull

  • 00:50:56 Why high technical uncertainty makes distributed teams better than vertical integration

  • 01:02:55 Why the UK keeps producing world-class neuroscience but not world-class neurotech companies

  • 01:11:04 What AI-driven hypothesis generation means for breakthroughs per pound

  • 01:20:40 From quantum computing to improv comedy to running £119M government brain programs

Links from the Podcast

Jacques Carolan

Jacques’ Programmes at ARIA

Research Papers + Technical References

Videos + Demonstrations

References Mentioned in Conversation

Books + Media

Links

Disclaimer⁠

]]><![CDATA[Ben Rapoport — Treating Paralysis and Digitizing Neural Data]]>https://www.juanbenetpodcast.com/p/ben-rapoport-treating-paralysis-andhttps://www.juanbenetpodcast.com/p/ben-rapoport-treating-paralysis-andMon, 11 May 2026 17:16:53 GMTBen is co-founder and CSO of Precision Neuroscience, Assistant Professor of Neurosurgery at the Icahn School of Medicine at Mount Sinai, and Scientific Director at Mount Sinai. Previously, he co-founded Neuralink and Simbionics (acquired by Apple).

Precision is building a minimally invasive brain-computer interface (BCI) that reads from thousands of points on the cortex without penetrating it. The Layer 7 device is implanted through a one-millimeter slit in the skull rather than the larger borehole other approaches require. It is also fully removable.

Precision seeks to help the 5 million people living with severe paralysis in the US (including 800,000 new stroke cases per year). In March 2025, Precision received FDA clearance for a temporary wired version of the system. Over 85 patients have been implanted with and used the device in clinical studies. Wireless implants are planned for 2027.

We go deep on the history of Neurotech from the 1980s to the ML inflection points that triggered Neuralink’s founding, why surface ECoG was a contrarian bet that’s now paying off, the path to treating paralysis and stroke at scale, and why Ben believes neural data is at the same inflection point genomic data was in 2000 — a whole class of biological problems about to become tractable as computer science problems.

Sections

  • 00:00:00 Introduction

  • 00:04:39 Paralysis as a lens to understand the brain

  • 00:05:36 The 1980s breakthrough: population encoding and the birth of BCI

  • 00:14:36 Google Translate, ML, and the founding of Neuralink

  • 00:23:08 What is the long-term vision of Precision Neuroscience

  • 00:31:56 Layer 7 and why transformative technology always looks impossible at first

  • 00:50:21 The surgery: a slit in the skull, not a borehole

  • 00:55:19 The clinical program: who are the patients

  • 01:04:16 FDA clearance and the path to wireless implants in 2027

  • 01:08:32 The patient population: paralysis and stroke at scale

  • 01:16:26 Neural data as the new genomics

  • 01:30:06 BCIs, AI, and the future of the human-machine interface

  • 01:31:22 From medical necessity to lifestyle technology

  • 01:40:36 Precision as a platform — and an optimistic vision

Links from the Podcast

Precision Neuroscience: https://www.precisionneuro.io

Layer 7 BCI: https://www.precisionneuro.io/our-technology

Icahn School of Medicine at Mount Sinai: https://icahn.mssm.edu

Podcast Episode Links

Precision Neuroscience

Juan Benet on X

Juan Benet Podcast

Protocol Labs

PL Neuro

Episode Links

Disclaimer⁠: https://bit.ly/PodcastDisclaimer

Transcript

Juan Benet

My guest today is Ben Rappaport. He’s the founder of Precision Neuroscience. He previously co-founded Neuralink and Symbiotic which was acquired by Apple. He’s assistant professor of neurosurgery at the Icahn School of Medicine at Mount Sinai. He leads research in brain computer interfaces and serves as scientific director of Mount Sinai Biodesign. He studied physics, mathematics.

He got a PhD in EE and computer science, went to med school and then became a neurosurgeon and treats lots of individual patients. He’s authored over 50 papers and 40 patents in neurotech, and medical innovation. So he’s just a legend in the field. I’m very honored to be chatting with you today.

Thanks for taking the time.

Ben Rapoport

Thank you so much for having me. It’s a pleasure. I’m flattered by the introduction.

Juan Benet

Thank you so much for all of the work that you’re doing for people globally. It takes a lot of individual drive and effort of people working for decades, both in the core science and the core innovation to actually get tech translated and available.

So thank you from all of the people that are benefiting from it. So let’s dive in. Let’s talk about the device, which I think is a beautiful work of art. Maybe walk us through the design of layer seven and kind of what were the core insights that you designed around, like why this particular form factor?

How does it sense neurons? Why is it in this particular way? It’s very different than than many others.

Ben Rapoport

It is different. Nothing is totally emerges out of a vacuum. So it’s similar in certain ways and, different in certain ways, but you’re totally right in the brain computer interface world when we started precision, it was heretical in a sense because the whole field really had developed around micro penetrating intracortical, micro electrodes that were developed to record kind of one neuron at a time. In the ‘80s, ‘90s, 2000s, in the academic field of brain computer interfaces, there was a real emphasis on trying to record from individual neurons. The neuron being thought to be the atomic unit of information processing in the brain and the atomic bit of information being the action potential, so-called colloquially referred to as a spike. So a one millisecond duration signal, like the ones and zeros of the brain. That was kind of how the brain computer interface world, academically was thinking about, interfacing with the brain.

In the medical world, there has been for many decades, routine use of electrocorticography. So surface contacting electrodes that sit on the cortex without penetrating in, and are used to map neural activity, with very precise timescales and, in the clinical domain, usually much less fine spatial scales but known to be safe and quite flexibly useful for many decades.

And one of the insights that I think we take for granted on the medical side is sort of that most of the conscious experience of human beings takes place at the cortical surface. So the brain is of course, this three dimensional structure, but really it’s optimized for surface area. If you’ll take a look at the neocortex, it’s kind of like a flat sheet that’s crumbled up into a ball and it has these hills and valleys, as a way of optimizing for surface area.

And the reason is that, at the surface, that’s where all of the conscious thinking happens. A lot of what’s on the inside is essentially white matter or wiring that connects what happens at the surface of the brain to the outside world through the brainstem and spinal cord. The intuition at the beginning of all this was that we really want to be sensing what’s happening at the cortex in a way that maps in the greatest spatial extent, relevant to what’s happening at the cortical surface, but not damage the brain by penetrating into it if we can avoid it.

What if there’s a way of doing that? Well, electrocorticography already existed. No one had built electrocorticography systems that were at comparable scales to the intracortical penetrating electrodes that had been used in the world of brain computer interfaces. So we said let’s just do that. If we do that and we get the electrodes down to the scale of individual neurons, we should be able to sense high quality neural information.

We also knew there are some kind of like in every field, there’s black magic, just like in AI. Like in early machine learning, there’s all the black magic, like when do you stop training?

How the actual stuff gets really done? There’s the stuff that you write papers about and then there’s the stuff that you have to learn by doing which nobody talks about. So one of the things that nobody talked about with intracortical electrodes was that very often you would stop recording from individual neurons, or you’d start recording from one, and then it shifts to another because the electrode moves a little bit or the brain responds a little bit. The impedance of the electrode changes and so you can’t record from individual neurons and you’re really recording from a bulk average of tissue. All of these things were happening and anybody who was in the field knew that was the case.

The reality was that recording from a local average field and recording from individual neurons was basically equally informative from the standpoint of can you decode what the brain wants to do?

Juan Benet

Mm-hmm.

Ben Rapoport

It’s not equally informative if you’re a neuroscientist trying to study the behavior of individual neurons. But from the standpoint of how the brain decides how to make the body move through the world, it’s equivalently informative.

Juan Benet

Is that primarily for motor cortex cases? Or will there turn out to be other areas, of thinking or behavior that end up with these populations of neurons?

Ben Rapoport

I’d say that it is definitely true of the motor cortex.

Juan Benet

Yeah.

Ben Rapoport

It also seems to be true of many other areas of the brain. Are there processes that require more and less fine detail? Probably. A hundred percent. But is there a huge application space that resides in this scale of hundreds of microns of spatial and few milliseconds of temporal resolution?

Absolutely. This shouldn’t surprise the machine learning world either, right? Because like, what was the TPU and the GPU all about? It was about reducing precision in order to increase the scale basically in order, and so the fact that we can lose a few bits of precision in order to increase spatial scale and scalability and addressability of different areas of the brain. and, and just to make a

Juan Benet

safer device

Ben Rapoport

isn’t easier. Yeah,

Juan Benet

Yeah.

Ben Rapoport

Without compromising efficacy at least in these class of problems.

Juan Benet

It seems to me like a classic case of a great contrarian secret when building a technology company. It’s like the rest of the field thinks and behaves a particular way. You have this core insight that, hey, actually the problem gets a lot easier and you can tap a whole range of use cases by applying this other case. You might not get the full holy grail of hundreds of thousands of individual neurons, but a whole wide range of applications are achieved.

Ben Rapoport

That’s not my Holy grail.

Juan Benet

Yeah, yeah,

Ben Rapoport

Like you asked me at the beginning, what’s my holy grail? My holy grail is making products that work for people. My holy grail is not studying every individual neuron in the brain simultaneously. For some people, that’s the holy grail. It’s not mine.

Juan Benet

How many people out there might qualify for this kind of treatment on the road?

Ben Rapoport

For the most severe forms of paralysis. Basically, spinal cord injury from the neck down, severe impairment of both hands. We’re talking tens of thousands of people in the United States per year newly affected. If you looked at a cross-sectional study of the United States today, about 5 million people existing in need. So of 5 million existing and a few tens of thousands per year newly affected. That’s for the most severe forms. So people who basically are paralyzed almost completely from the neck down and don’t have adequate use of their hands to engage in routine desk job work.

My hope is that, as long as the technology is safe and effective, other forms of paralysis will be amenable to treatment with this type of technology. I think that the Precision technology is well poised for this. I’m particularly thinking about stroke, because stroke is the most common form of paralyzing injury.

It’s different from a spinal cord injury in that it is more often incomplete. It does not usually affect both sides of the body in quite the same way. It has, in some ways, less focal impact because of the way disrupting blood flow to the brain works. But nevertheless, I know there is a huge need in restoration of function after stroke. There’s a big segment of the medical community that really believes that one of the next stages in the impact of brain-computer interfaces is for people with stroke. Because this is a little bit different from the AI futurist application world of how the user interface change and we can get there too.

If you think about it in terms of the medical need, in the last 10 years the treatment of stroke has been totally revolutionized. When I was growing up, when you were growing up, stroke was a medical disease.

There was nothing procedural to offer. That changed slowly over time. And in the last 10 years, the ability to intervene acutely and remove a blood clot from a blood vessel that is causing the paralysis, that is causing the stoppage of blood flow and oxygen to the brain, changed. That and a few other things made stroke, as a whole, a more procedurally amenable condition.

And I think there is a very influential stream of thought in medicine today that feels that, even though that has saved life and limb for many millions of people, many of those people whose lives are saved and improved live with a form of paralysis that we do not yet have a way of treating. There is a sense that what is waiting for those patients, and in the United States that is almost a million people per year who are affected by stroke.

Juan Benet

Wow.

Ben Rapoport

This is and was a contrarian position. At the beginning, we were told, “You can’t do that. You can’t get enough information out of the brain just from the surface.” And then, of course, like anything, you have to prove it. So that took time.

Now people ask other questions, and they don’t say, “Can you do it?” They ask, “How well can you do it?” That’s a natural evolution.

Juan Benet

Classic case of you can’t possibly do this. Well, here it is proof. Right. Okay. But you can’t possibly do this other thing. Right?

Ben Rapoport

But to your question, and to where you started, that was the insight.

So the notion was, what do we need to do if we want to get to those scales? We need to make electrocorticography arrays that are on that scale. Well, how do you build something at that scale? Actually, like we said, the real problems are not so easy to solve sometimes.

Because when you have to put a lot of electrodes in a plane array, how do you connect to them? How do you make such a thing? How do you make it small enough? How do you manufacture it in a reliable fashion? How do you manufacture it in a safe fashion? How do you route all the electrodes out? How do you power that device, and so on?

So the answer to how do you make a small patterned device was easy. You use photolithography. That’s how you make all kinds of small electronics.

Easy to say, but not so easy to do because, although well known and universally used in electronics, photolithography is really not used in medical devices. The number of medical devices that use photolithography in a sensor context, you can count on one hand. Precision is now one of them.

But also, this thing has to be flexible because it has to conform to the surface of the brain. By the way, the brain is delicate. It has a Jell-O-like consistency. So the interface itself has to be correspondingly delicate.

And all these electrodes have to be uniform in size, shape, and impedance. Then we had to solve questions like, how fine do you make them? What material do you make them out of? And how big should they be in order to record from the electrodes? These were all questions that had a theory behind them and that we then had to go and solve empirically.

But the fundamentals are that the modular device is a polymer thin film that contains tiny platinum electrodes, most of which are about the size of an individual neuron. The module that we use most frequently contains 1,024 electrodes, but we can place many of them side by side to tile the brain.

The traces are very thin platinum wires, essentially, that are embedded in this polymer thin film. Those traces then connect into a hermetically sealed can that contains the electronics that amplify, digitize, and condition the signal, and ultimately transmit it wirelessly outside of the body.

Juan Benet

It is beautiful and fascinating. You had this idea for how to produce this kind of device. How do you start? How do you make it?

Ben Rapoport

How do you make it at scale when you’re building something that demands incredible quality and reproducibility?

The truth is, there was no supply chain for this. There were research-grade houses that could make small batches of things, but not usually in a controllable fashion, in a reliable way, on time, and under quality controls.

When you’re building something that is going to be implanted in the human brain, it needs to adhere to medical-grade quality control. It needs to be highly well documented, predictable, testable, biocompatible, and meet all of these specifications that we appropriately require of a device that is going to be touching and implanted in the human brain.

We had to bring that capability in-house. We now own and operate a microfabrication facility just outside of Dallas. It is amazing.

Juan Benet

Do you build it from scratch or do you acquire it?

Ben Rapoport

We acquired it.

Juan Benet

Yeah.

Ben Rapoport

It’s very hard to build one of these from scratch.

Juan Benet

Yeah. You wanna make a BCI device, you must first get a fab.

Ben Rapoport

That may be the case.

Juan Benet

At least for the short to medium term.

Ben Rapoport

I think that’s true. As of five years ago, there was no supply chain. During COVID, many of us became acutely aware of manufacturing supply chains and how they limited the development of technology and the progress of industry.

So we started all this early in COVID, and we were thinking through supply chain with incredible sensitivity at that time. And there was, and remains, no piece of the medical technology supply chain that involves microfabrication.

Juan Benet

Especially if you want to tweak the process or do something that is a bit out of the ordinary, you then have to do it yourself.

Ben Rapoport

Yes. So if you have a locked design, then maybe that is one thing. But all of us who are seriously in the field know that it is evolving. So the ability to conduct R&D while we are building product is essential. You hit the nail on the head there.

Juan Benet

Yeah. If you’re serious about your hardware, you have to be serious about your fabs.

Ben Rapoport

Definitely.

Juan Benet

Yeah.

Ben Rapoport

Yes. You have to be serious about the R&D. You have to be building for today and also planning for tomorrow.

Juan Benet

So you design the device, and in order to make it, you try working with external groups. It does not work as well, so you need to bring it in-house and get your own fab. And so now you have this awesome device. We now have some insight into the approach in sensing and sensing populations of neurons. Potentially, some of these can get small enough to sense one or a few neurons at a time. But it also has this very super-flat. What is the thinking there? You also developed this new surgical procedure that is very different from what a lot of the other BCI groups are doing. What is the thinking there?

Ben Rapoport

Okay. So let’s unpack that.

But before we continue, as you were asking the question, I wanted to pause for a second. Because you frame a lot of the questions with “you,” and I want to clarify that I represent a plural “you.”

This is not just me. “You” represents a whole team of people, certainly at Precision and in the field as a whole. So I want to acknowledge that.

We were talking about the microfabrication facility. We did not just acquire a facility. We acquired an incredibly experienced team that had actually been working at that facility, for some of them, for more than a decade.

That team knew how to operate the facility and knew how to work together as a team. So that is not to be taken for granted. Of course, all of this science and technology is developed collectively by expert people. We are not quite at a millennium of experience yet, collectively, but it is a lot. It is a lot of people working together as a team.

So I am here having this conversation with you, but representing a huge team.

Juan Benet

And that know-how is part of what makes fabs so hard to reproduce and hard to scale, rather than just the depth of expertise of a team that is able to run them very well.

Ben Rapoport

No question. And it applies to basically every piece of what we do.

There is the clinical side, which we touched on. But the team that does the clinical interface, working with expert physicians and surgeons and patients and families, that itself is an expertise. The whole medical system and academic medical system in this country and abroad that enables early-stage technology to enter the medical system at an inchoate level, that itself is a whole other conversation.

The electrical engineering that happens on the R&D side, the machine learning, all of these are groups of experts, and the development of the technology and its rollout into the world depends on that.

So, you asked a series of questions, which include the electro array being planar, what the thinking is there, how it is delivered onto the brain and how that is different from how other groups have thought about it, and what signals we are actually recording from the brain and how we think about that.

We talked earlier about how the brain, conscious experience, kind of unfolds in the two-dimensional surface of the cortex. That was the reason the electrode array is flat, so to speak. We’re really focused on neocortical real estate for the most part, although we’ve also interacted with the brainstem and spinal cord. But again, the computing happens mostly at the surface, and the wiring happens under the surface.

So with that insight, we can build interfaces that focus on the computing elements of the brain and on sensing and stimulating it at those interfaces. That’s obviously a simplification, but it’s a really useful simplification.

The planar nature of the electrode array also allows us to deliver the array in some innovative ways. First of all, it makes the array placement in the brain, or on the brain, incredibly safe. Because basically, the array just caresses the brain surface. It sits delicately on the brain surface. It basically adheres through capillary action, and it does not penetrate the brain surface. And yet, it records with great spatial and temporal fidelity.

That means we can place the electrode array, and it can be moved along the surface. So a little bit of trial-and-error adjustment is possible in ways that are not possible when you have a needle-like electrode that needs to be withdrawn and replaced in order to optimize its position.

So that carries certain advantages. It is also possible to be extremely modular. We can place a lot of these electrode arrays simultaneously without having to worry much about whether there is a blood vessel or what have you.

We can also get into the nooks and crannies and folds of the brain, so we can get around onto surfaces of the brainstem that are inaccessible to conventional electrodes. We can place electrodes within the sulci, the folds of the brain, which are really not accessible to penetrating types of electrodes.

So there are certain things that this planar, film-like nature of the electrode allows us to do that are not possible with traditional electrodes.

One of those things is that we can reach parts of the brain that we do not need to expose. What does that mean? Traditionally, in order to place electrodes or sensors in or on the brain, you need to expose the part of the brain that you want to address by making a hole in the skull or cutting a piece of the skull out, and then penetrating the brain’s surface underneath where the skull has been removed.

The surface array that we have can be placed through a little slit in the skull. So we designed it to be placed through a slit in the skull, like a little incision in the skull and the dura, and it can be slid across the brain surface. It can be placed directly adjacent to this slit, or it can be placed many centimeters away.

And we have done that, or rather, our collaborating surgeons have done that. They have made small apertures in the skull and slid the electrode many centimeters away. That can be done with one electrode or with multiple electrodes.

So what the technology allows us to do is really decouple the invasiveness of the surgery from the total amount of information that you can exchange with the brain.

Juan Benet

Yeah.

Ben Rapoport

That was also kind of an “aha” for brain-computer interfaces: the minimally invasive nature, not just in terms of the way the electrode contacts the brain, but also in terms of the procedure itself, and hopefully the risk and risk-reward profile for patients considering undergoing the procedure.

Juan Benet

Maybe to help listeners understand the difference, describe how some of the other surgeries happen, how the devices are implanted, and then the approach you’re taking.

Ben Rapoport

In the research world where this all started, it was required to cut a piece of the skull away. That is a traditional technique of brain surgery. So when patients need brain access to remove a tumor, seal an aneurysm, or something like that, there are tried-and-true ways of accessing the brain that are totally appropriate when there is a need to get in.

But of course, the less you open the brain, from many perspectives, the better.

All of the prior-generation technologies were based on needle-like electrodes. The only way to get a needle, even a very fancy needle, into the brain is to expose the place where you want to put the needle. You need to expose the area directly underneath where you are removing the skull, because that is where you are going to be placing the needle.

So if you are trying to get to the surface of the brain, you need to remove skull over that whole area you want to address. And so that becomes a pretty invasive procedure.

That applies to Neuralink, for example. Even if it is a very small removal of skull, to scale the process, you do need to remove more skull in order to get more electrodes in.

Juan Benet

It seems like most of the BCI companies right now are following this kind of procedure, where they’re doing a borehole right on top of the area they’re trying to sense, especially if they’re trying to scale it out to multiple different sites. That would mean many holes in the skull.

Ben Rapoport

Exactly. So it would be nice to decouple the number of holes, or the size of the hole that you want to make in the skull, from the total number of electrodes, or from the functionality of the device.

And that’s what we did with the Precision system. We can make one small aperture, which in our case is either a small burr hole or a slit. Through that one-millimeter slit or very small burr hole, we can slide many electrode arrays, so many thousands of electrodes can be placed.

That decoupling of the invasiveness of the procedure from information exchange with the brain was a Precision innovation.

Juan Benet

That also seems like another great contrarian secret, where the rest of the industry is going in one particular direction. When you scale that up, or take it to its conclusion with many of the later applications, that seems like a long-term, maybe brittle path. And if you take this minimally invasive approach instead, and you focus on how you achieve scalability through that, that seems like a very good long-term strategy.

Ben Rapoport

That was deliberate on our part. And I think it comes a little bit from the starting point. It comes from combining an engineer’s orientation to the field with a physician’s sense of what is known to work and what is acceptable to people with a problem.

Juan Benet

So you have the device. You have this innovative surgery for delivering it. What is the range of applications that you’re using it for now? You’ve implanted around 50 people. What were those cases, and what were you looking at so far? And if I understand correctly, currently you implant it for a period of time, use it, and then remove it. It does not stay in there for now. What are the kinds of things you’re learning at the moment, and where is this headed?

Ben Rapoport

One of the things that we tried to do when we started Precision was to avoid getting into a situation where brain-computer interface is already high-complexity and high-stakes field. We’re talking about developing a fundamentally new technology for implantation in the human brain, and in the brains of people whose brains may already have been compromised in some way. So the risk tolerance is pretty low. How do we ensure that what we’re developing is going to work?

One way is to spend a lot of time building something in the laboratory, then prove that it’s safe through some incremental process, and then start clinical trials with the end-state device. But we know that something is not going to be quite right, and you would have wanted to know that at the beginning and fix it before you got all the way down the path.

There’s almost no field of technology in which that is how things are developed. You don’t build a multibillion-dollar rocket with many millions of parts without testing subsystems in the real world. You don’t build a high-performance automobile without iterative test laps and a team that can pick the thing up on a lift, get underneath it, and tweak stuff after five minutes of testing. Same thing with software, with consumer electronics, and with almost every other complex system.

So we knew that we wanted to be in a situation where we could test, validate, and verify pieces of the complex system in parallel. And the problem with medical technology, including brain-computer interfaces, is that the end user is not really part of the engineering team.

The end user is a patient, a surgeon, a neurologist, or some combination thereof. So how do we bring that part of the user experience into the design loop?

One of the good things about the Precision technology is that the electrode array we were just discussing is removable and reversible, because it does not penetrate into the brain. It can be moved and removed without traumatizing the brain. And so the risk profile of the device is fundamentally different from that of penetrating electrodes, which, however safe they may be, still do some amount of cutting into the brain.

So the ability to do that in a temporary fashion, or in a research-type fashion, is fundamentally different. Because of the nature of our electrodes, we were able to design and supervise studies with clinical partners at major academic sites, in which patients undergoing standard-of-care neurosurgery, sometimes asleep, sometimes awake, could volunteer to participate in an early-stage study involving the use of the Precision technology.

In that way, we learned a tremendous amount about how the devices interacted with brains in the real world, in real clinical settings, and what the signals looked like. Importantly, even from a very early stage, we proved that in people who were awake, and most of whom were able-bodied, they could execute and imagine movements, and speak, and that we could, using the machine-learning side of this technology platform, which we have only barely touched on, decode the neural correlates of intended, attempted, and executed movements.

That’s the fundamental insight and motivation of our clinical program. Number one, it is to validate the technology, make sure that we are getting the signal quality we need, that the signals we are getting are decodable, and that we have a signal-processing algorithmic pipeline that really works. So we could de-risk that, and not only de-risk it, but really push it forward in a nuanced and accelerated fashion, even as other parts of the system were coming online.

We are well beyond that at this point, five years into the company. But that was the insight early on. Let’s make sure that we are decoupling risks and testing pieces of the complex system together.

So, to your original question, who are the patients who are involved, and what is the nature of the surgery? With different clinical sites, we have different types of procedures in which the device has been used. I’ll give you a few examples.

In some brain surgeries where there is a tumor near language-related areas or movement-related areas, part of the standard of care is to electrically map where the so-called eloquent areas are, where the language-related or movement-related areas are, so that those areas are not removed along with the tumor.

Juan Benet

This is localizing what areas to make sure to really avoid.

Ben Rapoport

Exactly. Which areas to protect, and which areas represent abnormal tissue that can be removed. There are traditional techniques that are used to do that. So it has been of interest to understand, can higher-resolution electrode technologies help do that better?

And also, while that is happening in parallel, once you have identified those eloquent areas and are operating on the others to remove the tumor, can we place the Precision electrode on what is known to be a movement-related area or a language-related area, and while the patient is awake, have them engage in a behavior, in speech, in movement, hand gestures, and so on, that allows us to test and validate that we can identify distinct signatures of those neural behaviors, those intentions, those behaviors, and really make sure that when the technology rolls out to people who can intend the movements, who can intend the speech, but cannot execute them because they are paralyzed, the device and the system will be able to perform.

So a lot of the early work that we were doing involved situations like that. Some of the patients had brain tumors and were being operated on partially awake. Some of the surgeries were for Parkinson’s disease, in which an electrode was being placed in a different area of the brain, but nevertheless that allowed access to the movement-related areas of the brain in people who, for a portion of that procedure, are awake.

And I just want to, as a sidebar, note that in this type of clinical study, the people who are undergoing the surgery are volunteering to do something pretty extraordinary.

Juan Benet

Yeah.

Ben Rapoport

They’re really partnering with us and with their surgeons to say, “Look, we understand that there are people a little bit like us, or who we may know, who down the line are going to benefit from the data, knowledge, and insight that we’re contributing. And that is going to help other people who are not us.”

So we’re willing to engage in this, to have our surgeon, neurologist, or treatment team spend a little bit of extra time, in parallel with our treatment, to help push the boundary of what is known and help somebody else down the line.

And I think, to me, that is part of how medical science advances. It’s amazing.

Juan Benet

It is extraordinary pioneering work that just benefits so many other people downstream.

Ben Rapoport

Totally. I think that has to be acknowledged. These people and their families are amazing. It is a very special person who does this, and I think that needs to be said.

Juan Benet

Yeah, absolutely. So many people in the future will be deeply thankful to those people who took action now to help push the frontier, understand what’s going on, and shape therapies.

Ben Rapoport

They are usually energetic, incredibly fascinating, and curious people you want to be friends with and partners with. They feel as invested as we do in learning, pushing the boundaries forward, and contributing something to people beyond themselves. But nevertheless, those are the people who are really on the spot at that time, and they usually have their own things that they’re worried about.

So to zoom out a little bit and think about somebody who may come along later, who you may never know or meet, nobody will know your name and you may not know theirs, but somehow you’re contributing something, it’s special. It’s altruistic, and I think not everybody realizes that that’s how it works.

Those are the kinds of interactions that we’re having. Earlier this year, we did receive FDA clearance for a version of our device, which is a percutaneous, wired version. Of course, the permanent implant is a wireless version of the system that records the neural signals and wirelessly transmits them outside of the brain so that we can control computer systems.

But some of the R&D versions of the system that we rely on a lot in our R&D are wired, including the ones that we use during the temporary implantations. So we now, as of March, have FDA clearance for a temporary version of the system that can be implanted for up to 30 days. That type of system is the type that can be used for this kind of R&D work.

I have to be careful about exactly the language that I use with respect to what is actually cleared by the FDA. So just understand that the R&D work and our collaborations with all of the clinical sites are all investigational studies, which are not part of the clinical use of the technology, but they give you a sense of what the capabilities of the system are.

Juan Benet

Yeah. You’re doing this range of R&D work now, this set of studies. At what point do you make the wireless device for long-term implantation and actually start treating patients?

Ben Rapoport

Yes, as intended. Treating people with paralysis.

The current plan is that we have a wireless version of the system that is currently undergoing the validation and verification required for an implantable medical device. We expect that, in 2027, the first of those devices will be implanted in human patients in what’s called an early feasibility study.

That’s our current plan. That first group of patients, assuming things go smoothly, will proceed to a slightly larger pivotal clinical study. Between now and then, basically in 2026, while we’re doing the validation and verification required to advance into that feasibility study in human patients with the wireless device, we’re also spending a lot of time, in parallel, in clinical studies at partner sites to robustly and validate the algorithms and the software, so that we can decode intended movement and do all kinds of things that represent the functionality we would want to provide.

We want to be as certain as possible that we will be able to provide that functionality to the patients who enroll in the study starting in 2027.

Juan Benet

How long is that study? If I’m thinking about people who may have the forms of paralysis that you might be able to treat down the road, what does the timeline look like for them? So you’ll go into that 2027 study. At what point does this become commercially available for them?

Ben Rapoport

Let me say that, approximately, the 2027 feasibility study will be on the order of 10 patients, maybe fewer. And the pivotal study will be, we don’t know exactly, but hopefully a modest number of patients.

So I think we’re talking about a single-digit number of years before. Both of those studies need follow-up. You need to enroll the patients, and they need a certain amount of follow-up. Some of that is still to be negotiated and agreed upon with the FDA, so I don’t want to say too much in a premature fashion.

But I think it’s reasonable to say that we’re looking at a single-digit number of years, if all goes well, to devices becoming part of the standard of care and starting to treat people with paralysis, and being available for people with paralysis.

Juan Benet

Amazing.

Ben Rapoport

So it’s like 800,000-plus people a year in the United States have a stroke, some of whom will fully recover, some of whom are extremely severely debilitated, and about a third of whom go on to live a full life expectancy but with a significant paralyzing deficit. I think many of us know people like that.

So the question is, what do we have to offer them today? And there isn’t much, actually. So there’s a stream of thought, and I subscribe to this, that brain-computer interfaces will have something to offer that group of people. And that’s a lot of people.

There are other areas, but to me, if you think just about paralysis alone, without thinking about mood disorders, the visual system, the sensory system, executive functioning, memory, and all the different things that the brain does that can go right or wrong, or can be optimized or augmented, to me that’s an area where I see the future of medicine changing through brain-computer interfaces.

Juan Benet

Do those numbers look similar in the rest of the world as well?

Ben Rapoport

Yes. I would say, as a matter of prevalence, yes. It’s stroke. There are some segments of the world where it is a little more or a little less common, but stroke is a common condition the world over.

Juan Benet

So the outlook is a set of trials now through 2027, maybe 2028, and beyond, with a single-digit number of years before hopefully being clinically available for a range of potential problems, both in the US and abroad. That’s a phenomenal outlook. Once you get through the studies, how does that scale from there?

Ben Rapoport

I’ll say two things. One is that I also think one of the things we’re learning, that we did not set out to learn, is that by engaging with the world of experts, with medical and surgical experts and with patients, and by getting early versions of the technology out into the world, we’re discovering that there are other smart, knowledgeable people with experience and ideas about what to do with the Precision system as a platform technology.

Juan Benet

Yeah.

Ben Rapoport

We don’t pretend to have all of the ideas or insight. But what we are finding is that people are coming to us and saying, “We’re seeing these signals. We’re seeing this technology that you have. Can we use it? Can we partner with you?” And that is a kind of signal that you seem to be on the right track.

So we have to stay extremely focused on building one particular product, but we are also spending some time and capital on building out future use cases and supporting a community of users in doing additional exploratory work. Some of that we have done a little bit of ourselves.

I think we do have a sense that we do not have all the answers, and that what we are building has a platform nature to it. Of course, in order for there to be a platform, you have to have one application. I do not want the cart to go before the horse. But we do hope that what we build will be a tool that enables discovery, and that enables clinically oriented discovery into areas that may not be our first use case, and that we may not be thinking about now, but certainly we are able to access and gather data from all kinds of areas of the brain, brainstem, and spinal cord and we want to enable others to do that work, either independently from us or in partnership with us, to build on the platform, especially in a software-enabled fashion.

So that is part of the vision and I do hope that some of the other applications of the technology will be thought of and moved forward by others too. We want to set the stage for that.

In practical terms, we do have quite a few partners. Some of them are clinical research partners, and some of them are other types of partners. We are trying to balance how to prioritize the main thrust of what we have to do with building for the future.

Juan Benet

It seems to me that there is this huge bottleneck and overhang in the capability set, because as soon as you are able to have devices that can read and write to the brain at some reasonable level of bandwidth, that opens up a wide range of use cases and possibilities that just have not been done before.

So there seems to be a lot of not exactly low-hanging fruit, because it is very difficult to build these kinds of devices, make sure that they are safe, and make sure they work really well for patients and so on. But there is mid-hanging fruit, and a lot of it, that could open up over the next 5 to 15 years once you are able to cross this hard part.

Ben Rapoport

Yeah. Let me give two examples of how this is helpful.

One is MRI, magnetic resonance imaging. If you had told somebody in the early days of MRI, “You’re going to go into a superconducting magnet that costs many millions of dollars to build, and it needs to be maintained with liquid helium at four degrees, and you’re going to get a high-resolution picture of your brain because of it,” they would have said, “What are you talking about? That’s crazy.”

Maybe they had heard of nuclear magnetic resonance being used to study individual molecules for chemical structure. But if you told them, “I’m going to put my brain into that, and it’s going to give me an image of it,” they would have said, “You’re crazy,” because you can’t scale that up. How are you going to get the resolution? Too expensive. Crazy. Okay.

But over time, at scale, MRI is now an everyday reality that you can pay for. It is cost-effective. It has totally changed the way we diagnose diseases of the brain, and other parts of the body.

Juan Benet

Saved millions of lives if not tens of millions.

Ben Rapoport

Absolutely. And by the way, within probably a mile of us, there are probably a dozen MRI scanners. So it’s accessible to everybody. Insurance pays for it. It’s cost-effective. People know how to run them. It’s push-button, in a sense.

So at the beginning, in 1970, you would have said, “That’s insane. No way.” And who needs it, because there are other technologies that allow me to diagnose all of this stuff? So that’s one example. Maybe a little mundane, but nevertheless the technology behind MRI is nontrivial. It’s a lot of physics, electrical engineering, and understanding of biology and chemistry that goes into making that a real product.

Now, is it a consumer product? Not quite. Is it a medical standard of care that’s universally available, not even just in the developed world? A hundred percent. So it takes time. But that reality can come.

Another example that is a little more directly relevant is genomics, gene sequencing. This is something that, in your life and in mine, totally transformed medicine in ways that, at the beginning, in 2000 or 2001, would have seemed impossible. It cost many millions of dollars to sequence the first human genome. Many people were involved. It involved chemistry and computer science and biology, and a massive infrastructure and logistics to get all of this done.

That’s interesting because it’s a biological code. Once the code was read, the tools of computer science could be applied to do something in biology that was never possible before. And I think in neuroscience we’re seeing something similar. Not exactly analogous, but similar. The availability of data in a format that is portable, relatively uniform, and that packages neural data in a way that makes it amenable to the tools we’ve developed for other things, including image and video processing and modern machine learning in a highly parallel compute world.

Unlike the linear and fixed-in-time structure of the genetic code, the neural code is basically a two-plus-dimensional code. It unfolds on a planar surface at a timescale of a thousand hertz, on physical scales of tens of microns, hundreds of microns. But we now have a technology that interfaces on those spatial and temporal scales. We have the ability to store data on that scale. And the Precision technology, we’ve learned, is easily applied in many contexts to harvest data, save that data, and compute on it.

So I think we’re starting to see the edge of an exponential. It’s hard to say that 50 patients represents exponential growth. But if you think that, as of 2024, the number of patients who had ever been implanted with a brain-computer interface was about 75, so 50 patients is almost like a doubling of that, in one company in two years.

I don’t want to predict too much, but I think there will be a lot more patients in a year’s time. In one sense, that is a huge amount of data compared to the petabytes of data you might get in other fields, it is a thimbleful, but it definitely starts to add up over time. It shows you that there’s significant growth here.

Juan Benet

Let’s get into the neuro data and AI segment. As you collect data and use the device to map and understand what different signals correspond to, you’ve talked about being able to aggregate this data across a number of patients over time to build more robust models of what’s going on and then enable more patients.

We’ve already had some good results where, in a range of applications, it might take a lot of time for one person individually to train to use a particular BCI. But if you’re able to aggregate the information across many such users and build better ML models, you can cut that down significantly, to the point where you’re able to extract much better signal from brains in general, and supporting each additional patient is much easier.

Plus, you might also be able to decode other kinds of signals that previously were super difficult to decode, or that people did not think you could decode.

So where does that vision take you, and what are you thinking in terms of scale here? How many people might this take to start yielding really strong results, and what path are you thinking about taking over time?

Ben Rapoport

Absolutely. To both directions that you articulated, I would say yes.

Direction one is can we achieve accurate neural decoding more quickly in patient n+1 on the basis of patients 1 through n and what we have learned from them. The answer to that is absolutely yes. We are already seeing evidence of that.

By the way, that is incredibly powerful and not necessarily a given. It is easy to see in 20:20 hindsight, but it was not. In the early 2010s, when every patient was an n of one and a lot of time was spent calibrating and recalibrating these systems, for those who know the field closely, it was not a given that you would be able to build generalizable models that would be able to decode neural signals in a patient’s brain when they had never been seen before, sort of off-the-shelf models.

An analogy that makes it seem obvious is speech recognition, where Siri or Nuance PowerScribe, or whatever your chosen system is, works out of the box and works better the more you use it. So it is true of brain-computer interfaces, and we have proven that now.

To your question of how many it took, we got an inkling of it in the first handful of patients. But by patient 10, I think there was a sense that we were seeing some things that generalized, and we were able to show that to our own satisfaction. Of course, more is better, in a logarithmic fashion, like you would expect from image recognition and other areas.

So definitely, and that is really important from a product standpoint. It is not just true that you get things that generalize. It is also true that more data improves performance. Accuracy in particular, and in some cases speed, but definitely accuracy, benefits from having more data. That is not a surprise, but it is really important. Then, to your point of whether we learn that we can decode things that we did not think we could decode before, I will say yes to that, but I do not want to go into detail.

Juan Benet

Interesting.

Ben Rapoport

For the future, but it definitely is an extremely exciting area for us, and more on that will emerge over time.

I will say that genomics is a good example here. This is something that we’re really trying to do somewhat within Precision and definitely external to Precision with our partners. If you think about what we can do with the data and what we can do to move the field forward in a federated fashion, we cannot do everything ourselves. And we want to position the community for success because everybody is going to benefit from this.

This really means thinking about what it takes to map the electrical activity of the neural code across people’s brains, across time, and across function. This is not as discrete and finite a problem in neuroscience as it was in genomics, but there are many analogies.

In genomics, you kind of have the elements of your genetic code, your C, T, G, and A’s, and all the annotations there and then you have phenotype. Phenotype could be traits or diseases. We have all kinds of things. In a sense, the phenotypes are infinite, or certainly a large finite number. Those are your annotations, and you’re trying to map sequence elements to phenotype. But that sequence is linear and fixed in time.

In the neural code example, we think of it as a two-dimensional dynamic code. It is two-dimensional and unfolds in time. What are the annotations? From an ML standpoint, you want to make sure that the data is high quality, relatively uniform in some sense, and that you can compute on it using primitives and techniques that are relatively familiar. But it also has to be annotated. You really have to learn against something.

For most of this conversation, the implied annotations were related to movement or language, moving my hand in a particular way, making a particular gesture, moving my arm. We can talk about making a gesture, but how do we define that? Do we define movement in three-space? Do we define it in some other way? But you get the general idea that there are some elements of movement that are the dynamic, in-time annotations of the neural data.

But there are also other annotations that we can provide. Those may be disease-specific annotations, like whether somebody is having a seizure. There are certain ones that we care a lot about. Speech and language are some. Memory, vision, and sensation are others. And there are many more.

Juan Benet

You can probably find precursors to certain problems.

Ben Rapoport

Yeah, exactly right. So degenerative disease: is somebody going to develop Alzheimer’s disease? Is somebody going to have a memory problem? You can think of any number of things that you might want to annotate.

Those may be transient states, like movement is a transient state, or they may be less transient states, like a disease state. Some disease states are very permanent and are there all the time, and some of them, like an epileptic seizure, are also transient. But maybe there are features of abnormal activity that are present.

So part of what we are trying to do by mapping this space is to map the electrical activity patterns, label them on the anatomy of where the electrodes are receiving data from the brain, and on top of that layer in what these features are, speech, movement, and so on.

And to do that in a way that is curated, well structured, and accessible to people. I think we are in the early days of that. We have to build some of that pipeline for ourselves, and we are building a very limited version of that for our core products. But we are starting to build that in a more general-purpose fashion with some of our collaborators.

I think that is a tremendously exciting resource for the community. And I think it will be something, if we do it right, that really will be as impactful in this space as the genome was in cancer, infectious disease, the understanding of human origins, and many other things.

Juan Benet

Yeah. I would imagine also the data will be able to be aggregated across many different device types over time, and it’ll just kind of contribute to higher and higher quality models.

Ben Rapoport

So I hope that’s the case, and I have a little less insight into that. I know that you guys at Protocol Labs have been interested in trying to feel a way forward for the field to collaborate in a way that allows multiple platforms to interplay. And I think that’s a good aspiration.

I’ve spent a lot of time thinking about how we can do that for data coming from the Precision system. I’ve spent less time thinking about how it could be done for a general-purpose device. But I think there are ways. There are some open platforms now that people use for multiple device types.

Juan Benet

Yeah. And this might follow the path of other ML modalities, where certainly the data from the same device will be very useful for being able to use that device. But there will be a set of applications that become possible or useful once you start aggregating of these different modalities.

Ben Rapoport

I definitely see it from that perspective. If you have data on a problem from multiple sensor types or multiple device types, you should be able to learn about that specific domain from the different sensors. It does not matter what imagery you use if you are taking pictures of the relevant problem.

Being focused on my problem, it is not always clear that data gathered with somebody else’s sensors in my area of the brain. The truth we’ve learned from a lot of what other people have done.

Juan Benet

I’m sure it may be the sort of thing that only turns out to be very useful down the road. So first we have to go through this large collection process and start using it for the shorter-term applications, and then maybe later it becomes really useful.

Ben Rapoport

Yeah. There are quite a few areas, medical imaging and genomics and well beyond medicine, where federated approaches to sharing data, harmonizing data sets, and making sure that annotations are uniformly applied in a high-quality way have been a huge boost to the field. All of modern ML basically has groups like that to thank.

Juan Benet

Yeah. And this might also be a case where the Precision technology can be used to collect tons of data that may not directly lead to short-term applications, where the data that you have is maybe not useful enough or sufficient. But then down the road, it turns out that you’re able to decode various kinds of information that people just didn’t think were at all going to be possible today with this type of technology.

Ben Rapoport

I would not be surprised. We’ve already surprised ourselves in that sense. Sometimes you do something by accident that you didn’t realize.

Juan Benet

Yeah. It seems to me there are areas of technology where, once you start opening a bottleneck and you get the ability to start instrumenting and get a lot more insight into the problem, all kinds of things turn out to be way easier than people expected. Certainly still super hard, but certain things just become possible that people did not think at all were possible.

Ben Rapoport

That’s definitely true. And I think one of the things, in a general sense, in biology, in cancer biology, infectious disease, and other related areas, is that we did not necessarily know it at the time, but it turns out those areas were really data-impoverished.

The data were very difficult to compute on. And now we have discrete, universally agreed-upon data standards. They are digitized and amenable to compute.

Neuroscience is now coming into that domain where what’s happening at a fine-grained scale in the brain is accessible. It’s accessible in a way that is digitized and can be computed on.

Up until five years ago, if you wanted to get your hands on a dataset of what’s happening in the motor cortex or in the sensory cortex when somebody is doing something pretty sophisticated or thinking, there were only a few datasets like that in the world. And now we have almost 50 of them.

So I think you’re right, and that will scale. I think part of what we’re doing here is, because we’re at the beginning of it, it may be hard to recognize, but part of what we’re doing here is digitizing neural data in a way that has never been possible before. And in doing so, making it amenable to compute, and making some of the problems that seem like biological problems, or even impossible-to-articulate or impossible-to-crack problems, into problems that can be framed as a computer science problem.

That’s what happened in genomics. That’s what’s happening now. And some things that are very hard in the biological domain, like developing a vaccine, in the computer science domain are tractable.

So I think that’s what we’re going to start seeing now. But we have to be deliberate about how we handle the data, curate the data, and make them available. I think you’re right that some of the things we do now, if we’re careful about how we do them, will open doors.

Juan Benet

Yeah, it’s super exciting. The next 5 to 10 years of development and application finding may turn into being able to interface with computers, AI, the internet, and each other better.

It seems like the Precision technology will be able to do more than just restore basic mobility and the ability to navigate basic computers and digital life. It is also arriving at a time when the nature of interfaces is going to radically change as AI becomes much more capable.

So this opens the door for a whole range of other applications and uses of BCIs, where it becomes less about, or in addition to, all of the repair use cases, and more about being able to use a different range of interfaces to interact with AI, or with each other, and so on.

There are a whole bunch of possible use cases that have maybe been explored in sci-fi and in various vision-paper-type settings. It really feels like we’re on the cusp of that range of use cases becoming possible.

How much do you think about this in the long term, and how do you think this might play out over the next 10 to 15 years?

Ben Rapoport

Yeah. I find that a little hard to predict, and maybe I’ll be criticized for not being imaginative enough. But the way I see what has happened in the past being relevant to what is happening now is this: if you think about the way electronics have interfaced with the human body over the last several decades, things that were avant-garde a generation ago are now just part of everyday life.

Examples of that are implantable cardiac devices like pacemakers and defibrillators. A generation and a half ago, that was science fiction. Then it became an ordeal, but something that was possible. And now you may have a relative, or maybe several relatives, who have such devices, and it is basically an office procedure. They have a computer that controls their heart, and nobody even knows about it. They forget to tell the doctor that they have one. And by the way, their cardiac data is being uploaded and downloaded by their cardiologist, and maybe even by them on a smartphone.

So that became pervasive over time, and more highly functional, and almost kind of a lifestyle change for many people. It is basically a lifestyle choice at this point. It used to be a major surgery.

Cochlear implants are another example, maybe one that is a little closer to home for brain-computer interfaces. They were initially for people with severe deafness or congenital deafness, really a very medical type of application. They improved dramatically, and a big inflection point that was very influential in my own life was seeing the deaf community accept that parents of a deaf child could make a choice to have a cochlear implant implanted in an infant just a few months old.

To me, that signified that the technology was so robust, reliable, and effective that even a community that knows it has its own culture and language and grammar and literature could nevertheless decide that parents could reasonably choose to have an elective major surgery in an infant. And now, by the way, cochlear implants are used as effectively as a hearing aid. You have people who decide to have a surgery to have a cochlear implant implanted to essentially augment their hearing.

That process, going from cochlear implants coming into the world in the 1980s to becoming acceptable for people with a condition in the early 2000s, was 20 years later. Now we are another 20 years later, and you have people of our parents’ generation, or even our own generation, deciding to have a surgical implantation of a hearing aid device.

So that is the path this technology has taken, from explicitly and only medical technology, maybe an avant-garde medical technology, to kind of lifestyle modification. And there are other examples of that. Glucose monitors are an example. I could point to quite a few.

For all of these, in a way, this is the body interacting with the digital world. They are not quite what we are talking about in terms of interacting with modern artificial intelligence and the internet and so on. But they are advanced implantable electronic technologies interfacing with the world and shifting from something that is a medical necessity to something that is essentially a lifestyle choice.

Could I see something analogous happening in brain-computer interfaces? One hundred percent. We are at the very beginning of this process, where no matter what we do, opening the door to that possibility requires showing safety and efficacy in a medical context. So the next five years are devoted to that. We need to do that safely, effectively, and responsibly in order to open the door to possibilities for what may come next.

So it is fun to have these conversations about what may be here in 5, 10, 15, or 20 years. But I think what we need to do today to make that possible is clear.

Juan Benet

Yeah.

Ben Rapoport

Right. So the speculation is important. It’s fun, it’s interesting, and I think some of it is what we probably want to be doing now, because there is ethics, data curation, and some fundamentals that I think we probably want to put in place. No matter how the field evolves, I think we will be glad we did it right at the beginning.

But yeah, I could envision that. And by the way, people’s concept of what’s acceptable also changes. I’ve said this many times, but I have two kids, and they know a little bit about what I do. To them, it’s normal. So the next generation, the people who are five years old today and who are going to be the young engineers, physicians, and computer scientists coming of age and deciding what to do, when there is already a platform for development in this space in 5, 10, or 15 years, their concept of what’s possible, what’s normal, what’s acceptable, and what’s taboo is going to be very different from ours.

Juan Benet

Yeah, absolutely. But it sounds like you still imagine that taking many decades to develop, whereas a range of other folks in the field, and I personally, think it might happen a lot faster because the different piece now is that we have a much more online world, where people are interfacing with these systems much more actively, like the diffusion of something like ChatGPT, going from zero to “hey, this is possible now,” to percolating through society, happened in a few months to a year and a half or so, depending on how you look at it. But now it is widely used.

Ben Rapoport

Totally. This is an interesting conversation to have, because the diffusion of different technologies is limited by different things. Something that is purely an internet-based software application can diffuse globally in weeks or faster.

Juan Benet

Yeah. And obviously, the whole process of making sure that a device is safe to implant, which takes a long study process and so on, makes sense. What I’m getting at more is the level of shift that humans might go through over the next 10 to 20 years as AI becomes more capable and starts seeping into our societies and economies. There are a range of people now who are starting to have very significant and meaningful relationships with AI companion or counterpart. This is becoming a phenomenon now. So we’re on some shift curve that might end up being faster. I’m just kind of pushing on that.

Ben Rapoport

Yeah. I think that’s definitely true. There is no question that there are predominantly software-based, and wearable- and portable-device-based applications of artificial intelligence that we are all experiencing. You and I both experience the impact of changing AI tools many times a day.

So in the wearables and software world, that is a definite reality. I think the question is how implantable brain-computer interfaces play into that. I think there is maybe a slightly different timescale involved.

But certainly, once there is a cohort of people with implanted technologies, the ability to improve what is possible using the technology is an economy of scale. This hearkens back to something we were talking about earlier in the conversation, which is that the more people we are learning from how to decode, the more everybody benefits from our ability to decode effectively.

So there are a number of these kinds of economies of scale that I think we will see. And certainly, when you have a hardware platform that people are using, whether it is wearable, implantable, or something else, then the software that lives on that, or lives partly on that, whether that is AI-based software or anything else, will impact their lives at a rapid scale, at a scale that we have not seen before in other areas of medicine. It will be more familiar to pushing out software updates.

Juan Benet

That’s a great insight. There’s this hard bottleneck now of establishing the first set of interfaces that can actually be worn at length, are safe, and work really well, and where the entire product experience of having this device implanted, used, and removed, all of that lifecycle, is really figured out.

And once you’re able to do that, then a lot of this turns into primarily a software problem or at least a range of applications become possible is just purely software problem.

Ben Rapoport

I think that’s accurate. Exactly what that looks like and how much time it takes is not totally clear. But I think we can learn some things from other implantable medical devices. The nature of what a brain-computer interface does is obviously different from what a cardiac device does. It’s different from what a cochlear implant does. It’s different from what a glucose monitoring device does. But the interaction between software and an implanted medical device can give you some insight into that. Once you’re on the platform, the ability to adapt, program, and change is dramatically different.

Juan Benet

What first got you into neuroscience? What attracted you about the field?

Ben Rapoport

Well, I’m kind of a child of the field. My dad is a neurologist who also has both a PhD and an MD, and he’s an expert in clinical electrophysiology. His career kind of came of age with the beginning of clinical electrophysiology, at the very beginning of when electrodiagnostic tools started to be used in patients. Of course, the link between electrical engineering and neuroscience goes way back, almost to some of the pictures that are on our walls here. It really goes back to the beginning of the 20th century. So the electrophysiologic nature of the brain and nervous system has always been a part of neuroscience. But until the 1960s and 1970s, it was more a part of research neuroscience than clinical neuroscience.

My father started his career as an electrical engineer and became a neurologist, and his specialty became, and still is today, clinical electrophysiology, at the beginning of that era in the 1970s. So I grew up with that as a backdrop to my life. And what was emerging when I was in my late teens and early twenties was next-generation electrophysiology, which today brain-computer interfaces are a part of.

Juan Benet

When you think about the field of neuroscience, or neuroscience and neurotechnology together, what are some of the grand visions that the field has been trying to chase? Which of those have come true and been realized, and which ones are still forthcoming?

Ben Rapoport

Well, I think broadly speaking, the interest of neuroscience has been to understand the human brain. That is kind of a grand vision, and the specifics of what that means can be boiled down to different targeted questions. But in a broad sense, that is the grand vision.

In clinical neuroscience and medicine, the question is often: how can you understand what is going on in the brain in a way that heals a brain or a nervous system when something is not quite right? That means you have to understand the brain in the normal state, and you have to understand the disease process or the disorder well enough to address it and treat it in some way.

My focus growing up has always been that part, really understanding the brain in and of itself is, of course, super interesting. It is endlessly interesting. I do not think it will ever stop being interesting. To me, it is the most interesting thing in the world. That has driven my entire life and passion.

But more specifically, trying to understand when something is not right in the brain or nervous system, how do we fix it? That has been my lens.

And so the grand vision changes a little bit in a generational context, as sometimes problems become more important to a generation for one reason or another, for historical reasons.

In the early 2000s, various forms of paralysis became very important in the national and international conscience, partly because people were surviving injuries that were not survivable in other generations. And partly it was a technology push. So sometimes it is a clinical pull and a technology push.

There are various forms of paralysis that exist today that stem from stroke or spinal cord injury, or diseases like amyotrophic lateral sclerosis, so-called Lou Gehrig’s disease, which is a neurodegenerative disease. So paralysis ends up being an incredibly interesting lens through which to try to understand the brain.

A lot of the way we interact with the world is through movement, even in ways that we do not totally understand, because speech is movement too. That became a road into the modern world of brain-computer interfaces. So one of the grand challenges is: how do we treat paralysis?

Juan Benet

And how did you first become interested in building technology to treat these conditions? Were you encountering a lot of these cases as a neurosurgeon and then thinking about the problem space and realizing, “Hey, actually we might be able to start building some devices that can help patients here,”

Ben Rapoport

For me, it actually started in reverse, and I think this is true of a lot of people in the field. In the 1980s and 1990s, it was both a pure science push and a technology push, and some of what was being discovered then opened up possibilities for treating disease.

That is true of the beginning of the field of brain-computer interfaces. In the 1980s, it really was not understood how movement gets computed in the brain, how it gets planned, how it gets executed, and how the brain coordinates fine movement of the hands, the limbs, and so on.

One of the major discoveries in the field at that time was that fine motor control is coordinated by populations of neurons, not by one neuron at a time, and not by one bulk aggregate of brain tissue, but by populations of neurons firing in coordinated patterns to basically tell the muscles what to do in a time-synchronized fashion.

That set of discoveries was made in the 1980s, and those mechanisms were elucidated at that time, partly with the help of being able to bring multiple tiny electrodes into the brain, simultaneously record from them, and begin to parse out what the time-varying signals from multiple tiny electrodes meant.

So that population encoding became the basis for a whole movement into brain-computer interfaces. Really, it was basic science at that time, the basic science of how the brain coordinates movement. And early on in that process, people started to envision that if we can understand this, maybe this is a way to treat paralysis.

Juan Benet

In that case, these sets of neurons that are firing together, are they firing in parallel through different downstream sets of connections? Or are they ending up being joined by other neurons?

Ben Rapoport

One of the nice things about movement is that it is kind of an isolated system in the brain. Much of what happens in the brain is feedback among multiple circuits that interact with one another in ways that lead you to ask what is the chicken and what is the egg. It is very hard to untie these feedback loops and really understand causality.

Of course, there are some nuances to all of this, but in the coordination of movement in the brain. People who are knowledgeable in this area are going to listen and criticize, but let’s simplify. There is an area called the primary motor cortex, or M1, and that is located in one of the hills of the brain, right around here. Neurons in that area of the brain basically project long axons that go down the spinal cord and make one synapse to another neuron that actually connects to a muscle.

Juan Benet

Then to a single muscle fiber?

Ben Rapoport

Basically, yes. If we’re getting really technical, they’re called layer five pyramidal neurons. They’re large neurons. They’re called pyramidal because they have a pyramidal shape, and they project long axons, a meter or so long, that go all the way from the surface of the brain down through the brainstem and spinal cord. In the spinal cord, they connect with another neuron, an interneuron, and that interneuron then connects directly to a muscle endplate. So that one-two connection is what gives rise to movement.

Of course, one neuron firing is not enough to yield movement of a muscle. It takes the coordinated activity of many neurons firing together, synchronized in time and space, to generate a muscle endplate potential that contracts a muscle. But basically, that’s how it works.

On the sensory side, the body understands when a muscle has moved, and on the planning side, it understands how to move muscles in time to yield the desired movement. All of that is pretty nuanced. But the basic aspects of how movement happens work like that.

Then you get into asking, how do you actually move in a coordinated fashion to move a hand or an arm in a particular direction? Do you do it in order to achieve a result, or do you do it in a coordinate frame? Are you really moving in terms of kinematics, or are you moving in terms of concept? There are all kinds of questions that you can ask.

But the nice thing about movement is that a lot of the action happens in this one clearly labeled part of the brain that we can access and study. We learned in the 1980s that it was groups of neurons that gave rise to these movements, and that by electrically listening in on what they were doing, you could get a sense of what movement was being planned and executed.

The motor cortex, because of what I just described, kind of does it more or less in real time. The planning happens hundreds of milliseconds before, and the sensation and feedback happen 10 to hundreds of milliseconds after. But the real-time aspect of what the brain is doing to move is happening in the primary motor cortex. So you can listen to groups of neurons to understand what the brain is actually executing in real time.

That was a breakthrough in science. The corresponding breakthrough in engineering, more or less around the same time, was that whereas up until then neuroscientists had been listening to neurons, groups of neurons, muscles, anything electrical in nature in the nervous system, one electrode at a time, one amplifier at a time, there emerged the possibility to listen with many electrodes at a time.

There were many technologies used to do this, and part of it was that amplifiers became more readily available. Neuroscientists have always kind of been physicists and electrical engineers all rolled into one. Whatever was the data science of the era was also being used. Whatever was the computer science of the era was also being used.

When my father was starting out, everything was a physical trace of a pen on a rotating wheel paper script. Digital storage did not become commonplace until the 1980s and 1990s. So the ability to really apply what we think of as sophisticated compute did not come around until the 2010s.

Juan Benet

Wow.

Ben Rapoport

Fast-forwarding to where we are today, if you’re thinking about how brain-computer interfaces got to where we are today, it’s really because of the availability of multi-electrode arrays, of which there are different technologies that we can talk about, and the ability to store and compute in real time on all of that data, which didn’t come around until the GPU was basically a desktop reality and easily programmed.

Of course, it began with some understanding of the physiology. That was the first piece to emerge. The enabling technologies came next. And then, as you mentioned at the beginning of all of this, understanding that there were tools here that could be used not just to study the brain, but to understand how to try to heal the brain or the nervous system when there has been an injury or an insult of some kind.

That in itself is not just one enabling technology. That’s a team effort of many scientists and technologists. And even beyond pure science and technology, there’s policy and other realities in the world to actually try to get something from feasible to real and part of the standard of care.

And for me, going back to your grand challenge question, the goal is to have this technology available as part of the standard of care in a way that really touches people’s lives and helps to heal. And I think that is a palpable potential reality now. I think we’re very close to it.

We’re engaged now in clinical studies and with real patients who are either being helped or helping to help others who will soon come after them. And to me, that’s an incredibly exciting reality of today.

Juan Benet

Fantastic. Through studying the brain and learning about all of the advances in technology that could enable a range of things, it sounds like you then had the perspective and insight, and surely many other people in the neurotech field also thought this way, that you could finally start producing devices that could really help people heal. So from there to actually building one of these devices, inventing it, building a company, and so on, there were lots of steps. You co-founded Neuralink. How did you then go from there to Precision?

Ben Rapoport

Always thinking back through it, you try to make sense of it in a linear fashion.

But as I mentioned a few minutes ago, if you think back to what was happening in the mid-2010s, the first applications of machine learning were really starting to hit the public consciousness. One of the first, arguably, was Google Translate. People do not talk about that so much now. They talk about AlexNet, images, and computer vision. But one of the first overnight changes that happened was Google Translate.

Google Translate used to be terrible, almost a joke. And then almost overnight it was completely revamped to the point where you actually had high-quality translation across multiple languages. Very quickly, that moved from text-based translation to almost real time. There were apps where you could translate spoken words. This happened in the mid-2010s.

The reason I use that as an example is because, basically, in brain-computer interfaces, we are really solving a translation problem. One of the most important applications of machine learning in brain-computer interfaces is solving a translation problem between the electrical, electrophysiologic data streams that percolate in your brain and mine and everybody else’s, which are a similar grammar, but with different words and accents and so on. Nobody’s neural code is exactly the same.

So if you want to have a device that translates that neural code and makes it able to control other digital systems, you need to translate the individual neural code of somebody’s brain into the control output that you want to use.

Juan Benet

That whole generation of machine learning, including the transformer is critical in enabling this tech.

Ben Rapoport

Transformers didn’t even exist back then. At least not as, we think of it now.

Juan Benet

Certainly not the scale of transformation that we have now.

Ben Rapoport

But what was happening was that the last piece of the technological puzzle to fall into place was high-quality compute with an understanding of basic machine learning models that were obviously applicable to the translation problem that is neural decoding.

And with that, it kind of dawned on a number of people simultaneously, including Elon, that there was an opportunity to take what was a solution in the laboratory and make it into a technology that could actually do good in the world, and also perhaps have something to do with the way humans interacted with artificial intelligence in the future.

I think once people started to see a clear exponential growth in the capabilities of artificial intelligence and the speed of development of artificial intelligence, a number of questions emerged. One of them was, how is the human brain going to interact with artificial intelligence?

And that is still, as you know, a question that a lot of people ask with nuance and thought. What does that look like today, tomorrow, in five years, in ten years? And how does the technology we use to interface with artificial intelligence, the human brain interfacing with artificial intelligence, change over time? Is it fundamentally different from the way we currently use human-computer interfaces? Is it conceptually different? And I think there is this intuition that it is going to be very different.

So a number of groups at that time, back in 2016, Elon, Mark Zuckerberg, Brian Johnson at Kernel, and a bunch of others, decided to invest in trying to bring those emerging technologies out of the laboratory and into the real world, and to put significant investment dollars into doing that.

And I think there was also a consensus in the scientific community, which was small at that time, that the technology was as ready as it could be in an academic sense. A lot of the problems had “been solved”. And I say this with a laugh because it is never really quite the case.

Juan Benet

I describe it as R&D pipeline myopia, where no matter what part of the pipeline you’re working on, whether it’s the basic science, the invention, productizing, or even selling it into the market, the people working in that area encounter such large challenges that it feels like that’s the hard part. And that everything else is implementation detail.

But all of these parts are actually really hard. Doing the basic science is super hard. Doing the core invention is super hard. Doing the engineering is super hard. Building really high-quality products is super hard. Figuring out the strategy to deploy into the world is super hard. It’s all hard. One of the core things that I think ends up causing these super important technologies to be built and scaled is people who are able to span multiple segments in the pipeline and do the actual translation work.

Where this kind of breaks down is when you have such a division of labor that people do not span those areas well enough, and then things get stuck. There’s a lot of important science that is stuck in paper form, where the “hard problems of science” have been figured out, but there is no invention or downstream technology being built.

So by having that span over the R&D pipeline, you’re able to pull things forward, and you have the depth of knowledge of both the science and the technology and engineering to actually figure out what the core problems are to solve. So in that time, in that era, there were a range of people figuring out whether we could take this from the core science, and from the stage where things were, to now starting to build initial versions of products.

Ben Rapoport

“All the hard problems have been solved,” like you said, and all that was needed was capital to take it to the next step. That was not so far from the truth. A lot of really important problems had been solved.

Around that time, quite a few people who had been engaged in the science of brain-computer interfaces, which was a very small community then, much bigger now because of everything that has happened over the last ten years, pretty much everybody knew everybody, if not by one degree of separation then by two.

So if you were one of those people trying to get something started and bring it out of academia into the real world, it was not so hard to locate people who could help get things going.

Those of us who were a little younger at the time, and not established professors or established professionals, and at that time I was also single, the people who were most mobile, highest energy, and newly minted PhDs or professionals or engineers were the likeliest people to help get something going. That sort of describes me at that time in my life.

Of course, I was incredibly passionate about it. I definitely believed, as I do now, that there was something incredible to be done in brain-computer interfaces. And maybe I had, like you said, a slightly different skill set to bring to bear.

It obviously takes a huge team with a variety of different areas of expertise. There is the core engineering and the neuroscience, then there is a clinical understanding of how the systems work, and there is how the experimental neuroscience works, how you design studies around it, and how you scale things up.

There are so many aspects to how a team needs to be built to bring something that is a proof of concept into a scaled reality.

So yeah, that’s how it started. I was headhunted, and I was lucky to be headhunted and to join an incredibly talented team that grew very quickly.

I think it also became clear that the space was so huge, and the potential to do really high-impact things in the area of brain-computer interfaces was more than any one group could do with any one enabling technology.

So pretty early on, I also had a sense that to really scale the technology, to access multiple areas of the brain, to study those areas, and to deliver the technology to patients who already had some compromise to the brain, which itself is pretty delicate, the trade-offs you wanted to be able to offer had to emphasize, in my view, at least for some portion of the market, a technology that didn’t penetrate the brain but was also incredibly high in spatial and temporal resolution.

And so that’s how Precision was born. I left Neuralink in 2018, and basically in 2020, Michael Mager and I, who had known each other quite well before, got back together and started Precision with that vision of really bringing neural interface technology to the medical community to treat neurologic diseases that currently don’t have a treatment, with an emphasis at the beginning on paralysis, because that’s the clearest, most well-defined need, but with a view toward doing much more.

Juan Benet

What’s the long term vision of precision? What is the range of things that you wanna be able to do in the long term?

Ben Rapoport

I would say I want to be very concrete. We want to have, in the next five years and beyond, an effective technology for, I cannot exactly say treating paralysis, but something to offer people with various forms of paralysis to restore lost function in a really meaningful way that brings people who are not currently able to engage in the world in the ways that you and I take for granted back into that world.

For some people, that is about communicating with other people. For some people, it is about financial independence. For some people, it is about different things. But what we know we can do is deliver seamless integration with the digital world that, for many of us, is a major part of the reality that we take for granted. And for people with forms of paralysis, it is not.

So that is our goal over the next five-plus years. Very severe forms of paralysis, like spinal cord injury, will be the first that we address. But there are many forms of paralysis, and that includes paralysis caused by stroke, which is not always whole-body paralysis. It can be semibody paralysis, or it can even affect parts of the body. And there are other diseases of the nervous system that cause paralysis.

We have developed, over the last few years, a much more nuanced understanding of how forms of paralysis affect people in their lives, families, and communities, and the people around an individual who suffers from a paralyzing condition. So that is our five-plus-year goal.

At the same time, we have discovered that the Precision technology represents a platform for understanding and studying the brain, and for interfacing with not just the motor areas of the brain that we started this conversation on, but really, essentially, the entire brain.

We have now implanted the Precision technology in more than 50 people, and the electrode technology has touched parts of the brain that almost no other single-electrode technology has touched, everything from motor and sensory areas to prefrontal cortex for decision-making, to inside the valleys, inside the sulci of the cortex, which is the thinking part of the brain, the brainstem, the spinal cord, and areas of the brainstem that are very difficult to address.

So what we have discovered is that we have basically built a platform for discovery, diagnosis, and intervention. I think it is definitely the case that we cannot even envision today. We have a pipeline of use cases that we are working on, but I think that the possibilities are very significant. I won’t say endless, but like very significant. And we want to enable others to build on that platform.

Juan Benet

And maybe as you look ahead into the future, what is your optimistic vision of the future? The stories that tend to be told most often have high drama, which is why popular fiction ends up with highly dystopian future worlds, because that sells much better. So it is kind of up to us, and a lot of other people, to paint really positive visions of the future so we can aim for them.

What are the kinds of things you think about when you think 30, 50, or 100 years out? What is possible that you are working toward, that you see other people working toward, or that you want to inspire people out there to work toward?

Ben Rapoport

Let’s try to project a generation out, even though that is hard for me. If I think about what I am able to work on relative to what my father was able to work on, and what I think my kids will see, I think we are going to change the nature of what it means to have certain kinds of disability in society and in the world. I think that is going to be incredibly powerful.

Exactly how we change that, and exactly what we make possible, is hard to say. But just as cochlear implants have profoundly changed what it means to be hearing impaired, I think brain-computer interfaces are going to have a profound impact on what it means to be physically disabled in society. That is a massive change.

I think we are going to see that in the next decade. Are we going to cure all forms of paralysis? No, not in 10 years. But are we going to change that significantly? Yes, I think in the next 10 years.

Also, in 10 to 20 years, we are going to have brought forth a very powerful technology for understanding how the brain works. I think some problems that seem hard now are not going to be problems people care about anymore in 10 or 20 years, because they will be taught in grade school. That has been the case with genomics, and I think it is going to be the case with certain problems in neuroscience.

Some of the things that we think of as profound, like questions of consciousness and questions of neural computation, are just going to be things people understand routinely. Even my kids know how to query some of the AI software now.

So I think it is going to be an amazing step forward that some of this technology unlocks. I think there is going to be a big impact on disease. There is going to be an impact on understanding medicine and neuroscience. And whatever application layer is built on top of that, I think part of that is for us to enable, and part of that is for another generation of scientists and engineers to contribute to.

But I have a very optimistic view of the future, and I really do not worry too much about dystopian future states of neurotechnology. There are a lot of thoughtful people trying to build this in an appropriate manner, and I think we are going to see tremendous strides forward in a number of areas.

Juan Benet

Thank you very much. This was a phenomenal conversation. Thank you for spending the time and good luck with all of the work you’re doing.

Ben Rapoport

Thanks for being a partner in this and thank you for having me on the show.

Juan Benet

I hope you enjoyed this episode. This is a new podcast, so we need your help to get the word out. Like rate and subscribe on your favorite platform and please share it with people you think would find it interesting. Thank you and see you next time.

]]><![CDATA[Max Hodak — Restoring Sight, Growing Neurons on Silicon, and Expanding Human Intelligence]]>https://www.juanbenetpodcast.com/p/max-hodak-restoring-sight-growinghttps://www.juanbenetpodcast.com/p/max-hodak-restoring-sight-growingWed, 08 Apr 2026 16:37:34 GMTMax Hodak is the founder and CEO of Science Corp (previously co-founded Neuralink and Transcriptic). Science is building PRIMA, a retinal prosthetic that’s restoring meaningful vision for patients with blindness caused by age-related macular degeneration. The team is also developing a biohybrid brain implant that grows living neurons directly onto a silicon chip, then interfaces that system with the cortex.

In this conversation, we go deep on how both technologies work, how PRIMA restores vision, how the biohybrid BCI connects to the brain, what the next milestones are for neural interfaces, and what it would imply to add a new functional brain area to a human.

We also dig deep into how Max built and leads Science: his founder story, how the team drives Fast R&D, and how the team is able to speed through high-uncertainty, high-impact projects.

Hope you enjoy!

Watch on YouTube.

Timestamps

00:52 What counts as neurotech?

01:45 History of brain-computer interfaces and the iPhone dividend

07:25 PRIMA - How Science is restoring vision in blind patients

10:10 Why stimulating bipolar cells works when the optic nerve doesn’t

30:30 Are we bottlenecked by biology or engineering?

32:40 Expanding the brain’s bandwidth beyond 10 bits per second

37:00 Can we add new areas to the brain?

37:46 Biohybrid BCIs: neurons growing on a chip

39:20 What could neural augmentation look like?

01:13:20 How Science drives Fast R&D

01:44:00 How founders learn and level up

Referenced Links

Links

Juan Benet on X

PL Neuro: plneuro.xyz

Protocol Labs: protocol.ai

Transcript

Juan Benet

Today I’m interviewing Max Hodak, an extraordinary founder, scientist, engineer and investor at the frontier of neurotech. Max is the founder and CEO of Science Corp, where he and his team are developing breakthrough neural technologies, devices and platforms. Today, Science is focused on the PRIMA visual prosthesis, which literally restores sight in people with some forms of blindness, and they are also pioneering a biohybrid brain-computer interface, which I’m personally most excited about.

On top of that, Science Corp is building software and hardware platforms to drop the cost of neurotech R&D, enabling other companies and a new generation of startups. Before Science Corp, Max co-founded Neuralink and Transcriptic, now Strateos. His work cuts across neuroscience, engineering and philosophy, all united by the questions about how to expand human capability.

This interview is part of a series of neurotech, where we explore and discuss near and future breakthroughs, why they matter, and how they will benefit humanity. Let’s dive in. What exactly is neurotech?

Max Hodak

I mean, in some sense, neurotech is this incredibly broad idea because everything about our behavior is rooted in the brain. And so, I mean, there’s some interpretation where social media and the iPhone is kind of neurotech, but I think when we when we talk about it specifically in the context of brain computer interfaces, you’re talking about mostly implantable devices, some wearables, but instead of it’s — if you’ve lost the ability to use your hands or use your eyes or use your ears, maybe we can we restore those sensory or motor capabilities, can we bypass a broken, broken nerve or a lost function?

And there’s some groups that are starting to look ahead and we can understand really, how does the brain do these things and how does the brain form percepts and how does it reason and how does it remember. Then you can go from restoring to potentially extending.

Juan Benet

Can you maybe start with a quick history of the neuratech field, recently, like in the last few decades? From your perspective.

Max Hodak

A lot of what I consider brain-computer interface started in the ‘70s, the ‘60s and ‘70s. It was pretty quickly figured out that if you put an electrode in the brain of a monkey, if you got it to move a joystick, you could figure out how in the brain that was functioning. I mean, people have been doing this in humans really since the ‘90s.

So in the mid-’90s, the Utah Array was invented, which is a very classic neural probe. It’s like these silicon-like needles that can be placed into the brain to record neural activity. And in the late-’90s, early 2000, people were decoding cursor control and basic keyboard control in humans. Now that system is not really ready for prime time then, that probe in particular gets an immune reaction and it’s a relatively small number of electrodes. It doesn’t really match the material properties of the brain in some of the ways that you want. But kind of more to the point, it had a connector that came out through the scalp. So there was a pedestal that was anchored to the skull and then a connector coming out through the skin.

And the skin is a very important immune barrier. You really want the skin to be closed because if it’s not, there’s a risk that a bacteria could crawl down your connector and potentially into the brain, and then you’re gonna have a really bad time. And so a lot of the advancement over the last 10 years has just been miniaturizing the electronics and making them low power enough that they can be fully implanted, and the skin can be closed.

I think the fact that this is possible now is something that’s been built on the back of what we call the smartphone dividend. The fact that Apple and Samsung and others have poured tens of billions of dollars into building the modern electronics required to do this, and then we get to take advantage of. But the neuroscience is really not that different from what’s been understood. The basic neuroscience of motor decoding has been understood for decades. It’s the advances in the electronics and the materials that have led to the modern wave of new BCI.

BCI originally, I’d say, came out of neuroscience. And the field of neuroscience is concerned with things like what our brains, and how do they work. Like there’s a bunch of circuits in the brain, some that deal with affect or attention or motivation or memory or reasoning or motor planning or whatever. And if you want to understand what’s going on in there, then at some point you need to be able to see into the brain. You need to be able to record and detect all this activity, and the endeavor of neuroscience has been placing wires into the brain to record neural activity, basically since the discovery of neurons. And so BCI originally came out of neuroscience, and the tools that were being used in BCI were essentially neuroscience tools.

And I think only now we’re starting to see some new types of BCI probe approaches that could be used to understand things about the brain, but really begin to diverge a little bit. So, for example, the biohybrid work that we’re doing is, I think, a great example of this. With the biohybrid probes, we don’t place like a wire or an imaging system or a fiber into the brain. What we do is we load a device with living cells, with biological neurons, and then we make it so the cells don’t go anywhere. They’re stuck in the device, and then we can graft this into the brain. So the living cells project new biological connections. They form new chemical synapses with the host brain.

But they do this in a perfectly biocompatible, nondestructive way because the brain is a bunch of neurons. And so if you add some more neurons, they grow in and wire up. And this can be an incredibly powerful BCI technology, but it doesn’t necessarily help you understand exactly what those cells deep in the brain are doing, because you don’t necessarily recover the original representations.

You can in some ways, but you don’t know, like you’re kind of getting a bunch of information mixed together back at your device, or the way that you stimulate it to activate the brain is a little bit different than you would with, say, like a wire. And so I think we’re going to see a little bit of a divergence between neural engineering and neuroscience, even though they’re obviously highly synergistic.

Juan Benet

In some of the early neuroscience and neuro engineering successes, maybe led to things like — would you say a cochlear implant is like an early BCI?

Max Hodak

Totally. Like, absolutely. We include cochlear implants and retinal implants as brain-computer interfaces. The cochlear nerve is part of the brain. And I think a thing that isn’t widely appreciated is there’s already like a million, over a million people that are walking around with brain implants out there.

Yeah. here’s a lot of cochlear implants, and there’s also a lot of deep brain stimulators. So one of the biggest effects in medicine is a Parkinson’s patient who you turn on their deep brain stimulator for the first time, and they go from unable to hold a pot-like a thing of water to just steady.

Juan Benet

Wow.

Max Hodak

And it’s not — the disease will still degenerate. It’s not curative, but it is a huge quality of life improvement. It’s a huge effect size for a meaningful period of time for many patients. And yeah, I mean, we joke that it’s like the kind of the best patient testimonials or may your product demos be like a newborn having their cochlear implant turned on or DBS stimulator turned on for the first time.

These are effect sizes that you just don’t see in medicine, by and large. Like you do in a couple of cases. But this is one of the reasons I really like working with the brain — because when you have something that really works, the effect size can be huge. We’re not talking about extending a poor quality of life by two months in a way that is statistically significant, but not really meaningful to the patient.

Juan Benet

Yeah, there’s an amazing video. I’ll link it in the description of this where a woman has her cochlear implant enabled for, I think, for the first time, and she suddenly starts hearing people for the first time. And it’s just this amazing experience of expanding her sensory perception.

And it’s just like this wonderful testament to the good that all of these technologies can do. So with that, let’s get to the PRIMA device. So first, what is the problem that you’re trying to solve?

Max Hodak

There have been cochlear implants for decades. And even though things like those are amazing and are widespread, there’s still limits. By the way, I think better cochlear implants will be coming out — like they’re good enough to hear voices. They’re not necessarily good enough to take to a concert. And so there’s still ways to really expand applicability. But when you look at the retina, there’s nothing like that. There is no cochlear implant of the retina. And that is a huge unmet medical need. So sensorineural hearing loss is more common than some of these forms of blindness because the immune system learned a long time ago not to overreact in the eye because that is like really bad for the organism, but similar to Alzheimer’s, age-related macular degeneration is one of the things that many, many people are most worried about in getting older. And for these, there’s what we call outer retinal diseases, where the rods and cones, the the photoreceptors, the light sensitive cells in the back of the eye have died, but the optic nerve is still intact and the brain knows how to see because they they saw most of their life.

For these photoreceptor degeneration diseases, there’s millions of patients and there’s nothing available. So as a company, we’ve looked at a range of technologies for restoring vision in the retina. And we have a state of the art optogenetic gene therapy. And then we also have an electrical stimulator. The gene therapy is a little bit further away that needs to start clinical trials. And will take some time to really develop, but might be capable of really powerful things. And today we have a product called PRIMA, which I mean not to undersell it — the results are amazing. They finished a major clinical trial last summer. It’s the first time, to our knowledge, that some of these patients — the trial was in age-related macular degeneration — that some of these patients have been able to read again.

And I saw a video recently of a patient recognizing faces, which was a thing that some of the investigators weren’t even sure would work with this generation of device. We have new versions of the PRIMA implant already in development. We actually just got some of the first devices of the next generation back a month ago, and the next step there is to shrink the electrode size so that you can target fewer cells at a time.

But for the current version of the device, I mean, there’s clips online of patients who cannot see, definitely cannot read natively, filling in crossword puzzles. And that had never really been done before.

Juan Benet

What does a person with macular degeneration see? What is their experience? What is the form of blindness do, and then how does the device fix that?

Max Hodak

There’s three layers of cells in the retina that kind of matter. There’s the rods and cones that are light sensitive. There’s 150 million of those. Those connect to about 100 million bipolar cells, bipolar because they’ve got two add-ins. And those then can connect to a much smaller number, about 1.5 million retinal ganglion cells.

Those are the optic nerve. So the optic nerve cells reach all the way out from the eye, deep into the brain. And previously, many groups had had really primarily focused on stimulating the optic nerve cells. But the problem is that because the image is already so compressed, you can’t just excite them within, like a camera image that that doesn’t produce what we call like a form vision, like a form percept in the kind of in the mind’s eye that just produces these flashes of light that are called, that we call phosphenes.

Juan Benet

So that means the documented description is like somebody’s visual field when you stimulate too deep into the optical nerve, they have these flashes.

Max Hodak

You get these flashes of light that like, if you see an artistic representation of it, if you see a picture of a field of phosphene, you might see like, oh, there’s like a woman’s face or I can see letters from the flashes. But the way that the patients actually perceive this is really different than you seeing the artistic representation of it through your photoreceptors.

The brain does not piece phosphenes together into a really coherent whole. And so like ten years ago, there was a company that got a retinal stimulator to market. They sold 400 of them before it was eventually withdrawn. And they were able to stimulate these fields of phosphene onto the optic nerve cells.

The best patients could kind of read in the sense that they’d be like, oh, here’s a line, here’s a line. It’s connected — that’s an A. Here’s a shape. It’s bridged this other shape — that’s an N. But it wasn’t like they were reading off like sentences from a book. It was an empirical finding of this clinical trial that if you stimulate the bipolar cells. So the first way that our device is different is we stimulate that 100 million bipolar cell layer, not the 1.5 million optic nerve cell layer. And this requires us to place our device under the back of the retina instead of on the interior surface. If you go in through the eye, ironically, the layer that is most forward is not the rods and cones, it is that optic nerve cell layer, which is part of why you have a blind spot backward. So the blind spot is the point where the optic nerve dives and exits the eye. So our device sits under the retina, stimulates that first 100 million cell layer. And it’s an empirical finding of this trial. This had not really been done before that produces a clear form image in the brain.

Now it is black and white. We don’t get color with this. Color rolls up through the bipolar cells. We do think that in the next one or two device generations, we’ll probably get at least some red and green. But it acts as essentially an electronic photoreceptor. The implant is a light sensitive chip that has all these tiny little pixels on it, and the patient wears glasses that have a camera looking out at the world where you could get the video feed from anywhere, and then an infrared laser that projects into the back of the eye to strike the implant. And because your eyes are not normally light sensitive in the infrared, you can’t see the laser projection. But when it wherever the light energy falls on the implant, it just absorbs the light, converts that into an electric field that stimulates the bipolar cells and thereby kind of bypassing the dead rods and cones and getting the visual stimulus back into the visual processing pathway at the first possible opportunity beyond the dead photoreceptors.

Juan Benet

Basically, you’re creating a different technical pathway to bypass a layer that’s not working and then activate the layers below that are working and then generate the image. And so here, I think we didn’t describe the experience of a patient with the disease. You mentioned blind spots. There’s kind of like partial blindness, right? How does the disease work?

Max Hodak

Specifically, the trial was done in age-related macular degeneration. These patients are not in total darkness. They have some residual peripheral vision. They can use that to walk around and not run into walls. But only the central 6 or 7 degrees of your visual field is actually high acuity color vision.

This is the fovea, and your eyes are constantly darting around to figure out where in the scene should I look next in order to minimize my ongoing uncertainty. And what you see is not the image that actually falls on the retina. What you actually experience is this world model constructed by the brain, which is actually only relatively weakly updated by the senses.

This is one of the reasons that we’re so susceptible to things like illusions. And so, as the eye is darting around and filling in this world model, that is the thing that you end up perceiving. And so even though these patients, they have some residual peripheral vision that the area of their high acuity color vision is degraded — they think that they see.

Patients don’t perceive a dark spot because the brain fills it in. Now, if you’re really late stage blind, like in dark RP, that’s a little different. But the brain really wants to fill it in because it’s looking for information, even if it’s not real. One of the things that we can do with these patients is you can show a solid green bar that goes all the way through the visual field. And they’ll say that they see a contiguous bar that’s green, and then it turns white, and then it turns green again. But they see it as continuous because the brain fills in that area around the stoma.

Juan Benet

In a lot of these cases, what are the impacts on people’s lives. You mentioned they can maybe potentially walk around and so on, but certainly it sounds like they can’t read.

Max Hodak

Yeah. They can’t recognize faces. They can’t order off a menu. They can’t.

Juan Benet

Use that computer…

Max Hodak

They can’t definitely can’t use a computer, except to the degree that they can have it in their assistive device or talk to it or something.

Juan Benet

And so with a device you replace — you bypass the layer that’s not working. You use a laser mounted onto the glasses to then stimulate those cells. And now what do they see?

Max Hodak

This is one of the reasons that the crossword puzzle test is interesting. Because they’re — I mean, we don’t — but in theory, there are ways that you could cheat on other types of object recognition or these reading tasks. But here, this is a visually guided fine motor task.

Most motor control is not visually guided. It’s proprioceptive-guided, which is this feedback of your body position. You don’t need to look at your hands to catch your baseball or to type. But here, this is a visually guided fine-motor task. And by the way, the fact that this works actually says deeper things about how cool this technology is.

In prior devices where the electrode array moved with the eye as it moved, or if you put it, say, in cortex or in the thalamus, where as the eye moves around, the electrode mapping is the same, so then the eye movements are no longer meaningful, this breaks a very important connection to motor control, whereas with PRIMA, this is intact because the eye movements remain meaningful because they move relative to the laser.

And so the fact that you can do this visually guided fine motor task is very cool. But these are shapes and they have to see the lines of the grid squares. They have to read the letters, they have to fill in the letters between them. And so I think this is really very strong evidence that it — what does it look like? It looks like vision, like this is vision, structured form vision.

Juan Benet

Filling crosswords and like reading books.

Max Hodak

Yeah.

Juan Benet

Wow.

Max Hodak

They’re playing card games.

Juan Benet

Amazing.

Max Hodak

Yeah.

Juan Benet

How do the patients feel?

Max Hodak

The patients are pretty stoked. I mean, I have to be clear, this is a clinical trial. You cannot say that this would work for everybody. We’re going through the regulatory review process now. It’s not commercially available yet. This is an investigational device, but, I mean, the results from this trial are very, very — they’re great.

Juan Benet

Yes. Amazing. What are some of the people saying? I don’t know if you can talk about that, but, when I get a sense of the experience that people have.

Max Hodak

I’ve only spent a limited amount of time directly with the patients, but it’s pretty cool. Like when I was in France a couple of months ago, I went to a rehab session, and it’s pretty cool to see them holding a newspaper and reading it. And these patients are all pretty elderly, the average age of the trial was, I think, 81, and that it is a lot to ask an 81 year old to do anything, especially a blind 81 year old, to come into a clinic and engage with this clinical trial. And many of them, they really want to do it because this is a very special experience.

Juan Benet

So you mentioned France. And as far as I know, the clinical trials started in France and now you’re doing them in the US. Can you talk through what stage the tech is in and worldwide?

Max Hodak

The main clinical trial was done in five countries across Europe. It was 17 sites across France, the Netherlands, Germany, Italy and the UK, and we have currently submitted for the CE mark, which is the marketing approval system in Europe and a bunch of other countries. And we are going through the audits now. That seems to be going okay. In addition to the main pivotal trial for that approval, there is a small feasibility study in Europe and a small feasibility study in the US. So there’s a handful of patients that have been implanted in the US. We are currently engaged with the FDA to talk about the path to market in the US.

We also, like I said, have a next generation of the chip already being worked on, that we’re starting to think about human studies for that. It’s very similar to the current version, but better, and we’ll be doing a clinical trial for that, both in the US as well as Australia and some other countries.

Juan Benet

That’s amazing. And do you think that this device can then be used for a range of other diseases, or it is very specific to this one family of problems.

Max Hodak

Well, the family of problems are photoreceptor loss diseases.

Juan Benet

That sounds pretty general.

Max Hodak

There’s a bunch of different reasons people go blind. One of the most common are things like refractive errors, like cataracts that are easy to fix with surgery, done all the time — it’s one of the world’s most common surgeries. Then there’s glaucoma, which is loss of the optic high pressure, which can lead to loss of the optic nerve, or potentially other reasons why you would lose the optic nerve, say, trauma. If you’ve lost the connection of the brain, this does not work for that. But for a relatively broad range of diseases. I mean, one of the things that’s nice about our approach, both the gene therapy as well as PRIMA, is that it doesn’t really matter why the photoreceptors died, as long as you can stimulate the cells past them.

I think this is when we talk about neurotech, I think some people would say, well, this is ophthalmology. We’ve been doing ophthalmology for decades. Obviously people have been trying to restore vision for a long time. There’s all kinds of drugs, there’s gene therapies, there’s a bunch of devices — like what’s different. Neurotech is really — there is a different lens on the world. You can think about many of these things more in engineering terms than you could have before.

And it’s different from the other gene therapies and drugs that are out there because, like, we don’t necessarily care why the photoreceptors died. We just care that we can think in terms of the information and the representation that we need to inject into the brain in order for you to experience the visual information and get the visual experience.

And so beyond macular degeneration, we’re also thinking about retinitis pigmentosa Stargardt disease, which is something like macular degeneration affects young people. Diabetic retinopathy. There’s a bunch of different potential indications that will be needing to do separate studies on.

Juan Benet

Yeah. How many people does that represent globally?

Max Hodak

Macular degeneration is one of the largest of those. Glaucoma is big. But it’s in the millions of people.

Juan Benet

So if you if you succeed, if you get this device market, if it works out, if it’s successful, you could literally help millions of people around the world restore their vision.

Max Hodak

Yeah, that’s the idea.

Juan Benet

That’s great. That’s super awesome. I guess you have to go through the trials and that takes some amount of time. Can you walk through what that looks like? Because, of course, every day or year that passes without this kind of thing, there’s kind of significant loss there globally of like a day or a year that people don’t have these.

Mad Hodak

Medicine can move very slowly. And clinical research is like a very deliberate process. In some of the delays, to be totally honest, the risk benefits for the patients is if it takes another two or three years, they’re just not going to be there. They’re not going to get it. So clinical medical device R&D and translation is a very slow and deliberate process. The PRIMA technology was originally invented at Stanford by a professor there named Daniel Palanker. It’s been in development for, I think, a decade now. That’s the timescale that it takes to bring something like this to market.

Now, like I mentioned, we’re doing a trial in Australia. So the first version of almost anything is always pretty limited. The first iPhone was pretty cool. I mean, that was a huge advance. But the first iPod, they put together in six months. And that in itself, like if you look back now, like to that versus what we have now — the really cool stuff is in versions four, five, six and beyond. And so I think we now in PRIMA, we have this existence proof that you can create a form image in the mind and that this basic approach works.

But there’s a bunch of ways to make it better. We’re going to want every two years or so, to be coming out with a new version, to begin to realize the potential of the technology through these refinements and being able to do that quickly and easily to get feedback from human subjects and feed this back into technology development is really important.

We only work in sophisticated, modern developed health systems. We’re not going off the reservation to go do this somewhere sketchy, but we’re always interested in moving as fast as we can.

Juan Benet

There are probably some countries and jurisdictions that are starting to think about leaning forward and going faster on this. I’ve spoken to people in the UAE, for example, that I want to create pathways for neurotech to advance there. What sort of advice would you have for government folks globally to think about improving their systems?

Max Hodak

I don’t know that I would answer that specific to neurotech, necessarily. There’s on multiple levels opportunity for clinical trial reform. Healthcare, to a great degree, really represents a market failure. It’s something that everybody has to deal with. The willingness to pay is basically infinite. It requires big investments and very specialized labor and a lot of very specialized infrastructure. So on many levels it leads to these big economic distortions that need to be corrected somehow. I think one of the most valuable thing that any regulatory body can do is just engage actively, because if it takes two months to figure out if something is possible versus eight months, even if the conclusion is the same — whatever the rules are, the rules are fine — but being able to get feedback in two weeks versus two months versus six months, this has a really profound impact on the types of entities that can play in this. If it takes you a year to get even feedback on a clinical trial protocol, then the only people that can do this are super highly funded giant companies that think in decades, and it’s just too hard to venture fund a lot of this.

Even if the return could be there, bridging through that is very challenging. And so anything that we can do to compress these timelines is going to really encourage innovation. There are still biological time constants that can’t be avoided. Like at some point you need to just follow some number of patients for two years or three years and get the safety follow-ups before you go to more patients.

But just the paperwork. Like the paperwork — like if you send a letter saying, hey, we’ve interpreted this rule this way, and then there’s a three month delay that can make it hard to innovate.

Juan Benet

Exactly. The time that the biology needs to take and for safety. You have to allow the trial to pass for a certain amount of time. But if there are delays that are in paperwork or things like that, that seems like a huge problem. You mentioned upgrading the device over time. Would people then when the new upgrade rolls out then get another surgery and just replace the device? What does that path look like?

Max Hodak

Potentially. One of the things that’s nice about PRIMA is it might be upgradable. We’ve never done this in a human, but there’s animal evidence that this is possible. It’s a tiny, little fully wireless chip. PRIMA, being powered by the light that is used to activate it, is this, like, that’s a really cool trick.

Juan Benet

Yeah, that’s a great innovation.

Max Hodak

Yeah. There’s no cable. There’s no battery. Prior implants had a cable coming out of the eyeball. It’s like a titanium box. I can get rid of all that. You just have the tiny little silicon fleck. And. Yeah, we think that it’ll be possible to upgrade it now in practice. Probably in the first generation or two.

That won’t happen that often. And then as you start going to younger patients who are less disabled for a more powerful implant than you’ll, I think, start to see that more. Certainly, if you’re talking about like a 25 or 30 year old Stargardt patient, they’re going to expect to get a handful of upgrades over their life until we can get towards native acuity, color vision.

Juan Benet

Right now, the idea of having one surgery sounds intense. Multiple sounds even potentially scarier. How do we kind of enable this to be super easy and routine?

Max Hodak

It’s a super simple one-hour outpatient surgery. So this can be done under local anesthesia. It is often done under general anesthesia, but that’s as much for just nerves and for you can’t change your mind halfway through the surgery, but it can be done. You just make some injections next to the eye. It goes dark and numb for a few hours. They come in through the front of the eye. They leave the chip in the back of it. I remember when I got into this field almost 20 years ago — now it takes a little while to acclimate to photos of surgery and I think everybody has this experience going from like, I don’t really want to watch the videos of surgery to being, oh, check out that vasculature, that’s super defined and you can like see this part of the brain. And then when I moved to start working on the retina, it was a whole different adjustment to start looking at videos of eye surgeries. But you acclimate to it. And then you’re like, oh yeah, you can see that marker and it’s like, oh, you can see where the implant was placed relative to this blood vessel. It’s really not a big deal. The patients tolerate it well.

Juan Benet

And certainly the ability to recover a sense is probably a lot more of a difficult surgery and recovery process.

Max Hodak

Yeah, and this really is not a difficult surgery. I mean, like a brain surgery where you’re drilling a hole in the skull and tunneling into the brain, that is way more intense than this outpatient, leave a little wireless chip in the back of the eye. You don’t have to go through any bone to get to the eye. The eye is soft tissue. And so it’s surgically easy to access. You can look at it by looking in through the eye because the eye is clear. So on many levels, this is a much easier surgery than some of the other things out there.

Juan Benet

That’s a great segue. So let’s talk about the biohybrid. You mentioned it a little bit earlier, but let’s maybe start general and then dive in. So how do brain-computer interfaces work? I mean we’ve definitely been talking about maybe special purpose or special case BCIs, but what was the general vision of the BCI field, meaning being able to do read and write into the brain? What’s the possibility space here?

Max Hodak

I don’t know that the BCI field has a general theory of the vision of BCI. I think this is part of one of the issues right now where there’s some very classic BCI applications, like extracting motor representations to control a cursor, or a robotic arm or keyboard, or more recently, speech decoding, extracting speech from the brain from ALS patients. Or I actually saw some data recently where some non-verbal autistic people might have speech representations that could be decodable with with a speech prosthesis that hasn’t actually been done, but there’s evidence that that could be possible.

And that’s the type of neuroscience that isn’t new, but the devices are getting better. Now, does a better motor decoder — is that the big prize at the end of where a lot of people are imagining right now? I don’t know. There are vague gestures to maybe you could improve memory or maybe you could improve reasoning, or maybe you could have some other capability. I think that is kind of harder to reason about right now. I think that’s not really clear what that would mean.

But then at the other end, when I think about some biohybrid where you’re thinking, really, can you add whole new brain areas or can you add almost a new hemisphere to the brain? This takes you into even kind of more out there sounding territory. Where can you in some very profound sense, redraw the border around your brain? Can you add new things to that system? So then you go from thinking about just exchanging information with the brain, extracting motor information or writing sensory information, to kind of adding a whole new part of the brain it didn’t have before.

Juan Benet

Yeah. I mean, right now already, so many people are deeply integrated with their phone and their personal devices. These are kind of part of them in a way, but they just haven’t crossed the barrier. You still have to go through the eyes and your thumbs to type. But if you open the bottleneck and you then enable you to think with your computer — what does that look like? What could be possible here?

Max Hodak

So there’s a lot that I’d say is debated in the field right now. There’s some potential limits. Human language is about 40 bits per second. There’s different interpretations of that. But just think about the amount of number of bits of information that are kind of reduced, you’ve got a base of phonemes, like the words that can get put together. You choose one from that and some number of times per second. Some human languages are spoken more quickly and convey less information per token. Some are spoken more slowly and have more information per token, but in all cases it’s about 40 bits per second. Now there’s also some evidence that there’s this deeper cognitive bottleneck in the brain at about 10 bits per second for reasoning and experience.

So if you put someone with a photographic memory on a helicopter flight over Manhattan, then for an hour and then ask them to draw what they saw, and then you look at the details that they got averaged over the hour. It’s about ten bits per second. Or there’s many different ways to triangulate this number. And so that leads people to think that there’s this deep bottleneck which is co-evolved with all the different brain areas that kind of serializes thought at about 10 bits per second in terms of what you’re able to remember and act on. In that sense, just opening up some bottleneck may not necessarily help you.

You feel like you could speak faster, you feel like you have all these ideas. But the reality is that until you can until you serialize these in this way, they aren’t actually formed and you can’t actually use them now. On the other hand, there are even at 10 bits per second or at 40 bits per second. There are faculties that you don’t have or skills that you don’t have. This is kind of like the “I know kung fu” example. So that doesn’t necessarily cheat that limit. Those bottlenecks could still exist, but at those rates, the brain could have the ability to transform information in a different way than it would have otherwise had.

Juan Benet

So the “I know kung fu” moment from the Matrix, where Neo downloads this capability and is now suddenly able to control his body. That probably requires training the motor.

Max Hodak

Yeah, we don’t know how to do that right now.

Juan Benet

Yeah, that seems like surprisingly harder than, like, let me remember Wikipedia or, I don’t know, let me look at my message inbox with my brain. What are some of these other things that seem more likely?

Max Hodak

You can almost think of it as like a UI UX problem. What is the UI UX of a BCI? That is what you want for accessing something like Wikipedia, because like the iPhone, the iPhone is a crazy technology in multiple levels. If you want to talk about addiction, many people, if they’re physically away from their phone for more than a couple minutes, they get noticeably anxious. Like, that is a crazy technology for what that does to our brains and how it rewires it.

Juan Benet

Less a kind of dopamine addiction. More kind of a sense of self, evolving into it. Like, I’m addicted to walking with my legs, right?

Max Hodak

Kind of. Yeah. But when people rely on phones more, their memory degrades. Similarly, people that use GPSs to drive have worse spatial capabilities than those who don’t. And so the brain is definitely offloading. It’s something that’s not being exercised that it’s realized it can offload, that it can draw upon.

And so that is definitely already happening. But if you wanted to integrate this directly into the brain somehow, how should that be exposed? Like, should this be like an internal monologue? Not everybody has an internal monologue. How do you cue this thing? Do you want to just be able to have a thought, be like, oh, I wonder what is the GDP of like Micronesia or whatever? And then how should that be delivered to you?

Juan Benet

What do you think of I don’t know, maybe not a super long term future, but maybe a short near-term. I suppose that we get something like the biohybrid and we’re able to expand the connectivity. What UX capabilities would suddenly be unlocked?

Max Hodak

Well, to some degree this gets at like what is your unannounced research?

Juan Benet

Yeah, but what are some really cool stuff you want to tease.

Max Hodak

I think this idea of adding new brain areas entirely is underexplored. You have clinical needs for this in cases like stroke, where a patient might have lost some part of their brain that underlies a speech or some part of reasoning or some part of memory, and if you can restore that capability to them, then that is a really valuable medical need and also hints at potentially other types of expansion of capability.

Juan Benet

Concretely, this would be like adding a chip into your brain that would then carry along some amount of those capabilities. Would that connect to the phone or other devices?

Max Hodak

You could do it potentially either way. So with biohybrid, there would be a device that has these cells that the cells grow in — they form new bidirectional connections into the brain. And then those cells, we think of them as they kind of join the local cortical representations. For example, we know that if you have a bio implant in a mouse, and then you have a mouse and a treadmill, we can recover how fast the mouse is running through these cells.

We know that the dendrites of the biohybrid graft cells have grown into the brain and formed connections that join these, like Paw kinematic representations. And then also, if you have an animal in an environment, it’s making a decision. You can give it information that it can use to make that decision through a biohybrid environment.

So we know we’re getting these axonal synapses. And so when these things go into the brain, they form connections with the neighboring tissue. Biohybrids are pretty cool. They grow pretty deeply into the brain. You see connections all the way down to subcortical structures like the thalamus.

And so it’s totally possible to imagine that you could add a new cortical thalamic loop, that instead of going through a native part of cortex, kind of ends up in your device that then you could process either locally or you could send over a radio to some other larger model running elsewhere.

Juan Benet

And that would start hooking in entire subunits of computation into your brain. It seems like the brain could start calling out to these kinds of subunits to do complicated tasks.

Max Hodak

Exactly. Can you add new pretrained representations to cortex that you could draw upon is a really interesting idea. There’s a huge amount of basic neuroscience to do here.

Juan Benet

From a computer science perspective, this looks like opening up the whole operating system and programing application structure and then replacing interfaces in-between with calling out to APIs in the world or in your device, and filling in larger amounts of information processing or computation to then be able to access it as part of your intuition or your thinking.

Max Hodak

You kind of have to think of the brain as an information processing organ, and it’s really about these informational flows, and you can have this mental model of the brain as full of these abstract objects called representations. So there’s a representation of my hand that this state corresponds to whatever the motor control is and whatever the current proprioceptive state is. And you want to be able to access these so that you can decode them or recover them or drive them and create them. But this field is really young and moving pretty fast, and there’s a lot of basic research that we’ll need to do to understand how to use it in all of the implications.

And we do a lot of that internally, but we also try to collaborate with outside academics and get tools into their hands. So that, I mean, we’re a small part of a global community, and there’s a very deep well of neuroscience and biology to do with these types of devices over the coming ten plus years.

Juan Benet

Let’s go through maybe an explanation of biohybrid again, like walk through the description.

Max Hodak

The central idea is that instead of placing wires or optical fibers or anything like that into the brain, we can make a device that we can load in a dish before you put this into a patient and you have it in a dish, and you can seed this with living cells that will turn into neurons in a very predictable way.

There’s a bunch of different ways that you could do this. We have a bunch of different probe designs. But the key is that you have these cells growing really directly on the electrodes. Whether these are like actual electrodes or things like LEDs or imaging imaging pixels —

Juan Benet

How do you talk to those cells?

Max Hodak

So neurons store information in a voltage across their membrane. You can think of a cell as a fat bubble full of saline and a bunch of other molecules. Neurons are a type of so-called excitable cell, which have these proteins that go across the cell membrane, which allow them to pump charged particles around, basically, so they can control their membrane potential to store information and do computation.

This is detectable with an electrode. You can have what we call a capacitive electrode because of how the physics of the device works. And so if you’ve got a neuron next to your electrode, if the neuron pumps ions around, you can detect this with your electrodes. We can record the cell state with a conventional electrode.

Juan Benet

Like basically reading the state of a single neuron.

Max Hodak

Of a single neuron, yeah. You can also inject a charge through an electrode in order to depolarize a cell. So what you’re doing there is you’re kind of increasing the concentration of a charged particle next to the cell, which has the effect of moving its — the base of voltage is a differential measurement. If you’ve got, say, a 120 millivolts potential across the membrane of the cell that you can change by either pumping ions across the cell membrane or just changing the external concentration, which will have the effect of implicitly changing the membrane potential. So you can do that. That’s the most common approach. We call it micro-stimulation. We mostly don’t do that because you can’t stimulate and record at the same time. And there’s some other biological effects of that type of stimulation that aren’t ideal. So what we do is we express a special type of protein in these cells that make them light sensitive in a particular way.

So it’s a light-gated ion channel. It’s like one of those holes and the cell membrane that allow them to move ions around. Except this one is open or closed, depending on if there’s light of a specific color that is being shined on it. And so in addition to our electrodes, we also have micro LEDs in the device. So that if we want to fire a cell, you turn on the micro LED next to that cell, which will cause it to open the channel, and then that cell to fire.

Juan Benet

So, we have a tiny LED right next to the cell. It’s shooting photons into the cell that is activating.

Juan Benet

That opens the channel, causes it to fire. Yeah. Exactly.

Juan Benet

Yeah. Wow. And so that’s like the optogenetic opsin, right. Like maybe walk through why that was a breakthrough innovation.

Max Hodak

Those proteins are called opsins, and this is a field called optogenetics. They were originally discovered, I want to say almost like about 20 years ago, originally in some algae. And so optogenetics is a very widely used thing in neuroscience now, because it allows you to kind of causally influence these neural networks. So it’s one thing just to record neural activity. But if you want to say, well, what happens if I drive this cell or this population of cells in a particular way? Optogenetics is very powerful for that. And so we use optogenetics in a range of different things. We use it all over the place as a research tool.

But then in our products we use it in two places. One, we express our opsins in our biohybrid cells that make them light sensitive. And these opsins are a major area of research for us. We have an internal protein engineering group that has developed the world’s most sensitive opsins, and are continuing to improve their properties in a bunch of different ways.

Having very sensitive meaning, it takes a small amount of light to open or close them is important because if you want to have more channels and you have more cells, then if it takes less light to activate a cell, then you can have more LEDs in the same amount of power consumption and as importantly, in the same amount of, of heat generation, because one of the things that really limits what you can do with an implant is when you use it, how hot does it get, which is a function of how power efficient you are.

And so by having options that are very, very sensitive, that take much less light, you can have way more LEDs because each light, each LED is much dimmer. And so you don’t run in these thermal limits. We also use our opsins in the retina for, for our next generation retinal therapy. and it’s I think we have this intuition that nature is a great engineer.

And like, when nature has a problem to solve, it makes a protein. We should be inspired by this. This is something that’s really been enabled recently by the amino acid language models. I mean, this is really, I’d say AI driven innovation. We don’t talk that much about it like we don’t trust ourselves. It’s like, oh, we’re an AI company. And like, it’s AI powered and you should invest because of AI. But that’s a concrete example of how we use modern AI technologies like transformers for practical applications that can be really valuable.

Juan Benet

Yeah. It’s awesome. Without going super deep into the AI rabbit hole, how much do you feel like the latest models have accelerated your R&D?

Max Hodak

So in a couple of places they’ve been really impactful. For example, protein engineering is probably the single biggest impact. The other big place that we’ve used a lot of AI techniques is actually in regulatory documentation, and we have to generate thousands and thousands of pages of regulatory documentation.

And then we need to track that all of what we’ve done is compliant with hundreds of standards that all have super detailed criteria. And so that’s the type of thing that just as an automation tool can be useful. Everything we do is always like signed by a human. Everything is read by a human. But as a productivity lever it has been pretty useful.

Juan Benet

Yeah. Generating like thousands of drafts of things and so on before you actually settle on something. And it’s really happening on the other side of all of your applications and so on are probably being read by LLMs and distilled and summarized and so on before whatever response gets back to you.

Max Hodak

I hope so. I mean, they should be. That would definitely. I mean, anything that allows us to get responses faster would be great. And I mean, clearly these need to be reviewed by humans and signed by humans. But as a productivity tool, AI has a lot, a lot of potential.

Juan Benet

Okay. So then you have these cells that you can read from, electrically, and you can write to optogenetically. You can drive a single cell. The cell is living in a well in this whole sheet of silicon. You fab a chip with the LED to be able to drive it. And I guess you have, like a whole bunch of these cells in a grid and — how big do these grids get? Like how many?

Max Hodak

So we do have designs, which I think you’ve seen where there are wells that individual cells are in. We do some of that. We also have other designs. The microenvironment of a surface that these cells — it’s a pretty complicated co-culture of a couple different types of cells that are happy to survive, kind of at this silicon interface that is super detailed. A lot goes into making this microenvironment that these cells want to survive and thrive in. And we’ve learned a lot over the last few years. Probably the deepest area of IP on the biohybrid technology for us is actually the cells themselves, which we’ve heavily edited to make them compatible with patients’ immune systems and have things like these opsin proteins, but also have a bunch of other safety mechanisms built in.

But then second to that is a lot of what we’ve learned about building microenvironment is that these cells will survive including through the implantation procedure, which is a pretty harsh hypoxic, glycemic, other types of shock happening, these typically if you just take a bunch of cells and inject them into the brain, 99% of them die. And so there’s fairly deep technology around making them survive and thrive there.

Juan Benet

How big did the grids get? Like roughly how many like cells?

Max Hodak

We routinely engraft a million cells. Their standard probe right now is 4mm by 4mm. Cells are really small. There’s plenty of room at the bottom. The limit on the size of that end of the probe is just the brain is curved, and you want something that has more small little islands rather than a big chip that you’re going to try and squish on the surface of the brain. But within a 4x4mm probe active area, you can easily fit a million cells.

Juan Benet

Wow. That’s awesome.

Max Hodak

To be clear, that device right now does not have a million electrodes. Okay. It’s a way smaller number of electrodes than that. But it should be possible to get, and again, this is really limited primarily by power thermals and also our willingness to pay very large amounts of money on nice chip processes.

And so you can prototype this on some older chip nodes for hundreds of thousands to a million or so dollars and then start shrinking it. And then you quickly find yourself spending 10-plus million dollars at a time on a chip tape out. And I think we’ll get there within the next few years.

And that allows a very straightforward scaling. I think it is possible to get to a place where you’re talking about millions of channels with billions of cells. But one step at a time. We definitely get dendritic connections. We definitely get axonal connections. We’re translating this up to larger animals. We’re working methodically.

Juan Benet

Yeah. And the dendritic and external connections means when you implant a device, you are connecting into upstream of loops and downstream of loops? Like you’re making —

Max Hodak

Yeah, it just means that your cells are getting inputs and outputs, but they’re exchanging information bidirectional with the brain.

Juan Benet

So you take this grid of cells, you implant it on the brain. The cells grow their axons into the brain. And you were mentioning they go super deep.

Max Hodak

If you think about a motor decoder — so this is usually implanted in the pre central gyrus, so there’s this— Primary motor cortex is the strip at the very top of the brain. But from there to a muscle is like two synapses. The axons from the cells in primary motor cortex go all the way down the spine to the vertebrae. That’s at the level of wherever that connection is.

And there’s a synapse, and then it goes out to the muscle. And so neurons can be quite spatially extended. And I don’t know if this is

Juan Benet

Super amazing.

Max Benet

Yeah. Yeah. So neurons are used to being super long. They do this all the time.

Juan Benet

What sort of process drives this wiring. Do they just automatically do this?

Max Hodak

This is the developmental program. They do this — I don’t want to say totally on their own because again, the stem cell biology here is pretty cool.

Juan Benet

Neurons want to wire and learn?

Max Hodak

But yeah I mean this is what neurons do. When we graft these things, you see them wire up very broadly. And then they get pruned. And then there’s more things, active things that happen. But the neurons want to learn.

Juan Benet

Yeah. And so as soon as you connect it and so on, what have you detected from the types of systems that they wire into? What kind of experiments have you done?

Max Hodak

The things that we’ve published that we kind of have announced so far are very focused, like enabling studies on you want to show that the inputs to these cells are growing into the brain and forming functional connections. And you can recover information from the brain.

Juan Benet

Maybe even before the more sci-fi use cases of being able to kind of interact with a computer and integrate and so on, it sounds like this could even help with a bunch of the motor problems, like being able to wire parts of your brain to, I don’t know, across a separate spinal cord or something like that.

Max Hodak

Yeah, potentially. One of the labs that was into — I mean, they didn’t call it biohybrid at the time, but it was really the same idea — they called it living electrodes. There’s a guy at Penn, Kacy Cullen, who’s been into this idea for a long time. One of his ideas was, can you get axons to grow along these channels to build things like jumper cables in the spinal cord to bypass the break?

And so the spinal cord jumper cables idea is an old one that people have made progress on. And there’s some animal models that have really cool results. So that’s not something that we’re doing like in that embodiment specifically. But others are.

Juan Benet

If they’re wiring this way, couldn’t you start creating additional sensors like this that goes through your new brain area sort of thing, but could you add a sense?

Max Hodak

That’s certainly more speculative, but that’s exactly the type of thing that I’m excited about. You need a theory about, like — our senses like, where do they come from? Why do you see and hear and feel and not other stuff, which I have thoughts on, but getting to elaborate that and show some of these things.

I think that it’s been bottlenecked on probes and electronics. Like we just haven’t been able to get these bidirectional connections into the brain at a scale that allows you to really do this. And to seriously ask these questions about like, what would a novel sense be? I think that we’re going to be getting into the stuff within the next decade.

Juan Benet

Yeah. And what is the path? What is the science and engineering and then eventually the product-building look like there? Sounds like there’s a whole bunch of fundamental science to do right now with these new devices to figure out, what do you integrate into and so on. You’ve already done an enormous amount of engineering of figuring out the device and how to make it work and how to increase the channel counts and so on.

Seems like there’s a clear there’s probably a whole bunch of safety and health oriented testing to do. What does that look like? What does that track? How many years is that?

Max Hodak

You can have a debate about whether biohybrid is more or less invasive than other options. In some sense it’s less invasive. It does no damage. It’s perfectly biocompatible. The brain is happy to accept these things. On the other hand, you’ve had a biological cell therapy which is engrafted, which is forming these like very deep — you can’t really easily remove that unless you chemically cause these cells to die.

It has a bunch of structural advantages.They could last for decades. You can have massive bandwidth. They really enable things that might not be possible any other way. On the other hand, you have totally novel risks to think about. There’s actually a long history of people getting cell transplants into the brain.

And this was one of the things that made us think that this could work, because like, if you go on PubMed, the medical literature database run by the US government, and you search for Parkinson’s cell transplant or Alzheimer’s cell transplant, there’s actually a lot of doctors out there who’ve had a couple of patients who are like, you know what, this patient needs a stem cell injection to the spine or a cell engrafted to the basal ganglia. And those historically have not really helped the diseases. But they did teach us a couple of things.

One, the cells tend to survive and functionally integrate and last decades and nothing bad happens. And so there’s actually a pretty large body of like supporting evidence that even like like even like in some cases stem cells that I don’t think you would have necessarily wanted to inject into the brain that way, turned out to really be pretty safe and pretty well tolerated.

We think that that will turn out to be fine. But it’s definitely going to be systematic from whatever you can do in a dish, whatever you can do in mice, you do in mice or whatever you can do in a rabbit, And then you have to use some monkey studies. Then you go to humans. And certainly anything in humans is much slower and much more methodical than what you do in animal research. But at some point there has to be some first patients. And I think that it’ll probably most likely be stroke patients. There’ll be a population of stroke patients that I think are probably likely to be the first biohybrid recipients. It definitely won’t be next year, but I think it’ll be way less than five years from now.

Juan Benet

From there, what’s the path to like? How long is the trials landscape or something like this?

Max Hodak

It usually takes at least three to four years to get through clinical trial for something like this. Because you’ll spend a year getting set up. You’ll write your protocol, you’ll get IRB approval, you’ll get all your documentation — that takes like at least a year. And then, you’ll enroll your participants. That can take a while, and then you’ll have to follow them for at least 12 months. There’s no way that the FDA is not going to look at your data for less than 12 months, and they’ll probably want two and three year follow ups. But you can do that as you go. And so if you’ve got a two year endpoint and they really want to see a third year on follow up, then you have a year of setup. You have a year where you’re kind of implanting everybody. And then you’ll start hitting the 12 month check and 24 month check points at some rolling window. After that, once you’ve got a suitable number of patients out to two years, that’s probably enough to start thinking about submitting larger filings to the FDA.

And then while you’re going through that process and they’re reviewing it, you’ll start to get your 36 month data. And they’ll look at that as it comes in. After that you can imagine like another year to kind of get an approval and get on market. So that’s at least a four or so year process.

Juan Benet

Plus the sub five years that you mentioned. Now that could be within 10 years this might.

Max Hodak

Yeah, it could be on market. But at the same time we’re going to learn a lot. Going to market is one thing, and the way we actually architected the company allows some of that to be a little more back loaded. Our retinal prosthesis, specifically PRIMA in particular, we hope to be on market with that next summer. That could get delayed. We are going through the regulatory process now.

We don’t have the approval yet, but we think it’s pretty likely that we’ll get approved in the next year. And that alone is a big enough product that it can finance a lot of the rest of the stuff that we want to do. So the company, we’re thinking about how do we get profitable, not just like, oh, we want to raise another $5 billion of venture or something over the next five years.

Juan Benet

And so if you get profitable, what’s the timeline? This seems like one of the fastest biomed oriented companies to become profitable.

Max Hodak

Well, we can say that when we’ve done it. Let me get there first.

Juan Benet

If you can get there.

Max Hodak

Yeah, if you get there. Yeah, exactly. But I mean, I think I don’t know, running a startup is.

Juan Benet

Is there a website tracking these?

Max Hodak

I mean, it’s annoying to look at the software versions of this where it’s like they’re going to.

Juan Benet

Like 100 million in a few weeks.

Max Hodak

Or something. Yeah. It’s like, well, they can’t do that. I don’t know, it’s like the startup.

Juan Benet

Maybe you have a team doing that and then that funds the rest of the program.

Max Hodak

Yeah. Well, we’re grateful for PL support and investment over the years. But yeah the visual prosthesis is really a big enough business. If we don’t mess that up, there can be a multi-billion dollar a year source of cash fully developed. And that buys us a couple things. One, it buys you time to go through the full regulatory process for this — the world’s most complex combination cell therapy device ever made. And it allows us to fund some parallel research along the way that I think will really prove out some of the big concepts, like the missing theory prove that some of these really next generation type applications are possible in animal models and small numbers of humans, separate from a market approval study.

Because you can have these smaller studies where you’ll implant a couple patients like three, five, seven patients. Learn something very targeted, hopefully help them with some disease, but at least get a kind of prove out core elements that are missing. That is very different than a market approval study where you’ve got one thing that you’re going to do very little to tweak, and you implant 50 or 60 patients and you follow them for three years.

Those are very different types of things. Our plan, like the secret master plan here, is bring a breakthrough retinal prosthesis to market. Use the revenue from that to fund the development of the biohybrid core technology uses to prove out big prizes of emerging neurotech and then eventually, of course, translate this to market.

We have this vision of how we think the world is likely to look in the early 2030s. I think 2030 will probably still look a lot like today. I mean, this is also AI and other other things happening, but I think 2035 is likely to look quite a bit different than we might be imagining right now.

Juan Benet

Yeah. At some point we should do that conversation of like, what does 2030 look like and what do we think 2035 is going to look like? Stay tuned. Cool. So first of all, that’s super exciting. Like we’re talking about like if that happens within ten years of now, now having very high bandwidth BCIs that you could then start using to drive computers, interacting with AI, interacting with people and so on.

Max Hodak

Yeah. I mean, your entire experience of reality is rooted in the brain. And so this is also like it’s important to be very sensitive because this is like your sense of identity, your sense of self. Like people do not want you to mess with this. And I think we are very we hear that we were receptive to this.

We want to make sure what the goal is here.

Juan Benet

And at the same time people want to be able to experiment and explore and improve like change their own sense of self. So, just as much as a set of people want to be able to be have what they had in the past restored like they have some disease or some damage or an accident.

They have their sense of self and mobility be restored, there’s a population where a set of people want to be able to enhance their capabilities, like they want to be able to improve their senses, like see a different part of the color spectrum, or being able to add additional senses or interact with their phones or computers in deeper ways. What does what does that look like? What are the kinds of things that you want to be able to do?

Max Hodak

Yeah, some of these things will be possible. I think they’ll also be cultural discussions that happen along the way.

Juan Benet

And on that, I think OpenAI doesn’t get enough credit for shipping ChatGPT and having the big AI conversation in public. I think there was a lot of concerns. A lot of the AI community was very divided on whether you should or should not deployand have that conversation in public or not?

But from my perspective, I was always very pro having these kinds of significant changes that are possible appearing early while you can still like figure out what they’re going to look like in the long term and enable the broader groups around the world to come to terms with these kinds of potential futures and chart a path and figure it out together. Where should we go?

Max Hodak

AI is a very cool technology. It is going to enable us to do things that were never possible before. It is increasing the pace of innovation, and of society. But you can have some concern that it is kind of a dehumanizing technology that it could replace humans and have other existential concerns.

But it’s kind of an inherently dual use technology. It is possible that just like intelligence is itself dual use, any differentiated understanding of the universe is dual use. If you’ve got a better understanding of physics than anybody else you can build nuclear weapons, or if a civilization has calculus and one doesn’t, then one is going to be have an advantage.

Any different understanding of reality is dual use. And AI is the ultimate embodiment of that. Now on the other hand, BCI is in some ways a more intrinsically benign technology because somebody has a BCI does not necessarily mean there’s a different category of concern you might have versus like if you heard like somebody has an AGI and they’re the only one with it.

I think a lot of people are like there’s one person with a BCI that isn’t necessarily dangerous in the same category, and so I see this type of middle engineering.

Juan Benet

I mean, it depends, you know.

Max Hodak

Depends on what you do. But in itself, intrinsically, it is without some other thing happening. I think the promise of this technology is to increase and empower human agency. It is like a fundamentally human technology. And I think if you look at the endeavor of lower case science, like, what is the purpose of this?

We’re trying to understand reality. We’re trying to understand the world in order to improve the human condition. We use the knowledge that we get through research in order to improve our lives. Back in the Middle Ages, playing in the forest was dangerous, like a kid got a scratch on the wrong tree and would get infected and they would suddenly die.

And there’s nothing you could do about this, and there’s just this constant jeopardy that life was under, and that was just the human condition.

Juan Benet

It is astonishing. By the way, we do not today at all have an appreciation for how dangerous life was in the past.

Max Hodak

But even so, in the same way, people don’t think maybe I have an undetected cancer right now. That isn’t in the back of the minds of many people. And so this has changed and has certainly gotten better. But the fundamental human condition is still there in that way. So the endeavor of science is to improve the human condition, I see neural engineering as one of a very powerful deep nodes on the tech tree that begins to more dramatically allow you to maximize human agency and improve these things in kind of multiple different ways.

Juan Benet

Yeah. How much do you see this is an imperative for de-risking AI? From my perspective there are massive amounts of AI risks around capability explosion and generating a new age genetic species whether they might develop a degree and level of agency that is very difficult to prove anything about or to have certainty over.

So it can quickly become traditional sci fi, be in deep competition with humanity or in some ways that have been talked about in the last few years, not in harsh competition, just in this weird wau of taking control of the of the world and eroding human agency or control of the future.

Neurotech has seemed to me over the last five, ten years as one of the major avenues that we have to be able to stay ahead or even if not ahead, at least stay competitive in the long term and enable humanity to have a path into the future. How much do you think about that? How much of that is motivating? I am curious.

Max Hodak

There’s a real risk that AI does to us what we did to the chimpanzee 200,000 years ago. Humanity was an undifferentiated primate on the African savanna. And now our closest living relatives live in glass boxes so they don’t go extinct. What was different? Our intelligence. We were more adaptable, and we could collaborate better as a group.

And I think that’s the real thing. The only constant is change, because some people think this started in the 90s with the internet, or maybe in 2000 with the smartphone. This process has really been the story of human civilization since the mid-1800s. I think this really started with the Industrial Revolution, and there was a generation that went from a horse and buggy to seeing a man land on the moon and routine travel.

And I remember ten years ago, we used to joke around Silicon Valley, “We wanted flying cars, and we got 180 characters. What went wrong?” Will we ever see a situation like going from having a horse to the 747? Will we ever see something like that again? And it’s like, yeah, we’re going to see that again.

Juan Benet

We’re finally past the 90s and early 2000s slump. That was a horrible slump.

Max Hodak

I don’t even know if that’s fair, because one of the things that really strikes me about what’s happening in AI is that it feels like it happened as soon as it was possible. It wasn’t like there was a time when all the predicates were there and we were wandering.

But one of the prerequisites for training these AI models is that you need the internet, because you need 10 trillion tokens sitting there in a machine-readable form that you can consume.

Juan Benet

And have some pretty good semi-conductor.

Max Hodak

So you needed semiconductors, which were developed by gaming. Really, it was gaming, crypto, and all kinds of other forces that created the GPU. Those forces were strong and robust, and there were multiple of them. But then you also needed the internet, and you needed humanity to spend 20 years generating tokens, taking thoughts out of their minds and putting them into machines so that you had this machine-readable resource that you could then use to fit things. And really, as soon as you had that, we figured out the LLM.

Juan Benet

Though even without that, with other tokens, you could have the game-playing type of RL model from DeepMind and others. So if we did not have the tokens, we probably still would have been able to go in some direction. I think it’s a lot more constrained by Moore’s Law.

Max Hodak

I think for modern LLMs, the tokens are important. But AlphaGo happened first, so self-play was figured out first.

Juan Benet

And there will be even greater cases of it. I actually think that LLMs right now, and the hunger for improving them, have taken over a lot of the cycles that would have gone into self-play type results like AlphaFold. So I wonder what range of things we would be working on right now if our main modality were not chat and conversation.

Max Hodak

Yeah, I think you can say that Transformers and LLMs became one paradigm that sucked the oxygen out of the room. But I think the reality is that there’s way more oxygen. The total spending, the total amount of talent, and the total number of companies are massively larger than they were five years ago.

Juan Benet

3 or 4 times the entire R&D spend of the US on non-defense.

Max Hodak

Yeah. You could say that maybe we’d be exploring these other architectures if everything weren’t dominated by the LLM. But we are exploring the other architectures, and those efforts are as big now as what people were doing on the scaling hypothesis five years ago. So I think you’re getting all of that.

Juan Benet

Going to be a pretty interesting 2030.

Max Hodak

And BCI, I think, has one interpretation: there are four or five highly funded big companies, and they’re going to drive this. But I think it is going to look similar. In five years, as BCI really begins to translate and more of this is developed, there will be hundreds of companies. There will be a whole ecosystem. None of these things end up with one player in a really robust space.

Juan Benet

Let’s talk about company building and broad R&D in driving breakthroughs. I tend to give a talk about fast R&D, and you’re one of the examples I often give. It’s basically lessons from you, Elon, Sam, and Steve.

To set this up, Patrick Collison has this great post called Fast, where he collects a set of examples of people quickly accomplishing ambitious things together.

He has examples like the Alaska Highway, built in 234 days; the Empire State Building, built in 410 days; JavaScript, built in ten days; Unix, the first version, in three weeks; and the iPod, 290 days from concept to shipping to customers. Which is insane, right? A whole device, from Tony Fadell joining Apple to the first iPods shipping to customers, in 290 days, under a year.

Amazing. Now tons of R&D organizations and companies move excruciatingly slower than that. Sometimes it takes many years to get anything out the door. You’ve been able to do many generations of devices, multiple devices, different platforms. You have a fab. There are all kinds of fast R&D approaches that you’ve been able to do. So why does going fast matter?

Max Hodak

For a lot of this type of research-driven innovation, it is basically unknowable how long it’s going to take. You can’t plan out milestones or put it into your task tracker and say, “In December we’ll do this, and by February we’ll do this.” It’s totally different from building many other types of things.

The only thing you can really control is the rate at which you learn. So how long your iteration cycle is has a hugely determinative impact on the outcomes. Because you can’t see years, or even many quarters, into the future, you’re limited to how fast you can learn.

Then we apply resources to things that are making progress and are working. And you reallocate resources away from things that are stuck until there’s either a breakthrough or you have a better idea. So that is a central management mechanism for this type of research.

We learned that there’s really no substitute for vertical integration for much of this stuff. In some sense, it’s actually kind of a market failure. The fact that we need to have a lot of our own capabilities in-house, including, as you mentioned, a fab, but also animal research and a lot of chip design.

In many ways, it would be nicer if there were a robust ecosystem of companies that were really sophisticated and could move fast, and you could just buy from them. Because then we would raise less money, have a smaller org that was easier to manage, be more capital-light, and take less dilution.

And in some other parts of the world, which we should probably be paying attention to, they can do that. You can have these smaller companies that are part of an ecosystem that you can just order this stuff from.

Juan Benet

And it’s kind of like what the US was like.

Max Hodak

What the US was like 50 or 60 years ago. So now I think there have been a couple of really successful examples of vertical integration making a huge difference, with these companies having all of this in-house.

Juan Benet

And probably the first serious example of this was Apple. They were vertically integrated across the entire process of manufacturing, building all the pieces of the product, selling it, and delivering it all the way to the customer.

Max Hodak

Well, Apple famously partnered with Foxconn. So I don’t know if that’s the right example, because they make toys like SMC and Samsung and Foxconn.

Juan Benet

That’s true. So I guess they were still doing a lot of things. The level of integration basically got cranked up a notch, or several notches. Back then, about 20 years ago, people used to say Apple vertically integrated a lot of things. They were still building on top of semiconductors from other companies. Eventually, they developed Apple Silicon.

Max Hodak

Yeah. That’s true.

Juan Benet

They’re still not making their own stuff.

Max Hodak

Yeah. They’re certainly taking advantage of a very sophisticated Asian supply chain. They make their own silicon. The real magic of Apple is the deep integration. They really care about how it works from end to end. But even Apple uses contract manufacturers. They don’t manufacture internally.

Juan Benet

Do you think there’s going to be humanoids in 5 or 10 years?

Max Hodak

I think manufacturing is one of the places where humanoids make the least sense.

Juan Benet

May not humanoids, but just robots?

Max Hodak

Oh yeah, there are tons of robots. Automobile manufacturing lines are incredibly automated.

Juan Benet

But we still haven’t been able to do certain things, like wire harnessing and a bunch of other tasks.

Max Hodak

Yeah, totally. I’m definitely a believer in humanoid robotics. If there’s a meme out there that humanoid robots are stupid and should always be more specialized, I’m skeptical of that. Realistically, the world is built for humanoids, and the more you can fit into that interface, the better.

But in a manufacturing environment, where you’re going to spend $1 billion to build a manufacturing line, there is going to be more specialized equipment. I think people are underestimating how much even relatively basic robotics, with really smart software and pretty good sensors, will be able to do.

So I think that is really software-limited. This is a weirdly contrarian opinion. You say this to some robotics people and they’re like, “Oh no, hands are bad, these sensors are bad, you need more motor bandwidth,” and all this stuff. I don’t know.

I think you could take basically a Roomba with a sufficiently smart model, and it would impress you with what it could do.

Juan Benet

Yeah. A lot of the robot controls problem is just not solved well yet. There are all kinds of easy dexterity-type things that robots just fumble. I think people hide behind “the hardware is not good enough” because the robot software is so bad.

Max Hodak

Yeah, exactly. I think that’s the easiest bet in the world: the models will get better. But on the vertical integration point, there’s one thing I would say. In some sense, it’s kind of a failure. On the other hand, what it enables you to do is innovate more deeply. If you’re limited to things you can piece together from what is commercially available, you will always be somewhat limited in how novel a thing you can build.

Whereas if we can say, “This is the arrangement of matter that we want. I want to put these atoms in exactly these places, and I’m willing to void the warranty on a $2 million fab tool to place those nitrogens exactly where I want them,” that allows deeper innovation. But it’s very capital-intensive.

Juan Benet

And so one part of the approach is how you manage the team. Another part is vertical integration, maybe in the team and how you organize it. You mentioned this kind of control loop of limiting the resources you put into things and trying to maximize the learning rate.

Give us a concrete sense of what that looks like. How fast is fast? What does it mean to drive a project quickly?

Max Hodak

I mean, some of this is getting a sense of what’s fast. I think that’s a totally reasonable question: how do you know? One of the questions I ask people I interview is, “How do you know that you’re good at what you do? What evidence do you have for this?” And it depends on the context.

For some of these things, where it’s unknown how long they can take or should take, I think all you’re really left with is this: at the end of the day, we want to be convinced that there’s nobody else on earth, with our resources, who could have done it faster. In this case, competition can be great, because nobody sets a world record running an open lap, and you don’t really find out what you’re capable of until.

Juan Benet

And as soon as the four-minute mile was broken, a bunch of people did it.

Max Hodak

A bunch of people did it. So you never really find out what you’re capable of unless you feel that pressure to deliver.

Juan Benet

But often a lot of groups don’t describe it at all. You maybe see some of the public timelines and whatnot, but in terms of the whole host of small little projects that a big project breaks down into, how do you keep the internal clock cycle really fast? I’ve just seen so many large organizations start bloating.

This is what happened to all of the major government contractors that got really slow, and the major tech companies. How do you avoid this kind of ossification or this bloat?

Max Hodak

Yeah. Small teams of people doing things. All the prizes are for making things that work. So you want small teams of people working with their own hands. There are not a lot of other managers. There are not people sitting around whose job is just paperwork, unless it is quality and regulatory, where the job is literally paperwork, which is also important.

But when we interview, people ask, what do you look for? And I think there are two things that I look for, and they are not the hard skills. The technical team they are interviewing for will determine if they meet the threshold of hard skills. But even then, that can often be taught.

The two things that I care about the most, which I have found are the least teachable, are an intrinsic sense of urgency. Does it matter to you that this stuff happens sooner? And then judgment. Do you make good decisions?

My central measure of capacity is: how successful have you been in making your life look like you wish it were, whatever that means? Because people have different interests and different ambitions, and some people want maximum free time to spend with their kids.

Other people want to be at the very top of their field and are willing to make trade-offs for that. But whatever it is, how good are you at making your life look like you wish it were? And by the time you have gotten to your mid-career, there should be visible evidence of this. And so the sense of judgment.

Juan Benet

Why instill a sense of urgency when you can just hire for it.

Max Hodak

Yeah. And that is tough to teach. Sometimes you’ve had really smart people who were just in the wrong environment or had a different cultural example, but they knew that they wanted something faster. But if someone is happy on island time, then we’re not going to convince them otherwise.

Juan Benet

Yeah. Do you do a Netflix-style approach, where you get very clear about that really quickly and early? How do you approach that in the interview process to clarify to candidates the clock speed that you expect in the organization?

Max Hodak

I don’t know that there’s a way. It’s definitely a thing that gets talked about in every interview. How would this person fit in here? I don’t know that there’s a bright-line rule or guideline that you rely on, but it’s something that everybody is looking for.

Juan Benet

And how does the team embody this? I think historically, lots of organizations have degraded in part because the team, if the incentives are not set up right or if they do not have this very strong intrinsic motivation, lets everything start taking a little bit longer and a little bit longer.

Max Hodak

You’ve got to keep an eye on this. Certainly, keeping an eye out for evaporative cooling is important. A players hire A players. B players hire C players. It’s like a massive explosion. Then your best people get disenfranchised, they start to leave, and then you’ve got a real problem. So being very paranoid about that is important.

Juan Benet

Can you clarify that a bit? The “A players hire A players” idea comes from early Silicon Valley perspectives and so on. I’ve often thought that model of just these three buckets is a vastly oversimplistic view of a deep exponential, where there’s an enormous ladder of skills.

I think there’s something qualitatively different. You could have people who are at a deep mastery level in some field, and yet they don’t have the quality that the Silicon Valley “A player” idea meant, and that maybe you’re getting at with the sense of urgency and so on.

How do you define what an A player looks like when they’re very early in their career, or when they don’t yet have the degree of accomplishments that you can clearly look at from a LinkedIn profile perspective and say, “A player through and through”?

Max Hodak

Yeah. I think one early marker of this is how intentional you have been. Even early in your career, you have gotten through 20-something years of life, or 18 or so. How intentional were you through that?

One of the things I like to do is start with: where did you grow up? Where did you go to high school? Where did you go to college? What did you do after that? The thing I’m looking at is: did you make decisions, or were you just kind of swept along? Many people are just swept along.

So the first thing I look for in these junior candidates is whether they have made intentional decisions. Did they end up in a place they meant to be?

Juan Benet

Do you think it’s a personality trait, or is it learned? Have you encountered people who were maybe swept along for a while and then started taking agency, or vice versa? Maybe they had a lot of agency and then that sort of broke down.

Max Hodak

I mean, you see all kinds of patterns, but you’re looking for high-agency people. And you’re totally right. The very best people are much more dramatically effective than the average person.

Juan Benet

I think this is really not well appreciated by people globally, especially even in deep knowledge-work domains where the leverage is so significant. In Silicon Valley, people talk about it in terms of 10x, 100x, 1000x types of things, but these are deep exponentials. You’re getting into the 10,000 or 100,000 level of impact, where one person, in the same year of time, will be able to have an incredible degree of leverage over the same problem space. And it really does break down to typing on a keyboard. It’s the same mechanical range.

Max Hodak

But it’s not that they’re producing more lines of code or more PCBs. So in that sense, it’s tough to think about it in terms of 10x or 100x or whatever, because it’s not like they often just work longer hours. It’s not that they’re moving faster in that sense. It’s that the things they do work, unblock things, and there’s no amount of time others could have spent on it where they would have found as good a solution.

And I think one of the biggest mistakes really smart engineers make is highly optimizing a thing that just shouldn’t exist in the first place. And a famous saying out there now is, “The best part is no part.” And this is definitely true. I think the best systems are very simple. Those are also the most reliable. The closer our products get to a single block of covalently bonded matter, the higher performance, the lower power, and the better they’ll be. And this means that you have to find ways to really simplify it to whatever the minimum physical thing can be.

And that simplicity is not trivial. So when you think about someone being ten times as effective, that doesn’t mean they are doing ten times as much. In fact, they might write half as much code or a third as much. The thing they come up with might be fundamentally simpler, and in that sense, more powerful.

Juan Benet

You emphasize judgment and good judgment. I have also found that this is extremely critical. You kind of described interviewing for it and looking at people’s judgment and decision-making across their life. What are some of the ways it shows up when running a team? What does good judgment look like in a team?

Max Hodak

Do the things you propose tend to work? Do the ideas you have tend to end up working?

We give resources and responsibility to people when, if I see them get excited about something, I think, “This is probably going to work, and it’s probably going to be cool.” And you also know when people have pushed for things that turned out to be dead ends or did not work. So you know it when you see it.

Juan Benet

You encourage exploration on things where you might legitimately have to pursue a whole bunch of dead ends before you find the right one.

Max Hodak

I mean, you shouldn’t really need to pursue that many dead ends, or when you do, it’s fairly intentional. Sometimes, in practice, it’s not like this whole idea of, “Oh, you throw away 4,000 prototypes.” It doesn’t really work that way, at least in the stuff I’ve seen.

But there might be a thing where you’re like, for example, on the retina, we knew that there would be some way to get a visual signal into the brain past the photoreceptors. For that, you’ve got this two-by-two matrix of bipolar cells, optic nerve cells, optogenetic, electrical. You could extend that. You could do genetic. You could do other things.

But for us, we were like, you’ve got a two-by-two matrix, and we’re going to do a survey of all four quadrants. And we’re going to exhaustively explore this space so we understand the trade-offs of all four quadrants. And then we’re going to narrow in on the things that we think are worth exploring further.

And so sometimes you want to do a parameter sweep of what the space is, and you can understand the space in some cases almost exhaustively. But from that, you then want to narrow this down. And if you’re constantly doing a lot of effort, if by six months into a project it’s a big setback, that doesn’t actually happen that often.

Juan Benet

Tell us a bit about how you manage time in the company. How do you set goals, timelines, and deadlines?

Max Hodak

It’s tough to set arbitrary deadlines. Smart engineers and scientists do not really like arbitrary deadlines. If I come along and say, “You’ve got to do this by this date,” they ask, “Why?” And if I say, “Because I said so,” that is not convincing. That does not work very well.

Now, in some cases, you have exogenous deadlines. For example, there could be a big opportunity where, if you have something ready by then, you know you will be able to get people to check it out. Or there is some other dependency where, if you are not ready by then, something else will be blocked that could have proceeded.

Nobody wants to be the bottleneck. They all want to hold up their end of the bargain and have their parts ready by the time it needs to be integrated. So you have these mutual bottlenecks, where the chip people do not want to block things, the animal people do not want to block things, the pro people do not want to block things, and the cell people do not want to block things.

So they have a sense of how it comes together. In practice, there are trade-offs you can make in each of these so that you can ship something, and then you can make each part better over time. You just keep iterating. It is a high rate of iteration.

The first version is not going to be perfect, but you want to get to some threshold of performance so that you can start making those trade-offs and say, “Okay, this feature is not important.”

You can have a higher noise floor in this chip, or you can have a smaller number of channels, or you could have a cell that does not have some particular phenotype, or some thin film where we are going to go with two layers instead of four layers right now, because that is the trade-off to keep the thing moving. And then we are going to get that back on the schedule next time.

Juan Benet

But it requires a great manager to set those concrete external goals.

Max Hodak

This is what I do all day. This is what the leadership here does.

Juan Benet

Because I think if left to their own devices, teams will just expand the timeline and erode time. And then things will just take a lot longer. And there will always be really good reasons for doing it.

Max Hodak

I mean, at the team level, people still want to ship things, and they want to get things out, and they are able to do that in a decoupled way. But as part of the culture, you need to have this sense of paranoia. We do not know how long it will take. We do not know if we will need to respin a chip. We do not know if we will need to do another surgery, or if we are going to need to generate more evidence. And at some point, you do eventually run into these absolute limits. You can only raise some amount of money at some price, on some terms, and then you are out of money and everybody goes home.

Juan Benet

You thought you were going to be able to raise money in two years, and then the economy changed and you couldn’t.

Max Hodak

And so you have to have this paranoia that you do not know exactly how long this is going to take. Because of that, you have to go as fast as you can, because even that might not be enough. But at the end of it, you want to know there was nothing else you could have done.

Juan Benet

But there seems to be a quality in really great founders: they are able to pick the right threshold for maintaining this extremely fast pace and externalizing the deadline.

For example, one of the famous stories on this is the iPhone keynote, the iPhone launch keynote. It is this magical moment in Silicon Valley history that people reference a ton.

Many people know the iPhone was built in around two years, roughly from deciding to do it to shipping the thing, or from deciding to do it to announcing it in a keynote. And Steve gets onstage at the keynote, describes the price and the shipping date, and the manufacturing team learns about this at the keynote.

So they have no idea that they need to ship this device in six months, and the cost structure they need to fit. Of course, they had probably ballparked that, but there was not an agreement or a plan. And now they have to scramble and do this within six months.

Max Hodak

And I think part of what made Steve somebody like that is knowing what is possible. In some sense, he is making it up, but part of what makes him so special is that he knew what the bounds were, that they could do it, that they could be pushed to do it, and then he was right. And I think that is tough to know.

I think lots of other founders then model on that and are like, “Well, he was a dick, so I can be a dick.” And it’s like, you’re missing the point. It’s not about that. The thing that mattered was the revealed judgment. He was right about these things.

And the being a dick part, it wasn’t a feature. He was successful despite that.

Juan Benet

Yeah. I’ve been wanting to write an essay called Cargo Cult Founders, where they learn all the wrong lessons from great founders and forget to learn the really critical ones.

Max Hodak

Yeah. People who have worked with me know I’m not perfect. You care a lot, you want to move fast, and it’s a high-stakes, high-stress environment. But at the end of the day, it’s not about that. Those things are still to be overcome in these incredibly stressful, incredibly high-stakes situations.

Juan Benet

There’s a deep, unfortunate thing about being a very strong founder, which is that the demands of the technology and the product and the market and the people and the team massively compress the timeline in which you have to care about a range of things. So you can try extremely hard not to be a dick about a bunch of things and yet come off like one at various points in time.

So this is a hard balance. Many people do it way better than others. Of course, Steve certainly had lots of examples where he just did not need to be the kind of jerk that he was, and many people do it better. But at the end of the day, the time you have to be human with other people is compressed.

Max Hodak

Yeah. This is part of the trade-off of getting to work on the critical path of civilization. The reality is, the stuff that gets done here matters, and it changes the world. It’s a privilege to get to work on it. And it’s a lifestyle choice that is not for everybody.

Juan Benet

Yeah, I think that’s something Silicon Valley is probably a lot better about now than it was in the past. People are much more aware and self-selecting into a lot of this.

And I think this is where the Netflix culture of, you know, “good performance gets your severance package” type of mentality comes in, of saying, “No, no, no. We’re really trying to build an athlete-level team here, and we’re trying to win the Olympics of business.”

And that looks a certain way. You have to self-select into that if that’s what you want to do. Amazing. Great. If that’s not what you want to do, that’s okay. But this is just not the right place.

Max Hodak

Totally. Yeah.

Juan Benet

Deadlines, though, you don’t have the ability to externalize as many deadlines because you have to be a lot more private about everything. How do you set deadlines like that? Are you able to commit the team down a path to force things to happen?

Max Hodak

It depends. There are always other things that we’re interacting with. Like I said, there are these mutual interdependencies where you want things to come together at some iteration cadence. People do not want to let their teammates down, so they are going to make sure that they do not block them and make that other work a waste.

But then there are other things, like tape-out dates. There are shuttle runs, and that is a TSMC date. If you miss it, then you are going to wait two months. So you are going to make it. There are other things like that that you get attached to.

Juan Benet

You also use these internal demos to create a clock cycle for the company, where you demo certain things on a cadence and things have to be good for that demo.

Max Hodak

Yeah, exactly. We’ve done these about twice a year since the beginning of the company. We do not really have board meetings, but we invite the investors, we invite the whole company, and then we organize an hour-and-a-half presentation of what our progress has been in the last four to six months.

And that is as much for internal purposes as it is for updating our investors. Every once in a while, the story is complicated. There are a lot of moving parts. You should organize your thoughts and show what you have to show for yourself.

And for some of the employees, especially with how we have been growing, they do not see the whole story put together end to end because they are focusing on their part. They know that they have been working on the lifetime testing of implant packaging, or they have been working on new chips, or they have been working on some other part of it.

Then they see the whole story together. That is a morale-building experience for the team. It allows us to compact the story and make sure that what we are doing is sensible. And because there are these spinning blades of death every six months, it creates this forcing function of, “We’re going to have something to show for ourselves.”

Juan Benet

I love it. It’s like a whole-company board meeting structure, because board meetings have famously been used as a forcing function for the executive team to actually think through things carefully and report to a set of stakeholders who then get to reason about things and so on. So many teams use board meetings as a forcing function for high-quality thinking across the board.

But that often tends to happen only with a small fraction of the company or stakeholders. And if you do these larger demos with the whole company and the entire stakeholder set, that gives a shot in the arm to the whole group.

Max Hodak

Yeah. Getting the whole organization aligned is one of the central challenges. The idea of the company can be perfectly formed in my mind, but if that is not conveyed to 200 people who are touching it, then it does not really matter.

We have been fortunate that our governance is relatively simple, and we can act with high conviction very quickly. But then aligning the rest of the company to that, I think one of the lessons I learned early in my career was that these companies are really human organizations. You can usually get the technology to work, but aligning and motivating hundreds of humans to do anything in the same direction is not trivial.

Juan Benet

Yeah. How do you think that is going to start changing as AI systems get good enough to start planning and managing entire swaths of knowledge work, and then eventually teams?

Max Hodak

Yeah, I don’t know. Ironically, the company and our science are kind of architected around this. We’ve got this big internal software tool that we run most of the company through. So basically all of the information that the company generates is in this internal tool called Helix.

All of our purchasing, all the animals, all the parts, all of the meetings, it’s a system we built in-house. This is kind of a contrarian bet. Early on, I heard all these stories and saw these things secondhand about the power of internal tools at Facebook, YC, and others. And I’ve used a lot of ERP systems and other tools like that, and nobody likes their ERP install.

Juan Benet

Something like a common denominator for a user.

Max Hodak

Yeah, exactly. It’s this category of software that seems to resist generic solutions. But when one company grows around a piece of software like that, it can be very powerful. And because it’s all in one place, you can expose a lot of this to AI agents.

So, for example, all of the meeting notes, there’s a note taker that joins many internal meetings, takes notes, gets them into Helix, and then people can chat with it and be like, “Hey, what’s the status of this project?” And it can read all the meeting notes and give you a summary.

Juan Benet

Can you connect it to project management software and then figure out what?

Max Hodak

I’m sure that down the road, there are some parts that are really sophisticated. Every dollar the company spends flows through this thing. On the other hand, there’s an endless wish list of features that are not a high priority to add.

Juan Benet

Automatically schedule a new fab run?

Max Hodak

Someday, yeah. I joke that the next CEO of science will be an AGI, and I’ll know that we’re getting there when all of the control surfaces and all the information required to run the company are there. And I’m just hitting “accept, accept, accept, accept.” By 2035, I’ll be able to delegate that, and then I’ll get to go back and do the fun stuff.

Juan Benet

Let’s talk a bit about you as an individual. How did you grow up and become the modern Max Hodak? When did you first get into science and tech? What inspired you? I want to get your story.

Max Hodak

Yeah. I started programming when I was really little. My parents told me that I sat on the floor of a bookstore and cried until they bought me a “Learn QBasic” book or something like that. From the time I was a kid through being a teenager, the compiler was not especially concerned with how old I was. I was into science fiction.

Juan Benet

When you say you started programming really little, what years was that?

Max Hodak

I don’t know exactly. I think I started when I was as little as 5 or 6, and I became a good programmer when I was an early teenager. My dad knew how to program. He had an undergrad degree in aerospace engineering. My parents were kind of in business, but I grew up building model rockets and having exposure to STEM.

One of the big inspirations was definitely the movie The Matrix. I think one of my personal ambitions is to disappear into full VR, never to be found. Atoms are really annoying to work with. The speed of light is low. Earth is small. In the world of bits, it can be whatever we want, and I think there is something very inspiring about that.

That was one of many potential missions, but through that and other things, I got really interested in the brain. And I spent a lot of my teenage years reading about and learning about the brain.

Juan Benet

Through like textbooks or sci-fi.

Max Hodak

Textbook, sci-fi. All of the above. I remember when I was in high school, I discovered one book that stood out to me called The Biophysics of Computation by Christof Koch. The brain is extraordinarily cool, and the idea of being able to engineer that is such a transcendent goal that, if you can really do that... Sometimes I think my life would be easier if I had gone into AI instead of biotech, but let people figure that out. BCI remains to be solved. I think there are some important things there.

Juan Benet

We need to run a portfolio approach here.

Max Hodak

But I went to the college that I did. I have an undergrad degree in biomedical engineering from Duke, and one of the reasons I went there was because, at the time, one of the best labs in the world doing brain-computer interfacing in monkeys was there, Miguel Nicolelis’s lab.

So I talked my way into that lab freshman year. When I showed up at Duke as a freshman, I got asked for an advisor. They asked, “Is there any researcher that you want to work with?” And I told them that I wanted to work for Miguel. I was basically rejected. They were like, “No, no, no. He doesn’t take undergrads. Only grad students and postdocs. He’s in the medical center.”

Then I figured out that there was a chemistry course, a seminar in chemistry, that would place you in a lab, and I used that as a backdoor to sneak in. They took me as part of this chemistry seminar. It was not a chemistry lab, but I got in through that. And that was really where most of my education in college happened. I spent most of my time working in that lab.

That was also where I met a bunch of people who would later become some of the Neuralink co-founders. Tim Hanson was a grad student and then a postdoc in the lab, as was Joey O’Doherty, who is now one of the senior BCI engineers at Neuralink.

In fact, I remember that, a decade plus later, this would become the Neuralink surgical robot. I remember the first time Tim had this idea for building a neural sewing machine, and he ordered this old CNC machine, a pick-and-place machine, or a CNC, whatever it was. It was this extraordinarily busted thing, available for $1 on eBay plus $400 in shipping, that showed up and that, over the following 18 months, he turned into a very early prototype of this neurosurgical implantation robot.

Then they ended up moving out to San Francisco and becoming postdocs for Philip Sabes, who was later part of the founding team. But that lab was a formative experience.

Juan Benet

There are these very special labs, sometimes at universities, that end up shaping the people who then go on to create the whole field. This has happened a few times in computer science. For example, a lot of graphics came out of one lab at the beginning of the field, and AI came out of one or two labs.

Max Hodak

And Duke ’08 to ’12, there’s a lot of high-profile alumni there, but it is really overrepresented in Silicon Valley to some degree. Like Frederson, Byers, Zac Parrott, me, and a handful of others. I don’t know what was in the water there. Harvard, Stanford, yeah, that makes sense. But then there’s this whole group, especially in biotech, from Duke from that era that ended up being super overrepresented.

Juan Benet

How much did you learn at the university from the university itself, versus just getting connected to the relevant people in the relevant lab?

Max Hodak

I was not really into school. For my last two years of college, I actually ended up working out in Silicon Valley and commuting to college because I wanted to keep working in the lab, and that was interesting research. But I kind of showed up to exams and turned in problem sets and otherwise did not spend that much time thinking about the coursework.

Each semester, I would stuff all of the labs into either the first half or the back half of the week and spend the rest of the time in California. There isn’t really advice in that.

Juan Benet

I think it’s just a trade of how people go through college and so on. We both know a number of people who chose not to go down the university path and now do incredibly breakthrough R&D work in a range of places and are effectively self-taught, or they found ways of learning the relevant material.

Max Hodak

I did really feel like I needed to finish the undergrad degree because I’m enough of a dropout without having a PhD. Someday, if the stuff that we’re doing really works well, I’ll fix that at some point. But most of my education happened outside the classroom.

Juan Benet

Yeah. I think in your case, in neuro, you can’t be a university dropout. You have to be a PhD dropout.

Max Hodak

Something like that.

Juan Benet

What about after university? From there, I think you went to Transcriptic.

Max Hodak

Yeah. So when I graduated from college, I thought what I wanted to do was start a BCI company. But this was 2012, and I thought it would take at least $100 million. And this technology was at least ten years away from clinical translation, and I did not have $100 million.

So the choice was basically: go to grad school, in which case I could spend 6 or 7 years in grad school, and after that be kind of a postdoc who still did not have access to $100 million that nobody really cared about. Or I could move out to Silicon Valley, start a different company that I thought was more accessible with the kind of resources that I could raise at the time, and really learn how to build companies.

And there was another idea that I had that I thought was pretty good. In addition to working in the Nicolelis lab, I also spent a little bit of time in a synthetic biology lab where I had the experience of going into the lab to press a button on a machine every three hours for three days to take a growth curve with this bacteria that I was engineering.

And I was like, there’s got to be a better way.

Juan Benet

Yeah, it is astonishing.

Max Hodak

And to do that, you also needed all this lab space and millions of dollars of equipment. This was around the time that cloud computing was really starting to happen. And so the idea felt very obvious at the time, which was, what if instead of having your own lab, you had a cloud lab?

And that was not something you could simulate. You needed a physical laboratory with a bunch of robotics. The idea was that we’d have a set of APIs that scientists could use over the internet to run experiments. And one of the things that made me think this was possible was that I’d figured out by this point that you could buy lab equipment surprisingly cheaply at auction.

And so for the ten or so million dollars that was accessible, that I could come out and raise from Google Ventures and some others, you could get started on that. And so that became Transcriptic. And from 2012 to the end of 2016, I was founder and CEO of Transcriptic. We built a small business there.

Juan Benet

It was automating all of the flows of a specific synthetic biology process.

Max Hodak

Some parts of cell and molecular biology, primarily molecular biology.

Juan Benet

And this is automating it with robots. How much of it was software around existing machines versus robots on top of the machines?

Max Hodak

It was existing machines and custom robotic arms. We built custom refrigerators, freezers, incubators, and custom transport systems. The standard unit of device here is this thing called the microplate. So we made custom robots for moving microplates between devices. And all of the software is custom.

Juan Benet

By the way, this is kind of what TSMC and others look like today, with the wafers.

Max Hodak

Exactly. So Transcriptic ultimately did not revolutionize its industry. I think we made some key mistakes there. That was definitely on hard mode.

Early on, we were selling to academics because I was a student. That was what I knew. I knew these academic labs. I was not connected to big drug companies.

But automation in biology is good if you need to run an assay 10,000 times, or 100 times. It can do that quite well. But biology is still really pretty finicky. It is not the case that you can just write your protocol out as Python and then run that and have it work the first time. There is a lot of high-touch interaction there.

And with academic customers, if you gave them the option that their protocol could be twice as complicated, but they would save 20% per sample, they would do that every time. And that just turns out to not really be a good fit for this type of automation.

So a couple of years in, we had really figured out that where the business was, was in serving pharma and conventional drug discovery. And we eventually got some big contracts there. We ended up with this huge contract to run this $100 million Eli Lilly facility in San Diego, and that business grew to pretty substantial revenue.

That year was my last year as CEO, when I stepped down to co-found Neuralink. And ultimately, I do not think it lived up to its potential. There are elements of that business that I think should be tried again at some point.

But still, to this day, I think one of the structural things that I got wrong there, or that just was not in my worldview at the time and is only now becoming possible, was that biology does not happen in milliliters. It barely even happens in microliters. Biology happens in picoliters and nanoliters.

And microfluidics has been around for a long time. People have talked about building a lab on a chip for a long time. And there are lots of lab-on-a-chip single-purpose things. There are lots of ASICs, but there is no lab-on-a-chip CPU. And that has been a very resistant problem that a lot of people have thought about.

But things like modern DNA sequencers, and lots of other things like that, run on microfluidics. And whenever you can scale down, whenever you can scale down your automation to deal with biology on its length and time scales, things will get more predictable.

So I would approach it very differently if I were to do it today.

Juan Benet

As you mentioned, you have to step down from transcriptic.

Max Hodak

Yeah. In the summer of 2016, I got introduced to Elon, who already had the name Neuralink in mind. He knew he wanted to start this and was very concerned about what he saw on the horizon with AI, which I think has proved to be remarkably prescient.

And I think he deserves a lot of credit for creating the modern instantiation of this field. Without his bet on BCI, there certainly would not be the ecosystem in industry that exists today.

Juan Benet

Let’s end on this: what advice would you have for yourself coming out of high school or college, or maybe not necessarily yourself, just people right now staring into 2030 and 2035 coming up? What would you recommend brilliant people out there be focused on?

Max Hodak

Well, I think one thing to be very careful of is how social media and mobile devices have atrophied attention. I think there is a much more pernicious danger in copy-pasting from ChatGPT atrophying reasoning ability. So you have to keep thinking for yourself.

In fact, I think this is one of the central things that makes having any interesting career really hard. If you cheat on a test, then your grade will trend toward the average of the class. And certainly for startups, that would be failure. So you have to do better than that. And the only way to do better than that is to really think for yourself.

It is actually quite hard to do that because, by its nature, you will have people telling you otherwise, including objectively successful, smart people who you think are capable of giving you all this input. And nevertheless, you must pick things that make sense to you, even when you are alone in it. Because you might be wrong, but you cannot beat that, ultimately.

And as soon as you start delegating that, I mean, you can, in your judgment, delegate the judgment, but you still have to take all-cause responsibility for the results.

I cannot tell you how many times I have seen something where there is a consensus view, or literally everybody has some perspective on something, and it does not feel right to me. But then you are like, should I trust that? Because I do not think my judgment is perfect. So how do I know?

But ultimately, you just have to do things that make sense to you, and then it will get revealed how good your judgment is. That is the only way to get differentiated results.

And I worry as we start offloading more. Like we were talking about earlier, if you have your phone with you, your memory gets worse and your attention gets worse. And now you have this possibility of offloading thinking, so unless you want to be totally dependent on ChatGPT or Claude or Gemini, I think that is a real danger.

That is one of the AGI takeover scenarios that does not get talked about as much. It is not that there are robots out hunting humans, or even a single big agent that is clearly running the world. It is just that all of the humans have totally delegated their thinking to the machine, because it is producing better decisions than they used to have, and then suddenly you are kind of being puppets.

Juan Benet

Well, hopefully you’ll fix that by getting the biohybrid in place, and then other next-generation devices.

Max Hodak

So you have to be really thoughtful about how you integrate that with your own decision-making and your own thinking.

Juan Benet

When people are just starting out, it is sometimes hard to remember how hard certain things were, in terms of how you build your first project, your first attempt at a company, or raise your first money.

Max Hodak

Like, totally. I remember this clearly because I’d go ask experienced entrepreneurs, “How do you raise money?” or “How do you organize a team?” And they’d say, “Well, you need to have a really clear vision, and you need to have a good culture.”

And I’m like, okay, but what does that mean? How do I raise $1 million? “Having a good culture” is not practical advice. There are tactics that you can learn, and there are various things that are practical.

But then later on, you realize that really was the important thing. You actually did need it. Like you’re saying, how do you make sure that you’re always moving as fast as you can, that you have these short time constants, and that you don’t just bleed out by a death of a thousand cuts, these small slips that people don’t take seriously? The answer to that is culture.

And in some sense, these are the answers. But you also need the more tactical specifics.

Juan Benet

Yeah. There’s a great meme about how to draw an owl, which starts with two circles.

Max Hodak

And then you draw.

Juan Benet

You draw the rest of the fucking owl. So a lot of the advice is you start with a two circles, right?

Max Hodak

And so the way that I’ve characterized it, the apprenticeship that I did at the company I was at before Science was invaluable, because there aren’t really generic principles of startup advice where, if you hear the right thousand words, you can turn the crank.

A career is a long series of decisions, and you want to be able to look at those fact patterns and make local decisions that are best for those fact patterns. And even if this looks inconsistent over time, if it is a situation that is similar, but the facts look slightly different, you should feel empowered to make a different decision.

But the thing that you want is for your filters to be well tuned so that, when you’re faced with these things, you make high-judgment decisions. And that is, I think, a different lens than asking what the key tactics are. You just need to be good at making decisions.

And the best way that I’ve seen for that is, this is really an apprenticeship. It’s tough. You can’t really learn it through school. I tried to do it on my own, and that was very difficult. Humans are very mimetic, and I think the PayPal experience, for a whole cluster of Silicon Valley, was that they figured it out and they did it. And that was a really powerful founding moment, the culture that came out of that. And then I, like many others, ultimately got some of that imparted by doing the apprenticeship. But these cultures are oral traditions.

Juan Benet

And also, in practice, it’s kind of like a Jedi apprenticeship situation where you have to join the group and then go through it.

Max Hodak

And they’re tough. I had the experience where it was your best friend’s birthday, and you had been making plans for it, and then you get the text: “The plane leaves in 45 minutes. Get there.”

And the only way to learn these things is to be trying to make these decisions with jeopardy and stakes attached, looking forward with uncertainty, when it matters. And you go through a bunch of reps of that, and then you find out if you can learn it or not.

Juan Benet

Do you have a young Max style apprentice?

Max Hodak

Well, we’re fortunate to work with some really talented teammates. We still have a lot to prove. I do not want to sit here and say I’ve worked for very successful people, I have friends who are very successful, and we still have a long way to go. I think we should acknowledge that.

But yes, we’re fortunate to work with some really talented teammates, including others who I hope receive the oral tradition.

Juan Benet

Yeah. Let’s finish with any recommendations for very inspiring sci-fi, people to read, books, or maybe underrated sci-fi.

Max Hodak

Pantheon is great. It’s an animated TV show. Highly recommended. I joke that it’s actually a horror story about the extinguishing of consciousness in the universe, but people should go watch it.

Juan Benet

Is that a spoiler?

Max Hodak

I don’t know, maybe a very mild one. The Culture series. I think if you want to talk about people saying, “Oh, we want to build an optimistic future,” the Culture series probably best embodies that. And it’s kind of unique among science fiction in that it deals with artificial intelligence.

AI is kind of a tricky topic because as soon as you have it, it’s kind of like time travel. The story is only about AI or only about time travel. And the Culture paints a picture of a far-future humanity that is fully elaborated in these ways and is, in some ways, a utopia, but in other ways very complicated.

So many of the stories are told on the periphery of the civilization, where they interact with others and face these complex moral and ethical dilemmas. And you see how those get navigated even in a basically utopian society.

I also just love The Expanse and hope very much that Mr. Bezos decides to make the last two seasons.

Juan Benet

Yeah, that’d be great.

Max Hodak

Yeah.

Juan Benet

Hey, thank you so much for doing this. It’s great chatting.

Max Hodak

Thanks for having me.

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