The following is a conversation with Guido Van Rossum, creator of Python,

one of the most popular programming languages in the world, used in almost any application

that involves computers, from web backend development to psychology, neuroscience,

computer vision, robotics, deep learning, natural language processing, and almost any

subfield of AI. This conversation is part of MIT course on artificial general intelligence

and the artificial intelligence podcast. If you enjoy it, subscribe on YouTube, iTunes,

or your podcast provider of choice, or simply connect with me on Twitter at Lex Friedman,

spelled F R I D. And now here's my conversation with Guido Van Rossum.

You were born in the Netherlands in 1956. Your parents and the world around you was deeply

impacted by World War Two, as was my family from the Soviet Union. So with that context,

what is your view of human nature? Are some humans inherently good and some inherently

evil, or do we all have both good and evil within us?

Ouch, I did not expect such a deep one. I guess we all have good and evil potential in us,

and a lot of it depends on circumstances and context.

Out of that world, at least on the Soviet Union side in Europe, sort of out of suffering,

out of challenge, out of that kind of set of traumatic events, often emerges beautiful art,

music, literature. In an interview I read or heard, you said you enjoyed Dutch literature

when you were a child. Can you tell me about the books that had an influence on you in your

childhood? Well, as a teenager, my favorite writer was, my favorite Dutch author was

a guy named Willem Friedrich Hermans, whose writing, certainly his early novels, were all about

sort of ambiguous things that happened during World War Two. I think he was a young adult

during that time, and he wrote about it a lot and very interesting, very good books, I thought,

I think. In a nonfiction way? No, it was all fiction, but it was very much set in the ambiguous

world of resistance against the Germans, where often you couldn't tell whether someone

was truly in the resistance or really a spy for the Germans, and some of the characters in his

novels sort of crossed that line, and you never really find out what exactly happened.

And in his novels, there's always a good guy and a bad guy, in the nature of good and evil,

is it clear there's a hero? No, his main characters are often antiheroes, and so they're

not very heroic. They fail at some level to accomplish their lofty goals.

And looking at the trajectory through the rest of your life, has literature,

Dutch or English or translation had an impact outside the technical world that you existed in?

I still read novels. I don't think that it impacts me that much directly.

It doesn't impact your work. It's a separate world. My work is highly technical and sort of

the world of art and literature doesn't really directly have any bearing on it.

You don't think there's a creative element to the design of a language's art?

I'm not disagreeing with that. I'm just saying that I don't feel

direct influences from more traditional art on my own creativity.

All right, of course, you don't feel doesn't mean it's not somehow deeply there in your subconscious.

So let's go back to your early teens. Your hobbies were building electronic circuits,

building mechanical models. If you can just put yourself back in the mind of that

young widow, 12, 13, 14, was that grounded in a desire to create a system? So to create

something? Or was it more just tinkering? Just the joy of puzzle solving?

I think it was more the latter, actually. Maybe towards the end of my high school

period, I felt confident enough that I designed my own circuits that were sort of interesting.

Somewhat. But a lot of that time, I literally just took a model kit and followed the instructions,

putting the things together. I mean, I think the first few years that I built electronics kits,

I really did not have enough understanding of electronics to really understand what I was

doing. I could debug it and I could follow the instructions very carefully, which has always

stayed with me. But I had a very naive model of how a transistor works. I don't think that in

those days, I had any understanding of coils and capacitors, which actually was a major problem

when I started to build more complex digital circuits, because I was unaware of the analog

part of how they actually work. And I would have things that the schematic looked, everything

looked fine, and it didn't work. And what I didn't realize was that there was some

megahertz level oscillation that was throwing the circuit off, because I had a sort of,

two wires were too close or the switches were kind of poorly built.

But through that time, I think it's really interesting and instructive to think about,

because there's echoes of it in this time now. So in the 1970s, the personal computer was being

born. So did you sense in tinkering with these circuits, did you sense the encroaching revolution

in personal computing? So if at that point, you're sitting, we'll sit you down and ask you to predict

the 80s and the 90s, do you think you would be able to do so successfully to unroll this,

the process? No, I had no clue. I, I remember, I think in the summer after my senior year,

or maybe it was the summer after my junior year. Well, at some point, I think when I was 18,

I went on a trip to the math Olympiad in Eastern Europe. And there was like, I was part of the

Dutch team. And there were other nerdy kids that sort of had different experiences. And one of

them told me about this amazing thing called a computer. And I had never heard that word.

My own explorations in electronics were sort of about very simple digital circuits. And I,

I had sort of, I had the idea that I somewhat understood how a digital calculator worked. And

so there is maybe some echoes of computers there, but I didn't, didn't, I never made that connection.

I didn't know that when my parents were paying for magazine subscriptions using punched cards,

that there was something called a computer that was involved that read those cards and

transferred the money between accounts. I was actually also not really interested in those

things. It was only when I went to university to study math that I found out that they had a

computer and students were allowed to use it. And there were some, you're supposed to talk to that

computer by programming it. What did that feel like? Yeah, that was the only thing you could do

with it. The computer wasn't really connected to the real world. The only thing you could do was

sort of, you typed your program on a bunch of punched cards. You gave the punched cards to

the operator. And an hour later, the operator gave you back your printout. And so all you could do

was write a program that did something very abstract. And I don't even remember what my

first forays into programming were, but they were sort of doing simple math exercises and just to

learn how a programming language worked. Did you sense, okay, first year of college, you see this

computer, you're able to have a program and it generates some output. Did you start seeing the

possibility of this? Or was it a continuation of the tinkering with circuits? Did you start to

imagine that one, the personal computer, but did you see it as something that is a tool

to get tools like a word processing tool, maybe maybe for gaming or something? Or did you start

to imagine that it could be, you know, going to the world of robotics, like you, you know, the

Frankenstein picture that you could create an artificial being. There's like another entity

in front of you. You did not see it. I don't think I really saw it that way. I was really more

interested in the tinkering. It's maybe not a sort of a complete coincidence that I ended up

sort of creating a programming language, which is a tool for other programmers. I've always been

very focused on the sort of activity of programming itself and not so much what happens with

what happens with the program you write. I do remember, and I don't remember, maybe in my second

or third year, probably my second, actually, someone pointed out to me that there was this

thing called Conway's Game of Life. You're probably familiar with it. I think in the 70s,

I think, as long as you came up with it. So there was a Scientific American column by

someone who did a monthly column about mathematical diversions and also blinking out on the guy's

name. It was very famous at the time and I think up to the 90s or so. And one of his columns was

about Conway's Game of Life and he had some illustrations and he wrote down all the rules

and sort of there was the suggestion that this was philosophically interesting, that that was why

Conway had called it that. And all I had was like the two pages photocopy of that article.

I don't even remember where I got it. But it spoke to me and I remember implementing

a version of that game for the batch computer we were using where I had a whole Pascal program

that sort of read an initial situation from input and read some numbers that said,

do so many generations and print every so many generations and then out would come pages and

pages of sort of things. Patterns of different kinds and yeah. Yeah. And I remember much later

I've done a similar thing using Python, but I sort of that original version I wrote at the time

I found interesting because I combined it with some trick I had learned during my electronics

hobbyist times. I essentially first on paper I designed a simple circuit built out of logic gates

that took nine bits of input, which is the sort of the cell and its neighbors

and produce the new value for that cell. And it's like a combination of a half adder and some

other clipping. No, it's actually a full adder. And so I had worked that out and then I translated

that into a series of Boolean operations on Pascal integers where you could use the integers as

bitwise values. And so I could basically generate 60 bits of a generation in like eight instructions

or so. Nice. So I was proud of that. It's funny that you mentioned so for people who don't know

Conway's Game of Life is a cellular automata where there's single compute units that kind of

look at their neighbors and figure out what they look like in the next generation based on the

state of their neighbors and this is deeply distributed system in concept at least. And then

there's simple rules that all of them follow and somehow out of the simple rule when you step back

and look at what occurs, it's beautiful. There's an emergent complexity, even though the underlying

rules are simple, there's an emergent complexity. Now the funny thing is you've implemented this

and the thing you're commenting on is you're proud of a hack you did to make it run efficiently.

When you're not commenting on what like this is a beautiful implementation.

You're not commenting on the fact that there's an emergent complexity

that you've quoted a simple program and when you step back and you print out the

following generation after generation, that's stuff that you may have not predicted what

happened is happening. And is that magic? I mean that's the magic that all of us feel when we

program. When you create a program and then you run it and whether it's Hello World or it shows

something on screen if there's a graphical component, are you seeing the magic and the

mechanism of creating that? I think I went back and forth. As a student, we had an incredibly

small budget of computer time that we could use. It was actually measured. I once got in trouble with

one of my professors because I had overspent the department's budget. It's a different story.

But so I actually wanted the efficient implementation because I also wanted to explore

what would happen with a larger number of generations and a larger sort of size of the

board. And so once the implementation was flawless, I would feed it different patterns and then I

think maybe there was a follow up article where there were patterns that were like gliders,

patterns that repeated themselves after a number of generations but translated one or two positions

to the right or up or something like that. And there were, I remember things like glider guns.

Well, you can Google Conway's Game of Life. People still go on over it. For a reason because

it's not really well understood. I mean this is what Stephen Wolfram is obsessed about.

We don't have the mathematical tools to describe the kind of complexity that emerges

in these kinds of systems. And the only way you can do is to run it.

I'm not convinced that it's sort of a problem that lends itself to classic mathematical analysis.

No. And so one theory of how you create an artificial intelligence or an artificial being

is you kind of have to, same with the game of life, you kind of have to create a universe

and let it run. That creating it from scratch in a design way in the, you know, coding up a

Python program that creates a fully intelligent system may be quite challenging that you might

need to create a universe just like the game of life is. Well, you might have to experiment with

a lot of different universes before. There is a set of rules that doesn't essentially always just

and repeating itself in a trivial way. Yeah. And Steve Wolfram, Stephen Wolfram works with

these simple rules, says that it's kind of surprising how quickly you find rules that

create interesting things. You shouldn't be able to, but somehow you do. And so maybe our universe

is laden with rules that will create interesting things that might not look like humans, but

you know, emergent phenomena that's interesting may not be as difficult to create as we think.

Sure. But let me sort of ask, at that time, you know, some of the world, at least in popular press,

was kind of captivated, perhaps at least in America, by the idea of artificial intelligence,

that these computers would be able to think pretty soon. And did that touch you at all? Did

that in science fiction or in reality, in any way? I didn't really start reading science fiction

until much, much later. I think as a teenager, I read maybe one bundle of science fiction stories.

Was it in the background somewhere, like in your thoughts?

That sort of the using computers to build something intelligent always fell to me,

because I felt I had so much understanding of what actually goes on inside a computer.

I knew how many bits of memory it had and how difficult it was to program and sort of

I didn't believe at all that that you could just build something intelligent out of that,

that that would really sort of satisfy my definition of intelligence. I think the most

the most influential thing that I read in my early 20s was Gödel Escherbach.

That was about consciousness and that was a big eye opener, in some sense.

In what sense? So on your own brain, did you at the time or do you now see your

own brain as a computer? Or is there a total separation of the way? So yeah, you're very

pragmatically, practically know the limits of memory, the limits of this sequential computing,

or weekly paralyzed computing, and you just know what we have now and it's hard to see

how it creates, but it's also easy to see it was in the in the 40s, 50s, 60s, and now

at least similarities between the brain and our computers. Oh yeah, I mean, I

I totally believe that brains are computers in some sense. I mean, the rules they they use to

play by are pretty different from the rules we we can sort of implement in in our current

hardware. But I don't believe in like a separate thing that infuses us with intelligence or

consciousness or any of that. There's no soul. I've been an atheist probably

from when I was 10 years old, just by thinking a bit about math and the universe.

And well, my parents were atheists. Now, I know that you you you could be an atheist and still

believe that there is something sort of about intelligence or consciousness that cannot possibly

emerge from a fixed set of rules. I am not in that camp. I totally see that

that sort of given how many millions of years evolution took its time. DNA is is a particular

machine that that sort of encodes information and an unlimited amount of information in in

chemical form and has figured out a way to replicate itself. I thought that that was maybe

it's 300 million years ago, but I thought it was closer to half a half a billion years ago that that's

sort of originated and it hasn't really changed that the sort of the structure of DNA hasn't

changed ever since that is like our binary code that we have in hardware. I mean, the basic

programming language hasn't changed, but maybe the programming itself, obviously did sort of it.

It happened to be a set of rules that was good enough to to sort of develop

of endless variability and and sort of the the idea of self replicating molecules

competing with each other for resources and and one type eventually sort of always taking over

that happened before there were any fossils. So we don't know how that exactly happened, but

I believe it it's it's clear that that did happen and can you comment on consciousness and how you

see it? Because I think we'll talk about programming quite a bit. We'll talk about,

you know, intelligence connecting to programming fundamentally, but consciousness consciousness

is this whole other other thing. Do you think about it often as a developer of a programming

language and and as a human? Those those are pretty sort of separate topics.

Sort of my line of work working with programming does not involve anything that that goes in the

direction of developing intelligence or consciousness, but sort of privately as an avid reader of

popular science writing. I have some thoughts which which is mostly that

I don't actually believe that consciousness is an all or nothing thing. I have a feeling that and

and I forget what I read that influenced this, but I feel that if you look at a cat or a dog or a

mouse, they have some form of intelligence. If you look at a fish, it has some form of intelligence

and that evolution just took a long time. But I feel that the the sort of evolution of

more and more intelligence that led to to sort of the human form of intelligence

follow the the evolution of the senses, especially the visual sense.

I mean, there is an enormous amount of processing that's needed to interpret a scene. And humans are

still better at that than than computers are. Yeah, and so and and I have a feeling that

there is a sort of the reason that that like mammals is in particular developed the levels of

consciousness that they have and that eventually sort of going from intelligence to to self

awareness and consciousness has to do with sort of being a robot that has very highly developed

senses. Has a lot of rich sensory information coming in. So the that's a really interesting

thought that that whatever that basic mechanism of DNA, whatever that basic building blocks of

programming is you if you just add more abilities, more more high resolution sensors, more sensors,

you just keep stacking those things on top that this basic programming in trying to survive

develops very interesting things that start to us humans to appear like intelligence and

consciousness. Yeah, so in in as far as robots go, I think that the self driving cars have that sort

of the greatest opportunity of developing something like that because when I drive myself, I don't

just pay attention to the rules of the road. I also look around and I get clues from that Oh,

this is a shopping district. Oh, here's an old lady crossing the street. Oh, here is someone

carrying a pile of mail. There's a mailbox. I bet you they're gonna cross the street to reach

that mailbox. And I slow down. And I don't even think about that. Yeah. And so there is there is

so much where you turn your observations into an understanding of what other consciousnesses

are going to do or what what other systems in the world are going to be Oh, that tree is going to

fall. Yeah, I see sort of I see much more of I expect somehow that if anything is going to

become conscious, it's going to be the self driving car and not the network of a bazillion

computers at in a Google or Amazon data center that are all networked together to

to do whatever they do. So in that sense, so you actually highlight because that's what I work in

is an autonomous vehicles, you highlight the big gap between what we currently can't do and

what we truly need to be able to do to solve the problem. Under that formulation, then consciousness

and intelligence is something that basically a system should have in order to interact with us

humans, as opposed to some kind of abstract notion of a consciousness consciousness is

something that you need to have to be able to empathize to be able to

to fear the understand what the fear of death is. All these aspects that are important for

interacting with pedestrians need to be able to do basic computation based on our human

desires and if you sort of Yeah, if you if you look at the dog, the dog clearly knows, I mean,

I'm not the dog owner, my brother, I have friends who have dogs, the dogs clearly know

what the humans around them are going to do or at least they have a model of what those humans

are going to do when they learn the dog some dogs know when you're going out and they want to go

out with you, they're sad when you leave them alone, they cry. They're afraid because they were

mistreated when they were younger. We don't assign sort of consciousness to dogs or at least

not not all that much but I also don't think they have none of that. So I think it's it's

consciousness and intelligence are not all or nothing.

The spectrum is really interesting. But in returning to

programming languages and the way we think about building these kinds of things about building

intelligence, building consciousness, building artificial beings. So I think one of the exciting

ideas came in the 17th century. And with liveness, Hobbes, Descartes, where there's this feeling that

you can convert all thought all reasoning, all the thing that we find very special in our brains,

you can convert all of that into logic. You can formalize it, former reasoning. And then once

you formalize everything, all of knowledge, then you can just calculate. And that's what

we're doing with our brains is we're calculating. So there's this whole idea that we that this is

possible that this but they weren't aware of the concept of pattern matching in the sense that we

are aware of it now. They sort of thought you they had discovered incredible bits of mathematics

like Newton's calculus. And their sort of idealism there, their sort of extension of what they could

do with logic and math sort of went along those lines. And they thought there's there's like,

yeah, logic, there's there's like a bunch of rules, and a bunch of input, they didn't realize that how

you recognize a face is not just a bunch of rules, but is a shit ton of data, plus a circuit that

that sort of interprets the visual clues and the context and everything else. And somehow

how can massively parallel pattern match against stored rules? I mean, if I see you tomorrow here

in front of the Dropbox office, I might recognize you even if I'm wearing a different shirt. Yeah,

but if I if I see you tomorrow in a coffee shop in Belmont, I might have no idea that it was you

or on the beach or whatever. I make those mistakes myself all the time. I see someone that I only

know as like, Oh, this person is a colleague of my wife's. And then I see them at the movies and

I don't recognize them. But do you see those you call it pattern matching? Do you see that rules is

unable to encode that to you? Everything you see all the piece of information you look around

this room, I'm wearing a black shirt, I have a certain height, I'm a human all these you can

there's probably tens of thousands of facts you pick up moment by moment about this scene,

you take them for granted and you accumulate aggregate them together to understand the scene.

You don't think all of that could be encoded to weren't at the end of the day, you just put

it on the table and calculate. Oh, I don't know what that means. I mean, yes, in the sense that

there is no, there is no actual magic there, but there are enough layers of abstraction from sort

of from the facts as they enter my eyes and my ears to the understanding of the scene that I don't

think that that AI has really covered enough of that distance. It's like if you take a human body

and you realize it's built out of atoms, well, that that is a uselessly reductionist view, right?

The body is built out of organs, the organs are built out of cells, the cells are built out of

proteins, the proteins are built out of amino acids, the amino acids are built out of atoms,

and then you get to quantum mechanics. So that's a very pragmatic view. I mean, obviously as an

engineer, I agree with that kind of view, but I also you also have to consider the the with the

Sam Harris view of well, well, intelligence is just information processing. Do you just like

you said you take in sensory information, you do some stuff with it and you come up with actions

that are intelligent. That makes it sound so easy. I don't know who Sam Harris is. Oh, it's

philosopher. So like this is how philosophers often think, right? And essentially, that's what

Descartes was is, wait a minute, if there is, like you said, no magic. So you basically says it

doesn't appear like there's any magic, but we know so little about it that it might as well be magic.

So just because we know that we're made of atoms, just because we know we're made of organs,

the fact that we know very little how to get from the atoms to organs in a way that's recreatable

means it that you shouldn't get too excited just yet about the fact that you figured out that we're

made of atoms. Right. And and and the same about taking facts as our our sensory organs take them

in and turning that into reasons and actions that sort of there are a lot of abstractions that we

haven't quite figured out how to how to deal with those. I mean, I sometimes I don't know if I can

go on a tangent or not. Please. Drag you back in. Sure. So if I take a simple program that parses,

say I have a compiler, it parses a program. In a sense, the input routine of that compiler

of that parser is a sense, a sensing organ. And it builds up a mighty complicated internal

representation of the program it just saw it doesn't just have a linear sequence of bytes

representing the text of the program anymore, it has an abstract syntax tree. And I don't know how

many of your viewers or listeners are familiar with compiler technology, but there is fewer and

fewer these days, right? That's also true, probably. People want to take a shortcut, but there's sort

of this abstraction is a data structure that the compiler then uses to produce outputs that is

relevant like a translation of that program to machine code that can be executed by by hardware.

And then that data structure gets thrown away. When a fish or a fly sees

these sort of gets visual impulses. I'm sure it also builds up some data structure and for

the fly that may be very minimal, a fly may may have only a few. I mean, in the case of a fly's

brain, I could imagine that there are few enough layers of abstraction that it's not much more

than when it's darker here than it is here. Well, it can sense motion, because a fly sort of responds

when you move your arm towards it. So clearly, it's visual processing is intelligent, or well,

not intelligent, but is has an abstraction for motion. And we still have similar things in in

but much more complicated in our brains. I mean, otherwise, you couldn't drive a car if you,

you couldn't sort if you didn't have an incredibly good abstraction for motion.

Yeah, in some sense, the same abstraction for motion is probably one of the primary sources of

our of information for us, we just know what to do. I think we know what to do with that.

We've built up other abstractions on top. We build much more complicated data structures

based on that. And we build more persistent data structures, sort of after some processing,

some information sort of gets stored in our memory, pretty much permanently, and is available on

recall. I mean, there are some things that you sort of, you're conscious that you're remembering it,

like you give me your phone number, I, well, at my age, I have to write it down, but I could

imagine I could remember those seven numbers or 10, 10 digits, and reproduce them in a while.

If I sort of repeat them to myself a few times. So that's a fairly conscious form of memorization.

On the other hand, how do I recognize your face? I have no idea. My brain has a whole bunch of

specialized hardware that knows how to recognize faces. I don't know how much of that is sort of

coded in our DNA and how much of that is trained over and over between the ages of zero and three.

But somehow our brains know how to do lots of things like that that are useful in our interactions

with other humans without really being conscious of how it's done anymore.

Right. So our actual day to day lives, we're operating at the very highest level of abstraction.

We're just not even conscious of all the little details underlying it. There's compilers on top

of, it's like turtles on top of turtles or turtles all the way down. It's compilers all the way down.

But that's essentially, you say that there's no magic. That's what I, what I was trying to get at,

I think, is with Descartes started this whole train of saying that there's no magic. I mean,

there's others beforehand. Well, didn't Descartes also have the notion, though, that the soul and

the body were fundamentally separate? Yeah, I think he had to write in God in there for

political reasons. So I don't actually, I'm not historian, but there's notions in there that

all of reasoning, all of human thought can be formalized. I think that continued in the 20th

century with the Russell and with Gato's incompleteness theorem, this debate of what are

the limits of the things that could be formalized? That's where the Turing machine came along.

And this exciting idea, I mean, underlying a lot of computing, that you can do quite a lot

with a computer. You can, you can encode a lot of the stuff we're talking about in terms of

recognizing faces and so on, theoretically, in an algorithm that can then run on the computer.

And in that context, I'd like to ask programming in a philosophical way.

What, so what does it mean to program a computer? So you said you write a Python program

or compiled a C++ program that compiles to somebody code. It's forming layers.

You're, you're, you're programming in a layer of abstraction that's higher. How do you see programming

in that context? Can it keep getting higher and higher levels of abstraction?

I think at some, at some point, the higher level of levels of abstraction will not be called

programming and they will not resemble what we, we call programming at the moment. There will

not be source code. I mean, there will still be source code sort of at a lower level of the machine,

just like there's still molecules and electrons and sort of proteins in our brains. But, and so

there's still programming and system administration and who knows what keeping to keep the machine

running. But what the machine does is, is a different level of abstraction in a sense. And

as far as I understand the way that for last decade or more people have made progress with

things like facial recognition or the self driving cars is all by endless, endless amounts of

training data where at least as, as, as a layperson and I feel myself totally as a layperson in that

field, it looks like the researchers who publish the results don't necessarily know exactly how,

how their algorithms work. And I often get upset when I sort of read a sort of a fluff piece about

Facebook in the newspaper or social networks and they say, well, algorithms. And that's like a totally

different interpretation of the word algorithm. Because for me, the way I was trained or what I

learned when I was eight or 10 years old, an algorithm is a set of rules that you completely

understand that can be mathematically analyzed. And, and, and you can prove things, you can like

prove that Aristotle's Civ produces all prime numbers and only prime numbers.

Yeah. So the, I don't know if you know who Andre Capati is. I'm afraid not. So he's a

head of AI at Tesla now, but he was at Stanford before, and he has this cheeky way of calling

this concept software 2.0. So let me disentangle that for a second. So kind of what you're

referring to is the traditional, traditional, the algorithm, the concept of an algorithm,

something that's there, it's clear, you can read it, you understand it, you can prove it's

functioning as kind of software 1.0. And what software 2.0 is, is exactly what you describe,

which is you have neural networks, which is a type of machine learning that you feed a bunch

of data, and that neural network learns to do a function. All you specify is the inputs and

the outputs you want, and you can't look inside. You can't analyze it. All you can do is train

this function to map the inputs, the outputs by giving a lot of data. In that sense, programming

becomes getting a lot of cleaning, getting a lot of data. That's what programming is in this.

Well, that would be programming 2.0. 2.0 to programming 2.0.

I wouldn't call that programming. It's just a different activity, just like

building organs out of cells is not called chemistry.

Well, so let's just step back and think sort of more generally, of course, but it's like

as a parent teaching your kids, things can be called programming. In that same sense,

that's how programming is being used. You're providing them data, examples, use cases.

So imagine writing a function not with for loops and clearly readable text, but more saying,

well, here's a lot of examples of what this function should take, and here's a lot of

examples of when it takes those functions, it should do this, and then figure out the rest.

So that's the 2.0 concept. And the question I have for you is like, it's a very fuzzy way.

This is the reality of a lot of these pattern recognition systems and so on. It's a fuzzy way

of quote unquote programming. What do you think about this kind of world? Should it be called

something totally different than programming? If you're a software engineer, does that mean

you're designing systems that are very can be systematically tested, evaluated, they have a

very specific specification, and then this other fuzzy software 2.0 world machine learning world,

that's that's something else totally? Or is there some intermixing that's possible?

Well, the question is probably only being asked because we we don't quite know what

that software 2.0 actually is. And it sort of I think there is a truism that every task that

AI has has tackled in the past. At some point, we realized how it was done. And then it was no

longer considered part of artificial intelligence because it was no longer necessary to to use

that term. It was just, oh, now we know how to do this. And a new field of science or engineering

has been developed. And I don't know if sort of every form of learning or sort of controlling

computer systems should always be called programming. So I don't know, maybe I'm focused too much on

the terminology. I but I expect that that there just will be different concepts where people with

sort of different education and a different model of what they're trying to do will will develop those

concepts. Yeah, and I guess, if you could comment on another way to put this concept is, I think,

I think the kind of functions that neural networks provide is things as opposed to being able to

upfront prove that this should work for all cases you throw at it. All you're able, it's the worst

case analysis versus average case analysis, all you're able to say is it seems on everything

we've tested to work 99.9% of the time, but we can't guarantee it and it fails in unexpected ways.

We can even give you examples of how it fails in unexpected ways. But it's like really good

most of the time. Yeah, but there's no room for that in current ways we think about programming.

Programming 1.0 is actually sort of

getting to that point to where the sort of the ideal of a bug free program

has been abandoned long ago by most software developers. We only care about bugs that manifest

themselves often enough to be annoying. And we're willing to take the occasional crash or

outage or incorrect result for granted, because we can't possibly we don't have enough programmers

to make all the code bug free and it would be an incredibly tedious business. And if you try to

throw formal methods at it, it gets it becomes even more tedious. So every once in a while,

the user clicks on a link in and somehow they get an error. And the average user doesn't panic,

they just click again and see if it works better the second time, which often magically it does.

Or they go up and they try some other way of performing their tasks. So that's sort of an

end to end recovery mechanism and inside systems, there is all sorts of retries and timeouts and

fallbacks. And I imagine that that sort of biological systems are even more full of that

because otherwise they wouldn't survive. Do you think programming should be taught and thought of

as exactly what you just said before I come from is kind of you're you're always denying that fact

always in in sort of basic programming education, the sort of the programs you're, you're having

students write are so small and simple that if there is a bug, you can always find it and fix it.

Because the sort of programming as it's being taught in some even elementary middle schools

in high school, introduction to programming classes in college, typically, it's programming in the

small. Very few classes sort of actually teach software engineering building large systems. I

mean, every summer here at Dropbox, we have a large number of interns, every tech company

on the West Coast has the same thing. These interns are always amazed because this is the

first time in their life that they see what goes on in a really large software development environment.

And everything they've learned in college was almost always about a much smaller scale and

somehow that difference in scale makes a qualitative difference in how you how you

do things and how you think about it. If you then take a few steps back into decades,

70s and 80s, when you're first thinking about Python or just that world of programming languages,

did you ever think that there would be systems as large as underlying Google, Facebook and Dropbox?

Did you when you were thinking about Python? I was actually always caught by surprise by

every sort of this. Yeah, pretty much every stage of computing.

So maybe just because you spoke in other interviews, but I think the evolution of

programming languages are fascinating. And it's especially because it leads from my

perspective towards greater and greater degrees of intelligence. I learned the first programming

language I played with in Russia was with the turtle logo logo. Yeah. And if you look, I just

have a list of programming languages, all of which I've played with a little bit. And they're all

beautiful in different ways from Fortran, Cobalt, Lisp, Algal 60, basic logo again, C

as a few object oriented came along in the 60s, Simula, Pascal, small talk, all of that leads

all the classics, the classics. Yeah, the classic hits, right? Scheme built that's built on top of

Lisp on the database side SQL C plus plus and all that leads up to Python, Pascal to

and all that's before Python, MATLAB, these kind of different communities, different languages.

So can you talk about that world? I know that sort of Python came out of ABC, which actually

never knew that language. I just having researched this conversation went back to ABC and it looks

remarkably, it has a lot of annoying qualities. But underneath those like all caps and so on.

But underneath that, there's elements of Python that are quite they're already there.

That's where I got all the good stuff, all the good stuff. So but in that world,

you're swimming in these programming languages, were you focused on just the good stuff in your

specific circle? Or did you have a sense of what, what is everyone chasing? You said that every

programming language is built to scratch an itch. Were you aware of all the itches in the community

and if not, or if yes, I mean, what itch we try to scratch with Python?

Well, I'm glad I wasn't aware of all the itches because I would probably not have been able to

do anything. I mean, if you're trying to solve every problem at once,

you saw nothing. Well, yeah, it's, it's too overwhelming. And so I had a very, very focused

problem. I wanted a programming language that set somewhere in between shell scripting and C.

And now, arguably, there is like, one is higher level, one is lower level. And

Python is sort of a language of an intermediate level, although it's still pretty much at the

high level. And I was I was thinking about much more about I want a tool that I can use to be

more productive as a programmer in a very specific environment. And I also had given myself a time

budget for the development of the tool. And that was sort of about three months for both the design

like thinking through what are all the features of the language syntactically.

And semantically, and how do I implement the whole pipeline from parsing the source code to

executing it. So I think both with the timeline and the goals, it seems like productivity was

at the core of it as a goal. So, like for me, in the 90s, and the first decade of the 21st

century, I was always doing machine learning AI, programming for my research was always in C++.

Wow. And then the other people who are a little more mechanical engineering,

electrical engineering, are Matlabby. They're a little bit more Matlab focused. Those are the

world and maybe a little bit Java too, but people who are more interested in emphasizing

the object oriented nature of things. So within the last 10 years or so, especially with the

oncoming of neural networks and these packages that are built on Python to interface with

neural networks, I switched to Python. And it's just, I've noticed a significant boost that I

can't exactly, because I don't think about it, but I can't exactly put into words why I'm just

much, much more productive, just being able to get the job done much, much faster. So how do you

think whatever that qualitative difference is, I don't know if it's quantitative, it could be just

a feeling. I don't know if I'm actually more productive, but how do you think about? You probably

are. Yeah, well, that's right. I think there's elements. Let me just speak to one aspect that

I think that was affecting our productivity is C++ was, I really enjoyed creating performant code

and creating a beautiful structure where everything that, you know, this kind of going

into this, especially with the newer and newer standards of templated programming of just really

creating this beautiful, formal structure that I found myself spending most of my time doing that

as opposed to getting it parsing a file and extracting a few keywords or whatever the task

goes trying to do. So what is it about Python? How do you think of productivity in general as

you were designing it now? So through the decades, last three decades, what do you think it means

to be a productive programmer? And how did you try to design it into the language?

There are different tasks. And as a programmer, it's, it's useful to have different tools available

that sort of are suitable for different tasks. So I still write C code. I still write shell code.

But I write most of my, my things in Python.

Why do I still use those other languages? Because sometimes the task just demands it.

And, well, I would say most of the time, the task actually demands a certain language because

the task is not write a program that solves problem x from scratch, but it's more like

fix a bug in existing program x or add a small feature to an existing large program.

But even if, if you sort of, if you're not constrained in your choice of language

by context like that, there is still the fact that if you write it in a certain language, then you

sort of, you, you have this balance between how long does it time? Does it take you to write the

code? And how long does the code run? And when you're in sort of, in the phase of exploring

solutions, you often spend much more time writing the code than running it, because every time

you've sort of, you've run it, you see that the output is not quite what you wanted. And

you spend some more time coding. And a language like Python just makes that iteration much faster,

because there are fewer details. There is a large library, sort of there are fewer details that,

that you have to get right before your program compiles and runs. There are libraries that

do all sorts of stuff for you. So you can sort of very quickly take a bunch of

existing components, put them together and get your prototype application running just like

when I was building electronics, I was using a breadboard most of the time. So I had this like

sprawl out circuit that if you shook it, it would stop working because it was not put together

very well. But it functioned and all I wanted was to see that it worked and then move on to the next

next schematic or design or add something to it. Once you've sort of figured out, oh, this is the

perfect design for my radio or light sensor or whatever, then you can say, okay, how do we

design a PCB for this? How do we solder the components in a small space? How do we make it

so that it is robust against, say, voltage fluctuations or mechanical disruption? I mean,

I know nothing about that when it comes to designing electronics, but I know a lot about

that when it comes to writing code. So the initial steps are efficient, fast, and there's not much

stuff that gets in the way. But you're kind of describing from like Darwin described the evolution

of species, right? You're observing of what is true about Python. Now, if you take a step back,

if the act of creating languages is art, and you had three months to do it, initial steps,

so you just specified a bunch of goals, sort of things that you observe about Python. Perhaps

you had those goals, but how do you create the rules, the syntactic structure, the features

that result in those? So I have, in the beginning, and I have follow up questions about through the

evolution of Python, too. But in the very beginning, when you're sitting there, creating the lexical

analyze or whatever evolution was still a big part of it, because I sort of I said to myself,

I don't want to have to design everything from scratch. I'm going to borrow features from

other languages that I like. Oh, interesting. So you basically, exactly, you first observe what

you like. Yeah. And so that's why if you're 17 years old, and you want to sort of create a programming

language, you're not going to be very successful at it. Because you have no experience with other

languages. Whereas I was in my, let's say mid 30s. I had written parsers before. So I had worked on

the implementation of ABC, I had spent years debating the design of ABC with its authors,

it's with its designers, I had nothing to do with the design, it was designed

fully as it was ended up being implemented when I joined the team. But so

you borrow ideas and concepts and very concrete sort of local rules from different languages,

like the indentation and certain other syntactic features from ABC. But I chose to borrow string

literals and how numbers work from C and various other things. So in then, if you take that further,

so yet, you've had this funny sounding, but I think surprisingly accurate and at least practical

title of benevolent dictator for life for quite, you know, for the last three decades or whatever,

or no, not the actual title, but functionally speaking. So you had to make decisions, design

decisions. Can you maybe let's take Python two, so Python releasing Python three as an example.

Mm hmm. It's not backward compatible to Python two in ways that a lot of people know. So what was

that deliberation discussion decision like? Yeah, what was the psychology of that experience?

Do you regret any aspects of how that experience undergone that? Well, yeah, so it was a group

process really. At that point, even though I was BDFL in name, and certainly everybody sort of

respected my position as the creator and the current sort of owner of the language design,

I was looking at everyone else for feedback.

Sort of Python 3.0 in some sense was sparked by other people in the community pointing out,

oh, well, there are a few issues that sort of bite users over and over. Can we do something

about that? And for Python three, we took a number of those Python words as they were called at the

time. And we said, can we try to sort of make small changes to the language that address those words?

And we had sort of in the past, we had always taken backwards compatibility very seriously.

And so many Python words in earlier versions had already been resolved, because they could be resolved

while maintaining backwards compatibility or sort of using a very gradual path of evolution of the

language in a certain area. And so we were stuck with a number of words that were widely recognized

as problems, not like roadblocks, but nevertheless, sort of things that some people trip over. And you

know that that's always the same thing that that people trip over when they trip. And we could not

think of a backwards compatible way of resolving those issues. But it's still an option to not

resolve the issues. And so yes, for for a long time, we had sort of resigned ourselves to well,

okay, the language is not going to be perfect in this way, and that way, and that way. And we sort

of certain of these I mean, there are still plenty of things where you can say, well, that's

that particular detail is better in Java or in R or in visual basic or whatever. And we're okay with

that because well, we can't easily change it. It's not too bad, we can do a little bit with user

education, or we can have static analyzer or warnings in in the parse or something. But there

were things where we thought, well, these are really problems that are not going away, they're

getting worse. In the future, we should do something about it. Do something. But ultimately, there is

a decision to be made, right? Yes. So was that the toughest decision in the history of Python you

had to make as the benevolent dictator for life? Or if not, what are other maybe even on a smaller

scale? What was the decision where you were really torn up about? Well, the toughest decision was

probably to resign. All right, let's go there. Hold on a second, then let me just because in the

interest of time too, because I have a few cool questions for you. And let's touch a really

important one because it was quite dramatic and beautiful in certain kinds of ways. In July this

year, three months ago, you wrote, now that PEP 572 is done, I don't ever want to have to fight so

hard for a PEP and find that so many people despise my decisions. I would like to remove myself

entirely from the decision process. I'll still be there for a while as an ordinary core developer.

And I'll still be available to mentor people possibly more available. But I'm basically giving

myself a permanent vacation from being BDFL benevolent dictator for life. And you all will

be on your own. First of all, just this, it's almost Shakespearean. I'm not going to appoint a

successor. So what are you all going to do? Create a democracy, anarchy, a dictatorship,

a federation. So that was a very dramatic and beautiful set of statements. It's almost,

it's open ended nature, called the community to create a future for Python. This is kind of a

beautiful aspect to it. Wow. So what and dramatic, you know, what was making that decision like?

What was on your heart, on your mind, stepping back now, a few months later,

taking it to your mindset? I'm glad you liked the writing because it was actually written pretty

quickly. It was literally something like after months and months of going around in circles,

I had finally approved PEP 572, which I had a big hand in its design, although I didn't

initiate it originally. I sort of gave it a bunch of nudges in a direction that would be

better for the language. So sorry, just to ask, is async IO, is that the one or no? No, PEP 572 was

actually a small feature, which is assignment expressions. Oh, assignment expressions, okay.

That had been thought there was just a lot of debate where a lot of people claimed that

they knew what was Pythonic and what was not Pythonic, and they knew that this was going to

destroy the language. This was like a violation of Python's most fundamental design philosophy.

And I thought that was all bullshit because I was in favor of it. And I would think I know

something about Python's design philosophy. So I was really tired and also stressed of that thing.

And literally, after sort of announcing, I was going to accept it. A certain Wednesday evening,

I had finally send the email, it's accepted. Now let's just go implement it. So I went to bed

feeling really relieved. That's behind me. And I wake up Thursday morning, 7am. And I think,

well, that was the last one. That's going to be such such a terrible debate. And that's

going to be that's the last time that I let myself be so stressed out about a PEP decision.

I should just resign. I've been sort of thinking about retirement for half a decade. I've been

joking and sort of mentioning retirement, sort of telling the community, some point in the

future, I'm going to retire. Don't take that FL part of my title too literally. And I thought,

okay, this is it. I'm done. I had the day off. I wanted to have a good time with my wife. We

were going to a little beach town nearby. And in, I think maybe 15, 20 minutes, I wrote that thing

that you just called Shakespearean. And the funny thing is, I get so much crap for calling you

Shakespearean. I didn't even I didn't even realize what a monumental decision it was.

Because five minutes later, I read that a link to my message back on Twitter, where people were

already discussing on Twitter, Guido resigned as the BDFL. And I had, I had posted it on an internal

forum that I thought was only read by core developers. So I thought I would at least

have one day before the news would sort of get out. The on your own aspects, I had also an

element of quite, it was quite a powerful element of the uncertainty that lies ahead. But can you

also just briefly talk about, you know, like, for example, I play guitar as a hobby for fun.

And whenever I play, people are super positive, super friendly. They're like, this is awesome.

This is great. But sometimes I enter as an outside observer, enter the programming community.

And there seems to some sometimes be camps on whatever the topic. And in the two camps,

the two or plus camps, are often pretty harsh at criticizing the opposing camps.

As an onlooker, I may be totally wrong on this. Yeah, holy wars are sort of a favorite activity

in the programming community. And what is the psychology behind that? Is, is that okay for

a healthy community to have? Is that, is that a productive force ultimately for the evolution

of a language? Well, if everybody is batting each other on the back and never telling the truth,

yes, it would not be a good thing. I think there is a middle ground where sort of

being nasty to each other is not okay. But there there is is is a middle ground where there is

is healthy ongoing criticism and feedback that is very productive. And you mean at every level,

you see that I mean, someone proposes to fix a very small issue in a code base.

Chances are that some reviewer will sort of respond by saying, well, actually,

you can do it better the other way. When it comes to deciding on the future of the Python

core developer community, we now have, I think, five or six competing proposals for a constitution.

So that future, do you have a fear of that future? Do you have a hope for that future?

I'm very confident about that future. And by and large, I think that the debate has been very

healthy and productive. And I actually when when I wrote that resignation email, I knew that that

Python was in a very good spot and that the Python core development community that the group of

50 or 100 people who sort of write or review most of the code that goes into Python, those people

get along very well most of the time. A large number of different areas of expertise are

represented at different levels of experience in the Python core dev community, different levels

of experience completely outside it in software development in general, large systems, small

systems, embedded systems. So I felt okay, resigning because I knew that that the community can

really take care of itself. And out of a grab bag of future feature developments, let me ask if

you can comment, maybe on all very quickly, concurrent programming parallel computing,

async IO, these are things that people have expressed hope, complained about, whatever

I have discussed on Reddit, async IO, so the parallelization in general, packaging, I was totally

close on this, I just use pip install stuff, but apparently, there's pip end of poetry, there's

these dependency packaging systems that manage dependencies and so on, they're emerging, and

there's a lot of confusion about what's what's the right thing to use. Then also, functional

programming, the ever, are we going to get more functional programming or not, this kind of idea,

and of course, the GIL connected to the parallelization, I suppose, the global interpreter

lock problem. Can you just comment on whichever you want to comment on?

Well, let's take the GIL and parallelization and async IO as one one topic.

I'm not that hopeful that Python will develop into a sort of high concurrency, high parallelism

language. That's sort of the way the language is designed, the way most users use the language,

the way the language is implemented, all make that a pretty unlikely future.

So you think it might not even need to really the way people use it, it might not be something

that should be of great concern. I think I think async IO is a special case, because it sort of

allows overlapping IO and only IO. And that is is a sort of best practice of supporting very

high throughput IO, many connections per second. I'm not worried about that. I think async IO

will evolve. There are a couple of competing packages, we have some very smart people who are

sort of pushing us in sort of to make async IO better. Parallel computing, I think that

Python is not the language for that. There are there are ways to work around it.

But you sort of you can't expect to write an algorithm in Python and have a compiler

automatically paralyze that what you can do is use a package like NumPy and there are a bunch of

other very powerful packages that sort of use all the CPUs available, because you tell the package,

here's the data, here's the abstract operation to apply over it, go at it, and then then we're

back in the C++ world. But the those packages are themselves implemented usually in C++.

That's right. That's where TensorFlow and all these packages come in where they parallelize

across GPUs, for example, they take care of that for you. So in terms of packaging, can you comment

on the packaging? Yeah, my packaging has always been my least favorite topic. It's a really tough

problem because the OS and the platform want to own packaging. But their packaging solution is not

specific to a language. Like, if you take Linux, there are two competing packaging solutions for

Linux, or for Unix in general. And but they all work across all languages. And several languages,

like Node, JavaScript, and Ruby, and Python all have their own packaging solutions that only work

within the ecosystem of that language. Well, what should you use? That is a tough problem.

My own own approach is I use the system packaging system to install Python, and I use the Python

packaging system then to install third party Python packages. That's what most people do.

10 years ago, Python packaging was really a terrible situation. Nowadays, Pip is the future.

There is there is a separate ecosystem for numerical and scientific Python, Python based on

Anaconda. Those two can live together. I don't think there is a need for more than that.

Great. So that's that's packaging. That's, well, at least for me, that's that's where I've been

extremely happy. I didn't I didn't even know this was an issue until it was brought up. Well, in the

interest of time, let me sort of skip through a million other questions I have. So I watched the

five hour five five and a half hour oral history. They've done with the computer history museum.

And the nice thing about it, it gave this because of the linear progression of the interview, it

it gave this feeling of a life, you know, a life well lived with interesting things in it.

Sort of a pretty, I would say a good spend of of this little existence we have on earth.

So outside of your family, looking back, what about this journey are you really proud of?

Are there moments that stand out accomplishments ideas? Is it the creation of Python itself

that stands out as a thing that you look back and say, damn, I did pretty good there?

Well, I would say that Python is definitely the best thing I've ever done.

And I wouldn't sort of say just the creation of Python, but the way I sort of raised Python,

like a baby, I didn't just conceive a child, but I raised a child. And now I'm setting the child

free in the world. And I've set up the child to to sort of be able to take care of himself.

And I'm very proud of that. And as the announcer of Monty Python's Flying Circus used to say,

and now for something completely different, do you have a favorite Monty Python moment or a

moment in Hitchhiker's Guide or any other literature show or movie that cracks you up when you think

about it? Oh, you can always play me the Parrots, the dead Parrot sketch. Oh, that's brilliant.

Yeah, that's my favorite as well. Pushing up the daisies. Okay, Greta, thank you so much for

talking to me today. Lex, this has been a great conversation.