Good Disruption: Inside Brain-Computer Interfaces

UVA Darden.

The Disruption, a lively discussion between UVA Darden School of Business professors Yael Grushka Cocaine and Mike Lennox on cutting edge technologies and practices that are challenging the status quo.

Hey Mike.

Hey Ayel, How how are you doing today?

I am doing fantastic.

So, so for our loyal listeners out there, this is a very exciting time while we're recording this.

We are literally at a point in time of handoff.

Oh, baton handoff, handoff.

If you have not heard the news already, I have been serving as our interim dean for the last 7 months, and Yael is our incoming dean.

So we are the deans of Darden bringing to you.

Good disruption today.

I, I just gotta say I am just so excited for you and for the school, and uh you're gonna, you're gonna crush it.

We know that, uh, and you know, my one ask for our listeners here of YIL was she has to keep doing the podcast.

So she's promised me she's not gonna get too busy that she won't do the podcast, uh, but no, we're really, we're really thrilled.

I felt like we were coming as a like a high five moment.

Do a high five moment.

Can we do a high five on, on, yeah, there you go.

All right, you make that sound good in postproduction, Gary.

No, thank you.

I feel like the podcast has been a good omen in my, uh, in my appointment, and I'm, uh, very thankful and appreciative for this partnership and for this podcast.

And so I look forward to continuing to do it.

And, and in 2 years I want to have a podcast.

We debate Yael, good, bad, or no disruption.

Good.

I hope I go down on the record.

I hope you go a good disruption, good disruption and a disruptor of sorts.

So what do you want to talk about today?

I, um, what's top of mind to you, top of mind to me.

So as I'm stepping into the world and as I'm taking over the baton from you, um, I find myself in, uh, key moments thinking about things that I wish somebody could capture in my brain.

Like I'm driving the car, I'm in the shower, I'm running on the treadmill, you know, those moments where you're like, Oh, I thought about this thing, great thing.

Please can, can it get registered somewhere so I don't have to remember it because of course I forget later on.

Have you been in those situations.

Every day, every day, this happens to me.

And so, um, what is the solution there?

Like, you don't wanna just forget and constantly be thinking about this.

Imagine you could actually tap into what's going on in your brain and have it somehow signal that signal get registered and documented and activate something else.

Wouldn't that be awesome?

That, that sounds like the premise for a lot of sci-fi movies.

I know, I know, and you're a big sci-fi.

I love it.

I love it though.

Yeah.

So today we're gonna talk about brain-computer interface.

So BCI, yes, exactly.

BCI, BCI is an emerging field, a growing market.

We're gonna talk about all kinds of applications, but did you even know that this was a thing, right?

That this is a 11 I've heard of, which I think a lot of people have heard of is Neuralink, which I know Elon Musk is a.

You know, behind and, uh, um, but I actually haven't followed it that closely.

So, so where are we today?

So the global market size, um, is valued in 2025, so pretty recently at $240 million US dollars and relatively small, relatively small, but projected to grow to almost close to a billion dollars by 2034.

OK, so it's growing.

And primarily North America has been dominated a lot of dominating a lot of that with about 44% of the market share in 2020.

So given that there's already revenue here, like this is real, right?

This is not just sci-fi.

We are able to connect your brain directly to a computer interface.

Yes, and there's different modes of it.

There's more and less invasive, as you can imagine.

There's things that are more on the outside and they capture certain um activity, kind of sensors on the scalp, if you will.

There's, um, a lot more invasive kind of.

Technology, um, primarily use cases that we've seen are very much in the medical space, as you can imagine, paraplegics, correct, correct, where, uh, I can, you know, I get excited thinking about the opportunity to really connect to like messages that our brain wants to send that if for some reason our, our body currently can't cope with it, we can find ways to kind of bypass it.

Now, you know, you sometimes get the impression that these are like they're putting sensors into your brain.

Is, is that right as well?

Is that how this works?

So you, you mentioned that some of them are just kind of reading signals.

Yeah, some of it is really, um, electrodes that are really implanted kind of through surgery in your brain, um, much more high pre, you know, high precision, so much more accurate signaling of what the activity in the brain does, um, but of course it's a procedure, it's a surgery, you have to actually get in there, and that sometimes has its side, side effects and some risks.

Again, this is not my expertise at all, but it, it, it is what I seem to read on the popular press, like.

We're still learning a lot about the brain, right?

Like we, like some of our, uh, previous notions of how our brains work are probably completely outdated.

Is that, is that correct?

That's so true, and that's what's so fascinating about it is you would think, you know, the, the brain has been a fascination of researchers for so many, so many years, and we're really learning some basics on how it works and how we can get it to work better, or, or, or help send the message across the body.

To kind of deliver on the functionality that the brain is hoping to deliver as we were preparing for the podcast, I thought it was interesting to note that this old adage that, you know, we only use 10% of our brain, and if we could tap into false, yes, that this is apparently not true.

This is not like this is, you know, I don't know how it got started, but this is not actually correct, correct.

We, we virtually use 100% of our brain now, not all the time in all the ways, uh, for each and every moment that we, we leverage it.

But we do use 100% of our brain in different ways uh throughout our functionality.

And I imagine the other piece that maybe we, uh, misunderstood in kind of earlier eras was that like, there are very small specific parts of the brain and they do one thing, like, oh, they can.

Control your eyes and this over here controls your sense of taste and, and like, but I think that also isn't exactly right, right?

This is a complex system.

It's a complex system and it's a little bit different, right?

Obviously with individuals and the various circumstances that their brain operates in.

Um, but I feel like we're gonna quickly run out of our knowledge, um, and, um, we're so lucky at UVA because we have incredible experts, and one of those experts is a dear colleague from our school of data science here at UVA, um, Jack Van Horn.

Jack, we're gonna invite you to come and enlighten us and tell us a little bit more about not only how the brain works, but how the computer can.

Interface with it.

Thank you so much for having me this morning.

You, you guys are so fun.

Thank you so much.

So Jack, what do we got wrong so far?

Well, before, before, before we actually ask Jack to correct us, can I tell you a little bit about Jack just so our listeners know, because Jack is incredibly, um, incredibly talented and, and well respected academic.

He's a professor of psychology and quantitative foundation, distinguished professor, uh, um, at the School of Data Science, as I mentioned.

His research really centers on.

Um, informatics and data science of human neuroimaging, so really the expert in this field, um, and accompanying biomedical data for identification of patterns and biomarkers of in brain health and disease.

So, a lot of, uh, kind of application to the, to the medical space.

Um, and, uh, Jack has Has authored, you know, hundreds of articles, really accomplished academic, and he's the editor in chief of the Journal of Neuroinformatics, um, recipient of many recent awards, and we are just so honored.

Um, Jack, tell us a little bit about brain-computer interface.

What is going on right now and why is it, why is today the time for it?

There's a lot of excitement around brain-computer interfaces.

I think you're seeing a lot of it in the media and you're seeing a lot of it um kind of fictionalized in Hollywood.

Um, I think, you know, perseverance, I think got a lot of people excited about what this might mean for us real humans outside of Hollywood.

So brain-computer interface is a kind of, we like to call it a closed loop kind of a system where it's making a measurement of brain activity.

It's trying to decode that brain activity using data science.

And artificial intelligence kind of means, and then be able to actuate something that can help that person do some activity and then use feedback from that to help kind of adjust the measurement process and then keep that loop going so that somebody might be able to say, move a robotic arm or be able to move a cursor on a screen while they are physically unable to because of a neurological condition, for example.

It's really, and it's, it's a combination of an engineering problem.

Problem with a neurophysiological problem, with a data science problem.

And as you might imagine, all those things kind of coming together, making an exciting business opportunity for those people who are dipping their toe into this world because it's, it is kind of exciting to think about because I think what Mike was talking about with the, uh, kind of seeding us with all sorts of sci-fi related potential that something like this might have.

So, um, it's really interesting because it's It's probably not just one story.

It's probably multiple stories layered on top of each other.

Every day we're all using our phones, which in a sense are a brain-computer interface, right?

Where it's, we're using that, we're learning from our phones.

It's teaching us things.

But then you have the more invasive variety where it's something that as Yale was talking about, it's surgically implanted.

Your brain.

This is something that can be done to alleviate, say, Parkinson's disease related symptomatology.

It's being used in other areas as well, and those are seeing various levels of success, but only in a very few number of subjects.

So we have to kind of couch that in terms of, you know, watch this space.

It's not ready yet, but there's some interesting potential.

I, I'd love to start with the data science challenge here.

I, I have a systems engineering background, study complex systems, and as we were talking about the brain and the number of synapses and the like, tell us more about like that, that understanding of what the signals you're reading in the brain and why maybe it requires AI to help us make sense of it.

The brain is, I like to say, one of the most, if not the most complicated object in the known universe.

There are more synapses in the brain than there are known stars in the universe, and it's kind of dealing with that kind of challenge from a data science perspective is really, you know, orders of magnitude harder than a lot of other engineering and kind of physical challenges that, you know, we undertake.

So it's a real complex system.

It's changing all the time.

It's what we say is plastic, meaning that, you know, just the act of measuring the brain changes how it functions.

And so it's this interesting moving target, which is a real kind of a technological hurdle that companies that are interested in trying to, you know, come up with these solutions will have to kind of figure out that we're getting better at that.

All the time, but the signals that we can get are multifaceted.

We can get using human neuroimaging data.

We can get whole brain pictures of brain blood flow related activity.

We can understand structure, brain connectivity using electrophysiological methodologies like EEG.

We can get those surface electrode recordings of neural electrical activity, though at poorer resolution.

If you do things like What's called electrocorticography, where you can expose the surface of the brain and you can make measurements more directly on the surface of the actual brain tissue.

That's an extra level of resolution that a lot of these technologies we're talking about today, they're looking at that particular level of being able to interface with the brain.

And then using the data that you collect and using machine learning, using various different algorithms for decoding those signals as people.

Attempt to do something like speak or move something or move a cursor.

If you can understand what those signals look like, you can decode them and then you can use that information to invoke an action.

And then that basically starts that process of that kind of make a measurement, decode it, drive some sort of a system, take that feedback, pump it back into the system, and have that loop continue.

Amazing.

Can you make comparisons across these systems, so basically across brains, or is it that we each have our own very bespoke.

Set of neurons that then my brain is different than yours.

I know that's the case, um, but, or, or, or, or can we learn by looking at like a large sample of brains that like, oh, OK, common patterns seem to emerge.

Yes and yes.

So everybody's brain is unique to them.

It's like your own fingerprints of your brain.

Now you have the same sort of parts, but how they're actually kind of configured may vary from person to person.

I think some of the early brain-computer interface type stimulators, for example, got started in the 1950s, believe it or not, but they were very simple and they had to be kind of customized and bespoke for every.

Particular person.

That's still kind of true, but, um, obviously, with the emergence of large language models and computational approaches, um, that draw from multiple different data sources, we're able to kind of like expand that.

So something that gets built for, say, one person could in principle, be moved over and with a little bit of retraining, kind of, uh, bootstrapped so that somebody else could utilize that system.

Also, because of that brain, that.

Dynamical brain system.

One of the challenges early on was that almost every day you had to recalibrate your system or, you know, how your brain was performing that particular day or it had changed because of those plasticity, um, properties.

It needed to retrain itself.

Now, um, it's possible that that amount of recalibration can be done in a much more dynamic fashion so that somebody could just wake up and start using their brain-computer interface device.

Amazing.

I, I love the fact that it's such a complex system that is sitting in our bodies, and it's right there, and we can develop so many advanced technologies, and yet understanding the brain is so difficult.

That's why UVA has a whole institute for, for the brain and studying the brain more carefully.

Um, you, you mentioned, uh, and you described it very clearly how it relates to an action, so, uh, moving a pen, moving, you know, a, a mouse or a cursor, um, so we're not quite, we're not quite at the point where the The Computer can read our minds.

Is that correct?

That's correct.

We're that's sort of the stuff of Hollywood at the moment, and I'm, I'm gonna ask about that.

I'm happy to leave that there, but, um, yeah, these are for things like intentions.

So like if you're intending to say a word, you know, these machines can almost act like an autocomplete for you, right?

And that gets an idea of what you're after and it can kind of complete it for you, and through that process it can be successful.

The naturalistic speech is about, oh, say 160 words per minute, whereas some of these systems can get to 62 words per minute.

But even actually in the last couple of years, that's it actually that gap is closed.

And so with very low latency, you can almost have these naturalistic conversations with people who have a brain computer interface device implanted.

Now, that's though with an intentional speech, right?

So that it's not reading your brain.

It's not like downloading your internal monologue or, you know, any of your internal dialogue with yourself.

It's not doing that yet.

You can't upload all your memories to the cloud and then have them stored just yet.

But that what you can do now is where you're attempting to move a cursor or move a robotic arm, you can do those kinds of things, and it's really quite exciting to see that.

Amazing.

Let me, and I'd love to just push just for a second.

On the, the sci-fi piece that as I like to do that we were talking earlier that, uh, you know, there was a movie Brainstorm back in the 80s where uh Natalie Wood and, uh, and others coming together and being able to record basically your memories.

Um and then of course there's always like a Black Mirror episode.

There's a famous one where a husband and a wife get in an argument and they like recall the actual argument from their own uh mind.

There's also a Tom Cruise movie.

Um, usually it has this bad outcome, but But in the terms of the technology, is, is this always going to be the area of sci-fi, or is this something that there maybe is a path towards, whether it's a decade from now or longer?

You know, I don't think we really know yet.

It's really hard to put a timeline on these things.

I know that there's a lot of interest in helping, you know, that small population of clinical patients who have, you know, ALS or who've had a traumatic brain injury or they've had a tumor or something and they've, or, or, or spinal injury and they've lost some.

Capability that they would need and be able to act as a stimulator, um, act as a bridge, or act as a rehabilitation tool is very exciting.

Being able to do things that are a little more on the sci-fi end of things, like, you know, upload your consciousness and be an innie versus an Audi, um, is, is probably something that's, you know, that's going to be quite a long time in the future.

Tell, um, tell us a little bit about Jack, the, the ethical side of things.

Are there ethical concerns with this?

I mean, what you described it sounds incredibly um exciting to me, and, uh, to think about all the people in need that this can help and, and support.

I'm, I, I can't imagine any downside to that, but still, there's always ethical concerns when we're talking about technology with a lot of data and with invasive surgery.

So what are some of the ethical concerns here?

I think one of the things that is particularly challenging is that there's going to be a cost to these things.

The technologies are not going to be cheap.

They're probably going to be variable, variably reimbursable by insurance companies, for example.

And one of the things that we don't talk about very much is what happens if the company that say, Created the technology that's implanted in your brain.

What happens if that company goes out of business?

Oh.

So there is a black mirror about that actually.

You've got, now you've got this technology which is in your brain.

It needs a software, a firmware upgrade, and there's no one to do it, or it would have to be, um, replaced somehow, you know, who's going to do that?

How's that going to be managed and what happens to that technology?

You'd probably have to have it removed from your brain.

Um, there's all sorts of other things as well about some of the potential of side effects for these sorts of things.

So.

The different, um, uh, uh, kind of, um, the systems that can be implanted in your brain, they can cause inflammation, they can cause an infection, um, and infections in your brain are usually a very bad thing.

Um, what happens there?

And it also, are the people who are able to have this, are they just the rich, right, in contrast to basically people from, you know, the middle class or in, in other areas of our society that didn't. afford it.

And so while that, you know, we're talking, we were talking earlier about the, the marketization of this and, you know, the potential for this being a multibillion dollar enterprise, some of those companies are going to fail.

Some of those companies are not going to be able to manage and maintain the hardware that they produce and may, may or may not be responsible if there's something goes, you know, if something goes bad with them.

So these are things that I think are.

Worthy of conversations not only with ourselves today but with ethicists and other business leaders about, OK, what happens when these companies inevitably there will be some that fail.

What happens?

Yeah, the switching costs are huge.

Well, and it makes me curious if there is a push for any type of like standardization or, uh, you know, ways of creating interfaces that you could, even if it's not your technology, you could then implant like a new company coming.

In and says, all right, now we're, we're running these types of systems or even like open source some of the, the software side of things.

Has that been discussed, Jack, at all?

Um, you know, I think it's so new that people have yet to kind of solve those kind of problems.

Um, I know that um, the UNESCO people on the United Nations Educational, Scientific and Cultural Organization, um, they've come up with some, um, ethical standards about what can happen.

With the data.

So it's, you know, it's not like you can elect out of having your data recorded per se, right?

The neural, um, the brain-computer interface is going to be collecting that data and whether or not you're consciously aware of it or not.

And so that is an interesting thing where the ethics of neurotechnology is something that um has been not only the UNESCO people, but Colorado.

The state of California have written neural data into privacy statutes.

I think the country of Chile has done the furthest with a constitutional protection, and there are various other things with, um, you know, FDA and related IRB, you know, institutional requirements for how you deal with data, and especially if you're thinking about like EEG wearables and that sort of thing.

So there's a big conversation to have about, yeah, and I can, I mean, our brain works when we sleep too, right?

Like imagine it's tracking all along and somebody's collecting data all through your sleep.

Like I can imagine when it's a wearable, I was gonna say my watch is my watch is doing that too, but it feels more private even though maybe I'm just kidding myself.

Like the companies, the apps have access.

To it, but if it keeps recording what's going on in our brain, and that gets, you know, a third party, that's pretty, pretty scary.

I can see the, I can see the concern there. 11 of the things that came to mind that I could see as a concern, and maybe again, this is just the realm of sci-fi, if we're reading those signals and using them to interpret external devices, whether it's a robotic arm or the like.

Could it work the other way?

Could someone control the brain function externally?

Um, I don't know why my mind goes like the Cruciatus curse in Harry Potter, where like someone is able to like literally control someone else.

Is that, again, is that just the realm of sci-fi, or is there, is there a path where this could be possible?

The other direction, the other direction.

So I don't think, well.

Interesting to think about this.

Um, I imagine that that's possible to do with these particular, um, brain-computer interface, uh, devices.

Um, I can give you an example in the experimentation on mice.

Mice can be specially bred to have neurons that are stimulated by light.

And so you can actually take a fiber optic, um, piece of fiber optic, uh, wire, cable, uh, you know, glass, whatever they're made of, and stick them into the brain.

And by just turning on the light.

You can stimulate the neurons in various parts of the brain and change the behavior of the mouse.

And this is called optogenetics because it's optical and they're specially genetically bred mice, but it's really interesting to be able to, you can also record, not only stimulate, but you can also record at very high resolution, but it's for very specific localized populations of neurons, but it's a demonstration that being able to stimulate those neurons can alter the behavior patterns of mice in the laboratory.

All right, this is great because this gets me.

To where, you know, something I thought we would go, um, and it, and it's something that, that Musk, Elon Musk has talked about, I think, is this idea that we're going to move towards what's called it a cyborg future that these brain-computer interfaces are just going to be accepted and I know what's in the back of my mind is in part, um, this whole movement we see right now of like optimizing your body, right, and, uh, whether it's use of drugs or other things that allow you to do this, we talked about, for example, GLP ones before, um.

We've talked about people who have either disabilities or other ailments and using this technology to help improve their, their quality of life.

Are we going to see people who are relatively healthy start to adopt this in a way to get, uh, dare I say in some cases like superpowers or some, you know, be, be more human than human or more, uh, intelligent than human.

It is, again, are we just in the realm of sci-fi or is that the path we're on here?

It's, you'd have to, there's a cost-benefit analysis that probably people would have to weigh for themselves about, you have a, you know, I hope that I have a normally functioning brain.

Would I electively go in to augment that for some marginal cognitive gain with something which is expensive, invasive, I'm taking a medical risk with, you know, the risk of possible infection or inflammation or something, you know, is that mild.

Moderate cognitive gain, is that worth it to me?

What's that risk benefit look like to me and whether or not we have, you know, superpowered people or people with, you know, cognitive superpowers, I think it's too soon to say.

I don't think we know quite yet.

Again, being able to give back the functionality to patients that they have lost due to neurological disease or insults to their brains, that's, that's a really valuable thing.

Brain-computer interface devices that are implantable have really only been demonstrated in a few dozen people at this point around the world, and whether if we're going to do this properly, you'd need to do proper clinical trials in hundreds of people, really vet these at a very, very precise, rigorous level, and at the moment those studies have yet to be done.

And so I think there's a, there would be a risk for this to be widely adopted without those studies having been completed.

So now as we think of, so that's super helpful.

Thank you for all of that explanation, Jack.

I feel like I know so much more than I knew even a few minutes ago, but um, As we think about a disruption, right, um, this is clearly disrupting, um, the lifestyle for those patients that need this type of, uh, treatment.

Like, I, I don't think that there's a substitute beforehand that got even close to what this is able to produce, um, so what markets are being disrupted here?

What is it the physiotherapist?

Is it the aid care?

Is it, is it maybe?

Less disruption in that sense, like, where, where is the disruption here?

Well, and I think this is, I think where it's interesting to think about these other applications that extend beyond these maybe more narrow, uh, you know, brain trauma and brain disease, uh, issues, um.

I'd be curious, Jack, like, you know, I, I at least I've read about people thinking about this as an interface for us.

So instead of having to use either voice or or tactile commands on our phones, we could just literally control our technology using our, our brain.

Um, again, I keep asking the same question, like stuff is sci-fi or is, is this, is this on, are we on the path to that because that to me would be a much bigger societal disruption, disrupt iPhones, disrupt, you know, a whole bunch of different industries.

Uh, uh, there, you know, there are applications of this, which I think are kind of interesting.

Um, I was involved in this study a few years ago where, um, there was a special baseball cap created that had EEG sensors in the, in the inside brim, and golfers were using this to give themselves feedback to improve their putting.

So if you're a, if you're a golfer, Mike, I need this.

I'm a very bad golfer.

Any technology I can use to improve my golf game, that would be great.

They were using their EEG signals of their frontal lobes to kind of get into a cognitive kind of state, if you would, that would help to optimize their putting.

So, in terms of, you know, golf maxing, um, that might be one of the, uh, possible options.

Applications, but that does, that does suggest your previous comment, Mike, where that, that this type of signal can be leveraged for pushing us to the next level.

It reminds me a little bit of work that our colleague Ken Ono did with the swimmers at, uh, UVA at the, uh, at UVA here that obviously led to really big success on an international scale at the Olympics where.

Um, it wasn't brain sensors, so it's, it's not in that case, it's not the same application, but, uh, reading and tracking data in new ways and creative ways and trap tracking signals in creative ways, which this technology now allows us to do, can help us get to even higher performance, uh, in some instances.

Could we be using it for education to see like, students are struggling with calculus and then we can say, oh, here we're, we're seeing a pattern here of the The mistake they're making.

What do you think, Jack?

Could that be leveraged that way?

It could be, um, you know, various technologies again, the noninvasive variety, so like EEG or there's another one called near infrared spectroscopy, which is basically like a special cap that you put on that can record signals from the brain, could be used to help, you know, students optimize their performance in terms of, um, you know.

Either setting themselves up for putting themselves in the right cognitive frame of mind to perform well or to use those signals during times they're making an error so that they know how to use that feedback to not make that error in the future and hopefully optimize their performance.

So those kinds of things are possible, possible today, but they don't involve, you know, neurosurgery to implant anything.

That's fascinating.

And, and of course what's interesting that ethically it maybe reduces some of the risks that we talked about earlier, um, but it also has other ethical concerns around like equity and um, are, are is everybody as welcoming of that kind of assisted aid and assistance, external assistance as maybe others are, or can afford it.

Oh can afford it, right?

Like if only certain people get that type of, you know, head, uh, you know, head, uh, helmet there to wear and others don't, that, that could make a huge difference.

Amazing, fascinating, fascinating.

Um, Jack, what keeps you up at night when you think about this?

Uh, what gets you really excited, but also what keeps you up at night when you think about all the, uh, the growth in this field?

Um, this is, well, firstly, it's, it's a very exciting.

Technology to be utilized for again, these patients with.

You know, ALS, people who have multiple sclerosis or people who have other neuros, this is just such a game changer for their lives and the lives of their caregivers.

It really can be, you know, just amazing for them, and these technologies are now available for those patients, and that's very gratifying.

Things that keep me up at night are maybe some of the, the Hype cycle about these things and we don't want to get out over our ski tips on these things and find that we're saying that they can do things and they can't.

And, you know, this, it just kind of, once we get misaligned on what's possible with what, uh, you know, what's available and what might be possible, um, people start making, I don't know, they reach the wrong conclusions about what these things are.

I think there's a lot of potential here.

There's some definitely.

Room for growth.

I don't think that in the next 5 years we're going to see, you know, major changes here per se, because there's a lot of biology, not necessarily the engineering, but a lot of biology that has to be understood properly to make these things optimized.

And, you know, the brain has been evolving over a very long period of time.

It's very complicated.

I don't think we've solved it by any means, and so we have a lot of work left to do.

Amazing, and we are so thankful for the work that you do and your colleagues do.

Thank you so much for coming and teaching us about this.

Uh, um, we learned a lot and we're just, um, in awe of your work.

So thank you, uh, for spending a, a few minutes with us, Jack.

Yeah, thank you, Jack.

I feel smarter even though I, I don't have the other face.

No implants and you're implants and I'm feeling smarter.

That's right.

See, you don't even need the implant.

Well, thank you so much.

This was really, really fun.

I really appreciate the opportunity to talk to you.

Amazing.

Thank you, Jack.

So Mike, where do you, what do you think?

I'm struggling a little bit with this one.

So I'm not struggling at all, by the way.

OK, so I, I will start with the, uh, medical, uh, you know, applications that we've talked about.

This seems like a wonderful thing, and, and we need to continue to pursue this and a disruption, a good disruption and a good disruption.

I'm not sure a disruption in the, uh, market sense we typically talk about, but from a technology, this is a game changing technology that's improving quality of life for people who otherwise aren't.

So I think, uh, I'm 100% beyond that.

I think as we started to delve into, you know, again, a little bit of the sci-fi, but a little bit of like where our future might lead of uh performance maxing, uh, as we say these days, uh, using the technology and using it in non kind of medical situations.

It does give me a little pause, right?

It does give me a little pause on how it could be abused, like we said, you know, will only rich people be able to have access to this?

What types of performance advantages might it, uh, provide?

Uh, how does it change the ways we engage with technology?

We already have worries about, you know, the ways we engage in kind of an external environment.

Now, if it's just part and parcel to where we are, and then of course you do have those like Elon Musk and others who are kind of talking about this cyborg future.

I think that is a.

Popular concept in the Bay Area right now that that might be where we're evolving, where we're heading, um, and that gives me a lot of pause, and that, and that's where I might start to go bad disruption, right?

Like that's that maybe is not the thing we should be messing with in the, the human brain and the like here, yeah, and so I, I think, uh, for me what helps me to, and I, I, I totally hear your concerns, and I think they're important to discuss for me, um, the way that I think about it, maybe I kind of separate it between the actual.

In innovation here, the technology, what is capable versus the applications.

The applications always and almost every technology that we've talked about, the applications can go sour, and that's a little bit about the business side.

That's actually where we come in, right?

It's kind of educating kind of ethical leaders to make these smart business choices and including product, market, and use case, but the technology itself, the ability to understand our brain so much better, the fact that we can get in.

In there and start to record, you know, and link activity in the brain to actual movement or to, uh, um, other behavior, I think it's amazing.

I think it's groundbreaking, and I think it's the fact that we don't know, don't know as much on the brain as we know about space is like shame on us.

Like it's all here.

It's been in front of us all along, and we haven't been able to crack it.

I, I agree, and I look, when I think about the market dynamics too, it's easy for us to kind of tell stories that say, hey.

A brain-computer interface to control our technology that would be obvious.

We'd all do this, it would be disruptive.

But look, we have these examples like, um, wearable glasses with technology that are still out there.

They're making some impact, but it wasn't the disruption that a lot of people predicted, especially when Google Glass was like, this is like we're no longer gonna have phones, that form factor is gonna go away because sometimes, you know, the, the old school form factor, it works.

Works well enough that we're not willing to suddenly use our brain to directly control something.

So, but if this helps patients that are in need, um, and if it helps folks that, um, that, that current medicine and current applications cannot support, I think that will be at the end of the day a big win.

And anything, I like the fact that Jack described that there's a movement around ethical questions so that there's a whole community around it.

Um, but, but I think it's, can I answer good disruption today, and I'm reserving bad disruption for like a decade from now.

We'll do this a decade from now, and I can, you know, reassess my, uh, and I feel like, uh, when it comes to medical applications, uh, we, we had a similar tension when we talked about the GL.

Um, LP1 episode where and the the off medical use, correct, correct, correct, yeah, you're, if nothing else we're consistent, we're consistent, consistent.

Well, um, thank you so much, Mike.

Thank you for all you've done, uh, for Darden and for me personally over the last few months.

It's been a joy watching you lead.

Um, I'm excited about our new chapter together, but the podcast will continue.

The podcast will continue.

Well, as always, uh, thank you to Gary, our producer, to Becky, our researcher extraordinaire, uh, and thank you to all of our listeners.

This is good disruption.

Have a fun summer, everybody.

Good Disruption is a podcast from the University of Virginia Darden School of Business.

Darden.