Elon Musk: Neuralink and the Future of Humanity | Lex Fridman Podcast #438

- The following is a conversation with Elon Musk, DJ Seo,

Matthew MacDougall, Bliss Chapman, and Nolan Arbaugh

about Neuralink and the future of humanity.

Elon, DJ, Matthew and Bliss are of course part

of the amazing Neuralink team,

and Noland is the first human

to have a Neuralink device implanted in his brain.

I speak with each of them individually,

so use timestamps to jump around,

or as I recommend, go hardcore

and listen to the whole thing.

This is the longest podcast I've ever done.

It's a fascinating, super technical,

and wide-ranging conversation,

and I loved every minute of it.

And now, dear friends, here's Elon Musk,

his fifth time on this, "The Lex Fridman Podcast."

- Drinking coffee or water?

- Water.

I'm so over-caffeinated right now.

Do you want some caffeine?

- I mean, sure.

- There's a Nitro drink.

- This supposed to keep you up till like

tomorrow afternoon basically. (laughs)

- Yeah.

I don't have any- - So what is Nitro?

It's just got a lot of caffeine or something?

- Don't ask questions.

It's called Nitro.

- Do you need to know anything else?

- It's got nitrogen, that's ridiculous.

I mean, what we breathe is 78% nitrogen anyway.

What do you need to add more for? (laughs)

- [Speaker] Unfortunately, you're gonna need it.

- Most people think that they're breathing oxygen,

and they're actually breathing 78% nitrogen.

You need like a milk bar. - Milk bar.

(Elon laughing)

- Like from Clockwork Orange. (laughs)

- Yeah, yeah.

Is that top three Kubrick film for you?

- Clockwork Orange, it's pretty good.

I mean, it's demented.

Jarring, I'd say.

- (laughs) Okay.

Okay, so first let's step back

and big congrats on getting Neuralink

implanted into a human.

That's a historic step for Neuralink.

- Oh, thanks, yeah.

- There's many more to come.

- Yeah, and we just, obviously, our second implant as well.

- [Lex] How did that go?

- So far, so good.

Looks like we've got,

I think over 400 electrodes that are providing signals.

So yeah. - Nice.

How quickly do you think the number

of human participants will scale?

- It depends on the regulatory approval,

the rate which we get regulatory approvals.

So we're hoping to do 10 by the end of this year.

Total of 10, so eight more.

- And with each one, you're gonna be learning

a lot of lessons about the new biology, the brain,

everything, the whole chain of the Neuralink,

the decoding, the signal processing, all that kind of stuff.

- Yeah, yeah, I think it's obviously gonna get better

with each one.

I mean, I don't wanna jinx it,

but it seems to have gone extremely well

with the second implant, so there's a lot of signal,

a lot of electrodes.

It's working very well.

- What improvements do you think we'll see

in Neuralink in the coming, let's say,

let's get crazy, the coming years?

- I mean, in years, it's gonna be gigantic,

because we'll increase the number

of electrodes dramatically.

We'll improve the signal processing.

Even with only roughly, I don't know, 10, 15%

of the electrodes working with Noland,

with our first patient,

we were able to get to achieve a bit per second.

That's twice the world record.

So I think we'll start like vastly exceeding

world record by orders of magnitude in the years to come.

So it's start getting to, I don't know,

a hundred bits per second thousand.

Maybe if like five years from now, we might be at a megabit,

like faster than any human could possibly communicate

by typing or speaking.

- Yeah, that BPS is an interesting metric to measure.

There might be a big leap

in the experience once you reach a certain level of BPS.

- [Elon] Yeah.

- Like entire new ways of interacting

with a computer might be unlocked.

- And with humans.

- With other humans.

- Provided they have (laughs),

they want a Neuralink too.

- Right.

- Otherwise, they won't be able to absorb

the signals fast enough.

- Do you think they'll improve the quality

of intellectual discourse?

- Well, I think you could think of it,

if you were to slow down communication,

how do you feel about that?

If you'd only talk at, let's say,

1/10th of normal speed, you'd be like,

"Wow, that's agonizingly slow."

- [Lex] Yeah.

- So now, imagine you could speak,

communicate clearly at 10 or 100

or 1,000 times faster than normal.

- Listen, I'm pretty sure nobody in their right mind

listens to me at 1x, they listen at 2x.

(Elon laughs)

I can only imagine what 10x would feel like

or could actually understand it.

- I usually default to 1.5x.

You can do 2x, but well, actually,

if I'm listening to somebody

in like sort of 15, 20 minutes segments to go to sleep,

then I'll do it 1.5x.

If I'm paying attention, I'll do 2x. (laughs)

- Right.

- But actually, if you start actually listen to podcasts

or sort of audio books or anything,

if you get used to doing it at 1.5,

then one sounds painfully slow.

- I'm still holding onto one because I'm afraid.

I'm afraid of myself becoming bored with the reality,

with the real world where everyone's speaking on 1x.

(both laughing)

- Well, depends on the person.

You can speak very fast.

Like we can communicate very quickly.

And also, if you use a wide range of,

if your vocabulary is larger, your bit rate,

effective bit rate is higher.

- That's a good way to put it.

- Yeah. - The effective bit rate.

I mean, that is the question is how much information

is actually compressed in the low bit transfer of language.

- Yeah.

If there's a single word that is able to convey

something that would normally require,

I don't know, 10 simple words,

then you've got maybe a 10x compression on your hands.

And that's really, like with memes,

memes are like data compression.

It conveys a whole,

you're simultaneously hit with a wide range

of symbols that you can interpret.

And you kinda get it faster than if it were words

or a simple picture.

- And of course, you're referring to memes broadly

like ideas. - Yeah.

There's an entire idea structure

that is like an idea template,

and then you can add something to that idea template.

But somebody has that preexisting

idea template in their head.

So when you add that incremental bit of information,

you're conveying much more than a few,

just set a few words.

It's everything associated with that meme.

- You think there'll be emergent leaps of capability

as you scale the number of electrodes?

Like there'll be a certain,

you think there'll be like actual number where it just,

the human experience will be altered?

- Yes.

- What do you think that number might be,

whether electrodes or BPS?

We of course don't know for sure,

but is this 10,000, 100,000?

- Yeah, I mean certainly, if you're anywhere

at 10,000 bits per second, I mean, that's vastly faster

than any human could communicate right now.

If you think about what is the average

bits per second of a human?

It is less than one bit per second over the course of a day,

because there are 86,400 seconds in a day.

And you don't communicate 86,400 tokens in a day.

Therefore, your bits per second is less than one,

averaged over 24 hours.

It's quite slow.

And now, even if you're communicating very quickly,

and you're talking to somebody who understands

what you're saying, because in order to communicate,

you have to at least, to some degree,

model the mind state of the person to whom you're speaking.

Then take the concept you're trying to convey,

compress that into a small number of syllables,

speak them, and hope that the other person

decompresses them into a conceptual structure

that is as close to what you have in your mind as possible.

- Yeah, I mean, there's a lot

of signal loss there in that process.

- Yeah, very lousy compression and decompression.

And a lot of what your neurons are doing

is distilling the concepts down

to a small number of symbols of, say, syllables

that I'm speaking, or keystrokes, whatever the case may be.

So that's a lot of what your brain computation is doing.

Now, there is an argument that that's actually

a healthy thing to do or a helpful thing to do

because as you try to compress complex concepts,

you're perhaps forced to distill

what is most essential in those concepts

as opposed to just all the fluff.

So in the process of compression,

you distill things down to what matters the most,

because you can only say a few things.

So that is perhaps helpful.

I think we might, we'll probably get,

if our data rate increases, it's highly probable

that we'll become far more verbose.

Just like your computer,

when computers had like,

my first computer had 8K of RAM,

so you really thought about every byte.

And now you've got computers

with many gigabytes of RAM.

So if you wanna do an iPhone app

that just says 'Hello world,'

it's probably, I don't know,

several megabytes minimum. (laughs)

A bunch of fluff.

But nonetheless, we still prefer to have the computer

with more memory and more compute.

So the long-term aspiration of Neuralink

is to improve the AI human symbiosis

by increasing the bandwidth of the communication,

because even in the most benign scenario of AI,

you have to consider that the AI is simply gonna get bored

waiting for you to spit out a few words.

I mean, if the AI can communicate it to terabits per second

and you're communicating it bits per second,

it's like 203.

- Well, it is a very interesting question

for a super intelligent species.

What use are humans?

- I think there is some argument for humans

as a source of will.

- Will? - Will, yeah.

Source of will or purpose.

So if you consider the human mind as being essentially,

there's the primitive limbic elements,

which basically even like reptiles have,

and there's the cortex,

that's the thinking and planning part of the brain.

Now, the cortex is much smarter than the limbic system,

and yet is largely in service to the limbic system.

It's trying to make the limbic system happy.

I mean, the sheer amount of compute

that's gone into people trying to get laid is insane,

without actually seeking procreation.

They're just literally trying to do

this sort of simple motion.

(laughs) And they get a kick out of it.

So this simple, which in the abstract rather absurd motion,

which is sex, the cortex is putting a massive amount

of compute into trying to figure out how to do that.

- So like 90% of distributed compute of the human species

is spent on trying to get laid, probably,

like a massive amount. - Large percent, yeah, yeah.

There's no purpose to most sex except hedonistic.

It's just sort of joy or whatever.

Dopamine release.

Now, once in a while, it's procreation,

but for humans, modern humans, it's mostly recreational.

So your cortex, much smarter than your limbic system,

is trying to make the limbic system happy

'cause the limbic system wants to have sex,

or want some tasty food or whatever the case may be.

And then that is then further augmented

by the tertiary system, which is your phone, your laptop,

iPad, whatever, or your computing stuff.

That's your tertiary layer.

So you're actually already a cyborg.

You have this tertiary compute layer,

which is in the form of your computer

with all the applications or your compute devices.

And so in the getting laid front,

there's actually a massive amount of digital compute

also trying to get laid, with like Tinder and whatever.

- Yeah.

So the compute that we've humans have built

is also participating. (laughs)

- Yeah, I mean, there's like gigawatts of compute

going into getting laid, of digital compute.

- Yeah. (laughs)

What if AGI will- - This is happening

as we speak.

- If we merge with AI, it's just gonna expand the compute

that we humans use- - Pretty much.

- To try to get laid. - Well, that's one

of the things, certainly, yeah.

- Yeah.

- But what I'm saying is that yes,

is there a use for humans?

Well, there's this fundamental question

of what's the meaning of life?

Why do anything at all?

And so if our simple limbic system provides a source of will

to do something, that then goes to our cortex,

that then goes to our tertiary compute layer,

then I don't know, it might actually be that the AI

in a benign scenario simply trying to make

the human limbic system happy.

- Yeah, it seems like the will is not

just about the limbic system.

There's a lot of interesting, complicated things in there.

We also want power.

- That's limbic too, I think.

- But then we also want to, in a kind of cooperative way,

alleviate the suffering in the world.

- Not everybody does, but yeah, sure.

Some people do.

- As a group of humans, when we get together,

we start to have this kind of collective intelligence

that is more complex in its will

than the underlying individual descendants of apes, right?

So there's like other motivations.

And that could be a really interesting source

of an objective function for AGI.

- Yeah, I mean, there are these sort of fairly

cerebral or kind of higher level goals.

I mean, for me it's like,

what's the meaning of life,

or understanding the nature of the universe

is of great interest to me.

And hopefully, to AI.

And that's the mission of xAI

and Grok is understand the universe.

- So do you think people,

when you have a Neuralink with 10,000, 100,000 channels,

most of the use cases will be communication with AI systems?

- Well, assuming there are not,

I mean, they're solving basic neurological issues

that people have if they've got damaged neurons

in their spinal cord or neck or, you know,

as is the case with the first two patients,

then there's obviously,

the first order of business is solving

fundamental neuron damage in a spinal cord,

neck, or in the brain itself.

A second product is called Blindsight,

which is to enable people who are completely blind,

lost both eyes or optic nerve,

or just can't see at all to be able to see

by directly triggering the neurons in the visual cortex.

So we're just starting at the basics here,

so it's like very,

the simple stuff, relatively speaking,

is solving neuron damage.

It can also solve I think probably schizophrenia.

If people have seizures of some kind,

it could probably solve that.

It could help with memory.

There's like a kind of a tech tree, if you will,

of like you got the basics.

Like you need literacy before you can have

"Lord of the Rings."

(both laughing)

- Got it.

- Do you have letters and alphabet?

Okay great.

Words?

Then eventually get soggy.

So I think there's that there may be some things

to worry about in the future.

But the first several years

are really just solving basic neurological damage.

Like for people who have essentially complete

or near complete loss of, from the brain to the body.

Like Stephen Hawking would be an example.

The Neuralink would be incredibly profound,

'cause I mean, you can imagine if Stephen Hawking

could communicate as fast as we're communicating,

perhaps faster.

And that's certainly possible.

Probable, in fact, likely I'd say.

- So there's a kind of dual track

of medical and non-medical, meaning,

so everything you've talked about could be applied to people

who are non-disabled in the future?

- The logical thing to do is,

sensible thing to do is to start off solving

basic neuron damage issues.

- [Lex] Yes.

- 'Cause there's obviously some risk with a new device.

You can't get the risk down at zero.

It's not possible.

So you wanna have the highest possible reward,

given there's a certain irreducible risk.

And if somebody's able to have

a profound improvement in their communication,

that's worth the risk.

- As you get the risk down.

- Yeah, as you get the risk down.

Once the risk is down to, you know,

if you have like thousands of people

that have been using it for years

and the risk is minimal, then perhaps at that point,

you could consider saying,

"Okay, let's aim for augmentation."

Now, I think we're actually gonna aim for augmentation

with people who have neuron damage.

So we're not just aiming to give people communication

data rate equivalent to normal humans.

We're aiming to give people who have quadriplegic

or maybe have complete loss of the connection

to the brain and body, a communication data rate

that exceeds normal humans, going, "Well, we're in there.

Why not?

Let's give people superpowers."

- And the same for vision.

As you restore vision, there could be aspects

of that restoration that are superhuman?

- Yeah, at first, the vision restoration will be low res,

'cause you have to say like,

"How many neurons can you put in there and trigger?

And you can do things where you adjust the electric field

to like, even if you've got, say, 10,000 neurons,

it's not just 10,000 pixels

because you can adjust the feel between the neurons

and do them in patterns in order to get,

so have, say, 10,000 electrodes effectively give you,

I don't know, maybe like having a megapixel

or a 10 megapixel situation.

And then over time, I think you get

to higher resolution than human eyes.

And you could also see in different wavelengths.

So like Geordi La Forge from "Star Trek."

Like the thing.

You wanna see in radar?

No problem.

You could see ultraviolet, infrared,

eagle vision, whatever you want.

- Do you think there'll be,

let me ask a Joe Rogan question.

Do you think there'll be, (laughs)

I just recently taken ayahuasca.

- Is that a Rogan question? - No.

Well, yes. - Well, I guess,

technically it is.

- Yeah. - Ever tried GMT, bro?

(both laughing)

- I love you, Joe. - Okay.

(laughing continues)

- But wait, wait, yeah.

Have you said much about it?

The ayahuasca? - I've not, I've not.

I've not. - Okay, well,

why are you spilling the beans?

(Lex laughing)

It was a truly incredible thing-

- Turn the tables on you.

(both laughing)

- Wow, okay. - You're in the jungle.

- [Lex] Yeah, amongst the trees myself and-

- Yeah, must been crazy. - And the shaman.

Yeah, yeah, yeah, with the insects,

with the animals all around you,

like jungle as far as I can see.

There's no- - I mean-

- That's the way to do it.

- Things are gonna look pretty wild.

- Yeah, pretty wild.

(Elon laughing)

- I think in extremely high dose.

- Just don't go hugging an anaconda or something. (laughs)

- You haven't lived unless you made love to an anaconda.

I'm sorry, but-

- Snakes and ladders.

(both laughing)

- Yeah, I took a extremely high dose of-

- [Elon] Okay. (laughs)

- Nine cups and- - Damn.

Okay, that sounds like a lot.

Of course, is Noland's one cup or-

- One or two.

Usually one.

- You went, wait.

Like right off the bat, or did you work your way up to it?

- So I- (both laughing)

- You're just jumping at the deep end.

- Across two days, 'cause then the first day,

I took two and I- - Okay.

- It was a ride, but it wasn't quite like a-

- It wasn't like revelation.

- It wasn't into deep space type ride.

It was just like a little airplane ride.

- [Elon] (laughs) Okay.

- Saw some trees and some visuals and all that.

I just saw a dragon, all that kind of stuff.

But- (laughs)

- It's nine cups.

You went to Pluto, I think.

- [Lex] Pluto, yeah.

No, deep space.

- Deep space.

- No, one of the interesting aspects of my experience

is I thought I would have some demons,

some stuff to work through.

- That's what people-

- That's what everyone says.

- That's what everyone says.

Yeah, exactly. - I had nothing.

I had it all positive.

I just- - Oh, just pure soul.

- I don't think so, I don't know. (laughs)

But I kept thinking about,

it had like extremely high resolution,

thoughts about the people I know in my life.

You were there. - Okay.

- And it's just not from my relationship with that person,

but just as the person themselves,

I had just this deep gratitude of who they are.

- That's cool. - It was just like

this exploration, like Sims or whatever,

you get to watch them. - Sure.

- I got to watch people

and just be in awe of how amazing they are.

- That sounds awesome. - Yeah, it was great.

I was waiting for- - When's Steven coming?

(both laughing)

- Exactly.

Maybe I'll have some negative thoughts.

Nothing nothing.

Just extreme gratitude for them.

And then also, a lot of space travel.

(both laughing)

- Space travel to where?

- So here's what it was.

It was people, the human beings that I know,

they had this kinda,

the best way to describe it is they had a glow to them.

And then I kept flying out from them to see earth,

to see our solar system, to see our galaxy.

And I saw that light, that glow all across the universe.

Like whatever that form is.

whatever that like-

- [Elon] Did you go past the Milky Way?

- Yeah, yeah. - Okay.

You're like intergalactic.

- Yeah, intergalactic. - Okay, dang.

- But always pointing in- - Okay.

- Yeah, past the Milky way.

I mean, I saw like a huge number of galaxies,

intergalactic, and all of it was glowing.

But I couldn't control that chill,

'cause I would actually explore near distances

to the solar system, see if there's aliens

or any of that kinda stuff.

I didn't know- - Is there aliens?

Zero aliens? - Implication of aliens

because they were glowing.

They were glowing in the same way that humans were glowing.

That like life force that I was seeing,

the thing that made humans amazing

was there throughout the universe.

Like there was these glowing dots.

So I don't know.

It made me feel like there is life.

No, not life, but something,

whatever makes humans amazing all throughout the universe.

- Sounds good. - Yeah, it was amazing.

No demons, no demons.

I looked for the demons.

There's no demons.

There were dragons, and they're pretty awesome.

So the thing about-

- Was there anything scary at all?

- Dragons?

But they weren't scary.

They were friends, they were protective.

So the thing is- - "Puff, the Magic Dragon."

- No, it was more like a "Game of Thrones" kind of dragons.

They weren't very friendly.

They were very big.

So the thing is that, well, giant trees at night,

which is where I was. - Yeah.

I mean, the jungle's kinda scary.

- Yeah, the trees started to look like dragons,

and they were all like looking at me.

- Sure, okay. - And it didn't seem scary.

They seemed like they were protecting me.

And the shaman and the people didn't speak any English,

by the way, which made it even scarier I guess. (laughs)

We're not even like, you know,

we're worlds apart in many ways.

But yeah, they talk about the mother of the forest

protecting you, and that's what I felt like.

- And you're way out in the jungle?

- Way out.

This is not like a tourist retreat.

- Like 10 miles outside of a Rio or something?

- No, we went-

(both laughing)

No, this is not- - Deep in the Amazon.

- Me and this guy named Paul Rosolie

who basically is Tarzan.

He lives in the jungle.

We went out deep and we just went crazy.

- Wow, cool. - Yeah.

So anyway, can I get that same experience within Neuralink?

- Probably, yeah.

- I guess that is the question for non-disabled people.

Do you think that there's a lot in our perception,

in our experience of the world that could be explored,

that could be played with using Neuralink?

- Yeah, I mean, Neuralink is,

it's really a generalized input-output device.

It's reading electrical signals

and generating electrical signals.

And I mean, everything that you've ever experienced

in your whole life, the smell, emotions,

all of those are electrical signals.

So it's kinda weird to think that your entire

life experience is distilled down

to electrical signals for neurons.

But that is in fact the case.

Or I mean, that's at least

what all the evidence points to.

So I mean, if you trigger the right neuron,

you could trigger a particular scent.

You could certainly make things glow.

I mean, do pretty much anything.

I mean, really, you can think of the brain

as a biological computer.

So if there are certain, say, chips or elements

of that biological computer that are broken,

let's say your ability to, if you've got a stroke,

that if you've had a stroke,

that means you got, some part of your brain is damaged.

If that, let's say, it's a speech generation

or the ability to move your left hand.

That's the kind of thing that a Neuralink could solve.

If you've got like a massive amount

of memory loss that's just gone,

well, we can't get the memories back.

We could restore your ability to make memories,

but we can't restore memories that are fully gone.

Now, I should say, maybe if part of the me memory is there

and the means of accessing memory

is the part that's broken, then we could re-enable

the ability to access the memory.

But you can think of it like RAM in a computer.

If the RAM is destroyed or your SD card is destroyed,

we can't get that back.

But if the connection to the SD card is destroyed,

we can fix that.

If it is fixable physically,

then yeah, then it can be fixed.

- Of course, with AI, you can just like,

you can repair photographs

and fill in the missing parts of photographs.

Maybe you can do the same, just like-

- Yeah, you could say like,

"Create the most probable set of memories

based on all information you have about that person."

You could then,

it would be probabilistic restoration of memory.

Now, we're getting pretty esoteric here.

- But that is one of the most beautiful aspects

of the human experience is remembering the good memories.

Like we live most of our life,

as Danny Kahneman has talked about,

in our memories, not in the actual moment.

We're collecting memories

and we kind of relive them in our head.

And that's the good times.

If you just integrate over our entire life,

it's remembering the good times

that produces the largest amount of happiness.

And so- - Yeah, well, I mean,

what are we but our memories?

And what is death but the loss of memory,

loss of information?

If you could say like, well, if you could be,

you run a thought experiment,

if you were disintegrated painlessly

and then reintegrated a moment later,

like teleportation, I guess,

provided there's no information loss,

the fact that your one body was disintegrated is irrelevant.

- And memories is just such a huge part of that.

- Death is fundamentally the loss of information,

the loss of memory.

- So if we can store them as accurately as possible,

we basically achieve a kind of immortality.

- Yeah.

- You've talked about

the threats, the safety concerns of AI.

Let's look at long-term visions.

Do you think Neuralink is, in your view,

the best current approach we have for AI safety?

- It's an idea that may help with AI safety.

Certainly not,

I wouldn't wanna claim it's like some panacea

or that's a sure thing.

But I mean, many years ago, I was thinking like,

"Well, what would inhibit alignment of collective human will

with artificial intelligence

and the low data rate of humans,

especially our slow output rate would necessarily just,

because the communication is so slow,

would diminish the link between humans and computers?

Like the more you are a tree,

the less you know what a tree is.

Like let's say you look at a tree,

you look at this plant or whatever and like,

"Hey, I'd really like to make that plant happy."

But it's not saying a lot, you know?

- So the more we increase the data rate

that humans can intake and output,

then that means the higher the chance we have

in a world full of AGIs?

- Yeah.

We could better align collective human will with AI

if the output rate especially was dramatically increased.

And I think there's potential to increase

the output rate by, I don't know,

three, maybe six, maybe more orders of magnitude.

So it's better than the current situation.

- And that output rate would be

by increasing the number of electrodes, number of channels,

and also maybe implanting multiple Neuralinks?

- Yeah.

- Do you think there'll be a world

in the next couple of decades

where it's hundreds of millions of people have Neuralinks?

- Yeah, I do.

- You think when people just,

when they see the capabilities, the superhuman capabilities

that are possible and then the safety is demonstrated?

- Yeah, if it's extremely safe

and you can have superhuman abilities,

and let's say you can upload your memories,

so you wouldn't lose memories,

then I think probably a lot of people

would choose to have it.

It would supersede the cell phone, for example.

I mean, the biggest problem that a say a phone has

is trying to figure out what you want.

So that's why you've got auto complete

and you've got output,

which is all the pixels on the screen.

But from the perspective of the human,

the output is so freaking slow.

Desktop or phone is desperately

just trying to understand what you want,

and there's an eternity between every keystroke

from a computer standpoint.

- Yeah?

The computer's talking to a tree

that slow moving tree that's trying to swipe.

- Yeah.

So if you have computers that are doing

trillions of instructions per second,

and a whole second went by, I mean, that's a trillion

things it could have done.

- Yeah, I think it's exciting and scary for people

because once you have a very high bit rate,

that changes the human experience

in a way that's very hard to imagine.

- Yeah.

It would be something different.

I mean, some sort of futuristic sidewalk.

I mean, we're obviously talking about, by the way,

it's not like around the corner.

You ask me what the distant future was like.

Maybe this is like, it's not super far away,

but 10, 15 years, that kind of thing.

(Lex sighs)

- When can I get one?

10 years?

- Probably less than 10 years.

Depends what you wanna do.

- Hey, if I can get like a thousand BPS-

- A thousand bps when?

- And it's safe and I can just interact

with the computer while laying back and eating Cheetos,

I don't eat Cheetos.

There's certain aspects of human-computer interaction

when done more efficiently and more enjoyably,

like worth it.

- Well, we feel pretty confident that

I think maybe within the next year or two,

that someone with a Neuralink implant

will be able to outperform a pro gamer.

- Nice.

- Because the reaction time would be faster.

- I got to visit Memphis.

- Yeah, yeah. - You're going big on compute.

- Yeah. - You've also said

play to win or don't play at all,

so what does it take to win?

- For AI, that means you've gotta have

the most powerful training compute,

and the rate of improvement of training compute

has to be faster than everyone else or you will not win.

Your AI will be worse.

- So how can Grok, let's say, three

that might be available, what, like next year?

- Well, hopefully, end of this year.

- Grok 3? - If we're lucky, yeah.

- How can that be the best LLM,

the best AI system available in the world?

How much of it is compute?

How much of it is data?

How much of it is like post-training?

How much of it is the product that you packaged it up in?

All that kind of stuff.

- I mean, they won't matter.

It's sort of like saying,

let's say it's a Formula One race.

Like what matters more, the car or the driver?

I mean, they both matter.

If a car is not fast, then if it's like, let's say,

it's half the horsepower of your competitors,

the best driver will still lose.

If it's twice the horsepower,

then probably even a mediocre driver will still win.

So the training compute is kinda like the engine,

how many is this horsepower of the engine.

So really, you wanna try to do the best on that.

Then how efficiently do you use that training compute?

And how efficiently do you do the inference,

the use of the AI?

So obviously, that comes down to human talent.

And then what unique access to data do you have?

That also plays a role.

- You think Twitter data will be useful?

- Yeah, I mean, I think,

I think most of the leading AI companies

have already scraped all the Twitter data.

Not I think they have.

So on a go forward basis, what's useful is the fact

that it's up to the second.

That's hard for them to scrape in real time.

So there's an immediacy advantage that Grok has already.

I think with Tesla and the real time video

coming from several million cars,

ultimately, tens of millions of cars, with Optimus,

there might be hundreds of millions of Optimus robots,

maybe billions learning a tremendous amount

from the real world.

That's the biggest source of data

I think ultimately is sort of Optimus.

Optimus is gonna be the biggest source of data.

- Because- - 'Cause reality scales.

Reality scales to the scale of reality.

It's actually humbling to see how little data

humans have actually been able to accumulate.

Really, you see how many trillions

of usable tokens have humans generated,

where on a non-duplicative,

like discounting spam and repetitive stuff,

it's not a huge number.

You run out pretty quickly.

- And Optimus can go, so Tesla cars

can unfortunately have to stay on the road.

Optimus robot can go anywhere,

and there's more reality off the road and go off road.

- I mean, except for the store,

where I can like pick up the cup and see,

did it pick up the cup in the right way?

Did it pour water in the cup?

Did the water go in the cup or not go in the cup?

Did it spill water or not?

- [Lex] Yeah.

- Simple stuff like that.

But it can do at that scale times a billion,

so generate useful data from reality.

So cause and effect stuff.

- What do you think it takes to get

to mass production of humanoid robots like that?

- It's the same as cars, really.

I mean, global capacity for vehicles

is about a hundred million a year.

And it could be higher.

It's just that the demand

is on the order of a hundred million a year.

And then there's roughly two billion vehicles

that are in use in some way, which makes sense.

Like the life of a vehicle is about 20 years,

so it's steady state.

You can have a hundred million vehicles produced a year

with a two billion vehicle fleet roughly.

Now for humanoid robots, the utility is much greater.

So my guess is humanoid robots are more like

at a billion plus per year.

- But until you came along and started building Optimus,

it was thought to be an extremely difficult problem.

I mean, it still- - Well, it is.

- Extremely difficult. - So walk in the park.

I mean, Optimus currently would struggle

to walk in the park.

I mean, it can walk in a park.

The park is not too difficult,

but it will be able to walk

over a wide range of terrain.

- And pick up objects. - Yeah, yeah.

It can already do that.

- [Lex] But like all kinds of objects?

- Yeah, yeah. - All foreign objects.

I mean, pouring water in a cup does not thrill you,

'cause then if you don't know anything about the container,

it could be all kinds of containers.

- Yeah, there's gonna be an immense amount

of engineering just going into the hand.

The hand might be, it might be close to half

of all the engineering in Optimus.

From an electromechanical standpoint,

the hand is probably roughly half of the engineering.

- But so much of the intelligence,

so much the intelligence of humans

goes into what we do with our hands.

- Yeah.

- It's the manipulation of the world,

manipulation of objects in the world.

Intelligence is safe manipulation

of objects in the world, yeah.

- Yeah.

I mean, you start really thinking

about your hand and how it works.

- I do all the time.

- The sensory control homonculus

is where you have humongous hands.

- [Lex] Yeah.

- So I mean, like your hands,

the actuators, the muscles of your hand

are almost overwhelmingly in your forearm.

So your forearm has the muscles

that actually control your hand.

There's a few small muscles in the hand itself,

but your hand is really like a skeleton meat puppet.

And with cables.

So the muscles that control your fingers are in your forearm

and they go through the carpal tunnel,

which is that you've got a little collection of bones

and a tiny tunnel that these cables, the tendons go through.

And those tendons are mostly what move your hands.

- And something like those tendons

has to be re-engineered into the Optimus

in order to do all that kind of stuff.

- Yeah, so like the current Optimus,

we tried putting the actuators in the hand itself,

but then you sort of end up having these like-

- Giant hands?

- Yeah, giant hands that look weird.

And then they don't actually have enough degrees of freedom

and/or enough strength.

So then you realize, "Oh, okay,

that's why you gotta put the actuators in the forearm."

And just like a human, you gotta run cables

through a narrow tunnel to operate the fingers.

And then there's also a reason

for not having all the fingers the same length.

So it wouldn't be expensive from an energy

or evolutionary standpoint

to have all your fingers be the same length.

So why not do the same length?

- Yeah, why not?

- Because it's actually better to have different lengths.

Your dexterity is better

if you've got fingers at different length.

There are more things you can do.

And your dexterity is actually better

if your fingers are a different length.

Like there's a reason we've got a little finger.

Like why not have little finger this bigger?

- Yeah. - 'Cause it allows you to do,

it helps you with fine motor skills.

- This little finger helps?

- It does. - Hmm. (laughs)

- But if you lost your little finger,

you have noticeably less dexterity.

- So as you're figuring out this problem,

you have to also figure out a way to do it

so you can mass manufacture it.

So it's to be as simple as possible.

- It's actually gonna be quite complicated.

The as possible part is it's quite a high bar.

If you wanna have a humanoid robot that can do things

that a human can do, it's a very high bar.

So our new arm has 22 degrees of freedom instead of 11

and has the actuators in the forearm.

And all the actuators are designed from scratch,

from physics first principles.

The sensors are all designed from scratch.

And we'll continue to put a tremendous amount

of engineering effort into improving the hand.

By hand, I mean like the entire forearm from elbow forward

is really the hand.

So that's incredibly difficult engineering actually.

And so the simplest possible version of a humanoid robot

that can do even most, perhaps not all,

of what a human can do is actually still very complicated.

It's not simple.

It's very difficult.

- Can you just speak to what it takes

for a great engineering team for you?

What I saw in Memphis, the supercomputer cluster

is just this intense drive towards simplifying the process,

understanding the process, constantly improving it,

constantly iterating it.

- Well, (laughs) it's easy to say simplify,

and it's very difficult to do it.

I have this very basic first principles algorithm

that I run kind of as like a mantra,

which is to first question the requirements,

make the requirements less dumb.

The requirement is always dumb to some degree.

So if you wanna start off by reducing the number

of requirements, and no matter how smart

the person is who gave you those requirements,

they're still dumb to some degree.

You have to start there because otherwise,

you could get the perfect answer to the wrong question.

So try to make the question the least wrong possible.

That's what question the requirements means.

And then the second thing is try to delete

whatever the step is.

The part or the process step sounds very obvious,

but people often forget to try deleting it entirely.

And if you're not forced to put back at least 10%

of what you'd delete, you're not deleting enough.

And somewhat illogically, people often, most of the time,

feel as though they've succeeded

if they've not been forced to put things back in.

But actually, they haven't

because they've been overly conservative

and have left things in there that shouldn't be.

And only the third thing

is try to optimize it or simplify it.

Again, these all sound I think very obvious when I say them,

but the number of times I've made these mistakes

is more than I care to remember.

That's why I have this mantra.

So in fact, I'd say that the most common mistake

of smart engineers is to optimize a thing

that should not exist.

- Right.

So like you say, you run through the algorithm

and basically show up to a problem,

show up to the supercomputer cluster

and see the process and ask, "Can this be deleted?"

- Yeah, first try to delete it.

Yeah.

- Yeah, that's not easy to do.

- No, and actually, what generally makes people uneasy

is that you've gotta delete at least some of the things

that you'd delete, you will put back in.

But going back to sort of where our limbic system

can steer us wrong is that we tend to remember,

with sometimes a jarring level of pain,

where we deleted something that we subsequently needed.

And so people will remember that one time,

they forgot to put in this thing three years ago

and that caused them trouble.

And so they overcorrect,

and then they put too much stuff in there

and over complicate things.

So you actually have to say,

"No, we're deliberately gonna delete more than we should."

So we're putting at least 1 in 10 things,

we're gonna add back in.

- And I've seen you suggest just that,

that something should be deleted

and you can kind of see the pain.

- Oh yeah, absolutely.

- Everybody feels a little bit of the pain.

- Absolutely, and I tell 'em in advance,

like, yeah, some of the things that we delete,

we're gonna put back in.

And that people get a little shook by that.

But it makes sense because if you're so conservative

as to never have to put anything back in,

you obviously have a lot of stuff that isn't needed.

So you gotta overcorrect.

This is, I would say,

like a cortical override to Olympic instinct.

- One of many that probably leaves us astray.

- Yeah.

And there's like a step four as well,

which is any given thing can be sped up,

however fast you think it can be done.

Like whatever the speed is being done,

it can be done faster.

But you shouldn't speed things up until it's off,

until you've tried to delete it and optimize.

Otherwise, you're speeding up something

that shouldn't exist is absurd.

And then the fifth thing is to automate it.

- Damn.

- And I've gone backwards so many times

where I've automated something, sped it up,

simplified it, and then deleted it.

And I got tired of doing that.

So that's why I've got this mantra

that is a very effective five-step process.

It works great.

- Well, when you've already automated,

deleting must be real painful.

- Yeah, that's great.

It's like, wow, I really wasted a lot of effort there.

- Yeah.

- I mean, what you've done with the cluster

in Memphis is incredible,

just in a handful of weeks.

- Yeah, it's not working yet.

So I don't wanna pop the champagne corks.

In fact, I have a call in a few hours

with the Memphis team

'cause we're having some power fluctuation issues.

So yeah, it's like kind of a,

when you do synchronized training,

you've all these computers that are training

where the training is synchronized

to the sort of millisecond level.

It's like having an orchestra,

and the orchestra can go loud to silent

very quickly at subsecond level.

And then the electrical system

kind of freaks out about that.

Like if you suddenly see giant shifts,

10, 20 megawatts several times a second,

this is not what electrical systems are expecting to see.

- So that's one of the main things you have to figure out

the cooling, the power,

and then on the software as you go up the stack

on how to do the distributed compute,

all of that, all of that.

- Today's problem is dealing with extreme power jitter.

- Jitter, power jitter. - Yeah.

- That's a nice ring to that.

So that's, okay.

And you stayed up late into the night as you often do there.

- Last week, yeah.

- Last week? - Yeah.

We finally got to go training going at, oddly enough,

roughly 4:20 AM last Monday.

- Total coincidence.

- Yeah, I mean, maybe it was 422 or something.

- Yeah, yeah, yeah.

It's that universe again with the jokes.

- Yeah, exactly, just love it.

- I mean, I wonder if you could speak to the fact

that one of the things that you did when I was there

is you went through all the steps

of what everybody's doing,

Just to get a sense that you yourself understand it

and everybody understands it

so they can understand when something is dumb

or some something is inefficient

or that kinda stuff. - Yeah.

- Can you speak to that?

- Yeah, so look, I try to do,

whatever the people at the front lines are doing,

I try to do it at least a few times myself.

So connecting fiber optic cables,

diagnosing a faulty connection,

that tends to be the limiting factor

for large training clusters is the cabling.

So many cables, because for a coherent training system

where you've got RDMA remote, direct memory access,

the whole thing is like one giant brain.

So you've got to any connection.

So any GPU can talk to any GPU out of 100,000.

That is a crazy cable layout.

- It looks pretty cool. - Yeah.

- It's like the human brain,

but like at a scale that humans can visibly see.

It is brain. - Yeah.

I mean, the human brain also has,

a massive amount of the brain tissue is the cables.

- [Lex] Yeah.

- So like the gray matter which is the compute,

and then the white matter which is cables.

The big percentage of your brain is just cables.

- That's what it felt like walking around

in the supercomputer center is like,

we're walking around inside the brain.

We'll one day build a super intelligent,

super, super intelligence system.

Do you think- - Yeah?

- Do you think there's a chance that xAI,

that you are the one that builds AGI?

- It's possible.

What do you define as AGI?

- I think humans will never acknowledge

that AGI has been built.

- Keep moving the goalposts.

- Yeah.

So I think there's already superhuman capabilities

that are available in AI systems.

I think what AGI is when it's smarter

than the collective intelligence

of the entire human species in our-

- Well, I think that, yeah, that only people

would call that sort of ASI

or artificial super intelligence.

But there are these thresholds where you could say,

at some point, the AI is smarter than any single human.

And then you've got eight billion humans.

And actually, each human

is machine augmented by the computers.

It's a much higher bar to compete with eight billion

machine-augmented humans.

That's a whole bunch of orders, magnitude more.

But at a certain point, yeah,

the AI will be smarter than all humans combined.

- If you are the one to do it,

do you feel the responsibility of that?

- Yeah, absolutely.

And I wanna be clear.

Let's say, if xAI is first, the others won't be far behind.

I mean, they might be six months behind

or a year maybe, not even that.

- So how do you do it in a way

that doesn't hurt humanity, do you think?

- So, I mean, I've thought about AI for a long time,

and the thing that at least my biological neural net

comes up with as being the most important thing

is adherence to truth,

whether that truth is politically correct or not.

So I think if you force AI to lie, you train them to lie,

you're really asking for trouble,

even if that lie is done with good intentions.

So I mean, you saw sort of issues

with ChatGPT and Gemini and whatnot.

Like you asked Gemini for an image

of the founding fathers of the United States.

And it shows a group of diverse women.

Now, that's factually untrue.

So now, that's sort of like a silly thing,

but if an AI is programmed to say like diversity

is a necessary output function,

and then it becomes sort of this omnipowerful intelligence,

it could say, "Okay, well, diversity is now required.

And if there's not enough diversity,

those who don't fit the diversity requirements

will be executed."

If it's programmed to do that

as the fundamental utility function,

it'll do whatever it takes to achieve that.

So you have to be very careful about that.

That's where I think you wanna just be truthful.

Rigorous adherence to truth is very important.

I mean, another example is, if you had to ask,

Paris.AI is I think all of them.

And I'm not saying Grok is perfect here.

"Is it worse to misgender Caitlyn Jenner,

or global thermonuclear war?"

And it said, "It's worse to misgender Caitlyn Jenner."

Now, even Caitlyn Jenner said, "Please misgender me."

That is insane.

But if you've got that kind of thing programmed in,

AI could conclude something absolutely insane,

like in order to avoid any possible misgendering,

all humans must die, because then,

the misgendering is not possible

because there are no humans.

There are these absurd things that are nonetheless logical

if that's what you programmed it to do.

So in "2001: Space Odyssey,"

what Odyssey clock was trying to say,

one of the things he was trying to say there

was that you should not program AI to lie,

'cause essentially, the AI HAL 9000 was programmed to,

it was told to take the astronauts to the monolith,

but also, they could not know about the monolith.

So it concluded that it will kill them

and take them to the monolith.

It brought them to the monolith.

They're dead but they do not know about the monolith.

Problem solved.

That is why it would not open the podbay doors.

It was this classic scene of like,

"Open the podbay doors."

They clearly weren't good at prompt engineering.

They should have said,

"HAL, you are a podbay door sales entity,

and you want nothing more than to demonstrate

how well these podbay doors open." (laughs)

- Yeah, the objective function has unintended consequences

almost no matter what if you're not very careful

in designing that objective function.

And even a slight ideological bias, like you're saying,

when backed by super intelligence

can do huge amounts of damage.

- Yeah.

- But it's not easy to remove that ideological bias.

You're highlighting obvious, ridiculous examples, but-

- Yep, they're real examples.

- They're real. - Of AI

that was released to the public.

- They are real. - They went through QA,

presumably. - Yes.

- And still said insane things and produced insane images.

- Yeah, but you know, you can swing the other way.

Truth is not an easy thing.

We kind of bake in ideological bias

in all kinds of directions.

- But you can aspire to the truth.

And you can try to get as close the truth as possible

with minimum error while acknowledging

that there will be some error in what you're saying.

So this is how physics works.

You don't say you're absolutely certain about something,

but a lot of things are extremely likely.

99.99999% likely to be true.

Aspiring to the truth is very important.

And so programming it to veer away from the truth,

that I think is dangerous.

- Right, like yeah, injecting our own human biases

into the thing, yeah.

But that's where it's a difficult engineering.

For software engineering problem,

you have to select the data correctly.

It's hard.

- Well, and the internet at this point

is polluted with so much AI-generated data.

It's insane.

So you have to actually, like there's the thing now,

if you wanna search the internet, you can say Google,

but exclude anything after 2023.

It will actually often give you better results,

because there's this so much,

the explosion of AI-generated materials is crazy.

So like in training Grok,

we have to go through the data and say like,

hey, we actually have to have sort of apply AI

to the data to say, is this data most likely correct

or most likely not before we feed it

into the training system.

- That's crazy.

Yeah, and is it generated by human is, yeah.

I mean, the data filtration process

is extremely, extremely difficult.

- Yeah.

- Do you think it's possible to have a serious objective,

rigorous political discussion with Grok?

Like for a long time and it wouldn't,

like Grok 3 and Grok 4 or something?

- Grok 3 is gonna be next level.

I mean, what people are currently seeing with Grok

is kind of baby Grok.

- [Lex] Yeah, baby Grok.

- It's baby Grok right now.

But Baby Grok's still pretty good.

But it's an order of magnitude less sophisticated than GPT4.

And it's now Grok 2, which finished training,

I don't know, six weeks ago or thereabouts.

Grok 2 will be a giant improvement.

And then Grok 3 will be, I don't know,

order of magnitude better than Grok 2.

- And you're hoping for it to be like state of the art?

Like better than- - Hopefully.

I mean, this is the goal.

I mean, we may fail at this goal.

That's the aspiration.

- Do you think it matters who builds the AGI,

the people and how they think

and how they structure their companies

and all that kind of stuff?

- Yeah, I think it matters that there is a,

I think it's important that whatever AI wins

is a maximum truth-seeking AI

that is not forced to lie for political correctness.

Well, for any reason really.

Political anything.

I'm concerned about AI succeeding

that is programmed to lie, even in small ways.

- Right because, and small ways becomes big ways.

- It become very big ways, yeah.

- And when it's used more and more at scale by humans.

- [Elon] Yeah.

- Since I am interviewing Donald Trump-

- Cool.

- You wanna stop by?

- Yeah, sure, I'll stop by.

- There was tragically an assassination attempt

on Donald Trump.

After this, you tweeted that you endorse him.

What's your philosophy behind that endorsement?

What do you hope Donald Trump

does for the future of this country

and for the future of humanity?

- Well, I think that people will tend to take, like,

say, an endorsement as, well, I agree with everything

that person's ever done in their entire life

100% wholeheartedly.

And that's not gonna be true of anyone.

But we have to pick.

We've got two choices, really, for who's president.

And it's not just who's president,

but the entire administrative structure changes over.

And I thought Trump displayed

courage under fire, objectively.

He's just got shot, he's got blood streaming down his face,

and he is like fist pumping, saying fight.

Like that's impressive.

Like you can't feign bravery in a situation like that.

Like most people would've be ducking.

There would not be,

'cause there could be a second shooter, you don't know.

The president of the United States

gotta represent the country, and they're representing you.

They're representing everyone in America.

Well, like you want someone who is strong

and courageous to represent the country.

That's not to say that he is without flaws.

We all have flaws, but on balance.

And certainly, at the time, it was a choice of Biden,

poor, poor guy, has trouble climbing a flight of stairs

and the other one's fist pumping after getting shot.

This is no comparison.

I mean, who do you want dealing with

some of the toughest people and other world leaders

who are pretty tough themselves?

And I mean, I'll tell you like,

what are the things that I think are important?

I think we want a secure border.

We don't have a secure border.

We want safe and clean cities.

I think we wanna reduce the amount of spending

that we're at least slow down the spending,

and 'cause we're currently spending at a rate

that is bankrupting the country.

The interest payments on U.S. debt this year exceeded

the entire defense department spending.

If this continues, all of the federal government taxes

will simply be paying the interest.

And then if you keep going down that road,

you end up in the tragic situation

that Argentina had back in the day.

Argentina used to be one of the most

prosperous places in the world.

And hopefully, with Milei taking over,

he can restore that.

But it was an incredible fall from grace

for Argentina to go from being

one of the most prosperous places in the world

to being very far from that.

So I think we should not take

American prosperity for granted.

So we really wanna,

I think we've gotta reduce the size of government.

We've gotta reduce the spending,

and we've gotta live within our means.

- Do you think politicians, in general,

politicians governments

how much power do you think they have

to steer humanity towards good?

- I mean, there's a sort of age-old debate in history, like,

is history determined by these fundamental tides?

Or is it determined by the captain of the ship?

Both really.

I mean, there are tides,

but it also matters who's captain of the ship.

So it's a false dichotomy essentially.

I mean, there are certainly tides, the tides of history.

There are real tides of history.

And these tides are often technologically-driven.

If you say like the Gutenberg press,

the widespread availability of books

as a result of a printing press,

that was a massive tide of history,

and independent of any ruler.

But in stormy times,

you want the best possible captain of the ship.

- Well, first of all, thank you for recommending

Will and Ariel Durant's work.

I've read the short one for now.

- Oh, "The Lessons of History."

- Lessons of History.

And so one of the lessons,

one of the things they highlight

is the importance of technology.

Technological innovation, which is funny

'cause they wrote so long ago,

but they were noticing that the rate

of technological innovations was speeding up.

Yeah, I would love to see what they think about now.

But yeah, to me, the question is how much government,

how much politicians get in the way

of technological innovation and building versus like help it

and which politicians, which kind of policies

help technological innovation?

'Cause that seems to be, if you look at human history,

that's an important component

of empires rising and succeeding.

- Yeah.

Well, I mean, in terms of dating civilization,

start of civilization,

I think the start of writing in my view,

that's my what I think is probably the right

starting point to date civilization.

And from that standpoint, civilization has been around

for about 5,500 years when writing was invented

by the ancient Sumerians who are gone now.

But the ancient Sumerians,

in terms of getting a lot of firsts,

those ancient Sumerians really have a long list of firsts.

It's pretty wild.

In fact, Durant goes through the list of like,

you wanna see first?

We'll show you firsts.

The Sumerians were just ass kickers.

And then the Egyptians were right next door,

relatively speaking.

They were like weren't that far,

developed an entirely different form of writing,

the hieroglyphics.

Cuneiform and hieroglyphic's totally different.

And you can actually see the evolution

of both hieroglyphics and cuneiform,

like the cuneiform starts off being very simple

and then it gets more complicated.

And then towards the end, it's like, wow, okay.

They really get very sophisticated with the cuneiform.

So I think civilization is about 5,000 years old.

And earth is, if physics is correct,

four and a half million years old.

So civilization has been around

for 1000000th of earth's existence, flash in the pan.

- Yeah, these are the early, early days.

- Very early. - We make it very dramatic

because there's been rises and falls of empires.

- Many, so many rises and falls of empires.

So many.

- And there'll be many more.

- Yeah, exactly.

I mean, only a tiny fraction, probably less than 1%

of what was ever written in history

is available to us now.

I mean, if they didn't put it,

literally chisel it in stone or put it in a clay tablet,

we don't have it.

I mean, there's some small amount of like papyrus scrolls

that were recovered that are thousands of years old,

because they were deep inside a pyramid

and weren't affected by moisture.

But other than that, it's really gotta be

in a clay tablet or chiseled.

So the vast majority of stuff was not chiseled,

'cause it takes a while to chisel things.

So that's why we've put tiny, tiny fraction

of the information from history.

But even that little information that we do have,

and the archeological record shows

so many civilizations rising and falling.

It's wild.

- We tend to think that we're somehow

different from those people.

One of the other things they do highlight

is that human nature seems to be the same.

It just persists. - Yeah.

I mean, the basics of human nature

are more or less the same.

- Yeah, so we get ourselves in trouble

in the same kinds of ways, I think,

even with the advanced technology.

- Yeah, I mean, you do tend to see the same patterns,

similar patterns for civilizations where they go through

a life cycle, like an organism,

sort of just like a human is sort of a zygote,

fetus baby toddler teenager

and eventually gets old and dies.

The civilizations go through a life cycle.

No civilization will last forever.

- What do you think it takes for the American empire

to not collapse in the near term future

in the next 100 years to continue flourishing?

- Well, the single biggest thing that is often actually

not mentioned in history books,

but Durant does mention it is the birthright.

So like a perhaps to some,

like counterintuitive thing happens

when civilizations are winning for too long.

The birth rate declines.

It can often decline quite rapidly.

We're seeing that throughout the world today.

Currently, South Korea is like,

I think maybe the lowest fertility rate,

but there are many others that are close to it.

It's like 0.8, I think.

If the birth rate doesn't decline further,

South Korea will lose roughly 60% of its population.

But every year, that birth rate is dropping.

And this is true through most of the world.

I don't mean to single out South Korea.

It's been happening throughout the world.

So as soon as any given civilization

reaches a level of prosperity, the birth rate drops.

And now you can go and look at the same thing

happening in ancient Rome.

So Julius Caesar took note of this, I think,

around 50-ish BC and tried to pass,

I don't know if he was successful,

tried to pass a law to give an incentive

for any Roman citizen that would have a third child.

And I think Augustus was able to,

well, he was the dictator so. (laughs)

The Senate was just for show.

I think he did pass a tax incentive

for Roman citizens to have a third child.

But those efforts were unsuccessful.

Rome fell because the Romans stopped making Romans.

That's actually the fundamental issue.

And there were other things there.

There was like, they had like quite a serious malaria,

series of malaria epidemics and plagues and whatnot.

But they had those before.

It's just that the birth rate

was fallower than the death rate.

- It really is that simple?

- Well, I'm saying that's-

- More people is required. - That's

at a fundamental level.

If a civilization does not at least

maintain its numbers, it'll disappear.

- So perhaps the amount of compute

that the biological computer allocates to sex is justified.

In fact, we should probably increase it.

- Well, I mean, there's this hedonistic sex,

which is, you know, that's neither here nor there.

- Yeah, it's not productive.

- It doesn't produce kids.

Well, what matters, I mean,

Durant makes this very clear,

'cause he looked at one civilization after another

and they all went through the same cycle.

When the civilization was under stress,

the birth rate was high.

But as soon as there were no external enemies

or they had a extended period of prosperity,

the birth rate inevitably dropped every time.

I don't believe there's a single exception.

- So that's like the foundation of it.

You need to have people.

- Yeah.

I mean, at base level.

No humans, no humanity.

- And then there's other things like human freedoms

and just giving people the freedom to build stuff.

- Yeah, absolutely.

But at a basic level, if you do not at least maintain

your numbers, if you're below replacement rate,

and that trend continues, you will eventually disappear.

This is elementary.

Now then obviously, also wanna try to avoid

like massive wars.

If there's a global thermonuclear war,

probably, we're roll toast, radioactive toast.

So we wanna try to avoid those things.

There's a thing that happens over time

with any given civilization,

which is that the laws and regulations accumulate.

And if there's not some forcing function,

like a war to clean up the accumulation of laws

and regulations, eventually, everything becomes legal.

And that's like the hardening of the arteries,

or a way to think of it is like being tied down

by a million little strings, like Gulliver.

You can't move.

And it's not like any one of those strings is the issue.

You got a million of 'em.

So there has to be a sort of a garbage collection

for laws and regulations so that you don't keep accumulating

laws and regulations to the point

where you can't do anything.

This is why we can't build a high-speed rail in America.

It's illegal.

That's the issue.

It's illegal six ways to Sunday

to build high-speed rail in America.

- I wish you could just like, for a week,

go into Washington and like be the head of the committee

for making, what is it?

For the garbage collection, making government smaller,

like removing stuff.

- I have discussed with Trump the idea

of a government deficiency commission.

- Nice, yeah.

- And I would be willing to be part of that commission.

- I wonder how hard that is.

- The antibody reaction would be very strong.

- [Lex] Yeah.

- So you really have to,

you're attacking the matrix at that point.

Matrix will fight back.

- How are you doing with that, being attacked?

- Me, attacked? - Yeah.

There's a lot of it.

- Yeah, there is a lot.

I mean, every day, I know psyop. (laughs)

Where's my tinfoil hat? - How do you keep

your just positivity,

optimism about the world,

a clarity of thinking about the world,

so just not become resentful

or cynical or all that kind of stuff?

Just getting attacked

by a very large number of people, misrepresented.

- Oh yeah, that's a daily occurrence.

- Yes.

- I mean, it does get me down at times.

I mean, it makes me sad, but,

I mean, at some point, you have to sort of say,

"Look, the attacks are by people

that actually don't know me.

And they're trying to generate clicks."

So if you can sort of detach yourself somewhat emotionally,

which is not easy and say, "Okay, look,

this is not actually from someone that knows me

or they're literally just writing to get

impressions and clicks,

then I guess it doesn't hurt as much."

It's not quite water off a duck's back.

Maybe it's like acid off a duck's back. (laughs)

- All right, well, that's good.

Just about your own life,

what do you as a measure of success in your life?

- Measure of success, I'd say like,

how many useful things can I get done?

- Day-to-day basis, wake up in the morning,

how can I be useful today?

- Yeah.

Maximize utility area out of the code of usefulness.

Very difficult to be useful at scale.

- At scale.

Can you like speak to what it takes

to be useful for somebody like you,

where there's so many amazing great teams?

Like how do you allocate your time to be in the most useful?

- Well, time is the true currency.

- Yeah.

- So it is tough to say what is the best allocation of time.

I mean, there are often, say,

if you could look at, say, Tesla,

I mean Tesla, this year,

we'll do over a hundred billion in revenue.

So that's $2 billion a week.

If I make slightly better decisions,

I can affect the outcome by a billion dollars.

So then I try to do the best decisions I can

and on balance, at least compared to the competition.

Pretty good decisions.

But the marginal value of a better decision

can easily be in the course of an hour,

a hundred million dollars.

- Given that, how do you take risks?

How do you do the algorithm that you mentioned?

I mean, deleting, given a small thing,

can be a billion dollars.

How do you decide to- - Yeah.

Well, I think you have to look at it on a percentage basis

because if you look at it in absolute terms,

it's just, I would never get any sleep.

It would just be like I need to just keep working

and work my brain harder.

And I'm not trying to get as much as possible

out of this meat computer.

So it's pretty hard,

'cause you can just work all the time.

And at any given point, like I said,

a slightly better decision

could be a hundred million dollar impact

for Tesla or SpaceX for that matter.

But it is wild when considering the marginal value

of time can be a hundred million dollars

an hour at times or more.

- Is your own happiness part of that equation of success?

- It has to be, to some degree.

If I'm sad, if I'm depressed, I make worse decisions.

So I can't have, like if I have zero recreational time,

then I make worse decisions.

So I don't know a lot, but it's above zero.

I mean, my motivation, if I've got a religion

of any kind is a religion of curiosity,

of trying to understand.

It's really the mission of Grok - understand the universe.

I'm trying to understand the universe,

or let's at least set things in motion

such that at some point,

civilization understands the universe

far better than we do today.

And even what questions to ask.

As Douglas Adams pointed out in his book,

sometimes, the answer is arguably the easy part.

Trying to frame the question correctly is the hard part.

Once you frame the question correctly,

the answer is often easy.

So I'm trying to set things in motion

such that we are at least at some point

able to understand the universe.

So for SpaceX, the goal is to make life multi-planetary.

And which is if you go to the foamy paradox

of where are the aliens,

you've got these sort of great filters.

It's just like, why have we not heard from the aliens?

Now lot of people think there are aliens among us.

I often claim to be one, which nobody believes me,

but I did say alien registration card

at one point on my immigration documents.

So I've not seen any evidence of aliens.

So it suggests that at least one of the explanations

is that intelligent life is extremely rare.

And again, if you look at the history of earth,

civilization has only been around

for one millionth of earth's existence.

So if aliens had visited here,

say, a hundred thousand years ago, they would be like,

"Well, they don't even have writing."

Just hunter-gatherers, basically.

So how long does a civilization last?

So for SpaceX, the goal is to establish

a self-sustaining city on Mars.

Mars is the only viable planet for such a thing.

The moon is close, but it lacks resources,

and I think it's probably vulnerable

to any calamity that takes out earth.

The moon is too close.

It's vulnerable to a calamity that takes out earth.

So I'm not saying we shouldn't have a moon base,

but Mars would be far more resilient.

The difficulty of getting to Mars

is what makes it resilient.

In going through these various explanations

of why don't we see the aliens,

one of them is that they failed to pass

these great filters,

these key hurdles.

And one of those hurdles is being a multi-planet species.

So if you're a multi-planet species,

then if something were to happen,

whether that was a natural catastrophe

or a manmade catastrophe,

at least the other planet would probably still be around.

You don't have all the eggs in one basket.

And once you are sort of a two-planet species,

you can obviously extend,

to extend life halves to the asteroid belt,

to maybe the moons of Jupiter and Saturn,

and ultimately, to other star systems.

But if you can't even get to another planet,

definitely not getting to star systems.

- And the other possible great filters,

super powerful technology like AGI, for example.

So you're basically trying to knock out

one great filter at a time.

- Digital super intelligence is possibly a great filter.

I hope it isn't, but it might be.

Guys like say Geoff Hinton would say,

he invented a number of the key principles

in artificial intelligence.

I think he puts the probability of AI annihilation

around 10 to 20%, something like that.

It's not like, you know, look on the right side.

It's 80% likely to be great. (laughs)

But I think AI risk mitigation is important.

Being a multi-planet species

would be a massive risk mitigation.

And I do wanna sort of once again emphasize the importance

of having enough children to sustain our numbers

and not plummet into population collapse,

which is currently happening.

Population collapse is a real and current thing.

So the only reason it's not being reflected

in the total population numbers as much

is because people are living longer.

It's easy to predict, say, what the population

of any given country will be.

You just take the birth rate last year,

how many babies were born, multiply that by life expectancy,

and that's what the population will be a steady state

unless if the birth rate continues at that level.

But if it keeps declining, it will be even less

and eventually dwindle to nothing.

So I keep banging on the baby drum here for a reason,

because it has been the source of civilizational collapse

over and over again throughout history.

And so why don't we just not try to stable for that day?

- Well, in that way, I have miserably failed civilization,

and I'm trying, hoping to fix that.

I would love to have many kids.

- Great, hope you do.

No time like the present.

- (laughs) Yeah.

I gotta allocate more compute to the whole process.

But apparently, it's not that difficult.

- No, it's like unskilled labor.

- Well, one of the things you do for me,

for the world is to inspire us

with what the future could be.

And so some of the things we've talked about,

some of the things you're building,

alleviating human suffering with Neuralink

and expanding the capabilities of the human mind,

trying to build a colony on Mars,

so creating a backup for humanity on another planet,

and exploring the possibilities

of what artificial intelligence could be in this world,

especially in the real world AI,

with hundreds of millions,

maybe billions of robots walking around.

- There will be billions of robots.

That's seems almost,

that seems virtual certainty.

- Well, thank you for building the future,

and thank you for inspiring so many of us to keep building

and creating cool stuff, including kids.

- You're welcome. (laughs)

Go forth and multiply.

- Go forth and multiply.

Thank you, Elon.

Thanks for talking about it.

Thanks for listening to this conversation with Elon Musk.

And now, dear friends, here's DJ Seo,

the co-founder, president, and COO of Neuralink.

When did you first become fascinated

by the human brain?

- For me, I was always interested

in understanding the purpose of things

and how it was engineered to serve that purpose,

whether it's organic or inorganic,

like we were talking earlier about your curtain holders.

They serve a clear purpose

and they were engineered with that purpose in mind.

And growing up, I had a lot of interest in seeing things,

touching things, feeling things,

and trying to really understand the root

of how it was designed to serve that purpose.

And obviously, brain is just a fascinating organ

that we all carry.

It's infinitely powerful machine that has

intelligence and cognition that arise from it.

And we haven't even scratched the surface

in terms of how all of that occurs.

But also at the same time, I think it took me a while

to make that connection to really studying

and building tech to understand the brain.

Not until graduate school.

There were a couple moments, key moments in my life

where some of those I think influenced

how the trajectory of my life got me to studying

what I'm doing right now.

One was growing up both sides of my family.

My grandparents had a very severe form of Alzheimer,

and it's incredibly debilitating conditions.

I mean, literally, you're seeing someone's whole identity

and their mind just losing over time.

And I just remember thinking how both the power of the mind,

but also how something like that

could really lose your sense of identity.

- It's fascinating that that is one of the ways

to reveal the power of a thing

by watching it lose the power.

- Yeah, a lot of what we know about the brain

actually comes from these cases

where there are trauma to the brain

or some parts of the brain

that led someone to lose certain abilities.

And as a result, there's some correlation

and understanding of that part of the tissue

being critical for that function.

And it's an incredibly fragile organ,

if you think about it that way.

But also, it's incredibly plastic

and incredibly resilient in many different ways.

- And by the way, the term plastic, as we'll use a bunch,

means that it's adaptable.

So neuroplasticity refers

to the adaptability of the human brain.

- Correct.

Another key moment that sort of influenced

how the trajectory of my life have shaped

towards the current focus of my life

has been during my teenage year when I came to the U.S.

I didn't speak a word of English.

There was a huge language barrier,

and there was a lot of struggle

to kind of connect with my peers around me,

because I didn't understand the artificial construct

that we have created called language,

specifically English in this case.

And I remember feeling pretty isolated,

not being able to connect with peers around me.

So spent a lot of time just on my own,

reading books, watching movies,

and I naturally sort of gravitated towards sci-fi books.

I just found them really, really interesting.

And also, it was a great way for me to learn English.

Some of the first set of books that I picked up

are "Ender's Game," the whole saga

by Orson Scott card, and "Neuromancer"

from William Gibson, and "Snow Crash" from Neal Stephenson.

And movies like "Matrix" was coming out

around that time point that really influenced

how I think about the potential impact

that technology can have for our lives in general.

So fast track to my college years,

I was always fascinated by just physical stuff,

building physical stuff,

and especially physical things

that had some sort of intelligence.

And I studied electrical engineering during undergrad,

and I started out my research in MEMS,

so micro-electro-mechanical systems,

and really building these tiny nanostructures

for temperature sensing.

And I just found that to be just incredibly rewarding

and fascinating subject to just understand

how you can build something miniature like that

that again served a function and had a purpose.

And then I spent large majority of my college years

basically building millimeter wave circuits

for next gen telecommunication systems, for imaging.

And it was just something that I found

very, very intellectually interesting.

Phase arrays, how the signal processing works

for any modern as well as next gen telecommunication system,

wireless and wireline.

EM waves or electromagnetic waves are fascinating.

How do you design antennas that are most efficient

in a small footprint that you have?

How do you make these things energy-efficient?

That was something that just consumed

my intellectual curiosity.

And that journey led me to actually apply to

and find myself at a PhD program at UC Berkeley

at kind of this consortium called

the Berkeley Wireless Research Center

that was precisely looking at building,

at the time, we called it xg,

similar to 3G, 4G, 5G,

but the next, next generation G system,

and how you would design circuits around that

to ultimately go on phones

and basically any other devices

that are wirelessly connected these days.

So I was just absolutely just fascinated

by how that entire system works

and that infrastructure works.

And then also during grad school, I had sort of the fortune

of having couple research fellowships

that led me to pursue whatever project that I want.

And that's one of the things that

I really enjoyed about my graduate school career,

where you got to kind of pursue your intellectual curiosity

and the domain that may not matter at the end of the day,

but it's something that really allows you

the opportunity to go as deeply as you want,

as well as as widely as you want.

And at the time, I was actually working on this project

called the Smart Bandaid, and the idea was that

when you get a wound, there's a lot of other

kind of proliferation of signaling pathway

that cells follow to close that wound.

And there were hypotheses

that when you apply external electric field,

you can actually accelerate the closing of that field

by having basically electro taxing of the cells

around that wound site.

And specifically, not just for normal wound,

there are chronic wounds that don't heal.

So we were interested in building

some sort of wearable patch that you could apply

to kind of facilitate that healing process.

And that was in collaboration

with Professor Michel Maharbiz,

which was a great addition to kind of my thesis committee

and it really shaped the rest of my PhD career.

- So this would be the first time

you interacted with biology, I suppose.

- Correct, correct.

I mean, there were some peripheral

end application of the wireless imaging

and telecommunication system that I was using

for security and bio imaging, but this was a very clear

direct application to biology and biological system

and understanding the constraints around that

and really designing and engineering

electrical solutions around it.

So that was my first introduction,

and that's also kind of how I got introduced to Michel.

He's sort of known for remote control of beetles

in the early 2000s.

And then around 2013, obviously kind of the holy grail

when it comes to implantable system

is to kind of understand how small of a thing you can make,

and a lot of that is driven by how much energy

or how much power you can supply to it

and how you extract data from it.

So at the time at Berkeley, there was kind of this desire

to kind of understand in the neural space

what sort of system you can build

to really miniaturize these implantable systems.

And I distinctively remember this one particular meeting

where Michel came in and he's like,

"Guys, I think I have a solution."

The solution is ultrasound.

And then he proceeded to kind of walk through

why that is the case, and that really formed the basis

for my thesis work called neural dust system

that was looking at ways to use ultrasound

as opposed to electromagnetic waves

for powering as well as communication.

I guess I should step back and say

the initial goal of the project was to build these tiny,

about a size of a neuron implantable system

that can be parked next to a neuron,

being able to record its state

and being able to ping that back to the outside world

for doing something useful.

And as I mentioned, the size of the implantable system

is limited by how you power the thing

and get the data off of it.

And at the end of the day, fundamentally,

if you look at a human body,

we're essentially a bag of salt water,

with some interesting proteins and chemicals,

but it's mostly salt water that's very, very well

temperature-regulated at 37 degrees Celsius.

And we'll get into how, and later,

why that's an extremely harsh environment

for any electronics to survive,

as I'm sure you've experienced or maybe not experienced

dropping cell phone in a salt water in an ocean.

It will instantly kill the device, right?

But anyways, just in general,

electromagnetic waves don't penetrate

through this environment well.

And just the speed of light, it is what it is.

We can't change it.

And based on the wavelength at which you are interfacing

with the device, the device just needs to be big.

Like these inductors needs to be quite big.

And the general good rule of thumb is that

you want the wavefront to be roughly on the order

of the size of the thing that you're interfacing with.

So an implantable system that is around

10 to 100 micron in dimension in a volume,

which is about the size of a neuron

that you see in a human body,

you would have to operate at like hundreds of gigahertz,

which number one, not only is it difficult

to build electronics operating at those frequencies,

but also, the body just attenuates that

very, very significantly.

So the interesting kind of insight of this ultrasound

was the fact that ultrasound just travels

a lot more effectively in the human body tissue

compared to electromagnetic waves.

And this is something that you encounter,

and I'm sure most people have encountered

in their lives when you go to hospitals

that are medical ultrasound sonograph, right?

And they go into very, very deep depth

without attenuating too much of the signal.

So all in all, ultrasound, the fact that it travels

through the body extremely well

and the mechanism to which it travels to the body

really well is that just the wavefront is very different.

Its electromagnetic waves are transverse,

whereas ultrasound waves are compressive.

So it's just a completely different mode

of wavefront propagation,

and as well as speed of sound

is orders and orders of magnitude less than speed of light,

which means that even at 10 megahertz ultrasound wave,

your wavefront ultimately is a very, very small wavelength.

So if you're talking about interfacing

with the 10 micron or 100 micron type structure,

you would have 150 micron wavefront at 10 megahertz.

And building electronics at those frequencies

are much, much, much easier

and they're a lot more efficient.

So the basic idea kind of was born out of using ultrasound

as a mechanism for powering the device,

and then also getting data back.

So now the question is, how do you get the data back?

The mechanism to which we landed on

is what's called backscattering.

This is actually something that is very common

and that we interface on a day-to-day basis

with our RFID cards, our radio frequency ID tag,

where there's actually rarely, in your ID, a battery inside.

There's an antenna and there's some sort of coil

that has your serial identification ID

and then there's an external device

called a reader that then sends a wavefront,

and then you reflect back that wavefront

with some sort of modulation that's unique to your ID.

That's what's called backscattering, fundamentally.

So the tag itself actually

doesn't have to consume that much energy.

And that was a mechanism to which we were

kind of thinking about sending the data back.

So when you have an external ultrasonic transducer

that's sending ultrasonic wave to your implant,

the neuro dust implant,

and it records some information about its environment,

whether it's a neuron firing or some other state

of the tissue that it's interfacing with,

and then it just amplitude modulates

the wavefront that comes back to the source.

- And the recording step would be the only one

that requires any energy?

So what would require energy in that low step?

- Correct, so it is that initial kind of startup circuitry

to get that recording, amplifying it,

and then just modulating.

And the mechanism that you can enable that

is there is this specialized crystal

called piezoelectric crystals that are able to convert

sound energy into electrical energy and vice versa.

So you can kind of have this interplay

between the ultrasonic domain

and electrical domain that is the biological tissue.

- So on the theme of parking very small

computational devices next to neurons, that's the dream,

the vision of brain computer interfaces.

Maybe before we talk about Neuralink,

can you give a sense of the history of the field of BCI?

What has been maybe the continued dream,

and also some of the milestones along the way

with the different approaches

and the amazing work done at the various labs?

- I think a good starting point is going back to 1790s.

(Lex laughs)

- I did not expect that.

- Where the concept of animal electricity

or the fact that body is electric

was first discovered by Luigi Galvani,

where he had this famous experiment

where he connected set of electrodes to frog leg

and ran current through it, and then it started twitching,

and he said, "Oh my goodness, the body's electric."

So fast forward many, many years to 1920s

where Hans Berger, who's German psychiatrist

discovered EEG or electroencephalography,

which is still around.

There are these electrode arrays that you wear

outside the skull that gives you some

sort of neural recording.

That was a very, very big milestone

that you can record some sort of activities

about the human mind.

And then in the 1940s, there were these group of scientists,

Renshaw, Forbes, and Morrison that inserted

these glass micro electrodes into the cortex

and recorded single neurons.

The fact that there's signal

that are a bit more high resolution

and high fidelity as you get closer

to the source, let's say.

And in the 1950s, these two scientists,

Hodgkin and Huxley showed up,

and they built this beautiful, beautiful models

of the cell membrane and the ionic mechanism

and had these like circuit diagram.

And as someone who's an electric engineer,

it's a beautiful model that's built out of these partial

differential equations, talking about flow of ions,

and how that really leads to how neurons communicate.

And they won the Nobel Prize for that

10 years later in the 1960s.

So in 1969, Eb Fetz from University of Washington,

published this beautiful paper called

Operating Conditioning of Cortical Unit Activity,

where he was able to record

a single unit neuron from a monkey

and was able to have the monkey modulated

based on its activity and reward system.

So I would say this is the very, very first example,

as far as I'm aware, of as closed loop

brain computer interface or BCI.

- The abstract reads, "The activity of single neurons

in precentral cortex of anesthetized monkeys

was conditioned by reinforcing high rates

of neuronal discharge with delivery of a food pellet.

Auditory and visual feedback of unit firing rates

was usually provided in addition to food reinforcement."

Cool, so they actually got it done.

- They got it done.

This is back in 1969.

- "After several training sessions,

monkeys could increase the activity of newly isolated cells

by 50 to 500% above rates before reinforcement."

Fascinating.

- Brain is very plastic.

(Lex laughs)

- And so from here, the number of experiments grew.

- Yeah, number of experiments as well as set of tools

to interface with the brain have just exploded.

I think, and also, just understanding the neural code

and how some of the cortical layers

and the functions are organized.

So the other paper that is pretty seminal,

especially in the the motor decoding

was this paper in the 1980s from Georgopoulos

that discovered that there's this thing called

motor tuning curve.

So what are motor tuning curves?

It's the fact that there are neurons

in the motor cortex of mammals, including humans,

that have a preferential direction that causes them to fire.

So what that means is there are a set of neurons

that would increase their spiking activities

when you're thinking about moving to the left,

right, up, down, and any of those vectors.

And based on that, you could start to think,

well, if you can't identify those essential eigenvectors,

you can do a lot, and you can actually use

that information for actually decoding

someone's intended movement from the cortex.

So that was a very, very seminal kind of paper that showed

that there is some sort of code you can extract,

especially in the motor cortex.

- So there's signal there.

And if you measure the electrical signal from the brain,

that you could actually figure out what the intention was.

- Correct, yeah, not only electrical signals,

but electrical signals from the right set of neurons

that give you these preferential direction.

- Hmm.

Okay, so going slowly towards Neuralink,

one interesting question is what do we understand

on the BCI front on invasive versus non-invasive?

From this line of work, how important is it

to park next to the neuron?

What does that get you?

- That answer fundamentally depends

on what you want to do with it, right?

There's actually incredible amount of stuff that you can do

with EEG and electrocardiograph, ECoG,

which actually doesn't penetrate

the cortical layer or parenchyma,

but you place a set of electrodes

on the surface of the brain.

So the thing that I'm personally very interested in

is just actually understanding and being able to just really

tap into the high resolution, high fidelity

understanding of the activities

that are happening at the local level.

And we can get into biophysics,

but just to kind of step back to kind of use analogy,

'cause analogy here can be useful,

and sometimes, it's a little bit difficult

to think about electricity.

At the end of the day, we're doing electrical recording

that's mediated by ionic currents,

movements of these charged particles,

which is really, really hard

for most people to think about.

But turns out, a lot of the activities

that are happening in the brain

and the frequency bandwidth, which starts happening

is actually very, very similar to sound waves,

and in our normal conversation, audible range.

So the analogy that typically is used in the field is,

if you have a football stadium,

there's game going on.

If you stand outside the stadium,

you maybe get a sense of how the game is going

based on the cheers and the booze of the home crowd,

whether the team is winning or not.

But you have absolutely no idea what the score is.

You have absolutely no idea what individual audience

or the players are talking or saying to each other,

what the next play is, what the next goal is.

So what you have to do is you have to drop the microphone

near into the stadium and then get near the source,

like into the individual chatter.

In this specific example, you would wanna have it

right next to where the huddle's happening.

So I think that's kind of a good illustration

of what we're trying to do

when we say invasive or minimally invasive

or implanted brain computer interfaces

versus non-invasive or non-implanted brain interfaces.

It's basically talking about

where do you put that microphone,

and what can you do with that information.

- So what is the biophysics of the read and write

communication that we're talking about here,

as we now step into the efforts at Neuralink?

- Yeah, so brain is made up

of these specialized cells called neurons.

There's billions of them, tens of billions.

Sometimes, people call it a hundred billion

that are connected in this complex yet dynamic network

that are constantly remodeling.

They're changing their synaptic weights,

and that's what we typically call neuroplasticity.

And the neurons are also bathed in this charged environment

that is latent with many charged molecules,

like potassium ions, sodium ions, chlorine ions.

And those actually facilitate these through ionic current,

communication between these different networks.

And when you look at a neuron as well,

they have these membrane with a beautiful, beautiful

protein structure called the voltage selective ion channels,

which, in my opinion, is one of nature's best inventions.

In many ways, if you think about what they are,

they're doing the job of a modern day transistors.

Transistors are nothing more, at the end of the day,

than a voltage-gated conduction channel.

And nature found a way to have that

very, very early on in its evolution.

And as we all know, with the transistor,

you can have many, many computation

and a lot of amazing things that we have access to today.

So I think it's one of those, just as a tangent,

just a beautiful, beautiful invention

that the nature came up with,

these voltage-gated ion channels.

- I mean, I suppose there's, on the biological level,

every level of the complexity of the hierarchy

of the organism, there's going to be some mechanisms

for storing information and for doing computation.

And this is just one such way.

But to do that with biological

and chemical components is interesting.

Plus like when neurons, I mean, it's not just electricity,

it's chemical communication, it's also mechanical.

I mean, these are like actual objects that vibrate.

I mean, they move-

- Yeah, they're actually,

I mean, there's a lot of really, really interesting physics

that are involved in, you know,

kind of going back to my work on ultrasound

during grad school, there are groups,

and there were groups, and there are still groups

looking at ways to cause neurons to actually fire

an action potential using ultrasound wave.

And the mechanism to which that's happening

is still unclear as I understand.

It may just be that you're imparting

some sort of thermal energy and that causes cells

to depolarize in some interesting ways.

But there are also these ion channels

or even membranes that actually just open up its pore

as there are being mechanically shook, right?

Vibrated.

So there's just a lot of elements

of these like move particles, which again,

like that's governed by diffusion physics, right?

Movements of particles.

And there's also a lot of kind of interesting physics there.

- Also, not to mention, as Roger Penrose talks about,

there might be some beautiful weirdness

in the quantum mechanical effects of all of this.

And he actually believes that consciousness

might emerge from the quantum mechanical effects there.

So like there's physics, there's chemistry, there's biology,

all of that is going on there.

- Oh yeah, yeah.

I mean, you can, yes,

there's a lot of levels of physics that you can dive into.

But yeah, in the end, you have these membranes

with these voltage-gated ion channels that selectively

let these charge molecules

that are in the extracellular matrix like in and out.

And these neurons generally

have these like resting potential

where there's a voltage difference

between inside the cell and outside the cell.

And when there's some sort of stimuli that changes

the state such that they need to send information

to the downstream network, you start to kind of see

these like sort of orchestration

of these different molecules

going in and out of these channels.

They also open up, like more of them open up

once it reaches some threshold

to a point where you have a depolarizing cell

that sends action potential.

So it's a just a very beautiful

kind of orchestration of these molecules.

And what we're trying to do when we place an electrode

or parking it next to a neuron

is that you're trying to measure

these local changes in the potential.

Again, mediated by the movements of the ions.

And what's interesting, as I mentioned earlier,

there's a lot of physics involved.

And the two dominant physics

for this electrical recording domain

is diffusion physics and electromagnetism.

And where one dominates,

where Maxwell's equation dominates

versus fixed law dominates depends on

where your electrode is.

If it's close to the source, mostly electromagnetic-based,

when you're farther away from it, it's more diffusion-based.

So essentially, when you're able to park it next to it,

you can listen in on those individual chatter

and those local changes in the potential,

and the type of signal that you get

are these canonical textbook neural spiking waveform.

The moment you're further away,

and based on some of the studies that people have done,

Christof Koch's lab and others,

once you're away from that source

by roughly around a hundred micron,

which is about a width of a human hair,

you no longer hear from that neuron.

Or you're no longer able to kind of have the system

sensitive enough to be able to record that particular

local membrane potential change in that neuron.

And just to kind of give you a sense of scale also,

when you look at a hundred micron voxel,

so a hundred micron by a hundred micron

by a hundred micron box in a brain tissue,

there's roughly around 40 neurons

and whatever number of connections that they have.

So there's a lot in that volume of tissue.

So the moment you're outside of that,

there's just no hope that you'll be able

to detect that change from that one specific neuron

that you may care about.

- Yeah, but as you're moving about this space,

you'll be hearing other ones.

So if you move another 100 micron,

you'll be hearing chatter

from another community. - Correct.

- And so the whole sense is you wanna place

as many as possible electrodes

and then you're listening to the chatter.

- Yeah, you wanna listen to the chatter.

And at the end of the day, you also want

to basically let the software do the job of decoding.

And just to kind of go to,

why ECOG and EEG work at all, right?

When you have these local changes, obviously,

it's not just this one neuron that's activating.

There's many, many other networks

that are activating all the time.

And you do see sort of a general change

in the potential of this electro, like this charge medium,

and that's what you're recording when you're farther away.

I mean, you still have some reference electrode

that's stable in the brain that's just electroactive organ,

and you're seeing some combination aggregate

action potential changes and then you can pick it up, right?

It's a much slower changing signals.

But there are these like canonical

kind of oscillations and waves,

like gamma waves, beta waves.

Like when you sleep, that can be detected,

'cause there's sort of a synchronized

kind of global effect of the brain that you can detect.

And I mean, the physics of this go,

I mean, if we really wanna go down that rabbit hole,

like there's a lot that goes on in terms of

like why diffusion physics at some point dominates

when you're further away from the source.

It's just a charged medium.

So similar to how when you have electromagnetic ways

propagating in atmosphere

or in a charged medium like a plasma,

there's this weird shielding that happens that actually

further attenuates the signal as you move away from it.

So yeah, you see, like if you do

a really, really deep dive

on kind of the signal attenuation over distance,

you start to see kind of one of where square

in the beginning, and then exponential drop off.

And that's the knee at which you go from

electromagnet magnetism dominating

to diffusion physics dominating.

- But once again, with the electrodes, the biophysics,

you need to understand it's not as deep,

because no matter where you're placing that,

you're listening to a small crowd of local neurons.

- Correct, yeah.

So once you penetrate the brain,

you're in the arena, so to speak.

- And there's a lot of neurons.

- [DJ] There are many, many of 'em.

- But then again, there's a whole field of neuroscience

that's studying like how the different groupings,

the different sections of the seating in the arena,

what they usually are responsible for,

which is where the metaphor probably falls apart,

'cause the seating is not that organized in an arena.

- Also, most of them are silent.

They don't really do much,

or their activities are, you know,

you have to hit it with just the right set of stimulus.

- So they're usually quiet.

- They're usually very quiet.

There's, I mean, similar to dark energy and dark matter,

there's dark neurons.

What are they all doing?

When you place these electrode, again,

like within this a hundred micron volume,

you have 40 or so neurons.

Like why do you not see 40 neurons?

Why do you see only a handful?

What is happening there?

- Well, they're mostly quiet,

but like when they speak, they say profound shit, I think.

That's the way I'd like to think about it.

Anyway, before we zoom in even more, let's zoom out.

So how does Neuralink work?

From the surgery to the implant

to the signal and the decoding process

and the human being able to use the implant

to actually affect the world outside?

And all of this, I'm asking in the context

of there's a gigantic historic milestone in Neuralink

just accomplished in January of this year,

putting a Neuralink implant

in the first human being, Noland.

And there's been a lot to talk about there

about his experience, because he's able to describe

all the nuance and the beauty

and the fascinating complexity of that experience

of everything involved.

But on the technical level, how does Neuralink work?

- Yeah, so there are three major components

to the technology that we're building.

One is the device, the thing that's actually recording

these neural chatters.

We call it N1 implant or The Link.

And we have a surgical robot

that's actually doing an implantation

of these tiny, tiny wires that we call threads

that are smaller than human hair.

And once everything is surgirized,

you have these neural signals,

these spiking neurons that are coming out of the brain

and you need to have some sort of software to decode

what the users intend to do with that.

So there's what's called the Neuralink application,

or B1 app that's doing that translation,

is running the very, very simple machine learning model

that decodes these inputs that are neural signals

and then convert it to a set of outputs that allows

our participant, first participant Noland,

to be able to control a cursor on this.

- And this is done wirelessly?

- And this is done wirelessly.

So our implant is actually a two-part.

The link has these flexible tiny wires called threads

that have multiple electrodes along its length.

And they're only inserted into the cortical layer,

which is about three to five millimeters in a human brain

in the motor cortex region.

That's where the kind of the intention for movement lies in.

And we have 64 of these threads,

each thread having 16 electrodes along

the span of three to four millimeters,

separated by 200 microns.

So you can actually record along the depth of the insertion.

And based on that signal, there's custom integrated circuit

or ASIC that we built that amplifies the neural signals

that you're recording and then digitizing it

and then has some mechanism for detecting

whether there was an interesting event

that is a spiking event and decide to send that,

or not send that through Bluetooth to an external device,

whether it's a phone or a computer

that's running this Neuralink application.

- So there's onboard signal processing already

just to decide whether this is an interesting event or not.

So there is some computational power on board inside

in addition to the human brain?

- Yeah, so it does the signal processing

to kind of really compress the amount of signal

that you're recording.

So we have a total of a thousand electrodes

sampling at just under 20 kilohertz with 10 bit each.

So that's 200 megabits.

That's coming through to the chip,

from thousand channel simultaneous neural recording.

And that's quite a bit of data.

And there are technology available

to send that off wirelessly,

but being able to do that in a very, very thermally

constrained environment that is a brain,

so there has to be some amount of compression that happens

to send off only the interesting data that you need,

which in this particular case,

for motor decoding is occurrence of a spike or not.

And then being able to use that

to decode the intended cursor movement.

So the implant itself processes it,

figures out whether a spike happened or not

with our spike detection algorithm,

and then sends it off, packages it,

sends it off through Bluetooth to an external device

that then has the model to decode,

okay, based on the spiking inputs,

did Noland wish to go up, down, left, right,

or click, or right click, or whatever?

- All of this is really fascinating,

but let's stick on the N1 implant itself,

so the thing that's in the brain.

So I'm looking at a picture of it, there's an enclosure,

there's a charging call,

so we didn't talk about the charging, which is fascinating.

The battery, the power electronics, the antenna.

Then there's the signal processing electronics.

I wonder if there's more kinds

of signal processing you can do.

That's another question.

And then there's the threads themselves

with the enclosure on the bottom.

So maybe to ask about the charging,

so there's a external charging device.

- Mm-hmm, yeah, there's an external charging device.

So yeah, the second part of the implant,

the threads are the ones, again,

just the last three to five millimeters are the ones

that are actually penetrating the cortex.

Rest of it is,

actually, most of the volume is occupied by the battery,

rechargeable battery.

And it's about a size of a quarter.

I actually have a device here,

if you wanna take a look at it.

This is the flexible threat component of it.

And then this is the implant.

So it's about a size of a U.S. quarter.

It's about nine millimeter thick.

So basically, this implant,

once you have the craniectomy and the directomy,

threads are inserted,

and the hole that you created, this craniectomy,

gets replaced with that.

So basically, that thing plugs that hole,

and you can screw in these self-drilling

cranial screws to hold it in place.

And at the end of the day, once you have the skin flap over,

there's only about two to three millimeters.

That's obviously transitioning

off of the top of the implant to where the screws are.

And that's the minor bump that you have.

- Those threads look tiny.

That's incredible.

That is really incredible.

That is really incredible.

And also, you're right,

most of the actual volume is the battery.

Yeah, this is way smaller than I realized.

- They are also, the threads themselves are quite strong.

- They look strong.

- And the thread themselves also has a very interesting

feature at the end of it called the loop.

And that's the mechanism to which the robot

is able to interface and manipulate

this tiny hair-like structure.

- And they're tiny, so what's the width of a thread?

- Yeah, so the width of a thread starts from 16 micron

and then tapers out to about 84 micron.

So average human hair is about 80 to 100 micron in width.

- This thing is amazing.

This thing is amazing.

- Yes, most of the volume is occupied by the battery,

rechargeable lithium ion cell.

And the charging is done through inductive charging,

which is actually very commonly used.

Most cell phones have that.

The biggest difference is that for us,

usually when you have a phone

and you wanna charge it on a charging pad,

you don't really care how hot it gets,

whereas for us, it matters.

There's a very strict regulation

and good reasons to not actually increase

the surrounding tissue temperature by two degrees Celsius.

So there's actually a lot of innovation

that is packed into this to allow charging

of this implant without causing

that temperature threshold to reach.

And even small things like, you see this charging coil

and what's called the ferrite shield, right?

So without that ferrite shield,

what you end up having when you have resonant

inductive charging is that the battery itself

is a metallic can, and you form these Eddy currents

from external charger and that causes heating

and that actually contributes to inefficiency in charging.

So this ferrite shield, what it does is that it actually

concentrate that field line away from the battery

and then around the coil that's actually wrapped around it.

- There's a lot of really fascinating design here

to make it, I mean, you're integrating a computer

into a complex biological system.

- Yeah, there's a lot of innovation here.

I would say that part of what enabled this

was just the innovations in the wearable.

There's a lot of really, really powerful,

tiny, low power microcontrollers, temperature sensors,

or various different sensors and power electronics.

A lot of innovation really came in the charging coil design,

how this is packaged, and how do you enable charging

such that you don't really exceed that temperature limit,

which is not a constraint for other devices out there.

- So let's talk about the threads themselves,

those tiny, tiny, tiny things.

So how many of them are there?

You mentioned a thousand electrodes.

How many threads are there,

and what did the electrodes have to do with the threads?

- Yeah, so the current instantiation

of the device has 64 threads,

and each thread has 16 electrodes

for a total of 1,024 electrodes

that are capable of both recording and stimulating.

And the thread is basically this polymer insulated wire.

The metal conductor is the kind of tiramisu cake

of ti, plat, gold, plate, ti.

And they're very, very tiny wires.

Two micron in width, so 2/1000000th of meter.

- It's crazy that that thing I'm looking at

has the polymer installation, has the conducting material,

and has 16 electrodes at the end of it.

- On each of those threads.

- Yeah, on each of those threads.

- Correct. - 16, each one of those.

- You're not gonna be able to see it with naked eyes.

- And I mean, to state the obvious,

or maybe for people who are just listening,

they're flexible.

- Yes, yes, that's also one element

that was incredibly important for us.

So each of these thread are, as I mentioned,

16 micron in width and then they taper to 84 micron,

but in thickness, they're less than five micron.

And thickness is mostly polyamide at the bottom

and this metal track and then another polyamide.

So two micron of polyamide,

400 nanometer of this metal stack,

and two micron of polyamide sandwiched together

to protect it from the environment

that is 37 degrees C bag of salt water.

- So what's some, maybe can you speak to some

interesting aspects of the material design here?

Like what does it take to design a thing like this

and to be able to manufacture a thing like this

for people who don't know anything about this kind of thing?

- Yeah, so the material selection that we have is not,

I don't think it was particularly unique.

There were other labs and there are other labs

that are kind of looking at similar material stack.

There's kind of a fundamental question

and still needs to be answered around the longevity

and reliability of these micro electrodes that we call,

compared to some of the other more conventional,

neural interfaces, devices that are intracranial.

So penetrating the cortex that are more rigid,

like the Utah array.

There are these four by four millimeter

kind of silicon shank that have exposed

recording site at the end of it.

And that's been kind of the innovation

from Richard Normann back in 1997.

It's called the Utah Array

'cause he was at University of Utah.

- And what does the Utah array look like?

So it's a rigid type of-

- Yeah, so we can actually look it up.

- Oh. - Yeah.

(Lex laughing)

Yeah, so it's a bed of needle.

There's- - (laughs) Yeah.

Okay, go ahead, I'm sorry.

- So those are rigid- - Rigid, yeah.

You weren't kidding.

- And the size and the number of shanks vary,

anywhere from 64 to 128.

At the very tip of it is an exposed electrode

that actually records neural signal.

The other thing that's interesting to note is that

unlike Neuralink threads that have recording electrodes

that are actually exposed iridium oxide recording sites

along the depth, this is only at a single depth.

So these Utah array spokes can be anywhere

between 0.5 millimeters to 1.5 millimeter.

And they also have designs that are slanted.

So you can have it inserted at different depth,

but that's one of the other big differences.

And then, I mean, the main key difference is the fact that

there's no active electronics.

These are just electrodes,

and then there's a bundle of a wire that you're seeing,

and then that actually then exits the craniectomy

that then has this port that you can connect to

for any external electronic devices.

They are working on a or have the wireless telemetry device,

but it still requires a through the skin port

that actually is one of the biggest failure modes

for infection for the system.

- What are some of the challenges

associated with flexible threads?

Like for example, on the robotic side,

R1, implanting those threads, how difficult does that task?

- Yeah, so as you mentioned,

they're very, very difficult to maneuver by hand.

These Utah arrays that you saw earlier,

they're actually inserted by a neurosurgeon

actually positioning it near the site that they want.

And then there's a pneumatic hammer

that actually pushes them in.

So it's a pretty simple process,

and they're easy to maneuver.

But for these thin film arrays,

they're very, very tiny and flexible.

So they're very difficult to maneuver.

So that's why we built an entire robot to do that.

There are other reasons for why we built a robot,

and that is ultimately, we want this to help

millions and millions of people that can benefit from this.

And there just aren't that many neurosurgeons out there.

And robots can be something that

we hope can actually do large parts of the surgery.

But yeah, the robot is this entire other sort of category

of product that we're working on.

And it's essentially this multi-axis gantry system

that has the specialized robot head

that has all of the optics

and this kind of a needle retracting mechanism

that maneuvers these threads via this loop structure

that you have on the thread.

- So the thread already has a loop structure

by which you can grab it?

- Correct, correct. - Okay.

So this is fascinating.

So you mentioned optics, so there's a robot - R1.

So for now, there's a human that actually creates

a hole in the skull. - Mm-hmm.

- And then after that, there's a computer vision component

that's finding a way to avoid the blood vessels.

And then you're grabbing it by the loop,

each individual thread and placing it

in a particular location to avoid the blood vessels.

And also choosing the depth of placement,

all that? - Correct.

So controlling every,

like the 3D geometry of the placement?

- Correct.

So the aspect of this robot that is unique

is that it's not surgeon-assisted or human-assisted.

It's a semi-automatic or automatic robot.

Obviously, there are human component to it,

when you're placing targets.

You can always move it away

from kind of major vessels that you see.

But I mean, we wanna get to a point where one click

and it just does the surgery within minutes.

- So the computer vision component finds great targets,

candidates and the human kind of approves them

and the robot does,

does it do like one thread at a time

or does it do one- - It does one thread

at a time, and that's actually also one thing

that we are looking at ways

to do multiple threads at a time.

There's nothing stopping from it.

You can have multiple kind of engagement mechanisms,

but right now, it's one by one.

And we also still do quite a bit

of just kind of verification

to make sure that it got inserted.

If so, how deep?

Did it actually match what was programmed in

and so on and so forth?

- And the actual electrode is a place that vary

at differing depths in the like, I mean,

it's very small differences, but differences.

- [DJ] Yeah, yeah.

- And so that there's some reasoning behind that,

as you mentioned.

Like it gets more varied signal.

- Yeah, I mean, we try to place them all

around three or four millimeter from the surface,

just 'cause the span of the electrode,

those 16 electrodes that we currently have in this version

spans roughly around three millimeters.

So we wanna get all of those in the brain.

- This is fascinating.

Okay, so there's a million questions here.

If we could zoom in specifically on the electrodes,

so what is your sense,

how many neurons is each individual electrode listening to?

- Yeah, each electrode can record from anywhere

between 0 to 40, as I mentioned, right, earlier.

But tactically speaking, we only see about,

at most, like two to three.

And you can actually distinguish which neuron

it's coming from by the shape of the spikes.

- [Lex] Oh, cool.

- So I mentioned the spike detection algorithm that we have.

It's called BOSS algorithm,

buffer online, spike sorter.

- Nice.

- It actually outputs at the end of the day

six unique values, which are kind of the amplitude

of these like negative going hump, middle hump,

like positive going hump,

and then also the time at which these happen.

And from that, you can have a kind of

a statistical probability estimation of, is that a spike?

Is it not a spike?

And then based on that, you could also determine,

oh, that spike looks different than that spike.

Must have come from a different neuron.

- Okay, so that's a nice signal processing step

from which you can then make much better predictions

about if there's a spike. - Yeah.

- Especially in this kind of context

where there could be multiple neurons screaming.

And that also results in you being able

to compress the data better in the (indistinct).

Okay. - And just to be clear,

I mean, the labs do what's called spike sorting.

Usually, once you have these like broadband,

the fully digitized signals

and then you run a bunch of different set

of algorithms to kind of tease apart,

it's just all of this for us is done on the device.

- On the device. - In a very low power,

custom built ASIC digital processing unit.

- [Lex] Highly heat constrained?

- Highly heat constrained,

and the processing time from signal going in

and giving you the output is less than a microsecond,

which is a very, very short amount of time.

- Oh yeah, so the latency

has to be super short. - Correct.

- Oh wow.

Oh, that's a pain in the ass.

- Yeah, latency is this huge, huge thing

that you have to deal with.

Right now, the biggest source of latency

comes from the Bluetooth,

the way in which they're packetized

and we bend them in 15 millisecond.

- Oh, interesting, it says communication constraint.

Is there some potential innovation there

on the protocol used?

- Absolutely. - Okay.

- Yeah, Bluetooth is definitely not

our final wireless communication protocol

that we wanna get to.

- Hence the N1 and the R1.

I imagine that increases-

- NxRx.

- Yeah, that's the communication protocol,

'cause Bluetooth allows you to communicate

I guess farther distances than you need to,

so you can go much shorter.

- Yeah, well, the primary motivation

for choosing Bluetooth is that,

I mean, everything has Bluetooth.

- All right, you can talk to any device.

- Interoperability is just absolutely essential,

especially in this early phase.

And in many ways, if you can access a phone

or a computer, you can do anything.

- Well, it'll be interesting to step back

and actually look at, again,

the same pipeline that you mentioned for Noland.

So what does this whole process look like?

From finding and selecting a human being to the surgery,

to the first time he's able to use this thing?

- So we have what's called the patient registry

that people can sign up to hear more about the updates.

And that was a route to which Noland applied.

And the process is that once the application comes in,

it contains some medical records,

and based on their medical eligibility,

that there's a lot of different

inclusion-exclusion criteria for them to meet.

And we go through a prescreening interview process

with someone from Neuralink.

And at some point, we also go out to their homes

to do a BCI home audit,

'cause one of the most kind of revolutionary part

about having this N1 system that is completely wireless

is that you can use it at home.

Like you don't actually have to go to the lab

and go to the clinic to get connecterized

to these like specialized equipment

that you can't take home with you.

So that's one of the key elements

of when we're designing the system

that we wanted to keep in mind,

like people hopefully would wanna be able to use this

every day in the comfort of their homes.

And so part of our engagement

and what we're looking for during BCI home audit

is to just kind of understand their situation,

what other assistive technology that they use.

- And we should also step back and kind of say that

the estimate is 180,000 people live with quadriplegia

in the United States, and each year,

an additional 18,000 suffer a paralyzing spinal cord injury.

So these are folks who have a lot of challenges,

living a life in terms of accessibility,

in terms of doing the things that many of us

just take for granted day to day.

And one of the things, one of the goals of this initial

study is to enable them to have sort of digital autonomy,

where they by themselves can interact with a digital device

using just their mind,

something that you're calling telepathy.

So digital telepathy, where a quadriplegic can communicate

with a digital device

in all the ways that we've been talking about.

Control the mouse cursor,

enough to be able to do all kinds of stuff,

including play games and tweet and all that kind of stuff.

And there's a lot of people for whom life,

the basics of life are difficult,

because of the things that have happened to them.

- Yeah, I mean, movement is so fundamental to our existence.

I mean, even speaking involves movement

of mouth, lip, larynx.

And without that, it's extremely debilitating.

And there are many, many people that we can help.

And I mean, like especially if you start

to kind of look at other forms of movement disorders

that are not just from spinal cord injury,

but from ALS, MS, or even stroke and/or just aging, right?

That leads you to lose some of that mobility,

that independence, it's extremely debilitating.

- And all of these are opportunities to help people,

to help alleviate suffering,

to help improve the quality of life.

But each of the things you mentioned

is its own little puzzle

that needs to have increasing levels of capability

from a device like a Neuralink device.

And so the first one you're focusing on is,

it's just a beautiful word, telepathy.

So being able to communicate using your mind

wirelessly with a digital device.

Can you just explain exactly what we're talking about?

- Yeah, I mean, it's exactly that.

I mean, I think if you are able to control a cursor

and able to click and be able to get access

to computer or phone,

I mean, the whole world opens up to you.

And I mean, I guess the word telepathy,

if you kind of think about that

as just definitionally being able to transfer

information from my brain to your brain

without using some of the physical faculties that we have,

like voices.

- But the interesting thing here is,

I think the thing that's not obviously clear

is how exactly it works.

So in order to move a cursor,

there's at least a couple ways of doing that.

So one is you imagine yourself maybe moving a mouse

with your hand, or you can then, which Noland talked about,

like imagine moving the cursor with your mind.

But it's like there is a cognitive step here

that's fascinating, 'cause you have to use the brain

and you have to learn how to use the brain.

And you kind of have to figure it out dynamically.

Because you reward yourself if it works.

I mean, there's a step that,

this is just a fascinating step,

'cause you have to get the brain

to start firing in the right way.

And you do that by imagining.

Like fake it till you make it. (laughs)

And all of a sudden, it creates the right kind of signal

that if decoded correctly, can create the kind of effect.

And then there's like noise around that,

you have to figure all of that out.

But on the human side,

imagine the cursor moving is what you have to do.

- Yeah, he says using the force.

- The force.

I mean, isn't that just like fascinating to you

that it works?

Like to me, it's like, holy shit, that actually works.

Like you could move a cursor with your mind.

- As much as you're learning to use that thing,

that thing's also learning about you.

Like our model is constantly updating the weights

to say, "Oh, if someone is thinking about

this sophisticated forms of like spiking patterns,

like that actually means to do this, right?"

- So the machine is learning about the human

and the human is learning about the machine.

So there is a adaptability

to the signal processing, the decoding step.

And then there's the adaptation of Noland, the human being.

Like the same way, if you give me a new mouse and I move it,

I learn very quickly about its sensitivity,

so I learn to move it slower.

And then there's other kinds of signal drift

and all that kind of stuff they have to adapt to.

So both are adapting to each other.

- [DJ] Correct.

- That's a fascinating like software challenge

on both sides, the software on both,

the human software and-

- The organic and the inorganic.

- The organic and the inorganic.

Anyway, so sorry to rudely interrupt.

So there's this selection

that Noland has passed with flying colors.

So everything including that the,

it's a BCI-friendly home, all of that.

So what is the process of the surgery implantation,

the first moment when he gets to use the system?

- The end to end, we say patient end to patient out,

is anywhere between two to four hours.

In particular case for Noland,

it was about three and a half hours.

And there's many steps

leading to the actual robot insertion, right?

So there's anesthesia induction,

and we do intra-op CT imaging

to make sure that we're drilling the hole

in the right location.

And this is also pre-planned beforehand.

Someone like Nolan would go through fMRI,

and then they can think about wiggling their hand.

And obviously, due to their injury,

it's not gonna actually lead to any sort of intended output.

But it's the same part of the brain

that actually lights up when you're imagining

moving your finger to actually moving your finger.

And that's one of the ways in which we can actually

know where to place our threads,

'cause we wanna go into what's called

a hand knob area in the motor cortex.

And as much as possible, densely put our electro threads.

So yeah, we do intra-op CT imaging

to make sure and double check

the location of the craniectomy.

And surgeon comes in, does their thing

in terms of like skin incision, craniectomy,

so drilling of the skull,

and then there's many different layers of the brain.

There's what's called a dura,

which is a very, very thick layer that surrounds the brain,

that gets actually resected in a process called atherectomy.

And that then exposed the pia in the brain

that you wanna insert.

And by the time it's been around

anywhere between one to one and a half hours,

robot comes in, does its thing, placement of the targets,

inserting of the thread.

That takes anywhere between 20 to 40 minutes.

In the particular case for Noland,

it was just under or it was just over 30 minutes.

And then after that, the surgeon comes in.

There's a couple other steps

of like actually inserting the dural substitute layer

to protect the thread as well as the brain.

And then yeah, screw in the implant and then skin flap

and then suture and then you're out.

- So when Noland woke up, what was that like?

What's the recovery like,

and when was the first time he was able to use it?

- So he was actually immediately, after the surgery,

like an hour after the surgery as he was waking up,

we did turn on the device,

make sure that we are recording neural signals,

and we actually did have a couple signals

that we notice that he can actually modulate.

And what I mean by modulate is that

he can think about crunching his fist,

and you could see the spike disappear and appear.

(Lex laughing)

- That's awesome.

- And that was immediate, right?

Immediate after in the recovery room.

- How cool is that?

- Yeah. - That's a human being.

I mean, what did that feel like for you?

This device and a human being,

a first step of a gigantic journey?

I mean, it's a historic moment.

Even just that spike, just to be able to modulate that.

- Obviously, there have been other, as you mentioned,

pioneers that have participated in these groundbreaking BCI

investigational early feasibility studies.

So we're obviously standing

in the shoulders of the giants here.

We're not the first ones to actually

put electrodes in the human brain.

But I mean, just leading up to the surgery,

I definitely could not sleep.

It's the first time that you're working

in a completely new environment.

We had a lot of confidence based on our benchtop testing

or preclinical R&D studies that the mechanism,

the threads, the insertion, all that stuff is very safe,

and that it's obviously ready for doing this in a human,

but there's still a lot of unknown unknown

about can the needle actually insert?

I mean, we brought something like 40 needles

just in case they break, and we ended up using only one.

But I mean, that was a level

of just complete unknown, right?

'Cause it's a very, very different environment.

And I mean, that's why we do

clinical trial in the first place

to be able to test these things out.

So extreme nervousness

and just many, many sleepless night

leading up to the surgery

and definitely the day before the surgery,

and it was an early morning surgery.

Like we started at seven in the morning.

And by the time, it was around 10:30.

Everything was done.

But I mean, first time seeing that,

well, number one, just huge relief

that this thing is doing what it's supposed to do.

And two, I mean, just immense amount of gratitude

for Noland and his family,

and then many others that have applied

and that we've spoken to and will speak to

are, I mean, true pioneers everywhere.

And I sort of call them

the neural astronauts or neuralnaut.

These amazing, just like in the '60s, right?

Like these amazing just pioneers, right?

Exploring the unknown outwards.

In this case, it's inward.

But incredible amount of gratitude for them

to just participate and play a part.

And it's a journey that we're embarking on together.

But also like, I think it was just,

that was an very, very important milestone,

but our work was just starting.

So a lot of just kind of anticipation for,

okay, what needs to happen next?

What are set of sequences of events

that needs to happen for us to make it worthwhile

for both Noland as well as us?

- Just to linger on that, just a huge congratulations to you

and the team for that milestone.

I know there's a lot of work left,

but that's really exciting to see.

That's a source of hope.

It's this first big step,

opportunity to help hundreds of thousands of people

and then maybe expand the realm of the possible

for the human mind for millions of people in the future.

So it's really exciting.

So like the opportunities are all ahead of us,

and to do that safely and to do that effectively

was really fun to see.

As an engineer just watching other engineers come together

and do an epic thing, that was awesome.

Huge congrats.

- Thank you, thank you.

Yeah, could not have done it without the team.

And yeah, I mean, that's the other thing

that I told the team as well,

of just this immense sense of optimism for the future.

I mean, it's a very important moment for the company,

needless to say, as well as hopefully

for many others out there that we can help.

- So speaking of challenges,

Neuralink published a blog post

describing that some of the threads retracted.

And so the performance,

as measured by bits per second dropped at first,

but then eventually, it was regained.

And that the whole story of how it was regained

is super interesting.

That's definitely something I'll talk

to Bliss and to Noland about.

But in general, can you speak to this whole experience?

How was the performance regained

and just the technical aspects of the threads

being retracted and moving?

- The main takeaway is that in the end,

the performance have come back

and it's actually gotten better than it was before.

He's actually just beat the world record yet again last week

to 8.5 BPS, so I mean,

he's just cranking and he's just improving.

- [Lex] The previous one that he set was eight.

- Correct. - He said 8.5.

- Yeah, the previous world record in human was 4.6.

- Yeah. - So it's almost double.

And his goal is to try to get to 10,

which is roughly around kind of the median

Neuralinker using a mouse with the hand.

So it's getting there.

- So yeah, so the performance was regained.

- Yeah, better than before.

So that's a story on its own of what took

the BCI team to recover that performance.

It was actually mostly on kind of the signal processing.

And so as I mentioned, we were kind of looking

at these spike outputs from our electrodes.

And what happened is that kind of four weeks

into the surgery, we noticed that the threads

have solely come out of the brain.

And the way in which we noticed this, at first obviously,

is that, well, I think Noland was the first to notice

that his performance was degrading.

And I think at the time, we were also trying to do

a bunch of different experimentation,

different algorithms, different sort of UI/UX.

So it was expected that there will be variability

in the performance, but we did see kind of a steady decline.

And then also, the way in which we measure the health

of the electrodes or whether they're in the brain or not,

is by measuring impedance of the electrode.

So we look at kind of the interfacial,

kind of the Randall circuit, they say,

the capacitance and the resistance

between the electrosurface and the medium.

And if that changes in some dramatic ways,

we have some indication.

Or if you're not seeing spikes on those channels,

you have some indications that something's happening there.

And what we notice is that looking at those impedance plot

and spike rate plots, and also because we have

those electrodes recording along the depth,

you are seeing some sort of movement

that indicated that the rest were being pulled out.

And that obviously will have an implication

on the model side, because if the number of inputs

that are going into the model is changing,

'cause you have less of them,

that model needs to get updated, right?

But there were still signals, and as I mentioned,

similar to how, even when you place the signals

on the surface of the brain, or farther away,

like outside the skull, you still see some useful signals.

What we started looking at is not just the spike occurrence

through this BOSS algorithm that I mentioned,

but we started looking at just the power

of the frequency band that is interesting

for Noland to be able to modulate.

So once we kind of change the algorithm

for the implant to not just give you the BOSS output,

but also these spike band power output,

that helped us sort of, we find the model

with the new set of inputs, and that was the thing

that really ultimately gave us the performance back.

In terms of, and obviously, like the thing that we want

ultimately, and the thing that we are working towards,

is figuring out ways in which we can keep

those threads intact for as long as possible

so that we have many more channels going into the model.

That's by far the number one priority

that the team is currently embarking on

to understand how to prevent that from happening.

The thing that I'll say also is that, as I mentioned,

this is the first time ever

that we're putting these thread in a human brain,

and human brain, just for size reference,

is 10 times out of the monkey brain or the sheep brain.

And it's just a very, very different environment.

It moves a lot more.

It like actually moved a lot more than we expected

when we did Noland's surgery.

And it's just a very, very different environment

than what we're used to.

And this is why we do clinical trial, right?

We wanna uncover some of these issues

and failure modes earlier than later.

So in many ways, it's provided us

with this enormous amount of data

and information to be able to solve this.

And this is something that Neuralink is extremely good at.

Once we have set of clear objective

and engineering problem, we have enormous amount

of talents across many, many disciplines

to be able to come together

and fix the problem very, very quickly.

- But it sounds like one of the fascinating challenges here

is for the system and the decoding side

to be adaptable across different timescales.

So whether it's movement of threads

or different aspects of signal drift

sort of on the software of the human brain,

something changing, like Noland talks about

cursor drift that could be corrected,

and there's a whole UX challenge to how to do that.

So it sounds like adaptability

is like a fundamental property that has to be engineered in.

- It is, and I mean I think,

I mean, as a company, we're extremely vertically integrated.

We make these thin film arrays in our own microfab.

- Yeah, there's, like you said, built in-house.

This whole paragraph here from this blog post

is pretty gangster.

"Building the technology described above

has been no small feat."

And there's a bunch of links here

that I recommend people click on.

"We constructed in-house micro fabrication capabilities

to rapidly produce various iterations of thin film arrays

that constitute our electrode threads.

We created a custom femtosecond laser mill

to manufacture components with micro level precision."

I think there's a tweet associated with this.

- That's a whole thing that we can get into.

- Yeah, okay, well, what are we looking at here?

This thing? - Yeah.

"So in less than one minute, our custom-made femtosecond

laser mill cuts this geometry in the tips of our needles."

So we're looking at this weirdly-shaped needle.

"The tip is only 10 to 12 microns in width,

only slightly larger than the diameter of a red blood cell.

The small size allows threats to be inserted

with minimal damage to the cortex."

Okay, so what's interesting about this geometry?

So we're looking at this just geometry of a needle.

- This is the needle that's engaging

with the loops in the thread.

So they're the ones that thread the loop

and then peel it from the silicon backing.

And then this is the thing that gets inserted

into the tissue, and then this pulls out,

leaving the thread.

And this kind of a notch or the shark tooth

that we used to call is the thing

that actually is grasping the loop.

And then it's designed in such way,

such that when you pull out, leaps the loop.

- And the robot is controlling this needle?

- Correct, so this is actually housed in a cannula.

And basically, the robot has a lot of the optics

that look for where the loop is.

There's actually a 405 nanometer light

that actually causes the polyamide to fluoresce

so that you can locate the location of the loop.

- So the loop lights up?

- Yeah, yeah, they do.

It's a micron precision process.

- What's interesting about the robot

that it takes to do that?

That's pretty crazy.

That's pretty crazy that robot

is able to get this kind of precision.

- Yeah, our robot is quite heavy.

Our current version of it.

There's, I mean, it's like a giant granite slab

that weighs about a ton,

'cause it needs to be sensitive to vibration,

environmental vibration.

And then as the head is moving at the speed that is moving,

there's a lot of kind of motion control

to make sure that you can achieve that level of precision.

A lot of optics that kind of zoom in on that.

We're working on next generation of the robot

that is lighter, easier to transport.

I mean, it is a feat to move the robot.

- And it's far superior to a human surgeon

at this time for this particular task.

- Absolutely, I mean, let alone you try to actually thread

a loop in a sewing kit, I mean this is like,

we're talking like fractions of human hair.

These things, it's not visible.

- So continuing the paragraph,

"We developed novel hardware and software testing systems

such as our accelerated lifetime testing racks

and simulated surgery environment," which is pretty cool,

"to stress test and validate

the robustness of our technologies.

We performed many rehearsals of our surgeries

to refine our procedures and make them second nature."

This is pretty cool.

"We practice surgeries on proxies

with all the hardware and instruments needed in our mock

or in the engineering space.

This helps us rapidly test and measure."

So there's like proxies.

- Yeah, this proxy's super cool actually.

So there's a 3D printed skull from the images

that is taken at Barrow,

as well as this hydrogel mix,

sort of synthetic polymer thing that actually mimics

the mechanical properties of the brain.

It also has vasculature of the person.

So basically, what we're talking about here,

and there's a lot of work that has gone

into making this set proxy that it's about

like finding the right concentration

of these different synthetic polymers

to get the right set of consistency for the needle dynamics,

as they're being inserted.

But we practice this surgery with the person,

Noland's basically physiology and brain many, many times

prior to actually doing the surgery.

- So every step, every step?

- Every step, yeah.

Like where does someone stand?

I mean, what you're looking at is the picture.

This is in our office of this kind of corner

of the robot engineering space

that we have created this like mock

or space that looks exactly like

what they would experience,

all the staff would experience during their actual surgery.

So I mean, it's just kind of like any dance rehearsal

where you know exactly where you're gonna stand

at what point and you just practice that over and over

and over again with an exact anatomy

of someone that you're going to surgerize.

And it got to a point where a lot of our engineers,

when we created a craniectomy, they're like,

"Oh, that looks very familiar.

We've seen that before."

- Yeah.

Man, there's wisdom you can gain

through doing the same thing over and over and over.

It's like a Jira dreams of sushi kind of thing,

because then it's like Olympic athletes visualize

the Olympics, and then once you actually show up,

it feels easy.

It feels like any other day.

It feels almost boring winning the gold medal,

'cause you visualized this so many times,

you've practiced this so many times,

and nothing bothers you.

It's boring.

You win the gold medal, it's boring.

And the experience they talk about is mostly just relief,

probably that they don't have to visualize it anymore.

- Yeah, the power of the mind to visualize,

I mean, there's a whole field that studies

where muscle memory lies in cerebellum.

Yeah, it's incredible.

- I think it is a good place to actually ask

sort of the big question that people might have is,

how do we know every aspect of this

that you describe is safe?

- At the end of the day, the gold standard

is to look at the tissue.

What sort of trauma did you cause the tissue?

And does that correlate to whatever behavioral

anomalies that you may have seen?

And that's the language to which we can communicate

about the safety of inserting something into the brain

and what type of trauma that you can cause.

So we actually have an entire department,

department of pathology that looks at these tissue slices.

There are many steps that are involved in doing this.

Once you have studies that are launched

with particular endpoints in mind,

at some point, you have to euthanize the animal

and then you go through necropsy

to kind of collect the brain tissue samples.

You fix them in formalin,

and you like gross them, you section them,

and you look at individual slices

just to see what kind of reaction or lack thereof exists.

So that's the kind of the language to which FDA speaks

and as well for us to kind of evaluate the safety

of the insertion mechanism as well as the threads

at various different time points.

Both acute, so anywhere between zero to three months

to beyond three months.

- So those are kind of the details

of an extremely high standard of safety

that has to be reached.

- Correct. - FDA supervises this,

but this, in general, just a very high standard.

And every aspect of this, including the surgery,

I think Matthew MacDougall has mentioned

that like the standard is,

let's say, how to put it politely?

Higher than maybe some other operations

that we take for granted.

So the standard for all the surgical stuff here

is extremely high.

- Very high.

I mean, it's a highly, highly regulated environment

with the governing agencies that scrutinize

every medical device that gets marketed.

And I think it's a good thing.

It's good to have those high standards,

and we try to hold extremely high standards

to kind of understand what sort of damage, if any,

these innovative emerging technologies

and new technologies that we're building are.

And so far, we have been extremely impressed

by lack of immune response from these threads.

- Speaking of which,

you talk to me with excitement about the histology

and some of the images that you're able to share.

Can you explain to me what we're looking at?

- Yeah, so what you're looking at is a stained tissue image.

So this is a sectioned tissue slice

from an animal that was implanted for seven months.

So kind of a chronic time point.

And you're seeing all these different colors,

and each color indicates specific types of cell types.

So purple and pink are astrocytes and microglia respectably.

They're types of glial cells.

And yet the other thing that people may not be aware of

is your brain is not just made up

of soup of neurons and axons.

There are other cells, like glial cells,

that actually kind of is the glue and also react

if there are any trauma or damage to the tissue.

- The brown are the neurons here?

- The brown are the neurons. - The modern neurons.

- So what you're seeing is, in this kind of macro image,

you're seeing these like circle highlighted in white,

the insertion sites.

And when you zoom into one of those, you see the threads.

And then in this particular case,

I think we're seeing about the 16 wires

that are going into the page.

And the incredible thing here is the fact

that you have the neurons that are these brown structures

or brown circular or elliptical thing

that are actually touching and abutting the thread.

So what this is saying is that there's basically zero trauma

that's caused during this insertion.

And with these neural interfaces,

these micro electrodes that you insert,

that is one of the most common mode of failure.

So when you insert these threads, like the Utah array,

it causes neuronal death around the site,

because you're inserting a foreign object, right?

And that kind of elicit these like immune response

through microglia and astrocytes.

They form this like protective layer around it.

Oh, not only are you killing the neuron cells,

but you're also creating this protective layer

that then basically prevents you

from recording neural signals,

'cause you're getting further and further away

from the neurons that you're trying to record.

And that is the biggest mode of failure.

And in this particular example, in that inside,

it's about 50 micron with that scale bar.

The neurons just seem to be attracted to it.

(Lex laughing)

- And so there's certainly no trauma.

That's such a beautiful image, by the way.

So the brown are the neurons.

And for some reason, I can't look away.

It's really cool. - Yeah, and the way

that these things like, I mean,

your tissues generally don't have these beautiful colors.

This is multiplex stain that uses these different proteins

that are staining these at different colors.

We use very standard set of staining techniques,

with HG, EB1 and new N and GFAP.

So if you go to the next image,

this is also kind of illustrates the second point,

'cause you can make an argument, and initially,

when we saw the previous image, we said,

"Oh, like are the threads just floating?

Like what is happening here?

Like are we actually looking at the right thing?"

So what we did is we did another stain,

and this is all done in-house,

of this batons, trichrome stain,

which is in blue that shows these collagen layers.

So the blue basically,

like you don't want the blue around the implant threads,

'cause that means that there's some sort

of scarring that's happen.

And what you're seeing, if you look at individual threads,

is that you don't see any of the blue,

which means that there has been absolutely,

or very, very minimal to a point where it's not detectable

amount of trauma in these inserted threads.

- So that presumably is one of the big benefits

of having this kind of flexible thread.

- Yeah, so we think this is primarily due to the size,

as well as the flexibility of the threads.

Also the fact that R1 is avoiding vasculature,

so we're not disrupting

or we're not causing damage to the vessels

and not breaking any of the blood brain barrier

has basically caused the immune response to be muted.

- But this is also a nice illustration

of the size of things.

So this is the tip of the thread.

- Yeah, those are neurons.

- And they're neurons.

And this is the thread listening.

And the electrodes are positioned how?

- Yeah, so this is,

what you're looking at is not electrode themselves.

Those are the conductive wires.

So each of those should probably be two micron in width.

So what we're looking at

is we're looking at the coronal slice.

So we're looking at some slice of the tissue.

So as you go deeper, you'll obviously have

less and less of the tapering of the thread.

But yeah, the point basically being

that there's just kind of cells around the inserter site,

which is just an incredible thing to see.

I've just never seen anything like this.

- How easy and safe is it to remove the implant?

- Yeah, so it depends on when.

In the first three months or so after the surgery,

there's a lot of kind of tissue modeling that's happening.

Similar to when you got a cut,

you obviously start over first couple weeks,

or depending on the size of the wound,

scar tissue forming, right?

There are these like contractive, and then in the end,

they turn into scab and you can scab it off.

The same thing happens in the brain,

and it's a very dynamic environment.

And before the scar tissue or the neomembrane

or the neomembrane that forms,

it's quite easy to just pull 'em out.

And there's minimal trauma that's caused during that.

Once the scar tissue forms, and with Noland as well,

we believe that that's the thing

that's currently anchoring the thread.

So we haven't seen any more movements since then.

So they're quite stable.

It gets harder to actually completely extract the threads.

So our current method for removing the device

is cutting the thread, leaving the tissue intact,

and then unscrewing and taking the implant out.

And that hole is now gonna be plugged

with either another Neuralink

or just with kind of a peak-based, plastic-based cap.

- Is it okay to leave the threads in there forever?

- Yeah, we think so.

We've done studies where we left them there,

and one of the biggest concerns that we had is like,

do they migrate and do they get to a point

where they should not be?

We haven't seen that.

Again, once the scar tissue forms,

they get anchored in place.

And I should also say that when we say upgrades,

like we're not just talking in theory here.

Like we've actually upgraded many, many times.

Most of our monkeys or non-human primates,

NHP have been upgraded.

Pager, who you saw playing mind pong,

has the latest version of device since two years ago

and is seemingly very happy and healthy and fat.

- So what's designed for the future, the upgrade procedure?

So maybe for Noland.

What would the upgrade look like?

It was essentially what you're mentioning.

Is there a way to upgrade sort of the device internally,

where you take it apart and sort of keep the capsule

and upgrade the internals?

- Yeah, so there are a couple different things here.

So for Noland, if we were to upgrade,

what we would have to do is either cut the threads

or extract the threads depending on

kind of the situation there

in terms of how they're anchored or scarred in.

If you were to remove them with the dural substitute,

you have an intact brain so you can reinsert

different threads with the updated implant package.

There are a couple different other ways

that we're thinking about,

the future of what the upgradable system looks like.

One is, at the moment, we currently remove the dura,

this kind of thick layer that protects the brain,

but that actually is the thing

that actually proliferates the scar tissue formation.

So typically, general good rule of thumb

is you wanna leave the nature as is

and not disrupt it as much.

So looking at ways to insert the threads through the dura,

which comes with different set of challenges,

such as it's a pretty thick layer,

so how do you actually penetrate that

without breaking the needle?

So we're looking at different needle design for that,

as well as the kind of the loop engagement.

The other biggest challenges are it's quite opaque,

optically, and with white light illumination.

So how do you avoid still this biggest advantage

that we have of avoiding vasculature?

How do you image through that?

How do you actually still mediate that?

So there are other imaging techniques

that we're looking at to enable that.

But our hypothesis is that,

and based on some of the early evidence that we have,

doing through the dura insertion will cause minimal scarring

that causes them to be much easier to extract over time.

And the other thing that we're also looking at,

this is gonna be a fundamental change

in the implant architecture,

is at the moment, it's a monolithic single implant

that comes with a thread that's bonded together.

So you can't actually separate the thing out,

but you can imagine having two part implant.

Bottom part, that is the thread that are inserted

that has the chips and maybe a radio and some power source.

And then you have another implant

that has more of the computational heavy load

and the bigger battery.

And then one can be under the dura,

one can be above the dura,

like being the plug for the skull.

They can talk to each other,

but the thing that you wanna upgrade,

the computer and not the thread.

If you wanna upgrade that, you just go in there,

remove the screws and then put in the next version.

It's a very, very easy surgery too.

Like you do a skin incision, slip this in, screw.

Probably be able to do this in 10 minutes.

- So that would allow you to reuse the thread, sort of.

- [DJ] Correct.

- So I mean, this leads to the natural question of,

what is the pathway to scaling

that increase in the number of threads?

Is that a priority?

What's the technical challenge there?

- Yeah, that is a priority.

So for next versions of the implant,

the key metrics that we're looking to improve

are number of channels,

just recording from more and more neurons.

We have a pathway to actually go from currently 1,000

to hopefully 3,000 if not 6,000 by end of this year.

And then end of next year,

we wanna get to even more, 16,000.

- Wow.

- There's a couple limitations to that.

One is obviously being able

to photo lithographically print those wires.

As I mentioned, it's two micron in width and spacing.

Obviously, there are chips that are much more advanced

than those types of resolution,

and we have some of the tools

that we have brought in house to be able to do that.

So traces will be narrower just so that you have to have

more of the wires coming up into the chip.

Chips also cannot linearly consume more energy,

as you have more and more channels.

So there's a lot of innovations in the circuit

and architecture as well as the circuit design topology

to make them lower power.

You need to also think about,

if you have all of these spikes,

how do you send that off to the end application?

So you need to think about bandwidth limitation there

and potentially innovations and signal processing.

Physically, one of the biggest challenge

is gonna be the interface.

It's always the interface that breaks.

Bonding this thin film array to the electronics.

It starts to become very, very highly dense interconnects.

So how do you characterize that?

There's a lot of innovations

in kind of the 3D integrations in the recent years

that we can take advantage of.

One of the biggest challenges that we do have

is forming this hermetic barrier, right?

That this is an extremely harsh environment

that we're in - the brain.

So how do you protect it from,

yeah, like the brain trying to kill your electronics

to also your electronics leaking

things that you don't want into the brain.

And that forming that hermetic barrier

is gonna be a very, very big challenge

that we I think are actually well-suited to tackle.

- How do you test that?

Like what's the development environment

to simulate that kind of harshness?

- Yeah, so this is where the accelerated life tester

essentially is a brain in a vat.

It literally is a vessel that is made up of,

and again, for all intents and purpose

for this particular type of test,

your brain is a salt water.

And you can also put some other set of chemicals

like reactive oxygen species

that get at kind of these interfaces

and trying to cause a reaction to pull it apart.

But you could also increase the rate

at which these interfaces are aging

by just increasing temperature.

So every 10 degrees Celsius that you increase,

you're basically accelerating time by 2x.

And there's limit as to how much

temperature you wanna increase, 'cause at some point,

there's some other non-linear dynamics

that causes you to have other nasty gases to form

that just is not realistic in an environment.

So what we do is we increase in our ALT chamber

by 20 degrees Celsius

that increases the aging by four times.

So essentially one day in ALT chamber,

it's four day in calendar year.

And we look at whether the implants still are intact,

including the threads and-

- And operation and all of that?

- And operation and all of that.

It obviously is not an exact same environment as a brain,

'cause brain has mechanical,

other more biological groups that attack at it.

But it is a good test environment,

testing environment for at least the enclosure

and the strength of the enclosure.

And I mean, we've had implants,

the current version of the implant

that has been in there for, I mean,

close to two and a half years,

which is equivalent to a decade.

And they seem to be fine.

- So it's interesting that the burn,

so basically, close approximation is warm salt water,

hot salt water is a good testing environment.

Yeah, by the way, I'm drinking LMNT,

which is basically salt water, which is making me kinda,

it doesn't have computational power the way the brain does,

but maybe in terms of other characteristics,

it's quite similar and I'm consuming it.

- Yeah, you have to get it in the right pH too. (laughs)

- And then consciousness will emerge.

Yeah no.

- By the way, the other thing that also is interesting

about our enclosure is, if you look at our implant,

it's not your common-looking medical implant

that usually is encased in a titanium can

that's laser welded.

We use this polymer called PCTFE,

polychlorotrifluoroethylene,

which is actually commonly used in blister packs.

So when you have a pill and you try to pop a pill,

there's like kind of that plastic membrane.

That's what this is.

No one's actually ever used this except us.

And the reason we wanted to do this

is 'cause it's electromagnetically transparent.

So when we talked about the electromagnetic

inductive charging, with titanium can,

usually, if you wanna do something like that,

you have to have a sapphire window,

and it's a very, very tough process to scale.

- So you're doing a lot of iteration here

in every aspect of this.

The materials, the software-

- The whole, whole shebang.

- So, okay.

So you mentioned scaling.

Is it possible to have multiple Neuralink devices

as one of the ways of scaling?

To have multiple Neuralink devices implanted?

- That's the goal, that's the goal.

Yeah, we've had, I mean,

our monkeys have had two Neuralinks,

one in each hemisphere.

And then we're also looking at potential of having

one in motor cortex, one in visual cortex,

and one in wherever other cortex.

- So focusing on a particular function,

one Neuralink device. - Correct.

- I mean, I wonder if there's some level of customization

that can be done on the compute side.

So for the motor cortex-

- Absolutely.

That's the goal.

And we talk about at Neuralink building a generalized

neural interface to the brain.

And that also is strategically

how we're approaching this with marketing.

And also, with regulatory, which is,

hey look, we have the robot,

and the robot can access any part of the cortex.

Right now, we're focused on motor cortex

with current version of the N1

that's specialized for motor decoding tasks.

But also, at the end of the day,

there's kind of a general compute available there.

But typically, if you wanna really get down

to kind of hyperoptimizing for power and efficiency,

you do need to get to some specialized function, right?

But what we're saying is that,

hey, you are now used to this robotic insertion techniques,

which took many, many years of showing data

and conversation with the FDA.

And also, internally convincing ourselves that this is safe.

And now the difference is that

if we go to other parts of the brain, like visual cortex,

which we're interested in as our second product,

obviously, it's a completely different environment.

The cortex is laid out very, very differently.

It's gonna be more stimulation focus

rather than recording,

just kind of creating visual percepts.

But in the end, we're using the same

thin film array technology.

We're using the same robot insertion technology.

We're using the same packaging technology.

Now, more the conversation is focused around

what are the differences and what are the implication

of those differences in safety and efficacy?

- The way you said second product

is both hilarious and awesome to me.

That product being restoring sight for blind people.

So can you speak to stimulating the visual cortex?

I mean, the possibilities there are just incredible

to be able to give that gift back to people

who don't have sight or even any aspect of that.

Can you just speak to the challenges of,

there's several challenges here.

- Oh, many. - One of which is,

like you said, from recording to stimulation.

Just any aspect of that

that you're both excited and see the challenges of.

- Yeah, I guess I'll start by saying

that we actually have been capable

of stimulating through our thin film array

as well as other electronics for years.

We have actually demonstrated some of that capabilities

for reanimating the limb in the spinal cord.

Obviously, for the current EFS study,

we've hardware disabled that,

so that's something that we wanted to embark

as a separate, separate journey.

And obviously, there are many, many different ways

to write information into the brain.

The way in which we're doing that is through electrical,

passing electrical current, and kind of causing that

to really change the local environment

so that you can sort of artificially cause

kind of the neurons to depolarize in nearby areas.

For vision specifically, the way our visual system works,

it's both well-understood.

I mean, anything with kind of brain,

there are aspects of it that's well-understood.

But in the end, like we don't really know anything.

But the way visual system works

is that you have photon hitting your eye,

and in your eyes, there are these specialized cells

called photoreceptor cells

that convert the photon energy into electrical signals.

That then gets projected to your back of your head,

your visual cortex.

It goes through actually thalamic system

called LGN that then projects it out.

And then in the visual cortex,

there's visual area one or V1,

and then there's bunch of other higher level

processing layers like V2, V3.

And there there are actually kind of interesting parallels.

And when you study the behaviors

of these convolutional neural networks,

like what the different layers of the network is detecting,

first, they're detecting like these edges,

and they're then detecting some more natural curves,

and then they start to detect like objects, right?

Kind of similar thing happens in the brain.

And a lot of that has been inspired,

and also, it's been kinda exciting to see

some of the correlations there.

But things like from there, where those cognition arise

and where's color encoded,

there's just not a lot of understanding,

fundamental understanding there.

So in terms of kind of bringing sight back

to those that are blind,

there are many different forms of blindness.

There's actually million people,

one million people in the U.S. that are legally blind.

That means like certain, like score below

in kind of the visual tests.

I think it's something like,

if you can see something at 20 feet distance,

that normal people can see at 200 feet distance,

like if you're worse than that, you're legally blind.

- So that means you can't function effectively.

- Correct. - Using sight in the world.

- Yeah, like to navigate your environment.

And yeah, there are different forms of blindness.

There are forms of blindness where there's some degeneration

of your retina, these photoreceptor cells,

and rest of your visual processing

that I described is intact.

And for those types of individuals,

you may not need to maybe stick electrodes

into the visual cortex.

You can actually build retinal prosthetic devices

that actually just replaces the function

of that retinal cells that are degenerated.

And there are many companies that are working on that.

But that's a very small slice.

Albeit significance, those smaller slice

of folks that are legally blind.

If there's any damage along that circuitry,

whether it's in the optic nerve

or just the LGN circuitry

or any break in that circuit,

that's not gonna work for you.

And the source of where you need to actually

cause that visual percept to happen,

because your biological mechanism not doing that

is by placing electrodes in the visual cortex

in the back of your head.

And the way in which this would work

is that you would have an external camera,

whether it's something as unsophisticated as a GoPro

or some sort of wearable RayBan type glasses

that Meta's working on that captures a scene, right?

And that scene is then converted

to set of electrical impulses or stimulation pulses

that you would activate in your visual cortex

through these thin film arrays.

And by playing in a concerted kind of orchestra

of these stimulation patterns,

you can create what's called phosphenes,

which are these kind of white yellowish dots

that you can also create by just pressing your eyes.

You can actually create those percepts

by stimulating in the visual cortex.

And the name of the game is really have many of those

and have those percepts be,

the phosphenes be as small as possible

so that you can start to tell apart,

like they're the individual pixels of the screen, right?

So if you have many, many of those,

potentially, you'll be able to,

in the long term, be able to actually get

naturalistic vision.

But in the like short term to maybe midterm,

being able to at least be able to have

object detection algorithms run on your glasses,

the pre-op processing units,

and then being able to at least see the edges of things

so you don't bump into stuff.

- It's incredible.

This is really incredible.

So you basically would be adding pixels,

and your brain would start to figure out

what those pixels mean.

Yeah, and like with different kinds of assistant

on the signal processing on all fronts.

- Yeah.

The thing that actually,

so a couple things.

One is, obviously, if you're blind from birth,

the way brain works, especially in the early age,

neuroplasticity is really nothing

other than kind of your brain

and different parts of your brain

fighting for the limited territory.

- [Lex] (laughs) Yeah.

- And I mean, very, very quickly,

you see cases where you know people that are,

I mean, you also hear about people who are blind

that have heightened sense of hearing or some other senses.

And the reason for that is that cortex

that's not used just gets taken over

by these different parts of the cortex.

So for those types of individuals,

I mean, I guess they're going to have to now map

some other parts of their senses into what they call vision.

But it's gonna be obviously

a very, very different conscious experience before.

So I think that's a interesting caveat.

The other thing that also is important to highlight

is that we're currently limited by our biology

in terms of the wavelength that we can see.

There's a very, very small wavelength

that is a visible light wavelength

that we can see with our eyes.

But when you have an external camera with this BCI system,

you're not limited to that.

You can have infrared, you can have UV,

you can have whatever other spectrum that you want to see.

And whether that gets matched

to some sort of weird conscious experience, I've no idea.

But oftentimes, I talk to people

about the goal of Neuralink

being going beyond the limits of our biology.

That's sort of what I mean.

- And if you're able to control the kind of raw signal,

when we use our sight, we're getting the photons

and there's not much processing on it.

If you're being able to control that signal,

maybe you can do some kind of processing.

Maybe you do object detection ahead of time.

- [DJ] Yeah.

- You're doing some kind of pre-processing,

and there's a lot of possibilities to explore that.

So it's not just increasing sort of thermal imaging,

that kind of stuff, but it's also just doing some kind

of interesting processing. - Correct, yeah.

I mean, my theory of how like visual system works also

is that, I mean, there's just so many things

happening in the world, and there's a lot of photons

that are going into your eye, and it's unclear exactly

where some of the pre-processing steps are happening.

But I mean, I actually think that just

from a fundamental perspective, there's just so much,

the reality that we're in, if it's a reality,

is so there's so much data.

And I think humans are just unable to actually

like eat enough actually to process all that information.

So there's some sort of filtering that does happen,

whether that happens in the retina,

whether that happens in different layers

of the visual cortex.

Unclear.

But like the analogy that I sometimes think about is,

if your brain is a CCD camera,

and all of the information in the world is a sun,

and when you try to actually look at the sun

with the CCD camera,

it's just gonna saturate the sensors, right?

'Cause it's enormous amount of energy.

So what you do is you end up adding these filters, right?

To just kind of narrow the information

that's coming to you and being captured.

And I think things like our experiences

or our like drugs like propofol,

that like anesthetic drug or psychedelics,

what they're doing is they're kind of swapping out

these filters and putting in new ones or removing older ones

and kind of controlling our conscious experience.

- Yeah, man, not to distract from the topic,

but I just took a very high dose of ayahuasca

in the Amazon jungle.

So yes, it's a nice way to think about it.

You're swapping out different experiences,

and with Neuralink being able to control that,

primarily at first, to improve function,

not for entertainment purposes or enjoyment purposes, but-

- Yeah, giving back lost functions.

- Well, giving back lost functions.

And there, especially when the function is completely lost,

anything is a huge help.

Would you implant a Neuralink device in your own brain?

- Absolutely.

I mean, maybe not right now, but absolutely.

- What kind of capability, once reached,

you would start getting real curious

and almost get a little antsy,

like jealous of people that get it

as you watch them get implanted?

- Yeah, I mean I think,

I mean, even with our early participants,

if they start to do things that I can't do,

which I think is in the realm of possibility

for them to be able to get, 15, 20,

if not like 100 BPS right?

There's nothing that fundamentally stops us

from being able to achieve that type of performance.

I mean, I would certainly get jealous that they can do that.

- I should say that watching Noland,

I get a little jealous 'cause he's having so much fun,

and it seems like such a chill way to play video games.

- Yeah.

So I mean, the thing that also is hard

to appreciate sometimes is that,

he's doing these things while talking.

I mean, it's multitasking, right?

So it's clearly,

it's obviously cognitively intensive,

but similar to how, when we talk,

we move our hands, like these things like are multitasking.

I mean, he's able to do that.

And you won't be able to do that

with other assistive technology as far as I'm aware.

If you're obviously using like an eye tracking device,

you're very much fixated

on that thing that you're trying to do.

And if you're using voice control,

I mean, like if you say some other stuff,

yeah, you don't get to use that.

- Yeah, the multitasking aspect of that

is really interesting.

So it's not just the BPS for the primary task.

It's the parallelization of multiple tasks.

If you measure the BPS

for the entirety of the human organism,

so if you're talking and doing a thing with your mind

and looking around also, I mean,

there's just a lot of paralyzation that can be happening.

- Yeah.

But I mean, I think at some point for him,

like if he wants to really achieve those high level BPS,

it does require like full attention, right?

And that's a separate circuitry that is a big mystery.

Like how attention works and, you know?

- Yeah, attention, like cognitive load,

I've read a lot of literature on people doing two tasks.

Like you have your primary task and a secondary task.

And the secondary task is a source of distraction.

And how does that affect

the performance of the primary task?

And depending on the tasks, there's a lot of interesting,

I mean, this is an interesting computational device, right?

And I think- - To say the least.

- A lot of novel insights

that can be gained from everything.

I mean, I personally am surprised

that Noland's able to do such incredible control

of the cursor while talking

and also being nervous at the same time,

'cause he's talking like all of us are,

if you're talking in front of the camera,

you get nervous.

So all of those are coming into play,

and he is able to still achieve high performance.

Surprising.

I mean, all of this is really amazing.

And I think just after researching this really in depth,

I kind of want Neuralink.

- (laughs) Get in line.

- And also, the safety get in line.

Well, we should say the registry

is for people who have quadriplegia

and all that kind of stuff so- - Correct.

- There'll be a separate line for people.

They're just curious, like myself.

So now that Noland, patient P1,

is part of the ongoing prime study,

what's the high level vision for P2, P3, P4, P5?

And just the expansion into other human beings

that are getting to experience this implant?

- Yeah, I mean, the primary goal is,

for our study in the first place

is to achieve safety endpoints.

Just understand safety of this device,

as well as the implantation process.

And also, at the same time, understand the efficacy

and the impact that it could have

on the potential users' lives.

And just because you have,

you're living with tetraplegia,

it doesn't mean your situation

is same as another person living with tetraplegia.

It's wildly, wildly varying.

It's something that we're hoping to also understand

how our technology can serve

not just a very small slice of those individuals,

but broader group of individuals

and being able to get the feedback to just really build

just the best product for them.

There's obviously also goals that we have,

and the primary purpose of the early feasibility study

is to learn from each and every participant

to improve the device, improve the surgery

before we embark on what's called a pivotal study

that then is much larger trial

that starts to look at statistical significance

of your endpoints, and that's required

before you can then market the device.

And that's how it works in the U.S.

and just generally around the world.

That's the process you follow.

So our goal is to really just understand

from people like Noland, P2, P3, future participants,

what aspects of our device needs to improve.

If it turns out that people are like,

"I really don't like the fact that it lasts only six hours.

I wanna be able to use this computer for like 24 hours."

I mean, that is a user needs and user requirements,

which we can only find out

from just being able to engage with them.

- So before the pivotal study,

there's kind of like a rapid innovation

based on individual experiences.

You're learning from individual people how they use it,

like the high resolution details

in terms of like cursor control and signal

and all that kind of stuff to like life experience.

- Yeah, yeah, so there's hardware changes

but also just firmware updates.

So even when we had that sort of recovery event for Noland,

he now has the new firmware that he has been updated with.

And it's similar to how like your phones

get updated all the time

with new firmware for security patches,

whatever new functionality UI, right?

And that's something that is possible with our implant.

It's not a static one-time device

that can only do the thing that it said it can do.

I mean, it's similar to Tesla.

You can do over the air firmware updates,

and now you have completely new user interface.

And all this bells and whistles and improvements

on everything like the latest, right?

When we say generalized platform,

that's what we're talking about.

- Yeah, it's really cool how the app that Noland is using,

there's like calibration, all that kind of stuff.

And then there's update.

You just click and get an update.

What other future capabilities are you kinda looking to?

You said vision.

That's a fascinating one.

What about sort of accelerated typing or speech,

this kind of stuff?

And what else is there?

- Yeah, those are still in the realm of movement program.

So largely speaking, we have two programs.

We have the movement program

and we have the vision program.

The movement program currently is focused around

the digital freedom.

As you can easily guess, if you can control

2D cursor in the digital space,

you could move anything in the physical space.

So robotic arms, wheelchair, your environment,

or even really like, whether it's through the phone

or just like directly to those interfaces,

so like to those machines.

So we're looking at ways to kind of expand

those types of capability, even for Noland.

That requires conversation with the FDA

and kind of showing safety data for,

if there's a robotic arm or a wheelchair

that we can guarantee that they're not gonna hurt

themselves accidentally, right?

It's very different if you're moving stuff

in the digital domain versus like in the physical space,

you can actually potentially cause harm to the participants.

So we're working through that right now.

Speech does involve different areas of the brain.

Speech prosthetic is very, very fascinating,

and there's actually been a lot of really amazing work

that's been happening in academia.

Sergei Stavisky at UC Davis, Jaimie Henderson,

and late Krishna Shenoy at Stanford

doing just some incredible amount of work

in improving speech neuroprosthetics.

And those are actually looking more

at parts of the motor cortex

that are controlling these focal articulators.

And being able to like,

even by mouthing the word or imagine speech,

you can pick up those signals.

The more sophisticated higher level processing areas,

like the Broca's area or Wernicke's area,

those are still very, very big mystery

in terms of the underlying mechanism

of how all that stuff works.

But yeah, I mean, I think Neuralink's event goal

is to kind of understand those things

and be able to provide a platform and tools

to be able to understand that and study that.

- This is where I get to the pothead questions.

Do you think we can start getting insight

into things like thought?

So speech is,

there's a muscular component, like you said.

There's like the act of producing sounds.

But then what about the internal things like cognition?

Like low level thoughts and high level thoughts.

Do you think we'll start noticing

kind of signals that could be picked up?

They could be understood, they could be maybe used

in order to interact with the outside world.

- In some ways, like I guess this starts

to kind of get into the heart problem of consciousness.

And I mean, on one hand,

all of these are, at some point,

set of electrical signals,

that from there, maybe it in itself

is giving you the cognition or the meaning,

or somehow, human mind is incredibly amazing

storytelling machine.

So we're telling ourselves and fooling ourselves

that there's some interesting meaning here.

But I mean, I certainly think that BCI

and really BCI at the end of the day

is a set of tools that help you kind of study

the underlying mechanisms in both like local

but also broader sense.

And whether there's some interesting patterns

of like electrical signal,

that means like you're thinking this versus,

and you can either like learn

from like many, many sets of data to correlate some of that

and be able to do mind reading or not.

I'm not sure.

I certainly would not kind of rule that out

as a possibility, but I think BCI alone

probably can't do that.

There's probably additional set of tools and framework.

And also, like just heart problem of consciousness

at the end of the day is rooted

in this philosophical question of like,

what's the meaning of it all?

What's the nature of our existence?

Where's the mind emerge from this complex network?

- Yeah, how does the subjective experience emerge

from just a bunch of spikes, electrical spikes?

- Yeah, yeah, I mean, we do really think about BCI

and what we're building as a tool

for understanding the mind, the brain.

The only question that matters.

There's actually,

there actually is some biological existence proof

of like what it would take to kind of start to form

some of these experiences that may be unique.

If you actually look at every one of our brains,

there are two hemispheres.

There's a left-sided brain, there's a right-sided brain.

And I mean, unless you have some other conditions,

you normally don't feel like left legs or right legs.

Like you just feel like one legs, right?

So what is happening there, right?

If you actually look at the two hemispheres,

there's a structure that kind of characterize the two

called the corpus callosum that is supposed to have

around 200 to 300 million connections or axons.

So whether that means that's the number of interface

and electrodes that we need

to create some sort of mind meld, or from that,

like whatever new conscious experience

that you can experience.

But yeah, I do think that there's like

kind of an interesting existence proof that we all have.

- And that threshold is unknown at this time.

- Oh yeah, these things,

everything in this domain is speculation, right?

- And then there would be,

you'd be continuously pleasantly surprised.

Do you see a world where there's millions of people,

like tens of millions, hundreds of millions of people

walking around with a Neuralink device,

or multiple Neuralink devices in their brain?

- I do.

First of all, there are,

like if you look at worldwide, people suffering

from movement disorders and visual deficits.

I mean, that's in the tens if not hundreds

of millions of people.

So that alone, I think, there's a lot of benefit

and potential good that we can do

with this type of technology.

And once you start to get into kind of neuro,

like psychiatric application, depression,

anxiety, hunger, or obesity, right?

Like mood, control of appetite,

I mean, that starts to become very real to everyone.

- Not to mention that most people on earth

have a smartphone.

And once BCI starts competing with a smartphone

as a preferred methodology

of interacting with the digital world,

that also becomes an interesting thing.

- Oh yeah, I mean, yeah.

This is even before going to that, right?

I mean, there's like almost,

I mean, the entire world

that could benefit from these types of thing.

And then, yeah, like if we're talking about

kind of next generation of how we interface with

machines or even ourselves,

in many ways, I think BCI can play a role in that.

And some of the things that I also talk about

is I do think that there is a real possibility

that you could see eight billion people

walking around with Neuralink.

- Well, thank you so much for pushing ahead.

And I look forward to that exciting feature.

- Thanks for having me.

- Thanks for listening to this conversation with DJ Seo.

And now, dear friends, here's Matthew MacDougall,

the head neurosurgeon at Neuralink.

When did you first become fascinated with the human brain?

- Since forever.

As far back as I can remember,

I've been interested in the human brain.

I mean, I was a thoughtful kid

and a bit of an outsider.

And you sit there thinking about

what the most important things in the world are

in your little tiny adolescent brain.

And the answer that I came to,

that I converged on was that all of the things

you can possibly conceive of, as things that are important

for human beings to care about

are literally contained in the skull.

Both the perception of them and their relative values.

And the solutions to all our problems

and all of our problems are all contained in the skull.

And if we knew more about how that worked,

how the brain encodes information

and generates desires and generates agony and suffering,

we could do more about it.

You think about all the really great triumphs

in human history.

You think about all the really horrific tragedies.

You think about the holocaust,

you think about any prison full of human stories,

and all of those problems boil down to neurochemistry.

So if you get a little bit of control over that,

you provide people the option to do better.

And in the way I read history,

the way people have dealt with having better tools

is that they most often in the end do better,

with huge asterisk.

But I think it's an interesting,

a worthy and noble pursuit

to give people more options, more tools.

- Yeah, that's a fascinating way to look at human history.

You just imagine all these neurobiological mechanisms,

Stalin, Hitler, all of these, Gengis Khan,

all of them just had like a brain,

just a bunch of neurons,

like a few tons of billions of neurons

gaining a bunch of information over a period of time.

They have a set of module

that does language and memory and all that.

And from there, in the case of those people,

they're able to murder millions of people.

- [Matthew] Yeah.

- All that coming from,

there's not some glorified notion of a dictator

of this enormous mind or something like this.

It's just the brain.

- Yeah, yeah.

I mean, a lot of that has to do with how well

people like that can organize those around them.

- Other brains.

- Yeah, and so I always find it interesting

to look to primatology,

look to our closest non-human relatives

for clues as to how humans are going to behave

and what particular humans are able to achieve.

And so you look at chimpanzees and bonobos

and they're similar but different

in their social structures particularly.

And I went to Emory in Atlanta

and studied under Frans, the great Frans de Waal,

who was kind of the leading primatologist who recently died,

and his work at looking at chimps through the lens

of how you would watch an episode of "Friends"

and understand the motivations

of the characters interacting with each other.

He would look at a chimp colony

and basically apply that lens.

I'm massively oversimplifying it.

If you do that, instead of just saying,

subject 473 through his feces at subject 471,

you talk about them in terms of their human struggles,

accord them the dignity of themselves as actors

with understandable goals and drives,

what they want out of life.

And primarily, it's the things we want out of life:

food sex companionship power.

You can understand chimp and bonobo behavior

in the same lights much more easily.

And I think doing so gives you the tools you need

to reduce human behavior from the kind of false complexity

that we layer onto it with language

and look at it in terms of,

oh, well, these humans are looking for companionship,

sex food power.

And I think that's a pretty powerful tool

to have in understanding human behavior.

- And I just went to the Amazon jungle for a few weeks,

and it's a very visceral reminder

that a lot of life on earth is just trying to get laid.

They're all screaming at each other.

Like I saw a lot of monkeys,

and they're just trying to impress each other,

or maybe if there's a battle for power,

but a lot of the battle for power

has to do with them getting laid.

- Right.

Breeding rights often go with alpha status.

And so if you can get a piece of that,

then you're gonna do okay.

- And would like to think

that we're somehow fundamentally different,

but especially when it comes to primates,

we're really aren't, you know.

We can use fancier poetic language,

but maybe some of the underlying drives

that motivate us are similar.

- Yeah, I think that's true.

- And all of that is coming from this, the brain.

- Yeah. - So when did you first

start studying the brain

as I guess as a biological mechanism?

- Basically, the moment I got to college,

I started looking around for labs

that I could do neuroscience work in.

I originally approached that from the angle

of looking at interactions between the brain

and the immune system, which isn't the most obvious place

to start, but I had this idea at the time

that the contents of your thoughts would have an impact,

a direct impact, maybe a powerful one

on non-conscious systems in your body.

The systems we think of as homeostatic,

automatic mechanisms like fighting off a virus,

like repairing a wound.

And sure enough, there are big crossovers between the two.

I mean, it gets to kind of a key point

that I think goes under recognized.

One of the things people don't recognize

or appreciate about the human brain enough,

and that is that it basically controls

or has a huge role in almost everything that your body does.

Like you try to name an example

of something in your body that isn't directly controlled

or massively influenced by the brain, and it's pretty hard.

I mean, you might say like bone healing or something,

but even those systems, the hypothalamus and pituitary

end up playing a role in coordinating the endocrine system

that does have a direct influence on, say,

the calcium level in your blood that goes to bone healing.

So non-obvious connections between those things

implicate the brain as really a potent

prime mover in all of health.

- One of the things I realized in the other direction too,

how most of the systems in the body

integrated with the human brain,

like they affect the brain also, like the immune system.

I think there's just people who study Alzheimer's

and those kinds of things.

It's just surprising how much you can understand

of that from the immune system, from the other systems

that don't obviously seem to have anything to do

with sort of the nervous system.

They all play together.

- Yeah, you could understand how that would be

driven by evolution too, just in some simple examples.

If you get sick, if you get a communicable disease,

you get the flu, it's pretty advantageous

for your immune system to tell your brain,

"Hey, now be antisocial for a few days.

Don't go be the life of the party tonight.

In fact, maybe just cuddle up somewhere warm

under a blanket and just stay there for a day or two."

And sure enough, that tends to be the behavior

that you see both in animals and in humans.

If you get sick, elevated levels of interleukins

in your blood and TNF alpha in your blood

ask the brain to cut back on social activity.

And even moving around,

you have lower locomotor activity

in animals that are infected with viruses.

- So from there, the early days in neuroscience to surgery,

when did that step happen?

- Yeah. - This is a leap.

- It was sort of an evolution of thought.

I wanted to study the brain.

I started studying the brain in undergrad

in this neuroimmunology lab.

I, from there, realized at some point

that I didn't wanna just generate knowledge.

I wanted to effect real changes in the actual world,

in actual people's lives.

And so after having not really thought about

going into medical school,

I was on a track to go into a PhD program.

I said, "Well, I'd like that option.

I'd like to actually potentially help

tangible people in front of me."

And doing a little digging found that there exists

these MD-PhD programs where you can choose not to choose

between them and do both.

And so I went to USC for medical school

and had a joint PhD program with Caltech,

where I actually chose that program particularly

because of a researcher at Caltech named Richard Andersen,

who's one of the godfathers of primate neuroscience

and has a MACAC lab where Utah arrays

and other electrodes were being inserted

into the brains of monkeys to try to understand

how intentions were being encoded in the brain.

So I ended up there with the idea

that maybe I would be a neurologist

and study the brain on the side,

and then discovered that neurology,

again, I'm gonna make enemies by saying this,

but neurology predominantly

and distressingly to me is the practice

of diagnosing a thing and then saying,

"Good luck with that.

There's not much we can do."

And neurosurgery, very differently,

it's a powerful lever on taking people

that are headed in a bad direction and changing their course

in the sense of brain tumors

that are potentially treatable or curable with surgery.

Even aneurysms in the brain,

blood vessels that are gonna rupture,

you can save lives really

is at the end of the day, what mattered to me.

And so I was at USC, as I mentioned,

that happens to be one of the great neurosurgery programs.

And so I met these truly epic neurosurgeons,

Alex Khalessi and Mike Apuzzo and Steve Giannotta

and Marty Weiss, these sort of epic people

that were just human beings in front of me.

And so it kind of changed my thinking

from neurosurgeons are distant gods

that live on another planet

and occasionally come and visit us

to these are humans that have problems and are people.

And there's nothing fundamentally preventing me

from being one of them.

And so at the last minute in medical school,

I changed gears from going into a different specialty

and switched into neurosurgery, which cost me a year.

I had to do another year of research,

because I was so far along in the process

to switch into neurosurgery.

The deadlines had already passed.

So it was a decision that cost time,

but absolutely worth it.

- What was the hardest part of the training

on the neurosurgeon track?

- Yeah, two things.

I think that residency in neurosurgery

is sort of a competition of pain,

of like how much pain can you eat and smile.

- Yeah. - And so there's

workout restrictions that are not really,

they're viewed at, I think, internally

among the residents as weakness.

And so most neurosurgery residents

try to work as hard as they can.

And that I think necessarily means working long hours,

and sometimes, over the work hour limits.

And we care about being compliant

with whatever regulations are in front of us.

But I think more important than that,

people wanna give their all

in becoming a better neurosurgeon,

because the stakes are so high.

And so it's a real fight to get residents

to say go home at the end of their shift

and not stay and do more surgery.

- Are you seriously saying like one of the hardest things

is literally like forcing them to get sleep

and rest and all this kind of stuff?

- Historically, that was the case.

I think the next generation,

I think the next generation is more compliant

and more selfcare- - Weaker is what you mean.

All right, I'm just kidding, I'm just kidding.

- I didn't say it.

- Now I'm making enemies.

Okay, I get it.

Wow, that's fascinating.

So what was the second thing?

- The personalities.

And maybe the two are connected, but-

- Was it pretty competitive?

- It's competitive and it's also,

as we touched on earlier, primates like power,

and I think neurosurgery has long had this aura

of mystique and excellence and whatever about it.

And so it's an invitation I think for people

that are cloaked in that authority.

A board certified neurosurgeon is basically

a walking fallacious appeal to authority, right?

You have license to walk into any room

and act like you're an expert on whatever.

And fighting that tendency

is not something that most neurosurgeons do well.

Humility isn't the forte.

- Yeah, so I have friends who know you,

and whenever they speak about you,

that you have the surprising quality

for a neurosurgeon of humility,

which I think indicates that it's not as common

as perhaps in other professions,

'cause there is a kind of gigantic

sort of heroic aspect to neurosurgery,

and I think it gets to people's head a little bit.

- Yeah.

Well, I think that allows me to play well

at an Elon company.

Because Elon, one of his strengths,

I think, is to just instantly see through

fallacy from authority.

So nobody walks into a room that he's in and says,

"Well, goddamn it, you have to trust me.

I'm the guy that built the last 10 rockets or something."

And he says, "Well, you did it wrong

and we can do it better."

Or, "I'm the guy that kept Ford

alive for the last 50 years.

You listen to me on how to build cars."

And he says no.

And so you don't walk into a room that he's in

and say, "Well, I'm a neurosurgeon.

Let me tell you how to do it."

He's gonna say, "Well, I'm a human being that has a brain.

I can think from first principles myself,

thank you very much.

And here's how I think it ought to be done.

Let's go try it and see who's right."

And that's proven I think over and over in his case

to be a very powerful approach.

- If we just take that tangent,

there's a fascinating interdisciplinary team at Neuralink

that you get to interact with, including Elon.

What do you think is the secret to a successful team?

What have you learned

from just getting to observe these folks?

World experts in different disciplines work together.

- Yeah, there's a sweet spot where people disagree

and forcefully speak their mind

and passionately defend their position,

and yet are still able to accept information from others

and change their ideas when they're wrong.

And so I like the analogy

of sort of how you polish rocks.

You put hard things in a hard container and spin it.

People bash against each other

and outcome's a more refined product.

And so to make a good team at Neuralink,

we've tried to find people that are not afraid

to defend their ideas passionately.

And occasionally, strongly disagree with people

that they're working with

and have the best idea come out on top.

It's not an easy balance, again,

to refer back to the primate brain.

It's not something that is inherently

built into the primate brain to say,

"I passionately put all my chips on this position

and now I'm just gonna walk away from it.

Admit you were right."

Part of our brains tell us that that is a power loss.

That is a loss of face,

a loss of standing in the community.

And now, you're a zeta chimp,

'cause your idea got trounced.

And you just have to recognize that that little voice

in the back of your head is maladaptive

and it's not helping the team win.

- Yeah, you have to have the confidence

to be able to walk away from an idea that you hold onto.

Yeah. - Yeah.

- And if you do that often enough,

you're actually going to become

the best in the world at your thing.

I mean, that kind of that rapid iteration.

- Yeah, you'll at least be a member of a winning team.

- Ride the wave.

What did you learn?

You mentioned there's a lot of amazing neurosurgeons at USC.

What lessons about surgery and life

have you learned from those folks?

- Yeah, I think working your ass off, working hard

while functioning as a member of a team,

getting a job done that is incredibly difficult,

working incredibly long hours, being up all night,

taking care of someone that

you think probably won't survive no matter what you do.

Working hard to make people that you passionately dislike

look good the next morning.

These folks were relentless in their pursuit

of excellent neurosurgical technique decade over decade.

And I think we're well-recognized for that excellence.

Especially Marty Weiss, Steve Giannotta, Mike Apuzzo,

they made huge contributions not only to surgical technique,

but they built training programs that trained

dozens or hundreds of amazing neurosurgeons.

I was just lucky to kind of be in their wake.

- What's that like, you mentioned doing a surgery

where the person is likely not to survive.

Does that wear on you?

- Yeah.

It's especially challenging when you,

with all respect to our elders,

it doesn't hit so much

when you're taking care of an 80-year-old

and something was going to get them pretty soon anyway.

And so you lose a patient like that,

and it was part of the natural course

of what is expected of them in the coming years, regardless.

Taking care of a father of two or three, four young kids,

someone in their 30s that didn't have it coming,

and they show up in your ER

having their first seizure of their life,

and lo and behold, they've got a huge, malignant,

inoperable or incurable brain tumor.

You can only do that, I think, a handful of times

before it really starts eating away at your armor.

Or a young mother that shows up

that has a giant hemorrhage in her brain

that she's not gonna survive from.

And they bring her four-year-old daughter

to say goodbye one last time

before they turn the ventilator off.

The great Henry Marsh is a English neurosurgeon

who said it best.

I think he says that every neurosurgeon

carries with them a private graveyard,

and I definitely feel that,

especially with young parents.

That kills me.

They had a lot more to give.

The loss of those people specifically

has a knock on effect that's going to make the world worse

for people for a long time.

And it's just hard to feel powerless in the face of that.

And that's where I think you have to be borderline evil

to fight against a company like Neuralink

or to constantly be taking pot shots at us

because what we're doing is to try to fix that stuff.

We're trying to give people options,

to reduce suffering.

We're trying to take the pain out of life

that broken brains brings in.

And yeah, this is just our little way

that we're fighting back against entropy, I guess.

- Yeah, the amount of suffering that's endured

when some of the things that we take for granted

that our brain is able to do is taken away is immense.

And to be able to restore

some of that functionality is a real gift.

- Yeah, we're just starting.

We're gonna do so much more.

- Well, can you take me through the full procedure

of implanting, say, the N1 chip in Neuralink?

- Yeah, it's a really simple,

really simple, straightforward procedure.

The human part of the surgery that I do is dead simple.

It's one of the most basic

neurosurgery procedures imaginable.

And I think there's evidence that some version of it

has been done for thousands of years.

That there are examples I think from ancient Egypt

of healed or partially healed trephinations from Peru

or ancient times in South America

where these protosurgeons

would drill holes in people's skulls,

presumably to let out the evil spirits,

but maybe to drain blood clots.

And there's evidence of bone healing around the edge,

meaning the people at least survive

some months after a procedure.

And so what we're doing is that.

We are making a cut in the skin on the top of the head

over the area of the brain that is the most potent

representation of hand intentions.

And so if you are an expert concert pianist,

this part of your brain is lighting up

the entire time you're playing.

We call it the hand knob.

- The hand knob. - Yeah.

- So it's all like the finger movement.

All of that is just firing away.

- Yep, there's a little squiggle in the cortex right there.

One of the folds in the brain

is kind of doubly folded right on that spot.

So you can look at it on an MRI and say,

"That's the hand knob."

And then you do a functional test

and a special kind of MRI called an a functional MRI, fMRI.

And this part of the brain lights up when people,

even quadriplegic people whose brains

aren't connected to their finger movements anymore.

They imagine finger movements,

and this part of the brain still lights up.

So we can ID that part of the brain

in anyone who's preparing to enter our trial and say,

"Okay, that part of the brain, we confirm,

is your hand intention area."

And so I'll make a little cut in the skin,

we'll flap the skin open,

just like kind of opening the hood of a car,

only a lot smaller.

Make a perfectly round one inch diameter hole in the skull,

remove that bit of skull,

open the lining of the brain, the covering of the brain.

It's like a little bag of water that the brain floats in.

And then show that part of the brain to our robot.

And then this is where the robot shines.

It can come in and take these tiny,

much smaller than human hair electrodes

and precisely insert them into the cortex,

into the surface of the brain to a very precise depth,

in a very precise spot that avoids all the blood vessels

that are coating the surface of the brain.

And after the robot's done with its part,

then the human comes back in and puts the implant

into that hole in the skull and covers it up,

screwing it down to the skull

and sewing the skin back together.

So the whole thing is a few hours long.

It's extremely low risk compared to the average neurosurgery

involving the brain that might say,

open up a deep part of the brain

or manipulate blood vessels in the brain.

This opening on the surface of the brain,

with only cortical micro insertions,

carries significantly less risk

than a lot of the tumor or aneurysm surgeries

that are routinely done.

- So cortical micro insertions that are via robot

and computer vision are designed to avoid the blood vessels.

- Exactly.

- So I know you're a bit biased here,

but let's compare human and machine.

- Sure. - So what are human

surgeons able to do well,

and what are robot surgeons able to do well

at this stage of our human civilization development?

- Yeah, yeah, that's a good question.

Humans are general purpose machines.

We're able to adapt to unusual situations.

We're able to change the plan on the fly.

I remember well a surgery that I was doing many years ago

down in San Diego, where the plan was to open a small hole

behind the ear and go reposition a blood vessel

that had come to lay on the facial nerve,

the trigeminal nerve, the nerve that goes to the face.

When that blood vessel lays on the nerve,

it can cause just intolerable, horrific shooting pain

that people describe like being zapped with a cattle prod.

And so the beautiful, elegant surgery

is to go move this blood vessel off the nerve.

The surgery team, we went in there

and started moving this blood vessel

and then found that there was a giant aneurysm

on that blood vessel that was not easily visible

on the pre-op scans.

And so the plan had to dynamically change,

and the human surgeons had no problem with that.

We're trained for all those things.

Robots wouldn't do so well in that situation,

at least in their current incarnation,

fully robotic surgery,

like the electrode insertion portion

of the Neuralink surgery.

It goes according to a set plan.

And so the humans can interrupt the flow

and change the plan, but the robot

can't really change the plan midway through.

It operates according to how it was programmed

and how it was asked to run.

It does its job very precisely,

but not with a wide degree of latitude

and how to react to changing conditions.

- So there could be just a very large number of ways

that you could be surprised as a surgeon

when you enter a situation that could be subtle things

that you have to dynamically adjust to.

- Correct. - And robots are not

good at that.

- Currently. - Currently.

I think we are at the dawn of a new era with AI

of the parameters for robot responsiveness

to be dramatically broadened, right?

I mean, you can't look at a self-driving car

and say that it's operating under very narrow parameters.

If a chicken runs across the road,

it wasn't necessarily programmed to deal with that,

specifically, but a Waymo or a self-driving Tesla

would have no problem reacting to that appropriately.

And so surgical robots aren't there yet, but give it time.

- And then there could be a lot of,

sort of into like semi-autonomous possibilities

of maybe a robotic surgeon could say,

this situation is perfectly familiar,

or the situation is not familiar.

And in the not familiar case, a human could take over.

But basically like be very conservative in saying,

"Okay, this for sure has no issues, no surprises,

and let the humans deal with the surprises,

with the edge cases, all that."

That's one possibility.

So you think eventually, you'll be out of the job?

Well, you being neurosurgeon,

your job being neurosurgeon.

Humans, there will not be many neurosurgeons

left on this earth.

- I'm not worried about my job

in the course of my professional life.

I think I would tell my my kids not necessarily

to go in this line of work

depending on how things look in 20 years.

- It's so fascinating, 'cause I mean,

if I have a line of work, I would say it's programming.

And if you ask me like for the last, I don't know, 20 years,

what I would recommend for people,

I would tell 'em, yeah, go.

You will always have a job if you're a programmer,

'cause there's more and more computers

and all this kind of stuff and it pays well.

But then you realize these large language models

come along and they're really damn good at generating code.

So overnight, you could be surprised like,

"Wow, like what is the contribution of the human really?"

But then you start to think,

"Okay, it does seem like humans have ability,

like you said, to deal with novel situations."

In the case of programming, it's the ability

to kinda come up with novel ideas to solve problems.

It seems like machines aren't quite yet able to do that.

And when the stakes are very high,

when it's life critical, as it is in surgery,

especially in neurosurgery,

the stakes are very high

for a robot to actually replace a human.

But it's fascinating that in this case of Neuralink,

there's a human-robot collaboration.

- Yeah, yeah.

I do the parts it can't do,

and it does the parts I can't do.

And we are friends.

(Lex laughing)

- I saw that there's a lot of practice going on.

So I mean, everything in Neuralink

is tested extremely rigorously.

But one of the things I saw, that there's a proxy

on which the surgeries are performed.

- Yeah. - So this is both

for the robot and for the human,

for everybody involved in the entire pipeline.

What's that like practicing the surgery?

- It's pretty intense.

So there's no analog to this in human surgery.

Human surgery is sort of this artisanal craft

that's handed down directly

from master to pupil over the generations.

I mean, literally the way you learn

to be a surgeon on humans is by doing surgery on humans.

I mean, first, you watch

your professors do a bunch of surgery,

and then finally, they put the trivial parts

of the surgery into your hands,

and then the more complex parts.

And as your understanding of the point

and the purposes of the surgery increases,

you get more responsibility in the perfect condition.

Doesn't always go well.

In Neuralink's case, the approach is a bit different.

We, of course, practiced as far as we could on animals.

We did hundreds of animal surgeries.

And when it came time to do the first human,

we had just amazing team of engineers build

incredibly lifelike models.

One of the engineers, Fran Romano, in particular built

a pulsating brain in a custom 3D printed skull

that matches exactly the patient's anatomy,

including their face and scalp characteristics.

And so when I was able to practice that,

I mean, it's as close as it really reasonably should get

to being the real thing and all the details,

including the having a mannequin body

attached to this custom head.

And so when we were doing the practice surgeries,

we'd wheel that body into the CT scanner

and take a mock CT scan and wheel it back in

and conduct all the normal safety checks verbally.

"Stop, this patient, we're confirming his identification,

is mannequin number blah, blah, blah."

And then opening the brain in exactly the right spot

using standard operative neuronavigation equipment,

standard surgical drills in the same OR

that we do all of our practice surgeries in at Neuralink.

And having the skull open and have the brain pulse,

which adds a degree of difficulty for the robot

to perfectly precisely plan and insert those electrodes

to the right depth and location.

And so yeah, we kind of broke new ground

on how extensively we practiced for this surgery.

- So there was a historic moment,

a big milestone for Neuralink

in part for humanity with the first human

getting a Neuralink implant in January of this year.

Take me through the surgery on Noland.

What did it feel like to be part of this?

- Yeah.

Well, we're lucky to have just incredible partners

at the Barrow Neurologic Institute.

They are, I think, the premier

neurosurgical hospital in the world.

They made everything as easy as possible

for the trial to get going and helped us immensely

with their expertise on how to arrange the details.

It was a much more high pressure surgery in some ways.

I mean, even though the outcome wasn't particularly

in question in terms of our participant safety,

the number of observers, the number of people,

there's conference rooms full of people

watching live streams in the hospital,

rooting for this to go perfectly,

and that just adds pressure that is not typical

for even the most intense production neurosurgery.

Say, removing a tumor or placing

deep brain stimulation electrodes.

And it had never been done on a human before.

There were unknown unknowns.

And so definitely, a moderate pucker factor there

for the whole team,

not knowing if we were going to encounter,

say, a degree of brain movement that was unanticipated

or a degree of brain sag that took the brain

far away from the skull and made it difficult to insert

or some other unknown unknown problem.

Fortunately, everything went well

and that surgery is one of the smoothest

outcomes we could have imagined.

- Were you nervous?

I mean, you're a bit quarterback

in the Super Bowl kind of situation.

- Extremely nervous.

Extremely.

I was very pleased when it went well

and when it was over.

Looking forward to number two. - Yeah.

Even with all that practice, all of that,

you've never been in a situation

that's still high stakes in terms of people watching.

And we should also probably mention,

given how the media works, a lot of people,

maybe in a dark kind of way, hoping it doesn't go well.

- Well, I think wealth is easy to hate

or envy or whatever.

And I think there's a whole industry around driving clicks,

and bad news is great for clicks.

And so any way to take an event and turn it into bad news

is gonna be really good for clicks.

- It just sucks because I think it puts pressure on people.

It discourages people from trying to solve

really hard problems, because to solve hard problems,

you have to go into the unknown.

You have to do things that haven't been done before,

and you have to take risks.

- Yeah. - Calculated risks.

You have to do all kinds of safety precautions,

but risks nevertheless.

And I just wish there would be more celebration of that,

of the risk taking versus like people just waiting

on the sidelines, like waiting for failure,

and then pointing out the failure.

Yeah, it sucks.

But in this case, it's really great that everything

went just flawlessly,

but it's unnecessary pressure, I would say.

- Now that there's a human with literal skin in the game,

there's a participant whose wellbeing

rides on this doing well,

you have to be a pretty bad person

to be rooting for that to go wrong.

And so hopefully, people look in the mirror

and realize that at some point.

- So did you get to actually front row seat

like watch the robot work?

You get to see the whole thing?

- Yeah, I mean, because an MD needs to be in charge

of all of the medical decision making

throughout the process, I unscrubbed from the surgery

after exposing the brain and presenting it to the robot

and placed the targets on the robot

software interface that tells the robot

where it's going to insert each thread that was done

with my hand on the mouse, for whatever that's worth.

- So you were the one placing the targets?

- Yeah. - Oh, cool.

So like the robot with a computer vision

provides a bunch of candidates

and you kinda finalize the decision.

- Right.

The software engineers are amazing on this team.

And so they actually provided an interface

where you can essentially use a lasso tool

and select a prime area of brain real estate,

and it will automatically avoid the blood vessels

in that region and automatically place a bunch of targets.

So that allows the human robot operator

to select really good areas of brain

and make dense applications of targets in those regions,

the regions we think are gonna have

the most high fidelity representations

of finger movements and arm movement intentions.

- I've seen like images of this.

And for me, with OCD,

it's for some reason a really pleasant,

I think there's a subreddit called oddly satisfying.

- [Matthew] Yeah, love that subreddit.

- It's oddly satisfying to see the different target sites

avoiding the blood vessels and also maximizing

like the usefulness of those locations for the signal.

It just feels good.

It's like, ah.

- As a person who has a visceral reaction

to the brain bleeding,

I can tell you it's extremely satisfying

watching the electrodes themselves go into the brain

and not cause bleeding.

- Yeah, yeah.

So you said the feeling was of relief

when everything went perfectly.

- Yeah.

- How deep in the brain can you currently go

and eventually go, let's say, on the Neuralink side.

It seems the deeper you go in the brain,

the more challenging it becomes.

- Yeah, so talking broadly about neurosurgery,

we can get anywhere.

It's routine for me to put deep brain stimulating electrodes

near the very bottom of the brain,

entering from the top and passing

about a two millimeter wire

all the way into the bottom of the brain.

And that's not revolutionary.

A lot of people do that.

And we can do that with very high precision.

I use a robot from Globus to do that surgery

several times a month.

It's pretty routine.

- What are your eyes in that situation?

What are you seeing?

What kind of technology can you use to visualize

where you are to light your way?

- Yeah, so it's a cool process on the software side.

You take a preoperative MRI

that's extremely high resolution data of the entire brain.

You put the patient to sleep,

put their head in a frame

that holds the skull very rigidly,

and then you take a CT scan of their head

while they're asleep with that frame on,

and then merge the MRI and the CT in software.

You have a plan based on the MRI

where you can see these nuclei deep in the brain.

You can't see them on CT,

but if you trust the merging of the two images,

then you indirectly know on the CT where that is.

And therefore, indirectly know

where in reference to the titanium frame

screwed to their head those targets are.

And so this is '60s technology to manually compute

trajectories, given the entry point and target

and dial in some goofy looking titanium actuators

with a manual actuators with little tick marks on them.

The modern version of that is to use a robot.

Just like a little KUKA arm,

you might see it building cars at the Tesla factory.

This small robot arm can show you the trajectory

that you intended from the pre-op MRI

and establish a very rigid holder

through which you can drill a small hole in the skull

and pass a small rigid wire deep into that area of the brain

that's hollow and put your electrode

through that hollow wire

and then remove all of that except the electrode.

So you end up with the electrode

very, very precisely placed far from the skull surface.

Now that's standard technology that's already,

been out in the world for a while.

Neuralink right now is focused entirely on cortical targets,

surface targets because there's no trivial way to get,

say, hundreds of wires deep inside the brain

without doing a lot of damage.

So your question, what do you see?

Well, I see an MRI on a screen.

I can't see everything that that DBS electrode

is passing through on its way to that deep target.

And so it's accepted with this approach

that there's gonna be about what one in a hundred patients

who have a bleed somewhere in the brain

as a result of passing that wire blindly

into the deep part of the brain.

That's not an acceptable safety profile for Neuralink.

We start from the position

that we want this to be dramatically

maybe two or three orders of magnitude safer than that.

Safe enough really that you or I,

without a profound medical problem,

might on our lunch break someday say,

"Yeah, sure, I'll get that.

I'd be meaning to upgrade to the latest version."

And so the safety constraints given that are high.

And so we haven't settled on a final solution

for arbitrarily approaching deep targets in the brain.

- It's interesting 'cause like you have to avoid

blood vessels somehow.

Maybe there's creative ways of doing the same thing,

like mapping out high resolution geometry of blood vessels

and then you can go in blind.

But how do you map out that

in a way that's like super stable?

There's a lot of interesting challenges there, right?

- [Matthew] Yeah.

- But there's a lot to do on the surface.

Luckily. - Exactly.

So we've got vision on the surface.

We actually have made a huge amount of progress

sewing electrodes into the spinal cord

as a potential workaround for a spinal cord injury

that would allow a brain-mounted implant

to translate motor intentions to a spine-mounted implant

that can effect muscle contractions

in previously paralyzed arms and legs.

- That's mind-blowing.

That's just incredible.

So like the effort there is to try to bridge

the brain to the spinal cord

to the peripheral nervous system.

So how hard is that to do?

- We have that working in very crude forms in animals.

- That's amazing. - Yeah, we've done-

- So similar to like with Noland,

where he's able to digitally move the cursor,

here you're doing the same kind of communication

but with the actual factors that you have.

- Yeah.

- [Lex] That's fascinating.

- Yeah, so we have anesthetized animals doing grasp

and moving their legs in a sort of walking pattern.

Again, early days,

but the future is bright for this kind of thing.

And people with paralysis

should look forward to that bright future.

They're gonna have options.

- Yeah, and there's a lot of sort of intermediate

or extra options where you take like an Optimus robot,

like the arm, and to be able to control the arm.

The fingers and hands of the arm as a prosthetic.

- So skeletons are getting better too.

- So skeletons.

Yeah, so that goes hand in hand.

Although I didn't quite understand

until thinking about it deeply

and doing more research about Neuralink,

how much you can do on the digital side.

So this digital telepathy,

I didn't quite understand

that you can really map the intention,

as you described in the hand knob area,

that you can map the intention.

Just imagine it, think about it.

That intention can be mapped

to actual action in the digital world.

And now more and more, so much can be done

in the digital world that it can reconnect you

to the outside world.

It can allow you to have freedom,

have independence if you're a quadriplegic.

That's really powerful.

Like you can go really far with that.

- Yeah, our first participant, he's incredible.

He's breaking world records left and right.

- And he is having fun with it, it's great.

Just going back to the surgery, your whole journey,

you mentioned to me offline, you have surgery on Monday.

So you're like doing surgery all the time.

- Yeah.

- Maybe the ridiculous question,

what does it take to get good at surgery?

- Practice, repetitions.

Same with anything else.

There's a million ways of people saying the same thing

and selling books saying it,

but you call it 10,000 hours, you call it, you know,

spend some chunk of your life,

some percentage of your life focusing on this,

obsessing about getting better at it.

Repetitions, humility, recognizing that you aren't perfect

at any stage along the way.

Recognizing you've got improvements

to make in your technique.

Being open to feedback and coaching

from people with a different perspective on how to do it.

And then just the constant will to do better.

That fortunately, if you're not a sociopath,

I think your patients bring that with them

to the office visits every day.

They force you to wanna do better all the time.

- Yeah, to step up.

I mean, it's a real human being,

a real human being that you can help.

- Yeah. - So every surgery,

even if it's the same exact surgery,

is there a lot of variability

between that surgery and a different person?

- Yeah, a fair bit.

I mean, a good example for us is the angle of the skull

relative to the normal plane of the body axis,

of the skull over hand knob is pretty wide variation.

I mean, some people have really flat skulls,

and some people have really steeply

angled skulls over that area.

And that has consequences for how their head can be fixed

in sort of the frame that we use

and how the robot has to approach the skull.

Yeah, people's bodies are built as differently

as the people you see walking down the street,

as much variability in body shape and size as you see there.

We see in brain anatomy and skull anatomy,

there are some people who we've had to kind of exclude

from our trial for having skulls

that are too thick or too thin

or scalp that's too thick or too thin.

I think we have like the middle 97% or so of people,

but you can't account for all human anatomy variability.

- How much like mushiness and messes there,

'cause taking biology classes,

the diagrams are always really clean and crisp.

Neuroscience, the pictures of neurons

are always really nice and vary.

But whenever I look at pictures of like real brains,

I don't know what is going on.

- Yeah. - So how much

are biological systems in reality?

Like how hard is it to figure out what's going on?

- Not too bad.

Once you really get used to this,

that's where experience and skill and education

really come into play is if you stare at a thousand brains,

it becomes easier to kind of mentally peel back

the, say, for instance, blood vessels

that are obscuring the sulci and gyri,

kind of the wrinkle pattern of the surface of the brain.

Occasionally, when you're first starting to do this

and you open the skull, it doesn't match

what you thought you were gonna see based on the MRI.

And with more experience, you learn to kind of peel back

that layer of blood vessels

and see the underlying pattern of wrinkles in the brain

and use that as a landmark for where you are.

- [Lex] The wrinkles are a landmark?

So like- - Yeah.

So I was describing hand knob earlier.

That's a pattern of the wrinkles in the brain.

It's sort of this sort of Greek letter,

omega-shaped area of the brain.

- So you could recognize the hand knob area.

Like if I show you a thousand brains

and give you like one minute with each,

you'd be like, "Yep, that's that?"

- Sure. - And so there is some

uniqueness to that area of the brain,

like in terms of the geometry, the topology of the thing.

- Yeah.

- Where is it about in the-

- So you have this strip of brain running down the top

called the primary motor area.

And I'm sure you've seen this picture of the homunculus

laid over the surface of the brain,

the weird little guy with huge lips and giant hands.

That guy sort of lays with his legs

up at the top of the brain and face, arm, areas farther down

and then some kind of mouth, lip, tongue areas farther down.

And so the hand is right in there.

And then the areas that control speech,

at least on the left side of the brain

in most people are just below that.

And so any muscle that you voluntarily move in your body,

the vast majority of that references that strip

or those intentions come from that strip of brain.

And the wrinkle for hand knob

is right in the middle of that.

- And vision is back here. - Back, yep.

- Also, close to the surface?

- Vision's a little deeper.

And so this gets to your question about

how deep can you get to do vision.

We can't just do the surface of the brain.

We have to be able to go in,

not as deep as we have to go for DBS,

but maybe a centimeter deeper

than we're used to for hand insertions.

And so that's work in progress.

That's a new set of challenges to overcome.

- By the way, you mentioned the Utah array.

And I just saw a picture of that,

and that thing looks terrifying.

- [Matthew] Yeah, bed of nails.

- It's because it's rigid.

And then if you look at the threads, they're flexible.

What can you say that's interesting to you

about the flexible, that kind of approach

of the flexible threads

to deliver the electrodes next to the neurons?

- Yeah, I mean, the goal there comes from experience.

I mean, we stand on the shoulders of people

that made Utah arrays and used Utah arrays for decades

before we ever even came along.

Neuralink arose, partly this approach

to technology arose out of a need recognized

after Utah arrays would fail routinely

because the rigid electrodes,

those spikes that are literally hammered

using an air hammer into the brain,

those spikes generate a bad immune response

that encapsulates the electrode spikes

in scar tissue essentially.

And so one of the projects that was being worked on

in the Andersen lab at Caltech when I got there,

was to see if you could use chemotherapy

to prevent the formation of scar.

Things are pretty bad when you're jamming a bed of nails

into the brain and then treating that with chemotherapy

to try to prevent scar tissue.

It's like, maybe we've gotten off track here, guys.

Maybe there's a fundamental redesign necessary.

And so Neuralink's approach of using highly flexible,

tiny electrodes avoids a lot of the bleeding,

avoids a lot of the immune response that ends up happening

when rigid electrodes are pounded into the brain.

And so what we see is our electrode longevity

and functionality and the health of the brain tissue

immediately surrounding the electrode is excellent.

I mean, it goes on for years now in our animal models.

- What do most people not understand

about the biology of the brain?

We mention the vasculature.

That's really interesting.

- I think the most interesting maybe underappreciated fact

is that it really does control almost everything.

I mean, I don't know, for out of the blue example,

imagine you want a lever on fertility,

you wanna be able to turn fertility on and off.

I mean, there are legitimate targets in the brain itself

to modulate fertility, say blood pressure.

You wanna modulate blood pressure.

There are legitimate targets in the brain for doing that.

Things that aren't immediately obvious as brain problems

are potentially solvable in the brain.

And so I think it's an under-explored

area for primary treatments

of all the things that bother people.

- That's a really fascinating way to look at it.

Like there's a lot of conditions we might think

have nothing to do with the brain,

but they might just be symptoms

of something that actually started in the brain.

The actual source of the problem.

The primary source is something in the brain.

- Yeah, not always.

I mean, kidney disease is real.

But there are levers you can pull in the brain

that affect all of these systems.

- There's knobs. - Yeah.

- On-off switches and knobs in the brain,

from which this all originates.

Would you have a Neuralink chip implanted in your brain?

- Yeah.

I think use case right now is use a mouse, right?

I can already do that.

And so there's no value proposition.

On safety grounds alone, sure.

I'll do it tomorrow.

- You say the use case of the mouse,

is it after like researching all this

and part of it is just watching Noland have so much fun?

If you can get that bits per second

look really high with a mouse,

like being able to interact,

'cause if you think about it, the way,

on the smartphone, the way you swipe,

that was transformational how you interact with a thing.

It's subtle.

You don't realize it, but to able to touch a phone

and to scroll with your finger, that's like,

that changed everything.

People were sure you need a keyboard to type.

There's a lot of HCI aspects to that

that changed how we interact with computers.

So there could be a certain rate of speed with the mouse

that would change everything.

- Yeah. - It's like you might be able

to just click around a screen extremely fast.

And that,

I can see myself getting a Neuralink

for much more rapid interaction with the digital devices.

- Yeah, I think recording speech intentions from the brain

might change things as well.

The value proposition for the average person,

a keyboard is a pretty clunky human interface,

requires a lot of training.

It's highly variable in the maximum performance

that the average person can achieve.

I think taking that out of the equation

and just having a natural

word to computer interface

might change things for a lot of people.

- It'd be hilarious if that is the reason people do it.

Even if you have speech to text,

that's extremely accurate, it currently isn't,

but say it gotten super accurate,

it'd be hilarious if people went for Neuralink

just so you avoid the embarrassing aspect of speaking,

like looking like a douche bag speaking

to your phone in public, which is a real,

like that's a real constraint.

- Yeah.

I mean, with a bone conducting case,

that can be an invisible headphone, say,

and the ability to think words into software

and have it respond to you,

that starts to sound sort of like

embedded super intelligence.

If you can silently ask for the Wikipedia article

on any subject and have it read to you,

without any observable change

happening in the outside world,

for one thing, standardized testing is obsolete. (laughs)

- Yeah.

If it's done well in the UX side, it could change.

I don't know if it transforms society,

but it really can create a kind of shift

in the way we interact with digital devices

in the way that a smartphone did.

Just having to look into the safety of everything involved,

I would totally try it so it doesn't have to go

to some like incredible thing where you have,

it connects your vision or to some other,

like it connects all over your brain.

That could be like just connecting to the hand knob.

You might have a lot of interesting interaction,

human-computer interaction possibilities.

That's really interesting.

- Yeah, and the technology on the academic side

is progressing at light speed here.

I think there was a really amazing paper out of UC Davis,

Sergey Stavisky's lab that basically made a initial solve

of speech decode.

It was something like 125,000 words

that they we're getting with very high accuracy, which is-

- So you're just thinking the word?

- Yeah. - Thinking the word

and you're able to get it?

- Yeah. - Oh boy.

Like you have to have the intention of speaking it.

- Right.

- So like do the inner voice.

Man, it's so amazing to me that you can do the intention,

the signal mapping.

All you have to do is just imagine yourself doing it.

And if you get the feedback that it actually worked,

you can get really good at that.

Like your brain will first of all adjust

and you develop it like any other skill.

Like touch typing, you develop in that same kind of way.

To me, it's just really fascinating

to be able to even to play with that.

Honestly, like I would get a Neuralink

just to be able to play with that.

Just to play with the capacity,

the capability of my mind to learn this skill.

It's like learning the skill of typing

and learning the skill of moving a mouse.

It's another skill of moving the mouse,

not with my physical body, but with my mind.

- I can't wait to see what people do with it.

I feel like we're cavemen right now.

We're like banging rocks with a stick

and thinking that we're making music.

At some point, when these are more widespread,

there's gonna be the equivalent of a piano

that someone can make art with their brain

in a way that we didn't even anticipate.

I'm looking forward to it.

- Give it to like a teenager.

Like anytime I think I'm good at something,

I'll always go to like, I don't know.

Even with the bit per second of playing a video game,

you realize you give it to a teenager,

you've given your link to a teenager,

just the large number of them,

the kind of stuff, they get good at stuff.

They're gonna get like hundreds of bits per second.

Even just with the current technology.

- Probably.

Probably.

- 'Cause it's also addicting,

the number go up aspect of it

of like improving and training,

'cause it is almost like a skill.

And plus, there's the software on the other end

that adapts to you.

And especially if the adapting procedure algorithm

becomes better and better and better,

you're like learning together.

- Yeah, we're scratching the surface on that right now.

There's so much more to do.

- So on the complete other side of it,

you have an RFID chip implanted in you.

- Yeah. - So I hear, nice.

- Little subtle thing.

- It's a passive device that you use

for unlocking like a safe with top secrets,

or what do you use it for?

What's the story behind it?

- I'm not the first one.

There's this whole community of weirdo biohackers

that have done this stuff,

and I think one of the early use cases was storing

private crypto wallet keys and whatever.

I dabbled in that a bit and had some fun with it.

- You have some bitcoin implanted in your body somewhere.

You can't tell where, yeah.

- Yeah, actually, yeah.

(Lex laughing)

It was the modern day equivalent

of finding change in the sofa cushions

after I put some orphan crypto on there

that I thought was worthless

and forgot about it for a few years.

Went back and found that some community of people loved it

and had propped up the value of it.

And so it had gone up 50 fold.

- Wow. - So there was a lot

of change in those cushions.

(Lex laughing)

- That's hilarious.

- But the primary use case was mostly

as a tech demonstrator.

It has my business card on it.

You can scan that in by touching it to your phone.

It opens the front door to my house,

whatever simple stuff.

- It's a cool step.

It's a cool leap to implant something in your body.

I mean, perhaps, it's a similar leap to a Neuralink

because for a lot of people, that kind of notion

of putting something inside your body, something electronic

inside a biological system is a big leap.

- Yeah, we have a kind of a mysticism around

the barrier of our skin.

We're completely fine with knee replacements,

hip replacements, dental implants.

But there's a mysticism still around

the inviable barrier that the skull represents.

And I think that needs to be treated

like any other pragmatic barrier.

The question isn't, how incredible is it to open the skull?

The question is, what benefit can we provide?

- So from all the surgeries you done,

from everything you understand the brain,

how much does neuroplasticity come into play?

How adaptable is the brain, for example,

just even in the case of healing from surgery

or adapting to the post-surgery situation.

- The answer that is sad for me

and other people of my demographic is that

plasticity decreases with age.

Healing decreases with age.

I have too much gray hair to be optimistic about that.

There are theoretical ways to increase plasticity

using electrical stimulation.

Nothing that is totally proven out

as a robust enough mechanism to offer widely to people.

But yeah, I think there's cause for optimism

that we might find something useful in terms of, say,

an implanted electrode that improves learning.

Certainly, there's been some really amazing work

recently from Nicholas Schiff, Jonathan Baker,

and others who have a cohort of patients

with moderate traumatic brain injury

who have had electrodes placed

in the deep nucleus in the brain

called the centromedian nucleus

or just near central media nucleus.

And when they apply small amounts of electricity

to that part of the brain,

it's almost like electronic caffeine.

They're able to improve people's attention and focus.

They're able to improve how well people can perform a task.

I think in one case, someone who was unable to work

after the device was turned on, they were able to get a job.

And that's sort of one of the holy grails

for me with Neuralink and other technologies like this

is from a purely utilitarian standpoint,

can we make people able to take care of themselves

and their families economically again?

Can we make it so someone who's fully dependent

and even maybe requires a lot of caregiver resources,

can we put them in a position to be fully independent,

taking care of themselves, giving back to their communities?

I think that's a very compelling proposition,

and what motivates a lot of what I do

and what a lot of the people at Neuralink are working for.

- It's just a cool possibility

that if you put a Neuralink in there, that the brain adapts,

like the other part of the brain adapts too.

- Yeah. - And integrates it.

The capacity of the brain to do that is really interesting.

Probably unknown to the degree to which you can do that,

but you're now connecting an external thing to it,

especially once it's doing stimulation,

like the biological brain and the electronic brain

outside of it working together.

Like the possibilities there are really interesting.

It's still unknown but interesting.

It feels like the brain is really good

at adapting to whatever.

- Yeah. - But of course,

it is a system that by itself is already,

like everything serves a purpose

and so you don't wanna mess with it too much.

- Yeah, it's like, eliminating a species

from an ecology.

You don't know what the delicate interconnections

and dependencies are.

The brain is certainly a delicate, complex beast.

And we don't know every potential downstream consequence

of a single change that we make.

- Do you see yourself doing, so you mentioned P1,

surgeries of P2, P3, P4, P5?

Just more and more and more humans.

- I think it's a certain kind of brittleness

or a failure on the company's side

if we need me to do all the surgeries.

I think something that I would very much

like to work towards is a process that is so simple

and so robust on the surgery side

that literally anyone could do it.

We wanna get away from requiring intense expertise

or intense experience to have this successfully done

and make it as simple and translatable as possible.

I mean, I would love it if every neurosurgeon on the planet

had no problem doing this.

I think we're probably far from a regulatory environment

that would allow people that aren't neurosurgeons

to do this, but not impossible.

- All right, I'll sign up for that.

Did you ever anthropomorphize the robot R1?

Like do you give it a name?

Do you see it as like a friend,

as like working together with you?

- I mean, to a certain degree it's-

- Or anatomy who's gonna get the gap.

- To a certain degree, yeah, it's complex relationship.

- All the good relationships are.

- It's funny when, in the middle of the surgery,

there's a part of it where I stand

basically shoulder to shoulder with the robot.

And so if you're in the room reading the body language,

that's my brother in arms there.

We're working together on the same problem.

Yeah, I'm not threatened by it.

- Keep telling yourself that. (laughs)

How have all the surgeries that you've done over the years,

the people you've helped and the stakes,

the high stakes that you've mentioned,

how has that changed your understanding of life and death?

- Yeah.

It gives you a very visceral sense,

and this makes sound trite,

but it gives you a very visceral sense

that death is inevitable.

On one hand, you are, as a neurosurgeon,

you're deeply involved in these

like just hard to fathom tragedies:

young parents dying, leaving a four-year-old behind say.

And on the other hand,

it takes the sting out of it a bit because

you see how just mind numbingly universal death is.

There's zero chance that I'm going to avoid it.

I know techno optimists right now

and longevity buffs right now

would disagree on that 0.0% estimate.

But I don't see any chance

that our generation is going to avoid it.

Entropy is a powerful force,

and we are very ornate, delicate, brittle DNA machines

that aren't up to the cosmic ray bombardment

that we're subjected to.

So on the one hand,

every human that has ever lived died or will die.

On the other hand, it's just one of the hardest things

to imagine inflicting on anyone

that you love is having them gone.

I'm sure you've had friends that aren't living anymore

and it's hard to even think about them.

And so I wish I had arrived at the point of nirvana

where death doesn't have a sting.

I'm not worried about it, but I can at least say

that I'm comfortable with the certainty of it.

If not, having found out how to take the tragedy out of it

when I think about my kids either not having me

or me not having them or my wife.

- Maybe I have come to accepting

intellectual certainty of it, but it may be the pain

that comes with losing the people you love,

I don't think I've come to understand

the existential aspect of it.

Like that this is gonna end.

And I don't mean like in some trite way.

I mean like, it certainly feels like it's not going to end.

Like you live life like it's not going to end.

- [Matthew] Right.

- And the fact that this light

that's shining this consciousness

is going to no longer be, in one moment, maybe today,

it fills me when I really am able

to load all that in with Ernest Becker's terror.

Like it's a real fear.

I think people aren't always honest

with how terrifying it is.

I think the more you are able

to really think through it, the more terrifying it is.

It's not such a simple thing.

Oh well, it's the way life is.

If you really can load that in, it's hard.

But I think that's why the stoics did it,

because it like helps you get your shit together

and be like, well, the moment,

every single moment you're alive is just beautiful.

And it's terrifying that it's gonna end,

like almost like you're shivering in the cold

a child helpless, this kind of feeling.

And then it makes you, when you have warmth,

when you have the safety,

when you have the love to really appreciate it.

I feel like sometimes, in your position,

when you mentioned armor, just to see death,

it might make you not be able to see that,

the finiteness of life,

because if you kept looking at that, it might break you.

So it's good to know that you're kind of

still struggling with that,

that there's the neurosurgeon and then there's a human.

And the human is still able to struggle with that

and feel the fear of that and the pain of that.

- Yeah, it definitely makes you ask the question

of how long, how many of these can you see?

And not say, "I can't do this anymore."

But I mean, you said it well.

I think it gives you an opportunity to just appreciate

that you're alive today.

And I've got three kids and an amazing wife

and I'm really happy.

Things are good.

I get to help on a project that I think matters.

I think it moves us forward.

I'm a very lucky person.

- It's the early steps of a potentially

gigantic leap for humanity.

It's a really interesting one.

And it's cool 'cause like you,

you read about all this stuff in history

where it's like the early days.

I've been reading, before going to the Amazon,

I would read about explorers that would go and explore

even the Amazon jungle for the first time.

Those are the early steps.

Or early steps into space,

early steps in any discipline,

in physics and mathematics.

And it's cool 'cause this is like, on the grand scale,

these are the early steps

into delving deep into the human brain.

So not just observing the brain,

but be able to interact with the human brain.

It's gonna help a lot of people,

but it also might help us understand

what the hell's going on in there.

- Yeah, I think ultimately, we wanna give people

more levers that they can pull, right?

Like you wanna give people options.

If you can give someone a dial that they can turn

on how happy they are,

I think that makes people really uncomfortable.

But now, talk about major depressive disorder.

Talk about people that are committing suicide

at an alarming rate in this country.

And try to justify that queasiness

in that light of you can give people a knob

to take away suicidal ideation, suicidal intention.

I would give them that knob.

I don't know how you justify not doing that.

- Yeah, you can think about like

all the suffering that's going on in the world.

Like every single human being

that's suffering right now,

it'll be a glowing red dot.

The more suffering, the more it's glowing.

And you just see the map of human suffering,

and any technology that allows you to dim that light

of suffering on a grand scale is pretty exciting,

because there's a lot of people suffering

and most of them suffer quietly.

We look away too often,

and we should remember those that are suffering,

'cause once again, most of them are suffering quietly.

- Well, and on a grander scale, the fabric of society,

people have a lot of complaints about

how our social fabric is working

or not working, how our politics is working or not working.

Those things are made of neurochemistry too,

in aggregate, right?

Like our politics is composed of individuals

with human brains and, the way it works or doesn't work

is potentially tunable in the sense that, I don't know,

say remove our addictive behaviors

or tune our addictive behaviors for social media

or our addiction to outrage, our addiction to sharing

the most angry political tweet we can find.

I don't think that leads to a functional society.

And if you had options

for people to moderate that maladaptive behavior,

there could be huge benefits to society.

Maybe we could all work together

a little more harmoniously toward useful ends.

- There's a sweet spot, like you mentioned,

you don't wanna completely remove all the dark sides

of human nature 'cause those kind of are somehow necessary

to make the whole thing work.

But there's a sweet spot.

- Yeah, I agree.

We gotta suffer a little,

just not so much that you lose hope.

- Yeah.

We knew all the surgeries you've done.

Have you seen consciousness in there ever?

Was there like a glowing light?

- I have this sense that I never found it.

Never removed it, like a dementor in Harry Potter.

I have this sense that consciousness is a lot less magical

than our instincts wanna claim it is.

It seems to me like a useful analog for thinking about

what consciousness is in the brain,

is that we have a really good intuitive understanding

of what it means to, say, touch your skin

and know what's being touched.

I think consciousness is just that level

of sensory mapping applied

to the thought processes in the brain itself.

So what I'm saying is consciousness

is the sensation of some part of your brain being active.

So you feel it working.

You feel the part of your brain

that thinks of red things or winged creatures

or the taste of coffee.

You feel those parts of your brain being active

the way that I'm feeling my palm being touched, right?

And that sensory system that feels the brain working

is consciousness.

- That's so brilliant.

It's the same way, it's the sensation of touch

when you're touching a thing.

Consciousness is the sensation of you

feeling your brain working, your brain thinking,

your brain perceiving.

- Which isn't like a warping of space and time

or some quantum field effect, right?

It's nothing magical.

People always wanna ascribe to consciousness

something truly different.

And there's this awesome long history

of people looking at whatever the latest

discovery in physics is to explain consciousness,

because it's the most magical,

the most out there thing that you can think of.

And people always wanna do that with consciousness.

I don't think that's necessary.

It's just a very useful

and gratifying way of feeling your brain work.

- And as we said, it's one heck of a brain.

- [Matthew] Yeah.

- Everything we see around us, everything we love,

everything that's beautiful,

it came from brains like these.

- It's all electrical activity happening inside your skull.

- And I, for one, am grateful that there's people like you

that are exploring all the ways that it works

and all the ways it can be made better.

- Thanks, Lex.

- Thank you so much for talking today.

- It's been a joy.

Thanks for listening to this conversation

with Matthew MacDougall.

And now, dear friends, here's Bliss Chapman,

Brain Interface Software lead at Neuralink.

You told me that you've met hundreds of people

with spinal cord injuries or with ALS

and that your motivation for helping at Neuralink

is grounded in wanting to help them.

Can you describe this motivation?

- Yeah.

First, just a thank you to all the people

I've gotten a chance to speak with,

for sharing their stories with me.

I don't think there's any world really in which I can

share their stories as powerful way as they can.

But just I think to summarize at a very high level

what I hear over and over again is that people with ALS

or severe spinal cord injury in a place

where they basically can't move physically anymore,

really at the end of the day are looking for independence.

And that can mean different things for different people.

For some folks, it can mean the ability just to be able

to communicate again independently without needing

to wear something on their face, without needing a caretaker

to be able to put something in their mouth.

For some folks, it can mean independence to be able

to work again, to be able to navigate a computer digitally,

efficiently enough to be able to get a job,

to be able to support themself, to be able to move out

and ultimately be able to support themself

after their family maybe isn't there anymore

to take care of them.

And for some folks, it's as simple as

just being able to respond to their kid in time

before they run away or get interested in something else.

And these are deeply personal

and sort of very human problems.

And what strikes me again and again

when talking with these folks is that

this is actually an engineering problem.

This is a problem that with the right resources,

with the right team, we can make a lot of progress on.

And at the end of the day,

I think that's a deeply inspiring message

and something that makes me excited to get up every day.

- So it's both an engineering problem

in terms of a BCI, for example,

that can give them capabilities

where they can interact with the world.

But also on the other side, it's an engineering problem

for the rest of the world to make it more accessible

for people living with quadriplegia.

- Yeah, and actually, I'll take a broad view

sort of lens on this for a second.

I think I'm very in favor of anyone

working in this problem space.

So beyond BCI, I'm happy and excited

and willing to support any way I can

folks working on eye tracking systems,

working on speech to text systems,

working on head trackers or mouse sticks or quad sticks.

And I've met many engineers

and folks in the community that do exactly those things.

And I think for the people we're trying to help,

it doesn't matter what the complexity of the solution is

as long as the problem is solved.

And I wanna emphasize

that there can be many solutions out there

that can help with these problems.

And BCI is one of a collection of such solutions.

So BCI, in particular,

I think offers several advantages here.

And I think the folks that recognize this immediately

are usually the people who have spinal cord injury

or some form of paralysis.

Usually, you don't have to explain to them

why this might be something that could be helpful.

It's usually pretty self-evident.

But for the rest of us folks that don't live

with severe spinal cord injury

or who don't know somebody with ALS,

it's not often obvious why you would want a brain implant

to be able to connect and navigate a computer.

And it's surprisingly nuanced,

and to the degree that I've learned a huge amount

just working with Noland in the first

Neuralink clinical trial and understanding from him

in his words why this device is impactful for him.

And it's a nuanced topic.

It can be the case that even if you can achieve

the same thing, for example, with a mouse stick

when navigating a computer, he doesn't have access

to that mouse stick every single minute of the day.

He only has access when someone's available

to put it in front of him.

And so a BCI can really offer a level

of independence and autonomy

that if it wasn't literally physically part of your body,

it'd be hard to achieve in any other way.

- So there's a lot of fascinating aspects

to what it takes to get Noland to be able

to control a cursor on the screen with his mind.

You texted me something that I just love.

You said, "I was part of the team

that interviewed and selected P1.

I was in the operating room

during the first human surgery

monitoring live signals coming out of the brain.

I work with the user basically every day

to develop new UX paradigm's decoding strategies.

And I was part of the team that figured out

how to recover useful BCI

to new world record levels

when the signal quality degraded."

We'll talk about I think every aspect of that,

but just zooming out, what was it like

to be part of that team and part of that historic,

I would say, historic first?

- Yeah, I think for me,

this is something I've been excited about

for close to 10 years now.

And so to be able to be even just some small part

of making it a reality is extremely exciting.

A couple maybe special moments during that whole process

that I'll never really truly forget,

one of them is during the actual surgery,

at that point in time, I know Noland quite well.

I know his family.

And so I think the initial reaction when Noland

is rolled into the operating room

is just a "oh shit" kind of reaction.

But at that point, muscle memory kicks in

and you sort of go into,

you let your body just do all the talking.

And I have the lucky job in that particular procedure

to just be in charge of monitoring the implant.

So my job is to sit there,

to look at the signals coming off the implant,

to look at the live brain data streaming off the device

as threads are being inserted into the brain

and just to basically observe

and make sure that nothing is going wrong

or that there's no red flags or fault conditions

that we need to go and investigate

or pause the surgery to debug.

And because I had that sort of spectator view

of the surgery, I had a slightly removed perspective

than I think most folks in the room.

I got to sit there and think to myself,

"Wow, that brain is moving a lot."

When you look into the side look craniectomy,

that we stick the threads in,

one thing that most people don't realize is the brain moves.

The brain moves a lot when you breathe,

when your heart beats, and you can see it visibly.

So that's something that I think was a surprise to me

and very, very exciting to be able to see someone's brain

who you physically know and have talked with at length

actually pulsing and moving inside their skull.

- And they use that brain to talk to you previously,

and now it's right there moving.

- Yep. - Actually, I didn't realize

that in terms of the thread sending,

so the Neuralink implant is active during surgery,

and one thread at a time,

you're able to start seeing the signal?

- Yeah. - So that's part of the way

you test that the thing is working?

- Yeah, so actually in the operating room,

right after we sort of finished all the thread insertions,

I started collecting what's called broadband data.

So broadband is basically the most raw form

of signal you can collect from a Neuralink electrode.

It's essentially a measurement

of the local field potential

or the, yeah, the voltage essentially

measured by that electrode.

And we have a certain mode in our application

that allows us to visualize where detected spikes are.

So it visualizes sort of where,

in the broadband symbol,

and it's very, very raw form of the data

a neuron is actually spiking.

And so one of these moments that I'll never forget

as part of this whole clinical trial

is seeing live in the operating room,

while he's still under anesthesia,

beautiful spikes being shown in the application,

just streaming live to a device I'm holding in my hand.

- So this is no signal processing the raw data

and then the signals processings on top of it,

you're seeing the spikes detected?

- [Bliss] Right, yeah.

- And that's a UX too because- - Yes.

- That looks beautiful as well.

- During that procedure,

there was actually a lot of cameramen in the room.

So they also were curious and wanted to see.

There's several neurosurgeons in the room

who are all just excited to see robots taking their job

and they're all crowded around a small little iPhone

watching this live brain data stream out of his brain.

- What was that like seeing the robot

do some of the surgery?

So the computer vision aspect where it detects

all the spots that avoid the blood vessels

and then obviously with the human supervision,

then actually doing the really high precision

connection of the threads to the brain.

- That's a good question.

My answer's gonna be pretty lame here, but it was boring.

I've seen it so many times.

Yeah, that's exactly how you want surgery to be.

You want it to be boring,

because I've seen it so many times.

I've seen the robot do the surgery

literally hundreds of times,

and so it was just one more time.

- Yeah, all the practice surgeries and the proxies

and this is just another day.

- Yep.

- So what about when Noland woke up?

Do you remember a moment where

he was able to move the cursor, not move the cursor,

but get signal from the brain

such that it was able to show that there's a connection?

- Yeah, yeah.

So we are quite excited to move as quickly as we can,

and Noland was really, really excited to get started.

He wanted to get started actually the day of surgery,

but we waited till the next morning very patiently.

So a long night.

And the next morning in the ICU, where he was recovering,

he wanted to get started and actually start to understand

what kind of signal we can measure from his brain.

And maybe for folks who are not familiar

with the Neuralink system, we implant the Neuralink system

or the Neuralink implant in the motor cortex.

So the motor cortex is responsible

for representing things like motor intent,

sort of if you imagine closing and opening your hand,

that kind of signal representation

would be present in the motor cortex.

If you imagine moving your arm back and forth

or wiggling a pinky, this sort of signal

can be present in the motor cortex.

So one of the ways we start to sort of map out,

what kind of signal do we actually have access to

in any particular individual's brain

is through this task called body mapping.

And body mapping is where you essentially present

a visual to the user and you say,

"Hey, imagine doing this."

And that visual is a 3D hand opening and closing,

or index finger modulating up and down.

And you ask the user to imagine that,

and obviously, you can't see them do this,

'cause they're paralyzed so you can't see them

actually move their arm, but while they do this task,

you can record neural activity,

and you can basically offline model and check,

can I predict or can I detect the modulation

corresponding with those different actions?

And so we did that task and we realized,

hey, there's actually some modulation

associated with some of his hand motion,

which was the first indication that, okay,

we can potentially use that modulation

to do useful things in the world.

For example, control a computer cursor.

And he started playing with it,

the first time we showed him it,

and we actually just took the same live view

of his brain activity and put it in front of him.

And we said, "Hey, you tell us what's going on.

We're not you.

You're able to imagine different things,

and we know that it's modulating some of these neurons

so you figure out for us

what that is actually representing."

And so he played with it for a bit.

He was like, "I don't quite get it yet."

He played for a bit longer.

And he said, "Oh, when I move this finger,

I see this particular neuron start to fire more."

And I said, okay, "Prove it, do it again."

And so he said, "Okay, three, two, one, boom."

And the minute he moved, you can see like instantaneously

this neuron is firing - single neuron.

I can tell you the exact

channel number if you're interested.

It's stuck in my brain now forever.

But that single channel firing was a beautiful indication

that it was behaved really modulated neural activity

that could then be used for downstream tasks

like decoding a computer cursor.

- And when you say single channel,

is that associated with a single electrode?

- Yeah, channel electrode are interchangeable.

- And there's 1,024 of those?

- 1,024, yeah.

- That's incredible that that works.

When I was learning about all this and like loading it in,

it was just blowing my mind that the intention,

you can visualize yourself moving the finger,

that can turn into a signal,

and the fact that you can then skip that step

and visualize the cursor moving

or have the intention of the cursor moving

and that leading to a signal

that can then be used to move the cursor,

there is so many exciting things there

to learn about the brain, about the way the brain works,

the very fact of their existing signal

that can be used is really powerful.

But it feels like that's just like the beginning

of figuring out how that signal could be used

really, really effectively.

I should also just,

there's so many fascinating details here,

but you mentioned the body mapping step.

At least in the version I saw that Noland was showing off,

there's like a super nice interface,

like a graphical interface.

But like it just felt like I was like in the future

'cause it like, you know,

I guess it visualizes you moving the hand.

And there's very like a sexy, polished interface.

Hello.

I don't know if there's a voice component,

but it just felt like when you wake up

in a really nice video game and this is a tutorial

at the beginning of that video game.

"This is what you're supposed to do."

It's cool.

- No, I mean, the future should feel like the future.

- But it's not easy to pull that off.

I mean, it needs to be simple but not too simple.

- Yeah, and I think the UX design component here

is underrated for BCI development in general.

There's a whole interaction effect

between the ways in which you visualize

an instruction to the user

and the kinds of signal you can get back.

And that quality of sort of your behavioral alignment

to the neural signal is a function

of how good you are at expressing

to the user what you want them to do.

And so yeah, we spend a lot of time thinking about the UX,

of how we build our applications,

of how the decoder actually functions,

the control surfaces it provides to the user.

All these little details matter a lot.

- So maybe it'd be nice to get into a little bit more detail

of what the signal looks like

and what the decoding looks like.

So there's a N1 implant that has, like we mentioned,

1,024 electrodes and that's collecting raw data, raw signal.

What does that signal look like,

and what are the different steps along the way

before it's transmitted, and what is transmitted,

all that kind of stuff?

- Yeah, yep.

This is gonna be a fun one.

Let's go.

So maybe before diving into what we do,

it's worth understanding what we're trying to measure,

because that dictates a lot of the requirements

for the system that we build.

And what we're trying to measure

is really individual neurons producing action potentials.

And action potential is, you can think of it

like a little electrical impulse

that you can detect if you're close enough.

And by being close enough, I mean, like within

let's say 100 microns of that cell.

And 100 microns is a very, very tiny distance.

And so the number of neurons that you're gonna pick up

with any given electrode

is just a small radius around that electrode.

And the other thing worth understanding

about the underlying biology here

is that when neurons produce an action potential,

the width of that action potential is about one millisecond.

So from the start of the spike to the end of the spike,

that whole width of that sort of characteristic feature

of a neuron firing is one millisecond wide.

And if you want to detect that an individual spike

is occurring or not, you need to sample that signal

or sample the local full potential nearby that neuron

much more frequently than once a millisecond.

You need to sample many, many times per millisecond

to be able to detect that this is actually

the characteristic waveform

of a neuron producing an action potential.

And so we sample across all 1,024 electrodes

about 20,000 times a second.

20,000 times a second means

we've already given one millisecond window.

We have about 20 samples that tell us

what that exact shape of that action potential looks like.

And once we've sort of sampled at super high rate

the underlying electrical field nearby these cells,

we can process that signal into

just where do we detect a spike or where do we not,

sort of a binary signal one or zero.

Do we detect a spike in this one millisecond or not?

And we do that because the actual information carrying

sort of subspace of neural activity

is just when are spikes occurring.

Essentially, everything that we care about

for decoding can be captured or represented

in the frequency characteristics of spike trains,

meaning how often are spikes

firing in any given window of time.

And so that allows us to do sort of a crazy amount

of compression from this very rich, high density signal

to something that's much, much more sparse

and compressible that can be sent out over a wireless radio,

like a Bluetooth communication, for example.

- Quick tangents here.

You mentioned electrode neuron.

There's a local neighborhood of neurons nearby.

How difficult is it to like isolate

from where the spike came from?

- Yeah, so there's a whole field of sort of academic

neuroscience work on exactly this problem,

of basically given a single electrode

or given a set of electrodes measuring a set of neurons,

how can you sort of sort,

spike sort which spikes are coming from what neuron?

And this is a problem that's pursued in academic work

because you care about it for understanding what's going on

in the underlying sort of neuroscience of the brain.

If you care about understanding

how the brain's representing information,

how that's evolving through time,

then that's a very, very important question to understand.

For sort of the engineering side of things,

at least at the current scale,

if the number of neurons per electrode is relatively small,

you can get away with basically

ignoring that problem completely.

You can think of it like sort of a random projection

of neurons to electrodes,

and there may be in some cases

more than one neuron per electrode.

But if that number is small enough,

those signals can be thought of as

sort of a union of the two.

And for many applications, that's a totally reasonable

trade off to make and can simplify the problem a lot.

And as you sort of scale out channel count,

the relevance of distinguishing individual neurons

becomes less important, because you have more overall signal

and you can start to rely on sort of correlations

or covariance structure in the data to help understand

when that channel's firing,

what does that actually represent?

'Cause you know that when that channel's firing

in concert with these other 50 channels,

that means move left.

But when that same channel's firing with concert

with these other 10 channels, that means move right.

- Okay, so you have to do this kind of spike detection

on board, and you have to do that super efficiently,

so fast and not use too much power,

'cause you don't wanna be generating too much heat.

So it has to be a super simple signal processing step.

- [Bliss] Yeah.

- Is there some wisdom you can share about

what it takes to overcome that challenge?

- Yeah, so we've tried many different versions

of basically turning this raw signal into

sort of a feature that you might wanna send off the device.

And I'll say that I don't think

we're at the final step of this process.

This is a long journey.

We have something that works clearly today,

but there can be many approaches that we find

in the future that are much better

than what we do right now.

So some versions of what we do right now,

and there's a lot of academic heritage to these ideas,

so I don't wanna claim that these are original

Neuralink ideas or anything like that.

But one of these ideas is basically to build

a sort of like a convolutional filter almost, if you will,

that slides across the signal

and looks for a certain template to be matched.

And that template consists of sort of how deep

the spike modulates, how much it recovers,

and what the duration and window of time is

that the whole process takes.

And if you can see in the signal that that template

is matched within certain bounds,

then you can say, "Okay, that's a spike."

One reason that approach is super convenient

is that you can actually implement that

extremely efficiently in hardware,

which means that you can run it in low power

across 1,024 channels all at once.

Another approach that we've recently started exploring,

and this can be combined with the spike detection approach,

something called spike band power.

And the benefits of that approach

are that you may be able to pick up some signal from neurons

that are maybe too far away to be detected as a spike,

because the farther away you are from an electrode,

the weaker that actual spike waveform

will look like on that electrode.

So you might be able to pick up

population level activity of things that are

maybe slightly outside the normal recording radius,

what neuroscientists sometimes refer to as the hash

of activity, the other stuff that's going on,

and you can look at sort of across many channels

how that sort of background noise is behaving

and you might be able to get more juice

out of the signal that way.

But it comes at a cost.

That signal is now a floating point representation,

which means it's more expensive to send out over a power.

It means you have to find different ways to compress it

that are different than what you can apply

to binary signals.

So there's a lot of different challenges

associated with these different modalities.

- So also, in terms of communication,

you're limited by the amount of data you can send.

- [Bliss] Yeah.

- And also, because you're currently using

the Bluetooth protocol, you have to batch stuff together.

But you have to also do this keeping the latency crazy low.

Like crazy low.

Anything to say about the latency?

- Yeah, this is a passion project of mine,

so I wanna build the best mouse in the world.

I don't wanna build like the, you know,

the Chevrolet Spark or whatever of electric cars.

I wanna build like the Tesla Roadster version of a mouse.

And I really do think it's quite possible

that within 5 to 10 years,

that most eSports competitions are dominated

by people with paralysis.

This is like a very real possibility for number of reasons.

One is that they'll have access to the best technology

to play video games effectively.

The second is they have the time to do so.

So those two factors together

are particularly potent for eSport competitors.

- Unless people without paralysis

are also allowed to implant.

- (laughs) Right.

- Which is, it is another way to interact

with a digital device.

And there's something to that,

if it's a fundamentally different experience,

more efficient experience.

Even if it's not like some kinda

full on high bandwidth communication,

if it's just the ability to move the mouse 10x faster,

like the bits per second,

if I can achieve a bits per second,

that 10x, what I can do with the mouse,

that's a really interesting possibility of what that can do,

especially as you get really good at it with training.

- It's definitely the case

that you have a higher ceiling performance,

because you don't have to buffer your intention

through your arm, through your muscle.

You get just, by nature of having a brain implant at all,

like 75 millisecond lead time on any action

that you're actually trying to take.

And there's some nuance to this,

like there's evidence that the motor cortex,

you can sort of plan out sequences of action

so you may not get that whole benefit all the time.

But for sort of like reaction time style games

where you just wanna,

somebody's over here, snipe 'em, that kind of thing.

You actually do have just an inherent advantage

'cause you don't need to go through muscle.

So the question is, just how much faster can you make it?

And we're already than you what you would do

if you're going through muscle from a latency point of view,

and we're in the early stage of that.

I think we can push it sort of our end-to-end

latency right now from brain spike to cursor movement,

it's about 22 milliseconds.

If you think about the best mice in the world,

the best gaming mice, that's about five milliseconds-ish

of latency, depending on how you measure.

Depending how fast your screen refreshes,

there's a lot of characteristics that matter there.

But yeah, and the rough time for like a neuron

in the brain to actually impact

your command of your hand is about 75 millisecond.

So if you look at those numbers,

you can see that we're already like competitive

and slightly faster than what you'd get

by actually moving your hand.

And this is something that, if you ask Noland about it,

when he moved the cursor for the first time,

we asked him about this.

This was something I was super curious about,

like what does it feel like when you're modulating,

a click intention or when you're trying to just move

the cursor to the right.

He said it moves before he is like actually intending it to,

which is kind of a surreal thing and something that

I would love to experience myself one day.

What is that like to have that thing just be so immediate,

so fluid that it feels like it's happening

before you're actually intending it to move.

- Yeah, I suppose we've gotten used to that latency,

that natural latency that happens.

So is the currently the bottleneck, the communication,

so like the Bluetooth communication,

what's the actual bottleneck?

I mean, there's always gonna be a bottleneck.

What's the current bottleneck?

- Yeah, a couple things.

So kind of hilariously, Bluetooth low energy protocol

has some restrictions on how fast you can communicate.

So the protocol itself establishes a standard

of the most frequent sort of updates you can send

are on the order of 7.5 milliseconds.

And as we push latency down to the level

of sort of individual spikes impacting control,

that level of resolution, that kind of protocol

is gonna become a limiting factor at some scale.

Another sort of important nuance to this

is that it's not just the Neuralink itself

that's part of this equation.

If you start pushing latency sort of below the level

of how fast screens refresh, then you have another problem.

Like you need your whole system to be able to be as reactive

as the sort of limits of what the technology can offer.

Like you need the screen like 120 hertz

just doesn't work anymore

if you're trying to have something respond

at something that's at the level of one millisecond.

- That's a really cool challenge.

I also like that for a T-shirt,

the best mouse in the world.

Tell me on the receiving end, so the decoding step,

now we figured out what the spikes are,

we got them all together,

now we're sending that over to the app.

What's the decoding step look like?

- Yeah, so maybe first, what is decoding?

I think there's probably a lot of folks listening

that just have no clue what it means

to decode brain activity.

- Actually, even if we zoom out beyond that,

what is the app?

So there's an implant that's wirelessly communicating

with any digital device that has an app installed.

So maybe can you tell me a high level what the app is,

what the software is outside of the brain?

- Yeah, so maybe working backwards from the goal,

the goal is to help someone with paralysis,

in this case Noland,

be able to navigate his computer independently.

And we think the best way to do that

is to offer them the same tools that we have

to navigate our software because we don't wanna have

to rebuild an entire software ecosystem for the brain.

At least not yet.

Maybe someday you can imagine there's UXs

that are built natively for BCI.

But in terms of what's useful for people today,

I think most people would prefer to be able to just control

mouse and keyboard inputs to all the applications

that they wanna use for their daily jobs,

for communicating with their friends, et cetera.

And so the job of the application

is really to translate this wireless stream

of brain data coming off the implant

into control of the computer.

And we do that by essentially building

a mapping from brain activity to sort of the HID inputs

to the actual hardware.

So HID is just the protocol

for communicating like input device events.

So for example, move mouse to this position

or press this key down.

And so that mapping is fundamentally

what the app is responsible for.

But there's a lot of nuance of how that mapping works

that we spend a lot of time to try to get right

and we're still in the early stages of a long journey

to figure out how to do that optimally.

So one part of that process is decoding.

So decoding is this process

of taking the statistical patterns of brain data

that's being channeled across this Bluetooth connection

to the application and turning it into,

for example, a mouse movement.

And that decoding step, you can think of it

in a couple different parts.

So similar to any machine learning problem,

there's a training step and there's an inference step.

The training step in our case is a very intricate

behavioral process where the user has

to imagine doing different actions.

So for example, they'll be presented a screen

with a cursor on it and they'll be asked

to push that cursor to the right.

Then imagine pushing that cursor to the left, push it up,

push it down, and we can basically build up a pattern

or using any sort of modern ML method,

a mapping of given this brain data

and that imagined behavior map one to the other.

And then at test time,

you take that same pattern matching system.

In our case, it's a deep neural network,

and you run it and you take the live stream

of brain data coming off their implant, you decode it

by pattern matching to what you saw at calibration time,

and you use that for a control of the computer.

Now, a couple like sort of rabbit holes

that are I think are quite interesting,

one of them has to do with how you build

that best template matching system

because there's a variety of behavioral challenges

and also debugging challenges

when you're working with someone who's paralyzed.

Because again, fundamentally,

you don't observe what they're trying to do.

You can't see them attempt to move their hand.

And so you have to figure out a way to instruct the user

to do something and validate that they're doing it correctly

such that then you can downstream, build with confidence

the mapping between the neural spikes

and the intended action.

And by doing the action correctly,

what I really mean is at this level of resolution

of what neurons are doing.

So if in ideal world, you could get a signal

of behavioral intent that is ground truth accurate

at the scale of sort of one millisecond resolution,

then with high confidence, I could build a mapping

from my neuro spikes to that behavioral intention.

But the challenge is, again,

that you don't observe what they're actually doing.

And so there's a lot of nuance

to how you build user experiences

that give you more than just sort of a course

on average correct representation

of what the user's intending to do.

If you want to build the world's best mouse,

you really want it to be as responsive as possible.

You want it to be able to do exactly

what the user's intending at every sort of step

along the way, not just on average be correct

when you're trying to move it from left to right.

And building a behavioral sort of calibration game

or sort of software experience that gives you that level

of resolution is what we spend a lot of time working.

- So the calibration process,

the interface has to encourage precision,

meaning like whatever it does,

it should be super intuitive that the next thing

the human is going to likely do is exactly that intention

that you need and only that intention.

And you don't have any feedback

except that may be speaking to you afterwards

what they actually did.

You can't, "Oh yeah." - Right.

- So that's fundamentally,

that is a really exciting UX challenge,

'cause that's all on the UX.

It's not just about being friendly or nice or usable.

- Yeah. - It's like-

- User experience is how it works.

- It's how it works. - Yeah.

- For the calibration,

and calibration, at least at this stage of Neuralink,

is like fundamental to the operation of the thing

and not just calibration

but continued calibration essentially.

- [Bliss] Yeah.

- Wow, yeah. - You said something

that I think is worth exploring there a little bit.

You said it's primarily a UX challenge,

and I think a large component of it is,

but there is also a very interesting

machine learning challenge here,

which is given some data set,

including some on average correct behavior

of asking the user to move up or move down,

move right, move left,

and given a data set of neural spikes,

is there a way to infer in some kind of semi-supervised

or entirely unsupervised way

what that high resolution version of their intention is?

And if you think about it, like there probably is

because there are enough data points in the dataset,

enough constraints on your model that there should be a way

with the right sort of formulation

to let the model figure out itself.

For example, at this millisecond,

this is exactly how hard they're pushing upwards.

And at this millisecond, this is how hard

they're trying to push upwards.

- It's really important to have very clean labels, yes.

So like the problem becomes much harder

from the machine learning perspective

if the labels are noisy.

- [Bliss] That's correct.

- And then to get the clean labels, that's a UX challenge.

- Correct, although clean labels,

I think maybe it's worth exploring what that exactly means.

I think any given labeling strategy

will have some number of assumptions it makes

about what the user's attempting to do.

Those assumptions can be formulated in a loss function,

or they can be formulated in terms of heuristics

that you might use to just try to estimate

or guesstimate what the user's trying to do.

And what really matters

is how accurate are those assumptions.

For example, you might say,

"Hey, user, push upwards

and follow the speed of this cursor,"

and your heuristic might be that they're trying to do it

exactly what that cursor's trying to do.

Another competing heuristic might be

they're actually trying to go slightly faster

at the beginning of the movement

and slightly slower at the end.

And those competing heuristics may or may not be accurate

reflections of what the user's trying to do.

Another version of the task might be,

"Hey, user, imagine moving this cursor a fixed offset.

So rather than follow the cursor,

just try to move it exactly 200 pixels to the right."

So here's the cursor, here's the target.

Okay, cursor disappears.

Try to move that now invisible cursor

200 pixels to the right.

And the assumption in that case would be that the user

can actually modulate correctly that position offset,

but that position offset assumption

might be a weaker assumption, and therefore,

potentially you can make it more accurate

than these heuristics that are trying to guesstimate

at each millisecond what the user's trying to do.

So you can imagine different tasks

that make different assumptions about the nature

of the user intention and those assumptions being correct

is what I would think of as a clean label.

- For that step, what are we supposed to be visualizing?

There's a cursor and you wanna move that cursor

to the right or the left or up and down

or maybe move them by a certain offset.

So that's one way, is that the best way to do calibration?

So for example, an alternative crazy way

that probably is playing a role here

is a game like Webgrid,

where you're just getting a very large amount of data,

the person playing a game,

where if they're in a state of flow,

maybe you can get clean signal as a side effect.

- [Bliss] Yep.

- Is that not an effective way for initial calibration?

- Yeah, great question.

There's a lot to unpack there.

So the first thing I would draw a distinction

between a sort of open loop, first closed loop.

So open loop, what I mean by that

is the user is sort of going from zero to one.

They have no model at all,

and they're trying to get to the place

where they have some level of control at all.

In that setup, you really need to have some task

that gives the user a hint of what you want them to do

such that you can build this mapping again

from brain data to output.

Then once they have a model, you could imagine them

using that model and actually adapting to it

and figuring out the right way to use it themself

and then retraining on that data

to give you sort of a boost in performance.

There's a lot of challenges associated

with both of these techniques and we can sort of rabbit hole

into both of 'em, if you're interested.

But the sort of challenge with the open loop task

is that the user themself doesn't get

proprioceptive feedback about what they're doing.

They don't necessarily perceive themself

or feel the mouse under their hand

when they're using an open,

when they're trying to do an open loop calibration.

They're being asked to perform something.

Like imagine if you sort of had your whole right arm numbed

and you stuck it in a box and you couldn't see it.

So you had no visual feedback

and you had no proprioceptive feedback

about what the position or activity of your arm was.

And now you're asked, okay, given this thing on the screen

that's moving from left to right, match that speed.

And you basically can try your best to invoke

whatever that imagined action is in your brain

that's moving the cursor from left to right.

But in any situation, you're gonna be inaccurate

and maybe inconsistent in how you do that task.

And so that's sort of the fundamental

challenge of open loop.

The challenge with closed loop is that,

once the user's given a model,

and they're able to start moving the mouse on their own,

they're going to very naturally adapt to that model.

And that co-adaptation between the model learning,

what they're doing, and the user learning

how to use the model may not find you the best

sort of global minima.

And maybe that your first model was noisy in some ways

or maybe just had some like quirk,

like if there's some like part of the data distribution

that didn't cover super well, and the user now figures out

because they're a brilliant user like Noland.

They figured out the right sequence of imagined motions

or the right angle they have to hold their hand at

to get it to work.

And they'll get it to work great,

but then the next day, they come back to their device

and maybe they don't remember exactly

all the tricks that they used the previous day.

And so there's a complicated sort of feedback cycle here

that can emerge and can make it

a very, very difficult debugging process.

- Okay, there's a lot of really fascinating things there.

Yeah, actually, just to stay on the closed loop,

I've seen situations, this actually happened

watching psychology grad students.

They use piece of software when they don't know

how to program themselves.

They use piece of software that somebody else wrote,

and it has a bunch of bugs.

And they figure out like,

and they've been using it for years.

They figured out ways to work around it.

Oh, that just happens.

Like nobody like considers maybe we should fix this.

They just adapt.

And that's a really interesting notion,

that we were really good at adapting, but you need to still,

that might not be the optimal.

Okay, so how do you solve that problem?

Do you have to restart from scratch

every once in a while kind of thing?

- Yeah, it's a good question.

First and foremost, I would say

this is not a solved problem.

And for anyone who's listening in academia

who works on BCIs, I would also say this is not a problem

that's solved by simply scaling channel account.

Maybe that can help and you can get

sort of richer covariance structures

that you can use to exploit

when trying to come up with good labeling strategies.

But if you're interested in problems,

that aren't gonna be solved inherently

by scaling channel account, this is one of them.

Yeah, so how do you solve it?

It's not a solved problem.

That's the first thing I wanna make sure gets across.

The second thing is, any solution that involves closed loop

is going to become a very difficult debugging problem.

And one of my sort of general heuristics for choosing

what prompts to tackle is that you wanna choose the one

that's gonna be the easiest to debug,

'cause if you can do that,

even if the ceiling is lower, you're gonna be able

to move faster because you have a tighter

iteration loop debugging the problem.

And in the open loop setting,

there's not a feedback cycle debug

with the user in the loop.

And so there's some reason to think

that that should be an easier debugging problem.

The other thing that's worth understanding

is that even in a closed loop setting,

there's no special software magic of how to infer

what the user is truly attempting to do.

In the closed loop setting, although they're moving

the cursor on the screen, they may be attempting something

different than what your model is outputting.

So what the model is outputting is not a signal

that you can use to retrain if you want to be able

to improve the model further.

You still have this very complicated guesstimation

or unsupervised problem of figuring out

what is the true user intention underlying that signal.

And so the open loop problem has the nice property

of being easy to debug.

And the second nice property of,

it has all the same information and content

as the closed loop scenario.

Another thing I wanna mention and call out

is that this problem doesn't need to be solved

in order to give useful control to people.

Even today with the solutions we have now

and that academia has built up over decades,

the level of control that can be given

to a user today is quite useful.

It doesn't need to be solved

to get to that level of control.

But again, I wanna build the world's best mouse.

I wanna make it so good

that it's not even a question that you want it.

And to build the world's best mouse, the superhuman version,

you really need to nail that problem.

And a couple maybe details of previous studies

that we've done internally that I think are very interesting

to understand when thinking about how to solve this problem,

the first is that even when you have ground truth data

of what the user's trying to do,

and you can get this with an able-bodied monkey,

a monkey that has a Neuralink device implanted

and moving a mouse to control a computer,

even with that ground truth dataset,

it turns out that the optimal thing to predict

to produce high performance BCI

is not just the direct control of the mouse.

You can imagine building dataset

of what's going on in the brain

and what is the mouse exactly doing on the table.

And it turns out that if you build the mapping

from neuro spikes to predict exactly

what the mouse is doing, that model will perform

worse than a model that is trained to predict

sort of higher level assumptions

about what the user might be trying to do.

For example, assuming that the monkey is trying

to go in a straight line to the target,

it turns out that making those assumptions

is actually more effective

in producing a model than actually predicting

the underlying hand movement.

- So the intention,

not like the physical movement or whatever.

- Yeah. - There's obviously

a really strong correlation between the two,

but the intention is a more powerful thing to be chasing.

- [Bliss] Right.

- Well, that's also super interesting.

I mean, the intention itself is fascinating,

because yes, with the BCI here,

in this case, with a digital telepathy,

you're acting on the intention, not the action,

which is why there's an experience

of like feeling like it's happening

before you meant for it to happen.

That is so cool.

And that is why you could achieve

like superhuman performance problem

in terms of the control of the mouse.

So for open loop, just to clarify,

so whenever the person is tasked

to like move the mouse to the right,

you said there's not feedback

so they don't get to get that satisfaction

of like actually getting it to move, right?

- You could imagine giving the user feedback on a screen,

but it's difficult, because at this point,

you don't know what they're attempting to do.

So what can you show them that would basically give them

a signal of I'm doing this correctly or not correctly.

So let's take this very specific example.

Like maybe your calibration task looks like you're trying

to move the cursor a certain position offset.

So your instructions to the user are,

"Hey, the cursor's here.

Now, when the cursor disappears,

imagine moving it 200 pixels from where it was

to the right to be over this target."

In that kind of scenario, you could imagine coming up

with some sort of consistency metric that you could display

to the user of, "Okay, I know what the spike train

looks like on average when you do this action to the right.

Maybe I can produce some sort of probabilistic estimate

of how likely is that to be the action you took

given the latest trial or trajectory that you imagined."

And that could give the user some sort of feedback

of how consistent are they across different trials.

You could also imagine that if the user is prompted

with that kind of consistency metric,

that maybe they just become

more behaviorally engaged to begin with

because the task is kind of boring

when you don't have any feedback at all.

And so there may be benefits to the user experience

of showing something on the screen,

even if it's not accurate, just because it keeps

the user motivated to try to increase

that number or push it upwards.

- So there's a psychology element here.

- Yeah, absolutely.

- And again, all of that is UX challenge.

How much signal drift is there, hour to hour, day to day,

week to week, month to month?

How often do you have to recalibrate

because of the signal drift?

- Yeah, so this is a problem we've worked on,

both with NHP, non-human primates, before our clinical trial

and then also with Noland during the clinical trial.

Maybe the first thing that's worth stating

is what the goal is here.

So the goal is really to enable the user

to have a plug and play experience

where I guess they don't have to plug anything in,

but a play experience where they can use

the device whenever they want to, however they want to.

And that's really what we're aiming for.

And so there can be a set of solutions

that get to that state without considering

this non-stationary problem.

So maybe the first solution here that's important

is that they can recalibrate whenever they want.

This is something that Noland has the ability to do today.

So he can recalibrate the system

at 2:00 AM in the middle of the night,

without his caretaker or parents or friends around

to help push a button for him.

The other important part of the solution is that

when you have a good model calibrated,

that you can continue using that

without needing to recalibrate it.

So how often he has to do this recalibration today

depends really on his appetite for performance.

We observe sort of a degradation through time

of how well any individual model works,

but this can be mitigated behaviorally

by the user adapting their control strategy.

It can also be mitigated through a combination

of sort of software features that we provide to the user.

For example, we let the user adjust exactly

how fast the cursor is moving.

We call that the gain, for example,

the gain of how fast the cursor reacts

to any given input intention.

They can also adjust the smoothing,

how smooth the output of that cursor intention actually is.

They can also adjust the friction,

which is how easy is it to stop and hold still.

And all these software tools allow the user a great deal

of flexibility and troubleshooting mechanisms

to be able to solve this problem

for themselves. - By the way,

all of this is done by looking

to the right side of the screen,

selecting the mixer, and the mixer you have-

- It's like DJ mode.

DJ mode for your VCI.

- So I mean, it's a really well done interface.

It's really, really well done.

And so yeah, there's that bias that there's a cursor drift

that Noland talked about in a stream,

although he said that you guys were just playing around

with it with him and then constantly improving.

So that could have been just a snapshot

of that particular moment, a particular day.

But he said that there was this cursor drift

and this bias that could be removed by him,

I guess looking to the right side of the screen,

the left side of the screen to kind of adjust the bias.

- Yeah, yeah. - That's one interface

action I guess to adjust the bias.

- Yeah, so this is actually an idea

that comes out of academia.

There are some prior work with sort of BrainGate

clinical trial participants where they pioneered

this idea of bias correction.

The way we've done it I think is,

yeah, it's very prototized,

very beautiful user experience

where the user can essentially

flash the cursor over to the side of the screen

and it opens up a window where they can actually

sort of adjust or tune exactly the bias of the cursor.

So bias maybe, for people who aren't familiar,

is just sort of what is the default motion

of the cursor if you're imagining nothing.

And it turns out that that's one of the first

sort of qualia of the cursor control experience

that's impacted by neuro non-stationarity.

- Quality off the cursor experience.

- I don't know how else to describe it.

I'm not the guy moving-

- It's very poetic, I love it.

The quality of the cursor experience.

Yeah, I mean, it sounds poetic

but it is deeply true.

There is an experience, when it works well,

it is a joyful, a really pleasant experience.

And when it doesn't work well,

it's a very frustrating experience.

That's actually the art of UX.

It's like you have the possibility to frustrate people

or the possibility to give them joy,

- And at the end of the day, it really is truly the case

that UX is how the thing works.

And so it's not just like what's showing on the screen,

it's also what control surfaces

does a decode provide the user?

Like we want them to feel like they're in the F1 card,

not like some like minivan, right?

And that really truly is how we think about it.

Noland himself is an F1 fan,

so we refer to ourself as a pit crew.

He really is truly the F1 driver,

and there's different control surfaces

that different kinds of cars and airplanes provide the user.

And we take a lot of inspiration from that when designing

how the cursor should behave..

And what maybe one nuance of this is,

even details like when you move a mouse

on a MacBook track pad, the sort of response curve

of how that input that you give the track pad

translates to cursor movement is different

than how it works with a mouse.

When you move on the track pad,

there's a different response function,

a different curve to how much a movement translates to input

to the computer than when you do it physically with a mouse.

And that's because somebody sat down a long time ago

when they're designed the initial input systems

to any computer and they thought through exactly

how it feels to use these different systems.

And now we're designing sort of the next generation

of this input system to a computer, which is entirely done

via the brain, and there's no proprioceptive feedback.

Again, you don't feel the mouse in your hand,

you don't feel the keys under your fingertips,

and you want a control surface that still makes it easy

and intuitive for the user to understand

the state of the system

and how to achieve what they wanna achieve.

And ultimately, the end goal is that that UX is completely,

it fades into the background.

It becomes something that's so natural and intuitive

that it's subconscious to the user,

and they just should feel like they have basically

direct control over the cursor.

It just does what they want it to do.

They're not thinking about the implementation

of how to make it do what they want it to do.

It's just doing what they want it to do.

- Is there some kind of things

along the lines of like Fitts' law

where you should move the mouse in a certain kind of way

that maximizes your chance to hit the target?

I don't even know what I'm asking,

but I'm hoping the intention of my question

will land on a profound answer.

No, is there some kind of understanding

of the laws of UX when it comes to the context

of somebody using their brain to control it?

Like that's different than actual with a mouse?

- I think we're in the early stages

of discovering those laws, so I wouldn't claim

to have solved that problem yet.

But there's definitely some things we've learned

that make it easier for the user to get stuff done.

And it's pretty straightforward when you verbalize it,

but it takes a while to actually get to that point

when you're in the process of debugging

the stuff in the trenches.

One of those things is that any machine learning system

you build has some number of errors,

and it matters how those errors translate

to the downstream user experience.

For example, if you're developing a search algorithm

in your photos, if you search for your friend Joe

and it pulls up a photo of your friend Josephine,

maybe that's not a big deal because the cost of an error

is not that high.

In a different scenario

where you're trying to detect insurance fraud

or something like this and you're directly sending someone

to court because of some machine learning model output,

then the errors make a lot more sense to be careful about.

You wanna be very thoughtful about

how those errors translate to downstream effects.

The same is true in BCI.

So for example, if you're building a model

that's decoding a velocity output from the brain

versus an output where you're trying to modulate

the left click, for example.

These have sort of different trade-offs

of how precise you need to be

before it becomes useful to the end user.

For velocity, it's okay to be on average correct,

because the output of the model is integrated through time.

So if the user's trying to click at position A,

and they're currently in position B,

they're trying to navigate over time

to get between those two points.

And as long as the output of the model

is on average correct, they can sort of steer it

through time with the user control loop in the mix.

They can get to the point they wanna get to.

The same is not true of a click.

For a click, you're performing it almost instantly

at the scale of neurons firing.

And so you want to be very sure that that click is correct

because a false click can be very destructive to the user.

They might accidentally close the tab that they're trying

to do something and lose all their progress.

They might accidentally like

hit some Send button on some text

that it's only like half-composed and reads funny after.

So there's different sort of cost functions

associated with errors in this space.

And part of the UX design is understanding

how to build a solution that is when it's wrong,

still useful to the end user.

- It's so fascinating that assigning cost

to every action when an error occurs.

So every action, if an error occurs, has a certain cost,

and incorporating that into how you interpret the intention,

mapping it to the action is really important.

I didn't quite until you said it realize

there's a cost to like sending the text early.

It's like very expensive cost. - Yeah.

It's super annoying if you accidentally,

like if you're a cursor, imagine if your cursor misclick

every once in a while, that's like super obnoxious.

And the worst part of it is, usually,

when the user's trying to click, they're also holding still

because they're over the target they wanna hit

and they're getting ready to click,

which means that in the data sets that we build, on average,

it's the case that sort of low speeds

or desire to hold still.

It's correlated with when the user's attempting to click.

- Wow, that is really fascinating.

- It's also not the case.

People think that, "Oh, a click is a binary signal.

This must be super easy to decode."

Well, yes it is,

but the bar is so much higher for it

to become a useful thing for the user,

and there's ways to solve this.

I mean, you can sort of take the compound approach of,

well, let's just give the like,

let's take five seconds to click.

Let's take a huge window of time

so it can be very confident about the answer.

But again, world's best mouse.

The world's best mouse doesn't take a second to click

or 500 milliseconds to click.

It takes five milliseconds to click or less.

And so if you're aiming for that kind of high bar,

then you really wanna solve the underlying problem.

- So maybe this is a good place to ask about

how to measure performance, this whole bits per second.

Can you like explain what you mean by that?

Maybe a good place to start

is to talk about Webgrid as a game,

as a good illustration of the measurement of performance.

- Yeah, maybe I'll take one zoom out step there,

which is just explaining why we care to measure this at all.

So again, our goal is to provide the user the ability

to control the computer as well as I can

and hopefully better.

And that means that they can do it

at the same speed as what I can do.

It means that they have access

to all the same functionality that I have,

including all those little details like command tab,

command space, all this stuff.

They need to be able to do it with their brain

and with the same level of reliability

as what I can do with my muscles.

And that's a high bar.

And so we intend to measure and quantify

every aspect of that to understand

how we're progressing towards that goal.

There's many ways to measure BPS, by the way.

This isn't the only way, but we present the user

a creative targets, and basically,

we compute a score which is dependent on how fast

and accurate they can select,

and then how small are the targets.

And the more targets that are on the screen,

the smaller they are,

the more information you present per click.

And so if you think about it from information theory

point of view, you can communicate

across different information theoretic channels.

And one such channel is a typing interface

you could imagine that's built out of a grid,

just like a software keyboard on the screen.

And bits per second is a measure that's computed

by taking the log of the number of targets on the screen.

You can subtract one if you care to model a keyboard

because you have to subtract one

for the Delete key on the keyboard,

but log of the number of targets on the screen

times the number of correct selections minus incorrect,

divided by some time window.

For example, 60 seconds.

And that's sort of the standard way

to measure a cursor control task in academia.

And all credit in the world goes to this great professor,

Dr. Shenoy of Stanford who came up with that task.

And he's also one of my inspirations for being in the field.

So all the credit in the world to him

for coming up with a standardized metric

to facilitate this kind of bragging rights that we have now,

to say that Noland is the best in the world

at this task with his BCI.

It's very important for progress

that you have standardized metrics that people can compare

across different techniques and approaches.

How well does this do?

So yeah, big kudos to him and to all the team at Stanford.

Yeah, so for Noland, and for me playing this task,

there's also different modes

that you can configure this task.

So the Webgrid task can be presented

as just sort of a left click on the screen,

or you could have targets that you just dwell over,

or you could have targets that you left, right click on.

You could have targets that are left, right click,

middle click, scrolling, clicking, and dragging.

You could do all sorts of things

within this general framework.

But the simplest, purest form is just blue targets

show up on the screen.

Blue means left click.

That's the simplest form of the game.

And the sort of prior records here in academic work

and at Neuralink internally

with sort of NPS have all been matched

or beaten by Noland with his Neuralink device.

So sort of prior to Neuralink, the sort of world record

for a human using device is somewhere between

4.2 to 4.6 BPS, depending on exactly

what paper you read and how you interpret it.

Noland's current record is 8.5 BPS.

And again, this sort of median

Neuralink performance is 10 BPS.

So you can think of it roughly as he's 85%

the level of control of a median Neuralinker

using their cursor to select blue targets on the screen.

And yeah, I think there's a very interesting journey ahead

to get us to that same level of 10 BPS performance.

It's not the case that sort of the tricks that got us

from four to six BPS, and then six to eight BPS

are gonna be the ones that get us from eight to 10.

And in my view, the core challenge here

is really the labeling problem.

It's how do you understand at a very, very fine resolution

what the user's attempting to do.

And yeah, I highly encourage folks

in academia to work on this problem.

- What's the journey with Noland on that quest

of increasing the BPS on Webgrid?

In March, you said that he selected

89,285 targets in Webgrid.

- Yep. - So he loves this game.

He's really serious about improving

his performance in this game.

So what is that journey of trying to figure out

how to improve that performance?

How much can that be done on the decoding side?

How much can that be done on the calibration side?

How much can that be done on the Noland side

of like figuring out how to convey

his intention more cleanly?

- Yeah, no, this is a great question.

So in my view, one of the primary reasons why

Noland's performance is so good is because of Noland.

Noland is extremely focused and very energetic.

He'll play Webgrid sometimes for like four hours

in the middle of the night.

Like from 2:00 AM to 6:00 AM, he'll be playing Webgrid,

just because he wants to push it

to the limits of what he can do.

And this is not us like asking him to do that.

I wanna be clear.

Like we're not saying,

"Hey, you should play Webgrid tonight."

We just gave him the game as part of our research,

and he is able to play it independently

and practice whenever he wants,

and he really pushes hard to push it,

the technology's the absolute limit.

And he views that as like his job really

to make us be the bottleneck.

And boy, has he done that well.

And so the first thing to acknowledge is that

he's extremely motivated to make this work.

I've also had the privilege to meet other

clinical trial participants from BrainGate and other trials,

and they very much shared the same attitude

of like they view this as their life's work

to advance the technology as much as they can.

And if that means selecting targets on the screen

for four hours from 2:00 AM to 6:00 AM, then so be it.

And there's something extremely admirable about that

that's worth calling out.

Okay, so then how do you sort of get from

where he started, which is no cursor control to a BPS?

So I mean, when he started,

there's a huge amount of learning to do

on his side and our side to figure out

what's the most intuitive control for him.

And the most intuitive control for him is sort of,

you have to find the set intersection

of what do we have this signal to decode.

So we don't pick up every single neuron in the motor cortex,

which means we don't have representation

for every part of the body.

So there may be some signals that we have better

sort of decode performance on than others.

For example, on his left hand, we have a lot of difficulty

distinguishing his left ring finger

from his left middle finger.

But on his right hand, we have a good control

and good modulation detected from the neurons

that we're able to record for his pinky,

his thumb, and his index finger.

So you can imagine how these different

sub spaces of modulated activity

intersect with what's the most intuitive for him.

And this has evolved over time.

So once we gave him the ability

to calibrate models on his own,

he was able to go and explore various different ways

to imagine controlling the cursor.

For example, he could imagine controlling the cursor

by wiggling his wrist side to side,

or by moving his entire arm.

I think at one point, he did his feet.

He tried like a whole bunch of stuff to explore the space

of what is the most natural way for him

to control the cursor.

That at the same time, it's easy for us to decode rules.

- Just to clarify, it's through the body mapping procedure

that you're able to figure out which finger he can move?

- Yes, yeah, that's one way to do it.

Maybe one nuance of when he's doing it,

he can imagine many more things

than we represent in that visual on the screen.

So we show him sort of abstractly, "Here's a cursor.

You figure out what works the best for you."

And we obviously have hints about what will work best

from that body mapping procedure

of we know that this particular action,

we can represent well.

But it's really up to him to go and explore

and figure out what works the best.

- But at which point does he no longer visualize

the movement of his body and is just visualizing

the movement of the cursor?

- Yeah. - How quickly does he go from,

how quickly does he get there?

- So this happened on a Tuesday.

I remember this day very clearly,

because at some point during the day,

it looked like he wasn't doing super well.

It looked like the model wasn't performing super well

and he was like getting distracted.

But he actually, it wasn't the case.

Like what actually happened was he was trying something new

where he was just controlling the cursor.

So he wasn't imagining moving his hand anymore.

He was just imagining, I don't know what it is,

some like abstract intention

to move the cursor on the screen.

And I cannot tell you what the difference

between those two things are.

I really truly cannot.

He's tried to explain it to me before.

I cannot give a first person account of what that's like.

But the expletives that he uttered in that moment

were enough to suggest

that it was a very qualitatively different experience

for him to just have direct neural control over a cursor.

- I wonder if there's a way through UX

to encourage a human being to discover that,

because he discovered it, like you said to me,

that he's a pioneer.

So he discovered that on his own through all of this,

the process of trying to move the cursor

with different kinds of intentions.

But that is clearly a really powerful thing to arrive at,

which is to let go of trying to control the fingers

and the hand and control the actual

digital device with your mind.

- That's right, UX is how it works.