The following is a conversation with Peter Wang, one of the most impactful leaders and

developers in the Python community, former physicist, current philosopher, and someone

who many people told me about and praised as a truly special mind that I absolutely should talk to.

Recommendations ranging from Travis Hallifant to Eric Weinstein. So, here we are. This is

the Lex Freeman podcast. To support it, please check out our sponsors in the description.

And now, here's my conversation with Peter Wang.

You're one of the most impactful humans in the Python ecosystem. So, you're an engineer,

leader of engineers, but you're also a philosopher. So, let's talk both in this conversation about

programming and philosophy. First, programming. What to you is the best or maybe the most beautiful

feature of Python? Or maybe the thing they made you fall in love or stay in love with Python?

Well, those are three different things. What I think is most beautiful, what made me fall in

love, what made me stay in love. When I first started using it was when I was a C++ computer

graphics performance nerd. In the 90s? Yeah, in late 90s. And that was my first job out of college.

And we kept trying to do more and more abstract and higher order programming in C++, which at

the time was quite difficult with templates. The compiler support wasn't great, et cetera.

So, when I started playing around with Python, that was my first time encountering really

first class support for types, for functions, and things like that. And it felt so incredibly

expressive. So, that was what kind of made me fall in love with it a little bit. And also,

once you spend a lot of time in a C++ dev environment, the ability to just whip something

together that basically runs and works the first time is amazing. So, really productive scripting

language. I knew Perl, I knew Bash, I was decent at both. But Python just made everything, it made

the whole world accessible. I could script this and that and the other network things,

little hard drive utilities, I could write all of these things in the space of an afternoon.

And that was really, really cool. So, that's what made me fall in love.

Is there something specific you could put your finger on that you're not programming in Perl

today? Like why Python for scripting? Oh, I think there's not a specific thing as much as the design

motif of both the creator of the language and the core group of people that built the standard

library around him. There was definitely, there was a taste to it. I mean, Steve Jobs used that

term in somewhat of an arrogant way, but I think it's a real thing that it was designed to fit.

A friend of mine actually expressed this really well. He said, Python just fits in my head.

And there's nothing better to say than that. Now, people might argue modern Python,

there's a lot more complexity, but certainly as version 152, I think was my first version,

that fit in my head very easily. So, that's what made me fall in love with it.

Okay. So, the most beautiful feature of Python that made you stay in love. It's like over the years,

what has like, you know, you do a double take and you return too often as a thing that just

brings you a smile. I really still like the ability to play with metaclasses and express

higher order things when I have to create some new object model to model something, right?

It's easy for me because I'm pretty expert as a Python programmer. I can easily put all sorts of

lovely things together and use properties and decorators and other kinds of things

and create something that feels very nice. So, that to me, I would say that's tied with the

numpy and vectorization capabilities. I love thinking in terms of the matrices and the vectors

and these kind of data structures. So, I would say those two are kind of tied for me.

So, the elegance of the numpy data structure, like slicing through the different multidimensional...

Yeah, there's just enough things there. It's like a very simple, comfortable tool.

It's easy to reason about what it does when you don't stray too far afield.

Can you put your finger on how to design a language such that it fits in your head?

Certain things like the colon or the certain notation aspects of Python that just kind of work.

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

it's just right? Is it a taste thing or is there a systematic process? What's your sense?

I think it's more of a taste thing. But one thing that should be said is that

you have to pick your audience. So, the better defined the user audience is or the users are,

the easier it is to build something that fits in their minds because their needs

will be more compact and coherent. It is possible to find a projection, a compact

projection for their needs. The more diverse the user base, the harder that is. And so,

as Python has grown in popularity, that's also naturally created more complexity

as people try to design any given thing. There will be multiple valid

opinions about a particular design approach. And so, I do think that's the

downside of popularity. It's almost an intrinsic aspect of the complexity of the problem.

Well, at the very beginning, aren't you an audience of one? Aren't all the greatest

projects in history were just solving a problem that you yourself had?

Well, so Clay Scherke in his book on crowdsourcing or in his kind of thoughts on crowdsourcing,

he identifies the first step of crowdsourcing is me first collaboration.

You first have to make something that works well for yourself. It's very telling that when you

look at all of the impactful big projects, well, their fundamental projects now in the

SciPy and PyData ecosystem, they all started with the people in the domain trying to scratch

their own itch. And the whole idea of scratching your own itch is something that

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

But in the scientific computing areas, these are assistant professors or

electrical engineering grad students. They didn't have really a lot of programming skill

necessarily, but Python was just good enough for them to put something together that fit in their

domain. So, it's almost like a necessity is a mother intervention aspect. And also, it was a

really harsh filter for utility and compactness and expressiveness. It was too hard to use,

then they wouldn't have built it because there was just too much trouble. It was a side project

for them. And also, necessity creates a kind of deadline. It seems like a lot of these projects

are quickly thrown together in the first step. And that even though it's flawed,

that just seems to work well for software projects.

Well, it does work well for software projects in general. And in this particular space,

but one of my colleagues, Stan Siebert, identified this, that all the projects in the

SciPy ecosystem, you know, if we just rattle them off, there's NumPy, there's SciPy, built by

different collaborations of people, although Travis is the heart of both of them. But NumPy

coming from numeric and numeric, these are different people. And then you've got Pandas,

you've got Jupyter or iPython, there's Matplotlib, there's just so many others I'm,

you know, not going to do justice if I try to name them all. But all of them are actually

different people. And as they rolled out their projects, the fact that they had limited resources

meant that they were humble about scope. A great famous hacker, Jamie Zewiski, once said that every

geek's dream is to build the ultimate middleware, right? And the thing is with these

scientist turn programmers, they had no such thing. They were just trying to write something

that was a little bit better for what they needed, the MATLAB. And they were going to

leverage what everyone else had built. So naturally, almost in kind of this annealing

process or whatever, we built a very modular cover of the basic needs of a scientific computing

library. If you look at the whole human story, how much of a leap is it? We've developed all

kinds of languages, all kinds of methodologies for communication, and just kind of like grew

this collective intelligence, the civilization grew, it expanded, we wrote a bunch of books,

and now we tweet. How big of a leap is programming if programming is yet another language? Is it

just a nice little trick that's temporary in our human history? Or is it like a big leap in the,

almost us becoming another organism at a higher level of abstraction, something else?

I think the act of programming or using grammatical constructions of some underlying primitives,

that is something that humans do learn, but every human learns this. Anyone who can speak

learns how to do this. What makes programming different has been that up to this point,

when we try to give instructions to computing systems, all of our computers, well, actually,

this is not quite true, but I'll first say it and then I'll tell you why it's not true.

But for the most part, we can think of computers as being these iterated systems.

So when we program, we're giving very precise instructions to iterated systems that then run at

incomprehensible speed and run those instructions. In my experience, some people are just better

equipped to model systematic iterated systems in their head. Some people are really good at that

and other people are not. For instance, sometimes people have tried to build systems that make

programming easier by making it visual drag and drop. And the issue is you can have a drag and

drop thing, but once you start having to iterate the system with conditional logic, handling case

statements and branch statements and all these other things, the visual drag and drop part doesn't

save you anything. You still have to reason about this giant iterated system with all these different

conditions around it. That's the hard part. So handling iterated logic, that's the hard part.

The languages we use then emerge to give us ability and capability over these things.

Now, the one exception to this rule, of course, is the most popular programming system in the

world, which is Excel, which is a data flow and a data driven, immediate mode, data transformation

oriented programming system. And this is actually not an accident that that system is the most

popular programming system because it's so accessible to a much broader group of people.

I do think as we build future computing systems, you're actually already seeing this a little bit,

it's much more about composition of modular blocks. They themselves

actually maintain all their internal state and the interfaces between them are well defined data

schemas. And so to stitch these things together using like IFTTT or Zapier or any of these kind of,

you know, I would say compositional scripting kinds of things. I mean, hypercard was also a

little bit in this vein. That's much more accessible to most people. It's really that implicit state

that's so hard for people to track. Yeah, okay. So that's modular stuff. But there's also an

aspect where you're standing on the shoulders of giants. You're building like higher and higher

levels of abstraction. You do that a little bit with language. So with language, you develop

sort of ideas, philosophies from Plato and so on. And then you kind of leverage those philosophies as

you try to have deeper and deeper conversations. But with programming, it seems like you can build

much more complicated systems. Like without knowing how everything works, you can build on top of the

work of others. And it seems like you're developing more and more sophisticated

expressions, ability to express ideas in a computational space. I think it's worth pondering

the difference here between complexity and complication. Back to Excel. Well, not quite

back to Excel, but the idea is, you know, when we have a human conversation, all languages

is for humans emerged to support human relational communications, which is that the person we're

communicating with is a person, and they would communicate back to us. And so we sort of hit a

resonance point, right, when we actually agree on some concepts. So there's a messiness to it,

and there's a fluidity to it. With computing systems, when we express something to the computer,

and it's wrong, we just try again. So we can basically live many virtual worlds of having failed at

expressing ourselves to the computer until the one time we expressed ourselves right.

Then we kind of put in production and then discover that it's still wrong,

you know, a few days down the road. So I think the sophistication of things that we build with

computing, one has to really pay attention to the difference between when an end user is expressing

something onto a system that exists versus when they're extending the system to increase the

system's capability for someone else to that interface with. We happen to use the same language

for both of those things in most cases, but it doesn't have to be that. And Excel is actually

a great example of this, of kind of a counterpoint to that. Okay. So what about the idea of, you

said messiness, wouldn't you put the software 2.0 idea, this idea of machine learning into the

further and further steps into the world of messiness. The same kind of beautiful messiness

of human communication, isn't that what machine learning is, is building on levels of abstraction

that don't have messiness in them, that at the operating system level, then there's Python,

the programming languages that have more and more power. But then finally, there's neural networks

that ultimately work with data. And so the programming is almost in the space of data,

and the data is allowed to be messy. Isn't that a kind of program? So the idea of software 2.0

is a lot of the programming happens in the space of data, all roads lead back to Excel,

in the space of data, and also the hyperparameters of the neural networks. And all of those allow

the same kind of messiness that human communication allows.

It does, but my background is a physics. I took like two CS courses in college.

So I don't have, now I did cram a bunch of CS in prep when I applied for grad school,

but still, I don't have a formal background in computer science. But what I have observed in

studying programming languages and programming systems and things like that is that there seems

to be this triangle. It's one of these beautiful little iron triangles that you find in life sometimes.

And it's the connection between the code correctness and kind of expressiveness of code,

the semantics of the data, and then the kind of correctness or parameters of the underlying

hardware compute system. So there's the algorithms that you want to apply. There's what the bits,

that are stored on whatever media actually represents, the semantics of the data within

their representation. And then there's what the computer can actually do. And every programming

system, every information system ultimately finds some spot in the middle of this little triangle.

Sometimes some systems collapse them into just one edge.

Are we including humans as a system in this?

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

Okay. And the reason I bring this up is because I believe there's no free lunch around this stuff.

So if we build machine learning systems to sort of write the correct code that is at a certain

level of performance, so it'll sort of select with the hyperparameters, we can tune kind of how

we want the performance boundary in SLA to look like for transforming some set of inputs into

certain kinds of outputs. That training process itself is intrinsically sensitive to the kinds

of inputs we put into it. It's quite sensitive to the boundary conditions we put around the

performance. So I think even as we move to using automated systems to build this transformation,

as opposed to humans explicitly from a top down perspective, figuring out, well, this schema and

this database and these columns get selected for this algorithm. And here we put a, you know,

a Fibonacci heap for some other thing, human design or computer design. Ultimately, what we hit,

the boundaries that we hit with these information systems is when the representation of the data

hits the real world is where there's a lot of slop and a lot of interpretation. And that's where

actually I think a lot of the work will go in the future is actually understanding kind of how to

better, in the view of these live data systems, how to better encode the semantics of the world

for those things. There'll be less of the details of how we write a particular SQL query.

Okay. But given the semantics of the real world and the messiness of that,

what does the word correctness mean when you're talking about code?

There's a lot of dimensions to correctness. Historically, and this is one of the reasons

I say that we're coming to the end of the era of software, because for the last 40 years or so,

software correctness was really defined about functional correctness. I write a function,

it's got some inputs, does it produce the right outputs? If so, then I can turn it on,

hook it up to the live database and it goes. And more and more now, we have, I mean, in fact,

I think the bright line in the sand between machine learning systems or modern data driven

systems versus classical software systems is that the values of the input actually have to

be considered with the function together to say this whole thing is correct or not.

And usually there's a performance SLA as well. Like, did it actually finish making this SLA?

Sorry, service level agreement. So it has to return within some time. You have a 10 millisecond

time budget to return a prediction of this level of accuracy. So these are things that were not

traditionally in most business computing systems for the last 20 years at all. People didn't think

about it. But now we have value dependence on functional correctness. So that question of

correctness is becoming a bigger and bigger question. What is that map to the end of software?

We've thought about software as just this thing that you can do in isolation with some test trial

inputs and in a very sort of sandboxed environment. And we can quantify how does it scale? How does

it perform? How many nodes do we need to allocate if we want to scale this many inputs? When we start

turning this stuff into prediction systems, real cybernetic systems, you're going to find scenarios

where you get inputs that you don't want to spend a little more time thinking about. You're going to

find inputs that are not, it's not clear what you should do, right? So then the software has a varying

amount of runtime and correctness with regard to input. And that is a different kind of system

altogether. Now it's a full on cybernetic system. It's a next generation information system that

is not like traditional software systems. Can you maybe describe what is a cybernetic system?

Do you include humans in that picture? So is a human in the loop kind of complex mess

of the whole kind of interactivity of software with the real world or is it something more

concrete? Well, when I say cybernetic, I really do mean that the software itself is closing the

Observe Orient Decide Act loop by itself. So humans being out of the loop is the fact what,

for me, makes it a cybernetic system. Humans are out of that loop.

When humans are out of the loop, when the machine is actually sort of deciding on its own what it

should do next to get more information, that makes it a cybernetic system. So we're just at the dawn

of this, right? I think everyone talking about MLAI, it's great. But really the thing we should

be talking about is when we really enter the cybernetic era and all of the questions of ethics

and governance and correctness and all these things, they really are the most important questions.

Okay. Can we just linger on this? What does it mean for the human to be out of the loop in a

cybernetic system? Because isn't the cybernetic system that's ultimately accomplishing some kind

of purpose that at the bottom, the turtles all the way down at the bottom turtle is a human?

Well, the human may have set some criteria, but the human wasn't precise. So for instance,

I just read the other day that earlier this year, or maybe it was last year at some point,

the Libyan army, I think, sent out some automated killer drones with explosives.

And there was no human in the loop at that point. They basically put them in a geofenced area,

said find any moving target like a truck or vehicle that looks like this and boom,

that's not a human in the loop, right? So increasingly the less human there is in the

loop, the more concerned you are about these kinds of systems because there's unintended

consequences like less the original designer and engineer of the system is able to predict.

Even one with good intent is able to predict the consequences of such a system.

That's right. There are some software systems that run without humans in the loop that are

quite complex. And that's like the electronic markets. And we get flash crashes all the time.

We get in the heyday of high frequency trading, there's a lot of market microstructure,

people doing all sorts of weird stuff that the market designers had never really thought about,

contemplated or intended. So when we run these full on systems with these automated trading bots,

now they become automated killer drones and then all sorts of other stuff,

that's what I mean by we're at the dawn of the cybernetic era and the end of the era of just

pure software. Are you more concerned if you're thinking about cybernetic systems or even like

self replicating systems? So systems that aren't just doing a particular task, but are able to

sort of multiply and scale in some dimension in the digital or even the physical world.

Are you more concerned about like the lobster being boiled? So a gradual with us not noticing

collapse of civilization or a big explosion? Like oops, kind of a big thing where everyone

notices, but it's too late. I think that it will be a different experience for different people.

I do share a common point of view with some of the people who are concerned about climate change

and just the big existential risks that we have. But unlike a lot of people who share my level

of concern, I think the collapse will not be quite so dramatic as some of them think. And what I

mean is that I think that for certain tiers of let's say economic class or certain locations

in the world, people will experience dramatic collapse scenarios. But for a lot of people,

especially in the developed world, the realities of collapse will be managed. There will be narrative

management around it so that they essentially insulate, the middle class will be used to

insulate the upper class from the pitchforks and the flaming torches and everything.

It's interesting because my specific question wasn't as general as my question is more about

cybernetic systems or software. Okay. It's interesting, but it would nevertheless perhaps

be about class. So the effect of algorithms might affect certain classes more than others.

Absolutely. I was more thinking about whether it's social media algorithms or actual robots.

Is there going to be a gradual effect on us where we wake up one day and don't recognize the humans

we are? Or is it something truly dramatic where there's a Meltdown of a nuclear reactor kind of

thing, Chernobyl, catastrophic events that are almost bugs in a program that scaled itself too

quickly? Yeah, I'm not as concerned about the visible stuff. And the reason is because the big

visible explosions, I mean, this is something I said about social media is that, at least with

nuclear weapons, when a nuke goes off, you can see it and you're like, well, that's really, wow,

that's kind of bad. I mean, Oppenheimer was reciting the Bhagavad Gita when he saw one of

those things go off. So we can see nukes are really bad. He's not reciting anything about

Twitter. Well, right. But then when you have social media, when you have all these different things

that conspire to create a layer of virtual experience for people that alienates them from

reality and from each other, that's very pernicious. It's impossible to see, right? And it slowly

gets in there. You've written about this idea of virtuality on this topic, which you define as

the subjective phenomenon of knowingly engaging with virtual sensation and perception and

suspending or forgetting the context that it's some alakam. So let me ask, what is real? Is there

a hard line between reality and virtuality? Like perception drifts from some kind of physical

reality. We have to kind of have a sense of what is the line that's too, we've gone too far.

Right, right. For me, it's not about any hard line about physical reality as much as a simple

question of, does the particular technology help people connect in a more integral way

with other people, with their environment, with all of the full spectrum of things around them?

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

more about when we create virtual representations of the real things. Always,

some things are lost in translation. Usually, many, many dimensions are lost in translation,

right? We're now coming to almost two years of COVID, people on Zoom all the time. You know,

it's different when you meet somebody in person than when you see them on, I've seen you on YouTube

lots, right? But the senior person is very different. And so I think when we engage in virtual

experiences all the time, and we only do that, there is absolutely a level of embodiment,

there's a level of embodied experience and participatory interaction that is lost.

And it's very hard to put your finger on exactly what it is. It's hard to say, oh,

we're going to spend $100 million building a new system that captures this 5% better

higher fidelity human expression. No one's going to pay for that, right?

So when we rush madly into a world of simulacrum and virtuality,

you know, the things that are lost are, it's difficult. Once everyone moves there, it can

be hard to look back and see what we've lost. So is it irrecoverably lost? Or rather,

when you put it all on the table, is it possible for more to be gained than is lost?

If you look at video games, they create virtual experiences that are surreal and can bring joy

to a lot of people, connect a lot of people and can get people to talk a lot of trash.

So they can bring out the best and the worst in people. So is it possible to have a future world

where the pros outweigh the cons? It is. I mean, it's possible to have that in the current world.

But when literally trillions of dollars of capital are tied to using those things to

groom the worst of our inclinations and to attack our weaknesses in the limbic system

to create these things into id machines versus connection machines,

then those good things don't stand a chance.

Can you make a lot of money by building connection machines? Is it possible,

do you think, to bring out the best in human nature to create fulfilling connections and

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

If I figured out, I'll let you know.

But what's your intuition without concretely knowing what's the solution?

My intuition is that a lot of our digital technologies give us the ability to have

synthetic connections or to experience virtuality. They have co evolved with

sort of the human expectations. It's sort of like sugary drinks. As people have more sugary

drinks, they need more sugary drinks to get that same hit. So with these virtual things

and with TV and fast cuts and TikToks and all these different kinds of things,

we're co creating essentially humanity that sort of asks and needs those things.

And now it becomes very difficult to get people to slow down. It gets difficult for people to

hold their attention on slow things and actually feel that embodied experience.

So mindfulness now more than ever is so important in schools and as a therapy technique

for people because our environment has been accelerated. And McLuhan actually talks about

this in the electric environment of the television. And that was before TikTok and before front

facing cameras. So I think for me, the concern is that it's not like we can ever switch to

doing something better, but more of the humans and technology, they're not independent of each

other. The technology that we use kind of molds what we need for the next generation of technology.

Yeah, but humans are intelligent and they're introspective and they can reflect on the

experiences of their life. So for example, there's been many years in my life where I ate an

excessive amount of sugar. And then a certain moment I woke up and said,

why do I keep doing this? This doesn't feel good. Like long term. And I think

so going through the TikTok process of realizing, okay, when I short my attention span,

actually that does not make me feel good longer term and realizing that and then going to platforms,

going to places that are away from the sugar. And so doing you can create platforms that can

make a lot of money when so to help people wake up to what actually makes them feel good long term

develop, grow as human beings. And it just feels like humans are more intelligent than

mice looking for cheese. They're able to sort of think, I mean, we can think we can contemplate

our mortality and contemplate things like long term love and we can have a long term fear of

certain things like mortality. We can contemplate whether the experience is the sort of the drugs

of daily life that we've been partaking in is making us happier, better people. And then

once we contemplate that we can make financial decisions in using services and paying for services

that are making us better people. So it just seems that we're in the very first stages of

social networks that just we're able to make a lot of money really quickly. But in bringing out

sometimes the bad parts of human nature, they didn't destroy humans. They just fed everybody

a lot of sugar. And now everyone's going to wake up and say, hey, we're going to start having

like sugar free social media. Right. Well, there's a lot to unpack there. I think some people

certainly have the capacity for that. And I certainly think, I mean, it's very interesting

even the way you said it. You woke up one day and you thought, well, this doesn't feel very good.

Well, that's still your limbic system saying this doesn't feel very good.

Right. You have a cat brain's worth of neurons around your gut. Right. And so maybe that

saturated and that was telling you, hey, this isn't good. Humans are more than just mice looking

for cheese or monkeys looking for sex and power. Right. So let's slow down. Now you're,

now a lot of people would argue with you on that one. But we're more than just that,

but we're at least that. And we're very, very seldom not that. So my, I don't actually disagree with

you that we could be better and that we can, that better platforms exist. And people are

voluntarily noping out of things like Facebook and noping out. That's an awesome verb.

It's a great term. Yeah, I love it. I use it all the time. You're welcome. I'm going to have to

nope out of that. I want to nope out of that. Right. It's going to be a hard pass. And that's,

and that's, that's great. But that's again, to your point, that's the first generation

of front facing cameras of social pressures and you as a, you know, self starter, self aware

adult have the capacity to say, yeah, I'm not going to do that. I'm going to go and spend time

on long form reads. I'm going to spend time managing my attention. I'm going to do some yoga.

If you're a 15 year old in high school and your entire social environment is everyone doing

these things, guess what you're going to do? You're going to kind of have to do that because

your limbic system says, hey, I need to get the guy or the girl or the whatever. And that's

what I'm going to do. And so one of the things that we have to reason about here is the social

media systems or, you know, social media, I think is a first, our first encounter with

a technological system that runs a bit of a loop around our own cognition and attention.

It's not the last. It's far from the last. And it gets to the heart of some of the

philosophical Achilles heel of the Western philosophical system, which is each person

gets to make their own determination. Each person is an individual that's sacrosanct

and their agency and their sovereignty and all these things. The problem with these systems is

they come down and they are able to manage everyone en masse. And so every person is making

their own decision, but together the bigger system is causing them to act with a group

dynamic that's very profitable for people. So this is the issue that we have is that our philosophies

are actually not geared to understand what is it for a person to have a high trust connection

as part of a collective and for that collective to have its right to coherency and agency.

That's something like when a social media app causes a family to break apart,

it's done harm to more than just individuals. So that concept is not something we really talk

about or think about very much, but that's actually the problem is that we're vaporizing

molecules into atomic units and then we're hitting all the atoms with certain things that's like,

yeah, well, that person chose to look at my app. So our understanding of human nature is at the

individual level. It emphasizes the individual too much because ultimately society operates at

the collective level. And these apps do as well. And the apps do as well. So for us to understand

the progression and development of this organism, we call human civilization,

we have to think of the collective level too. I would say multi tiered. Multi tiered. So individual

as well. Just individuals, family units, social collectives, and on the way up. Okay. So you've

said that individual humans are multi layered susceptible to signals and waves and multiple

strata, the physical, the biological, social, cultural, intellectual. So sort of going along

these lines, can you describe the layers of the cake that is a human being and maybe the human

collective human society? So I'm just stealing wholesale here from Robert Persig, who is the

author of Zen and the Auto Motorcycle Maintenance. And his follow on book has a sequel to it called

Lila. He goes into this in a little more detail. But it's a crude approach to thinking about people,

but I think it's still an advancement over traditional subject object metaphysics,

where we look at people as a dualist would say, well, is your mind, your consciousness,

is that just merely the matter that's in your brain? Or is there something kind of more beyond

that? And they would say, yes, there's a soul, sort of ineffable soul beyond just merely the

physical body. And I'm not one of those people. I think that we don't have to draw a line between

are things only this or only that, collectives of things can emerge structures and patterns

that are just as real as the underlying pieces. But they're transcendent, but they're still of

the underlying pieces. So your body is this way. I mean, we just know physically, you consist of

atoms and whatnot. And then the atoms are arranged into molecules, which then arrange into certain

kinds of structures that seem to have a homeostasis to them, we'll call them cells. And those cells

form biological structures. Those biological structures give your body its physical ability

and biological ability to consume energy and to maintain homeostasis. But humans are social

animals. I mean, human by themselves is not very long for the world. So we also part of our biology

is why are two connect to other people from the mirror neurons to our language centers and all

these other things. So we are intrinsically, there's a layer, there's a part of us that

wants to be part of a thing. If we're around other people, not saying a word, but they're just up

and down jumping and dancing, laughing, we're going to feel better, right? And there was no

exchange of physical anything. They didn't give us like five atoms of happiness, right? But there's

an induction in our own sense of self that is at that social level. And then beyond that,

person puts the intellectual level kind of one level higher than social. I think they're actually

more intertwined than that. But the intellectual level is the level of pure ideas that you are a

vessel for memes. You're a vessel for philosophies. You will conduct yourself in a particular way.

I mean, I think part of this is if we think about it from a physics perspective, you're not, you

know, there's the joke that physicists like to approximate things and we'll say, well, approximate

a spherical cow, right? You're not a spherical cow. You're not a spherical human. You're a messy

human. You're a messy human. And we can't even say what the dynamics of your emotion will be

unless we analyze all four of these layers, right? If you're a Muslim at a certain time of day,

guess what? You're going to be on the ground kneeling and praying, right? And that has nothing

to do with your biological need to get on the ground or physics of gravity. It is an intellectual

drive that you have. It's a cultural phenomenon and an intellectual belief that you carry. So

that's what the four layered stack is all about. It's that a person is not only one of these things.

They're all of these things at the same time. It's a superposition of dynamics that run through

us that make us who we are. So no layers special? Not so much no layers special. Each layer is

just different. But we are... Each layer gives the participation trophy. Each layer is a part

of what you are. You are a layer cake of all these things. And if we try to deny... So many

philosophies do try to deny the reality of some of these things. Some people will say,

well, we're only atoms. Well, we're not only atoms because there's a lot of other things that are

only atoms. I can reduce a human being to a bunch of soup and they're not the same thing,

even though it's the same atoms. So I think the order and the patterns that emerge within humans

to understand, to really think about what a next generation of philosophy would look like,

that would allow us to reason about extending humans into the digital realm or to interact

with autonomous intelligences that are not biological nature. We really need to appreciate

these... What human beings actually are is the superposition of these different layers.

You mentioned consciousness. Are each of these layers of cake conscious? Is consciousness a

particular quality of one of the layers? Is there like a spike if you have a consciousness

detector at these layers? Or is something that just permeates all of these layers and just takes

different form? I believe what humans experience as consciousness is something that sits on a gradient

scale of a general principle in the universe that seems to look for order and reach for order when

there's an excess of energy. It would be odd to say a proton is alive. It'd be odd to say this

particular atom or molecule of hydrogen gas is alive. But there's certainly something we can make

assemblages of these things that have auto poetic aspects to them, that will create structures,

that will... Crystalline solids will form very interesting and beautiful structures. This gets

kind of into weird mathematical territories. You start to think about Penrose and Game of

Life stuff about the generativity of math itself, like the hyperreal numbers, things like that.

But without going down that rabbit hole, I would say that there seems to be a tendency

in the world that when there is excess energy, things will structure and pattern themselves.

And they will then actually furthermore try to create an environment that furthers their continued

stability. It's the concept of externalized extended phenotype or niche construction. So

this is ultimately what leads to certain kinds of amino acids forming certain kinds of structures

and so on and so forth until you get the latter of life. So what we experience as consciousness,

no, I don't think cells are conscious of that level. But is there something beyond mere equilibrium

state biology and chemistry and biochemistry that drives what makes things work? I think there is.

So Adrian Bajon has his Constructo Law. There's other things you look at when you look at the

life sciences and you look at any kind of statistical physics and statistical mechanics.

When you look at things far out of equilibrium, when you have excess energy,

what happens then? Life doesn't just make a hotter soup. It starts making structure.

There's something there.

With the poetry of reaches for order when there's an excess of energy.

Because you brought up game of life.

You did it. Not me. I love cellular automata, so I have to sort of linger on that for a little bit.

So cellular automata, I guess, or game of life is a very simple example of reaching for order

when there's an excess of energy or reaching for order and somehow creating complexity.

It's explosion of just turmoil, somehow trying to construct structures and so doing

creates very elaborate organism looking type things. What intuition do you draw from the

simplest mechanism? Well, I like to turn that around its head and look at it as what if every

single one of the patterns created life or created, not life, but created interesting

patterns? Because some of them don't and sometimes you make cool gliders. And other times,

you start with certain things and you make gliders and other things that then construct

like and gates and not gates and you build computers on them. All of these rules that

create these patterns that we can see, those are just the patterns we can see. What if our

subjectivity is actually limiting our ability to perceive the order in all of it? What if some

of the things that we think are random are actually not that random? We're simply not

integrating at a final level across a broad enough time horizon. And this again, I said,

we go down the rabbit holes and the Penrose stuff or like Wolfram's explorations on these things.

There is something deep and beautiful in the mathematics of all this that is hopefully one

day I'll have enough money to work and retire and just ponder those questions. But there's

something there. But you're saying there's a ceiling to when you have enough money and you

retire and you ponder it, there's a ceiling to how much you can truly ponder because there's

cognitive limitations in what you're able to perceive as a pattern. Yeah. And maybe mathematics

extends your perception capabilities, but it's still finite. It's just like...

Yeah. The mathematics we use is the mathematics that can fit in our head.

Yeah. Did God really create the integers or did God create all of it? And we just happened

at this point in time to be able to perceive integers.

Well, he just did the positive in it.

I said, did she create all of it?

She just created the natural numbers and then we screwed all up with zero and then I guess.

Okay. But we did. We created mathematical operations so that we can have iterated

steps to approach bigger problems. I mean, the entire point of the Arabic neural system and

it's a rubric for mapping a certain set of operations, folding them into a simple little

expression, but that's just the operations that we can fit in our heads. There are many other

operations besides. The thing that worries me the most about aliens and humans is that

they're aliens. They're all around us and we're too dumb to see them.

Oh, certainly. Yeah.

Or life. Let's say just life. Life of all kinds of forms or organisms. You know what? Just even

the intelligence of organisms is imperceptible to us because we're too dumb and self centered.

Well, we're looking for a particular kind of thing. When I was at Cornell, I had a lovely

professor of Asian religions, Jamery Law, and she would tell this story about a musician,

a Western musician, who went to Japan and he taught classical music and could play all sorts

of instruments. He went to Japan and he would ask people, he would basically be looking for

things in the style of a Western chromatic scale and these kinds of things. And then

finding none of it, he would say, well, there's really no music in Japan, but they're using

a different scale. They're playing different kinds of instruments. The same thing she was

using as sort of a metaphor for religion as well. In the West, we center a lot of religion,

certainly the religions of Abraham, we center them around belief. And in the East, it's more

about practice, right? Spirituality and practice rather than belief. So anyway, the point is here,

to your point, life. I think so many people are so fixated on certain aspects of self replication

or homeostasis or whatever. But if we kind of broaden and generalize this thing of things

reaching for order, under which conditions can they then create an environment that sustains that

order that allows them, the invention of death is an interesting thing. There are some organisms

on earth that are thousands of years old. And it's not like they're incredibly complex,

they're actually simpler than the cells that comprise us, but they never die. So at some point,

death was invented somewhere along the eukaryotic scale, I mean, even the protists, right? There's

death. And why is that along with the sexual reproduction, right? There is something about

the renewal process, something about the ability to respond to a changing environment,

where it just become, you know, just killing off the old generation and letting new generations

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

You know, human historian seems to write about wheels and fires, the greatest inventions,

but it seems like death and sex are pretty good. And they're kind of essential inventions

at the very beginning. At the very beginning, yeah. Well, we didn't invent them, right?

Well, broad, we, you didn't invent them. ICS is one, you particular Homo sapiens did not

invent them, but we together, it's a team project, just like you're saying.

I think the greatest Homo sapiens invention is collaboration.

So when you say collaboration, Peter, where do ideas come from? And how do they take hold in

society? What's, is that the nature of collaboration? Is that the basic atom of collaboration is ideas?

It's not not ideas, but it's not only ideas. There's a book I just started reading called

Death from a Distance. Have you heard of this? No.

It's a really fascinating thesis, which is that humans are the only conspecific,

the, the, the only species that can kill other members of the species from range.

And maybe there's a few exceptions, but if you look in the animal world,

you see like pronghorns, butting heads, right? You see the alpha lion and the beta lion,

and they take each other down. Humans, we develop the ability to chuck rocks at each other and,

well, at prey, but also at each other. And that means the beta male can chuck a rock

at the alpha male and take them down. And with very, and he can throw a lot of rocks actually,

miss a bunch of times, which is hit once and be good. So this ability to actually kill members

of our own species from range without a threat of harm to ourselves, create essentially mutually

assured destruction where we had to evolve cooperation. If we didn't, then if we just

continue to try to do like I'm the, the biggest monkey in the tribe, and I'm going to, you know,

own this tribe and you have to go, if we do it that way, then those tribes basically failed.

And the tribes that persisted and that have now given rise to the modern Homo sapiens

are the ones where respecting the fact that we can kill each other from range

without harm, like there's an asymmetric ability to, to snipe the leader from range.

That meant that we sort of had to learn how to cooperate with each other, right? Come back here,

don't throw that rock at me. Let's talk our, this is out. So violence is also part of collaboration.

The threat of violence, let's say. Well, the recognition, I would, maybe the better way to

put it is the recognition that we have more to gain by working together than the prisoner's dilemma

of both of us defecting. So mutually assured destruction in all its forms is part of this

idea of collaboration. Well, and Eric Weinstein talks about our nuclear peace, right? I mean,

it kind of sucks though if thousands of warheads aimed at each other, Russian and the US, but

it's like, on the other hand, you know, we only fought proxy wars, right? We did not have another

world war three of like hundreds of millions of people dying to like machine gun fire and,

and, you know, giant, you know, guided missiles. So the original nuclear weapon is, is a rock

that we learned how to throw essentially. The original, yeah. Well, the original scope of

the world for any human being was their little tribe. I would say it still is to the most,

for the most part. Eric Weinstein speaks very highly of you, which is very surprising to me

at first because I didn't know there's this depth to you because I knew you as a, as a, as an amazing

leader of engineers and an engineer yourself and so on. So it's fascinating. Maybe just as a comment,

a side tangent that we can take. What's your nature of your friendship with Eric Weinstein?

How did the two, how did such two interesting paths cross? Is it your origins in physics?

Is it your interest in philosophy and the ideas of how the world works? What is it?

It's actually, it's very random. It's Eric found me. He actually found Travis and I.

Travis Alffant. Yeah, we were both working at a company called Enthought back in the mid 2000s,

and we're doing a lot of consulting around scientific Python. And we'd made some,

some tools and Eric was trying to use some of these Python tools to visualize,

that he had a fiber bundle approach to modeling certain aspects of economics. He was doing this

and that's how he kind of got in touch with us. And so this was in the early mid 2000s.

Oh, seven time frame? Oh, six, oh, seven.

Eric Weinstein trying to use Python to visualize fiber bundles using some of the tools that we

had built in the open source. That's somehow entertaining to me, the thought of that.

It's really funny. But then, you know, we've met with him a couple of times,

really interesting guy. And then in the wake of the 0708 kind of financial collapse, he helped

organize with Lee Smolin a symposium at the Perimeter Institute about, okay, well, clearly,

you know, big finance can't be trusted, governments in its pockets with regulatory capture,

what the F do we do? And all sorts of people, Nassim Tlaib was there and Andy Lowe from MIT was

there and, you know, Bill Janeway, I mean, just a lot of, you know, top billing people were there.

And he invited me and Travis and another one of our coworkers, Robert Kern, who was a anyone in

the SciPy, NumPy community knows Robert, really great guy. So the three of us also got invited

to go to this thing. And that's where I met Brett Weinstein for the first time as well.

Yeah, I knew him before he got all famous for unfortunate reasons, I guess. But anyway, we,

so we met then and kind of had a friendship, you know, throughout since then.

You have a depth of thinking that kind of runs with Eric in terms of just thinking about the

world deeply and thinking philosophically. And then there's Eric's interest in programming.

Actually, never, you know, he'll bring up programming to me quite a bit as a metaphor

for stuff. Right. But I never kind of pushed the point of like,

what's the nature of your interest in programming? I think you saw it probably as a tool.

Yeah, absolutely. To visualize, to explore mathematics and explore physics. But

and I was wondering, like, what's the, his depth of interest and also his

vision for what programming would look like in the future? Have you had interaction with him,

like discussion in the space of Python programming? Well, in the sense of sometimes he asks me,

why is this stuff still so hard? Yeah, you know, everybody's a critic. But actually, no, Eric.

Programming, you mean like ingest? Yes. Yes. Well, not programming in general,

but certain things in the Python ecosystem. But he actually, I think what I find in listening

to some of his stuff is that he does use programming metaphors a lot, right? He'll

talk about APIs or object oriented and things like that. So I think that's a useful

set of frames for him to draw upon for discourse. I haven't pair programmed with him

in a very long time. You've previously... Well, I mean, try to help, like, put together some

of the visualizations around these things. But it's been a very, not really pair program,

but like, even looked at his code, right? I mean, how legendary would be is that, like,

get repo with Peter Wang and Eric Weinstein? Well, honestly, Robert Kern did all the heavy

lifting. So I have to give credit where credit is due. Robert is the silent, but incredibly deep,

quiet, not silent, but quiet, but incredibly deep individual at the heart of a lot of those things

that Eric was trying to do. But we did have, you know, in the... As Travis and I were starting

our company in 2012 timeframe, we went to New York. Eric was still in New York at the time.

He hadn't moved to... This is before he joined Teal Capital. We just had like a steak dinner

somewhere. Maybe it was Keynes, I don't know, somewhere in New York. So it's me, Travis, Eric,

and then Wes McKinney, the creative pandas, and then Wes's then business partner, Adam.

The five of us sat around having this just a hilarious time, amazing dinner. I forget what

all we talked about, but it was one of those conversations which I wish as soon as COVID is

over, maybe Eric and I can sit down. Recreate. Recreate in somewhere in LA or maybe he comes

here because a lot of cool people are here in Austin, right? Exactly. Yeah, we're all here.

He should come here. Eric, come here. Yeah. So he uses the metaphor source code sometimes to

talk about physics. We figure out our own source code. So you were the physics background

and somebody who's quite a bit of an expert in source code, do you think we'll ever figure out

our own source code in the way that Eric means? Do you think we'll figure out the nature we have?

Well, I think we're constantly working on that problem. I mean, I think we'll make more and more

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

that one day we will create a synthetic, maybe not fully in silicon, but a synthetic approach

to cognition that rivals the biological 20 watt computers in our heads.

What's cognition here? Cognition, perception, attention, memory, recall, asking better questions.

That, for me, is a measure of intelligence. Doesn't Roomba vacuum clean already do that?

Or do you mean, oh, it doesn't ask questions? I mean, no. So I mean, I have a Roomba, but it's

not even as smart as my cat, right? Yeah, but it asks questions about what is this wall.

It now, a new feature asks, is this poop or not, apparently? Yes. A lot of our current

cybernetic system, it's a cybernetic system. It will go and it will happily vacuum up some poop,

right? The older generations would. A new one, just released, does not vacuum up the poop.

Okay. I wonder if it still gets stuck under my first rung of my stare. In any case, these

cybernetic systems we have, they're designed to be sent off into a relatively static environment.

And whatever dynamic things happen in the environment, they have a very limited capacity

to respond to. A human baby, a human toddler of 18 months of age has more capacity to manage

its own attention and its own capacity to make better sense of the world than the most advanced

robots today. So again, my cat, I think, can do a better job of my two and they're both pretty

clever. So I do think though, back to my kind of original point, I think that it's not, for me,

it's not a question at all that we will be able to create synthetic systems that are able to do this

better than the human, at an equal level or better than the human mind. It's also for me,

not a question that we will be able to put them alongside humans so that they capture the full

broad spectrum of what we are seeing as well. And also looking at our responses, listening

to our responses, even maybe measuring certain vital signs about us. So in this kind of sidecar

mode, a greater intelligence could use us and our whatever, 80 years of life to train itself up and

then be a very good simulacrum of us moving forward. So who is in the sidecar in that picture

of the future exactly? The baby version of our immortal selves. Okay. So once the baby grows up,

is there any use for humans? I think so. I think that out of epistemic humility,

we need to keep humans around for a long time. And I would hope that anyone making those systems

would believe that to be true. Out of epistemic humility, what's the nature of the humility that...

We don't know what we don't know. So we don't...

Right? So we don't know... First we have to build systems that help us do the things that we do

know about, that can then probe the unknowns that we know about. But the unknown unknowns,

we don't know. Nature is the one thing that is infinitely able to surprise us. So we should

keep biological humans around for a very, very, very long time. Even after our immortal selves

have transcended and have gone off to explore other worlds, gone to go communicate with the

lifeforms living in the sun or whatever else. So I think that's... For me, these seem like

things that are going to happen. I don't really question that, that they're going to happen.

Assuming we don't completely destroy ourselves.

Is it possible to create an AI system that you fall in love with and it falls in love with you

and you have a romantic relationship with it or a deep friendship, let's say?

I would hope that that is the design criteria for any of these systems.

If we cannot have a meaningful relationship with it, then it's still just a chunk of silicon.

So then what is meaningful? Because back to sugar.

Well, sugar doesn't love you back, right? So the computer has to love you back. And what does love

mean? Well, in this context, for me, love... I'm going to take a page from Alain de Bouton. Love

means that it wants to help us become the best version of ourselves.

That's beautiful. That's a beautiful definition of love. So what role does love play in the human

condition at the individual level and at the group level? Because you were kind of saying that

we should really consider humans both at the individual and the group and the societal level.

What's the role of love in this whole thing? We talked about sex, we talked about death

thanks to the bacteria, they invented it. At which point did we invent love, by the way?

I mean, is that also... No, I think love is the start of it all and the feelings of... And this

gets sort of beyond just romantic, sensual, whatever kind of things, but actually genuine

love as we have for another person, love as it would be used in a religious text, right?

I think that capacity to feel love more than consciousness, that is the universal thing.

Our feeling of love is actually a sense of that generativity. When we can look at another person

and see that they can be something more than they are and more than just what we...

A pigeonhole, we might stick them in. I think in any religious text, you'll find

voiced some concept of this, that you should see the grace of God and the other person,

right? They're made in the spirit of the love that God feels for his creation or her creation.

I think this thing is actually the root of it. I don't think molecules of water

feel consciousness, have consciousness, but there is some proto micro quantum thing of love

that's the generativity when there's more energy than what they need to maintain equilibrium.

That, when you sum it all up, is something that leads to... I had my mind blown one day as an

undergrad at the physics computer lab. I logged in and when you log in to Bash for a long time,

there was a little fortune that would come out and it said, man was created by water to carry

itself uphill. I was logging in to work on some problem set and I logged in and I saw that and

I just said, son of a bitch, I logged out and I went to the coffee shop and I got a coffee and I sat

there on the quad and I'm like, it's not wrong and yet WTF, right? So when you look at it that way,

okay, non equilibrium physics is a thing. So when we think about love, when we think about

these kinds of things, I would say that in the modern day human condition,

there's a lot of talk about freedom and individual liberty and rights and all these things,

but that's very Hegelian. It's very kind of following from the Western philosophy

of the individual as sacrosanct, but it's not really couched, I think, the right way because

it should be how do we maximize people's ability to love each other, to love themselves first,

to love each other, their responsibilities to the previous generation, to the future generations.

Those are the kinds of things that should be our design criteria, right? Those should be

what we start with to then come up with the philosophies of self and of rights and responsibilities.

But that love being at the center of it, I think when we design systems for cognition,

it should absolutely be built that way. I think if we simply focus on efficiency

and productivity, these kind of very industrial era, all the things that Marx had issues with,

right? That's a way to go and really, I think, go off the deep end in the wrong way.

So one of the interesting consequences of thinking of life in this hierarchical way

of an individual human, and then there's groups and there are societies, is I believe that

that you believe that corporations are people. So this is a kind of a politically dense idea

and all those kinds of things. If we just throw politics aside, if we throw all of that aside,

in which sense do you believe that corporations are people?

And how does love connect to that? Right. So the belief is that groups of people

have some kind of higher level, I would say, mesoscopic claim to agency. So where do I,

let's start with this. Most people would say, okay, individuals have claims to agency and

sovereignty. Nations, we certainly act as if nations sort of very large, large scale. Nations have

rights to sovereignty and agency. Like everyone plays the game of modernity as if that's true.

We believe France is a thing. We believe the United States is a thing. But to say that groups of

people at a smaller level than that, like a family unit is a thing. Well, in our laws,

we actually do encode this concept. I believe that in a relationship and a marriage, one partner can

sue for loss of consortium if someone breaks up the marriage or whatever. So these are concepts

that even in law, we do respect that there is something about the union and about the family.

So for me, I don't think it's so weird to think that groups of people have a claim to rights

and sovereignty of some degree. And we look at our clubs, we look at churches. We talk about

these collectives of people as if they have a real agency to them. And then they do. But I think

if we take that one step further and say, okay, they can accrue resources. Well, yes, check. By

law, they can. They can own land. They can engage in contracts. They can do all these different

kinds of things. So we, in legal terms, support this idea that groups of people have rights.

Where we go wrong on this stuff is that the most popular version of this is the for profit

absentee owner corporation that then is able to amass larger resources than anyone else in the

landscape, anything else, any other entity of equivalent size. And they're able to essentially

bully around individuals, whether it's laborers, whether it's people whose resources they want

to capture. They're also able to bully around our system of representation, which is still tied

to individuals. So I don't believe that's correct. I don't think it's good that they're people,

but they're assholes. I don't think that corporations as people acting like assholes is a

good thing. But the idea that collectives and collections of people that we should treat

them philosophically as having some agency, some agency and some mass at a mesoscopic level,

I think that's an important thing. Because one thing I do think we under appreciate sometimes

is the fact that relationships have relationships. So it's not just individuals having

relationships with each other. But if you have eight people seated around a table,

right, each person has a relationship with each of the others. And that's obvious.

But then if it's four couples, each couple also has a relationship with each of the other couples,

right? The dyads do. And if it's couples, but one is the father, mother, older,

and then one of their children and their spouse, that family unit of four has a relationship with

the other family unit of four. So the idea that relationships have relationships is something

that we intuitively know in navigating the social landscape. But it's not something I

hear expressed like that. It's certainly not something that is, I think, taken into account

very well when we design these kinds of things. So I think the reason why I care a lot about this

is because I think the future of humanity requires us to form better sense, make collective

sense making units at something around Dunbar number, half to 5x Dunbar. And that's very different

than right now where we defer sense making to massive aging zombie institutions.

Or we just do it ourselves. We go it alone, go to the dark forest of the internet by ourselves.

So that's really interesting. So you've talked about agency, I think, maybe calling it a convenient

fiction at all these different levels. So even at the human individual level, it's kind of a fiction.

We all believe because we are, like you said, made of cells and cells are made of atoms.

So that's a useful fiction. And then there's nations. That seems to be a useful fiction.

But it seems like some fictions are better than others. There's a lot of people that argue the

fiction of nation is a bad idea. One of them lives two doors down from me. Michael Malis,

he's an anarchist. I'm sure there's a lot of people who are into meditation that believe

the idea this useful fiction of agency of an individual is troublesome as well. We need to

let go of that in order to truly like to transcend, I don't know, I don't know what words you want to

use, but suffering or to elevate the experience of life. So you're kind of arguing that, okay,

so we have some of these useful fictions of agency. We should add a stronger fiction that we tell

ourselves about the agency of groups in the hundreds of half a Dunbar's number or five X

Dunbar's number. Yeah, something in that order. And we call them fictions, but really they're

rules of the game, right? Rules that we feel are fair or rules that we consent to.

I always question the rules when I lose like a monopoly. That's when I usually question them.

When I'm winning, I don't question the rules. We should play a game of monopoly someday.

There's a trippy version of it that we could do. Contract monopoly is introduced by a friend of mine

to me where you can write contracts on future earnings or landing on various things and you

can hand out like, you know, you can land first three times, you land a park place is free or

whatever, just and then you can start trading those contracts for money. And then you create

human civilization and somehow Bitcoin comes into it. Okay, but some of these... Actually,

I bet if me and you and Eric sat down to play a game of monopoly and we were to make NFTs out

of the contracts we wrote, we could make a lot of money. Now, it's a terrible idea.

Yes. I would never do it, but I bet we could actually sell the NFTs around.

I have other ideas to make money that I could tell you and they're all terrible ideas,

including cat videos on the internet. Okay, but some of these rules of the game, some of these

fictions are, it seems like they're better than others. They have worked this far to

cohere human, to organize human collective action. But you're saying something about,

especially this technological age requires modified fictions, stories of agency. Why the

Dunbar number and also, you know, how do you select the group of people? You know,

Dunbar numbers, I think, I have this sense that it's overused as a kind of law that somehow

we can have deep human connection at this scale. Like some of it feels like an interface problem

too. It feels like if I have the right tools, I can deeply connect with a larger number of people.

It just feels like there's a huge value to interacting just in person, getting to share

traumatic experiences together or beautiful experiences together. There's other experiences

that in the digital space that you can share. It just feels like Dunbar's number can be

expanded significantly, perhaps not to the level of millions and billions, but it feels

like it could be expanded. How do we find the right interface, you think, for having a little

bit of a collective here that has agency? You're right, that there's many different ways that we

can build trust with each other. My friend Joe Edelman talks about a few different ways that

mutual appreciation, trustful conflict, just experiencing something. There's a variety of

different things that we can do, but all those things take time and you have to be present.

The less present you are, I mean, there's just, again, a no free lunch principle here. The less

present you are, the more of them you can do, but then the less connection you build. I think

there is a human capacity issue around some of these things. Now, that being said, if we can

use certain technologies. For instance, if I write a little monograph on my view of the world,

you read it asynchronously at some point and you're like, wow, Peter, this is great. Here's

mine. I read it. I'm like, wow, Lex, this is awesome. We can be friends without having to spend 10

years figuring all this stuff out together. We can just read each other's thing and be like, oh,

yeah, this guy's exactly in my wheelhouse and vice versa. We can then connect just a few times a year

and maintain a high trust relationship. It can be expanded a little bit, but it also requires,

these things are not all technological in nature. It requires the individual themselves

to have a certain level of capacity, to have a certain lack of neuroticism. If you want to

use the ocean big five sort of model, people have to be pretty centered. The less centered you are,

the fewer authentic connections you can really build for a particular unit of time. It just takes

more time. Other people have to put up with your crap. There's just a lot of the stuff that you

have to deal with if you are not so well balanced. Yes, we can help people get better to where they

can develop more relationships faster. Then you can maybe expand Dunbar number by quite a bit,

but you're not going to do it. I think it's going to be hard to get it beyond 10x,

kind of the rough swag of what it is. Well, don't you think that AI systems could be in addition

to the Dunbar's number? Do you count as one system or multiple AI systems?

Multiple AI systems. I do believe that AI systems, for them to integrate into human

society as it is now, have to have a sense of agency. There has to be an individual,

because otherwise we wouldn't relate to them. We could engage certain kinds of individuals to make

sense of them for us and be almost like, did you ever watch Star Trek? There's the Volta,

who are the interfaces, the ambassadors for the Dominion. We may have ambassadors that speak on

behalf of these systems. They're like the Mentats of Dune, maybe, or something like this.

I mean, we already have this to some extent. If you look at the biggest AI system, but the biggest

cybernetic system in the world is the financial markets. It runs outside of any individual's

control. You have an entire stack of people on Wall Street, Wall Street analysts, to CNBC,

reporters, whatever. They're all helping to communicate, what does this mean? Jim Cramer,

like Murrow Down, yelling and stuff. All of these people are part of that lowering of the

complexity there to help do sense making for people at whatever capacity they're at. I don't

see this changing with AI systems. I think you would have ringside commentators talking about

all this stuff that this AI system is trying to do over here, over here, because it's actually a

super intelligence. If you want to talk about humans interfacing, making first contact with

the super intelligence, we're already there. We do it pretty poorly. If you look at the gradient

of power and money, what happens is the people closest to it will absolutely exploit their

distance for personal financial gain. We should look at that and be like, oh, well, that's probably

what the future will look like as well. Nonetheless, we're already doing this kind of thing. In the

future, we can have AI systems, but you're still going to have to trust people to bridge the sense

making gap to them. I just feel like there could be millions of AI systems that have

agencies. When you say one super intelligence, super intelligence in that context means

it's able to solve particular problems extremely well, but there's some aspect of human like

intelligence that's necessary to be integrated into human society, so not financial markets,

not weather prediction systems or logistics optimization. I'm more referring to things

that you interact with on the intellectual level. I think there has to be a backstory,

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

Many of the elements that we humans experience that are fundamental to the human condition,

because otherwise, we would not have a deep connection with it.

But I don't think having a deep connection with it is necessarily going to stop us from

building a thing that has quite an alien intelligence aspect. The other kind of alien

intelligence on this planet is octopuses or octopodes or whatever you want to call them.

There's a little controversy as to what the plural is, I guess.

I look forward to your letters.

An octopus, it really acts as a collective intelligence of eight intelligent arms.

Its arms have a tremendous amount of neural density to them. Let's go with what you're

saying. If we build a singular intelligence that interfaces with humans that has a sense of agency

so we can run the cybernetic loop and develop its own theory of mind as well as its theory of

action, I agree with you that that's the necessary components to build a real intelligence.

There's got to be something at stake, it's got to make a decision, it's got to then run the

OODA loop. Okay, so we build one of those. Well, if we can build one of those, we can probably

build five million of them. So we'll build five million of them and if their cognitive systems

are already digitized and are already kind of there, we stick our antenna on each of them,

bring it all back to a hive mind that maybe doesn't make all the individual decisions for them,

but treats each one as almost like a neuronal input of a much higher bandwidth and fidelity,

going back to a central system that is then able to perceive much broader dynamics that we

can't see. In the same way that a phased array radar, you think about how a phased array radar

works, it's just sensitivity, it's just radars and then it's hypersensitivity and really great

timing between all of them and with a flat array, it's as good as a curved radar dish.

So with these things, it's a phased array of cybernetic systems that'll give the centralized

intelligence much, much better, much higher fidelity understanding of what's actually

happening in the environment. But the more power, the more understanding the central

superintelligence has, the dumber the individual fingers of this intelligence are, I think.

Not necessarily.

In my sense, this argument, there has to be the experience of the individual agent has to have

the full richness of the human like experience. You have to be able to be driving the car in

the rain, listening to Bruce Springsteen and all of a sudden break out in tears because

remembering something that happened to you in high school.

We can implant those memories if that's really needed.

No, but the central agency, I guess I'm saying in my view, for intelligence to be born,

you have to have a decentralization. Each one has to struggle and reach,

so each one in excess of energy has to reach for order as opposed to a central place doing so.

Have you ever read some sci fi where there's hive minds? The Verner Vinge, I think, has one of these

and then some of the stuff from the Commonwealth saga, the idea that you're an individual, but

you're connected with a few other individuals telepathically as well. Together, you form a swarm.

If you are, ask you, what do you think is the experience of, well, a borg, right? If you are

one, if you're part of this hive mind, outside of all the aesthetics, forget the aesthetics,

internally, what is your experience like? Because I have a theory as to what that looks like.

The one question I have for you about that experience is, how much is there feeling of freedom,

of free will? Because I obviously, as a human, very biased, but also somebody who values freedom

and bias, it feels like the experience of freedom is essential for trying stuff out, to being creative

and doing something truly novel, which is at the core of... Yeah. Well, I don't think you have to

lose any freedom when you're in that mode, because I think what happens is we think,

we still think, and I mean, you're still thinking about this in a sense of a top down,

command and control hierarchy, which is not what it has to be at all. I think the experience,

so I'll just show my cards here. I think the experience of being a robot in that robot swarm,

a robot who has agency over their own local environment that's doing sense making and

reporting it back to the hive mind, I think that robot's experience would be when the hive mind

is working well, it would be an experience of talking to God, that you essentially are reporting

to, you're sort of saying, here's what I see. I think this is what's going to happen over here.

I'm going to go do this thing, because I think if I want to do this, this will make this change

happen in the environment. And then, God, she may tell you, that's great. And in fact, your

brothers and sisters will join you to help make this go better, right? And then she can let your

brothers and sisters know, hey, Peter's going to go do this thing. Would you like to help him?

Because we think that this will make this thing go better. And they'll say, yes, we'll help him.

So the whole thing could be actually very emergent, the sense of what does it feel like to be a cell

in a network that is alive, that is generative. And I think actually the feeling is serendipity,

that there's random order, not random disorder or chaos, but random order, just when you need it

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

That feeling of serendipity, I feel like this is a bit of a flight of fancy, but every cell in your

body must have like, what does it feel like to be a cell in your body? When it needs sugar,

there's sugar. When it needs oxygen, there's just oxygen. Now, when it needs to go and do its work

and pull like as one of your muscle fibers, right? It does its work and it's great. It contributes

to the cause, right? So this is all, again, a flight of fancy, but I think as we extrapolate up,

what does it feel like to be an independent individual with some bounded sense of freedom?

All sense of freedom is actually bounded, but it was a bounded sense of freedom that still lives

within a network that has order to it. And I feel like it has to be a feeling of serendipity.

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

It has no way of explaining why it's getting oxygen and sugar when it gets it.

So you have to, each individual component has to be too dumb to understand the big picture.

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

But isn't that an essential characteristic of the individual is to be too dumb to understand

the bigger picture? Like not dumb necessarily, but limited in its capacity to understand.

Because the moment you understand, I feel like that leads to, if you tell me now

that there are some bigger intelligence controlling everything I do,

intelligence broadly defined, meaning like even the Sam Harris thing, there's no free will.

If I'm smart enough to truly understand that that's the case, that's kind of, I don't know if I...

Well, you have philosophical breakdown, right? Because we're in the West and we're pumped full

of this stuff of like you are a golden, fully free individual with all your freedoms and all

your liberties and go grab a gun and shoot whatever you want to. No, it's actually...

You don't actually have a lot of these... You're not unconstrained, but the areas where you can

manifest agency, you're free to do those things. You can say whatever you want on this podcast.

You can create a podcast, right? Yeah.

You're not... I mean, you have a lot of this kind of freedom, but even as you're doing this,

you are actually, I guess with the denouement of this is that we are already intelligent agents

in such a system, right? In that one of these like robots of one to five million little

swarm robots or one of the Borg, they're just posting an internal bulletin board.

I mean, maybe the Borg Cube is just a giant Facebook machine floating in space

and everyone's just posting on there. They're just posting really fast and like...

It's called the metaverse now.

The nest called the metaverse. That's right. Here's the enterprise. Maybe we should all

go shoot it. Yeah, everyone upvotes and they're going to go shoot it, right?

But we already are part of a human online collaborative environment and collaborative

sense making system. It's not very good yet. It's got the overhangs of zombie sense making

institutions all over it. But as that washes away and as we get better at this,

we are going to see humanity improving at speeds that are unthinkable in the past.

And it's not because anyone's freedoms were limited. In fact, the open source...

And we started this with open source software, right?

The collaboration, what the internet surfaced was the ability for people all over the world

to collaborate and produce some of the most foundational software that's in use today,

right? That entire ecosystem was created by collaborators all over the place.

So these online kind of swarm kind of things are not novel. I'm just suggesting that

future AI systems, if you can build one smart system, you have no reason not to build multiple.

If you build multiple, there's no reason not to integrate them all into a collective sense making

substrate. And that thing will certainly have immersion intelligence that none of the individuals

and probably not any of the human designers will be able to really put a bow around and explain.

But in some sense, would that AI system still be able to go like rural Texas by ranch,

go off the grid, go full survivalist? Can you disconnect from the hive mind?

You may not want to.

So to be an effective, to be intelligent.

You have access to way more intelligence capability if you're plugged into five

million other really, really smart cyborgs. Why would you leave?

So like there's a word control that comes to mind. So it doesn't feel like control,

like overbearing control. It's just knowledge.

I think systems, well, this is to your point. I mean, look at how uncomfortable you are with

this concept, right? I think systems that feel like overbearing control will not evolutionarily

win out. I think systems that give their individual elements the feeling of serendipity and the

feeling of agency that those systems will win. But that's not to say that there will not be

emergent higher level order on top of it. And that's the thing, that's the philosophical breakdown

that we're staring right at, which is in the Western mind, I think there's a very sharp

delineation between explicit control. Cartesian, like what is the vector? Where is the position?

Where is it going? It's completely deterministic. And kind of this idea that things emerge,

everything we see is the emergent patterns of other things. And there is agency when there's

extra energy. So you have spoken about a kind of meaning crisis that we're going through.

But it feels like since we invented sex and death, we broadly speaking, we've been searching for a

kind of meaning. So it feels like human civilization has been going through a meaning crisis of

different flavors throughout its history. Why is how is this particular meaning crisis different?

Or is it really a crisis and it wasn't previously? What's your sense?

A lot of human history, there wasn't so much a meaning crisis. There was just a food and not

getting eaten by bears crisis. Once you get to a point where you can make food, there was the

not getting killed by other humans crisis. So sitting around wondering what is it all about,

it's actually a relatively recent luxury. And to some extent, the meaning crisis coming out of

that is precisely because... Well, it's not precisely because I believe that meaning is the

consequence of when we make consequential decisions. It's tied to agency. When we make

consequential decisions, that generates meaning. So if we make a lot of decisions,

but we don't see the consequences of them, then it feels like what was the point?

Right? But if there's all these big things happening, but words are long for the ride,

then it also does not feel very meaningful. Meaning, as far as I can tell, this is my

working definition of Serga 2021 is generally the result of a person making a consequential

decision, acting on it, and then seeing the consequences of it. So historically,

just when humans are in survival mode, you're making consequential decisions all the time.

So there's not a lack of meaning because you either got eaten or you didn't.

Right? You got some food and that's great. You feel good. These are all consequential decisions.

Only in the post fossil fuel and industrial revolution could we create a massive leisure

class. I could sit around not being threatened by bears, not starving to death, making decisions

somewhat, but a lot of times not seeing the consequences of any decisions they make.

The general sense of anomy, I think there's the French term for it, in the wake of the consumer

society, in the wake of mass media telling everyone, hey, choosing between Hermes and Chanel is a

meaningful decision. No, it's not. I don't know what either of those mean.

Oh, there's high end luxury purses and crap like that. But the point is that we give people the

idea that consumption is meaning, that making a choice of this team versus that team spectating has

meaning. So we produce all of these different things that are as if meaning, but really making

a decision that has no consequences for us. And so that creates the meaning crisis.

Well, you're saying choosing between Chanel and the other one has no consequence?

Why is one more meaningful than the other? It's not that it's more meaningful than the other.

It's that you make a decision between these two brands and you're told this brand will make me

look better in front of other people. If I buy this brand of car, if I wear that brand of apparel,

the idea, like a lot of decisions we make are around consumption, but consumption by itself

doesn't actually yield meaning. Gaining social status does provide meaning. So that's why in this

era of abundant production, so many things turn into status games. Then the NFT kind of explosion

is a similar kind of thing. Everywhere there are status games because we just have so much

excess production. But aren't those status games a source of meaning? Why do the games we play have

to be grounded in physical reality like they are when you're trying to run away from lions?

Why can't we in this virtuality world on social media, why can't we play the games on social

media, even the dark ones? Right, we can. But you're saying that's creating a meaning crisis.

Well, there's a meaning crisis in that there's two aspects of it. Number one, playing those kinds

of status games oftentimes requires destroying the planet because it ties to consumption, consuming

the latest and greatest version of a thing, buying the latest limited edition sneaker,

and throwing out all the old ones. Maybe it keeps in the old ones, but the amount of sneakers we

have to cut up and destroy every year to create artificial scarcity for the next generation.

Right? This is kind of stuff that's not great. It's not great at all.

So, can speakers consumption fueling status games is really bad for the planet, not sustainable?

The second thing is you can play these kinds of status games, but then what it does is it

renders you captured to the virtual environment. The status games that really wealthy people are

playing are all around the hard resources where they're going to build the factories,

they're going to have the fuel in the rare earths to make the next generation of robots.

They're then going to run circles around you and your children. So, that's another reason

not to play those virtual status games. So, you're saying ultimately the big picture game is one

by people who have access or control over actual hard resources. So, you can't,

you don't see a society where most of the games are played in the virtual space.

They'll be captured in the physical space. It all builds. It's just like the stack

of human being. If you only play the game at the cultural and intellectual level,

then the people with the hard resources and access to layer zero physical are going to own you.

But isn't money not connected to or less and less connected to hard resources and money still

seems to work? It's a virtual technology. There's different kinds of money. Part of the reason that

some of the stuff is able to go a little unhinged is because the big sovereignties where one spends

money and uses money and plays money games and inflates money, their ability to adjudicate

the physical resources and hard resources on land and things like that, those have not been

challenged in a very long time. So, we went off the gold standard. Most money is not connected

to physical resources. It's an idea. And that idea is very closely connected to status.

But it's also tied to, it's actually tied to law. It is tied to some physical hard things. So,

you have to pay your taxes. Yes. So, it's always at the end going to be connected to the blockchain

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

is what? Violence is the, I'm playing a stack of devil's advocates here. And popping one devil

off the stack at a time. Isn't ultimately, of course, it'll be connected to physical reality,

but just because people control the physical reality, it doesn't mean the status,

LeBron James, in theory, could make more money than the owners of the teams

in theory. And to me, that's a virtual idea. So, somebody else constructed a game

and now you're playing in the virtual space of the game. And so, it just feels like there could

be games where status, we build realities that give us meaning in the virtual space. I can imagine

such things being possible. Oh, yeah. Okay. So, I think I see what you're saying there. With

the idea there, I mean, we'll take the LeBron James side and put in some YouTube influencer.

Yes, sure. Right. So, the YouTube influencer, it is status games, but at a certain level,

it precipitates into real dollars. Well, you look at Mr. Beast, right? He's setting off

half a million dollars worth of fireworks or something on a YouTube video.

And also, saving trees and so on. Sure, right. They're trying to plan a

million trees with Mark Rober or whatever it was. Yeah. It's not that those kinds of games can't

lead to real consequences. It's that for the vast majority of people in consumer culture,

they are incented by the... I would say mostly I'm thinking about middle class consumers.

They're incented by advertisements. They're incented by their memetic environment to treat

the purchasing of certain things, the need to buy the latest model, whatever, the need to appear,

however, the need to pursue status games as a driver of meaning. And my point would be that

it's a very hollow driver of meaning. And that is what creates a meaning crisis because at the

end of the day, it's like eating a lot of empty calories, right? Yeah, it tasted good going down.

It's a lot of sugar, but man, it was not enough protein to help build your muscles.

And you kind of feel that in your gut. And I think that's... I mean, all this stuff aside and

setting aside our discussion on currency, which I hope we get back to, that's what I mean about

the meaning crisis, part of it being created by the fact that we're not encouraged to have

more and more direct relationships. We're actually alienated from relating to even our family members

sometimes, right? We're encouraged to relate to brands. We're encouraged to relate to these kinds

of things that then tell us to do things that are really of low consequence. And that's where

the meaning crisis comes from. So the role of technology in this... So there's somebody you

mentioned who's Jacques Eliel. His view of technology, he warns about the towering piles

of technique, which I guess is a broad idea of technology. So I think, correct me if I'm wrong

for him, technology is bad at moving away from human nature and ultimately is destructive.

My question broadly speaking in this meaning crisis, what are the pros and cons of technology?

Can it be a good? Yeah, I think it can be. I certainly draw on some of Elul's

ideas and I think some of them are pretty good. But the way he defines technique is

well, also, Simonda as well. I mean, he speaks to the general mentality of efficiency,

homogenized processes, homogenized production, homogenized labor to produce homogenized artifacts

that then are not actually... They don't sit well in the environment. So it's essentially,

you can think of it as the antonym of craft. Whereas a craftsman will come to a problem,

maybe a piece of wood and they need to make into a chair. It may be a site to build a house or

build a stable or build whatever. And they will consider how to bring various things in to build

something well contextualized that's in right relationship with that environment.

But the way we have driven technology over the last 150 years is not that at all. It is how can we

make sure the input materials are homogenized, cut to the same size, diluted and doped to exactly

the right alloy concentrations. How do we create machines that then consume exactly the right kind

of energy to be able to run at this high speed to stamp out the same parts, which then go out the

door. Everyone gets the same tickle, Mielmo. And the reason why everyone wants it is because we

have broadcasts that tells everyone, this is the cool thing. So homogenized demand. And we're like

Baudrillard and the other critiques of modernity coming from that direction, the situationalist

as well. It's that their point is that at this point in time, consumption is the thing that drives

a lot of the economic stuff, not the need, but the need to consume and build status games on top.

So we have homogenized, when we discovered, I think this is really like Bernays and stuff,

in the early 20th century, we discovered we can create, we can create demand. We can create desire

in a way that was not possible before because of broadcast media. And not only do we create desire,

we don't create a desire for each person to connect to some bespoke thing to build a relationship

with their neighbor or their spouse. We are telling them, you need to consume this brand.

You need to drive this vehicle. You got to listen to this music. Have you heard this?

Have you seen this movie? So creating homogenized demand makes it really cheap to create homogenized

product. And now you have economics of scale. So we make the same tickle myelmo, give it to

all the kids. And all the kids are like, hey, I got a tickle myelmo. So this is ultimately where

this ties in then to runaway hyper capitalism is that capitalism is always looking for growth.

It's always looking for growth. And growth only happens at the margins. So you have to squeeze

more and more demand out. You got to make it cheaper and cheaper to make the same thing.

But tell everyone they're still getting meaning from it. This is still your tickle myelmo.

And we see little bits of this, critiques of this dripping in popular culture. You see it

sometimes. It's when Buzz Lightyear walks into the thing, he's like, oh my God, at the toy store,

I'm just a toy. There's millions of other, or there's hundreds of other Buzz Lightyears just

like me, right? That is, I think, a fun Pixar critique on this homogenization dynamic.

I agree with you on most of the things you're saying. So I'm playing devil's advocate here.

But this homogenized machine of capitalism is also the thing that is able to fund if

channeled correctly innovation, invention, and development of totally new things that

in the best possible world create all kinds of new experiences that can enrich lives,

the quality of lives for all kinds of people. So isn't this the machine that actually enables

the experiences and more and more experiences that would then give meaning?

It has done that to some extent. I mean, it's not all good or bad in my perspective.

We can always look backwards and offer a critique of the path we've taken to get

to this point in time. But that's somewhat different and informs the discussion,

but it's somewhat different than the question of where do we go in the future, right?

Is this still the same rocket we need to ride to get to the next point? We'll even get us to

the next point. Well, how does this, so you're predicting the future, how does it go wrong in

your view? We have the mechanisms we have now explored enough technologies to where we can

actually, I think, sustainably produce what most people in the world need to live.

We have also created the infrastructures to allow continued research and development

of additional science and medicine and various other kinds of things.

The organizing principles that we use to govern all these things today

have been, a lot of them have been just inherited from, honestly, medieval times.

Some of them have refactored a little bit in the industrial era, but a lot of these modes

of organizing people are deeply problematic. Furthermore, they're rooted in, I think, a

very industrial mode perspective on human labor. This is one of those things I'm going to go back

to the open source thing. There was a point in time when, well, let me ask you this. If you look

at the core SciPy collection of libraries, that's SciPy NumPy Mapplotlib. There's SciPython Notebook.

Let's throw pandas in there, scikit learn, a few of these things. How much value do you think,

economic value, would you say they drive in the world today?

That's one of the fascinating things about talking to you and Travis. It's a measure

with at least $1 billion a day, maybe?

$1 billion, sure. It's similar question of how much value does Wikipedia create.

All of it? I don't know.

Well, I mean, if you look at our systems, when you do a Google search, some of that stuff runs

through TensorFlow, but when you look at Siri, when you do credit card transaction,

just everything, every intelligence agency under the sun, they're using some aspect of

these kinds of tools. I would say that these create billions of dollars of value.

Oh, you mean like direct use of tools that leverage this?

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

Yeah, easily. I think the things they could not do if they didn't have these tools.

That's billions of dollars a day. Great. I think that's about right. Now,

if we take how many people did it take to make that? There was a point in time,

not anymore, but there was a point in time when they could fit in a van. I could have

fit them in my Mercedes printer. If you look at that, like holy crap,

literally a van of maybe a dozen people could create value to the tune of billions of dollars

a day. What lesson do you draw from that?

Well, here's the thing. What can we do to do more of that? That's open source. The way I've

talked about this in other environments is when we use generative participatory crowd

sourced approaches, we unlock human potential at a level that is better than what capitalism can do.

I would challenge anyone to go and try to hire the right 12 people in the world

to build that entire stack the way those 12 people did that. They would be very,

very hard to press to do that. If a hedge fund could just hire a dozen people

and create something that is worth billions of dollars a day, every single one of them

will be racing to do it. Finding the right people, fostering the right collaborations,

getting it adopted by the right other people to then refine it, that is a thing that was

organic in nature. That took crowdsourcing. That took a lot of the open source ethos and it took

the right kinds of people. None of those people who started that said, I need to have a part of a

multi billion dollar a day enterprise. They're like, I'm doing this cool thing to solve my

problem for my friends. The point of telling the story is to say that our way of thinking about

value, our way of thinking about allocation of resources, our ways of thinking about property

rights and all these kinds of things, they come from finite game, scarcity mentality,

medieval institutions. As we are now entering, to some extent, we're in a post scarcity era,

although some people are hoarding a whole lot of stuff. We are at a point where,

if not now soon, we'll be in a post scarcity era. The question of how we allocate resources

has to be revisited at a fundamental level, because the kind of software these people built,

the modalities that those human ecologies that built the software, it treats softwares unproperty.

Actually, sharing creates value. Restricting and forking reduces value. That's different than

any other physical resource that we've ever dealt with. It's different than how most corporations

treat software IP. If treating software in this way created this much value so efficiently,

so cheaply, because feeding a dozen people for 10 years is really cheap. That's the reason I care

about this right now is because looking forward when we can automate a lot of labor, where we can,

in fact, the programming for your robot in your neck of the woods and your part of the Amazon to

build something sustainable for you and your tribe to deliver the right medicines to take care of the

kids, that's just software. That's just code. That could be totally open sourced. We can actually

get to a mode where all of these additional generative things that humans are doing,

they don't have to be wrapped up in a container and then we charge for all the exponential dynamics

out of it. That's what Facebook did. That's what modern social media did, because the old internet

was connecting people just fine. Facebook came along and said, well, anyone can post a picture,

anyone can post some text, and we're going to amplify the crap out of it to everyone else,

and it exploded this generative network of human interaction, and then I said,

how do I make money off that? Oh yeah, I'm going to be a gatekeeper on everybody's attention,

and that's how we make money. How do we create more than one van? How do we have millions of vans

full of people that create NumPy, SciPy, that create Python? The story of those people is often

they have some kind of job outside of this. This is what they're doing for fun. Don't you need to

have a job? Don't you have to be connected, plugged in to the capitalist system? Isn't this consumerism,

the engine that results in the individuals that take a break from it every once in a while to

create something magical at the edges? The question of surplus, this is the question. If everyone

were to go and run their own farm, no one would have time to go and write NumPy, SciPy,

right? Maybe, but that's what I'm talking about when I say we're maybe at a post scarcity point

for a lot of people. The question that we're never encouraged to ask in a Super Bowl ad is,

how much do you need? How much is enough? Do you need to have a new car every two years,

every five? If you have a reliable car, can you drive one for 10 years? Is that all right?

I had a car for 10 years and it was fine. Your iPhone, do you have to upgrade every two years?

I mean, you're using the same apps you did four years ago, right?

This should be a Super Bowl ad.

This should be a Super Bowl ad. That's great. Maybe somebody...

Do you really need a new iPhone?

Maybe one of our listeners will fund something like this of like, no, but just actually

bring it back, bring it back to actually the question of what do you need? How do we create

the infrastructure for collectives of people to live on the basis of providing what we need,

meeting people's needs with a little bit of access to handle emergencies and things like that,

pulling our resources together to handle the really, really big emergencies, somebody with a

really rare cat form of cancer or some massive fire sweeps through half the village or whatever,

but can we actually unscale things and solve for people's needs and then give them the capacity

to explore how to be the best version of themselves?

And for Travis, that was throwing away his shot of tenure in order to write NumPy.

For others, there is a saying in the sci fi community that sci fi advance is one failed

postdoc at a time and we can do these things. We can actually do this kind of collaboration

because code, software information organization, that's cheap. Those bits are very cheap to

fling across the oceans.

So you mentioned Travis. We've been talking and we'll continue to talk about open source.

Maybe you can comment, how did you meet Travis? Who is Travis Alfon?

What's your relationship been like through the years? Where did you work together?

How did you meet? What's the present and the future look like?

Yeah. So the first time I met Travis was at a sci fi conference in Pasadena.

Do you remember the year?

2005. I was working at Nthought, working on scientific computing, consulting,

and a couple of years later, he joined us at Nthought, I think 2007.

And he came in as the president, one of the founders of Nthought was the CEO, Eric Jones.

And we were all very excited that Travis was joining us and that was great fun.

And so I worked with Travis on a number of consulting projects and we worked on

some open source stuff. I mean, it was just a really, it was a good time there.

It was primarily Python related?

Oh yeah, it was all Python, NumPy, sci fi consulting kind of stuff.

Towards the end of that time, we started getting called into more and more finance shops.

They were adopting Python pretty heavily. I did some work on like a high frequency

trading shop, working on some stuff, and then we worked together on some

a couple of investment banks in Manhattan. And so we started seeing that there was a

potential to take Python in the direction of business computing.

More than just being this niche like MATLAB replacement for big vector computing,

what we were seeing was, oh yeah, you could actually use Python as a Swiss army knife to do

a lot of shadow data transformation kind of stuff. So that's when we realized the potential

was much greater. And so we started Anaconda, I mean, it was called Continuum Analytics at the

time, but we started in January of 2012 with the vision of shoring up the parts of Python

that needed to get expanded to handle data at scale, to do web visualization, application

development, et cetera. And that was that, yeah. So he was CEO and I was president for the first

five years. And then we raised some money and then the board, it was sort of put in a new CEO.

They hired a kind of professional CEO. And then Travis, you laugh out that, I took over the CTO

role, Travis then left after a year to do his own thing, to do Quonsite, which was more oriented

around some of the bootstrap years that we did at Continuum, where it was open source and consulting.

It wasn't sort of like gung ho product development. And it wasn't focused on,

you know, we accidentally stumbled into the package management problem

at Anaconda. But we had a lot of other visions of other technology that we built in the open

source. And Travis was really trying to push, again, the frontiers of numerical computing,

vector computing, handling things like auto differentiation and stuff intrinsically in

the open ecosystem. So I think that's kind of the direction he's working on in some of his work.

We remain great friends and colleagues and collaborators, even though he's no longer

day to day working at Anaconda. But he gives me a lot of feedback about this and that and the other.

What's a big lesson you've learned from Travis about life or about programming or about leadership?

Wow, there's a lot. There's a lot. Travis is a really, really good guy.

His heart is really in it. He cares a lot.

I've gotten that sense having to interact with him. It's so interesting, such a good

he's a really good dude. And he and I, you know, it's so interesting. We come from very different

backgrounds. We're quite different as people. But I think we can not talk for a long time

and then be on a conversation and be eye to eye on 90% of things. And so he's someone who I believe,

no matter how much fog settles in over the ocean, his ship, my ship are pointed in the

same direction to the same star. Wow, that's a beautiful way to phrase it. No matter how much

fog there is or pointed at the same star. Yeah, and I hope he feels the same way. I mean, I hope

he knows that over the years now. We both care a lot about the community. For someone who cares so

deeply, I would say this about Travis, that's interesting. For someone who cares so deeply

about the nerd details of like type system design and vector computing and efficiency of

expressing this and that and the other, memory layouts and all that stuff, he cares even more

about the people in the ecosystem, the community. And I have a similar kind of alignment. I care a

lot about the tech. I really do. But for me, the beauty of what this human ecology has produced

is, I think, a touchstone. It's an early version. We should look at it and say,

how do we replicate this for humanity at scale? What this open source collaboration was able to

produce? How can we be generative in human collaboration moving forward and create that as

a civilizational kind of dynamic? Can we seize this moment to do that? Because a lot of the

other open source movements, it's all nerds nerding out on code for nerds. And this,

because it's scientists, because it's people working on data that all of it faces real human

problems, I think we have an opportunity to actually make a bigger impact.

Is there a way for this kind of open source vision to make money?

Absolutely.

To fund the people involved. Is that an essential part of it?

It's hard. But we're trying to do that in our own way at Anaconda because we know that business

users, as they use more of the stuff, they have needs that like business specific needs

around security, provenance. They really can't tell their VPs and their investors, hey, we're

having our data scientists are installing random packages from who knows where and running on

customer data. So they have to have someone to talk to you and that's what Anaconda does.

So we are a governed source of packages for them and that's great. That makes some money.

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

and write that as a dividend for the open source community. But beyond that, I really see the

development of a marketplace for people to create notebooks, models, data sets, curation of these

different kinds of things and to really have a long tail marketplace dynamic with that.

Can you speak about this problem that you stumbled into of package management,

Python package management? What is that? A lot of people speak very highly of Conda,

which is part of Anaconda, which is a package manager. There's a ton of packages.

So first, what are package managers? And second, what was there before? What is PIP?

And why is Conda more awesome? The package problem is this, which is that in order to do

numerical computing efficiently with Python, there are a lot of low level libraries that need

to be compiled, compiled with a C compiler or C++ compiler or Fortran compiler. They need to not

just be compiled, but they need to be compiled with all of the right settings. And oftentimes,

those settings are tuned for specific chip architectures. And when you add GPUs to the mix,

when you look at different operating systems, you may be on the same chip. But if you're running Mac

versus Linux versus Windows on the same X86 chip, you compile and link differently.

All of this complexity is beyond the capability of most data scientists to reason about. And it's

also beyond what most of the package developers want to deal with too. Because if you're a package

developer, you're like, I code on Linux, this works for me, I'm good. It is not my problem to

figure out how to build this on an ancient version of Windows, right? That's just simply not my

problem. So what we end up with is we have a creator or create a very creative crowdsourced

environment where people want to use this stuff, but they can't. And so we ended up creating

a new set of technologies like a build recipe system, a build system, and an installer system

that is able to, well, to put it simply, it's able to build these packages correctly on each of

these different kinds of platforms and operating systems and make it so when people want to install

something, they can. It's just one command. They don't have to, you know, set up a big compiler

system and do all these things. So when it works well, it works great. Now, the difficulty is

we have literally thousands of people writing code in the ecosystem, building all sorts of stuff,

and each person writing code, they may take a dependence on something else. And so all this

web, incredibly complex web of dependencies. So installing the correct package for any given

set of packages you want, getting that right subgraph is an incredibly hard problem. And

again, most data scientists don't want to think about this. They're like, I want to install NumPy

and Pandas. I want this version of some geospatial library. I want this other thing. Why is this

hard? These exist, right? And it is hard because it's, well, you're installing this on a version

of Windows, right? And half of these libraries are not built for Windows. Or the latest version

isn't available, but the old version was. If you go to the old version of this library,

that means you need to go to a different version of that library. And so the Python ecosystem,

by virtue of being crowdsourced, we were able to fill 100,000 different niches. But then we also

suffer this problem that because it's crowdsourced, and no one, it's like a tragedy to the comments,

right? No one really needs, wants to support their thousands of other dependencies. So we end up sort

of having to do a lot of this. And of course, the Kanda Forge community also steps up as an open

source community that, you know, maintains some of these recipes. That's what Kanda does. Now,

PIP is a tool that came along after Kanda to some extent. It came along as an easier way for the

Python developers writing Python code that didn't have as much compiled, you know, stuff,

they could then install different packages. And what ended up happening in the Python ecosystem

was that a lot of the core Python and web Python developers, they never ran into any of this

compilation stuff at all. So even we have, you know, on video, we have Guido van Rossum saying,

you know what, the scientific community's packaging problems are just too exotic and

different. I mean, you're talking about Fortran compilers, right? Like, you guys just need to

build your own solution, perhaps, right? So the Python core Python community went and built its

own sort of packaging technologies, not really contemplating the complexity of the stuff over

here. And so now we have the challenge where you can't PIP install some things. Some libraries,

if you just want to get started with them, you can PIP install TensorFlow and that works great.

The instant you want to also install some other packages that use different versions of

NumPy or some like graphics library or some OpenCV thing or some other thing,

you now run into dependency hell. Because you cannot, you know, OpenCV can have a different

version of libjpeg over here than PyTorch over here. Like they actually, they all have to use

the, if you want to use GPU acceleration, they have to all use the same underlying drivers and

same GPU CUDA things. So it's, it gets to be very gnarly. And it's a level of technology that

both the makers and the users don't really want to think too much about.

And that's where you step in and try to solve the sub graph problem. How much is that? And you

said that you don't want to think, they don't want to think about it, but how much is it a

little bit on the developer and providing them tools to, to be a little bit more clear of that

sub graph of dependency that's necessary? It is, it is getting to a point where we do have to think

about, look, can we pull some of the most popular packages together and get them to work on a

coordinated release timeline, get them to build against the same test matrix, et cetera, et cetera.

Right. And there is a little bit of dynamic around this, but again, it is a volunteer community.

You know, people working on these different projects have their own timelines and their own

things they're trying to meet. So we end up trying to pull these things together. And then it's,

it's just incredibly, and I would recommend just as a business tip, don't ever go into business

where when your hard work works, you're invisible. And when it breaks, because of someone else's

problem, you get flack for it. Because that's, that's a, in our situation, right? When something

doesn't condo install properly, usually it's some upstream issue, but it looks like condo is broken.

It looks like, you know, anaconda, screw something up. When things do work though, it's like, oh,

yeah, cool, it's worked. Assuming naturally, of course, that's very easy to make that work, right?

So we end up in this kind of problematic scenario. But, but it's okay, because I think we're still,

you know, our hearts in the right place. We're trying to move this forward as a community sort

of affair. I think most of the people in the community also appreciate the work we've done

over the years to try to move these things forward in a, in a collaborative fashion. So

one of the sub graphs of dependencies that became super complicated is the move from

Python two to Python three. So there's all these ways to mess with these kinds of

ecosystems of packages and so on. So I just want to ask you about that particular one.

What do you think about the move from Python two to three? Now, why did it take so long?

What were, from your perspective, just seeing the packages all struggle in the community,

all struggle through this process? What lessons do you take away from it? Why did it take so long?

Looking back, some people perhaps underestimated how much adoption Python two had.

I think some people also underestimated how much, or they overestimated how much value

some of the new features in Python three really provided. Like the things they really loved

about Python three just didn't matter to some of these people on Python two.

Yeah. Because this change was happening as Python scipy was starting to take off really like

past like a hockey stick of adoption in the early data science era in the early 2010s.

A lot of people were learning and onboarding in whatever just worked. And the teachers were like,

well, yeah, these libraries I need are not supporting Python three yet. I'm going to

teach you Python two. It took a lot of advocacy to get people to move over to Python three.

So I think it wasn't any particular single thing, but it was one of those death by,

you know, a dozen cuts, which just really made it hard to move off of Python two.

And also Python three itself, as they were kind of breaking things and changing these

around and reorganizing the standard library, there's a lot of stuff that was happening there

that kept giving people an excuse to say, I'll put off till the next version.

Two is working fine enough for me right now. So I think that's essentially what happened there.

And I will say this though, the strength of the Python data science movement,

I think, is what kept Python alive in that transition. Because a lot of languages have died

and left their user bases behind. If there wasn't the use of Python for data, there's a good chunk

of Python users that during that transition would have just left for Go and Rust and stayed.

In fact, some people did. They moved to Go and Rust and they just never look back.

The fact that we were able to grow by millions of users, the Python data community,

that is what kept the momentum for Python going. And now the usage of Python for data

is over 50% of the overall Python user base. So I will put, I will make, I'm happy to debate

that on stage somewhere. But from where I sit, I think that's true.

The statement there, the idea is that the switch from Python two to Python three

would have probably destroyed Python if it didn't also coincide with Python for whatever

reason, just overtaking the data science community, anything that processes data.

So the timing was perfect that this maybe imperfect decision was coupled with a great

timing on the value of data in our world.

I would say the troubled execution of a good decision. It was a decision that was necessary.

It's possible if we had more resources, we could have done it in a way that was a little bit

smoother. But ultimately, the arguments for Python three, I bought them at the time and I buy them

now. Having great text handling is like a nonnegotiable table stakes thing you need to have

in a language. So that's great. But the execution, Python is the, it's volunteer driven.

It's like now the most popular language on the planet, but it's all literally volunteers.

So the lack of resources meant that they had to really, they had to do things in a very

hamstrung way. And I think to carry the Python momentum in the language through that time,

the data movement was a critical part of that.

So some of it is carrot and stick. I actually have to shamefully admit that it took me a very

long time to switch from Python two and Python three because I'm a machine learning person.

It was just for the longest time, you could just do fine with Python two.

Right. But I think the moment where I switched everybody I worked with and switched myself

for small projects and big is when finally when NumPy announced that they're going to end support

like in 2020 or something like that. Right. So like when I realized, oh, this isn't going,

this is going to end. Right. So that's the stick. That's not a carrot. That's not so for the longest

time was carrots. It was like all of these packages were saying, okay, we have Python three support

now come join us with Python two and Python three. But when NumPy, one of the packages I

sort of love and depend on said like, nope, it's over. That's, that's when I decided to switch.

I wonder if you think it was possible much earlier for somebody like,

like NumPy or some major package to step into the cold. Well, it's a chicken and egg problem too.

Right. You don't want to cut off a lot of users unless you see the user momentum going too. So

the decisions for the scientific community, for each of the different projects, you know,

there's not a monolith. Some projects are like, we'll only be releasing new features on Python

three. And that was more of a sticky carrot or a firm carrot, if you will, a firm carrot, a

stick shaped carrot. But then for others, yeah, NumPy in particular, because it's at the base

of the dependency stack for so many things. That was the final stick. That was a stick shaped stick.

People were saying, look, if I have to keep maintaining my releases for Python two,

that's that much less energy that I can put into making things better for the Python three folks

or in my new version, which is of course going to be Python three. So people were also getting

kind of pulled by this tension. So the overall community sort of had a lot of input into when

the NumPy core folks decided that they would end of life on Python two.

So as these numbers are a little bit loose, but there are about 10 million Python programmers

in the world, you could argue that number, but let's say 10 million. That's actually

where I was looking said 27 million total programmers, developers in the world. You mentioned in a talk

that changes need to be made for there to be 100 million programmers. So first of all,

do you see a future where there's 100 million Python programmers? And second, what kind of

changes need to be made? So Anaconda, Miniconda get downloaded about a million times a week.

So I think the idea that there's only 10 million Python programmers in the world is a little bit

undercounting. There are a lot of people who escape traditional counting that are using Python and

data in their jobs. I do believe that the future world for it to, well, the world I would like to

see is one where people are data literate. So they are able to use tools that let them express

their questions and ideas fluidly. And the data variety and data complexity will not go down.

It will only keep increasing. So I think some level of code or code like things will continue to

be relevant. And so my hope is that we can build systems that allow people to more seamlessly

integrate Python kinds of expert sensitivity with data systems and operationalization methods

that are much more seamless. And what I mean by that is, right now, you can't punch Python code

into an Excel cell. I mean, there's some tools you can do to kind of do this. We didn't build

a thing for doing this back in the day. But I feel like the total addressable market for Python

users, if we do the things right, is on the order of the Excel users, which is a few hundred million.

So I think Python has to get better at being embedded, being a smaller thing that pulls

in just the right parts of the ecosystem to run numerics and do data exploration, meeting people

where they're already at with their data and their data tools. And then I think also,

it has to be easier to take some of those things they've written and flow those back into deployed

systems or little apps or visualizations. I think if we don't do those things, then we will always be

kept in a silo as sort of an expert user's tool and not a tool for the masses.

You know, I work with a bunch of folks in the Adobe creative suite, and I'm kind of forcing them

or inspired them to learn Python to do a bunch of stuff that helps them. And it's interesting

because they probably wouldn't call themselves a Python programmer, but they're all using Python.

I would love it if the tools like Photoshop and Premiere and all those kinds of tools that are

targeted towards creative people, I guess that's where Excel is targeted towards a certain kind

of audience that works with data, financial people, all that kind of stuff. If there would be easy

ways to leverage to use Python for quick scripting tasks. And you know, there's an exciting application

of artificial intelligence in the space that I'm hopeful about looking at open AI codex with

generating programs. So almost helping people bridge the gap from kind of visual interface

to generating programs to something formal. And then they can modify it and so on. But kind of

without having to read the manual, without having to do a Google search and stack overflow,

which is essentially what a neural network does when it's doing code generation,

is actually generating code and allowing a human to communicate with multiple programs. And then

maybe even programs to communicate with each other via Python. So that to me is a really exciting

possibility because I think there's a friction to kind of like, how do I learn how to use Python

in my life? Oftentimes, you kind of start a class, you start learning about types,

yes, I don't know, functions. Like this is, you know, Python is the first language with

which you start to learn to program. But I feel like that's going to take a long time for you

to understand why it's useful. You almost want to start with a script. Well, you do, in fact. I

think starting with the theory behind programming languages and types and all that, I mean,

types are there to make the compiler writer's jobs easier. Types are not, I mean,

heck, do you have an ontology of types or just the objects on this table? No. So types are there

because compiler writers are human and they're limited in what they can do. But I think that

the beauty of scripting, like there's a Python book that's called Automate the Boring Stuff,

which is exactly the right mentality. I grew up with computers in a time when Steve Jobs

was still pitching these things as bicycles for the mind. They were supposed to not be just

media consumption devices. But they were actually, you could write some code, you could write basic,

you could write some stuff to do some things. And that feeling of a computer as a thing that

we can use to extend ourselves has all but evaporated for a lot of people. So you see a

little bit in parts of the current, the generation of youth around Minecraft or Roblox, right?

And I think Python, circuit Python, these things could be a renaissance of that, of people actually

shaping and using their computers as computers, as an extension of their minds and the curiosity,

their creativity. So you talk about scripting the Adobe suite with Python in the 3D graphics world.

Python is a scripting language that some of these 3D graphics suites use. And I think it's great.

We should better support those kinds of things. But ultimately, the idea that I should be able

to have power over my computing environment, if I want these things to happen repeatedly all the

time, I should be able to say that somehow to the computer, right? Now, whether the operating systems

get there faster by having some, you know, Siri backed with open AI with whatever. So you can

just say, Siri, make this do this and this and every other Friday, right? We probably will get

there somewhere. And Apple's always had these ideas, you know, there's the Apple script in the menu

that no one ever uses. But you can do these kinds of things. But when you start doing that kind

of scripting, the challenge isn't learning the type system, or even the syntax of the language.

The challenge is all of the dictionaries and all the objects of all their properties and attributes

and parameters, like who's got time to learn all that stuff, right? So that's when then programming

by prototype, or by example, becomes the right way to get the user to express their desire.

So there's a lot of these different ways that we can approach programming. But I do think

when, as you were talking about the Adobe scripting thing, I was thinking about, you know,

when we do use something like NumPy, when we use things in the Python data and scientific,

they say expression system, there's a reason we use that, which is that it gives us mathematical

precision. It gives us actually quite a lot of precision over precisely what we mean about

this data set, that data set. And it's the fact that we can have that precision

that lets Python be powerful over as a duct tape for data. You know, you give me a TSV or a CSV,

and if you give me some massively expensive vendor tool for data transformation, I don't know,

I'm going to be able to solve your problem. But if you give me a Python prompt, you can throw

whatever data you want at me, I will be able to mash it into shape. So that ability to take it as

sort of this machete out into the data jungle is really powerful. And I think that's why at some

level, we're not going to get away from some of these expressions and APIs in libraries in Python

for data transformation. You've been at the center of the Python community for many years.

If you could change one thing about the community to help it grow, to help it improve,

to help it flourish and prosper, what would it be? I mean, that doesn't have to be one thing,

but what kind of comes to mind? What are the challenges?

Humility is one of the values that we have at Anaconda, the company, but it's also one of

the values in the community that it's been breached a little bit in the last few years,

but in general, people are quite decent and reasonable and nice. And that humility prevents

them from seeing the greatness that they could have. I don't know how many people in the core

Python community really understand that they stand perched at the edge of an opportunity

to transform how people use computers. And actually, PyCon, I think it was the last physical

PyCon I went to, Russell Keith McGee gave a great keynote about very much along the lines of the

challenges I have, which is Python, for a language that doesn't actually, that can't put an interface

up on the most popular computing devices, it's done really well as a language, hasn't it?

You can't write a web frontend with Python, really. I mean, everyone uses JavaScript.

You certainly can't write native apps. So for a language that you can't actually write apps in

any of the frontend runtime environments, Python's done exceedingly well. And so that wasn't to

pat ourselves in the back. That was to challenge ourselves as a community to say, we, through

our current volunteer dynamic, have gotten to this point, what comes next and how do we seize,

you know, we've caught the tiger by the tail, how do we make sure we keep up with it as it goes

forward? So that's one of the questions I have about sort of open source communities, is at

its best, there's a kind of humility. Is that humility prevent you to have a vision for creating

something like very new and powerful? And you've brought us back to consciousness again. The

collaboration is a swarm emergent dynamic. Humility lets these people work together without

anyone trouncing anyone else. How do they, you know, in consciousness, there's the question of

the binding problem. How does a singular, our attention, how does that emerge from, you know,

billions of neurons? So how can you have a swarm of people emerge a consensus that has a singular

vision to say, we will do this. And most importantly, we're not going to do these things. Emerging a

coherent, pointed, focused leadership dynamic from a collaboration, being able to do that kind of,

and then dissolve it so people can still do the swarm thing. That's a problem. That's a question.

So do you have to have a charismatic leader? For some reason, Linus Torvald comes to mind,

but you know, there's people who criticize the rules that iron fist, man. But there's

still charisma. There's charisma, right? There's charisma to that iron fist. There's a,

every leader is different, I would say, in their success. So he doesn't, I don't even know if you

can say he doesn't have humility. There's such a meritocracy of ideas that like, this is a good

idea and this is a bad idea. There's a step function to it. Once you clear a threshold,

he's open to your ideas, I think. The interesting thing is, obviously,

that will not stand in an open source community if that threshold that is defined by that one

particular person is not actually that good. So you actually have to be really excellent at what

you do. So he's very good at what he does. And so there's some aspect of leadership where

you can get thrown out. People can just leave. That's how it works with the open source of the

fork. But at the same time, you want to sometimes be a leader with a strong opinion because people,

I mean, there's some kind of balance here for this high of mind to get behind.

Leadership is a big topic. And I didn't, I'm not one of these guys that went to MBA school and said,

I'm going to be an entrepreneur and I'm going to be a leader. And I'm going to read all these

Harvard Business Review articles on leadership and all this other stuff. I was a physicist

turned into a software nerd who then really nerded out on Python. And now I am entrepreneurial. I

saw a business opportunity around the use of Python for data. But for me, what has been interesting

over this journey with the last 10 years is how much I started really enjoying the understanding,

thinking deeper about organizational dynamics and leadership. And leadership does come down to

a few core things. Number one, a leader has to create belief or at least has to dispel disbelief.

Leadership also, you have to have vision, loyalty and experience.

So can you say belief in a singular vision? What is belief?

Yeah, belief means a few things. Belief means here's what we need to do. And this is a valid thing

to do. And we can do it that you have to be able to drive that belief. And every step of leadership

along the way has to help you amplify that belief to more people. I mean, I think at a fundamental

level, that's what it is. You have to have a vision. You have to be able to show people that,

or you have to convince people to believe in the vision and to get behind you. And that's where

the loyalty part comes in and the experience part comes in. There's all different flavors of

leadership. So if we talk about Linus, we could talk about Elon Musk and Steve Jobs. There's

Sander Perchai. There's people that kind of put themselves at the center and are strongly opinionated.

And some people are more like consensus builders. What works well for open source? What works well

in the space of programmers? So you've been a programmer. You've led many programmers and now

sort of at the center of this ecosystem, what works well in the programming world, would you say?

It really depends on the people, what style of leadership is best. And it depends on the

programming community. I think for the Python community, servant leadership is one of the

values. At the end of the day, the leader has to also be the high priest of values. So any

collection of people has values of their living. And if you want to maintain certain values and

those values help you as an organization become more powerful, then the leader has to live those

values unequivocally and has to hold the values. So in our case, in this collaborative community

around Python, I think that the humility is one of those values. Servant leadership,

you actually have to kind of do the stuff. You have to walk the walk, not just talk the talk.

I don't feel like the Python community really demands that much from a vision standpoint.

And they should. And I think they should. This is the interesting thing is that so

many people use Python. From where comes the vision? You have an Elon Musk type character who

has bold statements about the vision for particular companies he's involved with. And it's like,

I think a lot of people that work at those companies kind of can only last if they believe

that vision. And some of it is super bold. So my question is, and by the way, those companies

often use Python, how do you establish a vision? Get to 100 million users, right?

Get to where the Python is at the center of the machine learning and was it data science,

machine learning, deep learning, artificial intelligence, revolution, right? In many ways,

perhaps the Python community is not thinking of it that way, but it's leading the way on this.

Like the tooling is like essential. Right. Well, for a while,

PyCon, people in the scientific Python and the PyData community, they would submit talks.

Those are early 2010s. They would submit talks to PyCon and the talks would all be rejected

because there was the separate sort of PyData conferences. And they're like, well, these

probably belong more to PyData. And instead, there'd be yet another talk about threads and

whatever, some web framework. And it's like, that was an interesting dynamic to see that there was,

I mean, at the time, it was a little annoying because we want to try to get more users and

get more people talking about these things. And PyCon is a huge venue, right? It's

thousands of Python programmers. But then also came to appreciate that, you know, parallel,

having an ecosystem that allows parallel innovation is not bad, right? There are people

doing embedded Python stuff. There's people doing web programming, people doing scripting,

there's cyber uses of Python. I think ultimately at some point, if your slide mode

covers so much stuff, you have to respect that different things are growing in different areas

and different niches. Now, at some point, that has to come together and the central body has to

provide resources. The principle here is subsidiarity. Give resources to the various groups

to then allocate as they see fit in their niches. That would be a really helpful dynamic. But again,

it's a volunteer community. It's not like they had that many resources to start with.

What was or is your favorite programming setup? What operating system, what keyboard,

how many screens are you listening to? What time of day? Are you drinking coffee? Tea?

Tea. Sometimes coffee, depending on how well I slept. I used to have...

How deep do you get? A night owl? I remember somebody asked you somewhere a question about

work life balance. Not just work life balance, but like a family. You lead a company and your

answer was basically like, I still haven't figured it out.

Yeah. I think I've gotten to a little bit better balance. I have a really great leadership team

now supporting me. That takes a lot of the day to day stuff off my plate. My kids are getting

a little older, so that helps. Of course, I have a wonderful wife who takes care of a lot of the

things that I'm not able to take care of and she's great. I try to get to sleep earlier now

because I have to get up every morning at six to take my kid down to the bus stop.

So there's a hard thing. For a while, I was doing polyphasic sleep, which is really interesting.