Peter Wang: Python and the Source Code of Humans, Computers, and Reality

Lex Fridman (01:10.040)

In the 90s?

Peter Wang (01:10.880)

Yeah, in the late 90s.

Lex Fridman (01:12.080)

And that was my first job out of college.

Lex Fridman (01:15.160)

And we kept trying to do more and more abstract

Lex Fridman (01:18.680)

and higher order programming in C++,

Peter Wang (01:20.520)

which at the time was quite difficult.

Lex Fridman (01:23.040)

With templates, the compiler support wasn't great, et cetera.

Peter Wang (01:26.560)

So, when I started playing around with Python,

Lex Fridman (01:28.720)

that was my first time encountering

Peter Wang (01:30.480)

really first class support for types, for functions,

Lex Fridman (01:33.600)

and things like that.

Lex Fridman (01:34.440)

And it felt so incredibly expressive.

Lex Fridman (01:37.200)

So, that was what kind of made me fall in love

Peter Wang (01:39.120)

with it a little bit.

Lex Fridman (01:39.960)

And also, once you spend a lot of time

Peter Wang (01:42.280)

in a C++ dev environment,

Lex Fridman (01:44.160)

the ability to just whip something together

Peter Wang (01:46.200)

that basically runs and works the first time is amazing.

Lex Fridman (01:49.680)

So, really productive scripting language.

Peter Wang (01:51.960)

I mean, I knew Perl, I knew Bash, I was decent at both.

Lex Fridman (01:55.280)

But Python just made everything,

Peter Wang (01:57.160)

it made the whole world accessible.

Lex Fridman (01:59.680)

I could script this and that and the other,

Peter Wang (02:01.240)

network things, little hard drive utilities.

Lex Fridman (02:04.000)

I could write all of these things

Peter Wang (02:05.040)

in the space of an afternoon.

Lex Fridman (02:06.520)

And that was really, really cool.

Peter Wang (02:07.640)

So, that's what made me fall in love.

Lex Fridman (02:08.600)

Is there something specific you could put your finger on

Lex Fridman (02:11.560)

that you're not programming in Perl today?

Lex Fridman (02:14.080)

Like, why Python for scripting?

Peter Wang (02:17.120)

I think there's not a specific thing

Lex Fridman (02:19.640)

as much as the design motif of both the creator

Peter Wang (02:22.800)

of the language and the core group of people

Lex Fridman (02:25.280)

that built the standard library around him.

Peter Wang (02:28.920)

There was definitely, there was a taste to it.

Lex Fridman (02:32.160)

I mean, Steve Jobs used that term

Peter Wang (02:34.320)

in somewhat of an arrogant way,

Lex Fridman (02:35.680)

but I think it's a real thing,

Peter Wang (02:37.120)

that it was designed to fit.

Lex Fridman (02:39.200)

A friend of mine actually expressed this really well.

Peter Wang (02:40.880)

He said, Python just fits in my head.

Lex Fridman (02:42.960)

And there's nothing better to say than that.

Peter Wang (02:45.200)

Now, people might argue modern Python,

Lex Fridman (02:47.880)

there's a lot more complexity,

Lex Fridman (02:49.240)

but certainly as version 1.5.2,

Lex Fridman (02:51.760)

I think was my first version,

Peter Wang (02:53.360)

that fit in my head very easily.

Lex Fridman (02:54.800)

So, that's what made me fall in love with it.

Peter Wang (02:56.560)

Okay, so the most beautiful feature of Python

Lex Fridman (03:01.400)

that made you stay in love.

Peter Wang (03:03.040)

It's like over the years, what has like,

Lex Fridman (03:06.560)

you do a double take and you return too often

Peter Wang (03:09.520)

as a thing that just brings you a smile.

Lex Fridman (03:11.560)

I really still like the ability to play with meta classes

Lex Fridman (03:17.000)

and express higher order of things.

Lex Fridman (03:19.000)

When I have to create some new object model

Lex Fridman (03:22.040)

to model something, right?

Lex Fridman (03:23.360)

It's easy for me,

Peter Wang (03:24.400)

cause I'm pretty expert as a Python programmer.

Lex Fridman (03:27.080)

I can easily put all sorts of lovely things together

Lex Fridman (03:29.800)

and use properties and decorators and other kinds of things

Lex Fridman (03:32.920)

and create something that feels very nice.

Peter Wang (03:34.680)

So, that to me, I would say that's tied

Lex Fridman (03:37.320)

with the NumPy and vectorization capabilities.

Peter Wang (03:40.640)

I love thinking in terms of the matrices and the vectors

Lex Fridman (03:43.800)

and these kind of data structures.

Peter Wang (03:46.080)

So, I would say those two are kind of tied for me.

Lex Fridman (03:49.400)

So, the elegance of the NumPy data structure,

Peter Wang (03:52.720)

like slicing through the different multi dimensional.

Lex Fridman (03:54.760)

Yeah, there's just enough things there.

Peter Wang (03:56.200)

It's like a very, it's a very simple, comfortable tool.

Lex Fridman (04:00.040)

Just, it's easy to reason about what it does

Peter Wang (04:02.800)

when you don't stray too far afield.

Lex Fridman (04:05.000)

Can you put your finger on how to design a language

Lex Fridman (04:09.960)

such that it fits in your head?

Lex Fridman (04:11.880)

Certain things like the colon

Peter Wang (04:14.040)

or the certain notation aspects of Python

Lex Fridman (04:17.160)

that just kind of work.

Peter Wang (04:18.640)

Is it something you have to kind of write out on paper,

Lex Fridman (04:22.200)

look and say, it's just right?

Lex Fridman (04:24.640)

Is it a taste thing or is there a systematic process?

Lex Fridman (04:27.600)

What's your sense?

Peter Wang (04:28.800)

I think it's more of a taste thing.

Lex Fridman (04:31.520)

But one thing that should be said

Lex Fridman (04:33.560)

is that you have to pick your audience, right?

Lex Fridman (04:36.360)

So, the better defined the user audience is

Peter Wang (04:39.160)

or the users are, the easier it is to build something

Lex Fridman (04:42.280)

that fits in their minds because their needs

Peter Wang (04:45.200)

will be more compact and coherent.

Lex Fridman (04:47.240)

It is possible to find a projection, right?

Peter Wang (04:49.160)

A compact projection for their needs.

Lex Fridman (04:50.920)

The more diverse the user base, the harder that is.

Lex Fridman (04:54.480)

And so, as Python has grown in popularity,

Lex Fridman (04:57.120)

that's also naturally created more complexity

Peter Wang (05:00.080)

as people try to design any given thing.

Lex Fridman (05:01.800)

There'll be multiple valid opinions

Peter Wang (05:04.120)

about a particular design approach.

Lex Fridman (05:06.240)

And so, I do think that's the downside of popularity.

Peter Wang (05:10.240)

It's almost an intrinsic aspect

Lex Fridman (05:11.440)

of the complexity of the problem.

Peter Wang (05:13.040)

Well, at the very beginning,

Lex Fridman (05:14.440)

aren't you an audience of one, isn't ultimately,

Peter Wang (05:17.440)

aren't all the greatest projects in history

Lex Fridman (05:19.480)

were just solving a problem that you yourself had?

Peter Wang (05:21.800)

Well, so Clay Shirky in his book on crowdsourcing

Lex Fridman (05:25.400)

or his kind of thoughts on crowdsourcing,

Peter Wang (05:27.520)

he identifies the first step of crowdsourcing

Lex Fridman (05:29.520)

is me first collaboration.

Peter Wang (05:31.440)

You first have to make something

Lex Fridman (05:32.480)

that works well for yourself.

Peter Wang (05:34.280)

It's very telling that when you look at all of the impactful

Lex Fridman (05:37.720)

big project, well, they're fundamental projects now

Peter Wang (05:40.000)

in the SciPy and Pydata ecosystem.

Lex Fridman (05:42.560)

They all started with the people in the domain

Peter Wang (05:46.720)

trying to scratch their own itch.

Lex Fridman (05:48.200)

And the whole idea of scratching your own itch

Peter Wang (05:49.720)

is something that the open source

Lex Fridman (05:51.280)

or the free software world has known for a long time.

Lex Fridman (05:53.640)

But in the scientific computing areas,

Lex Fridman (05:56.800)

these are assistant professors

Peter Wang (05:58.240)

or electrical engineering grad students.

Lex Fridman (06:00.520)

They didn't have really a lot of programming skill

Peter Wang (06:03.000)

necessarily, but Python was just good enough

Lex Fridman (06:05.520)

for them to put something together

Lex Fridman (06:06.720)

that fit in their domain, right?

Lex Fridman (06:09.400)

So it's almost like a,

Peter Wang (06:11.120)

it's a necessity is the mother of invention aspect.

Lex Fridman (06:13.960)

And also it was a really harsh filter

Peter Wang (06:16.880)

for utility and compactness and expressiveness.

Lex Fridman (06:20.880)

Like it was too hard to use,

Peter Wang (06:22.400)

then they wouldn't have built it

Lex Fridman (06:23.360)

because that was just too much trouble, right?

Peter Wang (06:24.960)

It was a side project for them.

Lex Fridman (06:26.160)

And also necessity creates a kind of deadline.

Peter Wang (06:28.120)

It seems like a lot of these projects

Lex Fridman (06:29.440)

are quickly thrown together in the first step.

Lex Fridman (06:32.320)

And that, even though it's flawed,

Lex Fridman (06:35.560)

that just seems to work well for software projects.

Peter Wang (06:38.840)

Well, it does work well for software projects in general.

Lex Fridman (06:41.280)

And in this particular space,

Peter Wang (06:43.520)

one of my colleagues, Stan Siebert identified this,

Lex Fridman (06:46.320)

that all the projects in the SciPy ecosystem,

Peter Wang (06:50.360)

if we just rattle them off,

Lex Fridman (06:51.200)

there's NumPy, there's SciPy

Peter Wang (06:53.040)

built by different collaborations of people.

Lex Fridman (06:55.000)

Although Travis is the heart of both of them.

Lex Fridman (06:57.680)

But NumPy coming from numeric and numery,

Lex Fridman (06:59.360)

these are different people.

Lex Fridman (07:00.680)

And then you've got Pandas,

Lex Fridman (07:01.840)

you've got Jupyter or IPython,

Peter Wang (07:04.480)

there's Matplotlib,

Lex Fridman (07:06.880)

there's just so many others, I'm not gonna do justice

Peter Wang (07:09.680)

if I try to name them all.

Lex Fridman (07:10.720)

But all of them are actually different people.

Lex Fridman (07:12.800)

And as they rolled out their projects,

Lex Fridman (07:15.280)

the fact that they had limited resources

Peter Wang (07:17.560)

meant that they were humble about scope.

Lex Fridman (07:21.600)

A great famous hacker, Jamie Zawisky,

Peter Wang (07:23.560)

once said that every geek's dream

Lex Fridman (07:26.040)

is to build the ultimate middleware, right?

Lex Fridman (07:29.280)

And the thing is with these scientists turned programmers,

Lex Fridman (07:32.280)

they had no such dream.

Peter Wang (07:33.120)

They were just trying to write something

Lex Fridman (07:34.800)

that was a little bit better for what they needed,

Peter Wang (07:36.560)

the MATLAB,

Lex Fridman (07:37.640)

and they were gonna leverage what everyone else had built.

Lex Fridman (07:39.920)

So naturally, almost in kind of this annealing process

Lex Fridman (07:42.640)

or whatever, we built a very modular cover

Peter Wang (07:46.480)

of the basic needs of a scientific computing library.

Lex Fridman (07:50.280)

If you look at the whole human story,

Lex Fridman (07:51.920)

how much of a leap is it?

Lex Fridman (07:53.680)

We've developed all kinds of languages,

Peter Wang (07:55.480)

all kinds of methodologies for communication.

Lex Fridman (07:57.720)

It just kind of like grew this collective intelligence,

Peter Wang (08:00.560)

civilization grew, it expanded, wrote a bunch of books,

Lex Fridman (08:04.400)

and now we tweet how big of a leap is programming

Lex Fridman (08:08.680)

if programming is yet another language?

Lex Fridman (08:10.880)

Is it just a nice little trick

Peter Wang (08:12.880)

that's temporary in our human history,

Lex Fridman (08:15.000)

or is it like a big leap in the,

Peter Wang (08:19.680)

almost us becoming another organism

Lex Fridman (08:23.120)

at a higher level of abstraction, something else?

Peter Wang (08:26.160)

I think the act of programming

Lex Fridman (08:28.240)

or using grammatical constructions

Peter Wang (08:32.360)

of some underlying primitives,

Lex Fridman (08:34.920)

that is something that humans do learn,

Lex Fridman (08:37.520)

but every human learns this.

Lex Fridman (08:38.840)

Anyone who can speak learns how to do this.

Lex Fridman (08:41.160)

What makes programming different

Lex Fridman (08:42.440)

has been that up to this point,

Peter Wang (08:44.840)

when we try to give instructions to computing systems,

Lex Fridman (08:49.000)

all of our computers, well, actually this is not quite true,

Lex Fridman (08:51.560)

but I'll first say it,

Lex Fridman (08:53.080)

and then I'll tell you why it's not true.

Lex Fridman (08:55.000)

But for the most part,

Lex Fridman (08:56.000)

we can think of computers as being these iterated systems.

Lex Fridman (08:58.880)

So when we program,

Lex Fridman (08:59.920)

we're giving very precise instructions to iterated systems

Peter Wang (09:04.200)

that then run at incomprehensible speed

Lex Fridman (09:07.360)

and run those instructions.

Peter Wang (09:08.800)

In my experience,

Lex Fridman (09:10.160)

some people are just better equipped

Peter Wang (09:12.720)

to model systematic iterated systems,

Lex Fridman (09:16.880)

well, whatever, iterated systems in their head.

Peter Wang (09:20.120)

Some people are really good at that,

Lex Fridman (09:21.760)

and other people are not.

Lex Fridman (09:23.800)

And so when you have like, for instance,

Lex Fridman (09:26.080)

sometimes people have tried to build systems

Peter Wang (09:27.640)

that make programming easier by making it visual,

Lex Fridman (09:30.080)

drag and drop.

Lex Fridman (09:31.240)

And the issue is you can have a drag and drop thing,

Lex Fridman (09:33.640)

but once you start having to iterate the system

Peter Wang (09:35.200)

with conditional logic,

Lex Fridman (09:36.120)

handling case statements and branch statements

Lex Fridman (09:37.880)

and all these other things,

Lex Fridman (09:39.480)

the visual drag and drop part doesn't save you anything.

Peter Wang (09:42.040)

You still have to reason about this giant iterated system

Lex Fridman (09:44.680)

with all these different conditions around it.

Lex Fridman (09:46.360)

That's the hard part, right?

Lex Fridman (09:48.160)

So handling iterated logic, that's the hard part.

Peter Wang (09:52.240)

The languages we use then emerge

Lex Fridman (09:54.160)

to give us the ability and capability over these things.

Peter Wang (09:57.240)

Now, the one exception to this rule, of course,

Lex Fridman (09:58.880)

is the most popular programming system in the world,

Peter Wang (10:00.680)

which is Excel, which is a data flow

Lex Fridman (10:03.280)

and a data driven, immediate mode,

Peter Wang (10:05.440)

data transformation oriented programming system.

Lex Fridman (10:08.360)

And this actually not an accident

Peter Wang (10:10.400)

that that system is the most popular programming system

Lex Fridman (10:12.840)

because it's so accessible

Peter Wang (10:14.440)

to a much broader group of people.

Lex Fridman (10:16.920)

I do think as we build future computing systems,

Peter Wang (10:21.240)

you're actually already seeing this a little bit,

Lex Fridman (10:22.920)

it's much more about composition of modular blocks.

Peter Wang (10:25.680)

They themselves actually maintain all their internal state

Lex Fridman (10:29.600)

and the interfaces between them

Peter Wang (10:31.040)

are well defined data schemas.

Lex Fridman (10:32.960)

And so to stitch these things together using like IFTTT

Peter Wang (10:35.880)

or Zapier or any of these kind of,

Lex Fridman (10:38.720)

I would say compositional scripting kinds of things,

Peter Wang (10:42.000)

I mean, HyperCard was also a little bit in this vein.

Lex Fridman (10:44.960)

That's much more accessible to most people.

Peter Wang (10:47.560)

It's really that implicit state

Lex Fridman (10:49.920)

that's so hard for people to track.

Peter Wang (10:52.000)

Yeah, okay, so that's modular stuff,

Lex Fridman (10:53.400)

but there's also an aspect

Peter Wang (10:54.440)

where you're standing on the shoulders of giants.

Lex Fridman (10:55.920)

So you're building like higher and higher levels

Peter Wang (10:58.720)

of abstraction, but you do that a little bit with language.

Lex Fridman (11:02.160)

So with language, you develop sort of ideas,

Peter Wang (11:05.160)

philosophies from Plato and so on.

Lex Fridman (11:07.600)

And then you kind of leverage those philosophies

Peter Wang (11:09.520)

as you try to have deeper and deeper conversations.

Lex Fridman (11:12.640)

But with programming,

Peter Wang (11:13.560)

it seems like you can build much more complicated systems.

Lex Fridman (11:17.040)

Like without knowing how everything works,

Peter Wang (11:18.920)

you can build on top of the work of others.

Lex Fridman (11:21.320)

And it seems like you're developing

Peter Wang (11:22.840)

more and more sophisticated expressions,

Lex Fridman (11:27.920)

ability to express ideas in a computational space.

Peter Wang (11:31.280)

I think it's worth pondering the difference here

Lex Fridman (11:35.040)

between complexity and complication.

Peter Wang (11:40.400)

Okay, right. Back to Excel.

Lex Fridman (11:42.240)

Well, not quite back to Excel,

Lex Fridman (11:43.280)

but the idea is when we have a human conversation,

Lex Fridman (11:47.560)

all languages for humans emerged

Peter Wang (11:51.240)

to support human relational communications,

Lex Fridman (11:55.560)

which is that the person we're communicating with

Peter Wang (11:57.720)

is a person and they would communicate back to us.

Lex Fridman (12:01.240)

And so we sort of hit a resonance point, right?

Peter Wang (12:05.640)

When we actually agree on some concepts.

Lex Fridman (12:07.480)

So there's a messiness to it and there's a fluidity to it.

Peter Wang (12:10.280)

With computing systems,

Lex Fridman (12:11.880)

when we express something to the computer and it's wrong,

Peter Wang (12:14.280)

we just try again.

Lex Fridman (12:15.400)

So we can basically live many virtual worlds

Peter Wang (12:17.520)

of having failed at expressing ourselves to the computer

Lex Fridman (12:20.200)

until the one time we expressed ourselves right.

Peter Wang (12:22.840)

Then we kind of put in production

Lex Fridman (12:23.960)

and then discover that it's still wrong

Peter Wang (12:25.920)

a few days down the road.

Lex Fridman (12:27.160)

So I think the sophistication of things

Peter Wang (12:30.480)

that we build with computing,

Lex Fridman (12:32.520)

one has to really pay attention to the difference

Peter Wang (12:35.600)

between when an end user is expressing something

Lex Fridman (12:38.240)

onto a system that exists

Peter Wang (12:39.960)

versus when they're extending the system

Lex Fridman (12:42.520)

to increase the system's capability

Peter Wang (12:45.480)

for someone else to then interface with.

Lex Fridman (12:47.480)

We happen to use the same language for both of those things

Peter Wang (12:49.560)

in most cases, but it doesn't have to be that.

Lex Fridman (12:52.080)

And Excel is actually a great example of this,

Peter Wang (12:54.360)

of kind of a counterpoint to that.

Lex Fridman (12:56.240)

Okay, so what about the idea of, you said messiness.

Peter Wang (13:01.440)

Wouldn't you put the software 2.0 idea,

Lex Fridman (13:06.240)

this idea of machine learning

Peter Wang (13:08.720)

into the further and further steps

Lex Fridman (13:12.560)

into the world of messiness.

Peter Wang (13:14.560)

The same kind of beautiful messiness of human communication.

Lex Fridman (13:17.640)

Isn't that what machine learning is?

Peter Wang (13:19.120)

Is building on levels of abstraction

Lex Fridman (13:23.560)

that don't have messiness in them,

Peter Wang (13:25.520)

that at the operating system level,

Lex Fridman (13:27.400)

then there's Python, the programming languages

Peter Wang (13:29.400)

that have more and more power.

Lex Fridman (13:30.920)

But then finally, there's neural networks

Peter Wang (13:34.640)

that ultimately work with data.

Lex Fridman (13:38.200)

And so the programming is almost in the space of data

Lex Fridman (13:40.560)

and the data is allowed to be messy.

Lex Fridman (13:42.480)

Isn't that a kind of program?

Lex Fridman (13:43.880)

So the idea of software 2.0 is a lot of the programming

Lex Fridman (13:47.360)

happens in the space of data, so back to Excel,

Peter Wang (13:52.320)

all roads lead back to Excel, in the space of data

Lex Fridman (13:55.080)

and also the hyperparameters of the neural networks.

Lex Fridman (13:57.400)

And all of those allow the same kind of messiness

Lex Fridman (14:02.240)

that human communication allows.

Peter Wang (14:04.400)

It does, but my background is in physics.

Lex Fridman (14:07.760)

I took like two CS courses in college.

Lex Fridman (14:09.960)

So I don't have, now I did cram a bunch of CS in prep

Lex Fridman (14:13.800)

when I applied for grad school,

Lex Fridman (14:15.520)

but still I don't have a formal background

Lex Fridman (14:18.160)

in computer science.

Lex Fridman (14:19.720)

But what I have observed in studying programming languages

Lex Fridman (14:22.520)

and programming systems and things like that

Peter Wang (14:25.000)

is that there seems to be this triangle.

Lex Fridman (14:27.360)

It's one of these beautiful little iron triangles

Peter Wang (14:30.440)

that you find in life sometimes.

Lex Fridman (14:32.080)

And it's the connection between the code correctness

Lex Fridman (14:35.640)

and kind of expressiveness of code,

Lex Fridman (14:37.440)

the semantics of the data,

Lex Fridman (14:39.920)

and then the kind of correctness or parameters

Lex Fridman (14:42.520)

of the underlying hardware compute system.

Lex Fridman (14:44.960)

So there's the algorithms that you wanna apply,

Lex Fridman (14:48.440)

there's what the bits that are stored on whatever media

Peter Wang (14:52.440)

actually represent, so the semantics of the data

Lex Fridman (14:55.800)

within the representation,

Lex Fridman (14:56.920)

and then there's what the computer can actually do.

Lex Fridman (14:59.760)

And every programming system, every information system

Peter Wang (15:02.840)

ultimately finds some spot in the middle

Lex Fridman (15:05.480)

of this little triangle.

Peter Wang (15:07.440)

Sometimes some systems collapse them into just one edge.

Lex Fridman (15:11.120)

Are we including humans as a system in this?

Peter Wang (15:13.480)

No, no, I'm just thinking about computing systems here.

Lex Fridman (15:15.960)

And the reason I bring this up is because

Peter Wang (15:17.800)

I believe there's no free lunch around this stuff.

Lex Fridman (15:20.120)

So if we build machine learning systems

Peter Wang (15:23.000)

to sort of write the correct code

Lex Fridman (15:25.520)

that is at a certain level of performance,

Lex Fridman (15:27.160)

so it'll sort of select with hyperparameters

Lex Fridman (15:30.120)

we can tune kind of how we want the performance boundary

Peter Wang (15:32.880)

in SLA to look like for transforming some set of inputs

Lex Fridman (15:37.280)

into certain kinds of outputs.

Peter Wang (15:39.600)

That training process itself is intrinsically sensitive

Lex Fridman (15:43.640)

to the kinds of inputs we put into it.

Peter Wang (15:45.560)

It's quite sensitive to the boundary conditions

Lex Fridman (15:47.960)

we put around the performance.

Lex Fridman (15:49.320)

So I think even as we move to using automated systems

Lex Fridman (15:52.200)

to build this transformation,

Peter Wang (15:53.480)

as opposed to humans explicitly

Lex Fridman (15:55.480)

from a top down perspective, figuring out,

Peter Wang (15:57.360)

well, this schema and this database and these columns

Lex Fridman (15:59.960)

get selected for this algorithm,

Lex Fridman (16:01.720)

and here we put a Fibonacci heap for some other thing.

Lex Fridman (16:04.880)

Human design or computer design,

Peter Wang (16:06.800)

ultimately what we hit,

Lex Fridman (16:08.160)

the boundaries that we hit with these information systems

Peter Wang (16:10.640)

is when the representation of the data hits the real world

Lex Fridman (16:14.240)

is where there's a lot of slop and a lot of interpretation.

Lex Fridman (16:17.560)

And that's where actually I think

Lex Fridman (16:18.960)

a lot of the work will go in the future

Peter Wang (16:20.880)

is actually understanding kind of how to better

Lex Fridman (16:23.360)

in the view of these live data systems,

Lex Fridman (16:26.240)

how to better encode the semantics of the world

Lex Fridman (16:29.320)

for those things.

Peter Wang (16:30.160)

There'll be less of the details

Lex Fridman (16:31.080)

of how we write a particular SQL query.

Peter Wang (16:33.480)

Okay, but given the semantics of the real world

Lex Fridman (16:35.520)

and the messiness of that,

Lex Fridman (16:36.920)

what does the word correctness mean

Lex Fridman (16:38.640)

when you're talking about code?

Peter Wang (16:40.800)

There's a lot of dimensions to correctness.

Lex Fridman (16:42.960)

Historically, and this is one of the reasons I say

Peter Wang (16:45.160)

that we're coming to the end of the era of software,

Lex Fridman (16:47.640)

because for the last 40 years or so,

Peter Wang (16:49.880)

software correctness was really defined

Lex Fridman (16:52.440)

about functional correctness.

Peter Wang (16:54.840)

I write a function, it's got some inputs,

Lex Fridman (16:56.400)

does it produce the right outputs?

Peter Wang (16:57.880)

If so, then I can turn it on,

Lex Fridman (16:59.280)

hook it up to the live database and it goes.

Lex Fridman (17:01.480)

And more and more now we have,

Lex Fridman (17:03.200)

I mean, in fact, I think the bright line in the sand

Peter Wang (17:05.120)

between machine learning systems

Lex Fridman (17:06.800)

or modern data driven systems

Peter Wang (17:08.200)

versus classical software systems

Lex Fridman (17:10.840)

is that the values of the input

Peter Wang (17:14.320)

actually have to be considered with the function together

Lex Fridman (17:17.440)

to say this whole thing is correct or not.

Lex Fridman (17:19.480)

And usually there's a performance SLA as well.

Lex Fridman (17:21.840)

Like did it actually finish making this?

Lex Fridman (17:23.160)

What's SLA?

Lex Fridman (17:24.000)

Sorry, service level agreement.

Lex Fridman (17:25.440)

So it has to return within some time.

Lex Fridman (17:27.120)

You have a 10 millisecond time budget

Lex Fridman (17:29.240)

to return a prediction of this level of accuracy, right?

Lex Fridman (17:32.760)

So these are things that were not traditionally

Peter Wang (17:35.040)

in most business computing systems for the last 20 years

Lex Fridman (17:37.560)

at all, people didn't think about it.

Lex Fridman (17:39.400)

But now we have value dependence on functional correctness.

Lex Fridman (17:42.720)

So that question of correctness

Peter Wang (17:44.160)

is becoming a bigger and bigger question.

Lex Fridman (17:45.760)

What does that map to the end of software?

Peter Wang (17:48.160)

We've thought about software as just this thing

Lex Fridman (17:50.520)

that you can do in isolation with some test trial inputs

Lex Fridman (17:54.760)

and in a very sort of sandboxed environment.

Lex Fridman (17:58.640)

And we can quantify how does it scale?

Lex Fridman (18:00.640)

How does it perform?

Lex Fridman (18:02.040)

How many nodes do we need to allocate

Lex Fridman (18:03.240)

if we wanna scale this many inputs?

Lex Fridman (18:05.120)

When we start turning this stuff into prediction systems,

Peter Wang (18:08.360)

real cybernetic systems,

Lex Fridman (18:10.120)

you're going to find scenarios where you get inputs

Peter Wang (18:12.320)

that you're gonna wanna spend

Lex Fridman (18:13.160)

a little more time thinking about.

Peter Wang (18:14.560)

You're gonna find inputs that are not,

Lex Fridman (18:15.840)

it's not clear what you should do, right?

Lex Fridman (18:17.480)

So then the software has a varying amount of runtime

Lex Fridman (18:20.360)

and correctness with regard to input.

Lex Fridman (18:22.280)

And that is a different kind of system altogether.

Lex Fridman (18:24.000)

Now it's a full on cybernetic system.

Peter Wang (18:25.960)

It's a next generation information system

Lex Fridman (18:27.640)

that is not like traditional software systems.

Lex Fridman (18:30.200)

Can you maybe describe what is a cybernetic system?

Lex Fridman (18:33.160)

Do you include humans in that picture?

Lex Fridman (18:35.080)

So is a human in the loop kind of complex mess

Lex Fridman (18:38.920)

of the whole kind of interactivity of software

Lex Fridman (18:41.520)

with the real world or is it something more concrete?

Lex Fridman (18:44.760)

Well, when I say cybernetic,

Peter Wang (18:45.760)

I really do mean that the software itself

Lex Fridman (18:47.720)

is closing the observe, orient, decide, act loop by itself.

Lex Fridman (18:51.600)

So humans being out of the loop is the fact

Lex Fridman (18:54.280)

what for me makes it a cybernetic system.

Lex Fridman (18:58.440)

And humans are out of that loop.

Lex Fridman (19:00.920)

When humans are out of the loop,

Peter Wang (19:01.960)

when the machine is actually sort of deciding on its own

Lex Fridman (19:05.240)

what it should do next to get more information,

Peter Wang (19:07.680)

that makes it a cybernetic system.

Lex Fridman (19:09.760)

So we're just at the dawn of this, right?

Peter Wang (19:11.400)

I think everyone talking about MLAI, it's great.

Lex Fridman (19:15.400)

But really the thing we should be talking about

Peter Wang (19:16.880)

is when we really enter the cybernetic era

Lex Fridman (19:20.360)

and all of the questions of ethics and governance

Lex Fridman (19:22.520)

and all correctness and all these things,

Lex Fridman (19:24.640)

they really are the most important questions.

Lex Fridman (19:27.200)

Okay, can we just linger on this?

Lex Fridman (19:28.600)

What does it mean for the human to be out of the loop

Peter Wang (19:30.640)

in a cybernetic system, because isn't the cybernetic system

Lex Fridman (19:34.120)

that's ultimately accomplishing some kind of purpose

Peter Wang (19:37.440)

that at the bottom, the turtles all the way down,

Lex Fridman (19:41.840)

at the bottom turtle is a human.

Peter Wang (19:44.240)

Well, the human may have set some criteria,

Lex Fridman (19:45.920)

but the human wasn't precise.

Lex Fridman (19:47.280)

So for instance, I just read the other day

Lex Fridman (19:49.240)

that earlier this year,

Peter Wang (19:51.360)

or maybe it was last year at some point,

Lex Fridman (19:52.480)

the Libyan army, I think,

Peter Wang (19:55.240)

sent out some automated killer drones with explosives.

Lex Fridman (19:58.200)

And there was no human in the loop at that point.

Lex Fridman (1:00:00.180)

So I would say, I don't think molecules of water

Lex Fridman (1:00:04.800)

feel consciousness, have consciousness,

Lex Fridman (1:00:06.280)

but there is some proto micro quantum thing of love.

Lex Fridman (1:00:10.920)

That's the generativity when there's more energy

Peter Wang (1:00:14.080)

than what they need to maintain equilibrium.

Lex Fridman (1:00:16.280)

And that when you sum it all up is something that leads to,

Peter Wang (1:00:19.800)

I mean, I had my mind blown one day as an undergrad

Lex Fridman (1:00:23.500)

at the physics computer lab.

Peter Wang (1:00:24.560)

I logged in and when you log into bash for a long time,

Lex Fridman (1:00:28.300)

there was a little fortune that would come out.

Lex Fridman (1:00:29.600)

And it said, man was created by water

Lex Fridman (1:00:32.400)

to carry itself uphill.

Lex Fridman (1:00:33.940)

And I was logging into work on some problem set

Lex Fridman (1:00:37.960)

and I logged in and I saw that and I just said,

Peter Wang (1:00:40.540)

son of a bitch, I just, I logged out

Lex Fridman (1:00:43.120)

and I went to the coffee shop and I got a coffee

Lex Fridman (1:00:45.080)

and I sat there on the quad and I'm like,

Lex Fridman (1:00:47.040)

you know, it's not wrong and yet WTF, right?

Lex Fridman (1:00:53.440)

So when you look at it that way,

Lex Fridman (1:00:55.320)

it's like, yeah, okay, non equilibrium physics is a thing.

Lex Fridman (1:00:59.040)

And so when we think about love,

Lex Fridman (1:01:00.960)

when we think about these kinds of things, I would say

Peter Wang (1:01:05.360)

that in the modern day human condition,

Lex Fridman (1:01:08.160)

there's a lot of talk about freedom and individual liberty

Lex Fridman (1:01:12.300)

and rights and all these things,

Lex Fridman (1:01:14.600)

but that's very Hegelian, it's very kind of following

Peter Wang (1:01:18.120)

from the Western philosophy of the individual as sacrosanct,

Lex Fridman (1:01:22.760)

but it's not really couched I think the right way

Peter Wang (1:01:26.360)

because it should be how do we maximize people's ability

Lex Fridman (1:01:29.460)

to love each other, to love themselves first,

Peter Wang (1:01:32.100)

to love each other, their responsibilities

Lex Fridman (1:01:34.560)

to the previous generation, to the future generations.

Peter Wang (1:01:37.800)

Those are the kinds of things

Lex Fridman (1:01:39.280)

that should be our design criteria, right?

Peter Wang (1:01:41.840)

Those should be what we start with to then come up

Lex Fridman (1:01:45.720)

with the philosophies of self and of rights

Lex Fridman (1:01:48.280)

and responsibilities, but that love being at the center

Lex Fridman (1:01:52.080)

of it, I think when we design systems for cognition,

Peter Wang (1:01:56.680)

it should absolutely be built that way.

Lex Fridman (1:01:58.700)

I think if we simply focus on efficiency and productivity,

Peter Wang (1:02:02.240)

these kind of very industrial era,

Lex Fridman (1:02:05.900)

all the things that Marx had issues with, right?

Peter Wang (1:02:08.200)

Those, that's a way to go and really I think go off

Lex Fridman (1:02:11.880)

the deep end in the wrong way.

Lex Fridman (1:02:13.600)

So one of the interesting consequences of thinking of life

Lex Fridman (1:02:19.200)

in this hierarchical way of an individual human

Lex Fridman (1:02:22.440)

and then there's groups and there's societies

Lex Fridman (1:02:25.040)

is I believe that you believe that corporations are people.

Lex Fridman (1:02:31.680)

So this is a kind of a politically dense idea,

Lex Fridman (1:02:36.600)

all those kinds of things.

Peter Wang (1:02:37.440)

If we just throw politics aside,

Lex Fridman (1:02:39.100)

if we throw all of that aside,

Lex Fridman (1:02:41.300)

in which sense do you believe that corporations are people?

Lex Fridman (1:02:46.260)

And how does love connect to that?

Peter Wang (1:02:47.760)

Right, so the belief is that groups of people

Lex Fridman (1:02:52.040)

have some kind of higher level, I would say mesoscopic

Peter Wang (1:02:55.680)

claim to agency.

Lex Fridman (1:02:57.960)

So where do I, let's start with this.

Peter Wang (1:03:00.920)

Most people would say, okay, individuals have claims

Lex Fridman (1:03:03.720)

to agency and sovereignty.

Peter Wang (1:03:05.140)

Nations, we certainly act as if nations,

Lex Fridman (1:03:07.640)

so at a very large, large scale,

Peter Wang (1:03:09.520)

nations have rights to sovereignty and agency.

Lex Fridman (1:03:13.120)

Like everyone plays the game of modernity

Lex Fridman (1:03:15.000)

as if that's true, right?

Lex Fridman (1:03:16.320)

We believe France is a thing,

Peter Wang (1:03:17.380)

we believe the United States is a thing.

Lex Fridman (1:03:18.800)

But to say that groups of people at a smaller level

Peter Wang (1:03:23.100)

than that, like a family unit is the thing.

Lex Fridman (1:03:26.640)

Well, in our laws, we actually do encode this concept.

Peter Wang (1:03:30.440)

I believe that in a relationship and a marriage, right,

Lex Fridman (1:03:33.800)

one partner can sue for loss of consortium, right?

Peter Wang (1:03:37.720)

If someone breaks up the marriage or whatever.

Lex Fridman (1:03:39.820)

So these are concepts that even in law,

Peter Wang (1:03:41.560)

we do respect that there is something about the union

Lex Fridman (1:03:44.720)

and about the family.

Lex Fridman (1:03:45.980)

So for me, I don't think it's so weird to think

Lex Fridman (1:03:48.640)

that groups of people have a right to,

Peter Wang (1:03:51.820)

a claim to rights and sovereignty of some degree.

Lex Fridman (1:03:54.640)

I mean, we look at our clubs, we look at churches.

Peter Wang (1:03:59.040)

These are, we talk about these collectives of people

Lex Fridman (1:04:02.000)

as if they have a real agency to them, and they do.

Lex Fridman (1:04:05.800)

But I think if we take that one step further and say,

Lex Fridman (1:04:08.720)

okay, they can accrue resources.

Peter Wang (1:04:10.320)

Well, yes, check, you know, and by law they can.

Lex Fridman (1:04:13.760)

They can own land, they can engage in contracts,

Peter Wang (1:04:17.060)

they can do all these different kinds of things.

Lex Fridman (1:04:18.840)

So we in legal terms support this idea

Peter Wang (1:04:22.600)

that groups of people have rights.

Lex Fridman (1:04:26.200)

Where we go wrong on this stuff

Peter Wang (1:04:28.040)

is that the most popular version of this

Lex Fridman (1:04:31.280)

is the for profit absentee owner corporation

Peter Wang (1:04:35.360)

that then is able to amass larger resources

Lex Fridman (1:04:38.440)

than anyone else in the landscape, anything else,

Peter Wang (1:04:40.840)

any other entity of equivalent size.

Lex Fridman (1:04:42.760)

And they're able to essentially bully around individuals,

Peter Wang (1:04:45.480)

whether it's laborers, whether it's people

Lex Fridman (1:04:47.040)

whose resources they want to capture.

Peter Wang (1:04:48.960)

They're also able to bully around

Lex Fridman (1:04:50.480)

our system of representation,

Lex Fridman (1:04:52.160)

which is still tied to individuals, right?

Lex Fridman (1:04:55.540)

So I don't believe that's correct.

Peter Wang (1:04:58.520)

I don't think it's good that they, you know,

Lex Fridman (1:05:01.160)

they're people, but they're assholes.

Peter Wang (1:05:02.320)

I don't think that corporations as people

Lex Fridman (1:05:03.680)

acting like assholes is a good thing.

Lex Fridman (1:05:05.520)

But the idea that collectives and collections of people

Lex Fridman (1:05:08.440)

that we should treat them philosophically

Peter Wang (1:05:10.120)

as having some agency and some mass,

Lex Fridman (1:05:15.120)

at a mesoscopic level, I think that's an important thing

Peter Wang (1:05:18.000)

because one thing I do think we underappreciate sometimes

Lex Fridman (1:05:22.400)

is the fact that relationships have relationships.

Lex Fridman (1:05:26.200)

So it's not just individuals

Lex Fridman (1:05:27.240)

having relationships with each other.

Lex Fridman (1:05:29.120)

But if you have eight people seated around a table, right?

Lex Fridman (1:05:32.080)

Each person has a relationship with each of the others

Lex Fridman (1:05:34.200)

and that's obvious.

Lex Fridman (1:05:35.640)

But then if it's four couples,

Peter Wang (1:05:37.880)

each couple also has a relationship

Lex Fridman (1:05:39.720)

with each of the other couples, right?

Peter Wang (1:05:41.720)

The dyads do.

Lex Fridman (1:05:42.720)

And if it's couples, but one is the, you know,

Peter Wang (1:05:45.600)

father and mother older, and then, you know,

Lex Fridman (1:05:48.200)

one of their children and their spouse,

Peter Wang (1:05:50.960)

that family unit of four has a relationship

Lex Fridman (1:05:53.980)

with the other family unit of four.

Lex Fridman (1:05:55.760)

So the idea that relationships have relationships

Lex Fridman (1:05:57.640)

is something that we intuitively know

Peter Wang (1:05:59.920)

in navigating the social landscape,

Lex Fridman (1:06:01.780)

but it's not something I hear expressed like that.

Peter Wang (1:06:04.720)

It's certainly not something that is,

Lex Fridman (1:06:06.680)

I think, taken into account very well

Peter Wang (1:06:08.160)

when we design these kinds of things.

Lex Fridman (1:06:09.480)

So I think the reason why I care a lot about this

Peter Wang (1:06:14.080)

is because I think the future of humanity

Lex Fridman (1:06:16.520)

requires us to form better sense make,

Peter Wang (1:06:19.660)

collective sense making units at something, you know,

Lex Fridman (1:06:23.680)

around Dunbar number, you know, half to five X Dunbar.

Lex Fridman (1:06:28.160)

And that's very different than right now

Lex Fridman (1:06:30.640)

where we defer sense making

Peter Wang (1:06:33.600)

to massive aging zombie institutions.

Lex Fridman (1:06:37.040)

Or we just do it ourselves.

Peter Wang (1:06:38.520)

Go it alone.

Lex Fridman (1:06:39.360)

Go to the dark force of the internet by ourselves.

Lex Fridman (1:06:41.120)

So that's really interesting.

Lex Fridman (1:06:42.360)

So you've talked about agency,

Peter Wang (1:06:45.340)

I think maybe calling it a convenient fiction

Lex Fridman (1:06:47.760)

at all these different levels.

Lex Fridman (1:06:49.620)

So even at the human individual level,

Lex Fridman (1:06:52.080)

it's kind of a fiction.

Peter Wang (1:06:53.160)

We all believe, because we are, like you said,

Lex Fridman (1:06:55.080)

made of cells and cells are made of atoms.

Lex Fridman (1:06:57.720)

So that's a useful fiction.

Lex Fridman (1:06:58.980)

And then there's nations that seems to be a useful fiction,

Lex Fridman (1:07:02.900)

but it seems like some fictions are better than others.

Lex Fridman (1:07:06.900)

You know, there's a lot of people that argue

Peter Wang (1:07:08.400)

the fiction of nation is a bad idea.

Lex Fridman (1:07:11.000)

One of them lives two doors down from me,

Peter Wang (1:07:13.880)

Michael Malice, he's an anarchist.

Lex Fridman (1:07:16.160)

You know, I'm sure there's a lot of people

Peter Wang (1:07:18.280)

who are into meditation that believe the idea,

Lex Fridman (1:07:21.860)

this useful fiction of agency of an individual

Peter Wang (1:07:24.920)

is a troublesome as well.

Lex Fridman (1:07:26.680)

We need to let go of that in order to truly,

Peter Wang (1:07:29.460)

like to transcend, I don't know.

Lex Fridman (1:07:32.320)

I don't know what words you want to use,

Lex Fridman (1:07:33.760)

but suffering or to elevate the experience of life.

Lex Fridman (1:07:38.440)

So you're kind of arguing that,

Peter Wang (1:07:40.800)

okay, so we have some of these useful fictions of agency.

Lex Fridman (1:07:44.680)

We should add a stronger fiction that we tell ourselves

Peter Wang (1:07:49.000)

about the agency of groups in the hundreds

Lex Fridman (1:07:52.700)

of the half a Dunbar's number, 5X Dunbar's number.

Peter Wang (1:07:57.920)

Yeah, something on that order.

Lex Fridman (1:07:58.760)

And we call them fictions,

Lex Fridman (1:07:59.800)

but really they're rules of the game, right?

Lex Fridman (1:08:01.580)

Rules that we feel are fair or rules that we consent to.

Peter Wang (1:08:05.720)

Yeah, I always question the rules

Lex Fridman (1:08:07.000)

when I lose like a monopoly.

Peter Wang (1:08:08.600)

That's when I usually question the rules.

Lex Fridman (1:08:09.840)

When I'm winning, I don't question the rules.

Peter Wang (1:08:11.600)

We should play a game Monopoly someday.

Lex Fridman (1:08:12.880)

There's a trippy version of it that we could do.

Lex Fridman (1:08:15.240)

What kind?

Lex Fridman (1:08:16.320)

Contract Monopoly is introduced by a friend of mine to me

Peter Wang (1:08:19.200)

where you can write contracts on future earnings

Lex Fridman (1:08:23.120)

or landing on various things.

Lex Fridman (1:08:24.600)

And you can hand out like, you know,

Lex Fridman (1:08:26.720)

you can land the first three times you land

Peter Wang (1:08:28.080)

in a park place, it's free or whatever.

Lex Fridman (1:08:30.160)

And then you can start trading those contracts for money.

Lex Fridman (1:08:33.480)

And then you create a human civilization

Lex Fridman (1:08:36.760)

and somehow Bitcoin comes into it.

Peter Wang (1:08:38.280)

Okay, but some of these.

Lex Fridman (1:08:40.520)

Actually, I bet if me and you and Eric sat down

Peter Wang (1:08:43.040)

to play a game Monopoly and we were to make NFTs

Lex Fridman (1:08:45.320)

out of the contracts we wrote, we could make a lot of money.

Peter Wang (1:08:48.000)

Now it's a terrible idea.

Lex Fridman (1:08:49.320)

I would never do it,

Lex Fridman (1:08:50.200)

but I bet we could actually sell the NFTs around.

Lex Fridman (1:08:52.920)

I have other ideas to make money that I could tell you

Lex Fridman (1:08:56.800)

and they're all terrible ideas.

Lex Fridman (1:08:58.400)

Yeah, including cat videos on the internet.

Peter Wang (1:09:02.280)

Okay, but some of these rules of the game,

Lex Fridman (1:09:04.600)

some of these fictions are,

Peter Wang (1:09:06.040)

it seems like they're better than others.

Lex Fridman (1:09:09.280)

They have worked this far to cohere human,

Peter Wang (1:09:13.320)

to organize human collective action.

Lex Fridman (1:09:14.840)

But you're saying something about,

Peter Wang (1:09:16.600)

especially this technological age

Lex Fridman (1:09:19.240)

requires modified fictions, stories of agency.

Lex Fridman (1:09:23.640)

Why the Dunbar number?

Lex Fridman (1:09:25.040)

And also, you know, how do you select the group of people?

Peter Wang (1:09:28.240)

You know, Dunbar numbers, I think I have the sense

Lex Fridman (1:09:31.760)

that it's overused as a kind of law

Peter Wang (1:09:36.360)

that somehow we can have deep human connection at this scale.

Lex Fridman (1:09:41.280)

Like some of it feels like an interface problem too.

Peter Wang (1:09:45.440)

It feels like if I have the right tools,

Lex Fridman (1:09:48.080)

I can deeply connect with a larger number of people.

Peter Wang (1:09:51.840)

It just feels like there's a huge value

Lex Fridman (1:09:55.480)

to interacting just in person, getting to share

Peter Wang (1:09:59.400)

traumatic experiences together,

Lex Fridman (1:10:00.960)

beautiful experiences together.

Peter Wang (1:10:02.720)

There's other experiences like that in the digital space

Lex Fridman (1:10:06.920)

that you can share.

Peter Wang (1:10:07.760)

It just feels like Dunbar's number

Lex Fridman (1:10:09.360)

could be expanded significantly,

Peter Wang (1:10:10.800)

perhaps not to the level of millions and billions,

Lex Fridman (1:10:15.000)

but it feels like it could be expanded.

Lex Fridman (1:10:16.280)

So how do we find the right interface, you think,

Lex Fridman (1:10:21.680)

for having a little bit of a collective here

Lex Fridman (1:10:24.840)

that has agency?

Lex Fridman (1:10:26.080)

You're right that there's many different ways

Peter Wang (1:10:28.080)

that we can build trust with each other.

Lex Fridman (1:10:30.000)

Yeah.

Peter Wang (1:10:30.840)

My friend Joe Edelman talks about a few different ways

Lex Fridman (1:10:33.960)

that, you know, mutual appreciation, trustful conflict,

Peter Wang (1:10:39.360)

just experiencing something like, you know,

Lex Fridman (1:10:41.320)

there's a variety of different things that we can do,

Lex Fridman (1:10:43.640)

but all those things take time and you have to be present.

Lex Fridman (1:10:48.480)

The less present you are, I mean, there's just, again,

Peter Wang (1:10:50.320)

a no free lunch principle here.

Lex Fridman (1:10:51.560)

The less present you are, the more of them you can do,

Lex Fridman (1:10:54.200)

but then the less connection you build.

Lex Fridman (1:10:56.800)

So I think there is sort of a human capacity issue

Peter Wang (1:10:59.440)

around some of these things.

Lex Fridman (1:11:00.280)

Now, that being said, if we can use certain technologies,

Lex Fridman (1:11:04.800)

so for instance, if I write a little monograph

Lex Fridman (1:11:07.520)

on my view of the world,

Peter Wang (1:11:08.720)

you read it asynchronously at some point,

Lex Fridman (1:11:10.600)

and you're like, wow, Peter, this is great.

Peter Wang (1:11:11.920)

Here's mine.

Lex Fridman (1:11:12.800)

I read it.

Peter Wang (1:11:13.640)

I'm like, wow, Lex, this is awesome.

Lex Fridman (1:11:15.320)

We can be friends without having to spend 10 years,

Peter Wang (1:11:18.720)

you know, figuring all this stuff out together.

Lex Fridman (1:11:20.560)

We just read each other's thing and be like,

Peter Wang (1:11:22.200)

oh yeah, this guy's exactly in my wheelhouse

Lex Fridman (1:11:24.800)

and vice versa.

Lex Fridman (1:11:26.080)

And we can then, you know, connect just a few times a year

Lex Fridman (1:11:30.520)

and maintain a high trust relationship.

Peter Wang (1:11:33.040)

It can be expanded a little bit,

Lex Fridman (1:11:34.320)

but it also requires,

Peter Wang (1:11:35.840)

these things are not all technological in nature.

Lex Fridman (1:11:37.320)

It requires the individual themselves

Peter Wang (1:11:39.640)

to have a certain level of capacity,

Lex Fridman (1:11:41.680)

to have a certain lack of neuroticism, right?

Peter Wang (1:11:44.760)

If you want to use like the ocean big five sort of model,

Lex Fridman (1:11:48.080)

people have to be pretty centered.

Peter Wang (1:11:49.680)

The less centered you are,

Lex Fridman (1:11:50.640)

the fewer authentic connections you can really build

Peter Wang (1:11:52.840)

for a particular unit of time.

Lex Fridman (1:11:54.800)

It just takes more time.

Peter Wang (1:11:55.840)

Other people have to put up with your crap.

Lex Fridman (1:11:57.160)

Like there's just a lot of the stuff

Peter Wang (1:11:58.120)

that you have to deal with

Lex Fridman (1:12:00.000)

if you are not so well balanced, right?

Lex Fridman (1:12:02.240)

So yes, we can help people get better

Lex Fridman (1:12:04.760)

to where they can develop more relationships faster,

Lex Fridman (1:12:06.760)

and then you can maybe expand Dunbar number by quite a bit,

Lex Fridman (1:12:09.560)

but you're not going to do it.

Peter Wang (1:12:10.640)

I think it's going to be hard to get it beyond 10X,

Lex Fridman (1:12:12.880)

kind of the rough swag of what it is, you know?

Peter Wang (1:12:16.280)

Well, don't you think that AI systems could be an addition

Lex Fridman (1:12:20.840)

to the Dunbar's number?

Lex Fridman (1:12:22.640)

So like why?

Lex Fridman (1:12:23.480)

Do you count as one system or multiple AI systems?

Peter Wang (1:12:25.600)

Multiple AI systems.

Lex Fridman (1:12:26.560)

So I do believe that AI systems,

Peter Wang (1:12:28.600)

for them to integrate into human society as it is now,

Lex Fridman (1:12:31.360)

have to have a sense of agency.

Lex Fridman (1:12:32.600)

So there has to be a individual

Lex Fridman (1:12:35.280)

because otherwise we wouldn't relate to them.

Peter Wang (1:12:37.600)

We could engage certain kinds of individuals

Lex Fridman (1:12:40.240)

to make sense of them for us and be almost like,

Lex Fridman (1:12:42.680)

did you ever watch Star Trek?

Lex Fridman (1:12:44.960)

Like Voyager, like there's the Volta,

Peter Wang (1:12:46.480)

who are like the interfaces,

Lex Fridman (1:12:47.640)

the ambassadors for the Dominion.

Peter Wang (1:12:50.480)

We may have ambassadors that speak

Lex Fridman (1:12:53.120)

on behalf of these systems.

Peter Wang (1:12:54.400)

They're like the Mentats of Dune, maybe,

Lex Fridman (1:12:56.160)

or something like this.

Peter Wang (1:12:57.280)

I mean, we already have this to some extent.

Lex Fridman (1:12:59.320)

If you look at the biggest sort of,

Peter Wang (1:13:01.160)

I wouldn't say AI system,

Lex Fridman (1:13:02.120)

but the biggest cybernetic system in the world

Peter Wang (1:13:04.080)

is the financial markets.

Lex Fridman (1:13:05.160)

It runs outside of any individual's control,

Lex Fridman (1:13:08.000)

and you have an entire stack of people on Wall Street,

Lex Fridman (1:13:09.920)

Wall Street analysts to CNBC reporters, whatever.

Lex Fridman (1:13:13.240)

They're all helping to communicate what does this mean?

Lex Fridman (1:13:16.920)

You know, like Jim Cramer,

Peter Wang (1:13:18.120)

like coming around and yelling and stuff.

Lex Fridman (1:13:19.560)

Like all of these people are part of that lowering

Peter Wang (1:13:22.560)

of the complexity there to meet sense,

Lex Fridman (1:13:26.320)

you know, to help do sense making for people

Peter Wang (1:13:28.440)

at whatever capacity they're at.

Lex Fridman (1:13:29.800)

And I don't see this changing with AI systems.

Peter Wang (1:13:31.560)

I think you would have ringside commentators

Lex Fridman (1:13:33.400)

talking about all this stuff

Peter Wang (1:13:34.560)

that this AI system is trying to do over here, over here,

Lex Fridman (1:13:36.600)

because it's actually a super intelligence.

Lex Fridman (1:13:39.120)

So if you want to talk about humans interfacing,

Lex Fridman (1:13:40.800)

making first contact with the super intelligence,

Peter Wang (1:13:42.480)

we're already there.

Lex Fridman (1:13:43.600)

We do it pretty poorly.

Lex Fridman (1:13:44.800)

And if you look at the gradient of power and money,

Lex Fridman (1:13:47.240)

what happens is the people closest to it

Peter Wang (1:13:48.800)

will absolutely exploit their distance

Lex Fridman (1:13:50.960)

for personal financial gain.

Lex Fridman (1:13:54.360)

So we should look at that and be like,

Lex Fridman (1:13:56.080)

oh, well, that's probably what the future

Peter Wang (1:13:57.320)

will look like as well.

Lex Fridman (1:13:58.880)

But nonetheless, I mean,

Peter Wang (1:14:00.240)

we're already doing this kind of thing.

Lex Fridman (1:14:01.360)

So in the future, we can have AI systems,

Lex Fridman (1:14:03.800)

but you're still gonna have to trust people

Lex Fridman (1:14:05.720)

to bridge the sense making gap to them.

Peter Wang (1:14:08.400)

See, I just feel like there could be

Lex Fridman (1:14:10.800)

like millions of AI systems that have,

Peter Wang (1:14:15.400)

have agencies, you have,

Lex Fridman (1:14:17.080)

when you say one super intelligence,

Peter Wang (1:14:19.480)

super intelligence in that context means

Lex Fridman (1:14:22.280)

it's able to solve particular problems extremely well.

Lex Fridman (1:14:26.080)

But there's some aspect of human like intelligence

Lex Fridman (1:14:29.240)

that's necessary to be integrated into human society.

Lex Fridman (1:14:32.320)

So not financial markets,

Lex Fridman (1:14:33.720)

not sort of weather prediction systems,

Peter Wang (1:14:36.760)

or I don't know, logistics optimization.

Lex Fridman (1:14:39.680)

I'm more referring to things that you interact with

Peter Wang (1:14:43.240)

on the intellectual level.

Lex Fridman (1:14:45.120)

And that I think requires,

Peter Wang (1:14:47.120)

there has to be a backstory.

Lex Fridman (1:14:48.920)

There has to be a personality.

Peter Wang (1:14:50.080)

I believe it has to fear its own mortality in a genuine way.

Lex Fridman (1:14:53.320)

Like there has to be all,

Peter Wang (1:14:56.560)

many of the elements that we humans experience

Lex Fridman (1:14:59.680)

that are fundamental to the human condition,

Peter Wang (1:15:01.920)

because otherwise we would not have

Lex Fridman (1:15:03.840)

a deep connection with it.

Lex Fridman (1:15:05.840)

But I don't think having a deep connection with it

Lex Fridman (1:15:07.800)

is necessarily going to stop us from building a thing

Peter Wang (1:15:10.600)

that has quite an alien intelligence aspect here.

Lex Fridman (1:15:13.360)

So the other kind of alien intelligence on this planet

Peter Wang (1:15:16.640)

is the octopuses or octopodes

Lex Fridman (1:15:18.640)

or whatever you wanna call them.

Peter Wang (1:15:19.720)

Octopi. Octopi, yeah.

Lex Fridman (1:15:21.000)

There's a little controversy

Peter Wang (1:15:22.400)

as to what the plural is, I guess.

Lex Fridman (1:15:23.720)

But an octopus. I look forward to your letters.

Peter Wang (1:15:26.560)

Yeah, an octopus,

Lex Fridman (1:15:30.360)

it really acts as a collective intelligence

Lex Fridman (1:15:32.120)

of eight intelligent arms, right?

Lex Fridman (1:15:34.320)

Its arms have a tremendous amount of neural density to them.

Lex Fridman (1:15:37.000)

And I see if we can build,

Lex Fridman (1:15:40.360)

I mean, just let's go with what you're saying.

Peter Wang (1:15:42.000)

If we build a singular intelligence

Lex Fridman (1:15:44.400)

that interfaces with humans that has a sense of agency

Lex Fridman (1:15:48.080)

so it can run the cybernetic loop

Lex Fridman (1:15:49.600)

and develop its own theory of mind

Peter Wang (1:15:51.080)

as well as its theory of action,

Lex Fridman (1:15:52.960)

all these things, I agree with you

Peter Wang (1:15:54.040)

that that's the necessary components

Lex Fridman (1:15:56.240)

to build a real intelligence, right?

Peter Wang (1:15:57.800)

There's gotta be something at stake.

Lex Fridman (1:15:58.720)

It's gotta make a decision.

Peter Wang (1:16:00.000)

It's gotta then run the OODA loop.

Lex Fridman (1:16:01.280)

Okay, so we build one of those.

Peter Wang (1:16:03.000)

Well, if we can build one of those,

Lex Fridman (1:16:03.880)

we can probably build 5 million of them.

Lex Fridman (1:16:05.640)

So we build 5 million of them.

Lex Fridman (1:16:07.400)

And if their cognitive systems are already digitized

Lex Fridman (1:16:09.960)

and already kind of there,

Lex Fridman (1:16:12.000)

we stick an antenna on each of them,

Peter Wang (1:16:13.720)

bring it all back to a hive mind

Lex Fridman (1:16:15.440)

that maybe doesn't make all the individual decisions

Peter Wang (1:16:17.640)

for them, but treats each one

Lex Fridman (1:16:19.360)

as almost like a neuronal input

Peter Wang (1:16:21.480)

of a much higher bandwidth and fidelity,

Lex Fridman (1:16:23.880)

going back to a central system

Peter Wang (1:16:25.920)

that is then able to perceive much broader dynamics

Lex Fridman (1:16:30.240)

that we can't see.

Lex Fridman (1:16:31.120)

In the same way that a phased array radar, right?

Lex Fridman (1:16:32.600)

You think about how phased array radar works.

Peter Wang (1:16:34.440)

It's just sensitivity.

Lex Fridman (1:16:36.200)

It's just radars, and then it's hypersensitivity

Lex Fridman (1:16:39.120)

and really great timing between all of them.

Lex Fridman (1:16:41.160)

And with a flat array,

Lex Fridman (1:16:42.600)

it's as good as a curved radar dish, right?

Lex Fridman (1:16:44.740)

So with these things,

Peter Wang (1:16:45.580)

it's a phased array of cybernetic systems

Lex Fridman (1:16:47.800)

that'll give the centralized intelligence

Peter Wang (1:16:51.280)

much, much better, a much higher fidelity understanding

Lex Fridman (1:16:55.040)

of what's actually happening in the environment.

Lex Fridman (1:16:56.600)

But the more power,

Lex Fridman (1:16:57.720)

the more understanding the central super intelligence has,

Peter Wang (1:17:02.480)

the dumber the individual like fingers

Lex Fridman (1:17:06.600)

of this intelligence are, I think.

Peter Wang (1:17:08.080)

I think you...

Lex Fridman (1:17:08.920)

Not necessarily.

Peter Wang (1:17:09.760)

In my sense...

Lex Fridman (1:17:10.580)

I don't see what has to be.

Peter Wang (1:17:11.420)

This argument, there has to be,

Lex Fridman (1:17:13.560)

the experience of the individual agent

Peter Wang (1:17:15.660)

has to have the full richness of the human like experience.

Lex Fridman (1:17:20.840)

You have to be able to be driving the car in the rain,

Peter Wang (1:17:23.800)

listening to Bruce Springsteen,

Lex Fridman (1:17:25.240)

and all of a sudden break out in tears

Peter Wang (1:17:28.280)

because remembering something that happened to you

Lex Fridman (1:17:30.580)

in high school.

Peter Wang (1:17:31.420)

We can implant those memories

Lex Fridman (1:17:32.240)

if that's really needed.

Lex Fridman (1:17:33.080)

But no, I'm...

Lex Fridman (1:17:33.920)

No, but the central agency,

Peter Wang (1:17:34.960)

like I guess I'm saying for, in my view,

Lex Fridman (1:17:37.720)

for intelligence to be born,

Peter Wang (1:17:39.620)

you have to have a decentralization.

Lex Fridman (1:17:43.860)

Like each one has to struggle and reach.

Lex Fridman (1:17:47.280)

So each one in excess of energy has to reach for order

Lex Fridman (1:17:51.920)

as opposed to a central place doing so.

Peter Wang (1:17:54.420)

Have you ever read like some sci fi

Lex Fridman (1:17:55.640)

where there's like hive minds?

Peter Wang (1:17:58.820)

Like the Wernher Vinge, I think has one of these.

Lex Fridman (1:18:01.080)

And then some of the stuff from the Commonwealth Saga,

Peter Wang (1:18:05.160)

the idea that you're an individual,

Lex Fridman (1:18:06.960)

but you're connected with like a few other individuals

Peter Wang (1:18:09.240)

telepathically as well.

Lex Fridman (1:18:10.300)

And together you form a swarm.

Lex Fridman (1:18:12.560)

So if you are, I ask you,

Lex Fridman (1:18:14.580)

what do you think is the experience of if you are like,

Lex Fridman (1:18:18.040)

well, a Borg, right?

Lex Fridman (1:18:18.920)

If you are one, if you're part of this hive mind,

Peter Wang (1:18:22.600)

outside of all the aesthetics, forget the aesthetics,

Lex Fridman (1:18:25.360)

internally, what is your experience like?

Peter Wang (1:18:28.360)

Because I have a theory as to what that looks like.

Lex Fridman (1:18:30.600)

The one question I have for you about that experience is

Lex Fridman (1:18:34.280)

how much is there a feeling of freedom, of free will?

Lex Fridman (1:18:38.560)

Because I obviously as a human, very unbiased,

Lex Fridman (1:18:43.280)

but also somebody who values freedom and biased,

Lex Fridman (1:18:46.120)

it feels like the experience of freedom is essential for

Peter Wang (1:18:52.800)

trying stuff out, to being creative

Lex Fridman (1:18:55.960)

and doing something truly novel, which is at the core of.

Peter Wang (1:18:59.080)

Yeah, well, I don't think you have to lose any freedom

Lex Fridman (1:19:00.920)

when you're in that mode.

Peter Wang (1:19:02.040)

Because I think what happens is we think,

Lex Fridman (1:19:04.560)

we still think, I mean, you're still thinking about this

Peter Wang (1:19:06.920)

in a sense of a top down command and control hierarchy,

Lex Fridman (1:19:09.800)

which is not what it has to be at all.

Peter Wang (1:19:12.280)

I think the experience, so I'll just show by cards here.

Lex Fridman (1:19:16.040)

I think the experience of being a robot in that robot swarm,

Peter Wang (1:19:19.720)

a robot who has agency over their own local environment

Lex Fridman (1:19:22.840)

that's doing sense making

Lex Fridman (1:19:23.880)

and reporting it back to the hive mind,

Lex Fridman (1:19:25.880)

I think that robot's experience would be one,

Peter Wang (1:19:28.840)

when the hive mind is working well,

Lex Fridman (1:19:31.000)

it would be an experience of like talking to God, right?

Peter Wang (1:19:34.360)

That you essentially are reporting to,

Lex Fridman (1:19:37.480)

you're sort of saying, here's what I see.

Peter Wang (1:19:38.760)

I think this is what's gonna happen over here.

Lex Fridman (1:19:40.200)

I'm gonna go do this thing.

Peter Wang (1:19:41.240)

Because I think if I'm gonna do this,

Lex Fridman (1:19:42.840)

this will make this change happen in the environment.

Lex Fridman (1:19:45.400)

And then God, she may tell you, that's great.

Lex Fridman (1:19:50.400)

And in fact, your brothers and sisters will join you

Lex Fridman (1:19:52.400)

to help make this go better, right?

Lex Fridman (1:19:54.040)

And then she can let your brothers and sisters know,

Peter Wang (1:19:56.440)

hey, Peter's gonna go do this thing.

Lex Fridman (1:19:58.680)

Would you like to help him?

Peter Wang (1:19:59.880)

Because we think that this will make this thing go better.

Lex Fridman (1:20:01.640)

And they'll say, yes, we'll help him.

Lex Fridman (1:20:03.000)

So the whole thing could be actually very emergent.

Lex Fridman (1:20:05.560)

The sense of, what does it feel like to be a cell

Lex Fridman (1:20:09.160)

and a network that is alive, that is generative.

Lex Fridman (1:20:11.880)

And I think actually the feeling is serendipity.

Peter Wang (1:20:16.040)

That there's random order, not random disorder or chaos,

Lex Fridman (1:20:20.360)

but random order, just when you need it to hear Bruce Springsteen,

Lex Fridman (1:20:24.040)

you turn on the radio and bam, it's Bruce Springsteen, right?

Lex Fridman (1:20:28.040)

That feeling of serendipity, I feel like,

Peter Wang (1:20:30.600)

this is a bit of a flight of fancy,

Lex Fridman (1:20:31.880)

but every cell in your body must have,

Lex Fridman (1:20:35.240)

what does it feel like to be a cell in your body?

Lex Fridman (1:20:37.320)

When it needs sugar, there's sugar.

Peter Wang (1:20:39.320)

When it needs oxygen, there's just oxygen.

Lex Fridman (1:20:41.640)

Now, when it needs to go and do its work

Lex Fridman (1:20:43.080)

and pull like as one of your muscle fibers, right?

Lex Fridman (1:20:46.120)

It does its work and it's great.

Lex Fridman (1:20:48.120)

It contributes to the cause, right?

Lex Fridman (1:20:49.560)

So this is all, again, a flight of fancy,

Lex Fridman (1:20:51.960)

but I think as we extrapolate up,

Lex Fridman (1:20:53.880)

what does it feel like to be an independent individual

Lex Fridman (1:20:56.520)

with some bounded sense of freedom?

Lex Fridman (1:20:58.200)

All sense of freedom is actually bounded,

Lex Fridman (1:20:59.640)

but it was a bounded sense of freedom

Lex Fridman (1:21:01.240)

that still lives within a network that has order to it.

Lex Fridman (1:21:04.360)

And I feel like it has to be a feeling of serendipity.

Lex Fridman (1:21:06.600)

So the cell, there's a feeling of serendipity, even though.

Peter Wang (1:21:10.680)

It has no way of explaining why it's getting oxygen

Lex Fridman (1:21:12.680)

and sugar when it gets it.

Lex Fridman (1:21:13.480)

So you have to, each individual component has to be too dumb

Lex Fridman (1:21:17.400)

to understand the big picture.

Peter Wang (1:21:19.240)

No, the big picture is bigger than what it can understand.

Lex Fridman (1:21:22.840)

But isn't that an essential characteristic

Peter Wang (1:21:24.680)

of the individual is to be too dumb

Lex Fridman (1:21:27.880)

to understand the bigger picture.

Peter Wang (1:21:29.480)

Like not dumb necessarily,

Lex Fridman (1:21:31.160)

but limited in its capacity to understand.

Peter Wang (1:21:33.960)

Because the moment you understand,

Lex Fridman (1:21:36.680)

I feel like that leads to, if you tell me now

Peter Wang (1:21:41.000)

that there are some bigger intelligence

Lex Fridman (1:21:43.480)

controlling everything I do,

Peter Wang (1:21:45.640)

intelligence broadly defined, meaning like,

Lex Fridman (1:21:47.960)

you know, even the Sam Harris thing, there's no free will.

Peter Wang (1:21:51.480)

If I'm smart enough to truly understand that that's the case,

Lex Fridman (1:21:56.360)

that's gonna, I don't know if I.

Lex Fridman (1:21:58.840)

We have philosophical breakdown, right?

Lex Fridman (1:22:00.920)

Because we're in the West and we're pumped full of this stuff

Peter Wang (1:22:03.560)

of like, you are a golden, fully free individual

Lex Fridman (1:22:06.760)

with all your freedoms and all your liberties

Lex Fridman (1:22:08.360)

and go grab a gun and shoot whatever you want to.

Lex Fridman (1:22:10.520)

No, it's actually, you don't actually have a lot of these,

Peter Wang (1:22:14.360)

you're not unconstrained,

Lex Fridman (1:22:15.800)

but the areas where you can manifest agency,

Peter Wang (1:22:20.120)

you're free to do those things.

Lex Fridman (1:22:21.720)

You can say whatever you want on this podcast.

Lex Fridman (1:22:23.160)

You can create a podcast, right?

Lex Fridman (1:22:24.280)

Yeah.

Peter Wang (1:22:24.760)

You're not, I mean, you have a lot of this kind of freedom,

Lex Fridman (1:22:27.720)

but even as you're doing this, you are actually,

Peter Wang (1:22:30.040)

I guess where the denouement of this is that

Lex Fridman (1:22:33.480)

we are already intelligent agents in such a system, right?

Peter Wang (1:22:37.720)

In that one of these like robots

Lex Fridman (1:22:39.960)

of one of 5 million little swarm robots

Peter Wang (1:22:42.280)

or one of the Borg,

Lex Fridman (1:22:43.480)

they're just posting on internal bulletin board.

Peter Wang (1:22:45.320)

I mean, maybe the Borg cube

Lex Fridman (1:22:46.200)

is just a giant Facebook machine floating in space

Lex Fridman (1:22:48.440)

and everyone's just posting on there.

Lex Fridman (1:22:50.360)

They're just posting really fast and like, oh yeah.

Peter Wang (1:22:52.680)

It's called the metaverse now.

Lex Fridman (1:22:53.720)

That's called the metaverse, that's right.

Peter Wang (1:22:54.840)

Here's the enterprise.

Lex Fridman (1:22:55.480)

Maybe we should all go shoot it.

Lex Fridman (1:22:56.440)

Yeah, everyone upvotes and they're gonna go shoot it, right?

Lex Fridman (1:22:58.840)

But we already are part of a human online

Peter Wang (1:23:02.120)

collaborative environment

Lex Fridman (1:23:03.640)

and collaborative sensemaking system.

Peter Wang (1:23:05.640)

It's not very good yet.

Lex Fridman (1:23:07.240)

It's got the overhangs of zombie sensemaking institutions

Peter Wang (1:23:10.760)

all over it, but as that washes away

Lex Fridman (1:23:13.240)

and as we get better at this,

Peter Wang (1:23:15.400)

we are going to see humanity improving

Lex Fridman (1:23:18.520)

at speeds that are unthinkable in the past.

Lex Fridman (1:23:21.800)

And it's not because anyone's freedoms were limited.

Lex Fridman (1:23:23.640)

In fact, the open source,

Lex Fridman (1:23:24.520)

and we started this with open source software, right?

Lex Fridman (1:23:26.680)

The collaboration, what the internet surfaced

Peter Wang (1:23:29.240)

was the ability for people all over the world

Lex Fridman (1:23:31.160)

to collaborate and produce some of the most

Lex Fridman (1:23:32.920)

foundational software that's in use today, right?

Lex Fridman (1:23:35.800)

That entire ecosystem was created

Peter Wang (1:23:36.920)

by collaborators all over the place.

Lex Fridman (1:23:38.840)

So these online kind of swarm kind of things

Peter Wang (1:23:42.760)

are not novel.

Lex Fridman (1:23:44.280)

It's just, I'm just suggesting that future AI systems,

Peter Wang (1:23:47.320)

if you can build one smart system,

Lex Fridman (1:23:49.480)

you have no reason not to build multiple.

Peter Wang (1:23:51.400)

If you build multiple,

Lex Fridman (1:23:52.120)

there's no reason not to integrate them all

Peter Wang (1:23:53.800)

into a collective sensemaking substrate.

Lex Fridman (1:23:57.720)

And that thing will certainly have immersion intelligence

Peter Wang (1:24:00.360)

that none of the individuals

Lex Fridman (1:24:01.720)

and probably not any of the human designers

Peter Wang (1:24:03.400)

will be able to really put a bow around and explain.

Lex Fridman (1:24:06.680)

But in some sense, would that AI system

Peter Wang (1:24:09.160)

still be able to go like rural Texas,

Lex Fridman (1:24:13.160)

buy a ranch, go off the grid, go full survivalist?

Lex Fridman (1:24:16.840)

Like, can you disconnect from the hive mind?

Lex Fridman (1:24:20.200)

You may not want to.

Lex Fridman (1:24:25.080)

So to be ineffective, to be intelligent.

Lex Fridman (1:24:27.960)

You have access to way more intelligence capability

Peter Wang (1:24:30.200)

if you're plugged into five million other

Lex Fridman (1:24:31.560)

really, really smart cyborgs.

Lex Fridman (1:24:33.320)

Why would you leave?

Lex Fridman (1:24:34.840)

So like there's a word control that comes to mind.

Lex Fridman (1:24:37.400)

So it doesn't feel like control,

Lex Fridman (1:24:39.800)

like overbearing control.

Peter Wang (1:24:43.080)

It's just knowledge.

Lex Fridman (1:24:44.600)

I think systems, well, this is to your point.

Peter Wang (1:24:46.360)

I mean, look at how much,

Lex Fridman (1:24:47.640)

how uncomfortable you are with this concept, right?

Peter Wang (1:24:49.800)

I think systems that feel like overbearing control

Lex Fridman (1:24:52.280)

will not evolutionarily win out.

Peter Wang (1:24:54.760)

I think systems that give their individual elements

Lex Fridman (1:24:57.480)

the feeling of serendipity and the feeling of agency

Peter Wang (1:25:00.280)

that that will, those systems will win.

Lex Fridman (1:25:04.280)

But that's not to say that there will not be

Peter Wang (1:25:05.720)

emergent higher level order on top of it.

Lex Fridman (1:25:09.560)

And that's the thing, that's the philosophical breakdown

Peter Wang (1:25:11.320)

that we're staring right at,

Lex Fridman (1:25:13.480)

which is in the Western mind,

Peter Wang (1:25:14.840)

I think there's a very sharp delineation

Lex Fridman (1:25:17.560)

between explicit control,

Lex Fridman (1:25:21.240)

Cartesian, like what is the vector?

Lex Fridman (1:25:23.320)

Where is the position?

Lex Fridman (1:25:24.280)

Where is it going?

Lex Fridman (1:25:25.560)

It's completely deterministic.

Lex Fridman (1:25:27.240)

And kind of this idea that things emerge.

Lex Fridman (1:25:30.840)

Everything we see is the emergent patterns

Peter Wang (1:25:32.840)

of other things.

Lex Fridman (1:25:33.960)

And there is agency when there's extra energy.

Lex Fridman (1:25:38.760)

So you have spoken about a kind of meaning crisis

Lex Fridman (1:25:42.200)

that we're going through.

Lex Fridman (1:25:44.520)

But it feels like since we invented sex and death,

Lex Fridman (1:25:50.920)

we broadly speaking,

Peter Wang (1:25:52.520)

we've been searching for a kind of meaning.

Lex Fridman (1:25:54.760)

So it feels like a human civilization

Peter Wang (1:25:56.840)

has been going through a meaning crisis

Lex Fridman (1:25:58.200)

of different flavors throughout its history.

Lex Fridman (1:26:00.840)

Why is, how is this particular meaning crisis different?

Lex Fridman (1:26:05.800)

Or is it really a crisis and it wasn't previously?

Lex Fridman (1:26:09.000)

What's your sense?

Lex Fridman (1:26:09.960)

A lot of human history,

Peter Wang (1:26:11.400)

there wasn't so much a meaning crisis.

Lex Fridman (1:26:13.080)

There was just a like food

Lex Fridman (1:26:14.280)

and not getting eaten by bears crisis, right?

Lex Fridman (1:26:16.920)

Once you get to a point where you can make food,

Peter Wang (1:26:18.840)

there was the like not getting killed

Lex Fridman (1:26:20.200)

by other humans crisis.

Lex Fridman (1:26:21.960)

So sitting around wondering what is it all about,

Lex Fridman (1:26:24.760)

it's actually a relatively recent luxury.

Lex Fridman (1:26:26.600)

And to some extent, the meaning crisis coming out of that

Lex Fridman (1:26:29.640)

is precisely because, well, it's not precisely because,

Peter Wang (1:26:33.000)

I believe that meaning is the consequence of

Lex Fridman (1:26:37.320)

when we make consequential decisions,

Lex Fridman (1:26:40.200)

it's tied to agency, right?

Lex Fridman (1:26:42.440)

When we make consequential decisions,

Peter Wang (1:26:44.760)

that generates meaning.

Lex Fridman (1:26:46.600)

So if we make a lot of decisions,

Lex Fridman (1:26:47.960)

but we don't see the consequences of them,

Lex Fridman (1:26:50.040)

then it feels like what was the point, right?

Lex Fridman (1:26:52.200)

But if there's all these big things

Lex Fridman (1:26:53.640)

that we don't see the consequences of,

Peter Wang (1:26:55.160)

right, but if there's all these big things happening,

Lex Fridman (1:26:57.000)

but we're just along for the ride,

Peter Wang (1:26:58.280)

then it also does not feel very meaningful.

Lex Fridman (1:27:00.680)

Meaning, as far as I can tell,

Peter Wang (1:27:01.880)

this is my working definition of CERCA 2021,

Lex Fridman (1:27:04.600)

is generally the result of a person

Peter Wang (1:27:08.280)

making a consequential decision,

Lex Fridman (1:27:09.720)

acting on it and then seeing the consequences of it.

Lex Fridman (1:27:12.120)

So historically, just when humans are in survival mode,

Lex Fridman (1:27:16.520)

you're making consequential decisions all the time.

Lex Fridman (1:27:19.400)

So there's not a lack of meaning

Lex Fridman (1:27:20.760)

because like you either got eaten or you didn't, right?

Peter Wang (1:27:23.320)

You got some food and that's great, you feel good.

Lex Fridman (1:27:25.480)

Like these are all consequential decisions.

Peter Wang (1:27:27.400)

Only in the post fossil fuel and industrial revolution

Lex Fridman (1:27:33.640)

could we create a massive leisure class.

Peter Wang (1:27:36.760)

I could sit around not being threatened by bears,

Lex Fridman (1:27:39.240)

not starving to death,

Peter Wang (1:27:43.480)

making decisions somewhat,

Lex Fridman (1:27:44.680)

but a lot of times not seeing the consequences

Peter Wang (1:27:47.320)

of any decisions they make.

Lex Fridman (1:27:49.080)

The general sort of sense of anomie,

Peter Wang (1:27:51.400)

I think that is the French term for it,

Lex Fridman (1:27:53.240)

in the wake of the consumer society,

Peter Wang (1:27:55.400)

in the wake of mass media telling everyone,

Lex Fridman (1:27:58.520)

hey, choosing between Hermes and Chanel

Peter Wang (1:28:01.960)

is a meaningful decision.

Lex Fridman (1:28:03.080)

No, it's not.

Peter Wang (1:28:04.120)

I don't know what either of those mean.

Lex Fridman (1:28:05.560)

Oh, they're high end luxury purses and crap like that.

Lex Fridman (1:28:10.840)

But the point is that we give people the idea

Lex Fridman (1:28:13.480)

that consumption is meaning,

Peter Wang (1:28:15.000)

that making a choice of this team versus that team,

Lex Fridman (1:28:17.480)

spectating has meaning.

Lex Fridman (1:28:20.040)

So we produce all of these different things

Lex Fridman (1:28:22.440)

that are as if meaning, right?

Lex Fridman (1:28:25.240)

But really making a decision that has no consequences for us.

Lex Fridman (1:28:28.840)

And so that creates the meaning crisis.

Peter Wang (1:28:30.920)

Well, you're saying choosing between Chanel

Lex Fridman (1:28:33.240)

and the other one has no consequence.

Lex Fridman (1:28:35.240)

I mean, why is one more meaningful than the other?

Lex Fridman (1:28:38.120)

It's not that it's more meaningful than the other.

Peter Wang (1:28:39.480)

It's that you make a decision between these two brands

Lex Fridman (1:28:42.440)

and you're told this brand will make me look better

Peter Wang (1:28:45.080)

in front of other people.

Lex Fridman (1:28:45.800)

If I buy this brand of car,

Lex Fridman (1:28:47.560)

if I wear that brand of apparel, right?

Lex Fridman (1:28:50.040)

Like a lot of decisions we make are around consumption,

Lex Fridman (1:28:54.120)

but consumption by itself doesn't actually yield meaning.

Lex Fridman (1:28:57.080)

Gaining social status does provide meaning.

Lex Fridman (1:28:59.960)

So that's why in this era of abundant production,

Lex Fridman (1:29:05.400)

so many things turn into status games.

Peter Wang (1:29:07.320)

The NFT kind of explosion is a similar kind of thing.

Lex Fridman (1:29:09.880)

Everywhere there are status games

Peter Wang (1:29:11.880)

because we just have so much excess production.

Lex Fridman (1:29:16.040)

But aren't those status games a source of meaning?

Peter Wang (1:29:18.360)

Like why do the games we play have to be grounded

Lex Fridman (1:29:22.360)

in physical reality like they are

Lex Fridman (1:29:24.120)

when you're trying to run away from lions?

Lex Fridman (1:29:26.040)

Why can't we, in this virtuality world, on social media,

Lex Fridman (1:29:30.280)

why can't we play the games on social media,

Lex Fridman (1:29:32.200)

even the dark ones?

Peter Wang (1:29:33.320)

Right, we can, we can.

Lex Fridman (1:29:35.160)

But you're saying that's creating a meaning crisis.

Peter Wang (1:29:37.640)

Well, there's a meaning crisis

Lex Fridman (1:29:39.080)

in that there's two aspects of it.

Peter Wang (1:29:41.000)

Number one, playing those kinds of status games

Lex Fridman (1:29:44.520)

oftentimes requires destroying the planet

Peter Wang (1:29:46.600)

because it ties to consumption,

Lex Fridman (1:29:51.800)

consuming the latest and greatest version of a thing,

Peter Wang (1:29:54.280)

buying the latest limited edition sneaker

Lex Fridman (1:29:56.840)

and throwing out all the old ones.

Peter Wang (1:29:58.120)

Maybe it keeps in the old ones,

Lex Fridman (1:29:59.000)

but the amount of sneakers we have to cut up

Lex Fridman (1:30:01.000)

and destroy every year

Lex Fridman (1:30:02.680)

to create artificial scarcity for the next generation, right?

Peter Wang (1:30:05.640)

This is kind of stuff that's not great.

Lex Fridman (1:30:07.720)

It's not great at all.

Lex Fridman (1:30:09.880)

So conspicuous consumption fueling status games

Lex Fridman (1:30:13.400)

is really bad for the planet, not sustainable.

Peter Wang (1:30:16.040)

The second thing is you can play these kinds of status games,

Lex Fridman (1:30:19.640)

but then what it does is it renders you captured

Peter Wang (1:30:22.520)

to the virtual environment.

Lex Fridman (1:30:24.360)

The status games that really wealthy people are playing

Peter Wang (1:30:26.760)

are all around the hard resources

Lex Fridman (1:30:29.240)

where they're gonna build the factories,

Peter Wang (1:30:30.440)

they're gonna have the fuel in the rare earths

Lex Fridman (1:30:31.800)

to make the next generation of robots.

Peter Wang (1:30:33.160)

They're then going to run game,

Lex Fridman (1:30:34.760)

run circles around you and your children.

Lex Fridman (1:30:37.240)

So that's another reason not to play

Lex Fridman (1:30:38.760)

those virtual status games.

Lex Fridman (1:30:40.040)

So you're saying ultimately the big picture game is won

Lex Fridman (1:30:44.040)

by people who have access or control

Peter Wang (1:30:46.840)

over actual hard resources.

Lex Fridman (1:30:48.360)

So you can't, you don't see a society

Peter Wang (1:30:51.080)

where most of the games are played in the virtual space.

Lex Fridman (1:30:55.400)

They'll be captured in the physical space.

Peter Wang (1:30:57.160)

It all builds.

Lex Fridman (1:30:57.960)

It's just like the stack of human being, right?

Peter Wang (1:31:00.840)

If you only play the game at the cultural

Lex Fridman (1:31:04.360)

and then intellectual level,

Peter Wang (1:31:05.960)

then the people with the hard resources

Lex Fridman (1:31:07.320)

and access to layer zero physical are going to own you.

Lex Fridman (1:31:10.920)

But isn't money not connected to,

Lex Fridman (1:31:13.480)

or less and less connected to hard resources

Lex Fridman (1:31:15.560)

and money still seems to work?

Lex Fridman (1:31:17.080)

It's a virtual technology.

Peter Wang (1:31:18.520)

There's different kinds of money.

Lex Fridman (1:31:20.520)

Part of the reason that some of the stuff is able

Peter Wang (1:31:22.280)

to go a little unhinged is because the big sovereignties

Lex Fridman (1:31:29.960)

where one spends money and uses money

Lex Fridman (1:31:32.040)

and plays money games and inflates money,

Lex Fridman (1:31:34.600)

their ability to adjudicate the physical resources

Lex Fridman (1:31:38.440)

and hard resources and the resources

Lex Fridman (1:31:40.120)

and hard resources on land and things like that,

Peter Wang (1:31:42.200)

those have not been challenged in a very long time.

Lex Fridman (1:31:45.480)

So, you know, we went off the gold standard.

Peter Wang (1:31:47.640)

Most money is not connected to physical resources.

Lex Fridman (1:31:51.640)

It's an idea.

Lex Fridman (1:31:53.640)

And that idea is very closely connected to status.

Lex Fridman (1:31:59.880)

But it's also tied to like, it's actually tied to law.

Peter Wang (1:32:03.000)

It is tied to some physical hard things

Lex Fridman (1:32:04.680)

so you have to pay your taxes.

Peter Wang (1:32:06.120)

Yes, so it's always at the end going to be connected

Lex Fridman (1:32:09.880)

to the blockchain of physical reality.

Lex Fridman (1:32:12.600)

So in the case of law and taxes, it's connected to government

Lex Fridman (1:32:17.240)

and government is what violence is the,

Peter Wang (1:32:21.480)

I'm playing with stacks of devil's advocates here

Lex Fridman (1:32:27.720)

and popping one devil off the stack at a time.

Peter Wang (1:32:30.520)

Isn't ultimately, of course,

Lex Fridman (1:32:31.560)

it'll be connected to physical reality,

Lex Fridman (1:32:33.080)

but just because people control the physical reality,

Lex Fridman (1:32:35.560)

it doesn't mean the status.

Peter Wang (1:32:36.600)

I guess LeBron James in theory could make more money

Lex Fridman (1:32:39.720)

than the owners of the teams in theory.

Lex Fridman (1:32:43.320)

And to me, that's a virtual idea.

Lex Fridman (1:32:44.920)

So somebody else constructed a game

Lex Fridman (1:32:47.320)

and now you're playing in the virtual space of the game.

Lex Fridman (1:32:51.880)

So it just feels like there could be games where status,

Peter Wang (1:32:55.480)

we build realities that give us meaning in the virtual space.

Lex Fridman (1:33:00.280)

I can imagine such things being possible.

Peter Wang (1:33:02.840)

Oh yeah, okay, so I see what you're saying.

Lex Fridman (1:33:04.440)

I think I see what you're saying there

Peter Wang (1:33:05.560)

with the idea there, I mean, we'll take the LeBron James side

Lex Fridman (1:33:08.840)

and put in like some YouTube influencer.

Peter Wang (1:33:10.760)

Yes, sure.

Lex Fridman (1:33:11.480)

So the YouTube influencer, it is status games,

Lex Fridman (1:33:15.000)

but at a certain level, it precipitates into real dollars

Lex Fridman (1:33:18.920)

and into like, well, you look at Mr. Beast, right?

Peter Wang (1:33:21.160)

He's like sending off half a million dollars

Lex Fridman (1:33:23.160)

worth of fireworks or something, right?

Peter Wang (1:33:24.440)

Not a YouTube video.

Lex Fridman (1:33:25.640)

And also like saving, like saving trees and so on.

Peter Wang (1:33:28.520)

Sure, right, trying to find a million trees

Lex Fridman (1:33:29.880)

with Mark Rober or whatever it was.

Peter Wang (1:33:30.920)

Yeah, like it's not that those kinds of games

Lex Fridman (1:33:33.000)

can't lead to real consequences.

Peter Wang (1:33:34.520)

It's that for the vast majority of people in consumer culture,

Lex Fridman (1:33:40.200)

they are incented by the, I would say mostly,

Peter Wang (1:33:44.360)

I'm thinking about middle class consumers.

Lex Fridman (1:33:46.920)

They're incented by advertisements,

Peter Wang (1:33:48.760)

they're scented by their memetic environment

Lex Fridman (1:33:50.840)

to treat the purchasing of certain things,

Peter Wang (1:33:54.760)

the need to buy the latest model, whatever,

Lex Fridman (1:33:56.440)

the need to appear, however,

Peter Wang (1:33:58.280)

the need to pursue status games as a driver of meaning.

Lex Fridman (1:34:02.360)

And my point would be that it's a very hollow

Peter Wang (1:34:04.440)

driver of meaning.

Lex Fridman (1:34:05.960)

And that is what creates a meaning crisis.

Peter Wang (1:34:08.280)

Because at the end of the day,

Lex Fridman (1:34:10.040)

it's like eating a lot of empty calories, right?

Peter Wang (1:34:12.120)

Yeah, it tasted good going down, a lot of sugar,

Lex Fridman (1:34:13.960)

but man, it did not, it was not enough protein

Peter Wang (1:34:15.800)

to help build your muscles.

Lex Fridman (1:34:17.080)

And you kind of feel that in your gut.

Lex Fridman (1:34:18.920)

And I think that's, I mean, so all this stuff aside

Lex Fridman (1:34:21.240)

and setting aside our discussion on currency,

Peter Wang (1:34:22.840)

which I hope we get back to,

Lex Fridman (1:34:24.360)

that's what I mean about the meaning crisis,

Peter Wang (1:34:27.480)

part of it being created by the fact that we don't,

Lex Fridman (1:34:30.120)

we're not encouraged to have more and more

Peter Wang (1:34:32.520)

direct relationships.

Lex Fridman (1:34:34.200)

We're actually alienated from relating to,

Lex Fridman (1:34:37.880)

even our family members sometimes, right?

Lex Fridman (1:34:40.360)

We're encouraged to relate to brands.

Peter Wang (1:34:43.880)

We're encouraged to relate to these kinds of things

Lex Fridman (1:34:46.280)

that then tell us to do things

Peter Wang (1:34:49.240)

that are really of low consequence.

Lex Fridman (1:34:51.240)

And that's where the meaning crisis comes from.

Lex Fridman (1:34:52.920)

So the role of technology in this,

Lex Fridman (1:34:54.760)

so there's somebody you mentioned who's Jacques,

Peter Wang (1:34:57.240)

his view of technology, he warns about the towering piles

Lex Fridman (1:35:01.400)

of technique, which I guess is a broad idea of technology.

Lex Fridman (1:35:05.400)

So I think, correct me if I'm wrong for him,

Lex Fridman (1:35:08.120)

technology is bad at moving away from human nature

Lex Fridman (1:35:12.440)

and it's ultimately is destructive.

Lex Fridman (1:35:14.680)

My question, broadly speaking, this meaning crisis,

Lex Fridman (1:35:16.920)

can technology, what are the pros and cons of technology?

Lex Fridman (1:35:19.640)

Can it be a good?

Peter Wang (1:35:21.000)

Yeah, I think it can be.

Lex Fridman (1:35:22.280)

I certainly think it can be a good thing.

Peter Wang (1:35:24.600)

Can it be a good? Yeah, I think it can be.

Lex Fridman (1:35:27.240)

I certainly draw on some of Alol's ideas

Lex Fridman (1:35:29.720)

and I think some of them are pretty good.

Lex Fridman (1:35:32.920)

But the way he defines technique is,

Peter Wang (1:35:36.200)

well, also Simondon as well.

Lex Fridman (1:35:37.720)

I mean, he speaks to the general mentality of efficiency,

Peter Wang (1:35:41.240)

homogenized processes, homogenized production,

Lex Fridman (1:35:43.640)

homogenized labor to produce homogenized artifacts

Peter Wang (1:35:47.160)

that then are not actually,

Lex Fridman (1:35:50.920)

they don't sit well in the environment.

Peter Wang (1:35:53.080)

Essentially, you can think of it as the antonym of craft.

Lex Fridman (1:35:57.880)

Whereas a craftsman will come to a problem,

Peter Wang (1:36:02.040)

maybe a piece of wood and they make into a chair.

Lex Fridman (1:36:04.280)

It may be a site to build a house or build a stable

Peter Wang (1:36:06.600)

or build whatever.

Lex Fridman (1:36:08.520)

And they will consider how to bring various things in

Peter Wang (1:36:12.280)

to build something well contextualized

Lex Fridman (1:36:15.080)

that's in right relationship with that environment.

Lex Fridman (1:36:20.360)

But the way we have driven technology

Lex Fridman (1:36:22.360)

over the last 100 and 150 years is not that at all.

Peter Wang (1:36:25.720)

It is how can we make sure the input materials

Lex Fridman (1:36:30.480)

are homogenized, cut to the same size,

Peter Wang (1:36:33.400)

diluted and doped to exactly the right alloy concentrations.

Lex Fridman (1:36:36.840)

How do we create machines that then consume exactly

Peter Wang (1:36:38.680)

the right kind of energy to be able to run

Lex Fridman (1:36:39.960)

at this high speed to stamp out the same parts,

Peter Wang (1:36:42.600)

which then go out the door,

Lex Fridman (1:36:44.080)

everyone gets the same tickle of Mielmo.

Lex Fridman (1:36:45.760)

And the reason why everyone wants it

Lex Fridman (1:36:46.800)

is because we have broadcasts that tells everyone

Peter Wang (1:36:49.280)

this is the cool thing.

Lex Fridman (1:36:50.520)

So we homogenize demand, right?

Lex Fridman (1:36:52.560)

And we're like Baudrillard and other critiques

Lex Fridman (1:36:55.440)

of modernity coming from that direction,

Peter Wang (1:36:57.520)

the situation lists as well.

Lex Fridman (1:36:59.240)

It's that their point is that at this point in time,

Peter Wang (1:37:02.080)

consumption is the thing that drives

Lex Fridman (1:37:04.560)

a lot of the economic stuff, not the need,

Lex Fridman (1:37:06.680)

but the need to consume and build status games on top.

Lex Fridman (1:37:09.400)

So we have homogenized, when we discovered,

Lex Fridman (1:37:12.120)

I think this is really like Bernays and stuff, right?

Lex Fridman (1:37:14.800)

In the early 20th century, we discovered we can create,

Peter Wang (1:37:17.920)

we can create demand, we can create desire

Lex Fridman (1:37:20.880)

in a way that was not possible before

Peter Wang (1:37:23.560)

because of broadcast media.

Lex Fridman (1:37:25.560)

And not only do we create desire,

Peter Wang (1:37:27.680)

we don't create a desire for each person

Lex Fridman (1:37:29.360)

to connect to some bespoke thing,

Peter Wang (1:37:31.080)

to build a relationship with their neighbor or their spouse.

Lex Fridman (1:37:33.640)

We are telling them, you need to consume this brand,

Peter Wang (1:37:36.080)

you need to drive this vehicle,

Lex Fridman (1:37:37.200)

you gotta listen to this music,

Lex Fridman (1:37:38.320)

have you heard this, have you seen this movie, right?

Lex Fridman (1:37:40.920)

So creating homogenized demand makes it really cheap

Peter Wang (1:37:44.800)

to create homogenized product.

Lex Fridman (1:37:46.520)

And now you have economics of scale.

Lex Fridman (1:37:48.520)

So we make the same tickle me Elmo,

Lex Fridman (1:37:50.000)

give it to all the kids and all the kids are like,

Lex Fridman (1:37:52.800)

hey, I got a tickle me Elmo, right?

Lex Fridman (1:37:54.400)

So this is ultimately where this ties in then

Peter Wang (1:37:58.640)

to runaway hypercapitalism is that we then,

Lex Fridman (1:38:03.040)

capitalism is always looking for growth.

Peter Wang (1:38:04.800)

It's always looking for growth

Lex Fridman (1:38:05.960)

and growth only happens at the margins.

Lex Fridman (1:38:07.960)

So you have to squeeze more and more demand out.

Lex Fridman (1:38:09.960)

You gotta make it cheaper and cheaper

Peter Wang (1:38:11.040)

to make the same thing,

Lex Fridman (1:38:12.280)

but tell everyone they're still getting meaning from it.

Lex Fridman (1:38:15.080)

You're still like, this is still your tickle me Elmo, right?

Lex Fridman (1:38:18.040)

And we see little bits of this dripping critiques

Peter Wang (1:38:21.400)

of this dripping in popular culture.

Lex Fridman (1:38:22.800)

You see it sometimes it's when Buzz Lightyear

Peter Wang (1:38:25.920)

walks into the thing, he's like,

Lex Fridman (1:38:27.760)

oh my God, at the toy store, I'm just a toy.

Peter Wang (1:38:30.640)

Like there's millions of other,

Lex Fridman (1:38:31.760)

or there's hundreds of other Buzz Lightyear's

Lex Fridman (1:38:33.400)

just like me, right?

Lex Fridman (1:38:34.720)

That is, I think, a fun Pixar critique

Peter Wang (1:38:38.080)

on this homogenization dynamic.

Lex Fridman (1:38:40.080)

I agree with you on most of the things you're saying.

Lex Fridman (1:38:42.880)

So I'm playing devil's advocate here,

Lex Fridman (1:38:44.560)

but this homogenized machine of capitalism

Peter Wang (1:38:50.600)

is also the thing that is able to fund,

Lex Fridman (1:38:54.240)

if channeled correctly, innovation, invention,

Lex Fridman (1:38:59.120)

and development of totally new things

Lex Fridman (1:39:00.760)

that in the best possible world,

Peter Wang (1:39:02.280)

create all kinds of new experiences that can enrich lives,

Lex Fridman (1:39:06.680)

the quality of lives for all kinds of people.

Lex Fridman (1:39:09.840)

So isn't this the machine

Lex Fridman (1:39:12.360)

that actually enables the experiences

Lex Fridman (1:39:15.120)

and more and more experiences that will then give meaning?

Lex Fridman (1:39:18.640)

It has done that to some extent.

Peter Wang (1:39:21.040)

I mean, it's not all good or bad in my perspective.

Lex Fridman (1:39:24.680)

We can always look backwards

Lex Fridman (1:39:26.760)

and offer a critique of the path we've taken

Lex Fridman (1:39:29.120)

to get to this point in time.

Lex Fridman (1:39:31.640)

But that's a different, that's somewhat different

Lex Fridman (1:39:33.760)

and informs the discussion,

Lex Fridman (1:39:35.880)

but it's somewhat different than the question

Lex Fridman (1:39:37.680)

of where do we go in the future, right?

Peter Wang (1:39:40.600)

Is this still the same rocket we need to ride

Lex Fridman (1:39:42.720)

to get to the next point?

Lex Fridman (1:39:43.560)

Will it even get us to the next point?

Lex Fridman (1:39:44.560)

Well, how does this, so you're predicting the future,

Lex Fridman (1:39:46.240)

how does it go wrong in your view?

Lex Fridman (1:39:48.760)

We have the mechanisms,

Peter Wang (1:39:51.040)

we have now explored enough technologies

Lex Fridman (1:39:53.920)

to where we can actually, I think, sustainably produce

Lex Fridman (1:39:59.520)

what most people in the world need to live.

Lex Fridman (1:40:03.360)

We have also created the infrastructures

Peter Wang (1:40:07.640)

to allow continued research and development

Lex Fridman (1:40:10.400)

of additional science and medicine

Lex Fridman (1:40:13.040)

and various other kinds of things.

Lex Fridman (1:40:16.080)

The organizing principles that we use

Peter Wang (1:40:18.520)

to govern all these things today have been,

Lex Fridman (1:40:21.840)

a lot of them have been just inherited

Peter Wang (1:40:25.520)

from honestly medieval times.

Lex Fridman (1:40:28.440)

Some of them have refactored a little bit

Peter Wang (1:40:30.160)

in the industrial era,

Lex Fridman (1:40:31.920)

but a lot of these modes of organizing people

Peter Wang (1:40:35.920)

are deeply problematic.

Lex Fridman (1:40:38.280)

And furthermore, they're rooted in,

Peter Wang (1:40:41.640)

I think, a very industrial mode perspective on human labor.

Lex Fridman (1:40:46.160)

And this is one of those things,

Peter Wang (1:40:47.800)

I'm gonna go back to the open source thing.

Lex Fridman (1:40:49.720)

There was a point in time when,

Peter Wang (1:40:51.920)

well, let me ask you this.

Lex Fridman (1:40:53.640)

If you look at the core SciPy sort of collection of libraries,

Lex Fridman (1:40:57.080)

so SciPy, NumPy, Matplotlib, right?

Lex Fridman (1:40:59.360)

There's iPython Notebook, let's throw pandas in there,

Peter Wang (1:41:01.440)

scikit learn, a few of these things.

Lex Fridman (1:41:03.400)

How much value do you think, economic value,

Lex Fridman (1:41:07.400)

would you say they drive in the world today?

Lex Fridman (1:41:10.800)

That's one of the fascinating things

Peter Wang (1:41:12.640)

about talking to you and Travis is like,

Lex Fridman (1:41:16.000)

it's a measure, it's like a...

Lex Fridman (1:41:18.160)

At least a billion dollars a day, maybe?

Lex Fridman (1:41:20.080)

A billion dollars, sure.

Peter Wang (1:41:21.240)

I mean, it's like, it's similar question of like,

Lex Fridman (1:41:23.680)

how much value does Wikipedia create?

Peter Wang (1:41:26.080)

Right.

Lex Fridman (1:41:26.920)

It's like, all of it, I don't know.

Peter Wang (1:41:30.520)

Well, I mean, if you look at it,

Lex Fridman (1:41:31.840)

all of it, I don't know.

Peter Wang (1:41:33.440)

Well, I mean, if you look at our systems,

Lex Fridman (1:41:34.720)

when you do a Google search, right?

Peter Wang (1:41:36.120)

Now, some of that stuff runs through TensorFlow,

Lex Fridman (1:41:37.760)

but when you look at Siri,

Peter Wang (1:41:40.080)

when you do credit card transaction fraud,

Lex Fridman (1:41:42.080)

like just everything, right?

Peter Wang (1:41:43.360)

Every intelligence agency under the sun,

Lex Fridman (1:41:45.240)

they're using some aspect of these kinds of tools.

Lex Fridman (1:41:47.680)

So I would say that these create billions of dollars

Lex Fridman (1:41:51.200)

of value.

Peter Wang (1:41:52.040)

Oh, you mean like direct use of tools

Lex Fridman (1:41:53.560)

that leverage this data?

Peter Wang (1:41:54.400)

Yes, direct, yeah.

Lex Fridman (1:41:55.240)

Yeah, even that's billions a day, yeah.

Peter Wang (1:41:56.720)

Yeah, right, easily, I think.

Lex Fridman (1:41:58.800)

Like the things they could not do

Lex Fridman (1:41:59.800)

if they didn't have these tools, right?

Lex Fridman (1:42:01.160)

Yes.

Lex Fridman (1:42:02.000)

So that's billions of dollars a day, great.

Lex Fridman (1:42:04.880)

I think that's about right.

Peter Wang (1:42:05.760)

Now, if we take, how many people did it take

Lex Fridman (1:42:07.880)

to make that, right?

Lex Fridman (1:42:09.960)

And there was a point in time, not anymore,

Lex Fridman (1:42:11.720)

but there was a point in time when they could fit

Peter Wang (1:42:12.960)

in a van.

Lex Fridman (1:42:13.800)

I could have fit them in my Mercedes winter, right?

Lex Fridman (1:42:15.960)

And so if you look at that, like, holy crap,

Lex Fridman (1:42:19.240)

literally a van of maybe a dozen people

Peter Wang (1:42:22.480)

could create value to the tune of billions of dollars a day.

Lex Fridman (1:42:28.320)

What lesson do you draw from that?

Peter Wang (1:42:30.080)

Well, here's the thing.

Lex Fridman (1:42:31.400)

What can we do to do more of that?

Peter Wang (1:42:35.240)

Like that's open source.

Lex Fridman (1:42:36.360)

The way I've talked about this in other environments is

Peter Wang (1:42:39.760)

when we use generative participatory crowdsourced

Lex Fridman (1:42:43.440)

approaches, we unlock human potential

Peter Wang (1:42:47.600)

at a level that is better than what capitalism can do.

Lex Fridman (1:42:52.280)

I would challenge anyone to go and try to hire

Peter Wang (1:42:55.520)

the right 12 people in the world

Lex Fridman (1:42:58.360)

to build that entire stack

Lex Fridman (1:43:00.240)

the way those 12 people did that, right?

Lex Fridman (1:43:02.520)

They would be very, very hard pressed to do that.

Peter Wang (1:43:04.160)

If a hedge fund could just hire a dozen people

Lex Fridman (1:43:06.760)

and create like something that is worth

Peter Wang (1:43:08.480)

billions of dollars a day,

Lex Fridman (1:43:10.120)

every single one of them would be racing to do it, right?

Lex Fridman (1:43:12.400)

But finding the right people,

Lex Fridman (1:43:13.680)

fostering the right collaborations,

Peter Wang (1:43:15.160)

getting it adopted by the right other people

Lex Fridman (1:43:16.840)

to then refine it,

Peter Wang (1:43:18.080)

that is a thing that was organic in nature.

Lex Fridman (1:43:21.080)

That took crowdsourcing.

Peter Wang (1:43:22.200)

That took a lot of the open source ethos

Lex Fridman (1:43:24.160)

and it took the right kinds of people, right?

Peter Wang (1:43:26.480)

Now those people who started that said,

Lex Fridman (1:43:27.880)

I need to have a part of a multi billion dollar a day

Peter Wang (1:43:30.880)

sort of enterprise.

Lex Fridman (1:43:32.440)

They're like, I'm doing this cool thing

Lex Fridman (1:43:33.560)

to solve my problem for my friends, right?

Lex Fridman (1:43:35.480)

So the point of telling the story

Peter Wang (1:43:37.880)

is to say that our way of thinking about value,

Lex Fridman (1:43:40.760)

our way of thinking about allocation of resources,

Peter Wang (1:43:42.880)

our ways of thinking about property rights

Lex Fridman (1:43:44.920)

and all these kinds of things,

Peter Wang (1:43:46.200)

they come from finite game, scarcity mentality,

Lex Fridman (1:43:50.040)

medieval institutions.

Peter Wang (1:43:52.160)

As we are now entering,

Lex Fridman (1:43:54.200)

to some extent we're sort of in a post scarcity era,

Peter Wang (1:43:57.080)

although some people are hoarding a whole lot of stuff.

Lex Fridman (1:43:59.800)

We are at a point where if not now soon,

Peter Wang (1:44:02.200)

we'll be in a post scarcity era.

Lex Fridman (1:44:03.920)

The question of how we allocate resources

Peter Wang (1:44:06.480)

has to be revisited at a fundamental level

Lex Fridman (1:44:08.720)

because the kind of software these people built,

Peter Wang (1:44:11.000)

the modalities that those human ecologies

Lex Fridman (1:44:13.960)

that built that software,

Peter Wang (1:44:15.840)

it treats offers unproperty.

Lex Fridman (1:44:17.960)

Actually sharing creates value.

Peter Wang (1:44:20.480)

Restricting and forking reduces value.

Lex Fridman (1:44:23.080)

So that's different than any other physical resource

Peter Wang (1:44:26.360)

that we've ever dealt with.

Lex Fridman (1:44:27.200)

It's different than how most corporations

Lex Fridman (1:44:28.720)

treat software IP, right?

Lex Fridman (1:44:31.240)

So if treating software in this way

Peter Wang (1:44:34.600)

created this much value so efficiently, so cheaply,

Lex Fridman (1:44:37.560)

because feeding a dozen people for 10 years

Lex Fridman (1:44:39.160)

is really cheap, right?

Lex Fridman (1:44:41.640)

That's the reason I care about this right now

Peter Wang (1:44:44.680)

is because looking forward

Lex Fridman (1:44:46.040)

when we can automate a lot of labor,

Peter Wang (1:44:48.000)

where we can in fact,

Lex Fridman (1:44:49.560)

the programming for your robot in your part,

Peter Wang (1:44:52.200)

neck of the woods and your part of the Amazon

Lex Fridman (1:44:54.120)

to build something sustainable for you

Lex Fridman (1:44:55.960)

and your tribe to deliver the right medicines,

Lex Fridman (1:44:58.200)

to take care of the kids,

Peter Wang (1:45:00.240)

that's just software, that's just code

Lex Fridman (1:45:02.880)

that could be totally open sourced, right?

Lex Fridman (1:45:05.480)

So we can actually get to a mode

Lex Fridman (1:45:07.360)

where all of this additional generative things

Peter Wang (1:45:10.920)

that humans are doing,

Lex Fridman (1:45:12.400)

they don't have to be wrapped up in a container

Lex Fridman (1:45:16.200)

and then we charge for all the exponential dynamics

Lex Fridman (1:45:18.360)

out of it.

Peter Wang (1:45:19.200)

That's what Facebook did.

Lex Fridman (1:45:20.400)

That's what modern social media did, right?

Peter Wang (1:45:22.400)

Because the old internet was connecting people just fine.

Lex Fridman (1:45:24.920)

So Facebook came along and said,

Peter Wang (1:45:25.960)

well, anyone can post a picture,

Lex Fridman (1:45:26.960)

anyone can post some text

Lex Fridman (1:45:28.440)

and we're gonna amplify the crap out of it to everyone else.

Lex Fridman (1:45:31.120)

And it exploded this generative network

Peter Wang (1:45:33.280)

of human interaction.

Lex Fridman (1:45:34.720)

And then I said, how do I make money off that?

Peter Wang (1:45:36.080)

Oh yeah, I'm gonna be a gatekeeper

Lex Fridman (1:45:38.160)

on everybody's attention.

Lex Fridman (1:45:39.880)

And that's how I'm gonna make money.

Lex Fridman (1:45:41.040)

So how do we create more than one van?

Lex Fridman (1:45:45.640)

How do we have millions of vans full of people

Lex Fridman (1:45:47.840)

that create NumPy, SciPy, that create Python?

Lex Fridman (1:45:51.000)

So the story of those people is often they have

Lex Fridman (1:45:55.120)

some kind of job outside of this.

Peter Wang (1:45:57.080)

This is what they're doing for fun.

Lex Fridman (1:45:58.880)

Don't you need to have a job?

Peter Wang (1:46:00.960)

Don't you have to be connected,

Lex Fridman (1:46:02.240)

plugged in to the capitalist system?

Peter Wang (1:46:04.960)

Isn't that what,

Lex Fridman (1:46:07.280)

isn't this consumerism,

Peter Wang (1:46:09.160)

the engine that results in the individuals

Lex Fridman (1:46:13.880)

that kind of take a break from it every once in a while

Lex Fridman (1:46:15.880)

to create something magical?

Lex Fridman (1:46:17.320)

Like at the edges is where the innovation happens.

Peter Wang (1:46:19.160)

There's a surplus, right, this is the question.

Lex Fridman (1:46:21.360)

Like if everyone were to go and run their own farm,

Lex Fridman (1:46:24.400)

no one would have time to go and write NumPy, SciPy, right?

Lex Fridman (1:46:27.320)

Maybe, but that's what I'm talking about

Peter Wang (1:46:29.960)

when I say we're maybe at a post scarcity point

Lex Fridman (1:46:32.800)

for a lot of people.

Peter Wang (1:46:34.160)

The question that we're never encouraged to ask

Lex Fridman (1:46:37.240)

in a Super Bowl ad is how much do you need?

Lex Fridman (1:46:40.640)

How much is enough?

Lex Fridman (1:46:41.960)

Do you need to have a new car every two years, every five?

Peter Wang (1:46:45.040)

If you have a reliable car,

Lex Fridman (1:46:46.160)

can you drive one for 10 years, is that all right?

Peter Wang (1:46:48.480)

I had a car for 10 years and it was fine.

Lex Fridman (1:46:50.520)

Your iPhone, do you have to upgrade every two years?

Peter Wang (1:46:52.800)

I mean, it's sort of, you're using the same apps

Lex Fridman (1:46:54.320)

you did four years ago, right?

Peter Wang (1:46:56.680)

This should be a Super Bowl ad.

Lex Fridman (1:46:58.320)

This should be a Super Bowl ad, that's great.

Lex Fridman (1:46:59.680)

Maybe somebody. Do you really need a new iPhone?

Lex Fridman (1:47:01.400)

Maybe one of our listeners will fund something like this

Peter Wang (1:47:03.920)

of like, no, but just actually bringing it back,

Lex Fridman (1:47:06.960)

bringing it back to actually the question

Lex Fridman (1:47:09.440)

of what do you need?

Lex Fridman (1:47:11.480)

How do we create the infrastructure

Peter Wang (1:47:13.560)

for collectives of people to live on the basis

Lex Fridman (1:47:17.640)

of providing what we need, meeting people's needs

Peter Wang (1:47:21.000)

with a little bit of access to handle emergencies,

Lex Fridman (1:47:23.200)

things like that, pulling our resources together

Peter Wang (1:47:26.320)

to handle the really, really big emergencies,

Lex Fridman (1:47:28.760)

somebody with a really rare form of cancer

Peter Wang (1:47:30.880)

or some massive fire sweeps through half the village

Lex Fridman (1:47:34.120)

or whatever, but can we actually unscale things

Lex Fridman (1:47:38.200)

and solve for people's needs

Lex Fridman (1:47:41.560)

and then give them the capacity to explore

Lex Fridman (1:47:45.160)

how to be the best version of themselves?

Lex Fridman (1:47:47.320)

And for Travis, that was throwing away his shot of tenure

Peter Wang (1:47:51.000)

in order to write NumPy.

Lex Fridman (1:47:52.880)

For others, there is a saying in the SciFi community

Peter Wang (1:47:56.840)

that SciFi advances one failed postdoc at a time.

Lex Fridman (1:48:00.920)

And that's, we can do these things.

Peter Wang (1:48:03.800)

We can actually do this kind of collaboration

Lex Fridman (1:48:05.600)

because code, software, information, organization,

Peter Wang (1:48:08.360)

that's cheap.

Lex Fridman (1:48:09.880)

Those bits are very cheap to fling across the oceans.

Lex Fridman (1:48:13.000)

So you mentioned Travis.

Lex Fridman (1:48:14.760)

We've been talking and we'll continue to talk

Peter Wang (1:48:16.560)

about open source.

Lex Fridman (1:48:19.480)

Maybe you can comment.

Lex Fridman (1:48:20.440)

How did you meet Travis?

Lex Fridman (1:48:21.960)

Who is Travis Aliphant?

Lex Fridman (1:48:24.080)

What's your relationship been like through the years?

Lex Fridman (1:48:28.440)

Where did you work together?

Lex Fridman (1:48:30.160)

How did you meet?

Lex Fridman (1:48:31.800)

What's the present and the future look like?

Peter Wang (1:48:35.120)

Yeah, so the first time I met Travis

Lex Fridman (1:48:36.600)

was at a SciFi conference in Pasadena.

Lex Fridman (1:48:39.360)

Do you remember the year?

Lex Fridman (1:48:40.920)

2005.

Peter Wang (1:48:42.040)

I was working at, again, at nthought,

Lex Fridman (1:48:44.400)

working on scientific computing consulting.

Lex Fridman (1:48:47.000)

And a couple of years later,

Lex Fridman (1:48:51.160)

he joined us at nthought, I think 2007.

Lex Fridman (1:48:55.240)

And he came in as the president.

Lex Fridman (1:48:58.240)

One of the founders of nthought was the CEO, Eric Jones.

Lex Fridman (1:49:01.880)

And we were all very excited that Travis was joining us

Lex Fridman (1:49:04.080)

and that was great fun.

Lex Fridman (1:49:05.080)

And so I worked with Travis

Lex Fridman (1:49:06.960)

on a number of consulting projects

Lex Fridman (1:49:08.920)

and we worked on some open source stuff.

Lex Fridman (1:49:12.120)

I mean, it was just a really, it was a good time there.

Lex Fridman (1:49:15.080)

And then...

Lex Fridman (1:49:15.920)

It was primarily Python related?

Peter Wang (1:49:17.840)

Oh yeah, it was all Python, NumPy, SciFi consulting

Lex Fridman (1:49:19.800)

kind of stuff.

Peter Wang (1:49:21.000)

Towards the end of that time,

Lex Fridman (1:49:23.240)

we started getting called into more and more finance shops.

Peter Wang (1:49:27.720)

They were adopting Python pretty heavily.

Lex Fridman (1:49:29.840)

I did some work on like a high frequency trading shop,

Peter Wang (1:49:33.320)

working on some stuff.

Lex Fridman (1:49:34.160)

And then we worked together on some,

Peter Wang (1:49:36.520)

at a couple of investment banks in Manhattan.

Lex Fridman (1:49:39.840)

And so we started seeing that there was a potential

Peter Wang (1:49:42.680)

to take Python in the direction of business computing,

Lex Fridman (1:49:45.720)

more than just being this niche like MATLAB replacement

Peter Wang (1:49:48.120)

for big vector computing.

Lex Fridman (1:49:50.520)

What we were seeing was, oh yeah,

Peter Wang (1:49:51.800)

you could actually use Python as a Swiss army knife

Lex Fridman (1:49:53.880)

to do a lot of shadow data transformation kind of stuff.

Lex Fridman (1:49:56.840)

So that's when we realized the potential is much greater.

Lex Fridman (1:50:00.520)

And so we started Anaconda,

Peter Wang (1:50:03.360)

I mean, it was called Continuum Analytics at the time,

Lex Fridman (1:50:05.240)

but we started in January of 2012

Peter Wang (1:50:07.520)

with a vision of shoring up the parts of Python

Lex Fridman (1:50:10.760)

that needed to get expanded to handle data at scale,

Peter Wang (1:50:13.760)

to do web visualization, application development, et cetera.

Lex Fridman (1:50:17.200)

And that was that, yeah.

Lex Fridman (1:50:18.040)

So he was CEO and I was president for the first five years.

Lex Fridman (1:50:23.880)

And then we raised some money and then the board,

Peter Wang (1:50:27.480)

it was sort of put in a new CEO.

Lex Fridman (1:50:28.960)

They hired a kind of professional CEO.

Lex Fridman (1:50:31.320)

And then Travis, you laugh at that.

Lex Fridman (1:50:34.080)

I took over the CTO role.

Peter Wang (1:50:35.240)

Travis then left after a year to do his own thing,

Lex Fridman (1:50:37.920)

to do Quonsight, which was more oriented

Peter Wang (1:50:41.120)

around some of the bootstrap years that we did at Continuum

Lex Fridman (1:50:43.920)

where it was open source and consulting.

Peter Wang (1:50:46.200)

It wasn't sort of like gung ho product development.

Lex Fridman (1:50:48.600)

And it wasn't focused on,

Peter Wang (1:50:50.120)

we accidentally stumbled

Lex Fridman (1:50:51.120)

into the package management problem at Anaconda,

Lex Fridman (1:50:55.560)

but we had a lot of other visions of other technology

Lex Fridman (1:50:57.760)

that we built in the open source.

Lex Fridman (1:50:58.920)

And Travis was really trying to push,

Lex Fridman (1:51:02.080)

again, the frontiers of numerical computing,

Peter Wang (1:51:04.160)

vector computing,

Lex Fridman (1:51:05.320)

handling things like auto differentiation and stuff

Peter Wang (1:51:07.720)

intrinsically in the open ecosystem.

Lex Fridman (1:51:09.960)

So I think that's kind of the direction

Peter Wang (1:51:14.320)

he's working on in some of his work.

Lex Fridman (1:51:18.280)

We remain great friends and colleagues and collaborators,

Peter Wang (1:51:22.520)

even though he's no longer day to day working at Anaconda,

Lex Fridman (1:51:25.760)

but he gives me a lot of feedback

Peter Wang (1:51:27.000)

about this and that and the other.

Lex Fridman (1:51:29.000)

What's a big lesson you've learned from Travis

Lex Fridman (1:51:32.200)

about life or about programming or about leadership?

Lex Fridman (1:51:35.440)

Wow, there's a lot.

Peter Wang (1:51:36.480)

There's a lot.

Lex Fridman (1:51:37.320)

Travis is a really, really good guy.

Peter Wang (1:51:39.600)

He really, his heart is really in it.

Lex Fridman (1:51:41.920)

He cares a lot.

Peter Wang (1:51:44.760)

I've gotten that sense having to interact with him.

Lex Fridman (1:51:46.920)

It's so interesting.

Peter Wang (1:51:47.760)

Such a good human being.

Lex Fridman (1:51:48.600)

He's a really good dude.

Lex Fridman (1:51:49.720)

And he and I, it's so interesting.

Lex Fridman (1:51:51.360)

We come from very different backgrounds.

Peter Wang (1:51:53.240)

We're quite different as people,

Lex Fridman (1:51:56.000)

but I think we can like not talk for a long time

Lex Fridman (1:52:00.800)

and then be on a conversation

Lex Fridman (1:52:03.280)

and be eye to eye on like 90% of things.

Lex Fridman (1:52:06.400)

And so he's someone who I believe

Lex Fridman (1:52:08.280)

no matter how much fog settles in over the ocean,

Peter Wang (1:52:10.600)

his ship, my ship are pointed

Lex Fridman (1:52:12.200)

sort of in the same direction of the same star.

Peter Wang (1:52:14.120)

Wow, that's a beautiful way to phrase it.

Lex Fridman (1:52:16.840)

No matter how much fog there is,

Peter Wang (1:52:18.600)

we're pointed at the same star.

Lex Fridman (1:52:20.400)

Yeah, and I hope he feels the same way.

Peter Wang (1:52:21.880)

I mean, I hope he knows that over the years now.

Lex Fridman (1:52:23.760)

We both care a lot about the community.

Peter Wang (1:52:27.000)

For someone who cares so deeply,

Lex Fridman (1:52:28.120)

I would say this about Travis that's interesting.

Peter Wang (1:52:29.880)

For someone who cares so deeply about the nerd details

Lex Fridman (1:52:33.360)

of like type system design and vector computing

Lex Fridman (1:52:36.000)

and efficiency of expressing this and that and the other,

Lex Fridman (1:52:38.760)

memory layouts and all that stuff,

Peter Wang (1:52:40.440)

he cares even more about the people

Lex Fridman (1:52:43.280)

in the ecosystem, the community.

Lex Fridman (1:52:45.880)

And I have a similar kind of alignment.

Lex Fridman (1:52:49.760)

I care a lot about the tech, I really do.

Lex Fridman (1:52:53.080)

But for me, the beauty of what this human ecology

Lex Fridman (1:52:58.080)

has produced is I think a touchstone.

Peter Wang (1:53:01.680)

It's an early version, we can look at it and say,

Lex Fridman (1:53:03.600)

how do we replicate this for humanity at scale?

Lex Fridman (1:53:05.800)

What this open source collaboration was able to produce?

Lex Fridman (1:53:08.760)

How can we be generative in human collaboration

Peter Wang (1:53:11.600)

moving forward and create that

Lex Fridman (1:53:12.760)

as a civilizational kind of dynamic?

Lex Fridman (1:53:15.080)

Like, can we seize this moment to do that?

Lex Fridman (1:53:17.440)

Because like a lot of the other open source movements,

Peter Wang (1:53:19.720)

it's all nerds nerding out on code for nerds.

Lex Fridman (1:53:23.600)

And this because it's scientists,

Peter Wang (1:53:25.840)

because it's people working on data,

Lex Fridman (1:53:27.160)

that all of it faces real human problems.

Peter Wang (1:53:31.480)

I think we have an opportunity

Lex Fridman (1:53:32.600)

to actually make a bigger impact.

Peter Wang (1:53:34.360)

Is there a way for this kind of open source vision

Lex Fridman (1:53:37.480)

to make money?

Peter Wang (1:53:39.040)

Absolutely.

Lex Fridman (1:53:40.080)

To fund the people involved?

Lex Fridman (1:53:41.640)

Is that an essential part of it?

Lex Fridman (1:53:43.040)

It's hard, but we're trying to do that

Peter Wang (1:53:45.640)

in our own way at Anaconda,

Lex Fridman (1:53:48.560)

because we know that business users,

Peter Wang (1:53:49.880)

as they use more of the stuff, they have needs,

Lex Fridman (1:53:52.000)

like business specific needs around security, provenance.

Peter Wang (1:53:54.840)

They really can't tell their VPs and their investors,

Lex Fridman (1:53:59.200)

hey, we're having, our data scientists

Peter Wang (1:54:01.040)

are installing random packages from who knows where

Lex Fridman (1:54:03.520)

and running on customer data.

Lex Fridman (1:54:04.680)

So they have to have someone to talk to you.

Lex Fridman (1:54:05.920)

And that's what Anaconda does.

Lex Fridman (1:54:07.360)

So we are a governed source of packages for them,

Lex Fridman (1:54:10.400)

and that's great, that makes some money.

Peter Wang (1:54:12.160)

We take some of that and we just take that as a dividend.

Lex Fridman (1:54:16.160)

We take a percentage of our revenues

Lex Fridman (1:54:17.200)

and write that as a dividend for the open source community.

Lex Fridman (1:54:20.000)

But beyond that, I really see the development

Peter Wang (1:54:23.440)

of a marketplace for people to create notebooks,

Lex Fridman (1:54:27.560)

models, data sets, curation of these different kinds

Peter Wang (1:54:30.840)

of things, and to really have

Lex Fridman (1:54:33.120)

a long tail marketplace dynamic with that.

Lex Fridman (1:54:37.280)

Can you speak about this problem

Lex Fridman (1:54:38.800)

that you stumbled into of package management,

Lex Fridman (1:54:41.800)

Python package management?

Lex Fridman (1:54:43.120)

What is that?

Peter Wang (1:54:46.080)

A lot of people speak very highly of Conda,

Lex Fridman (1:54:48.160)

which is part of Anaconda, which is a package manager.

Peter Wang (1:54:50.720)

There's a ton of packages.

Lex Fridman (1:54:52.400)

So first, what are package managers?

Lex Fridman (1:54:55.080)

And second, what was there before?

Lex Fridman (1:54:57.240)

What is pip?

Lex Fridman (1:54:58.600)

And why is Conda more awesome?

Lex Fridman (1:55:01.840)

The package problem is this, which is that

Peter Wang (1:55:04.200)

in order to do numerical computing efficiently with Python,

Lex Fridman (1:55:11.640)

there are a lot of low level libraries

Peter Wang (1:55:14.480)

that need to be compiled, compiled with a C compiler

Lex Fridman (1:55:17.400)

or C++ compiler or Fortran compiler.

Peter Wang (1:55:19.880)

They need to not just be compiled,

Lex Fridman (1:55:21.120)

but they need to be compiled with all of the right settings.

Lex Fridman (1:55:23.680)

And oftentimes those settings are tuned

Lex Fridman (1:55:25.200)

for specific chip architectures.

Lex Fridman (1:55:27.440)

And when you add GPUs to the mix,

Lex Fridman (1:55:29.320)

when you look at different operating systems,

Peter Wang (1:55:32.280)

you may be on the same chip,

Lex Fridman (1:55:33.800)

but if you're running Mac versus Linux versus Windows

Peter Wang (1:55:37.200)

on the same x86 chip, you compile and link differently.

Lex Fridman (1:55:40.040)

All of this complexity is beyond the capability

Peter Wang (1:55:44.800)

of most data scientists to reason about.

Lex Fridman (1:55:46.720)

And it's also beyond what most of the package developers

Peter Wang (1:55:50.240)

want to deal with too.

Lex Fridman (1:55:51.880)

Because if you're a package developer,

Peter Wang (1:55:52.840)

you're like, I code on Linux.

Lex Fridman (1:55:54.280)

This works for me, I'm good.

Peter Wang (1:55:55.840)

It is not my problem to figure out how to build this

Lex Fridman (1:55:58.080)

on an ancient version of Windows, right?

Peter Wang (1:56:00.040)

That's just simply not my problem.

Lex Fridman (1:56:01.920)

So what we end up with is we have a creator economy

Peter Wang (1:56:05.120)

or create a very creative crowdsourced environment

Lex Fridman (1:56:08.560)

where people want to use this stuff, but they can't.

Lex Fridman (1:56:11.160)

And so we ended up creating a new set of technologies

Lex Fridman (1:56:15.720)

like a build recipe system, a build system

Lex Fridman (1:56:18.400)

and an installer system that is able to,

Lex Fridman (1:56:22.520)

well, to put it simply,

Peter Wang (1:56:24.640)

it's able to build these packages correctly

Lex Fridman (1:56:27.680)

on each of these different kinds of platforms

Lex Fridman (1:56:29.320)

and operating systems,

Lex Fridman (1:56:30.320)

and make it so when people want to install something,

Peter Wang (1:56:33.040)

they can, it's just one command.

Lex Fridman (1:56:34.400)

They don't have to set up a big compiler system

Lex Fridman (1:56:36.960)

and do all these things.

Lex Fridman (1:56:38.320)

So when it works well, it works great.

Peter Wang (1:56:40.440)

Now, the difficulty is we have literally thousands

Lex Fridman (1:56:43.920)

of people writing code in the ecosystem,

Peter Wang (1:56:46.280)

building all sorts of stuff and each person writing code,

Lex Fridman (1:56:48.760)

they may take a dependence on something else.

Lex Fridman (1:56:50.640)

And so you have all this web,

Lex Fridman (1:56:52.280)

incredibly complex web of dependencies.

Lex Fridman (1:56:54.840)

So installing the correct package

Lex Fridman (1:56:57.640)

for any given set of packages you want,

Peter Wang (1:57:00.360)

getting that right subgraph is an incredibly hard problem.

Lex Fridman (1:57:04.600)

And again, most data scientists

Peter Wang (1:57:05.800)

don't want to think about this.

Lex Fridman (1:57:06.640)

They're like, I want to install NumPy and pandas.

Peter Wang (1:57:09.160)

I want this version of some like geospatial library.

Lex Fridman (1:57:11.720)

I want this other thing.

Lex Fridman (1:57:13.080)

Like, why is this hard?

Lex Fridman (1:57:14.000)

These exist, right?

Lex Fridman (1:57:15.680)

And it is hard because it's, well,

Lex Fridman (1:57:17.680)

you're installing this on a version of Windows, right?

Lex Fridman (1:57:20.760)

And half of these libraries are not built for Windows

Lex Fridman (1:57:23.400)

or the latest version isn't available,

Lex Fridman (1:57:25.120)

but the old version was.

Lex Fridman (1:57:26.240)

And if you go to the old version of this library,

Peter Wang (1:57:27.560)

that means you need to go to a different version

Lex Fridman (1:57:28.600)

of that library.

Lex Fridman (1:57:30.040)

And so the Python ecosystem,

Lex Fridman (1:57:32.480)

by virtue of being crowdsourced,

Peter Wang (1:57:34.480)

we were able to fill a hundred thousand different niches.

Lex Fridman (1:57:38.000)

But then we also suffer this problem

Peter Wang (1:57:40.360)

that because it's crowdsourced and no one,

Lex Fridman (1:57:43.120)

it's like a tragedy of the commons, right?

Peter Wang (1:57:44.480)

No one really needs, wants to support

Lex Fridman (1:57:47.080)

their thousands of other dependencies.

Lex Fridman (1:57:49.200)

So we end up sort of having to do a lot of this.

Lex Fridman (1:57:52.160)

And of course the conda forge community

Peter Wang (1:57:53.480)

also steps up as an open source community that,

Lex Fridman (1:57:55.640)

you know, maintain some of these recipes.

Peter Wang (1:57:57.520)

That's what conda does.

Lex Fridman (1:57:58.680)

Now, pip is a tool that came along after conda,

Peter Wang (1:58:01.880)

to some extent, it came along as an easier way

Lex Fridman (1:58:04.480)

for the Python developers writing Python code

Peter Wang (1:58:09.520)

that didn't have as much compiled, you know, stuff.

Lex Fridman (1:58:12.920)

They could then install different packages.

Lex Fridman (1:58:15.360)

And what ended up happening in the Python ecosystem

Lex Fridman (1:58:17.800)

was that a lot of the core Python and web Python developers,

Peter Wang (1:58:20.800)

they never ran into any of this compilation stuff at all.

Lex Fridman (1:58:24.160)

So even we have, you know, on video,

Peter Wang (1:58:27.040)

we have Guido van Rossum saying,

Lex Fridman (1:58:29.520)

you know what, the scientific community's packaging problems

Peter Wang (1:58:31.600)

are just too exotic and different.

Lex Fridman (1:58:33.160)

I mean, you're talking about Fortran compilers, right?

Peter Wang (1:58:35.680)

Like you guys just need to build your own solution

Lex Fridman (1:58:37.680)

perhaps, right?

Lex Fridman (1:58:38.960)

So the Python core Python community went

Lex Fridman (1:58:41.720)

and built its own sort of packaging technologies,

Peter Wang (1:58:45.320)

not really contemplating the complexity

Lex Fridman (1:58:47.880)

of this stuff over here.

Lex Fridman (1:58:49.280)

And so now we have the challenge where

Lex Fridman (1:58:51.560)

you can pip install some things, some libraries,

Peter Wang (1:58:53.600)

if you just want to get started with them,

Lex Fridman (1:58:55.280)

you can pip install TensorFlow and that works great.

Peter Wang (1:58:57.560)

The instant you want to also install some other packages

Lex Fridman (1:59:00.200)

that use different versions of NumPy

Peter Wang (1:59:02.560)

or some like graphics library or some OpenCV thing

Lex Fridman (1:59:05.560)

or some other thing, you now run into dependency hell

Peter Wang (1:59:08.720)

because you cannot, you know,

Lex Fridman (1:59:09.880)

OpenCV can have a different version of libjpeg over here

Peter Wang (1:59:12.800)

than PyTorch over here.

Lex Fridman (1:59:14.320)

Like they actually, they all have to use the,

Peter Wang (1:59:15.800)

if you want to use GPU acceleration,

Lex Fridman (1:59:17.400)

they have to all use the same underlying drivers

Lex Fridman (1:59:18.840)

and same GPU CUDA things.

Lex Fridman (1:59:20.400)

So it's, it gets to be very gnarly

Lex Fridman (1:59:22.960)

and it's a level of technology

Lex Fridman (1:59:24.240)

that both the makers and the users

Peter Wang (1:59:26.120)

don't really want to think too much about.

Lex Fridman (1:59:28.560)

And that's where you step in and try to solve this.

Peter Wang (1:59:30.480)

We try to solve it.

Lex Fridman (1:59:31.320)

Subgraph problems.

Lex Fridman (1:59:32.160)

How much is that?

Lex Fridman (1:59:33.000)

I mean, you said that you don't want to think,

Peter Wang (1:59:34.960)

they don't want to think about it,

Lex Fridman (1:59:35.960)

but how much is it a little bit on the developer

Lex Fridman (1:59:38.000)

and providing them tools to be a little bit more clear

Lex Fridman (1:59:42.440)

of that subgraph of dependency that's necessary?

Peter Wang (1:59:44.920)

It is getting to a point where we do have to think about,

Lex Fridman (1:59:47.920)

look, can we pull some of the most popular packages together

Lex Fridman (1:59:51.280)

and get them to work on a coordinated release timeline,

Lex Fridman (1:59:53.640)

get them to build against the same test matrix,

Lex Fridman (1:59:55.880)

et cetera, et cetera, right?

Lex Fridman (1:59:57.000)

And there is a little bit of dynamic around this,

Lex Fridman (1:59:58.780)

but again, it is a volunteer community.

Lex Fridman (20:00.000)

They basically put them in a geofenced area,

Peter Wang (20:02.280)

said find any moving target, like a truck or vehicle

Lex Fridman (20:04.280)

that looks like this, and boom.

Lex Fridman (20:07.000)

That's not a human in the loop, right?

Lex Fridman (20:09.360)

So increasingly, the less human there is in the loop,

Peter Wang (20:12.760)

the more concerned you are about these kinds of systems,

Lex Fridman (20:15.520)

because there's unintended consequences,

Peter Wang (20:18.360)

like less the original designer and engineer of the system

Lex Fridman (20:22.000)

is able to predict, even one with good intent

Peter Wang (20:25.000)

is able to predict the consequences of such a system.

Lex Fridman (20:27.400)

Is that it? That's right.

Peter Wang (20:28.680)

There are some software systems, right,

Lex Fridman (20:30.160)

that run without humans in the loop

Peter Wang (20:31.920)

that are quite complex.

Lex Fridman (20:32.800)

And that's like the electronic markets.

Lex Fridman (20:34.320)

And we get flash crashes all the time.

Lex Fridman (20:35.920)

We get in the heyday of high frequency trading,

Peter Wang (20:40.440)

there's a lot of market microstructure,

Lex Fridman (20:41.800)

people doing all sorts of weird stuff

Peter Wang (20:43.600)

that the market designers had never really thought about,

Lex Fridman (20:47.240)

contemplated or intended.

Lex Fridman (20:48.840)

So when we run these full on systems

Lex Fridman (20:50.680)

with these automated trading bots,

Peter Wang (20:52.840)

now they become automated killer drones

Lex Fridman (20:55.440)

and then all sorts of other stuff.

Peter Wang (20:57.280)

We are, that's what I mean by we're at the dawn

Lex Fridman (20:59.560)

of the cybernetic era and the end of the era

Peter Wang (21:01.600)

of just pure software.

Lex Fridman (21:03.880)

Are you more concerned,

Peter Wang (21:06.000)

if you're thinking about cybernetic systems

Lex Fridman (21:08.280)

or even like self replicating systems,

Lex Fridman (21:10.040)

so systems that aren't just doing a particular task,

Lex Fridman (21:13.560)

but are able to sort of multiply and scale

Peter Wang (21:15.720)

in some dimension in the digital

Lex Fridman (21:18.040)

or even the physical world.

Lex Fridman (21:20.360)

Are you more concerned about like the lobster being boiled?

Lex Fridman (21:24.800)

So a gradual with us not noticing,

Peter Wang (21:29.160)

collapse of civilization or a big explosion,

Lex Fridman (21:34.320)

like oops, kind of a big thing where everyone notices,

Lex Fridman (21:38.680)

but it's too late.

Lex Fridman (21:40.160)

I think that it will be a different experience

Peter Wang (21:44.040)

for different people.

Lex Fridman (21:46.280)

I do share a common point of view

Peter Wang (21:49.800)

with some of the climate,

Lex Fridman (21:52.240)

people who are concerned about climate change

Lex Fridman (21:53.760)

and just the big existential risks that we have.

Lex Fridman (21:59.440)

But unlike a lot of people who share my level of concern,

Peter Wang (22:02.560)

I think the collapse will not be quite so dramatic

Lex Fridman (22:06.200)

as some of them think.

Lex Fridman (22:07.560)

And what I mean is that,

Lex Fridman (22:09.200)

I think that for certain tiers of let's say economic class

Peter Wang (22:12.320)

or certain locations in the world,

Lex Fridman (22:14.640)

people will experience dramatic collapse scenarios.

Lex Fridman (22:17.760)

But for a lot of people, especially in the developed world,

Lex Fridman (22:20.320)

the realities of collapse will be managed.

Peter Wang (22:24.040)

There'll be narrative management around it

Lex Fridman (22:26.640)

so that they essentially insulate,

Peter Wang (22:29.160)

the middle class will be used to insulate the upper class

Lex Fridman (22:31.320)

from the pitchforks and the flaming torches and everything.

Peter Wang (22:35.880)

It's interesting because,

Lex Fridman (22:37.000)

so my specific question wasn't as general.

Peter Wang (22:39.600)

My question was more about cybernetic systems or software.

Lex Fridman (22:42.480)

Okay.

Peter Wang (22:43.560)

It's interesting,

Lex Fridman (22:44.400)

but it would nevertheless perhaps be about class.

Lex Fridman (22:46.720)

So the effect of algorithms

Lex Fridman (22:48.360)

might affect certain classes more than others.

Peter Wang (22:50.200)

Absolutely.

Lex Fridman (22:51.040)

I was more thinking about

Peter Wang (22:52.680)

whether it's social media algorithms or actual robots,

Lex Fridman (22:57.320)

is there going to be a gradual effect on us

Peter Wang (23:00.000)

where we wake up one day

Lex Fridman (23:02.600)

and don't recognize the humans we are,

Peter Wang (23:05.160)

or is it something truly dramatic

Lex Fridman (23:07.560)

where there's like a meltdown of a nuclear reactor

Peter Wang (23:11.640)

kind of thing, Chernobyl, like catastrophic events

Lex Fridman (23:15.240)

that are almost bugs in a program that scaled itself

Lex Fridman (23:20.280)

too quickly?

Lex Fridman (23:21.120)

Yeah, I'm not as concerned about the visible stuff.

Lex Fridman (23:26.280)

And the reason is because the big visible explosions,

Lex Fridman (23:29.560)

I mean, this is something I said about social media

Peter Wang (23:31.200)

is that at least with nuclear weapons,

Lex Fridman (23:33.200)

when a nuke goes off, you can see it

Lex Fridman (23:34.840)

and you're like, well, that's really,

Lex Fridman (23:36.360)

wow, that's kind of bad, right?

Lex Fridman (23:37.760)

I mean, Oppenheimer was reciting the Bhagavad Gita, right?

Lex Fridman (23:40.640)

When he saw one of those things go off.

Lex Fridman (23:42.200)

So we can see nukes are really bad.

Lex Fridman (23:45.160)

He's not reciting anything about Twitter.

Peter Wang (23:48.000)

Well, but right, but then when you have social media,

Lex Fridman (23:51.160)

when you have all these different things that conspire

Peter Wang (23:54.400)

to create a layer of virtual experience for people

Lex Fridman (23:57.120)

that alienates them from reality and from each other,

Lex Fridman (24:00.960)

that's very pernicious, that's impossible to see, right?

Lex Fridman (24:03.840)

And it kind of slowly gets in there, so.

Peter Wang (24:07.160)

You've written about this idea of virtuality

Lex Fridman (24:09.600)

on this topic, which you define as the subjective phenomenon

Peter Wang (24:14.120)

of knowingly engaging with virtual sensation and perception

Lex Fridman (24:17.800)

and suspending or forgetting the context

Peter Wang (24:19.920)

that it's simulacrum.

Lex Fridman (24:22.040)

So let me ask, what is real?

Lex Fridman (24:26.440)

Is there a hard line between reality and virtuality?

Lex Fridman (24:30.440)

Like perception drifts from some kind of physical reality.

Peter Wang (24:33.440)

We have to kind of have a sense of what is the line

Lex Fridman (24:36.120)

that's too, we've gone too far.

Peter Wang (24:37.640)

Right, right.

Lex Fridman (24:38.760)

For me, it's not about any hard line about physical reality

Peter Wang (24:42.640)

as much as a simple question of,

Lex Fridman (24:47.160)

does the particular technology help people connect

Peter Wang (24:51.560)

in a more integral way with other people,

Lex Fridman (24:54.400)

with their environment,

Lex Fridman (24:56.000)

with all of the full spectrum of things around them?

Lex Fridman (24:58.560)

So it's less about, oh, this is a virtual thing

Lex Fridman (25:00.880)

and this is a hard real thing,

Lex Fridman (25:03.000)

more about when we create virtual representations

Peter Wang (25:05.760)

of the real things, always some things

Lex Fridman (25:09.360)

are lost in translation.

Peter Wang (25:10.760)

Usually many, many dimensions are lost in translation.

Lex Fridman (25:14.320)

We're now coming to almost two years of COVID,

Peter Wang (25:16.840)

people on Zoom all the time.

Lex Fridman (25:17.920)

You know it's different when you meet somebody in person

Peter Wang (25:19.760)

than when you see them on,

Lex Fridman (25:20.600)

I've seen you on YouTube lots, right?

Lex Fridman (25:22.560)

But then seeing a person is very different.

Lex Fridman (25:24.200)

And so I think when we engage in virtual experiences

Peter Wang (25:29.760)

all the time, and we only do that,

Lex Fridman (25:31.680)

there is absolutely a level of embodiment.

Peter Wang (25:34.280)

There's a level of embodied experience

Lex Fridman (25:36.560)

and participatory interaction that is lost.

Lex Fridman (25:40.080)

And it's very hard to put your finger on exactly what it is.

Lex Fridman (25:42.520)

It's hard to say, oh, we're gonna spend $100 million

Peter Wang (25:44.600)

building a new system that captures this 5% better,

Lex Fridman (25:49.480)

higher fidelity human expression.

Lex Fridman (25:51.240)

No one's gonna pay for that, right?

Lex Fridman (25:52.600)

So when we rush madly into a world of simulacrum

Lex Fridman (25:57.480)

and virtuality, the things that are lost are,

Lex Fridman (26:02.560)

it's difficult.

Peter Wang (26:04.240)

Once everyone moves there, it can be hard to look back

Lex Fridman (26:06.400)

and see what we've lost.

Lex Fridman (26:08.120)

So is it irrecoverably lost?

Lex Fridman (26:10.400)

Or rather, when you put it all on the table,

Lex Fridman (26:14.200)

is it possible for more to be gained than is lost?

Lex Fridman (26:17.160)

If you look at video games,

Peter Wang (26:18.680)

they create virtual experiences that are surreal

Lex Fridman (26:22.720)

and can bring joy to a lot of people,

Peter Wang (26:24.480)

can connect a lot of people,

Lex Fridman (26:26.680)

and can get people to talk a lot of trash.

Lex Fridman (26:29.880)

So they can bring out the best and the worst in people.

Lex Fridman (26:32.600)

So is it possible to have a future world

Lex Fridman (26:35.720)

where the pros outweigh the cons?

Lex Fridman (26:38.560)

It is.

Peter Wang (26:39.400)

I mean, it's possible to have that in the current world.

Lex Fridman (26:41.560)

But when literally trillions of dollars of capital

Peter Wang (26:46.600)

are tied to using those things

Lex Fridman (26:48.920)

to groom the worst of our inclinations

Lex Fridman (26:52.840)

and to attack our weaknesses in the limbic system

Lex Fridman (26:56.080)

to create these things into id machines

Peter Wang (26:57.640)

versus connection machines,

Lex Fridman (26:59.120)

then those good things don't stand a chance.

Lex Fridman (27:03.200)

Can you make a lot of money by building connection machines?

Lex Fridman (27:06.640)

Is it possible, do you think,

Peter Wang (27:09.280)

to bring out the best in human nature

Lex Fridman (27:10.920)

to create fulfilling connections and relationships

Lex Fridman (27:13.720)

in the digital world and make a shit ton of money?

Lex Fridman (27:18.720)

If I figure it out, I'll let you know.

Lex Fridman (27:21.120)

But what's your intuition

Lex Fridman (27:22.000)

without concretely knowing what's the solution?

Peter Wang (27:24.640)

My intuition is that a lot of our digital technologies

Lex Fridman (27:27.720)

give us the ability to have synthetic connections

Peter Wang (27:30.800)

or to experience virtuality.

Lex Fridman (27:33.040)

They have co evolved with sort of the human expectations.

Peter Wang (27:38.920)

It's sort of like sugary drinks.

Lex Fridman (27:40.840)

As people have more sugary drinks,

Lex Fridman (27:42.320)

they need more sugary drinks to get that same hit, right?

Lex Fridman (27:45.880)

So with these virtual things and with TV and fast cuts

Lex Fridman (27:50.400)

and TikToks and all these different kinds of things,

Lex Fridman (27:52.840)

we're co creating essentially humanity

Peter Wang (27:55.040)

that sort of asks and needs those things.

Lex Fridman (27:57.320)

And now it becomes very difficult

Peter Wang (27:58.880)

to get people to slow down.

Lex Fridman (28:00.360)

It gets difficult for people to hold their attention

Peter Wang (28:03.520)

on slow things and actually feel that embodied experience.

Lex Fridman (28:07.320)

So mindfulness now more than ever is so important in schools

Lex Fridman (28:11.120)

and as a therapy technique for people

Lex Fridman (28:13.560)

because our environment has been accelerated.

Lex Fridman (28:15.680)

And McLuhan actually talks about this

Lex Fridman (28:17.360)

in the electric environment of the television.

Lex Fridman (28:19.520)

And that was before TikTok and before front facing cameras.

Lex Fridman (28:22.440)

So I think for me, the concern is that

Peter Wang (28:25.360)

it's not like we can ever switch to doing something better,

Lex Fridman (28:28.160)

but more of the humans and technology,

Peter Wang (28:32.080)

they're not independent of each other.

Lex Fridman (28:33.560)

The technology that we use kind of molds what we need

Peter Wang (28:37.760)

for the next generation of technology.

Lex Fridman (28:39.120)

Yeah, but humans are intelligent and they're introspective

Lex Fridman (28:43.360)

and they can reflect on the experiences of their life.

Lex Fridman (28:45.840)

So for example, there's been many years in my life

Peter Wang (28:47.880)

where I ate an excessive amount of sugar.

Lex Fridman (28:50.920)

And then a certain moment I woke up and said,

Lex Fridman (28:54.840)

why do I keep doing this?

Lex Fridman (28:55.920)

This doesn't feel good.

Peter Wang (28:57.680)

Like longterm.

Lex Fridman (28:59.000)

And I think, so going through the TikTok process

Peter Wang (29:02.320)

of realizing, okay, when I shorten my attention span,

Lex Fridman (29:06.280)

actually that does not make me feel good longer term.

Lex Fridman (29:10.240)

And realizing that and then going to platforms,

Lex Fridman (29:13.160)

going to places that are away from the sugar.

Lex Fridman (29:18.280)

So in so doing, you can create platforms

Lex Fridman (29:21.080)

that can make a lot of money to help people wake up

Peter Wang (29:24.000)

to what actually makes them feel good longterm.

Lex Fridman (29:26.280)

Develop, grow as human beings.

Lex Fridman (29:28.280)

And it just feels like humans are more intelligent

Lex Fridman (29:31.040)

than mice looking for cheese.

Peter Wang (29:35.120)

They're able to sort of think, I mean,

Lex Fridman (29:36.960)

we can contemplate our own mortality.

Peter Wang (29:39.720)

We can contemplate things like longterm love

Lex Fridman (29:43.880)

and we can have a longterm fear

Peter Wang (29:46.080)

of certain things like mortality.

Lex Fridman (29:48.080)

We can contemplate whether the experiences,

Peter Wang (29:51.240)

the sort of the drugs of daily life

Lex Fridman (29:53.680)

that we've been partaking in is making us happier,

Peter Wang (29:57.280)

better people.

Lex Fridman (29:58.320)

And then once we contemplate that,

Peter Wang (2:00:01.880)

Yeah.

Lex Fridman (2:00:02.720)

You know, people working on these different projects

Peter Wang (2:00:04.980)

have their own timelines

Lex Fridman (2:00:06.040)

and their own things they're trying to meet.

Lex Fridman (2:00:07.600)

So we end up trying to pull these things together.

Lex Fridman (2:00:11.360)

And then it's this incredibly,

Lex Fridman (2:00:13.080)

and I would recommend just as a business tip,

Lex Fridman (2:00:15.440)

don't ever go into business

Peter Wang (2:00:16.560)

where when your hard work works, you're invisible.

Lex Fridman (2:00:19.600)

And when it breaks because of someone else's problem,

Peter Wang (2:00:21.720)

you get flagged for it.

Lex Fridman (2:00:23.200)

Because that's in our situation, right?

Peter Wang (2:00:25.640)

When something doesn't condensate all properly,

Lex Fridman (2:00:27.280)

usually it's some upstream issue,

Lex Fridman (2:00:28.980)

but it looks like condensate is broken.

Lex Fridman (2:00:30.280)

It looks like, you know, Anaconda screwed something up.

Peter Wang (2:00:32.640)

When things do work though, it's like, oh yeah, cool.

Lex Fridman (2:00:34.560)

It's worked.

Peter Wang (2:00:35.400)

Assuming naturally, of course,

Lex Fridman (2:00:36.240)

it's very easy to make that work, right?

Lex Fridman (2:00:38.200)

So we end up in this kind of problematic scenario,

Lex Fridman (2:00:41.960)

but it's okay because I think we're still,

Peter Wang (2:00:45.040)

you know, our heart's in the right place.

Lex Fridman (2:00:46.760)

We're trying to move this forward

Peter Wang (2:00:47.840)

as a community sort of affair.

Lex Fridman (2:00:49.220)

I think most of the people in the community

Peter Wang (2:00:50.520)

also appreciate the work we've done over the years

Lex Fridman (2:00:53.000)

to try to move these things forward

Peter Wang (2:00:54.280)

in a collaborative fashion, so.

Lex Fridman (2:00:57.320)

One of the subgraphs of dependencies

Peter Wang (2:01:01.200)

that became super complicated

Lex Fridman (2:01:03.560)

is the move from Python 2 to Python 3.

Lex Fridman (2:01:05.760)

So there's all these ways to mess

Lex Fridman (2:01:07.200)

with these kinds of ecosystems of packages and so on.

Lex Fridman (2:01:11.520)

So I just want to ask you about that particular one.

Lex Fridman (2:01:13.760)

What do you think about the move from Python 2 to 3?

Lex Fridman (2:01:18.000)

Why did it take so long?

Lex Fridman (2:01:19.440)

What were, from your perspective,

Peter Wang (2:01:20.960)

just seeing the packages all struggle

Lex Fridman (2:01:23.700)

and the community all struggle through this process,

Lex Fridman (2:01:26.280)

what lessons do you take away from it?

Lex Fridman (2:01:27.840)

Why did it take so long?

Peter Wang (2:01:29.480)

Looking back, some people perhaps underestimated

Lex Fridman (2:01:33.380)

how much adoption Python 2 had.

Peter Wang (2:01:38.120)

I think some people also underestimated how much,

Lex Fridman (2:01:41.800)

or they overestimated how much value

Peter Wang (2:01:44.440)

some of the new features in Python 3 really provided.

Lex Fridman (2:01:47.400)

Like the things they really loved about Python 3

Peter Wang (2:01:49.720)

just didn't matter to some of these people in Python 2.

Lex Fridman (2:01:52.600)

Because this change was happening as Python, SciPy,

Peter Wang (2:01:56.440)

was starting to take off really like past,

Lex Fridman (2:01:58.700)

like a hockey stick of adoption

Peter Wang (2:02:00.000)

in the early data science era, in the early 2010s.

Lex Fridman (2:02:02.880)

A lot of people were learning and onboarding

Peter Wang (2:02:04.880)

in whatever just worked.

Lex Fridman (2:02:06.280)

And the teachers were like,

Peter Wang (2:02:07.180)

well, yeah, these libraries I need

Lex Fridman (2:02:09.080)

are not supported in Python 3 yet,

Peter Wang (2:02:10.360)

I'm going to teach you Python 2.

Lex Fridman (2:02:12.040)

Took a lot of advocacy to get people

Peter Wang (2:02:13.960)

to move over to Python 3.

Lex Fridman (2:02:15.700)

So I think it wasn't any particular single thing,

Lex Fridman (2:02:18.860)

but it was one of those death by a dozen cuts,

Lex Fridman (2:02:21.820)

which just really made it hard to move off of Python 2.

Lex Fridman (2:02:25.480)

And also Python 3 itself,

Lex Fridman (2:02:27.280)

as they were kind of breaking things

Lex Fridman (2:02:28.820)

and changing things around

Lex Fridman (2:02:29.660)

and reorganizing the standard library,

Peter Wang (2:02:30.720)

there's a lot of stuff that was happening there

Lex Fridman (2:02:32.920)

that kept giving people an excuse to say,

Peter Wang (2:02:35.880)

I'll put off till the next version.

Lex Fridman (2:02:37.760)

2 is working fine enough for me right now.

Lex Fridman (2:02:39.680)

So I think that's essentially what happened there.

Lex Fridman (2:02:41.480)

And I will say this though,

Peter Wang (2:02:43.760)

the strength of the Python data science movement,

Lex Fridman (2:02:48.660)

I think is what kept Python alive in that transition.

Peter Wang (2:02:52.440)

Because a lot of languages have died

Lex Fridman (2:02:54.040)

and left their user bases behind.

Peter Wang (2:02:56.840)

If there wasn't the use of Python for data,

Lex Fridman (2:02:58.980)

there's a good chunk of Python users

Peter Wang (2:03:01.160)

that during that transition,

Lex Fridman (2:03:02.740)

would have just left for Go and Rust and stayed.

Peter Wang (2:03:04.920)

In fact, some people did.

Lex Fridman (2:03:06.220)

They moved to Go and Rust and they just never looked back.

Peter Wang (2:03:08.880)

The fact that we were able to grow by millions of users,

Lex Fridman (2:03:13.720)

the Python data community,

Peter Wang (2:03:15.820)

that is what kept the momentum for Python going.

Lex Fridman (2:03:18.320)

And now the usage of Python for data is over 50%

Peter Wang (2:03:21.760)

of the overall Python user base.

Lex Fridman (2:03:24.320)

So I'm happy to debate that on stage somewhere,

Peter Wang (2:03:27.960)

I don't know if they really wanna take issue

Lex Fridman (2:03:29.920)

with that statement, but from where I sit,

Peter Wang (2:03:31.600)

I think that's true.

Lex Fridman (2:03:32.560)

The statement there, the idea is that the switch

Peter Wang (2:03:35.280)

from Python 2 to Python 3 would have probably

Lex Fridman (2:03:39.040)

destroyed Python if it didn't also coincide with Python

Peter Wang (2:03:43.600)

for whatever reason,

Lex Fridman (2:03:45.780)

just overtaking the data science community,

Peter Wang (2:03:49.680)

anything that processes data.

Lex Fridman (2:03:51.800)

So like the timing was perfect that this maybe

Peter Wang (2:03:55.760)

imperfect decision was coupled with a great timing

Lex Fridman (2:03:59.080)

on the value of data in our world.

Peter Wang (2:04:02.080)

I would say the troubled execution of a good decision.

Lex Fridman (2:04:04.780)

It was a decision that was necessary.

Peter Wang (2:04:07.360)

It's possible if we had more resources,

Lex Fridman (2:04:08.840)

we could have done in a way that was a little bit smoother,

Lex Fridman (2:04:11.120)

but ultimately, the arguments for Python 3,

Lex Fridman (2:04:15.200)

I bought them at the time and I buy them now, right?

Peter Wang (2:04:17.400)

Having great text handling is like a nonnegotiable

Lex Fridman (2:04:20.800)

table stakes thing you need to have in a language.

Lex Fridman (2:04:23.440)

So that's great, but the execution,

Lex Fridman (2:04:29.480)

Python is the, it's volunteer driven.

Peter Wang (2:04:33.000)

It's like now the most popular language on the planet,

Lex Fridman (2:04:34.880)

but it's all literally volunteers.

Lex Fridman (2:04:37.080)

So the lack of resources meant that they had to really,

Lex Fridman (2:04:40.400)

they had to do things in a very hamstrung way.

Lex Fridman (2:04:43.600)

And I think to carry the Python momentum in the language

Lex Fridman (2:04:46.720)

through that time, the data movement

Peter Wang (2:04:48.460)

was a critical part of that.

Lex Fridman (2:04:49.920)

So some of it is carrot and stick, I actually have to

Peter Wang (2:04:54.080)

shamefully admit that it took me a very long time

Lex Fridman (2:04:57.040)

to switch from Python 2 and Python 3

Peter Wang (2:04:58.760)

because I'm a machine learning person.

Lex Fridman (2:05:00.320)

It was just for the longest time,

Peter Wang (2:05:01.960)

you could just do fine with Python 2.

Lex Fridman (2:05:03.820)

Right.

Lex Fridman (2:05:04.960)

But I think the moment where I switched everybody

Lex Fridman (2:05:09.200)

I worked with and switched myself for small projects

Lex Fridman (2:05:13.080)

and big is when finally, when NumPy announced

Lex Fridman (2:05:17.700)

that they're going to end support like in 2020

Peter Wang (2:05:21.480)

or something like that.

Lex Fridman (2:05:22.320)

Right.

Lex Fridman (2:05:23.160)

So like when I realized, oh, this isn't going,

Lex Fridman (2:05:26.200)

this is going to end.

Peter Wang (2:05:27.400)

Right.

Lex Fridman (2:05:28.240)

So that's the stick, that's not a carrot.

Peter Wang (2:05:29.720)

That's not, so for the longest time it was carrots.

Lex Fridman (2:05:31.720)

It was like all of these packages were saying,

Peter Wang (2:05:34.420)

okay, we have Python 3 support now, come join us.

Lex Fridman (2:05:37.460)

We have Python 2 and Python 3, but when NumPy,

Peter Wang (2:05:40.120)

one of the packages I sort of love and depend on

Lex Fridman (2:05:43.440)

said like, nope, it's over.

Peter Wang (2:05:47.660)

That's when I decided to switch.

Lex Fridman (2:05:50.360)

I wonder if you think it was possible much earlier

Peter Wang (2:05:53.840)

for somebody like NumPy or some major package

Lex Fridman (2:05:58.840)

to step into the cold and say like we're ending this.

Lex Fridman (2:06:03.840)

Well, it's a chicken and egg problem too, right?

Lex Fridman (2:06:05.320)

You don't want to cut off a lot of users

Peter Wang (2:06:07.580)

unless you see the user momentum going too.

Lex Fridman (2:06:09.400)

So the decisions for the scientific community

Peter Wang (2:06:12.900)

for each of the different projects,

Lex Fridman (2:06:14.080)

you know, there's not a monolith.

Peter Wang (2:06:15.280)

Some projects are like, we'll only be releasing

Lex Fridman (2:06:17.000)

new features on Python 3.

Lex Fridman (2:06:18.920)

And that was more of a sticky carrot, right?

Lex Fridman (2:06:21.400)

A firm carrot, if you will, a firm carrot.

Peter Wang (2:06:26.360)

A stick shaped carrot.

Lex Fridman (2:06:27.960)

But then for others, yeah, NumPy in particular,

Peter Wang (2:06:30.680)

cause it's at the base of the dependency stack

Lex Fridman (2:06:32.680)

for so many things, that was the final stick.

Peter Wang (2:06:36.160)

That was a stick shaped stick.

Lex Fridman (2:06:37.600)

People were saying, look, if I have to keep maintaining

Peter Wang (2:06:40.080)

my releases for Python 2, that's that much less energy

Lex Fridman (2:06:43.640)

that I can put into making things better

Peter Wang (2:06:45.700)

for the Python 3 folks or in my new version,

Lex Fridman (2:06:48.160)

which is of course going to be Python 3.

Lex Fridman (2:06:49.920)

So people were also getting kind of pulled by this tension.

Lex Fridman (2:06:53.320)

So the overall community sort of had a lot of input

Peter Wang (2:06:56.140)

into when the NumPy core folks decided

Lex Fridman (2:06:58.480)

that they would end of life on Python 2.

Lex Fridman (2:07:01.380)

So as these numbers are a little bit loose,

Lex Fridman (2:07:04.000)

but there are about 10 million Python programmers

Peter Wang (2:07:06.840)

in the world, you could argue that number,

Lex Fridman (2:07:08.400)

but let's say 10 million.

Peter Wang (2:07:10.320)

That's actually where I was looking,

Lex Fridman (2:07:12.280)

said 27 million total programmers, developers in the world.

Peter Wang (2:07:17.240)

You mentioned in a talk that changes need to be made

Lex Fridman (2:07:20.540)

for there to be 100 million Python programmers.

Lex Fridman (2:07:24.000)

So first of all, do you see a future

Lex Fridman (2:07:26.020)

where there's 100 million Python programmers?

Lex Fridman (2:07:28.280)

And second, what kind of changes need to be made?

Lex Fridman (2:07:31.320)

So Anaconda and Miniconda get downloaded

Peter Wang (2:07:33.200)

about a million times a week.

Lex Fridman (2:07:34.880)

So I think the idea that there's only

Peter Wang (2:07:37.720)

10 million Python programmers in the world

Lex Fridman (2:07:39.320)

is a little bit undercounting.

Peter Wang (2:07:41.960)

There are a lot of people who escape traditional counting

Lex Fridman (2:07:44.860)

that are using Python and data in their jobs.

Peter Wang (2:07:48.440)

I do believe that the future world for it to,

Lex Fridman (2:07:52.100)

well, the world I would like to see

Peter Wang (2:07:53.280)

is one where people are data literate.

Lex Fridman (2:07:56.240)

So they are able to use tools

Peter Wang (2:07:58.600)

that let them express their questions and ideas fluidly.

Lex Fridman (2:08:03.180)

And the data variety and data complexity will not go down.

Peter Wang (2:08:06.440)

It will only keep increasing.

Lex Fridman (2:08:08.320)

So I think some level of code or code like things

Peter Wang (2:08:12.460)

will continue to be relevant.

Lex Fridman (2:08:15.460)

And so my hope is that we can build systems

Peter Wang (2:08:19.760)

that allow people to more seamlessly integrate

Lex Fridman (2:08:22.900)

Python kinds of expressivity with data systems

Lex Fridman (2:08:26.040)

and operationalization methods that are much more seamless.

Lex Fridman (2:08:31.200)

And what I mean by that is, you know,

Peter Wang (2:08:32.380)

right now you can't punch Python code into an Excel cell.

Lex Fridman (2:08:35.660)

I mean, there's some tools you can do to kind of do this.

Peter Wang (2:08:37.960)

We didn't build a thing for doing this back in the day,

Lex Fridman (2:08:39.920)

but I feel like the total addressable market

Peter Wang (2:08:43.820)

for Python users, if we do the things right,

Lex Fridman (2:08:46.860)

is on the order of the Excel users,

Peter Wang (2:08:49.080)

which is, you know, a few hundred million.

Lex Fridman (2:08:51.180)

So I think Python has to get better at being embedded,

Peter Wang (2:08:57.300)

you know, being a smaller thing that pulls in

Lex Fridman (2:08:59.660)

just the right parts of the ecosystem

Peter Wang (2:09:01.720)

to run numerics and do data exploration,

Lex Fridman (2:09:05.560)

meeting people where they're already at

Peter Wang (2:09:07.920)

with their data and their data tools.

Lex Fridman (2:09:09.640)

And then I think also it has to be easier

Peter Wang (2:09:12.800)

to take some of those things they've written

Lex Fridman (2:09:14.720)

and flow those back into deployed systems

Peter Wang (2:09:17.540)

or little apps or visualizations.

Lex Fridman (2:09:19.460)

I think if we don't do those things,

Peter Wang (2:09:20.720)

then we will always be kept in a silo

Lex Fridman (2:09:23.160)

as sort of an expert user's tool

Lex Fridman (2:09:25.920)

and not a tool for the masses.

Lex Fridman (2:09:27.400)

You know, I work with a bunch of folks

Peter Wang (2:09:28.960)

in the Adobe Creative Suite,

Lex Fridman (2:09:32.520)

and I'm kind of forcing them or inspired them

Peter Wang (2:09:35.400)

to learn Python, to do a bunch of stuff that helps them.

Lex Fridman (2:09:38.560)

And it's interesting, because they probably

Peter Wang (2:09:39.840)

wouldn't call themselves Python programmers,

Lex Fridman (2:09:41.740)

but they're all using Python.

Peter Wang (2:09:43.820)

I would love it if the tools like Photoshop and Premiere

Lex Fridman (2:09:46.480)

and all those kinds of tools that are targeted

Peter Wang (2:09:48.760)

towards creative people, I guess that's where Excel,

Lex Fridman (2:09:52.080)

Excel is targeted towards a certain kind of audience

Peter Wang (2:09:54.220)

that works with data, financial people,

Lex Fridman (2:09:56.320)

all that kind of stuff, if there would be easy ways

Peter Wang (2:10:00.040)

to leverage to use Python for quick scripting tasks.

Lex Fridman (2:10:03.600)

And you know, there's an exciting application

Peter Wang (2:10:06.760)

of artificial intelligence in this space

Lex Fridman (2:10:09.760)

that I'm hopeful about, looking at open AI codecs

Peter Wang (2:10:13.280)

with generating programs.

Lex Fridman (2:10:16.940)

So almost helping people bridge the gap

Peter Wang (2:10:20.800)

from kind of visual interface to generating programs,

Lex Fridman (2:10:25.840)

to something formal, and then they can modify it and so on,

Lex Fridman (2:10:28.980)

but kind of without having to read the manual,

Lex Fridman (2:10:32.360)

without having to do a Google search and stack overflow,

Peter Wang (2:10:34.880)

which is essentially what a neural network does

Lex Fridman (2:10:36.760)

when it's doing code generation,

Peter Wang (2:10:39.040)

is actually generating code and allowing a human

Lex Fridman (2:10:42.840)

to communicate with multiple programs,

Lex Fridman (2:10:44.760)

and then maybe even programs to communicate

Lex Fridman (2:10:46.540)

with each other via Python.

Lex Fridman (2:10:48.420)

So that to me is a really exciting possibility,

Lex Fridman (2:10:51.220)

because I think there's a friction to kind of,

Lex Fridman (2:10:55.960)

like how do I learn how to use Python in my life?

Lex Fridman (2:10:58.920)

There's oftentimes you kind of start a class,

Peter Wang (2:11:03.000)

you start learning about types, I don't know, functions.

Lex Fridman (2:11:07.080)

Like this is, you know, Python is the first language

Peter Wang (2:11:09.600)

with which you start to learn to program.

Lex Fridman (2:11:11.920)

But I feel like that's going to take a long time

Peter Wang (2:11:16.680)

for you to understand why it's useful.

Lex Fridman (2:11:18.560)

You almost want to start with a script.

Peter Wang (2:11:20.300)

Well, you do, in fact.

Lex Fridman (2:11:22.000)

I think starting with the theory behind programming languages

Lex Fridman (2:11:24.840)

and types and all that, I mean,

Lex Fridman (2:11:26.100)

types are there to make the compiler writer's jobs easier.

Peter Wang (2:11:30.000)

Types are not, I mean, heck, do you have an ontology

Lex Fridman (2:11:32.760)

of types or just the objects on this table?

Peter Wang (2:11:34.320)

No.

Lex Fridman (2:11:35.560)

So types are there because compiler writers are human

Lex Fridman (2:11:39.160)

and they're limited in what they can do.

Lex Fridman (2:11:40.840)

But I think that the beauty of scripting,

Peter Wang (2:11:45.100)

like there's a Python book that's called

Lex Fridman (2:11:47.560)

"'Automate the Boring Stuff,'

Peter Wang (2:11:49.200)

which is exactly the right mentality.

Lex Fridman (2:11:51.120)

I grew up with computers in a time when I could,

Peter Wang (2:11:56.720)

when Steve Jobs was still pitching these things

Lex Fridman (2:11:58.840)

as bicycles for the mind.

Peter Wang (2:11:59.880)

They were supposed to not be just media consumption devices,

Lex Fridman (2:12:03.300)

but they were actually, you could write some code.

Peter Wang (2:12:05.920)

You could write basic, you could write some stuff

Lex Fridman (2:12:07.280)

to do some things.

Lex Fridman (2:12:09.000)

And that feeling of a computer as a thing

Lex Fridman (2:12:12.160)

that we can use to extend ourselves

Peter Wang (2:12:14.240)

has all but evaporated for a lot of people.

Lex Fridman (2:12:17.780)

So you see a little bit in parts

Peter Wang (2:12:19.520)

in the current, the generation of youth

Lex Fridman (2:12:21.560)

around Minecraft or Roblox, right?

Lex Fridman (2:12:23.560)

And I think Python, circuit Python,

Lex Fridman (2:12:25.120)

these things could be a renaissance of that,

Peter Wang (2:12:28.800)

of people actually shaping and using their computers

Lex Fridman (2:12:32.660)

as computers, as an extension of their minds

Lex Fridman (2:12:35.500)

and their curiosity, their creativity.

Lex Fridman (2:12:37.880)

So you talk about scripting the Adobe Suite with Python

Peter Wang (2:12:41.560)

in the 3D graphics world.

Lex Fridman (2:12:42.920)

Python is a scripting language

Peter Wang (2:12:46.320)

that some of these 3D graphics suites use.

Lex Fridman (2:12:48.760)

And I think that's great.

Peter Wang (2:12:49.600)

We should better support those kinds of things.

Lex Fridman (2:12:51.280)

But ultimately the idea that I should be able

Peter Wang (2:12:54.200)

to have power over my computing environment.

Lex Fridman (2:12:56.280)

If I want these things to happen repeatedly all the time,

Lex Fridman (2:12:59.640)

I should be able to say that somehow to the computer, right?

Lex Fridman (2:13:02.640)

Now, whether the operating systems get there faster

Peter Wang (2:13:06.520)

by having some Siri backed with open AI with whatever.

Lex Fridman (2:13:09.560)

So you can just say, Siri, make this do this

Lex Fridman (2:13:10.860)

and this and every other Friday, right?

Lex Fridman (2:13:12.640)

We probably will get there somewhere.

Lex Fridman (2:13:14.240)

And Apple's always had these ideas.

Lex Fridman (2:13:15.880)

There's the Apple script in the menu that no one ever uses,

Lex Fridman (2:13:19.160)

but you can do these kinds of things.

Lex Fridman (2:13:21.640)

But when you start doing that kind of scripting,

Peter Wang (2:13:23.700)

the challenge isn't learning the type system

Lex Fridman (2:13:25.920)

or even the syntax of the language.

Peter Wang (2:13:27.160)

The challenge is all of the dictionaries

Lex Fridman (2:13:29.240)

and all the objects of all their properties

Lex Fridman (2:13:31.160)

and attributes and parameters.

Lex Fridman (2:13:32.840)

Like who's got time to learn all that stuff, right?

Lex Fridman (2:13:35.420)

So that's when then programming by prototype

Lex Fridman (2:13:38.840)

or by example becomes the right way

Peter Wang (2:13:40.960)

to get the user to express their desire.

Lex Fridman (2:13:43.780)

So there's a lot of these different ways

Peter Wang (2:13:45.200)

that we can approach programming.

Lex Fridman (2:13:46.060)

But I do think when, as you were talking

Peter Wang (2:13:48.280)

about the Adobe scripting thing,

Lex Fridman (2:13:49.800)

I was thinking about, you know,

Peter Wang (2:13:51.340)

when we do use something like NumPy,

Lex Fridman (2:13:53.780)

when we use things in the Python data

Lex Fridman (2:13:55.760)

and scientific, let's say, expression system,

Lex Fridman (2:14:00.160)

there's a reason we use that,

Peter Wang (2:14:01.360)

which is that it gives us mathematical precision.

Lex Fridman (2:14:04.400)

It gives us actually quite a lot of precision

Peter Wang (2:14:06.800)

over precisely what we mean about this data set,

Lex Fridman (2:14:09.960)

that data set, and it's the fact

Peter Wang (2:14:11.800)

that we can have that precision

Lex Fridman (2:14:13.680)

that lets Python be powerful over as a duct tape for data.

Peter Wang (2:14:18.720)

You know, you give me a TSV or a CSV,

Lex Fridman (2:14:21.240)

and if you give me some massively expensive vendor tool

Peter Wang (2:14:25.120)

for data transformation,

Lex Fridman (2:14:26.460)

I don't know I'm gonna be able to solve your problem.

Lex Fridman (2:14:28.280)

But if you give me a Python prompt,

Lex Fridman (2:14:30.600)

you can throw whatever data you want at me.

Peter Wang (2:14:32.500)

I will be able to mash it into shape.

Lex Fridman (2:14:34.400)

So that ability to take it as sort of this like,

Peter Wang (2:14:38.360)

you know, machete out into the data jungle

Lex Fridman (2:14:40.320)

is really powerful.

Lex Fridman (2:14:41.600)

And I think that's why at some level,

Lex Fridman (2:14:44.300)

we're not gonna get away from some of these expressions

Lex Fridman (2:14:47.760)

and APIs and libraries in Python for data transformation.

Lex Fridman (2:14:53.960)

You've been at the center of the Python community

Peter Wang (2:14:57.280)

for many years.

Lex Fridman (2:14:58.400)

If you could change one thing about the community

Peter Wang (2:15:03.640)

to help it grow, to help it improve,

Lex Fridman (2:15:05.540)

to help it flourish and prosper, what would it be?

Peter Wang (2:15:09.440)

I mean, you know, it doesn't have to be one thing,

Lex Fridman (2:15:11.720)

but what kind of comes to mind?

Lex Fridman (2:15:13.880)

What are the challenges?

Lex Fridman (2:15:15.360)

Humility is one of the values that we have

Peter Wang (2:15:16.840)

at Anaconda at the company,

Lex Fridman (2:15:17.940)

but it's also one of the values in the community.

Peter Wang (2:15:21.320)

That it's been breached a little bit in the last few years,

Lex Fridman (2:15:24.480)

but in general, people are quite decent

Lex Fridman (2:15:27.360)

and reasonable and nice.

Lex Fridman (2:15:29.320)

And that humility prevents them from seeing

Peter Wang (2:15:34.420)

the greatness that they could have.

Lex Fridman (2:15:36.880)

I don't know how many people in the core Python community

Peter Wang (2:15:40.800)

really understand that they stand perched at the edge

Lex Fridman (2:15:46.280)

of an opportunity to transform how people use computers.

Lex Fridman (2:15:50.160)

And actually, PyCon, I think it's the last physical PyCon

Lex Fridman (2:15:52.760)

I went to, Russell Keith McGee gave a great keynote

Peter Wang (2:15:56.680)

about very much along the lines of the challenges I have,

Lex Fridman (2:16:00.560)

which is Python, for a language that doesn't actually,

Peter Wang (2:16:04.320)

that can't put an interface up,

Lex Fridman (2:16:05.720)

put an interface up on the most popular computing devices,

Lex Fridman (2:16:09.360)

it's done really well as a language, hasn't it?

Lex Fridman (2:16:11.800)

You can't write a web front end with Python, really.

Peter Wang (2:16:13.720)

I mean, everyone uses JavaScript.

Lex Fridman (2:16:15.080)

You certainly can't write native apps.

Lex Fridman (2:16:17.040)

So for a language that you can't actually write apps

Lex Fridman (2:16:20.580)

in any of those front end runtime environments,

Peter Wang (2:16:22.720)

Python's done exceedingly well.

Lex Fridman (2:16:26.000)

And so that wasn't to pat ourselves on the back.

Peter Wang (2:16:28.760)

That was to challenge ourselves as a community to say,

Lex Fridman (2:16:30.580)

we, through our current volunteer dynamic,

Peter Wang (2:16:32.520)

have gotten to this point.

Lex Fridman (2:16:34.460)

What comes next and how do we seize,

Peter Wang (2:16:36.720)

you know, we've caught the tiger by the tail.

Lex Fridman (2:16:38.620)

How do we make sure we keep up with it as it goes forward?

Lex Fridman (2:16:40.960)

So that's one of the questions I have

Lex Fridman (2:16:42.440)

about sort of open source communities,

Peter Wang (2:16:44.240)

is at its best, there's a kind of humility.

Lex Fridman (2:16:48.500)

Is that humility prevent you to have a vision

Lex Fridman (2:16:52.500)

for creating something like very new and powerful?

Lex Fridman (2:16:55.560)

And you've brought us back to consciousness again.

Peter Wang (2:16:57.640)

The collaboration is a swarm emergent dynamic.

Lex Fridman (2:17:00.700)

Humility lets these people work together

Peter Wang (2:17:02.640)

without anyone trouncing anyone else.

Lex Fridman (2:17:04.840)

How do they, you know, in consciousness,

Peter Wang (2:17:07.200)

there's the question of the binding problem.

Lex Fridman (2:17:08.680)

How does a singular, our attention,

Lex Fridman (2:17:10.720)

how does that emerge from billions of neurons?

Lex Fridman (2:17:13.880)

So how can you have a swarm of people emerge a consensus

Peter Wang (2:17:17.680)

that has a singular vision to say, we will do this.

Lex Fridman (2:17:20.620)

And most importantly, we're not gonna do these things.

Peter Wang (2:17:23.880)

Emerging a coherent, pointed, focused leadership dynamic

Lex Fridman (2:17:29.000)

from a collaboration, being able to do that kind of,

Lex Fridman (2:17:32.060)

and then dissolve it so people can still do

Lex Fridman (2:17:34.040)

the swarm thing, that's a problem, that's a question.

Lex Fridman (2:17:37.240)

So do you have to have a charismatic leader?

Lex Fridman (2:17:40.800)

For some reason, Linus Torvald comes to mind,

Lex Fridman (2:17:42.640)

but there's people who criticize.

Lex Fridman (2:17:44.640)

He rules with an iron fist, man.

Lex Fridman (2:17:46.640)

But there's still charisma to it.

Lex Fridman (2:17:48.520)

There is charisma, right?

Peter Wang (2:17:49.520)

There's a charisma to that iron fist.

Lex Fridman (2:17:51.760)

There's, every leader's different, I would say,

Peter Wang (2:17:55.320)

in their success.

Lex Fridman (2:17:56.700)

So he doesn't, I don't even know if you can say

Peter Wang (2:17:59.480)

he doesn't have humility, there's such a meritocracy

Lex Fridman (2:18:04.840)

of ideas that like, this is a good idea

Lex Fridman (2:18:09.000)

and this is a bad idea.

Lex Fridman (2:18:10.440)

There's a step function to it.

Peter Wang (2:18:11.640)

Once you clear a threshold, he's open.

Lex Fridman (2:18:13.920)

Once you clear the bozo threshold,

Lex Fridman (2:18:15.680)

he's open to your ideas, I think, right?

Lex Fridman (2:18:17.560)

But see, the interesting thing is obviously

Peter Wang (2:18:20.280)

that will not stand in an open source community

Lex Fridman (2:18:23.440)

if that threshold that is defined

Peter Wang (2:18:25.920)

by that one particular person is not actually that good.

Lex Fridman (2:18:30.320)

So you actually have to be really excellent at what you do.

Lex Fridman (2:18:33.720)

So he's very good at what he does.

Lex Fridman (2:18:37.120)

And so there's some aspect of leadership

Peter Wang (2:18:39.040)

where you can get thrown out, people can just leave.

Lex Fridman (2:18:42.920)

That's how it works with open source, the fork.

Lex Fridman (2:18:45.960)

But at the same time, you want to sometimes be a leader

Lex Fridman (2:18:49.940)

like with a strong opinion, because people,

Peter Wang (2:18:52.440)

I mean, there's some kind of balance here

Lex Fridman (2:18:54.440)

for this like hive mind to get like behind.

Peter Wang (2:18:57.400)

Leadership is a big topic.

Lex Fridman (2:18:58.520)

And I didn't, I'm not one of these guys

Peter Wang (2:18:59.880)

that went to MBA school and said,

Lex Fridman (2:19:01.120)

I'm gonna be an entrepreneur and I'm gonna be a leader.

Lex Fridman (2:19:03.560)

And I'm gonna read all these Harvard Business Review

Lex Fridman (2:19:05.280)

articles on leadership and all this other stuff.

Peter Wang (2:19:07.760)

Like I was a physicist turned into a software nerd

Lex Fridman (2:19:10.700)

who then really like nerded out on Python.

Lex Fridman (2:19:13.600)

And now I am entrepreneurial, right?

Lex Fridman (2:19:14.840)

I saw a business opportunity around the use

Peter Wang (2:19:16.280)

of Python for data.

Lex Fridman (2:19:17.120)

But for me, what has been interesting over this journey

Peter Wang (2:19:20.720)

with the last 10 years is how much I started really

Lex Fridman (2:19:25.320)

enjoying the understanding, thinking deeper

Peter Wang (2:19:28.520)

about organizational dynamics and leadership.

Lex Fridman (2:19:31.320)

And leadership does come down to a few core things.

Peter Wang (2:19:35.280)

Number one, a leader has to create belief

Lex Fridman (2:19:40.000)

or at least has to dispel disbelief.

Peter Wang (2:19:44.060)

Leadership also, you have to have vision,

Lex Fridman (2:19:46.480)

loyalty and experience.

Lex Fridman (2:19:49.360)

So can you say belief in a singular vision?

Lex Fridman (2:19:52.640)

Like what does belief mean?

Peter Wang (2:19:53.680)

Yeah, belief means a few things.

Lex Fridman (2:19:55.240)

Belief means here's what we need to do

Lex Fridman (2:19:57.000)

and this is a valid thing to do and we can do it.

Lex Fridman (2:20:01.600)

That you have to be able to drive that belief.

Lex Fridman (2:20:06.040)

And every step of leadership along the way

Lex Fridman (2:20:08.800)

has to help you amplify that belief to more people.

Peter Wang (2:20:12.680)

I mean, I think at a fundamental level, that's what it is.

Lex Fridman (2:20:15.920)

You have to have a vision.

Peter Wang (2:20:17.120)

You have to be able to show people that,

Lex Fridman (2:20:20.920)

or you have to convince people to believe in the vision

Lex Fridman (2:20:23.640)

and to get behind you.

Lex Fridman (2:20:25.240)

And that's where the loyalty part comes in

Lex Fridman (2:20:26.480)

and the experience part comes in.

Lex Fridman (2:20:28.200)

There's all different flavors of leadership.

Lex Fridman (2:20:30.200)

So if we talk about Linus, we could talk about Elon Musk

Lex Fridman (2:20:34.320)

and Steve Jobs, there's Sunder Prachai.

Peter Wang (2:20:38.440)

There's people that kind of put themselves at the center

Lex Fridman (2:20:40.760)

and are strongly opinionated.

Lex Fridman (2:20:42.560)

And some people are more like consensus builders.

Lex Fridman (2:20:45.160)

What works well for open source?

Lex Fridman (2:20:47.720)

What works well in the space of programmers?

Lex Fridman (2:20:49.680)

So you've been a programmer, you've led many programmers

Peter Wang (2:20:53.240)

that are now sort of at the center of this ecosystem.

Lex Fridman (2:20:55.600)

What works well in the programming world would you say?

Peter Wang (2:20:58.960)

It really depends on the people.

Lex Fridman (2:21:01.120)

What style of leadership is best?

Lex Fridman (2:21:02.600)

And it depends on the programming community.

Lex Fridman (2:21:04.160)

I think for the Python community,

Peter Wang (2:21:06.320)

servant leadership is one of the values.

Lex Fridman (2:21:08.560)

At the end of the day, the leader has to also be

Lex Fridman (2:21:11.720)

the high priest of values, right?

Lex Fridman (2:21:14.720)

So any collection of people has values of their living.

Lex Fridman (2:21:19.160)

And if you want to maintain certain values

Lex Fridman (2:21:23.680)

and those values help you as an organization

Peter Wang (2:21:26.280)

become more powerful,

Lex Fridman (2:21:27.520)

then the leader has to live those values unequivocally

Lex Fridman (2:21:30.560)

and has to hold the values.

Lex Fridman (2:21:33.600)

So in our case, in this collaborative community

Peter Wang (2:21:36.680)

around Python, I think that the humility

Lex Fridman (2:21:41.400)

is one of those values.

Peter Wang (2:21:42.920)

Servant leadership, you actually have to kind of do the stuff.

Lex Fridman (2:21:45.320)

You have to walk the walk, not just talk the talk.

Peter Wang (2:21:49.000)

I don't feel like the Python community really demands

Lex Fridman (2:21:52.040)

that much from a vision standpoint.

Lex Fridman (2:21:53.800)

And they should.

Lex Fridman (2:21:54.760)

And I think they should.

Peter Wang (2:21:56.680)

This is the interesting thing is like so many people

Lex Fridman (2:22:00.840)

use Python, from where comes the vision?

Peter Wang (2:22:04.720)

You know, like you have a Elon Musk type character

Lex Fridman (2:22:07.640)

who makes bold statements about the vision

Peter Wang (2:22:12.160)

for particular companies he's involved with.

Lex Fridman (2:22:14.640)

And it's like, I think a lot of people that work

Peter Wang (2:22:18.680)

at those companies kind of can only last

Lex Fridman (2:22:22.560)

if they believe that vision.

Lex Fridman (2:22:24.680)

And some of it is super bold.

Lex Fridman (2:22:26.240)

So my question is, and by the way,

Peter Wang (2:22:28.600)

those companies often use Python.

Lex Fridman (2:22:32.520)

How do you establish a vision?

Lex Fridman (2:22:33.760)

Like get to 100 million users, right?

Lex Fridman (2:22:37.360)

Get to where, you know, the Python is at the center

Peter Wang (2:22:42.280)

of the machine learning and was it data science,

Lex Fridman (2:22:46.760)

machine learning, deep learning,

Lex Fridman (2:22:48.440)

artificial intelligence revolution, right?

Lex Fridman (2:22:51.720)

Like in many ways, perhaps the Python community

Peter Wang (2:22:54.920)

is not thinking of it that way,

Lex Fridman (2:22:55.840)

but it's leading the way on this.

Peter Wang (2:22:58.160)

Like the tooling is like essential.

Lex Fridman (2:23:01.240)

Right, well, you know, for a while,

Peter Wang (2:23:03.320)

PyCon people in the scientific Python

Lex Fridman (2:23:05.840)

and the PyData community, they would submit talks.

Peter Wang (2:23:09.320)

Those are early 2010s, mid 2010s.

Lex Fridman (2:23:12.160)

They would submit talks for PyCon

Lex Fridman (2:23:14.080)

and the talks would all be rejected

Lex Fridman (2:23:15.760)

because there was the separate sort of PyData conferences.

Lex Fridman (2:23:18.680)

And like, well, these probably belong more to PyData.

Lex Fridman (2:23:21.200)

And instead there'd be yet another talk about, you know,

Peter Wang (2:23:23.520)

threads and, you know, whatever, some web framework.

Lex Fridman (2:23:26.400)

And it's like, that was an interesting dynamic to see

Peter Wang (2:23:29.880)

that there was, I mean, at the time it was a little annoying

Lex Fridman (2:23:32.680)

because we wanted to try to get more users

Lex Fridman (2:23:34.280)

and get more people talking about these things.

Lex Fridman (2:23:35.760)

And PyCon is a huge venue, right?

Peter Wang (2:23:37.320)

It's thousands of Python programmers.

Lex Fridman (2:23:40.240)

But then also came to appreciate that, you know,

Peter Wang (2:23:42.200)

parallel, having an ecosystem that allows parallel

Lex Fridman (2:23:45.400)

innovation is not bad, right?

Peter Wang (2:23:47.400)

There are people doing embedded Python stuff.

Lex Fridman (2:23:49.040)

There's people doing web programming,

Peter Wang (2:23:50.640)

people doing scripting, there's cyber uses of Python.

Lex Fridman (2:23:53.280)

I think the, ultimately at some point,

Peter Wang (2:23:55.120)

if your slide mold covers so much stuff,

Lex Fridman (2:23:58.040)

you have to respect that different things are growing

Peter Wang (2:24:00.160)

in different areas and different niches.

Lex Fridman (2:24:02.320)

Now, at some point that has to come together

Lex Fridman (2:24:04.160)

and the central body has to provide resources.

Lex Fridman (2:24:07.560)

The principle here is subsidiarity.

Peter Wang (2:24:09.160)

Give resources to the various groups

Lex Fridman (2:24:11.760)

to then allocate as they see fit in their niches.

Peter Wang (2:24:15.040)

That would be a really helpful dynamic.

Lex Fridman (2:24:16.360)

But again, it's a volunteer community.

Peter Wang (2:24:17.520)

It's not like they had that many resources to start with.

Lex Fridman (2:24:21.160)

What was or is your favorite programming setup?

Lex Fridman (2:24:23.920)

What operating system, what keyboard,

Lex Fridman (2:24:25.720)

how many screens are you listening to?

Lex Fridman (2:24:29.080)

What time of day are you drinking coffee, tea?

Lex Fridman (2:24:32.960)

Tea, sometimes coffee, depending on how well I slept.

Peter Wang (2:24:36.280)

I used to have.

Lex Fridman (2:24:37.120)

How much sleep do you get?

Lex Fridman (2:24:38.120)

Are you a night owl?

Lex Fridman (2:24:39.680)

I remember somebody asked you somewhere,

Peter Wang (2:24:41.520)

a question about work life balance.

Lex Fridman (2:24:44.880)

Not just work life balance, but like a family,

Peter Wang (2:24:47.720)

you lead a company and your answer was basically like,

Lex Fridman (2:24:52.080)

I still haven't figured it out.

Peter Wang (2:24:54.240)

Yeah, I think I've gotten to a little bit better balance.

Lex Fridman (2:24:56.320)

I have a really great leadership team now supporting me

Lex Fridman (2:24:58.880)

and so that takes a lot of the day to day stuff

Lex Fridman (2:25:01.680)

off my plate and my kids are getting a little older.

Lex Fridman (2:25:04.400)

So that helps.

Lex Fridman (2:25:05.240)

So, and of course I have a wonderful wife

Peter Wang (2:25:07.520)

who takes care of a lot of the things

Lex Fridman (2:25:09.240)

that I'm not able to take care of and she's great.

Peter Wang (2:25:11.680)

I try to get to sleep earlier now

Lex Fridman (2:25:13.760)

because I have to get up every morning at six

Peter Wang (2:25:15.160)

to take my kid down to the bus stop.

Lex Fridman (2:25:17.000)

So there's a hard thing.

Peter Wang (2:25:19.280)

For a while I was doing polyphasic sleep,

Lex Fridman (2:25:21.120)

which is really interesting.

Peter Wang (2:25:22.040)

Like I go to bed at nine, wake up at like 2 a.m.,

Lex Fridman (2:25:24.640)

work till five, sleep three hours, wake up at eight.

Peter Wang (2:25:27.320)

Like that was actually, it was interesting.

Lex Fridman (2:25:29.360)

It wasn't too bad.

Lex Fridman (2:25:30.200)

How did it feel?

Lex Fridman (2:25:31.160)

It was good.

Peter Wang (2:25:32.000)

I didn't keep it up for years, but once I have travel,

Lex Fridman (2:25:34.960)

then it just, everything goes out the window, right?

Peter Wang (2:25:37.360)

Because then you're like time zones and all these things.

Lex Fridman (2:25:39.120)

Socially was it, except like were you able to live

Lex Fridman (2:25:42.440)

outside of how you felt?

Lex Fridman (2:25:43.480)

Were you able to live normal society?

Peter Wang (2:25:45.720)

Oh yeah, because like on the nights

Lex Fridman (2:25:47.360)

that I wasn't out hanging out with people or whatever,

Peter Wang (2:25:48.920)

going to bed at nine, no one cares.

Lex Fridman (2:25:50.680)

I wake up at two, I'm still responding to their slacks,

Peter Wang (2:25:52.720)

emails, whatever, and you know, shitposting on Twitter

Lex Fridman (2:25:56.880)

or whatever at two in the morning is great, right?

Lex Fridman (2:25:59.200)

And then you go to bed for a few hours and you wake up,

Lex Fridman (2:26:02.440)

it's like you had an extra day in the middle.

Lex Fridman (2:26:04.520)

And I'd read somewhere that humans naturally

Lex Fridman (2:26:06.320)

have biphasic sleep or something, I don't know.

Peter Wang (2:26:09.160)

I read basically everything somewhere.

Lex Fridman (2:26:11.160)

So every option of everything.

Peter Wang (2:26:13.400)

Every option of everything.

Lex Fridman (2:26:14.520)

I will say that that worked out for me for a while,

Peter Wang (2:26:16.840)

although I don't do it anymore.

Lex Fridman (2:26:18.480)

In terms of programming setup,

Peter Wang (2:26:19.400)

I had a 27 inch high DPI setup that I really liked,

Lex Fridman (2:26:24.840)

but then I moved to a curved monitor

Peter Wang (2:26:26.200)

just because when I moved to the new house,

Lex Fridman (2:26:28.840)

I want to have a bit more screen for Zoom plus communications

Peter Wang (2:26:32.440)

plus various kinds of things.

Lex Fridman (2:26:33.280)

So it's like one large monitor.

Peter Wang (2:26:35.800)

One large curved monitor.

Lex Fridman (2:26:38.040)

What operating system?

Peter Wang (2:26:39.720)

Mac.

Lex Fridman (2:26:40.640)

Okay. Yeah.

Peter Wang (2:26:41.640)

Is that what happens when you become important,

Lex Fridman (2:26:43.920)

is you stop using Linux and Windows?

Peter Wang (2:26:46.680)

No, I actually have a Windows box as well

Lex Fridman (2:26:48.520)

on the next table over, but I have three desks, right?

Lex Fridman (2:26:54.360)

So the main one is the standing desk so that I can,

Lex Fridman (2:26:57.200)

whatever, when I'm like, I have a teleprompter set up

Lex Fridman (2:26:59.320)

and everything else.

Lex Fridman (2:27:00.160)

And then I've got my iMac and then eGPU and then Windows PC.

Peter Wang (2:27:06.480)

The reason I moved to Mac was it's got a Linux prompt

Lex Fridman (2:27:10.680)

or no, sorry, it's got a, it's got a Unix prompt

Lex Fridman (2:27:13.000)

so I can do all my stuff, but then I don't have to worry.

Lex Fridman (2:27:18.320)

Like when I'm presenting for clients

Peter Wang (2:27:19.640)

or investors or whatever, like it,

Lex Fridman (2:27:21.800)

I don't have to worry about any like ACPI related

Peter Wang (2:27:25.120)

fsic things in the middle of a presentation,

Lex Fridman (2:27:27.160)

like none of that.

Peter Wang (2:27:28.000)

It just, it will always wake from sleep

Lex Fridman (2:27:30.960)

and it won't kernel panic on me.

Lex Fridman (2:27:32.320)

And this is not a dig against Linux,

Lex Fridman (2:27:34.200)

except that I just, I feel really bad.

Lex Fridman (2:27:38.440)

I feel like a traitor to my community saying this, right?

Lex Fridman (2:27:40.180)

But in 2012, I was just like, okay, start my own company.

Lex Fridman (2:27:43.240)

What do I get?

Lex Fridman (2:27:44.060)

And Linux laptops were just not quite there.

Lex Fridman (2:27:47.520)

And so I've just stuck with Macs.

Lex Fridman (2:27:48.840)

Can I just defend something that nobody respectable

Peter Wang (2:27:51.360)

seems to do, which is, so I do a boot on Linux windows,

Lex Fridman (2:27:55.800)

but in windows, I have a windows subsystems

Peter Wang (2:27:59.560)

for Linux or whatever, WSL.

Lex Fridman (2:28:02.920)

And I find myself being able to handle everything I need

Lex Fridman (2:28:06.480)

and almost everything I need in Linux

Lex Fridman (2:28:08.360)

for basic sort of tasks, scripting tasks within WSL

Lex Fridman (2:28:11.280)

and it creates a really nice environment.

Lex Fridman (2:28:12.960)

So I've been, but like whenever I hang out with like,

Peter Wang (2:28:15.400)

especially important people,

Lex Fridman (2:28:17.920)

like they're all on iPhone and a Mac

Lex Fridman (2:28:20.720)

and it's like, yeah, like what,

Lex Fridman (2:28:23.360)

there is a messiness to windows and a messiness to Linux

Peter Wang (2:28:27.840)

that makes me feel like you're still in it.

Lex Fridman (2:28:31.880)

Well, the Linux stuff, windows subsystem for Linux

Peter Wang (2:28:34.700)

is very tempting, but there's still the windows

Lex Fridman (2:28:37.840)

on the outside where I don't know where,

Lex Fridman (2:28:40.280)

and I've been, okay, I've used DOS since version 1.11

Lex Fridman (2:28:44.160)

or 1.21 or something.

Lex Fridman (2:28:45.560)

So I've been a long time Microsoft user.

Lex Fridman (2:28:48.360)

And I will say that like, it's really hard

Peter Wang (2:28:52.120)

for me to know where anything is,

Lex Fridman (2:28:53.440)

how to get to the details behind something

Peter Wang (2:28:55.040)

when something screws up as an invariably does

Lex Fridman (2:28:57.600)

and just things like changing group permissions

Peter Wang (2:28:59.920)

on some shared folders and stuff,

Lex Fridman (2:29:01.400)

just everything seems a little bit more awkward,

Peter Wang (2:29:03.600)

more clicks than it needs to be.

Lex Fridman (2:29:06.440)

Not to say that there aren't weird things

Peter Wang (2:29:07.800)

like hidden attributes and all this other happy stuff

Lex Fridman (2:29:09.880)

on Mac, but for the most part,

Lex Fridman (2:29:14.060)

and well, actually, especially now

Lex Fridman (2:29:15.400)

with the new hardware coming out on Mac,

Peter Wang (2:29:16.880)

it'll be very interesting with the new M1.

Lex Fridman (2:29:20.080)

There were some dark years in the last few years

Peter Wang (2:29:21.640)

when I was like, I think maybe I have to move off of Mac

Lex Fridman (2:29:24.000)

as a platform, but this, I mean,

Peter Wang (2:29:27.400)

like my keyboard was just not working.

Lex Fridman (2:29:29.080)

Like literally my keyboard just wasn't working, right?

Peter Wang (2:29:31.200)

I had this touch bar, didn't have a physical escape button

Lex Fridman (2:29:33.540)

like I needed to because I used Vim,

Lex Fridman (2:29:35.200)

and now I think we're back, so.

Lex Fridman (2:29:37.440)

So you use Vim and you have a, what kind of keyboard?

Lex Fridman (2:29:40.340)

So I use a RealForce 87U, it's a mechanical,

Lex Fridman (2:29:44.080)

it's a Topre keyswitch.

Peter Wang (2:29:45.240)

Like it's a weird shape, there's a normal shape, okay.

Lex Fridman (2:29:48.520)

Well, no, because I say that because I use a Kinesis,

Lex Fridman (2:29:51.480)

and you said some dark, you said you had dark moments.

Lex Fridman (2:29:55.000)

I recently had a dark moment,

Lex Fridman (2:29:57.440)

I was like, what am I doing with my life?

Lex Fridman (2:29:58.780)

So I remember sort of flying in a very kind of tight space,

Lex Fridman (2:30:03.020)

and as I'm working, this is what I do on an airplane.

Lex Fridman (2:30:06.600)

I pull out a laptop, and on top of the laptop,

Peter Wang (2:30:09.600)

I'll put a Kinesis keyboard.

Lex Fridman (2:30:11.080)

That's hardcore, man.

Lex Fridman (2:30:12.040)

I was thinking, is this who I am?

Lex Fridman (2:30:13.800)

Is this what I'm becoming?

Lex Fridman (2:30:15.080)

Will I be this person?

Lex Fridman (2:30:16.200)

Because I'm on Emacs with this Kinesis keyboard,

Peter Wang (2:30:18.560)

sitting like with everybody around.

Lex Fridman (2:30:21.960)

Emacs on Windows.

Peter Wang (2:30:23.620)

On WSL, yeah.

Lex Fridman (2:30:25.600)

Yeah, Emacs on Linux on Windows.

Peter Wang (2:30:27.360)

Yeah, on Windows.

Lex Fridman (2:30:28.720)

And like everybody around me is using their iPhone

Peter Wang (2:30:32.200)

to look at TikTok.

Lex Fridman (2:30:33.140)

So I'm like in this land, and I thought, you know what?

Peter Wang (2:30:36.560)

Maybe I need to become an adult and put the 90s behind me,

Lex Fridman (2:30:40.880)

and use like a normal keyboard.

Lex Fridman (2:30:43.120)

And then I did some soul searching,

Lex Fridman (2:30:45.040)

and I decided like this is who I am.

Peter Wang (2:30:46.760)

This is me like coming out of the closet

Lex Fridman (2:30:48.520)

to saying I'm Kinesis keyboard all the way.

Peter Wang (2:30:50.760)

I'm going to use Emacs.

Lex Fridman (2:30:52.840)

You know who else is a Kinesis fan?

Peter Wang (2:30:55.160)

Wes McKinney, the creator of Pandas.

Lex Fridman (2:30:56.720)

Oh.

Peter Wang (2:30:57.560)

He banged out Pandas on a Kinesis keyboard, I believe.

Lex Fridman (2:31:00.000)

I don't know if he's still using one, maybe,

Lex Fridman (2:31:01.920)

but certainly 10 years ago, like he was.

Lex Fridman (2:31:04.200)

If anyone's out there,

Peter Wang (2:31:05.320)

maybe we need to have a Kinesis support group.

Lex Fridman (2:31:07.520)

Please reach out.

Lex Fridman (2:31:08.360)

Isn't there already one?

Lex Fridman (2:31:09.440)

Is there one?

Peter Wang (2:31:10.280)

I don't know.

Lex Fridman (2:31:11.100)

There's gotta be an RSC channel, man.

Peter Wang (2:31:12.100)

Oh no, and you access it through Emacs.

Lex Fridman (2:31:16.880)

Okay.

Lex Fridman (2:31:18.040)

Do you still program these days?

Lex Fridman (2:31:19.600)

I do a little bit.

Peter Wang (2:31:21.440)

Honestly, the last thing I did was I had written,

Lex Fridman (2:31:25.900)

I was working with my son to script some Minecraft stuff.

Lex Fridman (2:31:28.540)

So I was doing a little bit of that.

Lex Fridman (2:31:29.560)

That was the last, literally the last code I wrote.

Lex Fridman (2:31:33.100)

Oh, you know what?

Lex Fridman (2:31:33.940)

Also, I wrote some code to do some cap table evaluation,

Peter Wang (2:31:36.600)

waterfall modeling kind of stuff.

Lex Fridman (2:31:39.260)

What advice would you give to a young person,

Peter Wang (2:31:41.320)

you said your son, today, in high school,

Lex Fridman (2:31:44.200)

maybe even college, about career, about life?

Peter Wang (2:31:48.580)

This may be where I get into trouble a little bit.

Lex Fridman (2:31:51.120)

We are coming to the end.

Peter Wang (2:31:53.380)

We're rapidly entering a time between worlds.

Lex Fridman (2:31:56.520)

So we have a world now that's starting to really crumble

Peter Wang (2:31:59.920)

under the weight of aging institutions

Lex Fridman (2:32:01.900)

that no longer even pretend to serve the purposes

Peter Wang (2:32:04.460)

they were created for.

Lex Fridman (2:32:05.740)

We are creating technologies that are hurtling billions

Peter Wang (2:32:09.060)

of people headlong into philosophical crises

Lex Fridman (2:32:11.460)

who they don't even know the philosophical operating systems

Peter Wang (2:32:14.300)

in their firmware.

Lex Fridman (2:32:15.120)

And they're heading into a time when that gets vaporized.

Lex Fridman (2:32:17.840)

So for people in high school,

Lex Fridman (2:32:20.100)

and certainly I tell my son this as well,

Peter Wang (2:32:21.520)

he's in middle school, people in college,

Lex Fridman (2:32:24.920)

you are going to have to find your own way.

Peter Wang (2:32:29.740)

You're going to have to have a pioneer spirit,

Lex Fridman (2:32:31.700)

even if you live in the middle

Peter Wang (2:32:34.080)

of the most dense urban environment.

Lex Fridman (2:32:36.360)

All of human reality around you

Peter Wang (2:32:40.120)

is the result of the last few generations of humans

Lex Fridman (2:32:44.440)

agreeing to play certain kinds of games.

Peter Wang (2:32:47.360)

A lot of those games no longer operate

Lex Fridman (2:32:51.880)

according to the rules they used to.

Peter Wang (2:32:55.760)

Collapse is nonlinear, but it will be managed.

Lex Fridman (2:32:58.040)

And so if you are in a particular social caste

Peter Wang (2:33:02.200)

or economic caste,

Lex Fridman (2:33:03.880)

and I think it's not kosher to say that about America,

Lex Fridman (2:33:10.200)

but America is a very stratified and classist society.

Lex Fridman (2:33:14.140)

There's some mobility, but it's really quite classist.

Lex Fridman (2:33:17.080)

And in America, unless you're in the upper middle class,

Lex Fridman (2:33:20.800)

you are headed into very choppy waters.

Lex Fridman (2:33:23.520)

So it is really, really good to think

Lex Fridman (2:33:26.260)

and understand the fundamentals of what you need

Peter Wang (2:33:29.240)

to build a meaningful life for you, your loved ones,

Lex Fridman (2:33:32.760)

with your family.

Lex Fridman (2:33:35.480)

And almost all of the technology being created

Lex Fridman (2:33:38.000)

that's consumer facing is designed to own people,

Peter Wang (2:33:41.760)

to take the four stack of people, to delaminate them,

Lex Fridman (2:33:47.120)

and to own certain portions of that stack.

Lex Fridman (2:33:50.360)

And so if you want to be an integral human being,

Lex Fridman (2:33:52.280)

if you want to have your agency

Lex Fridman (2:33:54.400)

and you want to find your own way in the world,

Lex Fridman (2:33:57.640)

when you're young would be a great time to spend time

Peter Wang (2:34:00.580)

looking at some of the classics

Lex Fridman (2:34:02.960)

around what it means to live a good life,

Lex Fridman (2:34:05.880)

what it means to build connection with people.

Lex Fridman (2:34:08.080)

And so much of the status game, so much of the stuff,

Peter Wang (2:34:13.060)

one of the things that I sort of talk about

Lex Fridman (2:34:14.860)

as we create more and more technology,

Peter Wang (2:34:18.640)

there's a gradient of technology,

Lex Fridman (2:34:19.840)

and a gradient of technology

Peter Wang (2:34:20.820)

always leads to a gradient of power.

Lex Fridman (2:34:22.640)

And this is Jacques Leleu's point to some extent as well.

Peter Wang (2:34:25.200)

That gradient of power is not going to go away.

Lex Fridman (2:34:27.280)

The technologies are going so fast

Peter Wang (2:34:29.900)

that even people like me who helped create

Lex Fridman (2:34:32.040)

some of the stuff, I'm being left behind.

Peter Wang (2:34:33.640)

Some of the cutting edge research,

Lex Fridman (2:34:34.680)

I don't know what's going on against today.

Peter Wang (2:34:36.800)

You know, I go read some proceedings.

Lex Fridman (2:34:38.660)

So as the world gets more and more technological,

Peter Wang (2:34:42.620)

it will create more and more gradients

Lex Fridman (2:34:44.240)

where people will seize power, economic fortunes.

Lex Fridman (2:34:48.400)

And the way they make the people who are left behind

Lex Fridman (2:34:51.320)

okay with their lot in life is they create lottery systems.

Peter Wang (2:34:54.480)

They make you take part in the narrative

Lex Fridman (2:35:00.020)

of your own being trapped in your own economic sort of zone.

Lex Fridman (2:35:04.180)

So avoiding those kinds of things is really important.

Lex Fridman (2:35:07.920)

Knowing when someone is running game on you basically.

Lex Fridman (2:35:10.740)

So these are the things I would tell young people.

Lex Fridman (2:35:12.260)

It's a dark message, but it's realism.

Peter Wang (2:35:14.220)

I mean, that's what I see.

Lex Fridman (2:35:15.860)

So after you gave some realism, you sit back.

Peter Wang (2:35:18.500)

You sit back with your son.

Lex Fridman (2:35:19.760)

You're looking out at the sunset.

Lex Fridman (2:35:21.700)

What to him can you give as words of hope and to you

Lex Fridman (2:35:27.900)

from where do you derive hope for the future of our world?

Lex Fridman (2:35:32.120)

So you said at the individual level,

Lex Fridman (2:35:33.620)

you have to have a pioneer mindset

Peter Wang (2:35:36.460)

to go back to the classics,

Lex Fridman (2:35:38.100)

to understand what is in human nature you can find meaning.

Lex Fridman (2:35:41.580)

But at the societal level, what trajectory,

Lex Fridman (2:35:44.500)

when you look up possible trajectories, what gives you hope?

Lex Fridman (2:35:47.460)

What gives me hope is that we have little tremors now

Lex Fridman (2:35:52.780)

shaking people out of the reverie

Peter Wang (2:35:54.620)

of the fiction of modernity that they've been living in,

Lex Fridman (2:35:57.020)

kind of a late 20th century style modernity.

Peter Wang (2:36:00.540)

That's good, I think.

Lex Fridman (2:36:02.860)

Because, and also to your point earlier,

Peter Wang (2:36:06.400)

people are burning out on some of the social media stuff.

Lex Fridman (2:36:08.180)

They're sort of seeing the ugly side,

Peter Wang (2:36:09.300)

especially the latest news with Facebook

Lex Fridman (2:36:11.620)

and the whistleblower, right?

Peter Wang (2:36:12.820)

It's quite clear these things are not

Lex Fridman (2:36:15.740)

all they're cracked up to be.

Lex Fridman (2:36:16.740)

Do you believe, I believe better social media can be built

Lex Fridman (2:36:20.740)

because they are burning out

Lex Fridman (2:36:21.820)

and it'll incentivize other competitors to be built.

Lex Fridman (2:36:25.300)

Do you think that's possible?

Peter Wang (2:36:26.460)

Well, the thing about it is that

Lex Fridman (2:36:29.060)

when you have extractive return on returns

Peter Wang (2:36:33.820)

capital coming in and saying,

Lex Fridman (2:36:35.380)

look, you own a network,

Peter Wang (2:36:36.620)

give me some exponential dynamics out of this network.

Lex Fridman (2:36:39.020)

What are you gonna do?

Peter Wang (2:36:39.840)

You're gonna just basically put a toll keeper

Lex Fridman (2:36:41.460)

at every single node and every single graph edge,

Peter Wang (2:36:45.220)

every node, every vertex, every edge.

Lex Fridman (2:36:48.020)

But if you don't have that need for it,

Peter Wang (2:36:49.920)

if no one's sitting there saying,

Lex Fridman (2:36:51.220)

hey, Wikipedia, monetize every character,

Peter Wang (2:36:53.260)

every byte, every phrase,

Lex Fridman (2:36:54.980)

then generative human dynamics will naturally sort of arise,

Peter Wang (2:36:58.340)

assuming we respect a few principles

Lex Fridman (2:37:01.140)

around online communications.

Lex Fridman (2:37:03.020)

So the greatest and biggest social network in the world

Lex Fridman (2:37:05.800)

is still like email, SMS, right?

Lex Fridman (2:37:08.780)

So we're fine there.

Lex Fridman (2:37:10.580)

The issue with the social media, as we call it now,

Lex Fridman (2:37:13.220)

is they're actually just new amplification systems, right?

Lex Fridman (2:37:16.620)

Now it's benefited certain people like yourself

Peter Wang (2:37:18.660)

who have interesting content to be amplified.

Lex Fridman (2:37:23.140)

So it's created a creator economy, and that's cool.

Peter Wang (2:37:25.180)

There's a lot of great content out there.

Lex Fridman (2:37:26.820)

But giving everyone a shot at the fame lottery,

Peter Wang (2:37:29.300)

saying, hey, you could also have your,

Lex Fridman (2:37:31.500)

if you wiggle your butt the right way on TikTok,

Peter Wang (2:37:33.940)

you can have your 15 seconds of micro fame.

Lex Fridman (2:37:36.100)

That's not healthy for society at large.

Lex Fridman (2:37:38.180)

So I think if we can create tools that help people

Lex Fridman (2:37:41.700)

be conscientious about their attention,

Peter Wang (2:37:45.180)

spend time looking at the past,

Lex Fridman (2:37:46.700)

and really retrieving memory and calling,

Peter Wang (2:37:49.600)

not calling, but processing and thinking about that,

Lex Fridman (2:37:53.260)

I think that's certainly possible,

Lex Fridman (2:37:55.100)

and hopefully that's what we get.

Lex Fridman (2:37:57.260)

So the bigger question that you're asking

Peter Wang (2:38:01.020)

about what gives me hope

Lex Fridman (2:38:02.380)

is that these early shocks of COVID lockdowns

Lex Fridman (2:38:08.140)

and remote work and all these different kinds of things,

Lex Fridman (2:38:11.620)

I think it's getting people to a point

Peter Wang (2:38:13.980)

where they're sort of no longer in the reverie.

Lex Fridman (2:38:19.740)

As my friend Jim Rutt says,

Peter Wang (2:38:21.020)

there's more people with ears to hear now.

Lex Fridman (2:38:23.700)

With the pandemic and education,

Peter Wang (2:38:26.100)

everyone's like, wait, wait,

Lex Fridman (2:38:27.140)

what have you guys been doing with my kids?

Lex Fridman (2:38:28.820)

How are you teaching them?

Lex Fridman (2:38:30.140)

What is this crap you're giving them as homework?

Lex Fridman (2:38:32.420)

So I think these are the kinds of things

Lex Fridman (2:38:33.940)

that are getting, and the supply chain disruptions,

Peter Wang (2:38:36.860)

getting more people to think about,

Lex Fridman (2:38:38.220)

how do we actually just make stuff?

Peter Wang (2:38:40.200)

This is all good, but the concern is that

Lex Fridman (2:38:44.820)

it's still gonna take a while for these things,

Peter Wang (2:38:48.380)

for people to learn how to be agentic again,

Lex Fridman (2:38:51.860)

and to be in right relationship with each other

Lex Fridman (2:38:53.920)

and with the world.

Lex Fridman (2:38:55.860)

So the message of hope is still people are resilient,

Lex Fridman (2:38:58.420)

and we are building some really amazing technology.

Lex Fridman (2:39:01.380)

And I also, to me, I derive a lot of hope

Peter Wang (2:39:03.920)

from individuals in that van.

Lex Fridman (2:39:08.140)

The power of a single individual to transform the world,

Peter Wang (2:39:11.980)

to do positive things for the world is quite incredible.

Lex Fridman (2:39:14.700)

Now you've been talking about,

Peter Wang (2:39:16.000)

it's nice to have as many of those individuals as possible,

Lex Fridman (2:39:18.860)

but even the power of one, it's kind of magical.

Peter Wang (2:39:21.900)

It is, it is.

Lex Fridman (2:39:22.740)

We're in a mode now where we can do that.

Peter Wang (2:39:24.500)

I think also, part of what I try to do

Lex Fridman (2:39:26.960)

is in coming to podcasts like yours,

Lex Fridman (2:39:29.020)

and then spamming with all this philosophical stuff

Lex Fridman (2:39:31.740)

that I've got going on,

Peter Wang (2:39:33.540)

there are a lot of good people out there

Lex Fridman (2:39:34.800)

trying to put words around the current technological,

Peter Wang (2:39:40.060)

social, economic crises that we're facing.

Lex Fridman (2:39:43.220)

And in the space of a few short years,

Peter Wang (2:39:44.580)

I think there has been a lot of great content

Lex Fridman (2:39:46.340)

produced around this stuff.

Peter Wang (2:39:47.620)

For people who wanna see, wanna find out more,

Lex Fridman (2:39:50.660)

or think more about this,

Peter Wang (2:39:52.140)

we're popularizing certain kinds of philosophical ideas

Lex Fridman (2:39:54.540)

that move people beyond just the,

Peter Wang (2:39:56.580)

oh, you're communist, oh, you're capitalist kind of stuff.

Lex Fridman (2:39:58.700)

Like it's sort of, we're way past that now.

Lex Fridman (2:40:01.200)

So that also gives me hope,

Lex Fridman (2:40:03.400)

that I feel like I myself am getting a handle

Peter Wang (2:40:05.700)

on how to think about these things.

Lex Fridman (2:40:08.460)

It makes me feel like I can,

Peter Wang (2:40:09.940)

hopefully affect change for the better.

Lex Fridman (2:40:12.560)

We've been sneaking up on this question all over the place.

Peter Wang (2:40:15.700)

Let me ask the big, ridiculous question.

Lex Fridman (2:40:17.580)

What is the meaning of life?

Peter Wang (2:40:20.380)

Wow.

Lex Fridman (2:40:23.900)

The meaning of life.

Peter Wang (2:40:28.860)

Yeah, I don't know.

Lex Fridman (2:40:29.700)

I mean, I've never really understood that question.

Peter Wang (2:40:32.060)

When you say meaning crisis,

Lex Fridman (2:40:34.700)

you're saying that there is a search

Peter Wang (2:40:39.860)

for a kind of experience

Lex Fridman (2:40:42.300)

that could be described as fulfillment,

Peter Wang (2:40:45.480)

as like the aha moment of just like joy,

Lex Fridman (2:40:50.180)

and maybe when you see something beautiful,

Peter Wang (2:40:53.400)

or maybe you have created something beautiful,

Lex Fridman (2:40:55.220)

that experience that you get,

Peter Wang (2:40:57.200)

it feels like it all makes sense.

Lex Fridman (2:41:02.600)

So some of that is just chemicals coming together

Peter Wang (2:41:04.340)

in your mind and all those kinds of things.

Lex Fridman (2:41:06.420)

But it seems like we're building

Peter Wang (2:41:08.820)

a sophisticated collective intelligence

Lex Fridman (2:41:12.620)

that's providing meaning in all kinds of ways

Peter Wang (2:41:15.300)

to its members.

Lex Fridman (2:41:17.120)

And there's a theme to that meaning.

Lex Fridman (2:41:20.620)

So for a lot of history,

Lex Fridman (2:41:22.860)

I think faith played an important role.

Peter Wang (2:41:26.620)

Faith in God, sort of religion.

Lex Fridman (2:41:29.660)

I think technology in the modern era

Peter Wang (2:41:32.660)

is kind of serving a little bit

Lex Fridman (2:41:34.740)

of a source of meaning for people,

Peter Wang (2:41:36.120)

like innovation of different kinds.

Lex Fridman (2:41:39.580)

I think the old school things of love

Lex Fridman (2:41:43.500)

and the basics of just being good at stuff.

Lex Fridman (2:41:47.480)

But you were a physicist,

Lex Fridman (2:41:50.300)

so there's a desire to say, okay, yeah,

Lex Fridman (2:41:52.580)

but these seem to be like symptoms of something deeper.

Peter Wang (2:41:56.300)

Right.

Lex Fridman (2:41:57.140)

Like why?

Lex Fridman (2:41:57.960)

A little meaning, what's capital M meaning?

Lex Fridman (2:41:59.060)

Yeah, what's capital M meaning?

Lex Fridman (2:42:00.960)

Why are we reaching for order

Lex Fridman (2:42:03.080)

when there is excess of energy?

Peter Wang (2:42:06.940)

I don't know if I can answer the why.

Lex Fridman (2:42:09.340)

Any why that I come up with, I think, is gonna be,

Peter Wang (2:42:13.700)

I'd have to think about that a little more,

Lex Fridman (2:42:15.500)

maybe get back to you on that.

Lex Fridman (2:42:17.040)

But I will say this.

Lex Fridman (2:42:19.260)

We do look at the world through a traditional,

Peter Wang (2:42:22.280)

I think most people look at the world through

Lex Fridman (2:42:24.220)

what I would say is a subject object

Peter Wang (2:42:25.980)

to kind of metaphysical lens,

Lex Fridman (2:42:27.200)

that we have our own subjectivity,

Lex Fridman (2:42:29.620)

and then there's all of these object things that are not us.

Lex Fridman (2:42:34.220)

So I'm me, and these things are not me, right?

Lex Fridman (2:42:37.220)

And I'm interacting with them, I'm doing things to them.

Lex Fridman (2:42:39.880)

But a different view of the world

Peter Wang (2:42:41.380)

that looks at it as much more connected,

Lex Fridman (2:42:44.220)

that realizes, oh, I'm really quite embedded

Peter Wang (2:42:49.220)

in a soup of other things,

Lex Fridman (2:42:50.680)

and I'm simply almost like a standing wave pattern

Lex Fridman (2:42:54.060)

of different things, right?

Lex Fridman (2:42:55.920)

So when you look at the world

Peter Wang (2:42:58.020)

in that kind of connected sense,

Lex Fridman (2:42:59.140)

I've recently taken a shine

Peter Wang (2:43:02.980)

to this particular thought experiment,

Lex Fridman (2:43:04.540)

which is what if it was the case

Peter Wang (2:43:08.560)

that everything that we touch with our hands,

Lex Fridman (2:43:12.380)

that we pay attention to,

Peter Wang (2:43:13.880)

that we actually give intimacy to,

Lex Fridman (2:43:16.300)

what if there's actually all the mumbo jumbo,

Peter Wang (2:43:21.300)

like people with the magnetic healing crystals

Lex Fridman (2:43:25.100)

and all this other kind of stuff and quantum energy stuff,

Lex Fridman (2:43:28.240)

what if that was a thing?

Lex Fridman (2:43:30.340)

What if literally when your hand touches an object,

Peter Wang (2:43:34.140)

when you really look at something

Lex Fridman (2:43:35.420)

and you concentrate and you focus on it

Lex Fridman (2:43:36.840)

and you really give it attention,

Lex Fridman (2:43:39.020)

you actually give it,

Peter Wang (2:43:40.580)

there is some physical residue of something,

Lex Fridman (2:43:44.240)

a part of you, a bit of your life force that goes into it.

Peter Wang (2:43:48.120)

Okay, now this is of course completely mumbo jumbo stuff.

Lex Fridman (2:43:51.500)

This is not like, I don't actually think this is real,

Lex Fridman (2:43:53.680)

but let's do the thought experiment.

Lex Fridman (2:43:55.640)

What if it was?

Lex Fridman (2:43:57.720)

What if there actually was some quantum magnetic crystal

Lex Fridman (2:44:01.720)

and energy field thing that just by touching this can,

Peter Wang (2:44:05.320)

this can has changed a little bit somehow.

Lex Fridman (2:44:08.920)

And it's not much unless you put a lot into it

Lex Fridman (2:44:11.940)

and you touch it all the time, like your phone, right?

Lex Fridman (2:44:15.000)

These things gained, they gain meaning to you a little bit,

Lex Fridman (2:44:19.640)

but what if there's something that,

Lex Fridman (2:44:23.520)

technical objects, the phone is a technical object,

Peter Wang (2:44:25.240)

it does not really receive attention or intimacy

Lex Fridman (2:44:29.160)

and then allow itself to be transformed by it.

Lex Fridman (2:44:31.980)

But if it's a piece of wood,

Lex Fridman (2:44:33.360)

if it's the handle of a knife that your mother used

Lex Fridman (2:44:36.720)

for 20 years to make dinner for you, right?

Lex Fridman (2:44:40.340)

What if it's a keyboard that you banged out,

Lex Fridman (2:44:43.860)

your world transforming software library on?

Lex Fridman (2:44:46.400)

These are technical objects

Lex Fridman (2:44:47.840)

and these are physical objects,

Lex Fridman (2:44:48.780)

but somehow there's something to them.

Peter Wang (2:44:51.400)

We feel an attraction to these objects

Lex Fridman (2:44:53.320)

as if we have imbued them with life energy, right?

Lex Fridman (2:44:56.980)

So if you walk that thought experiment through,

Lex Fridman (2:44:58.760)

what happens when we touch another person,

Lex Fridman (2:45:00.440)

when we hug them, when we hold them?

Lex Fridman (2:45:03.160)

And the reason this ties into my answer for your question

Peter Wang (2:45:07.800)

is that if there is such a thing,

Lex Fridman (2:45:12.800)

if there is such a thing,

Peter Wang (2:45:13.680)

if we were to hypothesize, you know,

Lex Fridman (2:45:15.660)

hypothesize it's such a thing,

Peter Wang (2:45:18.480)

it could be that the purpose of our lives

Lex Fridman (2:45:23.440)

is to imbue as many things with that love as possible.

Peter Wang (2:45:30.640)

That's a beautiful answer

Lex Fridman (2:45:32.820)

and a beautiful way to end it, Peter.

Peter Wang (2:45:35.240)

You're an incredible person.

Lex Fridman (2:45:36.600)

Thank you.

Peter Wang (2:45:37.440)

Spanning so much in the space of engineering

Lex Fridman (2:45:41.520)

and in the space of philosophy.

Peter Wang (2:45:44.760)

I'm really proud to be living in the same city as you

Lex Fridman (2:45:49.000)

and I'm really grateful

Peter Wang (2:45:51.120)

that you would spend your valuable time with me today.

Lex Fridman (2:45:53.000)

Thank you so much.

Peter Wang (2:45:53.840)

Well, thank you.

Lex Fridman (2:45:54.660)

I appreciate the opportunity to speak with you.

Peter Wang (2:45:56.560)

Thanks for listening to this conversation with Peter Wang.

Lex Fridman (2:45:59.240)

To support this podcast,

Peter Wang (2:46:00.600)

please check out our sponsors in the description.

Lex Fridman (2:46:03.520)

And now let me leave you with some words

Peter Wang (2:46:05.680)

from Peter Wang himself.

Lex Fridman (2:46:07.960)

We tend to think of people

Peter Wang (2:46:09.480)

as either malicious or incompetent,

Lex Fridman (2:46:12.480)

but in a world filled with corruptible

Lex Fridman (2:46:15.200)

and unchecked institutions,

Lex Fridman (2:46:17.120)

there exists a third thing, malicious incompetence.

Peter Wang (2:46:21.080)

It's a social cancer

Lex Fridman (2:46:22.620)

and it only appears once human organizations scale

Peter Wang (2:46:26.000)

beyond personal accountability.

Lex Fridman (2:46:27.840)

Thank you for listening and hope to see you next time.

Peter Wang (30:00.200)

we can make financial decisions in using services

Lex Fridman (30:03.800)

and paying for services that are making us better people.

Lex Fridman (30:06.880)

So it just seems that we're in the very first stages

Lex Fridman (30:11.440)

of social networks that just were able to make a lot of money

Peter Wang (30:15.520)

really quickly, but in bringing out sometimes

Lex Fridman (30:20.080)

the bad parts of human nature, they didn't destroy humans.

Peter Wang (30:23.200)

They just fed everybody a lot of sugar.

Lex Fridman (30:26.040)

And now everyone's gonna wake up and say,

Peter Wang (30:28.600)

hey, we're gonna start having like sugar free social media.

Lex Fridman (30:31.720)

Right, right.

Peter Wang (30:33.280)

Well, there's a lot to unpack there.

Lex Fridman (30:34.800)

I think some people certainly have the capacity for that.

Lex Fridman (30:37.520)

And I certainly think, I mean, it's very interesting

Lex Fridman (30:39.680)

even the way you said it, you woke up one day

Lex Fridman (30:41.400)

and you thought, well, this doesn't feel very good.

Lex Fridman (30:44.160)

Well, it's still your limbic system saying

Lex Fridman (30:45.760)

this doesn't feel very good, right?

Lex Fridman (30:47.480)

You have a cat brain's worth of neurons around your gut,

Lex Fridman (30:50.040)

right?

Lex Fridman (30:50.880)

And so maybe that saturated and that was telling you,

Peter Wang (30:53.600)

hey, this isn't good.

Lex Fridman (30:55.000)

Humans are more than just mice looking for cheese

Lex Fridman (30:58.320)

or monkeys looking for sex and power, right?

Lex Fridman (31:00.800)

So.

Peter Wang (31:01.640)

Let's slow down.

Lex Fridman (31:02.640)

Now a lot of people would argue with you on that one,

Lex Fridman (31:05.960)

but yes.

Lex Fridman (31:06.800)

Well, we're more than just that, but we're at least that.

Lex Fridman (31:08.480)

And we're very, very seldom not that.

Lex Fridman (31:11.800)

So I don't actually disagree with you

Peter Wang (31:15.080)

that we could be better and that better platforms exist.

Lex Fridman (31:18.240)

And people are voluntarily noping out of things

Peter Wang (31:20.320)

like Facebook and noping out.

Lex Fridman (31:21.680)

That's an awesome verb.

Peter Wang (31:22.760)

It's a great term.

Lex Fridman (31:23.680)

Yeah, I love it.

Peter Wang (31:24.520)

I use it all the time.

Lex Fridman (31:25.720)

You're welcome, Mike.

Peter Wang (31:26.560)

I'm gonna have to nope out of that.

Lex Fridman (31:27.400)

I'm gonna have to nope out of that, right?

Peter Wang (31:28.600)

It's gonna be a hard pass and that's great.

Lex Fridman (31:32.840)

But that's again, to your point,

Peter Wang (31:34.240)

that's the first generation of front facing cameras

Lex Fridman (31:37.240)

of social pressures.

Lex Fridman (31:38.680)

And you as a self starter, self aware adult

Lex Fridman (31:43.560)

have the capacity to say, yeah, I'm not gonna do that.

Peter Wang (31:46.360)

I'm gonna go and spend time on long form reads.

Lex Fridman (31:48.520)

I'm gonna spend time managing my attention.

Peter Wang (31:50.280)

I'm gonna do some yoga.

Lex Fridman (31:52.080)

If you're a 15 year old in high school

Lex Fridman (31:54.960)

and your entire social environment

Lex Fridman (31:57.000)

is everyone doing these things,

Lex Fridman (31:58.240)

guess what you're gonna do?

Lex Fridman (31:59.520)

You're gonna kind of have to do that

Peter Wang (32:00.680)

because your limbic system says,

Lex Fridman (32:01.760)

hey, I need to get the guy or the girl or the whatever.

Lex Fridman (32:04.520)

And that's what I'm gonna do.

Lex Fridman (32:05.640)

And so one of the things that we have to reason about here

Peter Wang (32:07.800)

is the social media systems or social media,

Lex Fridman (32:10.800)

I think is our first encounter with a technological system

Peter Wang (32:15.800)

that runs a bit of a loop around our own cognition

Lex Fridman (32:20.800)

and attention.

Peter Wang (32:21.800)

It's not the last, it's far from the last.

Lex Fridman (32:25.600)

And it gets to the heart of some of the philosophical

Peter Wang (32:28.280)

Achilles heel of the Western philosophical system,

Lex Fridman (32:31.800)

which is each person gets to make their own determination.

Peter Wang (32:34.280)

Each person is an individual that's sacrosanct

Lex Fridman (32:37.280)

in their agency and their sovereignty and all these things.

Peter Wang (32:39.960)

The problem with these systems is they come down

Lex Fridman (32:42.640)

and they are able to make their own decisions.

Peter Wang (32:44.560)

They come down and they are able to manage everyone on mass.

Lex Fridman (32:48.080)

And so every person is making their own decision,

Lex Fridman (32:50.240)

but together the bigger system is causing them to act

Lex Fridman (32:53.720)

with a group dynamic that's very profitable for people.

Lex Fridman (32:58.760)

So this is the issue that we have is that our philosophies

Lex Fridman (33:02.200)

are actually not geared to understand

Lex Fridman (33:05.080)

what is it for a person to have a high trust connection

Lex Fridman (33:10.400)

as part of a collective and for that collective

Peter Wang (33:12.480)

to have its right to coherency and agency.

Lex Fridman (33:16.280)

That's something like when a social media app

Peter Wang (33:19.400)

causes a family to break apart,

Lex Fridman (33:21.720)

it's done harm to more than just individuals, right?

Lex Fridman (33:24.640)

So that concept is not something we really talk about

Lex Fridman (33:27.320)

or think about very much, but that's actually the problem

Peter Wang (33:30.160)

is that we're vaporizing molecules into atomic units

Lex Fridman (33:33.200)

and then we're hitting all the atoms with certain things.

Peter Wang (33:35.160)

That's like, yeah, well, that person chose to look at my app.

Lex Fridman (33:38.360)

So our understanding of human nature

Peter Wang (33:40.600)

at the individual level, it emphasizes the individual

Lex Fridman (33:43.800)

too much because ultimately society operates

Peter Wang (33:46.360)

at the collective level.

Lex Fridman (33:47.440)

And these apps do as well.

Lex Fridman (33:48.640)

And the apps do as well.

Lex Fridman (33:49.880)

So for us to understand the progression and the development

Peter Wang (33:53.360)

of this organism we call human civilization,

Lex Fridman (33:56.000)

we have to think at the collective level too.

Peter Wang (33:58.000)

I would say multi tiered.

Lex Fridman (33:59.280)

Multi tiered.

Lex Fridman (34:00.240)

So individual as well.

Lex Fridman (34:01.600)

Individuals, family units, social collectives

Lex Fridman (34:05.080)

and all the way up.

Lex Fridman (34:06.400)

Okay, so you've said that individual humans

Peter Wang (34:09.400)

are multi layered susceptible to signals and waves

Lex Fridman (34:12.200)

and multiple strata, the physical, the biological,

Peter Wang (34:15.280)

social, cultural, intellectual.

Lex Fridman (34:16.760)

So sort of going along these lines,

Lex Fridman (34:19.480)

can you describe the layers of the cake

Lex Fridman (34:22.880)

that is a human being and maybe the human collective,

Lex Fridman (34:27.360)

human society?

Lex Fridman (34:29.200)

So I'm just stealing wholesale here from Robert Persig,

Peter Wang (34:32.920)

who is the author of Zen and the Art of Motorcycle

Lex Fridman (34:34.600)

Maintenance and his follow on book has a sequel to it

Peter Wang (34:40.040)

called Lila.

Lex Fridman (34:40.720)

He goes into this in a little more detail.

Lex Fridman (34:42.440)

But it's a crude approach to thinking about people.

Lex Fridman (34:47.000)

But I think it's still an advancement

Peter Wang (34:48.920)

over traditional subject object metaphysics,

Lex Fridman (34:51.200)

where we look at people as a dualist would say,

Peter Wang (34:53.960)

well, is your mind, your consciousness,

Lex Fridman (34:57.200)

is that just merely the matter that's in your brain

Lex Fridman (35:01.120)

or is there something kind of more beyond that?

Lex Fridman (35:03.440)

And they would say, yes, there's a soul,

Peter Wang (35:05.080)

sort of ineffable soul beyond just merely the physical body.

Lex Fridman (35:09.280)

And I'm not one of those people.

Peter Wang (35:11.360)

I think that we don't have to draw a line between are things

Lex Fridman (35:15.560)

only this or only that.

Peter Wang (35:16.840)

Collectives of things can emerge structures and patterns

Lex Fridman (35:19.720)

that are just as real as the underlying pieces.

Lex Fridman (35:22.000)

But they're transcendent, but they're still

Lex Fridman (35:24.680)

of the underlying pieces.

Lex Fridman (35:26.520)

So your body is this way.

Lex Fridman (35:28.400)

I mean, we just know physically you consist of atoms

Lex Fridman (35:31.000)

and whatnot.

Lex Fridman (35:32.800)

And then the atoms are arranged into molecules

Peter Wang (35:34.680)

which then arrange into certain kinds of structures

Lex Fridman (35:37.120)

that seem to have a homeostasis to them.

Peter Wang (35:39.560)

We call them cells.

Lex Fridman (35:40.720)

And those cells form sort of biological structures.

Peter Wang (35:44.560)

Those biological structures give your body

Lex Fridman (35:46.840)

its physical ability and the biological ability

Peter Wang (35:49.000)

to consume energy and to maintain homeostasis.

Lex Fridman (35:51.800)

But humans are social animals.

Peter Wang (35:54.040)

I mean, human by themselves is not very long for the world.

Lex Fridman (35:57.320)

So part of our biology is why are two connect to other people?

Peter Wang (36:02.200)

From the mirror neurons to our language centers

Lex Fridman (36:04.920)

and all these other things.

Lex Fridman (36:06.280)

So we are intrinsically, there's a layer,

Lex Fridman (36:09.160)

there's a part of us that wants to be part of a thing.

Peter Wang (36:12.560)

If we're around other people, not saying a word,

Lex Fridman (36:14.640)

but they're just up and down jumping and dancing, laughing,

Peter Wang (36:17.140)

we're going to feel better.

Lex Fridman (36:18.580)

And there was no exchange of physical anything.

Peter Wang (36:21.800)

They didn't give us like five atoms of happiness.

Lex Fridman (36:24.840)

But there's an induction in our own sense of self

Peter Wang (36:27.520)

that is at that social level.

Lex Fridman (36:29.600)

And then beyond that, Persick puts the intellectual level

Peter Wang (36:33.560)

kind of one level higher than social.

Lex Fridman (36:35.100)

I think they're actually more intertwined than that.

Lex Fridman (36:37.220)

But the intellectual level is the level of pure ideas.

Lex Fridman (36:41.040)

That you are a vessel for memes.

Peter Wang (36:42.840)

You're a vessel for philosophies.

Lex Fridman (36:45.000)

You will conduct yourself in a particular way.

Peter Wang (36:47.400)

I mean, I think part of this is if we think about it

Lex Fridman (36:49.520)

from a physics perspective, you're not,

Peter Wang (36:52.200)

there's the joke that physicists like to approximate things.

Lex Fridman (36:55.080)

And we'll say, well, approximate a spherical cow, right?

Peter Wang (36:57.500)

You're not a spherical cow, you're not a spherical human.

Lex Fridman (36:59.640)

You're a messy human.

Lex Fridman (37:00.760)

And we can't even say what the dynamics of your emotion

Lex Fridman (37:04.000)

will be unless we analyze all four of these layers, right?

Lex Fridman (37:08.560)

If you're Muslim at a certain time of day, guess what?

Lex Fridman (37:11.760)

You're going to be on the ground kneeling and praying, right?

Lex Fridman (37:14.000)

And that has nothing to do with your biological need

Lex Fridman (37:15.960)

to get on the ground or physics of gravity.

Peter Wang (37:18.520)

It is an intellectual drive that you have.

Lex Fridman (37:20.440)

It's a cultural phenomenon

Lex Fridman (37:22.000)

and an intellectual belief that you carry.

Lex Fridman (37:23.760)

So that's what the four layered stack is all about.

Peter Wang (37:28.120)

It's that a person is not only one of these things,

Lex Fridman (37:30.360)

they're all of these things at the same time.

Peter Wang (37:31.760)

It's a superposition of dynamics that run through us

Lex Fridman (37:35.680)

that make us who we are.

Lex Fridman (37:37.360)

So no layer is special.

Lex Fridman (37:40.520)

Not so much no layer is special,

Peter Wang (37:41.720)

each layer is just different.

Lex Fridman (37:44.360)

But we are.

Peter Wang (37:45.840)

Each layer gets the participation trophy.

Lex Fridman (37:48.320)

Yeah, each layer is a part of what you are.

Peter Wang (37:50.320)

You are a layer cake, right, of all these things.

Lex Fridman (37:52.080)

And if we try to deny, right,

Lex Fridman (37:54.480)

so many philosophies do try to deny

Lex Fridman (37:56.580)

the reality of some of these things, right?

Peter Wang (37:58.960)

Some people will say, well, we're only atoms.

Lex Fridman (38:01.420)

Well, we're not only atoms

Peter Wang (38:02.480)

because there's a lot of other things that are only atoms.

Lex Fridman (38:04.080)

I can reduce a human being to a bunch of soup

Lex Fridman (38:07.000)

and they're not the same thing,

Lex Fridman (38:08.520)

even though it's the same atoms.

Lex Fridman (38:09.800)

So I think the order and the patterns

Lex Fridman (38:12.160)

that emerge within humans to understand,

Peter Wang (38:15.960)

to really think about what a next generation philosophy

Lex Fridman (38:18.480)

would look like, that would allow us to reason

Peter Wang (38:20.040)

about extending humans into the digital realm

Lex Fridman (38:22.960)

or to interact with autonomous intelligences

Peter Wang (38:25.560)

that are not biological in nature.

Lex Fridman (38:27.520)

We really need to appreciate these,

Peter Wang (38:29.640)

that human, what human beings actually are

Lex Fridman (38:32.280)

is the superposition of these different layers.

Peter Wang (38:34.760)

You mentioned consciousness.

Lex Fridman (38:36.640)

Are each of these layers of cake conscious?

Lex Fridman (38:39.800)

Is consciousness a particular quality of one of the layers?

Lex Fridman (38:43.760)

Is there like a spike if you have a consciousness detector

Peter Wang (38:46.920)

at these layers or is something that just permeates

Lex Fridman (38:49.360)

all of these layers and just takes different form?

Peter Wang (38:51.920)

I believe what humans experience as consciousness

Lex Fridman (38:54.400)

is something that sits on a gradient scale

Peter Wang (38:57.640)

of a general principle in the universe

Lex Fridman (39:00.540)

that seems to look for order and reach for order

Peter Wang (39:04.960)

when there's an excess of energy.

Lex Fridman (39:06.760)

You know, it would be odd to say a proton is alive, right?

Peter Wang (39:09.400)

It'd be odd to say like this particular atom

Lex Fridman (39:12.040)

or molecule of hydrogen gas is alive,

Lex Fridman (39:15.840)

but there's certainly something we can make assemblages

Lex Fridman (39:20.680)

of these things that have autopoetic aspects to them

Peter Wang (39:24.400)

that will create structures that will, you know,

Lex Fridman (39:26.520)

crystalline solids will form very interesting

Lex Fridman (39:28.640)

and beautiful structures.

Lex Fridman (39:29.960)

This gets kind of into weird mathematical territories.

Peter Wang (39:33.000)

You start thinking about Penrose and Game of Life stuff

Lex Fridman (39:35.320)

about the generativity of math itself,

Peter Wang (39:37.900)

like the hyperreal numbers, things like that.

Lex Fridman (39:39.480)

But without going down that rabbit hole,

Peter Wang (39:42.320)

I would say that there seems to be a tendency

Lex Fridman (39:45.540)

in the world that when there is excess energy,

Peter Wang (39:49.120)

things will structure and pattern themselves.

Lex Fridman (39:51.360)

And they will then actually furthermore try to create

Peter Wang (39:53.760)

an environment that furthers their continued stability.

Lex Fridman (39:58.040)

It's the concept of externalized extended phenotype

Peter Wang (40:00.800)

or niche construction.

Lex Fridman (40:02.240)

So this is ultimately what leads to certain kinds

Peter Wang (40:06.240)

of amino acids forming certain kinds of structures

Lex Fridman (40:09.180)

and so on and so forth until you get the ladder of life.

Lex Fridman (40:11.120)

So what we experience as consciousness,

Lex Fridman (40:12.880)

no, I don't think cells are conscious at that level,

Lex Fridman (40:15.560)

but is there something beyond mere equilibrium state biology

Lex Fridman (40:19.560)

and chemistry and biochemistry

Lex Fridman (40:21.960)

that drives what makes things work?

Lex Fridman (40:25.420)

I think there is.

Lex Fridman (40:27.620)

So Adrian Bajan has his ConstructoLaw.

Lex Fridman (40:29.560)

There's other things you can look at.

Peter Wang (40:31.200)

When you look at the life sciences

Lex Fridman (40:32.440)

and you look at any kind of statistical physics

Lex Fridman (40:36.040)

and statistical mechanics,

Lex Fridman (40:37.440)

when you look at things far out of equilibrium,

Lex Fridman (40:40.540)

when you have excess energy, what happens then?

Lex Fridman (40:43.400)

Life doesn't just make a hotter soup.

Peter Wang (40:45.800)

It starts making structure.

Lex Fridman (40:47.440)

There's something there.

Peter Wang (40:48.640)

The poetry of reaches for order

Lex Fridman (40:50.880)

when there's an excess of energy.

Peter Wang (40:54.160)

Because you brought up game of life.

Lex Fridman (40:57.200)

You did it, not me.

Peter Wang (40:59.160)

I love cellular automata,

Lex Fridman (41:00.360)

so I have to sort of linger on that for a little bit.

Lex Fridman (41:06.400)

So cellular automata, I guess, or game of life

Lex Fridman (41:09.340)

is a very simple example of reaching for order

Peter Wang (41:11.840)

when there's an excess of energy.

Lex Fridman (41:14.080)

Or reaching for order and somehow creating complexity.

Peter Wang (41:17.240)

Within this explosion of just turmoil,

Lex Fridman (41:22.440)

somehow trying to construct structures.

Lex Fridman (41:25.560)

And in so doing, create very elaborate

Lex Fridman (41:29.760)

organism looking type things.

Lex Fridman (41:32.480)

What intuition do you draw from this simple mechanism?

Lex Fridman (41:35.840)

Well, I like to turn that around its head.

Lex Fridman (41:37.840)

And look at it as what if every single one of the patterns

Lex Fridman (41:42.400)

created life, or created, not life,

Lex Fridman (41:45.800)

but created interesting patterns?

Lex Fridman (41:47.400)

Because some of them don't.

Lex Fridman (41:48.640)

And sometimes you make cool gliders.

Lex Fridman (41:50.520)

And other times, you start with certain things

Lex Fridman (41:52.240)

and you make gliders and other things

Lex Fridman (41:54.020)

that then construct like AND gates and NOT gates, right?

Lex Fridman (41:57.040)

And you build computers on them.

Lex Fridman (41:59.240)

All of these rules that create these patterns

Peter Wang (42:00.800)

that we can see, those are just the patterns we can see.

Lex Fridman (42:04.640)

What if our subjectivity is actually limiting

Lex Fridman (42:06.600)

our ability to perceive the order in all of it?

Lex Fridman (42:11.000)

What if some of the things that we think are random

Lex Fridman (42:12.320)

are actually not that random?

Lex Fridman (42:13.300)

We're simply not integrating at a final level

Peter Wang (42:16.120)

across a broad enough time horizon.

Lex Fridman (42:18.980)

And this is again, I said, we go down the rabbit holes

Lex Fridman (42:20.600)

and the Penrose stuff or like Wolfram's explorations

Lex Fridman (42:22.880)

on these things.

Peter Wang (42:24.960)

There is something deep and beautiful

Lex Fridman (42:27.300)

in the mathematics of all this.

Peter Wang (42:28.600)

That is hopefully one day I'll have enough money

Lex Fridman (42:30.400)

to work and retire and just ponder those questions.

Lex Fridman (42:33.440)

But there's something there.

Lex Fridman (42:34.560)

But you're saying there's a ceiling to,

Peter Wang (42:36.120)

when you have enough money and you retire and you ponder,

Lex Fridman (42:38.480)

there's a ceiling to how much you can truly ponder

Peter Wang (42:40.720)

because there's cognitive limitations

Lex Fridman (42:43.000)

in what you're able to perceive as a pattern.

Peter Wang (42:46.320)

Yeah.

Lex Fridman (42:47.160)

And maybe mathematics extends your perception capabilities,

Lex Fridman (42:51.520)

but it's still finite.

Lex Fridman (42:53.840)

It's just like.

Peter Wang (42:55.460)

Yeah, the mathematics we use is the mathematics

Lex Fridman (42:57.640)

that can fit in our head.

Peter Wang (42:59.000)

Yeah.

Lex Fridman (43:00.840)

Did God really create the integers?

Lex Fridman (43:02.600)

Or did God create all of it?

Lex Fridman (43:03.720)

And we just happen at this point in time

Peter Wang (43:05.360)

to be able to perceive integers.

Lex Fridman (43:07.240)

Well, he just did the positive in it.

Lex Fridman (43:09.400)

She, I just said, did she create all of it?

Lex Fridman (43:11.360)

And then we.

Peter Wang (43:14.200)

She just created the natural numbers

Lex Fridman (43:15.760)

and then we screwed it all up with zero and then I guess.

Peter Wang (43:17.680)

Okay.

Lex Fridman (43:18.560)

But we did, we created mathematical operations

Lex Fridman (43:21.600)

so that we can have iterated steps

Lex Fridman (43:23.700)

to approach bigger problems, right?

Peter Wang (43:26.000)

I mean, the entire point of the Arabic Neural System

Lex Fridman (43:29.040)

and it's a rubric for mapping a certain set of operations,

Peter Wang (43:32.600)

folding them into a simple little expression,

Lex Fridman (43:35.360)

but that's just the operations that we can fit in our heads.

Lex Fridman (43:38.740)

There are many other operations besides, right?

Lex Fridman (43:41.140)

The thing that worries me the most about aliens and humans

Peter Wang (43:46.020)

is that the aliens are all around us and we're too dumb.

Lex Fridman (43:50.920)

Yeah.

Peter Wang (43:51.760)

To see them.

Lex Fridman (43:52.580)

Oh, certainly, yeah.

Peter Wang (43:53.420)

Or life, let's say just life,

Lex Fridman (43:54.800)

life of all kinds of forms or organisms.

Peter Wang (43:58.080)

You know what, just even the intelligence of organisms

Lex Fridman (44:01.940)

is imperceptible to us

Peter Wang (44:04.160)

because we're too dumb and self centered.

Lex Fridman (44:06.600)

That worries me.

Peter Wang (44:07.440)

Well, we're looking for a particular kind of thing.

Lex Fridman (44:09.560)

Yeah.

Peter Wang (44:10.400)

When I was at Cornell,

Lex Fridman (44:11.220)

I had a lovely professor of Asian religions,

Peter Wang (44:13.860)

Jane Marie Law,

Lex Fridman (44:14.700)

and she would tell this story about a musical,

Peter Wang (44:17.760)

a musician, a Western musician who went to Japan

Lex Fridman (44:20.100)

and he taught classical music

Lex Fridman (44:21.880)

and could play all sorts of instruments.

Lex Fridman (44:24.000)

He went to Japan and he would ask people,

Peter Wang (44:27.480)

he would basically be looking for things in the style of

Lex Fridman (44:30.640)

a Western chromatic scale and these kinds of things.

Lex Fridman (44:34.080)

And then finding none of it,

Lex Fridman (44:35.380)

he would say, well, there's really no music in Japan,

Lex Fridman (44:37.520)

but they're using a different scale.

Lex Fridman (44:38.800)

They're playing different kinds of instruments, right?

Peter Wang (44:40.440)

The same thing she was using as a sort of a metaphor

Lex Fridman (44:42.620)

for religion as well.

Peter Wang (44:43.660)

In the West, we center a lot of religion,

Lex Fridman (44:45.780)

certainly the religions of Abraham,

Peter Wang (44:47.980)

we center them around belief.

Lex Fridman (44:50.040)

And in the East, it's more about practice, right?

Peter Wang (44:52.440)

Spirituality and practice rather than belief.

Lex Fridman (44:54.600)

So anyway, the point is here to your point,

Peter Wang (44:57.360)

life, we, I think so many people are so fixated

Lex Fridman (45:00.820)

on certain aspects of self replication

Peter Wang (45:03.320)

or homeostasis or whatever.

Lex Fridman (45:06.120)

But if we kind of broaden and generalize this thing

Peter Wang (45:08.840)

of things reaching for order,

Lex Fridman (45:10.840)

under which conditions can they then create an environment

Peter Wang (45:13.540)

that sustains that order, that allows them,

Lex Fridman (45:17.320)

the invention of death is an interesting thing.

Peter Wang (45:20.120)

There are some organisms on earth

Lex Fridman (45:21.480)

that are thousands of years old.

Lex Fridman (45:23.380)

And it's not like they're incredibly complex,

Lex Fridman (45:25.520)

they're actually simpler than the cells that comprise us,

Lex Fridman (45:28.560)

but they never die.

Lex Fridman (45:29.640)

So at some point, death was invented,

Peter Wang (45:33.320)

somewhere along the eukaryotic scale,

Lex Fridman (45:34.720)

I mean, even the protists, right?

Peter Wang (45:35.980)

There's death.

Lex Fridman (45:37.180)

And why is that along with the sexual reproduction, right?

Peter Wang (45:41.480)

There is something about the renewal process,

Lex Fridman (45:45.000)

something about the ability to respond

Peter Wang (45:46.520)

to a changing environment,

Lex Fridman (45:48.160)

where it just becomes,

Peter Wang (45:50.240)

just killing off the old generation

Lex Fridman (45:51.520)

and letting new generations try,

Peter Wang (45:54.240)

seems to be the best way to fit into the niche.

Lex Fridman (45:57.040)

Human historians seems to write about wheels and fires,

Peter Wang (46:00.320)

the greatest inventions,

Lex Fridman (46:01.640)

but it seems like death and sex are pretty good.

Lex Fridman (46:04.360)

And they're kind of essential inventions

Lex Fridman (46:06.560)

at the very beginning.

Peter Wang (46:07.400)

At the very beginning, yeah.

Lex Fridman (46:08.600)

Well, we didn't invent them, right?

Peter Wang (46:10.560)

Well, Broad, you didn't invent them.

Lex Fridman (46:13.300)

I see us as one,

Peter Wang (46:15.400)

you particular Homo sapiens did not invent them,

Lex Fridman (46:17.880)

but we together, it's a team project,

Peter Wang (46:21.000)

just like you're saying.

Lex Fridman (46:21.880)

I think the greatest Homo sapiens invention

Peter Wang (46:24.280)

is collaboration.

Lex Fridman (46:25.640)

So when you say collaboration,

Peter Wang (46:29.640)

Peter, where do ideas come from

Lex Fridman (46:32.200)

and how do they take hold in society?

Lex Fridman (46:35.280)

Is that the nature of collaboration?

Lex Fridman (46:36.960)

Is that the basic atom of collaboration is ideas?

Peter Wang (46:40.440)

It's not not ideas, but it's not only ideas.

Lex Fridman (46:43.200)

There's a book I just started reading

Peter Wang (46:44.440)

called Death From A Distance.

Lex Fridman (46:45.920)

Have you heard of this?

Peter Wang (46:46.740)

No.

Lex Fridman (46:47.580)

It's a really fascinating thesis,

Peter Wang (46:49.200)

which is that humans are the only conspecific,

Lex Fridman (46:53.200)

the only species that can kill other members

Peter Wang (46:55.800)

of the species from range.

Lex Fridman (46:58.200)

And maybe there's a few exceptions,

Lex Fridman (46:59.600)

but if you look in the animal world,

Lex Fridman (47:01.020)

you see like pronghorns butting heads, right?

Peter Wang (47:03.000)

You see the alpha lion and the beta lion

Lex Fridman (47:05.840)

and they take each other down.

Peter Wang (47:07.200)

Humans, we developed the ability

Lex Fridman (47:08.720)

to chuck rocks at each other,

Peter Wang (47:10.120)

well, at prey, but also at each other.

Lex Fridman (47:11.960)

And that means the beta male can chunk a rock

Peter Wang (47:14.920)

at the alpha male and take them down.

Lex Fridman (47:17.440)

And he can throw a lot of rocks actually,

Peter Wang (47:20.040)

miss a bunch of times, but just hit once and be good.

Lex Fridman (47:22.400)

So this ability to actually kill members

Peter Wang (47:25.800)

of our own species from range

Lex Fridman (47:27.280)

without a threat of harm to ourselves

Peter Wang (47:29.960)

created essentially mutually assured destruction

Lex Fridman (47:32.360)

where we had to evolve cooperation.

Peter Wang (47:34.000)

If we didn't, then if we just continue to try to do,

Lex Fridman (47:37.300)

like I'm the biggest monkey in the tribe

Lex Fridman (47:39.480)

and I'm gonna own this tribe and you have to go,

Lex Fridman (47:43.040)

if we do it that way, then those tribes basically failed.

Lex Fridman (47:46.720)

And the tribes that persisted

Lex Fridman (47:48.440)

and that have now given rise to the modern Homo sapiens

Peter Wang (47:51.440)

are the ones where respecting the fact

Lex Fridman (47:53.860)

that we can kill each other from a range

Peter Wang (47:56.240)

without harm, like there's an asymmetric ability

Lex Fridman (47:58.640)

to snipe the leader from range.

Peter Wang (48:00.840)

That meant that we sort of had to learn

Lex Fridman (48:03.920)

how to cooperate with each other, right?

Peter Wang (48:05.400)

Come back here, don't throw that rock at me.

Lex Fridman (48:06.600)

Let's talk our differences out.

Lex Fridman (48:07.960)

So violence is also part of collaboration.

Lex Fridman (48:10.240)

The threat of violence, let's say.

Peter Wang (48:12.320)

Well, the recognition, maybe the better way to put it

Lex Fridman (48:15.720)

is the recognition that we have more to gain

Peter Wang (48:17.480)

by working together than the prisoner's dilemma

Lex Fridman (48:21.140)

of both of us defecting.

Lex Fridman (48:23.520)

So mutually assured destruction in all its forms

Lex Fridman (48:26.380)

is part of this idea of collaboration.

Peter Wang (48:28.720)

Well, and Eric Weinstein talks about our nuclear peace,

Lex Fridman (48:31.040)

right, I mean, it kind of sucks

Peter Wang (48:32.440)

with thousands of warheads aimed at each other,

Lex Fridman (48:34.040)

we mean Russia and the US, but it's like,

Lex Fridman (48:36.320)

on the other hand, we only fought proxy wars, right?

Lex Fridman (48:39.880)

We did not have another World War III

Peter Wang (48:41.360)

of like hundreds of millions of people dying

Lex Fridman (48:43.520)

to like machine gun fire and giant guided missiles.

Lex Fridman (48:47.840)

So the original nuclear weapon is a rock

Lex Fridman (48:50.400)

that we learned how to throw, essentially?

Peter Wang (48:52.280)

The original, yeah, well, the original scope of the world

Lex Fridman (48:54.280)

for any human being was their little tribe.

Peter Wang (48:58.740)

I would say it still is for the most part.

Lex Fridman (49:00.680)

Eric Weinstein speaks very highly of you,

Peter Wang (49:05.800)

which is very surprising to me at first

Lex Fridman (49:08.000)

because I didn't know there's this depth to you

Peter Wang (49:10.800)

because I knew you as an amazing leader of engineers

Lex Fridman (49:15.560)

and an engineer yourself and so on, so it's fascinating.

Peter Wang (49:18.440)

Maybe just as a comment, a side tangent that we can take,

Lex Fridman (49:23.800)

what's your nature of your friendship with Eric Weinstein?

Lex Fridman (49:27.120)

How did the two, how did such two interesting paths cross?

Lex Fridman (49:30.620)

Is it your origins in physics?

Peter Wang (49:32.960)

Is it your interest in philosophy

Lex Fridman (49:35.620)

and the ideas of how the world works?

Lex Fridman (49:37.280)

What is it?

Lex Fridman (49:38.120)

It's very random, Eric found me.

Peter Wang (49:40.920)

He actually found Travis and I.

Lex Fridman (49:43.840)

Travis Oliphant.

Peter Wang (49:44.680)

Oliphant, yeah, we were both working

Lex Fridman (49:45.900)

at a company called Enthought back in the mid 2000s

Lex Fridman (49:48.520)

and we were doing a lot of consulting

Lex Fridman (49:50.840)

around scientific Python and we'd made some tools

Lex Fridman (49:54.400)

and Eric was trying to use some of these Python tools

Lex Fridman (49:57.400)

to visualize, he had a fiber bundle approach

Peter Wang (50:00.200)

to modeling certain aspects of economics.

Lex Fridman (50:03.440)

He was doing this and that's how he kind of got in touch

Peter Wang (50:05.200)

with us and so.

Lex Fridman (50:06.160)

This was in the early.

Peter Wang (50:08.160)

This was mid 2000s, oh seven timeframe, oh six, oh seven.

Lex Fridman (50:13.680)

Eric Weinstein trying to use Python.

Peter Wang (50:16.200)

Right, to visualize fiber bundles.

Lex Fridman (50:18.680)

Using some of the tools that we had built

Peter Wang (50:20.160)

in the open source.

Lex Fridman (50:21.280)

That's somehow entertaining to me, the thought of that.

Peter Wang (50:24.160)

It's very funny but then we met with him a couple times,

Lex Fridman (50:27.160)

a really interesting guy and then in the wake

Peter Wang (50:28.780)

of the oh seven, oh eight kind of financial collapse,

Lex Fridman (50:31.360)

he helped organize with Lee Smolin a symposium

Peter Wang (50:35.440)

at the Perimeter Institute about okay, well clearly,

Lex Fridman (50:39.960)

big finance can't be trusted, government's in its pockets

Lex Fridman (50:42.220)

with regulatory capture, what the F do we do?

Lex Fridman (50:45.360)

And all sorts of people, Nassim Tlaib was there

Lex Fridman (50:47.680)

and Andy Lowe from MIT was there and Bill Janeway,

Lex Fridman (50:51.300)

I mean just a lot of top billing people were there

Lex Fridman (50:54.880)

and he invited me and Travis and another one

Lex Fridman (50:58.480)

of our coworkers, Robert Kern, who is anyone

Peter Wang (51:01.320)

in the SciPy, NumPy community knows Robert.

Lex Fridman (51:03.960)

Really great guy.

Lex Fridman (51:04.780)

So the three of us also got invited to go to this thing

Lex Fridman (51:06.640)

and that's where I met Brett Weinstein

Peter Wang (51:07.800)

for the first time as well.

Lex Fridman (51:09.520)

Yeah, I knew him before he got all famous

Peter Wang (51:11.680)

for unfortunate reasons, I guess.

Lex Fridman (51:13.440)

But anyway, so we met then and kind of had a friendship

Peter Wang (51:19.600)

throughout since then.

Lex Fridman (51:21.400)

You have a depth of thinking that kind of runs

Peter Wang (51:26.080)

with Eric in terms of just thinking about the world deeply

Lex Fridman (51:28.600)

and thinking philosophically and then there's Eric's

Peter Wang (51:31.720)

interest in programming.

Lex Fridman (51:33.480)

I actually have never, you know, he'll bring up programming

Peter Wang (51:38.080)

to me quite a bit as a metaphor for stuff.

Lex Fridman (51:41.100)

But I never kind of pushed the point of like,

Lex Fridman (51:44.520)

what's the nature of your interest in programming?

Lex Fridman (51:46.820)

I think he saw it probably as a tool.

Peter Wang (51:48.840)

Yeah, absolutely.

Lex Fridman (51:49.680)

That you visualize, to explore mathematics

Lex Fridman (51:52.200)

and explore physics and I was wondering like,

Lex Fridman (51:55.020)

what's his depth of interest and also his vision

Peter Wang (51:59.520)

for what programming would look like in the future.

Lex Fridman (52:05.640)

Have you had interaction with him, like discussion

Lex Fridman (52:08.000)

in the space of Python, programming?

Lex Fridman (52:09.840)

Well, in the sense of sometimes he asks me,

Lex Fridman (52:11.920)

why is this stuff still so hard?

Lex Fridman (52:13.620)

Yeah, you know, everybody's a critic.

Lex Fridman (52:18.280)

But actually, no, Eric.

Lex Fridman (52:20.320)

Programming, you mean, like in general?

Peter Wang (52:21.440)

Yes, yes, well, not programming in general,

Lex Fridman (52:23.320)

but certain things in the Python ecosystem.

Lex Fridman (52:25.560)

But he actually, I think what I find in listening

Lex Fridman (52:29.560)

to some of his stuff is that he does use

Lex Fridman (52:31.640)

programming metaphors a lot, right?

Lex Fridman (52:33.280)

He'll talk about APIs or object oriented

Lex Fridman (52:35.420)

and things like that.

Lex Fridman (52:36.400)

So I think that's a useful set of frames

Peter Wang (52:39.240)

for him to draw upon for discourse.

Lex Fridman (52:42.880)

I haven't pair programmed with him in a very long time.

Peter Wang (52:45.440)

You've previously pair coded with Eric.

Lex Fridman (52:47.240)

Well, I mean, I look at his code trying to help

Peter Wang (52:49.520)

like put together some of the visualizations

Lex Fridman (52:50.960)

around these things.

Lex Fridman (52:51.800)

But it's been a very, not really pair programmed,

Lex Fridman (52:54.040)

but like even looked at his code, right?

Peter Wang (52:55.800)

I mean.

Lex Fridman (52:56.640)

How legendary would be is that like Git repo

Lex Fridman (53:01.040)

with Peter Wang and Eric Weinstein?

Lex Fridman (53:02.680)

Well, honestly, Robert Kern did all the heavy lifting.

Lex Fridman (53:05.480)

So I have to give credit where credit is due.

Lex Fridman (53:06.880)

Robert is the silent but incredibly deep, quiet,

Peter Wang (53:10.760)

not silent, but quiet, but incredibly deep individual

Lex Fridman (53:13.480)

at the heart of a lot of those things

Peter Wang (53:14.720)

that Eric was trying to do.

Lex Fridman (53:16.720)

But we did have, you know, as Travis and I

Peter Wang (53:19.160)

were starting our company in 2012 timeframe,

Lex Fridman (53:23.520)

we went to New York.

Peter Wang (53:24.640)

Eric was still in New York at the time.

Lex Fridman (53:26.120)

He hadn't moved to, this is before he joined Teal Capital.

Peter Wang (53:29.160)

We just had like a steak dinner somewhere.

Lex Fridman (53:31.560)

Maybe it was Keynes, I don't know, somewhere in New York.

Lex Fridman (53:33.400)

So it was me, Travis, Eric, and then Wes McKinney,

Lex Fridman (53:36.360)

the creative pandas, and then Wes's then business partner,

Peter Wang (53:39.520)

Adam, the five of us sat around having this,

Lex Fridman (53:42.040)

just a hilarious time, amazing dinner.

Peter Wang (53:45.520)

I forget what all we talked about,

Lex Fridman (53:46.960)

but it was one of those conversations,

Peter Wang (53:49.000)

which I wish as soon as COVID is over,

Lex Fridman (53:51.400)

maybe Eric and I can sit down.

Peter Wang (53:53.080)

Recreate.

Lex Fridman (53:53.920)

Recreate it somewhere in LA, or maybe he comes here,

Lex Fridman (53:56.720)

because a lot of cool people are here in Austin, right?

Lex Fridman (53:58.160)

Exactly.

Peter Wang (53:59.000)

Yeah, we're all here.

Lex Fridman (53:59.840)

He should come here.

Peter Wang (54:00.680)

Come here.

Lex Fridman (54:01.520)

Yeah.

Lex Fridman (54:02.360)

So he uses the metaphor source code sometimes

Lex Fridman (54:04.120)

to talk about physics.

Peter Wang (54:05.320)

We figure out our own source code.

Lex Fridman (54:07.160)

So you with a physics background

Lex Fridman (54:10.880)

and somebody who's quite a bit of an expert in source code,

Lex Fridman (54:14.160)

do you think we'll ever figure out our own source code

Lex Fridman (54:17.760)

in the way that Eric means?

Lex Fridman (54:19.040)

Do you think we'll figure out the nature of reality?

Peter Wang (54:20.360)

Well, I think we're constantly working on that problem.

Lex Fridman (54:21.760)

I mean, I think we'll make more and more progress.

Peter Wang (54:24.400)

For me, there's some things I don't really doubt too much.

Lex Fridman (54:28.120)

Like, I don't really doubt that one day

Peter Wang (54:29.960)

we will create a synthetic, maybe not fully in silicon,

Lex Fridman (54:34.480)

but a synthetic approach to

Peter Wang (54:39.240)

cognition that rivals the biological

Lex Fridman (54:42.680)

20 watt computers in our heads.

Lex Fridman (54:44.760)

What's cognition here?

Lex Fridman (54:46.080)

Cognition.

Lex Fridman (54:46.920)

Which aspect?

Lex Fridman (54:47.760)

Perception, attention, memory, recall,

Peter Wang (54:49.960)

asking better questions.

Lex Fridman (54:51.840)

That for me is a measure of intelligence.

Lex Fridman (54:53.200)

Doesn't Roomba vacuum cleaner already do that?

Lex Fridman (54:55.400)

Or do you mean, oh, it doesn't ask questions.

Peter Wang (54:57.080)

I mean, no, it's, I mean, I have a Roomba,

Lex Fridman (55:00.160)

but it's not even as smart as my cat, right?

Lex Fridman (55:03.080)

Yeah, but it asks questions about what is this wall?

Lex Fridman (55:05.320)

It now, new feature asks, is this poop or not, apparently.

Peter Wang (55:08.920)

Yes, a lot of our current cybernetic system,

Lex Fridman (55:11.320)

it's a cybernetic system.

Lex Fridman (55:12.640)

It will go and it will happily vacuum up some poop, right?

Lex Fridman (55:14.960)

The older generations would.

Peter Wang (55:16.640)

The new one, just released, does not vacuum up the poop.

Lex Fridman (55:19.400)

Okay.

Peter Wang (55:20.240)

This is a commercial for.

Lex Fridman (55:21.080)

I wonder if it still gets stuck

Peter Wang (55:21.920)

under my first rung of my stair.

Lex Fridman (55:23.800)

In any case, these cybernetic systems we have,

Peter Wang (55:27.160)

they are mold, they're designed to be sent off

Lex Fridman (55:32.080)

into a relatively static environment.

Lex Fridman (55:34.040)

And whatever dynamic things happen in the environment,

Lex Fridman (55:36.400)

they have a very limited capacity to respond to.

Peter Wang (55:38.920)

A human baby, a human toddler of 18 months of age

Lex Fridman (55:43.080)

has more capacity to manage its own attention

Lex Fridman (55:45.840)

and its own capacity to make better sense of the world

Lex Fridman (55:49.200)

than the most advanced robots today.

Lex Fridman (55:51.720)

So again, my cat, I think can do a better job of my two

Lex Fridman (55:55.160)

and they're both pretty clever.

Lex Fridman (55:56.400)

So I do think though, back to my kind of original point,

Lex Fridman (55:59.440)

I think that it's not, for me, it's not question at all

Peter Wang (56:02.720)

that we will be able to create synthetic systems

Lex Fridman (56:05.960)

that are able to do this better than the human,

Peter Wang (56:09.200)

at an equal level or better than the human mind.

Lex Fridman (56:11.720)

It's also for me, not a question that we will be able

Peter Wang (56:16.400)

to put them alongside humans

Lex Fridman (56:20.160)

so that they capture the full broad spectrum

Peter Wang (56:23.240)

of what we are seeing as well.

Lex Fridman (56:25.400)

And also looking at our responses,

Peter Wang (56:28.040)

listening to our responses,

Lex Fridman (56:28.920)

even maybe measuring certain vital signs about us.

Lex Fridman (56:32.040)

So in this kind of sidecar mode,

Lex Fridman (56:34.440)

a greater intelligence could use us

Lex Fridman (56:37.600)

and our whatever 80 years of life to train itself up

Lex Fridman (56:42.080)

and then be a very good simulacrum of us moving forward.

Lex Fridman (56:45.080)

So who is in the sidecar

Lex Fridman (56:48.120)

in that picture of the future exactly?

Peter Wang (56:50.440)

The baby version of our immortal selves.

Lex Fridman (56:52.960)

Okay, so once the baby grows up,

Lex Fridman (56:56.160)

is there any use for humans?

Lex Fridman (56:58.440)

I think so.

Peter Wang (56:59.960)

I think that out of epistemic humility,

Lex Fridman (57:03.240)

we need to keep humans around for a long time.

Lex Fridman (57:05.600)

And I would hope that anyone making those systems

Lex Fridman (57:07.960)

would believe that to be true.

Peter Wang (57:10.040)

Out of epistemic humility,

Lex Fridman (57:11.640)

what's the nature of the humility that?

Peter Wang (57:13.480)

That we don't know what we don't know.

Lex Fridman (57:16.440)

So we don't.

Lex Fridman (57:18.960)

Right?

Lex Fridman (57:19.800)

So we don't know.

Peter Wang (57:20.640)

First we have to build systems

Lex Fridman (57:21.680)

that help us do the things that we do know about

Peter Wang (57:24.400)

that can then probe the unknowns that we know about.

Lex Fridman (57:26.760)

But the unknown unknowns, we don't know.

Peter Wang (57:28.560)

We could always know.

Lex Fridman (57:30.040)

Nature is the one thing

Peter Wang (57:31.160)

that is infinitely able to surprise us.

Lex Fridman (57:33.800)

So we should keep biological humans around

Peter Wang (57:35.880)

for a very, very, very long time.

Lex Fridman (57:37.600)

Even after our immortal selves have transcended

Lex Fridman (57:40.440)

and have gone off to explore other worlds,

Lex Fridman (57:42.880)

gone to go communicate with the lifeforms living in the sun

Peter Wang (57:45.200)

or whatever else.

Lex Fridman (57:46.040)

So I think that's,

Peter Wang (57:49.200)

for me, these seem like things that are going to happen.

Lex Fridman (57:53.000)

Like I don't really question that,

Peter Wang (57:54.480)

that they're gonna happen.

Lex Fridman (57:55.720)

Assuming we don't completely destroy ourselves.

Peter Wang (57:58.240)

Is it possible to create an AI system

Lex Fridman (58:02.480)

that you fall in love with and it falls in love with you

Lex Fridman (58:06.160)

and you have a romantic relationship with it?

Lex Fridman (58:08.480)

Or a deep friendship, let's say.

Peter Wang (58:10.760)

I would hope that that is the design criteria

Lex Fridman (58:12.680)

for any of these systems.

Peter Wang (58:14.520)

If we cannot have a meaningful relationship with it,

Lex Fridman (58:18.480)

then it's still just a chunk of silicon.

Lex Fridman (58:20.320)

So then what is meaningful?

Lex Fridman (58:21.680)

Because back to sugar.

Lex Fridman (58:23.800)

Well, sugar doesn't love you back, right?

Lex Fridman (58:25.400)

So the computer has to love you back.

Lex Fridman (58:26.840)

And what does love mean?

Lex Fridman (58:28.200)

Well, in this context, for me, love,

Peter Wang (58:30.160)

I'm gonna take a page from Alain de Botton.

Lex Fridman (58:32.040)

Love means that it wants to help us

Peter Wang (58:34.400)

become the best version of ourselves.

Lex Fridman (58:36.640)

Yes, that's beautiful.

Peter Wang (58:39.440)

That's a beautiful definition of love.

Lex Fridman (58:40.760)

So what role does love play in the human condition

Lex Fridman (58:45.360)

at the individual level and at the group level?

Lex Fridman (58:48.720)

Because you were kind of saying that humans,

Peter Wang (58:51.120)

we should really consider humans

Lex Fridman (58:52.280)

both at the individual and the group and the societal level.

Lex Fridman (58:55.320)

What's the role of love in this whole thing?

Lex Fridman (58:56.960)

We talked about sex, we talked about death,

Peter Wang (58:59.640)

thanks to the bacteria that invented it.

Lex Fridman (59:02.320)

At which point did we invent love, by the way?

Lex Fridman (59:04.320)

I mean, is that also?

Lex Fridman (59:05.560)

No, I think love is the start of it all.

Lex Fridman (59:08.960)

And the feelings of, and this gets sort of beyond

Lex Fridman (59:13.080)

just romantic, sensual, whatever kind of things,

Lex Fridman (59:16.760)

but actually genuine love as we have for another person.

Lex Fridman (59:19.720)

Love as it would be used in a religious text, right?

Peter Wang (59:22.680)

I think that capacity to feel love

Lex Fridman (59:25.480)

more than consciousness, that is the universal thing.

Peter Wang (59:28.440)

Our feeling of love is actually a sense

Lex Fridman (59:30.320)

of that generativity.

Peter Wang (59:31.320)

When we can look at another person

Lex Fridman (59:33.120)

and see that they can be something more than they are,

Lex Fridman (59:37.440)

and more than just a pigeonhole we might stick them in.

Lex Fridman (59:42.480)

I mean, I think there's, in any religious text,

Peter Wang (59:44.160)

you'll find voiced some concept of this,

Lex Fridman (59:47.640)

that you should see the grace of God in the other person.

Peter Wang (59:50.920)

They're made in the spirit of the love

Lex Fridman (59:54.760)

that God feels for his creation or her creation.

Lex Fridman (59:57.120)

And so I think this thing is actually the root of it.