The following is a conversation with Jim Keller,
his second time in the podcast.
Jim is a legendary microprocessor architect
and is widely seen as one of the greatest
engineering minds of the computing age.
In a peculiar twist of space-time in our simulation,
Jim is also a brother-in-law of Jordan Peterson.
We talk about this and about computing,
artificial intelligence, consciousness, and life.
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As a side note, let me say that Jim is someone
who on a personal level inspired me to be myself.
There was something in his words on and off the mic
or perhaps that he even paid attention to me at all
that almost told me, you're all right, kid.
A kind of pat on the back that can make the difference
between a mind that flourishes
and a mind that is broken down
by the cynicism of the world.
So I guess that's just my brief few words
of thank you to Jim and in general,
gratitude for the people who have given me a chance
on this podcast and my work and in life.
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on Twitter, Alex Friedman.
And now here's my conversation with Jim Keller.
What's the value and effectiveness of theory
versus engineering, this dichotomy,
in building good software or hardware systems?
Well, it's good designs both.
I guess that's pretty obvious.
But engineering, do you mean, you know,
reduction of practice of known methods?
And then science is the pursuit of discovering things
that people don't understand
or solving unknown problems.
Definitions are interesting here,
but I was thinking more in theory,
constructing models that kind of generalize
about how things work.
And engineering is actually building stuff,
the pragmatic like, okay, we have these nice models,
but how do we actually get things to work?
Maybe economics is a nice example.
Like economists have all these models
of how the economy works
and how different policies will have an effect.
But then there's the actual, okay,
let's call it engineering
of like actually deploying the policies.
So computer design is almost all engineering
and reduction of practice of known methods.
Now, because of the complexity of the computers we build,
you know, you could think you're,
well, we'll just go write some code
and then we'll verify it and then we'll put it together.
And then you find out that the combination
of all that stuff is complicated.
And then you have to be inventive
to figure out how to do it, right?
So that's, that's definitely happens a lot.
And then every so often some big idea happens,
but it might be one person.
And that idea is in what,
in the space of engineering or is it in the space?
Well, I'll give you an example.
So one of the limits of computer performance
is branch prediction.
So, and there's a whole bunch of ideas
about how good you could predict the branch.
And people said, there's a limit to it.
It's an asymptotic curve.
And somebody came up with a better way
to do branch prediction.
That was a lot better.
And he published a paper on it
and every computer in the world now uses it.
And it was one idea.
So the engineers who build branch prediction hardware
were happy to drop the one kind of training array
and put it in another one.
So it was, it was a real idea.
And branch prediction is,
is one of the key problems underlying all of sort of
the lowest level of software it boils down to branch prediction.
It boils down to uncertainty.
Computers are limited by, you know,
single thread computers limited by two things.
The predictability of the path of the branches
and the predictability of the locality of data.
So we have predictors that now predict
both of those pretty well.
So memory is, you know, a couple hundred cycles away.
Local cash is a couple of cycles away.
When you're executing fast,
virtually all the data has to be in the local cash.
So a simple program says, you know,
add one to every element in an array.
It's really easy to see what the stream of data will be.
But you might have a more complicated program that's, you know,
says get a, get an element of this array,
look at something, make a decision,
go get another element.
It's kind of random.
And you can think that's really unpredictable.
And then you make this big predictor
that looks at this kind of pattern.
And you realize, well, if you get this data
and this data, then you probably want that one.
And if you get this one and this one
and this one, you probably want that one.
And is that theory or is that engineering?
Like the paper that was written, was it asymptotic
kind of discussion?
Or is it more like, here's a hack that works well?
It's a little bit of both.
Like there's information theory in it, I think somewhere.
Okay.
It's actually trying to prove some kind of stuff.
But once you know the method implementing it,
it is an engineering problem.
Now there's a flip side of this,
which is in a big design team,
what percentage of people think their plan
or their life's work is engineering
versus inventing things.
So lots of companies will reward you for filing patents.
Some many big companies get stuck
because to get promoted,
you have to come up with something new.
And then what happens is everybody's trying
to do some random new thing, 99% of which doesn't matter.
And the basics get neglected.
And, or they get to, there's a dichotomy, they think,
like the cell library and the basic CAD tools,
or basic software validation methods.
That's simple stuff.
They wanna work on the exciting stuff.
And then they spend lots of time trying to figure out
how to patent something.
And that's mostly useless.
But the breakthroughs are on simple stuff.
No, no, you have to do the simple stuff really well.
If you're building a building out of bricks,
you want great bricks.
So you go to two places to sell bricks.
So one guy says, yeah, they're over there in an ugly pile.
And the other guy is like lovingly tells you
about the 50 kinds of bricks and how hard they are
and how beautiful they are and how square they are.
And, you know, which one are you gonna buy bricks from?
Which is gonna make a better house.
So you're talking about the craftsman,
the person who understands bricks,
who loves bricks, who loves the variety.
That's a good word.
You know, good engineering is great craftsmanship.
And when you start thinking engineering is about invention
and you set up a system that rewards invention,
the craftsmanship gets neglected.
Okay, so maybe one perspective is the theory.
The science overemphasizes invention
and engineering emphasizes craftsmanship.
And therefore, like, so if you,
it doesn't matter what you do, theory.
But everybody does, like read the tech rags.
They're always talking about some breakthrough
or innovation and everybody thinks
that's the most important thing.
But the number of innovative ideas
is actually relatively low.
We need them, right?
And innovation creates a whole new opportunity.
Like when some guy invented the internet, right?
Like that was a big thing.
The million people that wrote software against that
were mostly doing engineering and software writing.
So the elaboration of that idea was huge.
I don't know if you know Brandon and I,
he wrote JavaScript in 10 days.
That's an interesting story.
It makes me wonder, and it was, you know,
famously for many years considered
to be a pretty crappy programming language.
Still is perhaps.
It's been improving sort of consistently.
But the interesting thing about that guy is,
you know, he doesn't get any awards.
You don't get a Nobel Prize or a Fields Medal
or a crappy piece of, you know, software code.
That is currently the number one programming language
in the world and runs now is increasingly running
the back end of the internet.
Well, does he know why everybody uses it?
Like that would be an interesting thing.
Was it the right thing at the right time?
Cause like when stuff like JavaScript came out,
like there was a move from, you know,
writing C programs and C++ to,
let's call what they call managed code frameworks
where you write simple code, it might be interpreted,
it has lots of libraries, productivity is high,
and you don't have to be an expert.
So, you know, Java was supposed to solve
all the world's problems, it was complicated.
JavaScript came out, you know,
after a bunch of other scripting languages.
I'm not an expert on it, but was it the right thing
at the right time or was there something, you know,
clever cause he wasn't the only one.
There's a few elements.
And maybe if you figured out what it was.
No, I didn't.
Then he'd get a prize.
Like that destructive theory.
Yeah, you know, maybe his problem is he hasn't defined this.
Or he just needs a good promoter.
Well, I think there was a bunch of blog posts written
about it, which is like, wrong is right,
which is like doing the crappy thing fast,
just like hacking together the thing
that answers some of the needs.
And then iterating over time, listening to developers,
like listening to people who actually use the thing.
This is something you can do more in software.
But the right time, like you have to sense,
you have to have a good instinct
of when is the right time for the right tool
and make it super simple and just get it out there.
The problem is, this is true with hardware.
This is less true with software.
There's a backward compatibility
that just drags behind you as, you know,
as you try to fix all the mistakes of the past.
But the timing was good.
There's something about that.
And it wasn't accidental.
You have to like give yourself over to the,
you have to have this like broad sense
of what's needed now, both scientifically
and like the community.
And just like this, it was obvious that there was no,
the interesting thing about JavaScript
is everything that ran in the browser at the time,
like Java and I think other like scheme,
other programming languages,
they were all in a separate external container.
And then JavaScript was literally just injected
into the webpage.
It was the dumbest possible thing
running in the same thread as everything else.
And like, it was inserted as a comment.
So JavaScript code is inserted as a comment in the HTML code.
And it was, I mean, it's either genius or super dumb,
but it's like-
Right, so it had no apparatus for like a virtual machine.
Like a container, it just executed in the framework
of the program that's already running.
And it was-
Yeah, that's cool.
And then because something about that accessibility,
the ease of its use resulted in then developers innovating
of how to actually use it.
I mean, I don't even know what to make of that,
but it does seem to echo across different software,
like stories of different software.
PHP has the same story, really crappy language.
They just took over the world.
Well, let's have a joke that the random length instructions,
variable length instructions, that's always one,
even though they're obviously worse.
Like nobody knows why x86 is,
or you'd be the worst architecture, you know,
on the planet is one of the most positive ones.
Well, I mean, isn't that also the story of risk
versus I mean, is that a simplicity?
There's something about simplicity
that us in this evolutionary process is valued.
If it's simple, it spreads faster, it seems like.
Or is that not always true?
That's not always true.
Yeah, it could be simple as good, but too simple as bad.
So why did risk win, you think, so far?
Did risk win?
In the long archivist tree, maybe not.
We don't know.
So who's gonna win?
What's risk, what's syscon, who's gonna win in that space?
And these instruction sets?
AISOffer's gonna win, but there'll be little computers
that run little programs like normal.
All over the place, but we're going through
another transformation, so.
But you think instruction sets underneath it all will change?
Yeah, they evolve slowly.
They don't matter very much.
They don't matter very much, okay.
I mean, the limits of performance are, you know,
predictability of instructions and data.
I mean, that's the big thing.
And then the usability of it is some, you know,
quality of design, quality of tools, availability.
Like right now, X86 is proprietary with Intel and AMD,
but they can change it any way they want independently.
Right, ARM is proprietary to ARM,
and they won't let anybody else change it.
So it's like a sole point.
And RISC-5 is open source, so anybody can change it,
which is super cool, but that also might mean
it gets changed in too many random ways
that there's no common subset of it that people can use.
Do you like open or do you like closed?
Like, if you were to bet all your money
on one or the other, RISC-5 versus it?
No idea.
It's case dependent?
Well, X86, oddly enough, when Intel first started
developing it, they licensed it like seven people.
So it was the open architecture.
And then they moved faster than others
and also bought one or two of them.
But there was seven different people making X86,
because at the time there was 6502 and Z80s and 8086.
And you could argue everybody thought
Z80 was the better instruction set,
but that was proprietary to one place.
Oh, and the 6800.
So there's like four or five different microprocessors.
Intel went open, got the market share,
because people felt like they had multiple sources from it,
and then over time it narrowed down to two players.
So why, you as a historian, why did Intel win
for so long with their processors?
I mean, I mean-
They were great.
Their process development was great.
So it's just looking back to JavaScript and Brandenike
is Microsoft and Netscape and all these internet browsers.
Microsoft won the browser game
because they aggressively stole other people's ideas.
Like right after they did it.
You know, I don't know if Intel
was stealing other people's ideas.
They started making-
In a good way.
Stealing them good way just to clarify.
They started making RAMs, random access memories.
And then at the time when the Japanese manufacturers came up,
you know, they were getting out competed on that
and they pivoted the microprocessors
and they made the first, you know,
integrated microprocessor program.
It was the 4004 or something.
Who was behind that pivot?
That's a hell of a pivot.
Andy Grove.
And he was great.
That's a hell of a pivot.
And then they led semiconductor industry.
Like they were just a little company, IBM,
all kinds of big companies had boatloads of money
and they out-innovated everybody.
Out-of-innovated.
Okay.
So it's not like marketing.
It's not any other stuff.
Their processor designs were pretty good.
I think the, you know, Core 2 was probably the first one
I thought was great.
It was a really fast processor
and then Haswell was great.
What makes a great processor?
Oh, if you just look at its performance
versus everybody else, it's, you know,
the size of it, the, you know, usability of it.
So it's not specific, some kind of element
that makes you beautiful.
It's just like literally just raw performance.
Is that how you think of bioprocessors?
It's just like raw performance?
Of course.
It's like a horse race.
The fastest one wins.
Now.
You don't care how.
Well, there's the fastest in the environment.
Like for years, you made the fastest one you could
and then people started to have power limits.
So then you made the fastest at the right PowerPoint.
And then, and then when we started doing multiprocessors,
like if you could scale your processors more
than the other guy, you could be 10% faster
on like a single thread, but you have more threads.
So there's lots of variability.
And then ARM really explored, like, you know,
they have the A series and the R series
and the M series, like a family of processors
for all these different design points
from like unbelievably small and simple.
And so then when you're doing the design,
it's sort of like this big palette of CPUs.
Like they're the only ones with a credible,
you know, top to bottom palette.
What do you mean a credible top to bottom?
Well, there's people that make microcontrollers
that are small, but they don't have a fast one.
There's people who make fast processors,
but don't have a little, a medium one or a small one.
Is that hard to do that full palette?
That seems like a...
Yeah, it's a lot of different.
So what's the difference in the ARM folks in Intel,
in terms of the way they're approaching this problem?
Well, Intel, almost all their processors or designs
were, you know, very custom high-end,
you know, for the last 15, 20 years.
It's the fastest horse possible in one horse.
Yeah, and the architecture, they're really good,
but the company itself was fairly insular
to what's going on in the industry with CAD tools and stuff.
And there's this debate about custom design versus synthesis.
And how do you approach that?
I'd say Intel was slow on the cutting
to synthesize processors.
ARM came in from the bottom and they generated IP,
which went to all kinds of customers.
So they had very little say
on how the customer implemented their IP.
So ARM is super friendly to the synthesis IP environment.
Whereas Intel said,
we're going to make this great client chip or server chip
with our own CAD tools, with our own process,
with our own, you know, other supporting IP
and everything only works with our stuff.
So is that, is ARM winning the mobile platform space
in terms of process?
Yeah.
And so in that way you're describing is why they're winning.
Well, they had lots of people doing
lots of different experiments.
So they controlled the processor architecture and IP,
but they let people put in lots of different chips.
And there was a lot of variability in what happened there.
Whereas Intel, when they made their mobile,
there were 4A in the mobile,
they had one team doing one part, right?
So it wasn't 10 experiments.
And then their mindset was PC mindset,
Microsoft software mindset,
and that brought a whole bunch of things along
that the mobile world, the embedded world don't do.
You think it was possible for Intel to pivot hard
and win the mobile market?
That's a hell of a difficult thing to do, right?
For a huge company to just pivot.
I mean, it's so interesting to,
because we'll talk about your current work.
It's like, it's clear that PCs were dominating
for several decades, like desktop computers.
And then mobile, it's unclear.
It's a leadership question.
Like Apple under Steve Jobs, when he came back,
they pivoted multiple times.
They built iPads and iTunes and phones and tablets
and great Macs, like who knew computers
should be made out of aluminum?
Nobody knew that, that they're great, it's super fun.
That was Steve?
Yeah, Steve Jobs, like they pivoted multiple times.
And the old Intel, they did that multiple times.
They made DRAMs and processors and processes
and I got to ask this,
what was it like working with Steve Jobs?
I didn't work with him.
Did you interact with him?
Twice, I said hi to him twice in the cafeteria.
What did you say?
Hi.
You said, hey fellas, he was friendly.
He was wandering around and with somebody,
you couldn't find the table
because the cafeteria was packed and I gave my table.
But I worked for Mike Colbert who talked to,
like Mike was the unofficial CTO of Apple
and a brilliant guy and he worked for Steve for 25 years,
maybe more and he talked to Steve multiple times a day.
And he was one of the people that could put up with Steve's,
let's say brilliance and intensity.
And Steve really liked him and Steve trusted Mike
to translate the shit he thought up
into engineering products at work
and then Mike ran a group called Platform Architecture
and I was in that group.
So many times I'd be sitting with Mike
and the phone would ring and it'd be Steve
and Mike would hold the phone like this
because Steve would be yelling about something or other.
Yeah, and he would translate.
And he'd translate and then he would say,
Steve wants us to do this.
So.
Was Steve a good engineer or no?
I don't know.
He was a great idea guy.
Idea person.
And he's a really good selector for talent.
Yeah, that seems to be one of the key elements
of leadership, right?
And then he was a really good first principles guy.
Like somebody say something couldn't be done
and he would just think that's obviously wrong, right?
But maybe it's hard to do, maybe it's expensive to do,
maybe we need different people.
There's like a whole bunch of,
if you want to do something hard,
maybe it takes time, maybe you have to iterate.
There's a whole bunch of things that you could think about
but saying it can't be done is stupid.
How would you compare?
So it seems like Elon Musk is more engineering centric
but it's also,
I think he considered himself a designer too.
He has a design mind.
Steve Jobs feels like he's much more idea space,
design space versus engineering.
Just make it happen.
Like the world should be this way.
Just figure it out.
But he used computers.
You know, he had computer people talk to him all the time.
Like Mike was a really good computer guy.
He knew computers could do.
Computer meaning computer hardware,
like hardware software, all the pieces.
And then he would have an idea about
what could we do with this next
that was grounded in reality.
It wasn't like he was just finger painting on the wall
and wishing somebody would interpret it.
So he had this interesting connection
because he wasn't a computer architect or designer
but he had an intuition from the computers we had
to what could happen.
And essentially you say intuition
because it seems like he was pissing off
a lot of engineers in his intuition
about what can and can't be done.
Those, like the, what is all these stories
about like floppy disks and all that kind of stuff.
Yeah, so in Steve's the first round,
like he'd go into a lab and look at what's going on
and hate it and fire people or assembly in the elevator,
what they're doing for Apple and not be happy.
When he came back, my impression was,
is he surrounded himself with this relatively small group
of people and didn't really interact outside of that as much.
And then the joke was, you'd see like somebody moving
a prototype through the quad with a black blanket over it.
And that was cause it was secret,
partly from Steve cause they didn't want Steve
to see it until it was ready.
Yeah, the dynamic with Johnny Ive and Steve is interesting.
It's like you don't wanna,
he ruins as many ideas as he generates.
Yeah, yeah.
It's a dangerous kind of line to walk.
If you have a lot of ideas,
like Gordon Bell was famous for ideas, right?
And it wasn't that the percentage of good ideas
was way higher than anybody else.
It was, he had so many ideas
and he was also good at talking to people
about it and getting into filters, right?
And, you know, seeing through stuff.
Whereas Elon was like, hey, I wanna build rockets.
So Steve was hire a bunch of rocket guys
and Elon would go read rocket manuals.
So Elon is a better engineer, a sense like,
or like more like a love and passion for the manuals.
Yeah, and the details and the data and the understanding.
The craftsmanship too, right?
Well, I guess Steve had craftsmanship too,
but of a different kind.
What do you make of the,
just the standard for just a little longer?
What do you make of like the anger and the passion
and all that, the firing and the mood swings
and the madness, the, you know, being emotional
and all of that.
That's Steve and I guess Elon too.
So what, is that a bugger feature?
It's a feature.
So there's a graph, which is y-axis productivity.
Yeah.
x-axis at zero is chaos.
And infinity, it's complete order.
Yeah.
Right.
So as you go from the, you know, the origin,
as you improve order, you improve productivity.
Yeah.
And at some point productivity peaks
and then it goes back down again.
Too much order, nothing can happen.
Yes.
But the question is, is the,
how close to the chaos is that?
No, no, no.
Here's the thing is once you start moving
the direction of order, the force factor to drive you
towards order is unstoppable.
Oh.
And every organization will move to the place
where their productivity is stymied by order.
So you need a...
So the question is, who's the counter force?
Like, cause it also feels really good.
As you get more organized and productivity goes up,
the organization feels it.
They orient towards it, right?
To hire more people.
They got more guys who couldn't run process.
You get bigger, right?
And then inevitably, the organization gets captured
by the bureaucracy that manages all the processes.
Yeah.
All right.
And then humans really like that.
And so if you just walk into a room and say,
guys, love what you're doing,
but I need you to have less order.
If you don't have some force behind that,
nothing will happen.
I can't tell you on how many levels that's profound.
So...
So that's why I'd say it's a feature.
Now, could you be nicer about it?
I don't know.
Any good examples of being nicer about it?
Well, the funny thing is to get stuff done.
You need people who can manage stuff and manage people
cause humans are complicated.
They need lots of care and feeding
and you need to tell them that they look nice
and they're doing good stuff and pat them on the back, right?
I don't know.
You tell me, is that needed?
Oh yeah.
If humans need that?
I had a friend, he started to manage the group and he said,
I figured it out.
You have to praise them before they do anything.
I was waiting till they were done
and they were always mad at me.
Now we tell them what a great job they're doing
while they're doing it.
But then you get stuck in that trap
cause then when they're not doing something,
how do you confront these people?
I think a lot of people that had trauma
in their childhood would disagree with you.
Successful people that you just first do the rough stuff
and then be nice later.
I don't know.
Okay, but you know, engineering companies are full
of adults who had all kinds of range of childhoods.
You know, most people had okay childhoods.
Well, I don't know if...
And lots of people only worked for praise, which is weird.
You mean like everybody.
I'm not that interested in it, but...
Well, you're probably looking for somebody's approval.
Even still.
Yeah, maybe.
I should think about that.
Maybe somebody who's no longer with us kind of thing.
I don't know.
I used to call up my dad and tell him what I was doing.
He was very excited about engineering and stuff.
You got his approval?
Yeah, a lot.
I was lucky.
Like he decided I was smart and unusual as a kid
and that was okay when I was really young.
So when I did poorly in school, I was dyslexic.
I didn't read until I was third or fourth grade.
They didn't care.
My parents were like, oh, he'll be fine.
So I was lucky.
That was cool.
Is he still with us?
You miss him?
Sure, yeah.
He had Parkinson's and then cancer.
His last 10 years were tough and I killed him.
Killing a man like that's hard.
The mind?
Well, it was pretty good.
Parkinson's caused a slow dementia
and the chemotherapy, I think, accelerated it.
But it was like hallucinogenic dementia.
So he was clever and funny and interesting
and it was pretty unusual.
Do you remember conversations?
Of course.
From that time?
Like what, do you have fond memories of the guy?
Yeah, oh yeah.
Anything come to mind?
A friend told me one time I could draw a computer
on the way forward faster than anybody you'd ever met
and I said, you should meet my dad.
Like when I was a kid, he'd come home and say,
I was driving by this bridge and I was thinking about it
and he pulled out a piece of paper
and he'd draw the whole bridge.
He was a mechanical engineer.
Yeah.
And he would just draw the whole thing
and then he would tell me about it
and then tell me how he would have changed it.
And he had this idea that he could understand
and conceive anything.
And I just grew up with that.
So that was natural.
So, like when I interview people,
I ask them to draw a picture of something
they did on the whiteboard and it's really interesting.
Like some people draw a little box and then they'll say,
and then this talks to this and I'll be like,
that's just frustrating.
And then I had this other guy come in one time.
He says, well, I designed a floating point in this chip
but I'd really like to tell you how the whole thing works
and then tell you how the floating point works inside of it.
Do you mind if I do that?
And he covered two whiteboards in like 30 minutes
and I hired him.
Like he was great.
There's craftsmen.
I mean, that's the craftsmanship to that.
Yeah, but also the mental agility
to understand the whole thing.
Right.
Put the pieces in contacts,
like real view of the balance of how the design worked
because if you don't understand it properly
when you start to draw it,
you'll fill up half the whiteboard
with like a little piece of it.
And like your ability to lay it out in an understandable way
takes a lot of understanding.
And be able to zoom in to the detail
and then zoom out to the big picture.
Zoom out really fast.
What about the impossible thing?
You see your dad believed that you can do anything.
That's a weird feature for a craftsman.
Yeah.
It seems that that echoes in your own behavior.
Like that's the...
Well, it's not that anybody can do anything right now.
Right?
It's that if you work at it, you can get better at it
and there might not be a limit.
And they did funny things like,
like he always wanted to play piano.
So at the end of his life, he started playing the piano.
When he had Parkinson's and he was terrible.
But he thought if he really worked at it in this life,
maybe the next life, he'd be better at it.
He might be onto something.
Yeah.
He enjoyed doing it.
Yeah.
It's pretty funny.
Do you think the perfect is the enemy of the good
in hardware and software engineering?
It's like we were talking about JavaScript a little bit
and the messiness of the 10-day building process.
Yeah.
It's, you know, creative tension, right?
So creative tension is you have two different ideas
that you can't do both, right?
And the, but the fact that you want to do both
causes you to go try to solve that problem.
That's the creative part.
So if you're building computers,
like some people say we have the schedule
and anything that doesn't fit in the schedule we can't do,
right?
And so they throw out the perfect cause I have a schedule.
I hate that.
Then there's other people to say,
we need to get this perfectly right.
And no matter what, you know, more people, more money, right?
And there's a really clear idea about what you want.
And some people are really good at articulating it, right?
So let's call that the perfect, yeah.
Yeah.
All right, but that's also terrible
cause they never ship anything.
You never hit any goals.
So now you have the, now you have your framework.
Yes.
You can't throw out stuff
cause you can't get it done today.
Cause maybe you'll get it done tomorrow
with the next project, right?
You can't, so you have to,
I work with a guy that I really like working with,
but he over filters his ideas.
Over filters?
He'd start thinking about something.
And as soon as he figured out what's wrong with it,
he'd throw it out.
And then I start thinking about it.
And I, you know, you come up with an idea
and then you find out what's wrong with it.
And then you give it a little time to set
cause sometimes, you know, you figure out how to tweak it
or maybe that idea helps some other idea.
So idea generation is really funny.
So you have to give your idea space,
like spaciousness of mind is key,
but you also have to execute programs and get shit done.
And then it turns out computer engineering is fun
because it takes, you know, a hundred people
to build a computer, 200 to 300, whatever the number is.
And people are so variable about, you know,
temperament and, you know, skill sets and stuff
that in a big organization,
you find that the people who love the perfect ideas
and the people that want to get stuffed on yesterday
and people like that come up with ideas
and people like the, let's say, shoot down ideas.
And it takes the whole, it takes a large group of people.
So some are good at generating ideas, some are good at filtering ideas
and then all in that giant mess, you're somehow,
I guess the goal is for that giant mess of people
to find the perfect path through the tension,
the creative tension.
But like, how do you know when you said
there's some people good at articulating
what perfect looks like, what a good design is?
Like if you're sitting in a room
and you have a set of ideas
about like how to design a better processor,
how do you know this is something special here?
This is a good idea, let's try this.
So if you have a brainstormed idea
with a couple of people that were really smart
and you kind of go into it
and you don't quite understand it
and you're working on it.
And then you start, you know, talking about it,
putting it on the whiteboard, maybe it takes days or weeks
and then your brain starts to kind of synchronize.
It's really weird.
And like you start to see what each other is thinking.
And it starts to work, like you can see work.
Like my talent in computer design
is I can see how computers work in my head like really well.
And I know other people can do that too.
And when you're working with people that can do that,
like it is kind of an amazing experience.
And then, and every once in a while,
you get to that place and then you find the flaw
that was just kind of funny
because you can fool yourself in, but.
The two of you kind of drifted along
in the direction that was useless.
Yeah, that happens too.
Like you have to, because, you know,
the nice thing about computer design
is always reduction of practice.
Like you come up with your good ideas
and I've noticed some architects who really love ideas
and then they work on them and they put it on the shelf
and they go work on the next idea and put it on the shelf
and they never reduce it to practice.
So they find out what's good and bad
because almost every time I've done something really new,
by the time it's done, like the good parts are good,
but I know all the flaws like.
Yeah.
Would you say your career, just your own experience
is your career defined by mostly by flaws or by successes?
Like if.
Again, there's great attention between those.
If you haven't tried hard, right,
and done something new, right,
then you're not gonna be facing the challenges
when you build it, then you find out all the problems with it.
And.
But when you look back, do you see problems or?
Okay.
Oh, when I look back.
What do you remember?
I think earlier in my career.
Yeah.
Like EV5 was the second alpha chip.
I was so embarrassed about the mistakes,
I could barely talk about it.
And it was in the Guinness Book of Rolls records
and it was the fastest processor on the planet.
Yeah.
So it was, and at some point I realized
that was really a bad mental framework
to deal with like doing something new.
We did a bunch of new things
and some of them worked out great and some were bad.
And we learned a lot from it.
And then the next one, we learned a lot.
That also, EV6 also had some really cool things in it.
I think the proportion of good stuff went up,
but it had a couple of fatal flaws in it
that were painful.
And then, yeah.
You learned to channel the pain into like pride.
Not pride really.
So just realization about how the world works
or how that kind of idea set works.
Life is suffering.
That's the reality.
What?
No, it's not.
Well.
I know the Buddhists have that
and a couple of other people are stuck on it.
No, it's, you know, there's just kind of weird combination
of good and bad and, you know, light and darkness
that you have to tolerate and, you know, deal with.
Yeah, there's definitely lots of suffering in the world.
Depends on the perspective.
It seems like there's way more darkness,
but that makes the light part really nice.
What computing hardware
or just any kind of, even software design,
are you defined beautiful from your own work,
from other people's work,
that you're just,
we were just talking about the battleground
of flaws and mistakes and errors,
but things that were just beautifully done.
Is there something that pops to mind?
Well, when things are beautifully done,
usually there's a well-thought-out set of abstraction layers.
So the whole thing works in unison nicely.
Yes.
And when I say abstraction layer,
that means two different components,
when they work together, they work independently.
They don't have to know what the other one is doing.
So that decoupling.
Yeah.
So the famous one was the network stack.
Like there's a seven-layer network stack,
you know, data transport and protocol and all the layers.
And the innovation was,
is when they really got that right.
Because networks before that didn't define those very well.
The layers could innovate independently.
And occasionally the layer boundary
would, you know, the interface would be upgraded.
And that let, you know, the design space breathe.
And you could do something new in layer seven
without having to worry about how layer four worked.
And so good design does that.
And you see it in processor designs.
When we did the Zen design at AMD,
we made several components very modular.
And, you know, my insistence at the top was,
I wanted all the interfaces defined
before we wrote the RTL for the pieces.
One of the verification leads said,
if we do this right, I can test the pieces
so well independently, when we put it together,
we won't find all these interaction bugs
because the floating point knows how the cache works.
And I was a little skeptical,
but he was mostly right.
That the modularity of design greatly improved the quality.
Is that universally true in general?
Would you say about good designs,
the modularity is like usually modular?
Well, we talked about this before.
Humans are only so smart.
Like, and we're not getting any smarter, right?
But the complexity of things is going up.
So, you know, a beautiful design
can't be bigger than the person doing it.
It's just, you know, their piece of it.
Like, the odds of you doing a really beautiful design
of something that's way too hard for you is low, right?
If it's way too simple for you, it's not that interesting.
It's like, well, anybody could do that.
But when you get the right match of your expertise
and, you know, mental power to the right design size,
that's cool, but that's not big enough
to make a meaningful impact in the world.
So now you have to have some framework to design the pieces
so that the whole thing is big and harmonious.
But, you know, when you put it together,
it's, you know, sufficiently interesting to be used.
And, you know, so that's like a beautiful design is.
Matching the limits of that human cognitive capacity
to the module you can create
and creating a nice interface between those modules.
And thereby, do you think there's a limit
to the kind of beautiful complex systems
we can build with this kind of modular design?
It's like, you know, if we build increasingly more complicated,
you can think of like the internet,
okay, let's scale it down.
Well, you can think of like social network,
like Twitter as one computing system.
And, but those are the little modules, right?
But it's built on, it's built on so many components,
nobody at Twitter even understands.
Right.
So, so, so if an alien showed up and looked at Twitter,
he wouldn't just see Twitter as a beautiful,
simple thing that everybody uses, which is really big.
You would see the network, it runs on the fiber optics,
the data is transported to the computers.
The whole thing is so bloody complicated,
nobody at Twitter understands it.
And so.
I think that's what the alien would see.
So yeah, if an alien showed up and looked at Twitter
or looked at the various different network systems
that you can see on Earth.
So imagine they were really smart
that could comprehend the whole thing.
And then they sort of, you know, evaluated the human
and thought, this is really interesting.
No human on this planet comprehends the system they built.
No individual, well, they even see individual humans
as like we humans are very human centric, entity centric.
And so we think of us as the central organism
and the networks as just the connection of organisms,
but from a perspective of an alien
from an outside perspective, it seems like.
Yeah, I get it.
We're the ants and they'd see the ant colony.
The ant colony, yeah.
Or the result of production of the ant colony,
which is like cities and it's in that sense,
humans are pretty impressive.
The modularity that we're able to
and how robust we are to noise and mutation
and all that kind of stuff.
Well, that's cause it's stress tested all the time.
Yeah.
You know, you build all these cities with buildings
and you get earthquakes occasionally
and you know, some, you know, wars, earthquakes.
Viruses every once in a while.
You know, changes in business plans
for, you know, like shipping or something.
Like, as long as it's all stress tested,
then it keeps adapting to the situation.
So that's a curious phenomenon.
Well, let's go, let's talk about Moore's Law a little bit.
It's at the broad view of Moore's Law
where it's just exponential improvement of computing
capability, like OpenAI, for example,
recently published this kind of papers
looking at the exponential improvement
in the training efficiency of neural networks.
For like ImageNet and all that kind of stuff,
we just got better on this.
This is purely software aside,
just figuring out better tricks and algorithms
for training neural networks.
And that seems to be improving significantly faster
than the Moore's Law prediction, you know?
So that's in the software space.
What do you think if Moore's Law continues
or if the general version of Moore's Law continues,
do you think that comes mostly from the hardware,
from the software, some mix of the two,
some interesting totally,
so not the reduction of the size of the transistor
kind of thing, but more in the totally interesting
kinds of innovations in the hardware space,
all that kind of stuff?
Well, there's like a half a dozen things
going on in that graph.
So one is there's initial innovations
that had a lot of headroom to be exploited.
So, you know, the efficiency of the networks
has improved dramatically.
And then the decomposability of those and the use,
you know, they started running on one computer,
then multiple computers, then multiple GPUs
and then arrays of GPUs and they're up to thousands.
And at some point, so it's sort of like,
they were consumed, they were going from like
a single computer application
to a thousand computer application.
So that's not really a Moore's Law thing.
That's an independent vector.
How many computers can I put on this problem?
Because the computers themselves are getting better
on like a Moore's Law rate,
but their ability to go from one to 10 to 100 to 1,000,
you know, was something.
And then multiplied by, you know,
the amount of computers it took to resolve
like AlexNet, ResNet, the transformers.
It's been quite, you know, steady improvements.
But those are like S-curves, aren't they?
That's the exactly kind of S-curves
that are underlying Moore's Law from the very beginning.
So what's the biggest, what's the most productive,
rich source of S-curves in the future, do you think?
Is it hardware or is it software?
So hardware is going to move along relatively slowly,
like, you know, double performance every two years.
There's still,
I like how you call that slow.
You know, it's the slow version.
The snail's pace of Moore's Law,
maybe we should, we should,
we should trademark that one.
Whereas the scaling by number of computers, you know,
can go much faster, you know.
I'm sure at some point Google had a, you know,
their initial search engine was running on a laptop, you know,
like, and at some point they really worked on scaling that.
And then they factored the index here from, you know,
this piece and this piece and this piece,
and they spread the data on more and more things.
And, you know, they did a dozen innovations,
but as they scaled up the number of computers on that,
it kept breaking, finding new bottlenecks in their software
and their schedulers and made them rethink,
like, it seems insane to do a scheduler
across a thousand computers to schedule parts of it
and then send the results to one computer.
But if you want to schedule a million searches,
that makes perfect sense.
So, so there's the scaling by just quantity
is probably the richest thing.
But then as you scale quantity,
like a network that was great on a hundred computers,
maybe completely the wrong one,
you may pick a network that's 10 times slower
on 10,000 computers, like per computer.
But if you go from a hundred to 10,000, that's a hundred times.
So that's one of the things that happened
when we did internet scaling.
This efficiency went down, not up.
The future of computing is inefficiency, not efficiency.
But scales, inefficient scale.
It's scaling faster than inefficiency bites you.
And as long as there's, you know, dollar value there,
like scaling costs lots of money.
But Google showed, Facebook showed, everybody showed
that the scale was where the money was at.
It was, and so it was worth the financial.
Do you think, is it possible that like basically
the entirety of earth will be like a computing surface?
Like this table will be doing computing.
This hedgehog will be doing computing.
Like everything really inefficient,
dumb computing will be lover.
So that's fiction books they call it computronium.
Computronium?
We turn everything into computing.
Well, most of the elements aren't very good for anything.
Like you're not gonna make a computer out of iron.
Like, you know, silicon and carbon
have like nice structures.
You know, we'll see what you can do with the rest of it.
People talk about, well, maybe you can turn the sun
into a computer, but it's hydrogen.
And a little bit of helium, so.
What I mean is more like actually just adding computers
to everything.
Oh, okay.
I thought you were just converting all the mass
of the universe into a computer.
No, no, no.
So not using-
To be ironic from the simulation point of view
is like the simulator build mass, the simulate.
Yeah, I mean, yeah.
So, I mean, ultimately this is all heading
towards the simulation.
Yeah, well, I think I might have told you this story.
At Tesla, they were deciding,
so they wanna measure the current coming out of the battery
and they decided between putting the resistor in there
and putting a computer with a sensor in there.
And the computer was faster than the computer
I worked on in 1982.
And we chose the computer
because it was cheaper than the resistor.
So, sure, this hedgehog costs $13
and we can put an AI that's as smart as you
in there for five bucks.
It'll have one.
So computers will be everywhere.
I was hoping it wouldn't be smarter than me because-
Well, everything's gonna be smarter than you.
But you were saying it's inefficient.
I thought it was better to have a lot of dumb things.
Well, Moore's Law will slowly compact that stuff.
So even the dumb things will be smarter than us.
The dumb things are gonna be smart.
Or they're gonna be smart enough to talk
to something that's really smart.
You know, it's like, well, just remember,
like a big computer chip, you know,
it's like an inch by an inch and, you know,
40 microns thick, it doesn't take very much,
very many atoms to make a high power computer.
And 10,000 of them can fit in the shoebox.
But, you know, you have the cooling and power problems,
but, you know, people are working on that.
But they still can't write compelling poetry or music
or understand what love is or have a fear of mortality.
So we're still winning.
Neither can most of humanity, so.
Well, they can write books about it.
So, but speaking about this walk along the path of innovation
towards the dumb things being smarter than humans,
you are now the CTO of 10 storey, as of two months ago,
they built hardware for deep learning.
How do you build scalable and efficient deep learning?
This is such a fascinating space.
Yeah, yeah, so it's interesting.
So up until recently, I thought there was two kinds
of computers, there are serial computers
that run like C programs and then there's parallel computers.
So the way I think about it is, you know,
parallel computers have given parallelism.
Like GPUs are great because you have a million pixels.
And modern GPUs run a program on every pixel.
They call it the shader program, right?
So, or like finite element analysis, you build something,
you know, you make this into little tiny chunks,
you give each chunk to a computer.
So you're given all these chunks of parallelism like that.
But most C programs, you write this linear narrative
and you have to make a go fast.
To make a go fast, you predict all the branches,
all the data fetches and you run that more in parallel,
but that's found parallelism.
AI is, I'm still trying to decide how fundamental this is.
It's a given parallelism problem.
But the way people describe the neural networks
and then how they write them in PyTorch, it makes graphs.
Yeah, that might be fundamentally different
than the GPU kind of.
Parallelism, yeah, it might be.
Because when you run the GPU program on all the pixels,
you're running, you know, it depends,
you know, this group of pixels say it's background blue
and it runs a really simple program.
This pixel is, you know, some patch of your face.
So you have some really interesting shader program
to give you the impression of translucency.
But the pixels themselves don't talk to each other.
There's no graph, right?
So you do the image and then you do the next image
and you do the next image.
And you run 8 million pixels, 8 million programs every time
and modern GPUs have like 6,000 thread engines in them.
So, you know, to get 8 million pixels,
each one runs a program on, you know, 10 or 20 pixels.
And that's how they work.
There's no graph.
But you think graph might be a totally new way
to think about hardware.
So Rajdhika Dori and I have been having this good conversation
about given versus found parallelism.
And then the kind of walk as we got more transistors,
like, you know, computers way back when did stuff
on scalar data.
Then we did on vector data, famous vector machines.
Now we're making computers that operate on matrices, right?
And then the category we said that was next was spatial.
Like imagine you have so much data that, you know,
you want to do the compute on this data.
And then when it's done,
it says send the result to this pile of data
on some software on that.
And it's better to think about it spatially
than to move all the data to a central processor
and do all the work.
So spatially, I mean, moving in the space of data
as opposed to moving the data.
Yeah, you have a petabyte data space
spread across some huge array of computers.
And when you do a computation somewhere,
you send the result of that computation
or maybe a pointer to the next program
to some other piece of data and do it.
But I think a better word might be graph
and all the AI neural networks are graphs.
Do some computations and the result here,
do another computation, do a data transformation,
do emerging, do a pooling, do another computation.
Is it possible to compress and say,
how we make this thing efficient,
this whole process efficient, this different?
So first, the fundamental elements in the graphs
are things like matrix multiplies, convolutions,
data manipulations and data movements.
Yeah.
So GPUs emulate those things with their little singles,
you know, basically running a single threaded program.
And then there's, you know, an Nvidia calls it a warp
where they group a bunch of programs
that are similar together.
So for efficiency and instruction use.
And then at a higher level, you kind of,
you take this graph and you say,
this part of the graph is a matrix multiplier
which runs on these 30 view threads.
But the model at the bottom was built for
running programs on pixels, not executing graphs.
So it's emulation, ultimately.
So is it possible to build something
that natively runs graphs?
Yes.
So that's what 10 storent did.
So.
Where are we on that?
How, like in the history of that effort,
are we in the early days?
Yeah, I think so.
10 storents started by a friend of mine,
Labisha Bajek and I was his first investor.
So I've been, you know, kind of following him
and talking to him about it for years
and in the fall when I was considering things to do.
I decided, you know, we held a conference last year
with a friend organized it and we wanted to bring in thinkers
and two of the people were Andre Carpathi and Chris Ladner.
And Andre gave this talk on YouTube called Software 2.0
which I think is great.
Which is, we went from programs computers
where you write programs to data program computers.
You know, like the futures, you know, of software as data
programs, the networks.
And I think that's true.
And then Chris has been working, he worked on LLVM,
the low level virtual machine which became
the intermediate representation for all compilers.
And now he's working on another project called MLIR
which is mid-level intermediate representation
which is essentially under the graph about
how do you represent that kind of computation
and then coordinate large numbers of potentially
heterogeneous computers.
And I would say technically 10 storents,
you know, two pillars of those two ideas,
software 2.0 and mid-level representation.
But it's in service of executing graph programs.
The hardware is designed to do that.
So it's including the hardware piece.
Yeah.
And then the other cool thing is
for a relatively small amount of money,
they did a test chip and two production chips.
So it's like a super effective team.
And unlike some AI startups where,
if you don't build the hardware to run the software
that they really want to do,
then you have to fix it by writing lots more software.
So the hardware naturally does,
matrix multiply, convolution, the data manipulations
and the data movement between processing elements
that you can see in the graph.
Which I think is all pretty clever.
And that's what I'm working on now.
So the, I think it's called the grace call processor
introduced last year.
It's, you know, there's a bunch of measures
of performance we're talking about, horses.
It seems to outperform 368 trillion operations per second.
Seems to outperform NVIDIA's Tesla T4 system.
So these are just numbers.
What do they actually mean in real world performance?
Like what are the metrics for you
that you're chasing in your horse racing?
What do you care about?
Well, first, so the native language of, you know,
people who write AI network programs is PyTorch now.
PyTorch, TensorFlow, there's a couple others.
The PyTorch is one over TensorFlow,
is it just a-
I'm not an expert on that.
I know many people have switched
from TensorFlow to PyTorch.
Yeah.
And there's technical reasons for it and-
I use both, both are still awesome.
Both are still awesome.
But the deepest love is for PyTorch currently.
Yeah.
There's more love for that.
And that may change.
So the first thing is, when they write their programs,
can the hardware execute it pretty much as it was written?
Right.
So PyTorch turns into a graph.
We have a graph compiler that makes that graph.
Then it fractions the graph down.
So if you have big matrix multiply,
we turn it into right size chunks
to run on the processing elements.
It hooks all the graph up.
It lays out all the data.
There's a couple of mid-level representations of it
that are also simulatable,
so that if you're writing the code,
you can see how it's gonna go through the machine,
which is pretty cool.
And then at the bottom, it schedules kernels,
like math, data manipulation, data movement kernels,
which do this stuff.
So we don't have to run,
write a little program to do matrix multiply,
because we have a big matrix multiplier.
Like there's no SIMT program for that.
But there is scheduling for that, right?
So one of the goals is,
if you write a piece of PyTorch code
that looks pretty reasonable,
you should be able to compile it, run it on the hardware
without having to tweak it
and do all kinds of crazy things to get performance.
There's not a lot of intermediate steps.
It's running directly as written.
Like on a GPU, if you write a large matrix multiply naively,
you'll get 5% to 10% of the peak performance of the GPU.
Right, and then there's a bunch of people
who publish papers on this.
And I read them about what steps do you have to do?
And it goes from pretty reasonable,
well, transpose one of the matrices.
So you do rotor, not column ordered,
you know, block it so that you can put a block
of the matrix on different SMs, you know, groups of threads.
But some of it gets into a little details,
like you have to schedule it just so,
so you don't have register conflicts.
So the, they call them CUDA ninjas.
CUDA ninjas, I love it.
To get to the optimal point,
you either write a pre, use a pre-written library,
which is a good strategy for some things,
or you have to be an expert
in micro architecture to program it.
Right, so the optimization step
is way more complicated with the GPU.
So our goal is, if you write PyTorch,
that's good PyTorch, you can do it.
Now there's, as the networks are evolving,
you know, they've changed from convolutional
to matrix multiply,
that people are talking about conditional graphs
or talking about very large matrices,
they're talking about sparsity,
they're talking about problems
that scale across many, many chips.
So the native, you know, data item is a packet.
Like, so you send the packet to a processor,
it gets processed, it does a bunch of work,
and then it may send packets to other processors
and they execute like a data flow graph kind of methodology.
Got it.
We have a big network on chip and then 16,
and that second chip has 16 ethernet ports
to help lots of them together.
And it's the same graph compiler across multiple chips.
So that's where the scale comes in.
So it's built to scale naturally.
Now, my experience with scaling is,
as you scale, you run into lots of interesting problems.
So scaling is the amount of the climb.
Yeah.
So the hardware is built to do this
and then we're in the process of...
Is there a software part to this?
Would the ethernet and all that?
Well, the, you know, the protocol at the bottom,
you know, we send, you know, it's an ethernet phi,
but the protocol basically says,
send the packet from here to there.
It's all point to point.
The header bit says which processor to send it to,
and we basically take a packet off our on-chip network,
put an ethernet header on it, send it to the other end,
strip the header off and send it to the local thing.
It's pretty straightforward.
Human-to-human interaction is pretty straightforward too,
but when you get a million of us,
we just do crazy stuff together.
Yeah, it can be fun.
So is that the goal is scale?
So like, for example, I've been recently
doing a bunch of robots at home
for my own personal pleasure.
Am I going to ever use 10 storey or is this more for...
There's all kinds of problems.
Like there's small inference problems
or small training problems or big training problems.
What's the big goal?
Is it the big training problems
or the small training problems or is it the goal?
One of the goals is to scale from 100 milliwatts
to a megawatt, you know,
so like really have some range on the problems.
And the same kind of AI programs work
at all different levels.
So that's the goal.
The natural, since the natural data item is a packet
that we can move around, it's built to scale,
but so many people have, you know, small problems.
Right, right.
But, you know...
Like inside that phone is a small problem to solve.
So do you see 10 storey potentially being inside a phone?
Well, the power efficiency of local memory,
local computation and the way we built it is pretty good.
And then there's a lot of efficiency
on being able to do conditional graphs in sparsity.
I think it's for complicated networks
that want to go in a small factor, it's going to be quite good.
But we have to prove that that's a fun problem.
And that's the early days of the company, right?
It's a couple of years, you said.
But you think, you invested, you think they're legit
as you join.
Well, it's also, it's a really interesting place to be.
Like the AI world is exploding, you know?
And I looked at some other opportunities
like build a faster processor, which people want.
But that's more on an incremental path
than what's going to happen in AI in the next 10 years.
So this is kind of, you know, an exciting place to be part of.
The revolutions will be happening in the very space that's...
And then lots of people are working on it,
but there's lots of technical reasons
why some of them, you know, aren't going to work out that well.
And that's interesting.
And there's also the same problem about getting the basics right.
Like we've talked to customers about exciting features.
And at some point, we realized that each of the networks
realizing they want to hear first about memory bandwidth,
local bandwidth, compute intensity, programmability.
They want to know the basics, power management,
how the network ports work.
What are the basics?
Do all the basics work?
Because it's easy to say we've got this great idea, you know,
the crack GPT-3.
But the people we talk to want to say,
if I buy the...
So we have a piece of express card with our chip on it.
If you buy the card, you plug it in your machine
to download the driver.
How long does it take me to get my network to run?
Right.
You know, that's a real question.
It's a very basic question.
So, yeah.
Is there an answer to that yet?
Or is it trying to get to it?
Our goal is like an hour.
Okay.
When can I buy a test one?
Pretty soon.
For my, for the small case training.
Pretty soon.
Months.
Good.
I love the idea of you inside the room with the
Carpathian, Andre Carpathian, Chris Ladner.
Very, very interesting, very brilliant people,
very out of the box thinkers,
but also like first principles thinkers.
Well, they both get stuff done.
They only get stuff done to get their own projects done.
They talk about it clearly.
They educate large numbers of people and they've created
platforms for other people to go do their stuff on.
Yeah.
The clear thinking that's able to be communicated
is kind of impressive.
It's kind of remarkable to, yeah, I'm a fan.
Well, let me ask,
because I talked to Chris actually a lot these days.
He's been, one of the, just to give him a shout out
and he's been so supportive as a human being.
So everybody's quite different.
Like great engineers are different,
but he's been like sensitive to the human element
in a way that's been fascinating.
Like he was one of the early people on this stupid podcast
that I do to say like, don't quit this thing.
And also talk to whoever the hell you want to talk to.
That kind of from a legit engineer to get like props
and be like, you can do this.
That was, I mean, that's what a good leader does, right?
It's just kind of let a little kid do his thing.
Like go do it.
Let's see what turns out.
That's a pretty powerful thing.
But what do you, what's your sense about,
he used to be, no, I think stepped away from Google, right?
He said, sci-fi, I think.
What's really impressive to you
about the things that Chris has worked on?
Because it's a, we mentioned the optimization,
the compiler design stuff, the LLVM, then there's,
he's also at Google worked at the TPU stuff.
He's obviously worked on Swift,
so the programming language side,
talking about people that work in the entirety of the stack.
What, from your time interacting with Chris
and knowing the guy, what's really impressive to you?
It just inspires you.
Well, like LLVM became the de facto platform
for compilers, like it's amazing.
And it was good code quality, good design choices.
He hit the right level of abstraction.
There's a little bit of the right time and the right place.
And then he built a new programming language called Swift,
which after, let's say some adoption resistance
became very successful.
I don't know that much about his work at Google,
although I know that, that was a typical,
they started TensorFlow stuff and they,
it was new, they wrote a lot of code
and then at some point it needed to be refactored to be,
because it's development slowed down,
why PyTorch started a little later and then passed it.
So he did a lot of work on that.
And then his idea about MLIR,
which is what people started to realize
is the complexity of the software stack above,
the low level IR, was getting so high
that forcing the features of that into the low level
was putting too much of a burden on it.
So he's splitting that into multiple pieces.
And that was one of the inspirations for our software stack
where we have several intermediate representations
that are all executable.
And you can look at them and do transformations on them
before you lower the level.
So that was, I think we started before MLIR
really got far enough along to use,
but we're interested in that.
He's really excited about MLIR.
That's just like little baby.
So he, and there seems to be some profound ideas on that
that are really useful.
So each one of those things has been,
as the world of software gets more and more complicated,
how do we create the right abstraction levels
to simplify it in a way that people can now work independently
on different levels of it?
So I would say all three of those projects,
LVM, Swift and MLIR did that successfully.
So I'm interested was what he's gonna do next
in the same kind of way.
Yes.
On either the TPU or maybe the NVIDIA GPU side,
how does 10 storey you think,
or the ideas underlying it doesn't have to be 10 storey.
Just this kind of graph focused,
graph centric hardware,
deep learning centric hardware, beat NVIDIAs.
Do you think it's possible
for it to basically overtake NVIDIA?
Sure.
What's that process look like?
What's that journey look like, do you think?
Well, GPUs were built around shader programs
on millions of pixels, not to run graphs.
Yes.
So there's a hypothesis that says,
the way the graphs are built is going to be really interesting
to be inefficient on computing this.
And then the primitives is not a SIMD program,
it's matrix multiply convolution.
And then the data manipulations are fairly extensive
about like how do you do a fast transpose with a program?
I don't know if you've ever written a transpose program.
They're ugly and slow, but in hardware
you can do really well.
Like I'll give you an example.
So when GPU accelerators started doing triangles,
like if you have a triangle,
which maps on the set of pixels.
So you build, it's very easy,
straightforward to build a hardware engine
that will find all those pixels.
And it's kind of weird because you walk along the triangle
to get to the edge, and then you have to go back
down to the next row and walk along.
And then you have to decide on the edge,
if the line of the triangle is like half on the pixel,
what's the pixel color?
Because it's half of this pixel and half the next one.
That's called rasterization.
You're saying that could be done in hardware?
No, that's an example of that operation
as a software program is really bad.
I've written a program that did rasterization.
The hardware that does it,
it's actually less code than the software program
that does it, and it's way faster.
Right, so there are certain times when
the abstraction you have rasterize a triangle,
execute a graph, components of a graph,
the right thing to do in the hardware software boundary
is for the hardware to naturally do it.
And so the GPU is really optimized
for the rasterization of triangles.
Well, no, that's just, well, like in a modern,
you know, that's a small piece of modern GPUs.
What they did is that they still rasterize triangles
when you're running a game, but for the most part,
most of the computation in the area,
the GPU is running shader programs,
but there's single threaded programs on pixels, not graphs.
That's to be honest, let's say I don't actually know
the math behind shader, shading and lighting
and all that kind of stuff.
I don't know what.
They look like little simple floating point programs
or complicated ones.
You can have 8,000 instructions in a shader program.
But I don't have a good intuition
why it could be parallelized so easily.
No, it's because you have eight million pixels
in every single, so when you have a light, right?
Yeah.
That comes down, the amount of light,
like say this is a line of pixels across this table, right?
The amount of light on each pixel is subtly different.
And each pixel is responsible for figuring out what it is.
Figuring it out, so that pixel says I'm this pixel,
I know the angle of the light,
I know the occlusion, I know the color I am.
Like every single pixel here is a different color,
every single pixel gets a different amount of light.
Every single pixel has a subtly different translucency.
So to make it look realistic,
the solution was you run a separate program on every pixel.
See, but I thought there's like reflection
from all over the place, is it every pixel?
Yeah, but there is.
So you build a reflection map,
which also has some pixelated thing.
And then when the pixel is looking at the reflection map,
it has to calculate what the normal of the surface is,
and it does it per pixel.
By the way, there's bull loads of hacks on that.
You may have a lower resolution light map,
reflection map, there's all these, you know, tax they do.
But at the end of the day, it's per pixel computation.
And it's so happening that you can map graph-like computation
onto this pixel-centric computation.
You can do floating-point programs
on convolution and matrices.
And NVIDIA invested for years in CUDA, first for HPC,
and then they got lucky with the AI trend.
But do you think they're going to essentially
not be able to hardcore pivot out of their hole?
We'll see.
That's always interesting.
How often do big companies hardcore pivot occasionally?
How much do you know about NVIDIA, folks?
Some.
Some?
I'm curious as well, who's ultimately, as a...
Well, they've innovated several times,
but they've also worked really hard on mobile.
They've worked really hard on radios.
You know, they're fundamentally a GPU company.
Well, they tried to pivot.
It's an interesting little game and play
in autonomous vehicles, right?
With, or semi-autonomous, like playing with Tesla
and so on and seeing that's a dipping a toe
into that kind of pivot.
They came out with this platform,
which is interesting technically,
but it was like a 3,000 watt, $3,000 GPU platform.
I don't know if it's interesting technically.
It's interesting philosophically.
I technically, I don't know if it's the execution
that craftsmanship is there.
I'm not sure, but I didn't get a sense.
I think they were repurposing GPUs
for an automotive solution.
Right, it's not a real pivot.
They didn't build a ground up solution.
Like the chips inside Tesla are pretty cheap.
Like Mobileye has been doing this.
They're doing the classic work from the simplest thing.
They were building 40 square millimeter chips.
And NVIDIA, their solution had 800 millimeter chips
and 200 millimeter chips.
And like, both those are really expensive DRAMs.
And it's a really different approach.
So Mobileye fit the, let's say,
automotive cost and form factor.
And then they added features as it was economically viable.
NVIDIA said, take the biggest thing
and we're going to go make it work.
And that's also influenced like Waymo.
There's a whole bunch of autonomous startups
where they have a 5,000 watt server in their trunk.
Right, but that's cause they think,
well, 5,000 watts and $10,000 is okay
because it's replacing a driver.
Elon's approach was that board has to be cheap enough
to put it in every single Tesla
whether they turn on autonomous driving or not.
Which, and Mobileye was like,
we need to fit in the bomb and cost structure
that car companies do.
So they may sell you a GPS for 1,500 bucks.
But the bomb for that's like $25.
Well, and for Mobileye, it seems like neural networks
were not first class citizens, like the computation.
They didn't start out as a...
Yeah, it was a CB problem.
Yeah.
And they did classic CB and found stop lights and lines.
They were really good at it.
Yeah, and they never, I mean,
I don't know what's happening now,
but they never fully pivoted.
I mean, it's like, it's the NVIDIA thing.
And then as opposed to,
so if you look at the new Tesla work,
it's like neural networks from the ground up, right?
Yeah, and even Tesla started with a lot of CB stuff in it.
And Andre's basically been eliminating it.
Move everything into the network.
So without, this isn't like confidential stuff,
but you sitting on a porch looking over the world,
looking at the work that Andre is doing,
that Elon's doing with Tesla autopilot.
Do you like the trajectory of where things are going
on the hardware side?
Well, they're making serious progress.
I like the videos of people driving the beta stuff.
Like it's taking some pretty complicated intersections
and all that, but it's still an intervention per drive.
I mean, I have autopilot, the current autopilot, my Tesla,
I use it every day.
Do you have full stop driving beta or no?
So you like where this is going?
They're making progress.
It's taking longer than anybody thought.
You know, my wonder was, you know, hardware three,
is it enough computing, off by two, off by five,
off by 10, off by a hundred?
Yeah.
And I thought it probably wasn't enough,
but they're doing pretty well with it now.
Yeah.
And one thing is the data set gets bigger,
the training gets better.
And then there's this interesting thing is
you sort of train and build an arbitrary size network
that solves the problem.
And then you refactor the network down to the thing
that you can afford to ship, right?
So the goal isn't to build a network that fits in the phone.
It's to build something that actually works.
And then how do you make that most effective
on the hardware you have?
And they seem to be doing that much better
than a couple of years ago.
Well, the one really important thing is also
what they're doing well is how to iterate that quickly,
which means like it's not just about one time deployment,
one building, it's constantly iterating the network
and trying to automate as many steps as possible, right?
And that's actually the principles of the software 2.0
I can mention with Andre is it's not just,
I mean, I don't know what the actual,
his description of software 2.0 is.
If it's just high level, full software, there's specifics,
but the interesting thing about what that actually looks
in the real world is it's that,
what I think Andre calls the data engine.
It's like, it's the iterative improvement of the thing.
You have a neural network that does stuff,
fails on a bunch of things and learns from it
over and over and over.
So you constantly discovering edge cases.
So it's very much about like data engineering,
like figuring out, it's kind of what you were talking about
with TensorFlow is you have the data landscape.
You have to walk along that data landscape in a way
that is constantly improving the neural network.
And that feels like that's the central piece of it.
And there's two pieces of it, like,
you find edge cases that don't work
and then you define something that goes get you data for that.
But then the other constraint is whether you have
to label it or not.
Like the amazing thing about like the GPT-3 stuff
is it's unsupervised.
So there's essentially infinite amount of data.
Now there's obviously infinite amount of data
available from cars of people who are successfully driving.
But, you know, the current pipelines
are mostly running on labeled data,
which is human limited.
So when that becomes unsupervised, right?
It'll create unlimited amount of data, which is on a scale.
Now the networks that may use that data
might be way too big for cars,
but then there'll be the transformation
from now we have unlimited data.
I know exactly what I want.
Now can I turn that into something that fits in the car?
And that process is gonna happen all over the place.
Every time you get to the place where you have unlimited data,
that's what software 2.0 is about.
Unlimited data training networks to do stuff
without humans writing code to do it.
And ultimately also trying to discover
like you're saying the self-supervised formulation
of the problem.
So the unsupervised formulation of the problem.
Like, you know, in driving there's this really interesting
thing which is you look at a scene that's before you
and you have data about what a successful human driver did
in that scene, you know, one second later.
It's a little piece of data that you can use
just like with GPT-3 as training.
Currently, even though Tesla says they're using that,
it's an open question to me.
How far can you, can you saw all of the driving
with just that self-supervised piece of data?
And like, I think-
That's what Cama AI is doing.
That's what Cama AI is doing.
But the question is how much data,
so what Cama AI doesn't have is as good
of a data engine, for example, as Tesla does.
That's where the, like the organization of the data.
I mean, as far as I know, I haven't talked to George
but they do have the data.
The question is how much data is needed?
Because we say infinite very loosely here.
And then the other question, which you said,
I don't know if you think it's still an open question,
is are we on the right order of magnitude
for the compute necessary?
That is this, is it like what Elon said,
this chip that's in there now is enough
to do full self-driving,
or do we need another order of magnitude?
I think nobody actually knows the answer to that question.
I like the confidence that Elon has, but...
Yeah, we'll see.
There's another funny thing is you don't learn
to drive with infinite amounts of data.
You learn to drive with an intellectual framework
that understands physics and color
and horizontal surfaces and laws and roads.
And, you know, all your experience
from manipulating your environment.
Like, look, there's so many factors go into that.
So then when you learn to drive,
like driving is a subset of this conceptual framework
that you have, right?
And so with self-driving cars right now,
we're teaching them to drive with driving data.
Like, you never teach a human to do that.
You teach a human all kinds of interesting things,
like language, like don't do that, you know, watch out.
You know, there's all kinds of stuff going on.
This is where you, I think,
previous time we talked about where you poetically
disagreed more with my naive notion about humans.
I just think that humans will make
this whole driving thing really difficult.
Yeah, all right.
I said, humans don't move that slow.
It's a ballistics problem.
It's a ballistics, humans are a ballistics problem,
which is like poetry to me.
It's very possible that in driving,
they're indeed purely a ballistics problem.
And I think that's probably the right way to think about it.
But I still, they still continue to surprise me
with those damp pedestrians, the cyclists,
other humans in other cars.
And yeah, but it's gonna be one of these compensating things.
So like when you're driving,
you have an intuition about what humans are going to do,
but you don't have 360 cameras and radars,
and you have an attention problem.
So the self-driving car comes in with no attention problems,
360 cameras, you know, a bunch of other features.
So they'll wipe out a whole class of accidents, right?
And, you know, emergency braking with radar,
and especially as it gets AI enhanced,
will eliminate collisions, right?
But then you have the other problems
of these unexpected things where, you know,
you think your human intuition is helping,
but then the cars also have, you know,
a set of hardware features that you're not even close to.
And the key thing of course,
is if you wipe out a huge number of kind of accidents,
then it might be just way safer than a human driver,
even though, even if humans are still a problem,
that's hard to figure out.
Yeah, that's probably what will happen.
The autonomous cars will have a small number of accidents,
humans would have avoided,
but they'll wipe, they'll get rid of the bulk of them.
What do you think about, like Tesla's dojo efforts,
or it can be bigger than Tesla in general,
it's kind of like the tense torrent,
trying to innovate, like this is the economy,
like should a company try to from scratch
build its own neural network training hardware?
Well, first I think it's great.
So we need lots of experiments, right?
And there's lots of startups working on this
and they're pursuing different things.
Now, I was there when we started Dojo,
and it was sort of like,
what's the unconstrained computer solution
to go do very large training problems.
And then there's fun stuff, like, you know, we said,
well, we have this 10,000 watt board to cool.
Well, you go talk to guys at SpaceX,
and they think 10,000 watts is a really small number,
not a big number.
And there's brilliant people working on it.
I'm curious to see how it'll come out.
I couldn't tell you, you know,
I know it pivoted a few times since I left, so.
So the cooling does seem to be a big problem.
I do like what Elon said about it,
which is like, we don't want to do the thing
unless it's way better than the alternative,
whatever the alternative is.
So it has to be way better than like racks or GPUs.
Yeah, and the other thing is just like, you know,
the Tesla autonomous driving hardware,
it was only serving one software stack.
And the hardware team and the software team
were tightly coupled.
You know, if you're building a general purpose AI solution,
then, you know, there's so many different customers
with so many different needs.
Now, something Andre said is,
I think this is amazing, 10 years ago,
like vision, recommendation, language
were completely different disciplines.
I said, the people literally couldn't talk to each other.
And three years ago, it was all neural networks,
but the very different neural networks.
And recently it's converging on one set of networks.
They vary a lot in size, obviously,
they vary in data, vary in outputs,
but the technology has converged a good bit.
Yeah, these transformers behind GPT-3,
it seems like they could be applied to video,
they could be applied to a lot of,
and it's like, and they're all really simple.
And it was like to literally replace letters with pixels.
It does vision, it's amazing.
And then size actually improves the thing.
So the bigger it gets,
the more compute you throw at it, the better it gets.
And the more data you have, the better it gets.
So then you start to wonder,
well, is that a fundamental thing,
or is this just another step
to some fundamental understanding
about this kind of computation?
Which is really interesting.
Us humans don't want to believe
that that kind of thing will achieve
conceptual understandings, as you were saying,
like you'll figure out physics, but maybe it will.
Maybe. Probably will.
Well, it's worse than that.
It'll understand physics in ways that we can't understand.
I liked your Stephen Wolfram talk,
where he said, you know, there's three generations of physics.
There was physics by reasoning,
well, big things should fall faster than small things,
right, that's reasoning.
And then there's physics by equations, like, you know,
but the number of programs in a world that are solved
with the single equations relatively low,
almost all programs have, you know,
more than one line of code,
maybe a hundred million lines of code.
So he said, now we're going to physics by equation,
which is his project, which is cool.
I might point out that there was two generations of physics
before reasoning, habit.
Like all animals, you know, know things fall
and, you know, birds fly and, you know,
predators know how to solve a differential equation
to cut off a accelerating, you know, curving animal path.
And then there was, you know, the gods did it, right?
So, right, so there was, you know, there's five generations.
Now, software 2.0 says programming things
is not the last step, data.
So there's going to be a physics
by Stephen Wolfram's comp.
That's not explainable to us humans.
And actually, there's no reason that I can see
all that, even that's the limit.
Like there's something beyond that.
I mean, usually when you have this hierarchy,
it's not like, well, if you have this step
and this step and this step
and they're all qualitatively different
and conceptually different.
It's not obvious why, you know,
six is the right number of hierarchy steps
and not seven or eight or.
Well, then it's probably impossible for us
to comprehend something that's beyond
the thing that's not explainable.
Yeah, but the thing that, you know,
understands the thing that's not explainable to us
will conceive the next one.
And like, I'm not sure why there's a limit to it.
Looks like your brain hurts.
That's the sad story.
If we look at our own brain,
which is an interesting illustrative example
in your work with Testorant
and trying to design deep learning architectures,
do you think about the brain at all?
Maybe from a hardware designer perspective,
if you could change something about the brain,
what would you change?
Or do you-
Funny question.
Like, how would you do that?
So your brain is really weird.
Like, you know, your cerebral cortex
where we think we do most of our thinking
is what, like six or seven neurons thick.
Yeah.
Like that's weird.
Like all the big networks are way bigger than that.
Like way deeper.
So that seems odd.
And then, you know, when you're thinking,
if the input generates a result you can lose,
it goes really fast.
But if it can't, that generates an output
that's interesting, which turns into an input
and then your brain,
to the point where you mull things over for days
and how many trips through your brain is that, right?
Like it's, you know, 300 milliseconds or something
to get through seven levels of neurons.
I forget the number exactly.
But then it does it over and over and over as it searches.
And the brain clearly is,
looks like some kind of graph
because you have a neuron with, you know,
connections and it talks to other ones.
And it's locally very computationally intense,
but it's also does sparse computations
across a pretty big area.
There's a lot of messy biological type of things.
And it's, it's meaning like, first of all,
there's mechanical, chemical and electrical signals.
It's all that's going on.
Then the, there's the acynchronicity of signals.
And there's like, there's just a lot of variability.
It seems continuous and messy
and just the mess of biology.
And it's unclear whether that's a good thing
or it's a bad thing.
Because if it's a good thing,
then we need to run the entirety of the evolution.
Well, we're gonna have to start with basic bacteria
to create something.
Imagine we could control,
you could build a brain with 10 layers.
Would that be better or worse?
Or more connections or less connections or, you know,
we don't know to what level our brains are optimized.
But if I was changing things, like,
you know, you can only hold like seven numbers in your head.
Like why not a hundred or a million?
There was a lot of that.
And why can't like,
why can't we have like a floating point processor
that can compute anything we want?
Like and see it all properly.
Like that would be kind of fun.
And why can't we see in four or eight dimensions?
Like, like it's three days kind of a drag.
Like all the hard mass transforms
are up in multiple dimensions.
So there's, you know,
you could imagine a brain architecture that,
you know, you could enhance with a whole bunch of features
that would be, you know,
really useful for thinking about things.
It's possible that the limitations you're describing
are actually essential for like,
the constraints are essential for creating
like the depth of intelligence.
Like that, the ability to reason, you know.
It's hard to say because like,
your brain is clearly a parallel processor.
You know, 10 billion neurons talking to each other
at a relatively low clock rate.
But it produces something that looks like
a serial thought process.
It's a serial narrative in your head.
That's true.
But then there are people famously who are visual thinkers.
Like, I think I'm a relatively visual thinker.
I can imagine any object and rotate it in my head
and look at it.
And there are people who say they don't think that way at all.
And recently I read an article about people
who say they don't have a voice in their head.
They can talk.
But when they, you know, it's like,
well, what are you thinking?
No, they'll describe something that's visual.
So that's curious.
Now, if you're saying,
if we dedicated more hardware to holding information,
like, you know, 10 numbers or a million numbers,
like, would that distract us from our ability
to form this kind of singular identity?
Like it dissipates somehow.
Right.
But maybe, you know, future humans will have many identities
that have some higher level organization,
but can actually do lots more things in parallel.
Yeah, there's no reason, if we're thinking modularly,
there's no reason we can have multiple consciousnesses
in one brain.
Yeah.
And maybe there's some way to make it faster
so that the, you know, the area of the computation
could still have a unified feel to it
while still having way more ability
to do parallel stuff at the same time.
Could definitely be improved.
Could be improved?
Yeah.
Okay.
Well, it's pretty good right now.
Actually, people don't give it enough credit.
The thing is pretty nice that, you know,
the fact that the ride ends seem to be given nice,
like spark of beauty to the whole experience.
So I don't know, I don't know if it can be improved easily.
It could be more beautiful.
I don't know.
What do you mean?
What do you mean how?
All the ways you can't imagine.
No, but that's the whole point.
I wouldn't be able to imagine.
The fact that I can imagine ways in which it could be
more beautiful means.
So do you know, you know, Ian Banks, his stories.
So the super smart AIs there live,
mostly live in the world of what they call infinite fun
because they can create arbitrary worlds.
So they interact and, you know, the story has it.
They interact in the normal world
and they're very smart and they can do all kinds of stuff.
And, you know, a given mind can, you know,
talk to a million humans at the same time
because we're very slow.
And for reasons, you know, artificial, the story,
they're interested in people and doing stuff,
but they mostly live in this other land of thinking.
My inclination is to think that the ability
to create infinite fun will not be so fun.
That's sad.
Well, there's so many things to do.
Imagine being able to make a star, move planets around.
Yeah, yeah.
But because we can imagine that as wildlife is fun,
if we actually were able to do it, it'd be a slippery slope
where fun wouldn't even have a meaning
because we just consistently desensitize ourselves
by the infinite amounts of fun we're having.
And the sadness, the dark stuff is what makes it fun.
I think that could be the Russian.
It could be the fun makes it fun
and the sadness makes it bittersweet.
Yeah, that's true.
Fun could be the thing that makes it fun.
So what do you think about the expansion,
not through the biology side,
but through the BCI, the brain-computer interfaces?
Yeah, you got a chance to check out the Neuralink stuff.
It's super interesting.
Like humans, like our thoughts manifest as action.
You know, like as a kid, you know,
like shooting a rifle was super fun.
Driving a mini bike, doing things.
And then computer games, I think,
for a lot of kids became the thing where they,
you know, they can do what they want.
They can fly a plane, they can do this, they can do this, right?
But you have to have this physical interaction.
Now imagine, you know, you could just imagine stuff
and it happens, right?
Like really richly and interestingly.
Like we kind of do that when we dream.
Like dreams are funny because like,
if you have some control or awareness in your dreams,
like it's very realistic looking
or not realistic, it depends on the dream,
but you can also manipulate that.
And, you know, what's possible there is odd
in the fact that nobody understands it, it's hilarious.
But do you think it's possible to expand
that capability through computing?
Sure.
Is there some interesting,
so from a hardware designer perspective,
is there, do you think it'll present totally new challenges
in the kind of hardware that required that like,
so this hardware isn't standalone computing.
Well, just take it from the brain.
Today, computer games are rendered by GPUs, right?
So, but you've seen the GAN stuff, right?
Where train neural networks render realistic images,
but there's no pixels, no triangles, no shaders,
no light maps, no nothing.
So the future of graphics is probably AI, right?
Now that AI is heavily trained by lots of real data, right?
So if you have an interface with a AI renderer, right?
So if you say render a cat,
it won't say, well, how tall is the cat
and how big, you know, it'll render a cat.
You might say, oh, a little bigger, a little smaller,
you know, make it a tabby, shorter hair.
You know, like you could tweak it.
Like the amount of data you'll have to send
to interact with a very powerful AI renderer could be low.
But the question is, well, brain-computer interfaces
would need to render not onto a screen,
but render onto the brain.
And like directly, so that there's a bandwidth.
Well, it could do it both ways.
I mean, our eyes are really good sensors.
It could render onto a screen
and we could feel like we're participating in it.
You know, they're gonna have, you know,
like the Oculus kind of stuff.
It's gonna be so good when a projection to your eyes,
you think it's real.
You know, they're slowly solving those problems.
And I suspect when the renderer of that information
into your head is also AI mediated,
they'll be able to give you the cues that, you know,
you really want for depth and all kinds of stuff.
Like your brain is partly faking your visual field, right?
Like your eyes are twitching around,
but you don't notice that.
Occasionally they blank, you don't notice that.
You know, there's all kinds of things.
Like you think you see over here,
but you don't really see there.
It's all fabricated.
Yeah, peripheral vision is fascinating.
So if you have an AI renderer that's trained
to understand exactly how you see
and the kind of things that enhance the realism
of the experience could be super real, actually.
So I don't know what the limits that are,
but obviously if we have a brain interface
that goes in inside your, you know, visual cortex
in a better way than your eyes do, which is possible.
It's a lot of neurons.
Maybe that'll be even cooler.
Well, the really cool thing is it has to do
with the infinite fun that you're referring to,
which is our brains have to be very limited.
And like you said, computations.
Also very plastic.
Very plastic, yeah.
So it's a interesting combination.
The interesting open question is the limits
of that neuroplasticity.
Like how flexible is that thing?
Because we haven't really tested it.
We know about that experience where they put
like a pressure pad on somebody's head
and had a visual transducer pressurize it
and somebody slowly learned to see.
Yep.
Especially at a young age, if you throw a lot at it,
like what can it completely,
so can you like arbitrarily expand it with computing power?
So connected to the internet directly somehow.
Yeah, the answer is probably yes.
So the problem with biology and ethics is like,
there's a mess there.
Like us humans are perhaps unwilling to take risks
into directions that are full of uncertainty.
So it's like-
90% of the population's unwilling to take risks.
The other 10% is rushing into the risks
unaided by any infrastructure whatsoever.
And that's where all the fun happens in societies.
There's been huge transformations
in the last couple of thousand years.
Yeah, it's funny.
I got the chance to interact with this Matthew Johnson
from Johns Hopkins.
He's doing this large scale study of psychedelics.
It's becoming more and more.
I've gotten a chance to interact
with that community of scientists working on psychedelics.
But because of that, that opened the door to me
to all these, what are they called?
Psychonauts, the people who, like you said, the 10%.
Like, I don't care.
I don't know if there's a science behind this.
I'm taking the spaceship to,
if I'm be the first on Mars,
I'll be, you know, psychedelics interesting in the sense
that in another dimension, like you said,
it's a way to explore the limits of the human mind.
Like, what does this thing capable of doing?
Because you kind of, like when you dream,
you detach it, I don't know exactly
in your science of it,
but you detach your like reality from what your mind,
the images your mind is able to conjure up
and your mind goes into weird places.
And like entities appear,
somehow Freudian type of like trauma
is probably connected in there somehow.
You start to have like these weird, vivid worlds that like.
So do you actively dream?
Do you, why not?
I have like six hours of dreams
and it's like real useful time.
I know, I don't, I don't for some reason, I just knock out
and I have sometimes like anxiety inducing kind of like
very pragmatic like nightmare type of dreams,
but nothing fun, nothing.
Nothing fun?
Nothing fun.
I try, I unfortunately have mostly have fun
in the waking world,
which is very limited in the amount of fun you can have.
It's not that limited either.
Yeah, that's why we'll have to talk.
Yeah, and your instructions.
Yeah.
There's like a manual for that.
You might wanna.
I looked it up.
I'll ask you on, what would you dream?
You know, years ago when I read about, you know,
like, you know, a book about how to have, you know,
become aware in your dreams.
I worked on it for a while.
Like there's this trick about, you know,
imagine you can see your hands and look out
and I got somewhat good at it.
Like, but my mostly, when I'm thinking about things
or working on problems,
I prep myself before I go to sleep.
It's like, I pull into my mind
all the things I wanna work on or think about.
And then that, let's say, greatly improves the chances
that I'll work on that while I'm sleeping.
And then I also, you know, basically ask to remember it.
And I often remember very detailed.
Within the dream.
Yeah.
Or outside the dream.
Well, to bring it up in my dreaming
and then to remember it when I wake up.
It's just, it's more of a meditative practice.
You say, you know, to prepare yourself to do that.
Like if you go to, you know, to sleep,
still gnashing your teeth about some random thing
that happened that you're not that really interested
in your dream about it.
That's really interesting.
Maybe.
But you can direct your dreams somewhat by prepping.
Yeah, I'm gonna have to try that.
It's really interesting.
Like the most important, the interesting,
not like what did this guy send an email
kind of like stupid worry stuff,
but like fundamental problems you're actually concerned about.
Yeah.
Prepping.
And interesting things you're worried about.
Interesting.
Or both of your reading or, you know,
some great conversation you had
or some adventure you want to have.
Like there's a lot of space there.
And it seems to work that, you know,
my percentage of interesting dreams and memories went up.
Is there, is that the source of,
if you were able to deconstruct like where
some of your best ideas came from,
is there a process that's at the core of that?
Like, so some people, you know, walk and think,
some people like in the shower, the best ideas hit them.
If you talk about like Newton,
Apple hitting them on the head.
No, I found out a long time ago,
I process things somewhat slowly.
So like in college, I had friends that could study it
the last minute and get an A next day.
I can't do that at all.
So I always front loaded all the work.
Like I do all the problems early, you know,
for finals, like the last three days,
I wouldn't look at a book because I want, you know,
cause like a new fact day before finals may screw up
my understanding of what I thought I knew.
So my goal was to always get it in
and give it time to soak.
And I used to, you know,
I remember when we were doing like 3D calculus,
I would have these amazing dreams of 3D surfaces
with normal, you know, calculating the gradient.
And just like all come up.
So it was like really fun, like very visual.
And if I got cycles of that, that was useful.
And the other is just don't over filter your ideas.
Like I like that process of brainstorming
where lots of ideas can happen.
I like people who have lots of ideas.
And then you just let them sit.
Then there's a, yeah, I'll let them sit
and let it breathe a little bit.
And then reduce it to practice.
Like at some point you really have to, does it really work?
Like, you know, is this real or not?
Right, but you have to do both.
There's creative tension there.
Like how do you be both open and, you know, precise?
Have you had ideas that you just,
that sit in your mind for like years before the?
Sure.
That's it.
It's an interesting way to just generate ideas
and just let them sit, let them sit there for a while.
I think I have a few of those ideas.
You know, it was so funny.
Yeah, I think that's, you know, creativity,
this one or something.
For the slow thinkers in the room, I suppose.
As I, some people, like you said, are just like, like the.
Yeah, it's really interesting.
There's so much diversity in how people think.
You know, how fast or slow they are,
how well they remember it don't, like, you know,
I'm not super good at remembering facts,
but processes and methods.
Like in our engineering, I went to Penn State
and almost all our engineering tests were open book.
I could remember the page and not the formula.
As soon as I saw the formula,
I could remember the whole method if I, if I'd learned it.
Yeah.
So it's just a funny, or some people could, you know,
I just watched friends like flipping through the book,
trying to find the formula,
even knowing that they'd done just as much work.
And I would just open the book and I was on page 27.
About a half, I could see the whole thing visually.
Yeah.
And, you know.
And you have to learn that about yourself
and figure out what the, what the function optimally.
I had a friend who, he was always concerned.
He didn't know how he came up with ideas.
He had lots of ideas,
but he said they just sort of popped up.
Like you'd be working on something, you have this idea.
Like, where does it come from?
But you can have more awareness of it.
Like, like, like, like how you,
how your brain works as a little murky
as you go down from the voice in your head
or the obvious visualizations.
Like when you visualize something, how does that happen?
Yeah, that's right.
You know, if I say, you know, visualize a volcano,
it's easy to do, right?
And what does it actually look like when you visualize it?
I can visualize to the point where I don't see
very much out of my eyes
and I see the colors of the thing I'm visualizing.
Yeah, but there's like a, there's a shape,
there's a texture, there's a color,
but there's also conceptual visualization.
Like, what are you actually visualizing
when you're visualizing a volcano?
Just like with peripheral vision,
you think you see the whole thing.
Yeah, yeah, yeah.
That's a good way to say it.
You know, you have this kind of
almost peripheral vision of your visualizations.
They're like these ghosts.
But, you know, if you, if you work on it,
you can get a pretty high level of detail.
And somehow you can walk along those visualizations
to come up with an idea, which is weird.
But when you're thinking about solving problems,
like you're putting information in, you're exercising,
the stuff you do know,
you're sort of teasing the area
that you don't understand and don't know,
but you can almost, you know, feel,
you know, that process happening.
You know, that's, that's how I, like,
like, I know sometimes when I'm working really hard
on something, like, like I get really hot
when I'm sleeping.
And, you know, it's like,
we got the blanket throw,
I wake up with all the blankets are on the floor.
And, you know, every time it's while we wake up
and think, wow, that was great, you know.
Oh, you're able to, to reverse engineer
what the hell happened there?
Well, sometimes it's vivid dreams.
And sometimes it's just kind of like you say,
like shadow thinking that you sort of have this feeling
you're, you're going through this stuff,
but it's, it's not that obvious.
Isn't that so amazing that the mind just does
all these little experiments.
I never, you know, I thought, I always thought,
it's like a river that you can't,
you're just there for the ride, but you're right.
If you prep it.
No, it's all understandable.
Meditation really helps.
You got to start figuring out,
you need to learn language if you're on mind.
And there's multiple levels of it, but.
The abstractions again, right?
It's somewhat comprehensible and observable
and feelable or whatever the right word is.
Yeah, you're not alone for the ride.
You are the ride.
I have to ask you, hardware engineer,
working on your own networks now,
what's consciousness?
What the hell is that thing?
Is that, is that just some little weird quirk
of our particular computing device?
Or is it something fundamental that we really need
to crack open it for, to, to build like good computers?
Do you ever think about consciousness?
Like why it feels like something to be.
I know it's, it's, it's really weird.
So.
Yeah.
I mean, everything about it's weird.
First is to half a second behind reality, right?
It's a post hoc narrative about what happened.
You've already done stuff by the time you're conscious of it.
And your consciousness generally is a single threaded thing,
but we know your brain is 10 billion neurons
running some crazy parallel thing.
And there's a really big sorting thing going on there.
It also seems to be really reflective
in the sense that you create a space in your head.
Like, we don't really see anything, right?
Like photons hit your eyes, it gets turned into signals,
it goes through multiple layers of neurons.
You know, like, I'm so curious that, you know,
that looks glassy and that looks not glassy.
Like, like how the resolution of your vision is so high,
you had to go through all this processing.
Where for most of it, it looks nothing like vision, right?
Like, like there's no theater in your mind, right?
So we have a world in our heads.
We're literally just isolated behind our sensors,
but we can look at it, speculate about it,
speculate about alternatives, problem solve, what if,
you know, there's so many things going on
and that process is lagging reality.
And it's single threaded,
even though the underlying thing is like massively parallel.
Yeah, so it's so curious.
So imagine you're building an AI computer,
if you wanted to replicate humans,
well, you'd have huge arrays of neural networks
and apparently only sixers have in deep, which is hilarious.
They don't even remember seven numbers,
but I think we can upgrade that a lot, right?
And then somewhere in there,
you would train the network to create basically
the world that you live in, right?
To tell stories to itself about the world
that it's perceiving.
Well, create the world, tell stories in the world,
and then have many dimensions of, you know,
like side jobs to it.
Like we have an emotional structure,
like we have a biological structure.
And that seems hierarchical too.
Like if you're hungry, it dominates your thinking.
If you're mad, it dominates your thinking.
Like, and we don't know if that's important
to consciousness or not,
but it certainly disrupts, you know,
intrudes in the consciousness.
Like, so there's lots of structure to that.
And we like to dwell on the past.
We like to think about the future.
We like to imagine, we like to fantasize, right?
And the somewhat circular observation of that
is the thing we call consciousness.
Now, if you created a computer system
that did all things, create worldviews,
create the future alternate histories,
you know, dwelled on past events, you know,
accurately or semi-accurately, you know, it's...
Well, consciousness just spring up like naturally.
Well, would that feel look and feel conscious to you?
Like you seem conscious to me, but I don't know.
External observer sense.
Do you think a thing that looks conscious is conscious?
Like, do you...
Again, this is like an engineering kind of question,
I think, because...
Like...
I don't know.
If we want to engineer consciousness,
is it okay to engineer something that just looks conscious?
Or is there a difference between some...
Well, we have all consciousness
because it's a super effective way to manage our affairs.
Yeah, this is a social element, yeah.
Well, it gives us a planning system,
you know, we have a huge amount of stuff.
Like when we're talking,
like the reason we can talk really fast is
we're modeling each other in a really high level of detail.
And consciousness is required for that.
Right, and well, all those components together
manifest consciousness, right?
So if we make intelligent beings that we want to interact with,
that we're like, you know, wondering what they're thinking,
you know, looking forward to seeing them.
You know, when they interact with them,
they're interesting, surprising, you know, fascinating.
You know, they will probably be feel conscious like we do
and we'll perceive them as conscious.
I don't know why not, but never know.
Another fun question on this,
because in from a computing perspective,
we're trying to create something that's human-like
or super human-like.
Let me ask you about aliens.
What kind of aliens?
Do you think there's intelligent alien civilizations out there?
And do you think their technology,
their computing, their AI bots,
their chips are of the same nature as ours?
Yeah, I got no idea.
I mean, if there's lots of aliens out there,
they've been awfully quiet.
You know, there's speculation about why
there seems to be more than enough planets out there.
There's a lot.
There's intelligent life on this planet
that seems quite different, you know, like, you know,
dolphins seem like plausibly understandable.
Octopuses don't seem understandable at all.
If they live longer than a year,
maybe they would be running the planet.
They seem really smart.
And their neuro architecture is completely different than ours.
Now, who knows how they perceive things?
I mean, that's the question is for us intelligent beings,
we might not be able to perceive other kinds of intelligence
if they become sufficiently different than us.
So we cannot understand all this.
We live in the current constrained world,
you know, it's three-dimensional geometry,
and the geometry defines a certain amount of physics.
And, you know, there's like how time work seems to work.
There's so many things that seem like
a whole bunch of the input parameters
to another conscious being are the same.
Yes.
Biological, biological things seem to be
in a relatively narrow temperature range, right?
Because, you know, organics aren't stable,
too cold or too hot.
You know, so there's, if you specify the list of things
that input to that,
but soon as we make really smart, you know, beings,
and they go solve about how to think about a billion numbers
at the same time and how to think in n dimensions,
there's a funny science fiction book
where all the society had uploaded into this matrix.
And at some point, some of the beings in the matrix thought,
I wonder if there's intelligent life out there.
So they had to do a whole bunch of work to figure out
like how to make a physical thing,
because their matrix was self-sustaining.
And they made a little spaceship
and they traveled to another planet.
When they got there, there was like life running around,
but there was no intelligent life.
And then they figured out that there was these huge,
you know, organic matrix all over the planet inside there
where intelligent beings had uploaded themselves
and into that matrix.
So everywhere intelligent life was,
soon as it got smart, it up leveled itself
into something way more interesting than 3D geometry and...
Yeah, it escaped whatever the...
No, not escaped, it's...
Upload was better.
The essence of what we think of as an intelligent being,
I tend to like the thought experiment of the organism,
like humans aren't the organisms.
I like the notion of like Richard Dawkins and memes
that ideas themselves are the organisms,
like they're just using our minds to evolve.
So like we're just like meat receptacles
for ideas to breed and multiply and so on.
And maybe those are the aliens.
Yeah.
So, Jordan Peterson has a line that says,
you know, you think you have ideas, but ideas have you.
Yeah, good line.
Which, and then we know about the phenomenon of groupthink
and there's so many things that constrain us.
But I think you can examine all that
and not be completely owned by the ideas
and completely sucked into groupthink.
And part of your responsibility as a human
is to escape that kind of phenomena, which isn't...
You know, it's one of the creative tension things again.
You're constructed by it, but you can still observe it
and you can think about it
and you can make choices about to some level
how constrained you are by it.
And, you know, it's useful to do that.
And...
But at the same time,
and it could be by doing that,
that, you know, the group and society you're part of
becomes collectively even more interesting.
So, you know, so the outside observer will think,
wow, you know, all these Lexis running around
with all these really independent ideas
have created something even more interesting
in the aggregate.
So, so I don't know.
Those are lenses to look at the situation,
but that'll give you some inspiration,
but I don't think they're constrained.
Right, you know.
As a small little quirk of history,
it seems like you're related to Jordan Peterson,
like you mentioned.
He's going through some rough stuff now.
Is there some comment you can make
about the roughness of the human journey,
the ups and downs?
Well, I became an expert in Benza withdrawal.
Like, which is, you took Benza's Aspen's and at some point,
they interact with GABA circuits,
you know, to reduce anxiety and do a hundred other things.
Like, there's actually no known list of everything they do
because they interact with so many parts of your body.
And then once you're on them, you habituate to them
and you're, you have a dependency.
It's not like you're a drug dependency.
We're trying to get high.
It's a, it's a metabolic dependency.
And then if you discontinue them,
there's a funny thing called kindling,
which is if you stop them and then go, you know,
you'll have a horrible withdrawal symptoms.
If you go back on them at the same level,
you won't be stable.
And that unfortunately happened to him.
Because it's so deeply integrated
into all the kinds of systems in the body.
It literally changes the size and numbers
of neurotransmitter sites in your brain.
So there's a, there's a process called the Ashton protocol
where you taper it down slowly over two years
to people go through that goes through unbelievable hell.
And what Jordan went through seemed to be worse
because on advice of doctors, you know,
we'll stop taking these and take this.
It was the disaster and he got some.
Yeah, it was pretty tough.
He seems to be doing quite a bit better intellectually.
You can see his brain clicking back together.
I spent a lot of time with him.
I've never seen anybody suffer so much.
Well, his brain is also like this powerhouse, right?
So I wonder, does a brain that's able to think deeply
about the world suffer more through these kinds of withdrawals?
Like, I don't know, I've watched videos
of people going through withdrawal.
They all seem to suffer unbelievably.
And, you know, my heart goes out to everybody.
And there's some funny math about this.
Some doctors said as best you can tell, you know,
there's the standard recommendations
don't take them for more than a month
and then taper over a couple of weeks.
Many doctors prescribe them endlessly,
which is against the protocol, but it's common, right?
And then something like 75% of people,
when they taper it's, you know,
half the people have difficulty,
but 75% get off okay.
20% have severe difficulty
and 5% have life-threatening difficulty.
And if you're one of those, it's really bad.
And the stories that people have on this
is heartbreaking and tough.
So you put some of the fault at the doctors.
They just not know what the hell they're doing.
Oh, that was hard to say.
It's one of those commonly prescribed things.
Like one doctor said, what happens is
if you're prescribed them for a reason
and then you have a hard time getting off,
the protocol basically says you're either crazy or dependent.
And you get kind of pushed
into a different treatment regime.
You're a drug addict or a psychiatric patient.
And so like one doctor said, you know,
I prescribed them for 10 years thinking
I was helping my patients
and I realized I was really harming them.
And you know, the awareness of that is slowly coming up.
The fact that they're casually prescribed to people
is horrible and it's bloody scary.
And some people are stable on them,
but they're on them for life.
Like once you, you know,
it's another one of those drugs that,
but Benzo's long range have real impacts on your personality.
People talk about the Benzo bubble
where you get disassociated from reality
and your friends a little bit.
It's really terrible.
The mind is terrifying.
We were talking about how the infinite possibility of fun,
but like it's the infinite possibility of suffering too,
which is one of the dangers of like expansion
of the human mind.
It's like, I wonder if all the possible human experiences
that intelligent computer can have,
is it mostly fun or is it mostly suffering?
So like if you brute force expand the set of possibilities,
like are you going to run into some trouble
in terms of like torture and suffering and so on?
Maybe our human brain is just protecting us
from much more possible pain and suffering.
Maybe the space of pain is like much larger
than we could possibly imagine and that.
The world's in a balance.
You know, all the literature on religion and stuff
is the struggle between good and evil
is balanced for very finely tuned
for reasons that are complicated.
But that's a long philosophical conversation.
Speaking of balance that's complicated,
I wonder because we're living through one
of the more important moments in human history
with this particular virus,
it seems like pandemics have at least the ability
to kill off most of the human population at their worst.
And there's just fascinating
because there's so many viruses in this world.
There's so many, I mean viruses basically run the world
in the sense that they've been around very long time.
They're everywhere.
They seem to be extremely powerful
and they're distributed kind of way,
but at the same time they're not intelligent
and they're not even living.
Do you have like high level thoughts about this virus
that like in terms of you being fascinated
or terrified or somewhere in between?
So I believe in frameworks, right?
So like one of them is evolution.
Like we're evolved creatures, right?
Yes.
And one of the things about evolution
is it's hyper competitive.
And it's not competitive out of a sense of evil.
It's competitive as a sense of there's endless variation
and variations that work better when.
And then over time there's so many levels
of that competition.
Like multi-solar life partly exists
because of the competition
between different kinds of life forms.
And we know sex partly exists to scramble our genes
so that we have genetic variation
against the invasion of the bacteria and the viruses
and it's endless.
Like I read some funny statistic.
Like the density of viruses and bacteria in the ocean
is really high.
And one third of the bacteria die every day
because the virus is invading them.
Like one third of them.
Wow.
Like I don't know if that number is true
but it was like there's like the amount of competition
and what's going on is stunning.
And there's a theory as we age,
we slowly accumulate bacteria and viruses
and as our immune system kind of goes down,
that's what slowly kills us.
It just feels so peaceful from a human perspective
when we sit back and they're able to have a relaxed
conversation and there's wars going on out there.
Like right now, you're harboring how many bacteria
and the ones, many of them are parasites on you
and some of them are helpful
and some of them are modifying your behavior
and some of them are, it's just really wild.
But this particular manifestation is unusual
in the demographic, how it hit and the political response
that it engendered and the healthcare response
that it engendered and the technology it engendered,
it's kind of wild.
Yeah, the communication on Twitter
that it led to all that kind of stuff,
at every single level, yeah.
But what usually kills is life.
The big extinctions are caused by meteors and volcanoes.
That's the one you're worried about
as opposed to human created bombs that we launch.
Solar flares are another good one.
You know, occasionally solar flares hit the planet.
So it's nature.
Yeah, it's all pretty wild.
On another historic moment, this is perhaps outside
but perhaps within your space of frameworks
that you think about that just happened,
I guess a couple of weeks ago is,
I don't know if you're paying attention at all,
it's the GameStop and Wall Street Beds.
What's the first one?
So it's really fascinating.
There's kind of a theme to this conversation today
because it's like neural networks,
it's cool how there's a large number of people
in a distributed way, almost having a kind of fun,
we're able to take on the powerful elite hedge funds,
centralized powers and overpower them.
Do you have thoughts on this whole saga?
I don't know enough about finance,
but it was like the Elon, Robin Hood guy when they talked.
Yeah, what'd you think about that?
Well, Robin Hood guy didn't know
how the finance system worked.
That was clear, right?
He was treating like the people who settled the transactions
as a black box and suddenly somebody called him up
and say, hey, black box calling you,
your transaction volume means you need
to put out $3 billion right now.
And he's like, I don't have $3 billion.
Like I don't even make any money on these trades.
Why do I have $3 billion while you're sponsoring a trade?
So there was a set of abstractions
that I don't think either, like now we understand it,
like this happens in chip design,
like you buy wafers from TSMC or Samsung or Intel
and they say it works like this
and you do your design based on that
and then chip comes back and it doesn't work.
And then suddenly you started having to open the black boxes.
The transistors really work like they said,
what's the real issue?
So there's a whole set of things
that created this opportunity and somebody spotted it.
Now, people spot these kinds of opportunities all the times.
There's been flash crashes, there's been,
there's always short squeezes that are fairly regular.
Every CEO I know hates the shorts
because they're trying to manipulate their stock
in a way that they make money
and deprive value from both the company
and the investors.
So the fact that some of these stocks were so short,
it's hilarious that this hasn't happened before.
I don't know why and I don't actually know why
some serious hedge funds didn't do it to other hedge funds.
And some of the hedge funds actually made a lot of money
on this.
So my guess is we know 5% of what really happened
and a lot of the players don't know what happened
and the people who probably made the most money
aren't the people that they're talking about.
Do you think there was something,
I mean, this is the cool kind of Elon.
You're the same kind of conversationalist,
which is like first principles questions of like,
what the hell happened?
Just very basic questions of like,
was there something shady going on?
What, you know, who are the parties involved?
That's the basic questions everybody wants to know about.
Yeah, so like we're in a very
hyper competitive world, right?
But transactions like buying and selling stock
is a trust event.
You know, I trust the company,
representative sells properly, you know,
I bought the stock because I think it's gonna go up.
I trust that the regulations are solid.
Now, inside of that, there's all kinds of places
where, you know, humans over trust.
And, you know, this, this expose,
let's say some weak points in the system.
I don't know if it's gonna get corrected.
I don't know if we have close to the real story.
You know, my suspicion is we don't.
And listen to that guy, he was like a little wide eyed
about, and then he did this and then he did that.
And I was like, I think you should know more
about that spit your business than that.
But again, there's many businesses when like,
this layer is really stable.
You stop paying attention to it.
You pay attention to the stuff that's bugging you or new.
You don't pay attention to the stuff
that just seems to work all the time.
You just, you know, the sky's blue every day, California.
And I remember once while it rains there,
I was like, what do we do?
Somebody go bring in the lawn furniture, you know,
like it's getting wet.
You don't know why it's getting wet.
Yeah, it doesn't know.
I was blue for 100 days and now it's, you know, so.
But part of the problem here with Vlad,
the CEO of Robinhood is the scaling
is that what they've been talking about
is there's a lot of unexpected things
that happen with the scaling.
And you have to be, I think the scaling forces you
to then return to the fundamentals.
Well, it's interesting because when you buy and sell stocks,
the scaling is, you know, the stocks don't only move
in a certain range.
And if you buy a stock, you can only lose that amount of money.
On the short market, you can lose a lot more
than you can benefit.
Like it has a weird cause, you know, cost function
or whatever the right word for that is.
So he was trading in a market
where he wasn't actually capitalized for the downside.
If it got outside a certain range.
Now, whether something that various has happened,
I have no idea.
But at some point, the financial risk,
both him and his customers was way outside
of his financial capacity.
And his understanding of how the system work
was clearly weak or he didn't represent himself.
I don't know the person.
When I listened to him,
it could have been the surprise question was
like, how did these guys call them?
You know, it sounded like he was treating stuff
as a black box, maybe he shouldn't have,
but maybe he has a whole pilot expert
somewhere else than it was going on.
I don't know.
Yeah.
I mean, this is one of the qualities of a good leader
is under fire, you have to perform.
And that means to think clearly and to speak clearly.
And he dropped the ball on those things
and understand the problem, quickly learn
and understand the problem at this basic level.
Like what the hell happened?
And my guess is, at some level it was amateurs trading
against experts slash insiders slash people
with special information.
Outsideers versus insiders.
Yeah.
And the insiders, my guess is the next time this happens,
we'll make money on it.
The insiders always win.
Well, they have more tools and more incentive.
I mean, this always happens.
Like the outsiders are doing this for fun.
The insiders are doing this 24-7.
But there's numbers in the outsiders.
This is the interesting thing.
Well, there's numbers on the insiders too.
Like, different kind of numbers.
Different kind of numbers.
But this could be a new era because I don't know,
at least I didn't expect that a bunch of editors could,
you know, there's, you know,
millions of people can get to you.
It was a surprise attack.
The next one will be a surprise.
But don't you think the crowd,
the people are planning the next attack?
We'll see.
But it has to be a surprise.
Can't be the same game.
And so the inside.
It could be, there's a very large number of games to play
and they can be agile about it.
I don't know, I'm not an expert.
Right, that's a good question.
The space of games, how restricted is it?
Yeah.
And the system is so complicated,
it could be relatively unrestricted.
And also like, you know,
during the last couple of financial crashes,
you know, what set it off was, you know,
sets of derivative events where, you know,
you know, Nesim Talib's, you know,
saying is they're, they're,
they're trying to lower volatility in the short run
by creating tail events.
And systems always evolve towards that.
And then they always crash.
Like, like S curve is the, you know,
star low ramp plateau crash.
It's a hundred percent effective.
In the long run.
Let me ask you some advice to put on your profound hat.
There's a bunch of young folks who listen to this thing
for no good reason whatsoever.
Undergraduate students, maybe high school students,
maybe just young folks, a young heart
looking for the next steps to taking life.
What advice would you give to a young person today
about life, maybe career, but also life in general?
Get good at some stuff.
Well, get to know yourself, right?
Like get good at something that you're actually interested in.
You have to love what you're doing to get good at it.
You really gotta find that.
Don't waste all your time doing stuff
that's just boring or bland or numbing.
Right.
Don't let old people screw you.
Well, people get talked into doing all kinds of shit
and up to huge student debts and like,
there's so much crap going on, you know?
And they drains your time and drains your energy.
They are quite a sign, you know,
thesis that, you know, the older generation won't let go.
They're trapping all the young people.
I think that's the truth to that.
Yeah.
Just because you're old doesn't mean you stop thinking.
I know that's a really original old people.
I'm an old person.
So, but you have to be conscious about it.
You can fall into the rut and then do that.
You know, when I hear young people spouting opinions
that sounds like they come from Fox News or CNN,
I think they've been captured by group thinking, memes,
and stuff.
I supposed to think on their own.
You know, so if you find yourself repeating
what everybody else is saying,
you're not gonna have a good life.
Like, that's not how the world works.
It may be, it seems safe,
but it puts you at great jeopardy for
while being boring or unhappy or...
How long did it take you to find the thing
that you have fun with?
Oh, I don't know.
I've been a fun person since I was pretty little.
So, everything.
I've gone through a couple of periods
of depression in my life.
Or a good reason or for the reason
that doesn't make any sense.
Yeah.
Like, some things are hard.
Like, you go through mental transitions in high school.
I was really depressed for a year
and I think I had my first midlife crisis at 26.
I kind of thought, is this all there is?
Like, I was working at a job that I loved.
And, but I was going to work
and all my time was consumed.
What's the escape out of that depression?
What's the answer to is, is this all there is?
Well, a friend of mine, I asked him,
because he was working his ass off.
I said, what's your work-life balance?
Like, there's work, friends, family, personal time.
Are you balancing in that?
And he said, work 80%, family 20%.
And I tried to, I tried to find some time to sleep.
Like, there's no personal time.
There's no passionate time.
Like, you know, the young people
are often passionate about work.
So, and I was starting to like that.
But you need to, you need to have some space
in your life for different things.
And that's, that creates, that makes you resistant
to the hold, the dip, the deep dips
into depression kind of thing.
Yeah, well, you have to get to know yourself too.
Meditation helps.
Some physical, something physically intense helps.
Like the weird places your mind goes kind of thing.
Like, and why does it happen?
Why do you do what you do?
Like triggers, like the things that cause your mind
to go to different places kind of thing.
Or like events, like-
Your upbringing for better or worse,
whether your parents are great people or not,
you come into adulthood with all kinds of emotional burdens.
Yeah.
And you can see some people are so bloody stiff
and restrained and they think, you know,
the world's fundamentally negative.
Like you maybe, you have unexplored territory.
Yeah.
Or you're afraid of something.
Definitely afraid of quite a few things.
But then you gotta go face them.
Like, what's the worst thing that can happen?
You're gonna die, right?
Like that's inevitable.
You might as well get over that.
Like a hundred percent, that's right.
Like people are worried about the virus,
but you know, the human condition is pretty deadly.
There's something about embarrassment
that's, I've competed a lot in my life.
And I think the, if I'm too introspective,
the thing I'm most afraid of is being like humiliated,
I think.
Nobody cares about that.
Like you're the only person on the planet
that cares about you being humiliated.
Yeah, exactly.
So it can really useless thought.
It is.
It's like, you're all humiliated.
Something happened in a room full of people
and they walk out and they didn't think about it.
One more second.
Or maybe somebody told a funny story
to somebody else and they didn't care either.
And then it just pays to throw out, yeah.
Yeah.
No, I know it too.
I mean, I've been really embarrassed about shit
that nobody cared about myself.
Yeah.
It's a funny thing.
So the worst thing ultimately is just.
Yeah, but that's a cage and then you have to get out of it.
Like once you, here's the thing.
Once you find something like that,
you have to be determined to break it.
Because otherwise you'll just, you know,
so you accumulate that kind of junk
and then you die as a, you know, a mess.
So the goal, I guess it's like a cage within a cage.
I guess the goal is to die in the biggest possible cage.
Well, ideally you'd have no cage.
Well.
You know, people do get enlightened.
I've got a few.
It's great.
You found a few?
There's a few out there?
I don't know.
Of course there are.
Either that or they have, you know,
it's a great sales pitch.
There's like enlightened people,
write books and do all kinds of stuff.
It's a good way to sell a book.
I'll give you that.
You've never met somebody you just thought,
they just kill me.
Like they just, like mental clarity, humor.
No, 100%, but I just feel like they're living
in a bigger cage.
They have their own.
You still think there's a cage?
There's still a cage.
You secretly suspect there's always a cage.
There's no, there's nothing outside the universe.
There's nothing outside the cage.
You work, you work, you work at a bunch of companies.
You led a lot of amazing teams.
I don't, I'm not sure if you've ever been like
at the early stages of a startup,
but do you have advice for somebody that wants to do a startup
or build a company, like build a strong team of engineers
that are passionate and just want to solve a big problem?
Like, is there a more specifically on that point?
Well, you have to be really good at stuff.
If you're going to lead and build a team,
you better be really interested in how people work and think.
The people or the solution to the problem.
So there's two things, right?
One is how people work and the other is the-
Actually, there's quite a few successful startups
that's really clear to the founders.
Don't know anything about people.
Like the idea was so powerful
that it propelled them.
But I suspect somewhere early,
they hired some people who understood people
because people really need a lot of care
and feeding the collaborate and work together
and feel engaged and work hard.
Like startups are all about out producing other people.
Like you're nimble because you don't have any legacy.
You don't have a bunch of people who are depressed
about life and just showing up.
So startups have a lot of advantages that way, you know?
Do you like the- Steve Jobs talked about this idea
of A players and B players?
I don't know if you know this formulation.
Yeah, no.
Organizations that get taken over by B player leaders
often really underperform their HRC players.
That said, in big organizations,
there's so much work to do.
And there's so many people who are happy to do what,
you know, like the leadership
or the big idea people would consider menial jobs.
And, you know, you need a place for them,
but you need an organization
that both values and rewards them,
but doesn't let them take over the leadership of it.
Got it.
So you need to have an organization
that's resistant to that.
But in the early days,
the notion with Steve was that like one B player in a room
of A players will be like destructive to the whole.
I've seen that happen.
I don't know if it's like always true.
Like, you know, you run into people who are clearly B players,
but they think they're A players.
And so they have a loud voice at the table
and they make lots of demands for that.
But there's other people are like, I know I am.
I just want to work with, you know, cool people on cool shit
and just tell me what to do and I'll go get it done.
Yeah.
You know, so you have to, again, this is like people skills.
Like, what kind of person is it?
You know, I've met some really great people I love working with.
That weren't the biggest ID people,
they're most productive ever, but they show up,
they get it done.
You know, they create connection and community
that people value, it's pretty diverse.
I don't think there's a recipe for that.
I got to ask you about love.
I heard you into this now.
Into this love thing?
Yeah.
Is this, do you think this is your solution to your depression?
No, I'm just trying to, like you said,
delight in people on occasion trying to sell a book.
I'm writing a book about love.
You're writing a book about love.
No, I'm not.
I'm not.
I'm a friend of mine, he's gonna,
somebody said, you should really write a book
about the, you know, your management philosophy.
He said, it'd be a short book.
Well, that one was all pretty well.
What role do you think love, family, friendship,
all that kind of human stuff play in a successful life?
You've been exceptionally successful in the space of
like running teams, building cool shit in this world,
creating some amazing things.
What, did love get in the way?
Did love help the family get in the way?
Did family help friendship?
You want the engineer's answer?
Please.
So, but first love is functional, right?
It's functional in what way?
So we habituate ourselves to the environment.
And actually Jordan told me,
Jordan Peterson told me this line.
So you go through life and you just get used to everything
except for the things you love.
They remain new.
Like this is really useful for, you know,
like other people's children and dogs and, you know, trees,
you just don't pay that much attention to them.
You're old kids, you're monitoring them really closely.
Like, and if they go off a little bit,
because you love them, if you're smart,
if you're gonna be a successful parent,
you notice it right away.
You don't habituate just things you love.
And if you wanna be successful at work,
if you don't love it,
you're not gonna put the time in somebody else.
It's somebody else that loves it.
Like, cause it's new and interesting
and that lets you go to the next level.
So it's a thing, it's just a function
that generates newness and novelty and surprises,
you know, all those kinds of things.
It's really interesting.
Like, and there's people figured out lots of, you know,
frameworks for this, you know, like,
like humans seem to go in partnership,
go through, you know, interests.
Like, suddenly somebody's interesting
and then you're infatuated with them
and then you're in love with them.
And then you, you know, different people have ideas
about parental love or mature love.
Like, you go through a cycle of that,
which keeps us together and it's, you know,
super functional for creating families
and creating communities and making you support somebody
despite the fact that you don't love them.
Like, and it can be really enriching.
You know, now in the work-life balance scheme,
if all you do is work,
you think you may be optimizing your work potential,
but if you don't love your work
or you don't have family and friends
and things you care about,
your brain isn't well balanced.
Like everybody knows experience,
if you worked on something all week,
you went home and took two days off
and you came back in.
The odds of you working on the thing,
picking up right where you left off is zero.
Your brain refactored it.
But being in love is great.
It's like changes the color of the light in the room.
It creates a spaciousness that's different.
It helps you think.
It makes you strong.
Bukowski had this line about love being a fog
that dissipates with the first light of reality.
In the morning.
It's death depressing.
I think it's the other way around.
It lasts.
Well, like you said, it's just a function.
It's a thing that generously helps.
It can be the light that actually enlivens your world
and creates the interest and the power and the strength
to go do something.
Well, it's like, that sounds like,
you know, there's like physical love,
emotional love, intellectual love, spiritual love, right?
Isn't it all the same thing kind of?
Nope.
You should differentiate that.
Maybe that's your problem.
In your book, you should refine that a little bit.
With different chapters.
Yeah, there's different chapters.
What's the, what's, these are,
aren't these just different layers of the same thing
or the stack of physical?
People, people, some people are addicted to physical love
and they have no idea about emotional or intellectual love.
I don't know if they're the same things.
I think they're different.
That's true.
They could be different.
It'd be, I guess the ultimate goal is for it to be the same.
Well, if you want something to be bigger and interesting,
you should find all its components
and differentiate them, not clown it together.
People do this all the time.
I mean, yeah, the modularity.
Get your abstraction layers right
and then you can, you have room to breathe.
Well, maybe you can write the forward to my book about love.
Or the afterwards.
And the after.
You really tried.
I feel like Lex has made a lot of progress with this book.
Well, you have things in your life that you love.
Yeah.
Yeah.
And they are, you're right.
They're modular.
It's, it's quite.
And you can have multiple things with the same person
or the same thing.
Yeah.
But yeah.
Depending on the moment of the day.
Yeah, there's, like what Bacowski described is that moment
when you go from being in love
to having a different kind of love.
Yeah.
Right.
And that's a transition.
But when it happens, if you've read the owner's manual
and you believed it, you would have said,
oh, this happened.
It doesn't mean it's not love.
It's a different kind of love.
But, but maybe there's something better about that
as you grow old, if all you do is regret
how you used to be, it's sad.
Right.
You should have learned a lot of things
because like who you can be in your future self is,
it's actually more interesting and possibly delightful
than, you know, being a mad kid
and love with the next person.
Like that's super fun when it happens,
but that's, that's, you know, 5% of the possibility.
But yeah, that's right.
That there's a lot more fun to be had
in the long lasting stuff.
Yeah. Or meaning, you know, if that's your thing.
Which is a kind of fun.
It's a deeper kind of fun.
And it's surprising, you know, that's like,
like the thing I like is surprises, you know,
and you just never know what's going to happen.
But you have to look carefully and you have to work at it.
You have to think about it.
Yeah. You have to see the surprises when they happen, right?
You have to be looking for it.
From the branching perspective, you mentioned regrets.
Do you have regrets about your own trajectory?
Oh yeah, of course.
Yeah, some of it's painful,
but you want to hear the painful stuff?
No.
I'd say like in terms of working with people,
when people did say stuff I didn't like,
especially if it was a bit nefarious,
I took it personally and I also felt it was personal
about them.
But a lot of times, like humans are, you know,
most humans are a mess, right?
And then they act out and they do stuff.
And this psychologist I heard long time ago said,
you tend to think somebody does something to you.
But really what they're doing is they're doing
what they're doing while they're in front of you.
It's not that much about you, right?
And as I got more interested in, you know,
when I work with people, I think about them
and probably analyze them and understand them a little bit.
And then when they do stuff, I'm way less surprised.
And I'm way, you know, and if it's bad, I'm way less hurt.
And I react way less.
Like I sort of expect everybody's got their shit.
Yeah.
And it's not about you.
It's not about me that much.
It's like, you know, you do something
and you think you're embarrassed, but nobody cares.
Like, and somebody's really mad at you.
The odds of it being about you,
no, they're getting mad the way they're doing that
because of some pattern they learned.
And you know, and maybe you can help them
if you care enough about it,
or you could see it coming and step out of the way.
Like, I wish I was way better at that.
I'm a bit of a hothead.
And in support of-
You regret that?
You said with Steve, that was a feature, not a bug.
Yeah, well, he was using it as the counter for orderliness
that would crush his work.
Well, you were doing the same.
Maybe.
I don't think my vision was big enough.
It was more like I just got pissed off and did stuff.
I'm sure that's the, yeah, you're telling-
I don't know if it had the,
it didn't have the amazing effect
of creating a trillion dollar company.
It was more like I just got pissed off and left
and or made enemies that he shouldn't have.
And yeah, it's hard.
Like, I didn't really understand politics
until I worked at Apple,
where, you know, Steve was a master player of politics
and his staff had to be or they wouldn't survive him.
And it was definitely part of the culture.
And then I've been in companies where they say it's political,
but it's all, you know, fun and games compared to Apple.
And it's not that the people at Apple are bad people,
it's just they operate politically at a higher level.
You know, it's not like, oh, somebody said something bad
about somebody, somebody else, which is most politics.
That's, you know, they had strategies
about accomplishing their goals,
sometimes, you know, over the dead bodies of their enemies,
you know, with sophistication,
yeah, more game of thrones and sophistication
and like a big time factor rather than a, you know.
Well, that requires a lot of control over your emotions,
I think, to have a bigger strategy in the way you behave.
Yeah, and it's effective in the sense
that coordinating thousands of people
to do really hard things,
where many of the people in there
don't understand themselves much less
how they're participating,
creates all kinds of, you know, drama and problems
that, you know, our solution is political in nature.
Like, how do you convince people?
How do you leverage them?
How do you motivate them?
How do you get rid of them?
How, you know, like, there's so many layers of that
that are interesting.
And even though some of it, let's say, may be tough,
it's not evil unless, you know,
you use that skill to evil purposes,
which some people obviously do.
But it's a skill set that operates.
You know, and I wish I'd, you know,
I was interested in it, but I, you know,
it was sort of like, I'm an engineer, I do my thing.
And, you know, there's times when I could have had
a way bigger impact if I, you know,
knew how to, if I paid more attention
and knew more about that.
Yeah, about the human layer of the stack.
Yeah, that human political power,
you know, expression layer of the stack,
which is complicated.
And there's lots to know about it.
I mean, people aren't good at it, they're just amazing.
And when they're good at it, and let's say,
relatively kind and oriented in a good direction,
you can really feel, you can get lots of stuff done
and coordinate things that you never thought possible.
But all people like that also have some pretty hard edges
because, you know, it's a heavy lift.
And I wish I'd spent more time with that when I was younger,
but maybe I wasn't ready.
You know, I was a wide-eyed kid for 30 years.
Still a bit of a kid.
Yeah, I know.
What do you hope your legacy is when there's a book,
like a H Hikers guy to the galaxy,
and this is like a one sentence entry about you
and brother from like that guy lived at some point.
There's not many, you know,
not many people would be remembered.
You're one of the sparkling little human creatures
that had a big impact on the world.
How do you hope you'll be remembered?
My daughter was trying to get,
she edited my Wikipedia page to say that I was a legend
and a guru, but they took it out.
So she put it back in, she's 15.
I think that was probably the best part of my legacy.
She got her sister and they were all excited.
They were like trying to put it in the references
because there's articles in that on the top.
So in the eyes of your kids, you're a legend.
Well, they're pretty skeptical
because they know be better than that.
They're like, dad.
So yeah, that's that's that kind of stuff is super fun
in terms of the big legend stuff, I don't care.
They don't care.
I don't really care.
You're just an engineer.
Yeah.
They've been thinking about building a big pyramid.
So I had a debate with a friend about whether pyramids
or craters or cooler.
And he realized that there's craters everywhere,
but you know, they built a couple pyramids 5,000 years ago.
And they remember you for a while.
We're still talking about it.
So I think that would be cool.
Those aren't easy to build.
Oh, I know.
And they don't actually know how they built them,
which is great.
It's either AGI or aliens could be involved.
So I think, I think you're gonna have to figure out
quite a few more things than just the basics
of civil engineering.
So I guess you hope your legacy is pyramids.
That would be cool.
And my Wikipedia page, you know,
getting updated by my daughter periodically.
Like those two things would pretty much make it.
Jim, it's a huge honor talking to you again.
I hope we talk many more times in the future.
I can't wait to see what you do with TenseTorrent.
I can't wait to use it.
I can't wait for you to revolutionize yet another space
in computing.
It's a huge honor to talk to you.
Thanks for talking to me today.
This was fun.
Thanks for listening to this conversation with Jim Keller.
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