SPEAKER_00: all right everybody today we have a special and fascinating interview get ready to talk about our brains joining me is the co-founder and chief science officer of precision neuroscience dr benjamin rapaport we have a fascinating conversation about how our brains communicate the scientific breakthroughs that led to the founding of neural link where he started doing this kind of work and then what led him to start a whole new company doing a different kind of brain interface precision's non-invasive layer 7 interface it's basically we live in the future as an interview about science that could have a profound impact on our lives it's going to be a fascinating show stick with us this week in startups is brought to you by squarespace turn your idea into a new website go to squarespace.com twist for a free trial when you're ready to launch use offer code twist again to save 10 off your first purchase of a website or domain contra is a commission-free marketplace for freelancers and independent creators get 500 off your first hire at contra.com twist and element is a tasty electrolyte drink mix with everything you need and nothing you don't that means lots of salt and no sugar get a free sample pack with any purchase at

Jason Calacanis: drink lmnt.com twist dr benjamin rapaport is uh with precision neuroscience and i'm going to let him

SPEAKER_09: explain what they're working on but by way of background precision uh has raised 53 million dollars to date to come up with minimally invasive uh neurosurgical implants right i'm going to let you

SPEAKER_10: take it from there welcome to the show thanks molly thanks for having me it's a pleasure to be here

SPEAKER_12: so please um tell me what you're working on with these brain implants well we're working on uh brain computer interfaces is the general term for the technology that we're building and uh yes they are a form of brain implant and they're designed to uh connect the brain directly to computer systems as ways of uh helping to treat some forms of neurologic disorder that are currently basically untreatable and those include things like certain forms of paralysis stroke traumatic brain injury forms of disorder in which the brain can think but the body can't act and uh and ring computer interfaces are designed to enable a direct communication between the brain and a computer bypassing the part of the body that isn't able to act in order to reconnect the brain to the digital world

Jason Calacanis: right how common are these disorders well there are definitely millions of patients uh millions of

SPEAKER_12: people in the united states alone living with some form of paralysis from spinal cord injury or other other disorders okay they're they're pretty almost everybody knows somebody right i this is i do not

SPEAKER_09: mean this in any way to sound insensitive but i just went through a version of this with my dog where he was losing the use of his back legs and it was simply you know the vet was like his brain is not telling his legs to work and it was super terrible um i want to kind of go through the history here because you're at precision now you were at neural link before that but i want to go all the way back to your background and what like what is the origin story here what got you interested in this and and more importantly what was the moment when you realized that this could be

SPEAKER_21: possible well uh well that's it that's a great way to start and you know no one ever really

SPEAKER_12: begins something completely de novo right and i i come from a family of doctors and engineers and in a way i guess i've been working on on this my my entire life my my dad is a neurologist who specializes in electrophysiology which is the the electrical aspects of the way the brain and nervous system work so um and my grandfather was a was an electrical engineer a radio operator in the in the second world war actually my grand my father uh trained to be an electrical engineer and uh was exposed to the very earliest forms of artificial intelligence and in a way that was how he made the transition to becoming a doctor so i grew up with electrophysiology um and clinical neuroscience is part of the everyday and uh by the time i was about 20 finishing college the most interesting thing in the world to me was what was what was just becoming possible or just seeming to become possible uh at that time in their late 90s early 2000s which was the notion that even though for a long time it was possible to interface electrically from a scientific and clinical perspective with the nervous system and in fact all through the 20th century um research neuroscientists and doctors had been using the electrical properties of the brain and nerves to diagnose and treat disease and to study the nervous system the electrical nature of the brain and nervous system is kind of what makes it special in in the human body but it was not possible to do that in a kind of high bandwidth way until the very end of the 20th century and what i mean by that is that you could maybe record from a small number of nerves or a small number of nerve cells at a time using specialty hardware until the very end of the 20th century it became possible all of a sudden uh through some breakthroughs throughs that maybe we'll talk about later to record from many many uh neurons at a time and that change in the in the bandwidth of our ability to interface with the brain and nervous system made the current generation of brain computer interfaces possible and that i saw that happening and to me it seemed uh incredible and and i basically spent the rest of my life have spent the rest of my life working in that in that space and early on very early on um scientists neuroscientists understood that it might be possible to restore function to paralyzed patients amputees spinal cord injury patients um even in some cases blind patients and so the promise of the technology has been around for for quite some time maybe 20 years now but it wasn't until about the late 20 teens that there was kind of a general consensus that it was ready to emerge from academia into the real tech world uh to really translate what had been proven in academic settings into uh clinical reality but that was something that i had wanted to do for you know

SPEAKER_21: basically a long long time i and many others and uh and that's what we're that's what we're set to do

SPEAKER_09: uh precision that's amazing so you were in the science fiction part of it where you imagined a future that could be possible and then that has become true within your lifetime which is amazing

SPEAKER_12: i guess i guess you could say that yeah i mean in a way that's how a lot of good science gets done

SPEAKER_21: right uh the the science the science fiction of the prior generation inspires the next generation

SPEAKER_09: to try to turn science fiction into into fact start with if you wouldn't mind actually the give us a primer on the electrical nature of the brain for people who may not be familiar you know you mentioned this is the thing that makes the brain in the nervous system special um if you wouldn't mind just give us that that kind of like 100 level why that is the case and why there became this idea that

SPEAKER_12: you could potentially tap into that in some way sure um well neurons which are the cell in the brain that are responsible for conscious thought and communication and many of the functions of the brain and body that we think of as making us human neurons communicate with one another using electrical impulses and those electrical neurons are tiny they're about um uh if you put them side to side the the the body of the neuron you might be able to put 20 in the space of a millimeter so they're very they're very small and the electrical signals that they produce are also tiny um but if you think about it if you make an analogy to kind of sound as i think sometimes that's an easy way to to think about it instead of thinking about electricity think about sound and think about the ways of interfacing with neurons and the brain uh kind of like listening to the brain instead of electrical just just because i think that's an easier way to associate with what's going on so um if we talk about in terms of listening electrically interfacing with the brain is kind of like in some ways listening to or speaking to the brain and interfacing with a neuron or with groups of neurons is kind of like building a tiny little microphone that we bring just up just up close to these tiny little neurons uh or that we place within a group of neurons and we try to listen to their chatter we listen to the way they speak to one another and uh and there are distinct patterns of electrical activity that we can that we can hear and make sense of um and and we call that decoding so the electrical chatter of neural single neurons and groups of neurons is a language every brain speaks a little bit differently the problem of learning how to interpret what the electrical signals are in the brain what they mean is a little bit different from individual to individual um and so that is uh that's kind of where the artificial intelligence angle comes from and that was part of the um technological change that occurred in the field of brain computer interfaces as we moved from the 90s into the 2000s and the 20 teens uh the ability to the material science uh that allowed us to record from many neurons or large groups of neurons at once coincided with increases in computational power and sophistication that allowed us to decode what those conversations among neurons meant and so that in a way that that confluence of technological paradigm shifts

SPEAKER_33: as what has given is what has given rise to a very powerful new technology which is the brain

SPEAKER_09: computer interface that's fascinating so you needed this combination of the ability to manufacture an implant that is smaller than a human hair as i understand it combined with the ability to have adaptive compute basically on the other end to say okay i i'm taking these signals i can decode them and i can put it's not a brute force problem you can't apply the same you know zap to every that's right so

SPEAKER_12: neural decoding is is a is essential to bring computer interfaces and so it's not like you uh it's not like you can um implant a tiny little microphone uh and and give the system a dictionary and say translate everyone speaks in with a little bit everyone's brain speaks with a slightly different language uh or or slightly different accent and the system needs to be programmed to adapt to that um and so that that's a sort of a fundamentally different paradigm of uh medical implant that we're talking about designing and has ever really been designed before hey everybody we're back with another show us your

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Jason Calacanis: domain so now let's talk about thank you for that that was an incredible primer also you're a total

SPEAKER_09: poet the analogies are perfect um so then talk to me about the process of of forming companies around this um so you started by founding or co-founding or partly founding Neuralink around this technology tell me about sort of the history there how you became involved with Neuralink

SPEAKER_12: i was one of the uh one of the eight co-founding team members at Neuralink uh back in 2016 set 2017.

SPEAKER_09: okay and then explain the talk to me about the innovations that were being worked on there

SPEAKER_21: yeah i mean what i'll what i'll say is that uh that uh i kind of mentioned historically the way

SPEAKER_12: the way the field has developed and um i can maybe take things back in history a little bit a little bit uh further just to give some context so you know the or the origin origin in a way uh which uh just to give some deep perspective is you know the 20th century was when biology really discovered the electrical nature of communication in the nervous system and that's what we were talking about earlier um and the way that scientists and doctors uh uh interrogated the brain and nervous system was with electrodes and an electrode is just uh it is just a device sometimes it is a wire sometimes it is another kind of conductive material um that allows you to either touch or come in close proximity with the part of the nervous system could be the brain could be a peripheral nerve could be a spinal cord um that's generating uh electrical signals and up until the uh the late 20th century there was no real standardized manufacturing process for manufacturing those electrodes uh and neither was there a really standardized way of processing the signals and so it's not like in audio engineering you know there was a there is a whole industry that standardizes the manufacture of microphones and uh you know equipment for uh amplifying and processing sound and filtering it and recording it you know there are standards and and known equipment that you can buy that didn't really exist very much in uh until the late 20th century and what did exist was relatively large scale devices and let's say on the scale of fractions of a millimeter okay so that's large in in uh in there lots of human hairs yeah a ponytail so when i say large it's still relative and then in the late 80s early 90s what i think was really a turning point for uh for neuroscience and and for what became the field of brain computer interfaces was the development of a of a device called the what's now called the utah electrode array and that was um that was a micro array of uh of tiny little electrodes space at a fraction of a millimeter from a part approximately 100 of them and they were made using the same manufacturing process the same microfabrication process that is used to manufacture microchips okay so you could make this electrode array of 96 or 100 electrodes you could make many of them they would all be exactly the same uh and you could you could give them to researchers to use and everyone would be uh recording using a standardized uh microfabricated device okay and looking back on it i think that was the moment when moore's law arrived in neuroscience wow okay and uh and remind us i'm sorry what year this was uh the late 80s early 90s okay um was when uh richard norman developed the utah electric array got it and so then you had the chip and and well that was the beginning you know it wouldn't it doesn't happen all of a sudden like that but but you you went from artisanal manufacturer of electrodes to a standardized uh microfabricated manufacturing technique and one that allowed high performance microelectronics to interface with the electrodes themselves and uh you know we are all familiar with the um you know the the kind of concept of moore's law the notion that some scaling property could be applied to the technology and uh the scalability of microelectronics is what is one aspect of what has powered uh the um uh you know the revolution in computing that has that began in the last century and continues today that scaling paradigm uh is essential and um the ability to connect the electronics to the end effector uh was essential so that only arrived in neuroscience and basically let's say 1990 um and uh that paradigm was pushed you know uh in into the early 2000s um remember that that you know high performance computing as we think about it today the kind of things that enable modern artificial intelligence applications that didn't exist until the mid-20-teens so things that we kind of take for granted high performance computing applications that we take for granted even in things like image processing uh you know did not exist uh at the at that time ironically it also required sort of a new chip architecture right the shift from cpus to gpus that's true correct so gpus did not exist at that time so these the early interfacing of software with um the new generation of microelectrodes was all cpu-based uh computing which uh which worked just fine for tens of of electrodes and and it was it was kind of strained at the hundreds of electrodes uh level um but now you know in the 20 teens computing was able to catch up and concurrent with all of that um you know there was a generation i include myself in this of uh you know engineers in training uh masters and doctoral level engineers who kind of cut our teeth on circuit architecture algorithm designed for how to interface with the signals coming off of these sorts of electrodes and by the early 20 teens i think there was a general consensus that um most of the major science problems or many of the major science problems even some of the engineering problems had basically been solved in other words how to how to build back-end electronics and how to encode software that would make sense of many simultaneous electrical signals coming out of the brain in a way that we could understand what the brain was trying to tell the arm or the leg or the mouth to speak um and at that point there was a sense that um to take the next step to really transition academic science into clinical reality that would benefit people it was time to to move into a commercial and industrial setting out of the lab and i want to give you one other piece of context which is that you know think about the historical backdrop uh of all this work as it was happening in the united states you know from the early 2000s 20 teens we were seeing a lot of you know wounded young people coming back from iraq and afghanistan and the um the national science foundation and darpa and other funding agencies had a strong mandate to try to do whatever was technologically possible to to take care of these motivated young people who had been serving our country and so that led to a tremendous amount of attention being paid to advanced prosthetics development not all of that was neural prosthetics it took the work took many forms but certainly that uh motivate that motivation catalyzed a lot of tremendously important work and so the the government funding agencies invested heavily in the development of this of this technology but at some point it became clear that uh that that form of investment and the time scales that were required to to um secure the government grants and do the work in an academic setting the funding wasn't it wasn't enough and the time scales were too long and in order to really translate the advances that had been developed in academia into clinical reality it was time to to move into uh an you know a commercial setting and so in 2016 uh a couple of a couple of major efforts shifted attention uh from the academic to the commercial setting neural link was one of them facebook had an initiative around brain computer interfaces company called kernel also um was started started around that time each of these entities put a huge what what what at the time seemed like a huge amount of capital behind um moving people and resources from academia into uh a more commercial research and development setting to build the brain computer interfaces that would actually go into the clinic and uh that has given rise to a small ecosystem of uh advanced startup companies and a tremendous amount of talent being brought to bear in the field and i would say that that that's the best thing that happened out of neural link uh is that um engineering talent uh has been really focused on what i think is a tremendously important problem for our generation and that the more the more talent we bring into the field uh the better so it was sort of it was sort of a beacon

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Jason Calacanis: bit more about that i think we're all familiar with the the kind of academic valley of death that can

SPEAKER_09: occur with r d where it doesn't become commercialized or there is just not quite enough investment right it's like you can get here but you can never make the final leap and yet there is also that question about what happens if you attract all of the best and brightest researchers and scientists to private industry that if that doesn't work then you've left universities unable to continue this research too like how do you tackle that tension behind doing this as private companies that have you know investment return expectations as a result you know because of their vcs versus having this

SPEAKER_12: happen kind of in an academic way that might be more open yeah it's a great question uh and uh there is that tension always there always will be that tension and um it's hard to solve the general case uh but i can speak to um i can speak to the specific case of how how that sort of dynamic has played out in in our field um and maybe to give a little bit of perspective i like to think about what's happening now in in neural interfaces as somewhat similar to the genomics revolution of the early 2000s and if you sort of think about it uh you know actually in the year 2000 it was not common for a computer scientist to be working in biology true right uh that was kind of a new thing yeah we don't think about it so much nowadays but it was not really a standard thing to have computer scientists working in biology but the human genome project gave them jobs in biology there was no such thing really as computational genomics you know right uh uh in the 80s it wasn't really it wasn't really a platform it wasn't enough data there there was not um kind of an infrastructure and then you know what happened was that uh the human genome project in the final stages became kind of a competition between uh you know industry and an academic consortium and uh it doesn't really matter so much who won it just matters that that competition made it clear that um it was time for for high throughput gene sequencing to to to take its place in industry as well as in academia and so it gave people who had been working in a purely academic sense uh an opportunity some of them to to move and to take care of the engineering and scaling challenges required to um to actually bring what was essentially an academic endeavor until then uh to patients and doctors and healthcare systems and um and that itself uh is is is has been challenging in all kinds of ways but but the that transition has been made by you know quite a few companies that have gone on to be tremendously successful to generate jobs and have economic impact that has uh been far greater than the total the total amount of federal investment that's gone into the field and most importantly um you know it has had a tremendous impact on on medicine you know to the point where now you can you know you you've had you've had some of these people on your podcast and uh you know in in the past but you know you can you can as a consumer you can have your genome sequence you can have all kinds of insight into your family and and past and future health uh as well as you know patients undergoing advanced medical therapy can have not just their own genome sequence but uh you know the genome of a tumor for example right um so i i mentioned that as as background because i think that something very similar is happening in neural interfaces today that uh to me i think the year 2016 was for neural interfaces kind of like the year 2000 for genomics uh in that several major entities were formed that drew tremendous talent out of uh out of academia into industry with the mandate to try to take uh academic science and bring it to patients and um you know like you say that that transition is fraught and it's challenging in all kinds of ways uh it's not usually usually it's not usually well done in an academic setting it kind of needs it kind of needs professional engineering and uh oftentimes a profit motive um to really to really develop a robust engineered system that meets all the quality control standards and regulatory standards

Jason Calacanis: that allow it to be patient facing right um so then on our sort of journey here from 2016 say

SPEAKER_09: to today what what if you don't mind my asking caused you to leave neural link and co-found a new competitor if you will i don't know if it's a competitor directly or not but what's what's happening at precision and what made you want to go do that yeah i mean i i think that um as was the

SPEAKER_12: case in as was the case in uh in genomics and high throughput gene sequencing we've seen that uh there there was tremendous opportunity there and a number of very successful high impact companies emerged all attacking aspects of that scientific endeavor from different ways and so maybe some of them are competitors and but nevertheless they've been able to uh have tremendous impact uh side by side and i sort of see something similar happening in neural interfaces today there are um there's no one size fits all neural interface there is i think a consensus that the ability to interface with many many neurons or to interface with the uh the brain and nervous system in a very high bandwidth manner is critical and that's that's the general trend that's what modern brain computer interfaces are they are high bandwidth connections between the brain uh and um and the digital world uh but there are different

SPEAKER_35: ways of doing that and so yeah i guess that's the question is is precision neuroscience solving a

SPEAKER_21: different problem than neural link was tackling it's solving it in a different way and uh and that way

SPEAKER_12: is going to have uh some different applications so um so one of the things that that i have uh one of the let's put it this way that the some of the founding principles of precision are a little bit different from what uh what others are doing in the field today and we feel that in order for um neural interfaces to really have a major impact clinically in patients we have to be able to reach many patients and uh and to do it very safely uh in a way that um poses minimal risk to patients for maximal benefit and in a way that is extremely scalable so the the performance of a brain computer interface um is very much dependent on the bandwidth of the interface which makes sense right i mean we all live through this transition from uh from dial up to uh high speed internet and uh all of the changes and advances in technology uh that we've seen go along with that have been completely transformative there are things that we can do with uh you know with high speed that we could never even have dreamed of uh with early generation modems and the same is true with neural interfaces um the scale the bandwidth is tremendously important and so um we have developed a platform around those principles that safety which which to us also comes with minimal invasiveness so that means that means basically not damaging the brain with the interface and yet being able to deploy an interface that scales to very high bandwidth those uh are kind of the guiding principles uh the guiding design principles at precision and so that resulted in us uh designing an electrode interface that rather than being um uh many tiny little penetrating electrodes that penetrate the surface of the brain um that is the nature of the utah electrode that we mentioned before that is the nature of the neural ink electrode and some others uh rather we're using tiny little electrodes uh think of it like saran wrap so a kind of saran wrap that coats the surface of the brain with um many many tiny little electrodes each one of which is about the size of a neuron uh and and yet it doesn't penetrate the brain so it can listen to the brain uh at very high resolution can even stimulate the brain so it can listen to and speak to uh the the brain cells um and yet it can be removed with no damage to the brain and um and it can be replaced or upgraded uh those are the that's the that's that's the nature of the system and it can be deployed in a way that doesn't require a very

SPEAKER_09: complex open brain surgery and that is not the case that is distinct from other solutions i'm not

SPEAKER_21: trying to get you to i'm not trying to be provocative here from other that is distinct from other from

SPEAKER_22: other solutions got it okay how safe is this like how far along on the road to true commercialization

SPEAKER_92: and widespread adoption are you so we are um we're getting ready for uh fda submission uh this this year

SPEAKER_12: our um we have we work we have done a lot of work in large animals uh and uh all the early work that we've done uh all the work that we've done to date suggests an extremely uh good safety profile so our goal is really to to to never damage the brain through the the interface and uh just to to make it clear that truly is a different paradigm from uh the brain computer interfaces of the 90s 2000s 20 teens all of which were most of which many of which let's put it this way many of which were developed around tiny little electrodes that uh penetrate the brain uh so so think of the little microphones that we discussed before the little microphones that listen to uh groups of neurons the way those are think of those as little wires or little needles and in order to in order for them to do their listening they need to be placed inside the brain itself like if this if this microphone in front of me was

SPEAKER_35: actually extending a little tendril into my vocal cord directly so that it could hear me i would prefer

SPEAKER_21: that it not do that exactly you prefer that it just listen to your voice and uh and so uh you know

SPEAKER_12: with with a microphone you can have something that's completely external to your body right uh so that's that's and and you know for for a long time uh people have been trying to ask the question can a high bandwidth neural interface be completely non-invasive right uh right and and there has been a lot of work that's gone into what kinds of electrical signals can we record from outside the brain completely from outside the body from the scalp or um you know something like that and certainly there are detectable electrical signals that one can detect from outside the head completely but they are uh they are not um they didn't that that kind of system does not permit high bandwidth information exchange between the uh the electrode and the brain just the the physics of the situation doesn't permit us to to see uh to listen at high resolution spatially or temporally so you need to be really close to the brain to get the to get the best quality information and so what the precision system is doing is to get as close to the brain as you can without damaging it so that sounds like it seems like what

SPEAKER_09: you're saying is this is a big deal that's a really big breakthrough yeah i think so that is a

SPEAKER_21: completely new uh completely new paradigm congratulations thank you thank you you're

SPEAKER_12: like i need you to understand this is major like this i mean nothing just you know nothing comes in isolation right i mean there there is we're conscious that there is um you know decades of neuroscience and engineering that have allowed us to you know to get to this point and uh and likewise there is an ecosystem around us that we are interfacing with and that is an ecosystem of um you know high performance manufacturer advanced manufacturer in the united states the whole medical device industry as it as it exists today the regulators the fda and insurers hospital systems neurologists and neurosurgeons and patients and their families i mean there is an entire ecosystem required uh to move you know a promising technology from uh the laboratory into patients lives and that is kind of what you were asking before is how do you make how do you decide when to make the leap how do you make it work uh and we're very conscious this is not tech development that happens in a bubble especially medical technology um the the environment is highly regulated and the ability to the ability to um to uh the ability to get early feedback from our users uh in medical technology is very much limited relative to almost any other high tech industry um and we're sensitive to that and i will say that is one thing also that is that is unique about the precision system um uh and you you asked before about um what our next steps are and how close we are to uh to actually being able to deploy the system and because our electrodes don't damage the brain they're what we call surface micro electrodes as opposed to penetrating micro electrodes they can be removed or they're designed to be removable without damaging the brain and so uh we're designing the first system to be a temporary system uh meaning it will be usable for a temporary interface early on for diagnostic use in conditions like epilepsy uh and then it will be able to be removed and that has certain advantages um one of them is that uh the the first generation device we hope will do a lot of good clinically uh it will certainly deliver to um we hope will deliver to clinical practice the ability to interface with the brain at spatial and temporal resolutions that have never been possible before uh we'll be able to see we we've seen it you know we've seen it uh in in development but hopefully we'll see it very soon in in patients uh the the real-time activity of the brain at um spatial resolutions that have really never been seen before and uh to us that's tremendously exciting we think it will have a significant impact on uh clinical care um but the fact that the first generation device uh is removable allows us access to a regulatory pathway called the 510k pathway which is what the fda calls the pathway for um devices that that are sufficiently similar to something that's been done before and so the path is somewhat expedited relative to uh what we call the pma pathway which is the for used for class 3 high risk permanent implant devices and um we're very excited about that because we feel that even as we deliver first generation benefit to uh clinical medicine we also will be able to understand how the device works in the hands of clinicians and in the lives of patients and uh that will be uh we hope the the first uh or one of the first if not the first you know approved high band with electrophysiology systems in uh in clinical medicine today and uh that we hope will set the stage for um the generation of parenin implants uh that that that come in the years to follow but that that will generate a lot of learnings in our in our view um and we hope will be good for the field yeah i mean not to

SPEAKER_09: oversimplify but you'll be effectively your first product will be both a diagnostic tool like it will

SPEAKER_12: be a test strip for the brain in a sense yes yeah it will it will it will deliver we hope that it will deliver clinical benefit while also breaking uh kind of breaking open the space of uh high bandwidth electrophysiology yeah uh it will it will be one of if not the first truly extremely high bandwidth neural interfaces available to patients so that's that's what we're that's what we're working towards

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SPEAKER_95: like let's have the sci-fi part now because there is still so little we know about the brain all things

Jason Calacanis: considered and it feels like this is a huge opportunity to really get in there yeah yeah well

SPEAKER_12: i i i i love that uh i love that way of thinking about it at the same time um you know when we think about this question that you asked before you know when is it when is it time to move from academic science into uh a commercial enterprise and um so in a sense we try to do as little new science as possible right we're really trying to uh to just professionalize the science and engineering that has

SPEAKER_118: already been done um because to be clear is already awesome i'm not trying to jump ahead

SPEAKER_12: this is already amazing it's a huge lift it's tremendously exciting and and i think very very high impact we everyone here at precision believes that i i just you know since we're talking about in a sense you know this this podcast is about strategy and ways of thinking about um startups and and the world of new technology and medical device development is high risk enough and uh we want to take as much of the risk out of that development process as possible or at least to quantify it to the extent that we can and to take on the risk in as small bite-sized chunks as we can and so uh you're right there is a lot that is unknown about the brain and we try to deal with the parts that we know the most about uh and so so when you ask you know what will we be able to learn uh what will we be able to learn from this uh device once it's cleared for use in human patients and the answer to that is i i hope that we learn a ton i hope that we uh you know are going to be delivering uh both the diagnostic tool and a scientific tool to the community uh that will teach us all kinds of things uh and certainly um you know the last generation of electrodes and electronics that were delivered to that were made available for clinical use we have learned unbelievable things you know uh really really i would say um transformative things you know that if you think about you know 50 years ago what the first generation of electrodes was able to teach us it taught us about where language was located in the brain in great detail and uh all kinds of all kinds of detailed things um and i think that we'll learn a ton uh when we can basically what we're what we're providing is is a tool that will um that will provide sub-millimeter resolution uh electrical information from the brain so it will provide a window that will allow us to see the active brain in real time um kind of at a microscopic scale so think about you know looking at the brain under a microscope but actually being able to see uh how the brain is computing not just being able to see what the brain looks like um at the same time you know we when we think about the clinical applications that we're designing around we try to design uh around those areas of brain physiology that we feel we know a lot about so um you know what one of the one of the patient populations that we're designing for are patients with uh various forms of paralysis and that includes paralysis of the limbs as well as paralysis of the articulatory muscles of speech so uh in a sense aphasia and certain forms of inability to speak are also forms of paralysis uh to the extent that that it's the the articulatory apparatus the mouth the tongue the pharyngeal muscles and so on that cannot move um and all of those uh muscle systems have um have spatial representations in the brain and we basically know where they are uh but being able to interface with them at uh the scale that we're talking about we hope will enable um unlock functionality for patients with those kinds of disorders that has not been possible with um you know with lower resolution electrode systems so for example understanding the uh the detailed structure of the articulatory muscles of speech or the fingers and fingers and where those lie on the what we call the motor cortex of the brain um only electrodes to the scale that we're talking about can really interface with uh the neural structures that give rise to function in those those subtle aspects of um you know of the body we kind of know where they live but we need to we need um sensors uh and computation that are um on the right scale to

SPEAKER_26: interface with them yeah so that that to me those are some of the more exciting applications that we're looking to uh to develop in the years ahead that's amazing and then one last question i promise i will

SPEAKER_09: let you go because we also talk about investment on this show talk to me about cost and business model the manufacturing process you've described you know i mean i know from just semiconductor manufacturing it's a clean room situation unbelievably expensive expensive boundaries how what does it cost to

SPEAKER_95: produce one of these layer seven devices and what will it cost on the you know other side of things

SPEAKER_12: uh that that that is a great set of questions and uh it's a it's not a seven minute discussion not going to answer exactly so please invite me back you know for i would love to but no it's a great question it's absolutely critical and i'll try to address it you know in a couple in a couple of minutes but that question gets at uh you know some deep aspects of how medical device development works uh in in the united states and in the world and in what medical device uh what the medical device industry is going to look like in the years ahead and um you know to date really there is almost no example of uh implantable medical technology that really depends on microfabricated sensors and actuators almost all medical devices today are artisanally finished meaning human hands uh are met are making and finishing the devices um so unlike the semiconductor industry uh which has all of the highly expensive infrastructure that you mentioned um medtech has not relied on that today i think that is going to change in in the you know in the coming years certainly uh the neural interfaces industry is is facing that and we are driving some aspects of that change meaning that certain the sensors that we're developing do require microfabrication uh they don't require the kinds of uh single digit nanometer uh resolution that um advanced semiconductor manufacturer requires today uh but nevertheless they require similar processes and so we are seeing a need for advanced manufacture um in medical technology and uh to me that's actually very exciting but also it does of course come with uh a number of considerations including investment dollars for how to scale up that manufacture but the question of um how much it costs to manufacture the device and uh and how that cost is borne it by uh insurers and so on is is a good one i can say that um these devices i think will be more expensive than the current generation of uh of um implantable neural devices like deep brain stimulators then pacemakers then cochlear implants uh and so on but not uh you know not probably not 10x and um and if you think about uh the kind of medical economics of what we're trying to do it's easy to understand how it makes sense what we're really trying to do with these devices is to enable uh say a young quadriplegic patient who may be 30 years old and have you know 50 years of life ahead of them and uh you know 35 of them maybe in the workplace uh we want those those patients to be able to have a level of independence and dignity and financial self-sufficiency um and the ability to go back to work if they want to and if you think about the change that that that is possible we firmly believe that that is possible we're not really that far away actually from enabling that transformation but if you think about the metal economics there it's not a hard case to make um you're taking somebody who right now uh forget about that we need to do it as a society i mean just that that's just a given okay uh but but from an economic standpoint you're taking you know people who uh whose medical care is largely borne by uh disability insurance and state-run programs and things like that and um in the workplace they they pay for for commercial insurance so the the the the impact of that economically on the medical system is a is a completely sensible model uh so even if the um even if the devices are several times more expensive than current generation devices uh the technology will uh the impact the impact on uh the system as a whole will pay for itself and uh and i will also say that um most likely i mean almost certainly we're gonna look we're looking at a slightly certainly hope we're looking at a different model in the years ahead uh there has been a shift um in uh the medical device medical technology industry towards more software as a service software as a medical device models and a lot of the functionality we spent a lot of time talking about the material science and the electronics and so on uh and we talked a little bit about the uh the computation involved but um a lot of the functionality uh over the years will come after well after the implant uh as software upgrades are pushed uh you know to the device um and that will the ability to push software upgrades uh and enhance functionality um will continue for years and years after the initial implant and one of the things that we say at precision is that uh you know every patient should should uh the data that is generated by every patient should provide some benefit to the data to to every other patient's care that comes after you know that every patient should be helping every patient that comes after them and uh machine learning does make that possible uh you know data science today makes that possible and so um but that will also be part of the economic model you know the software upgrades will not be free and uh and but also you know people will only be paying for uh for functionality that that benefits them yeah so i hope that answers your question in a nutshell

SPEAKER_09: definitely it's fascinating and now i want to have part two dr benjamin rapaport is co-founder and chief science officer at precision neuroscience your present really is our future it's fascinating

SPEAKER_104: world you live in thanks for having me real pleasure being here thanks for the time