Peter Wang: Python and the Source Code of Humans, Computers, and Reality | Lex Fridman Podcast #250

Lex Fridman 0:00 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 Oliphant to Eric Weinstein. So here we are. This is the Lex Fridman Podcast. To support it, please check out our sponsors in the description, and now here's my conversation with Peter Wang.

Lex Fridman 0:33 You're one of the most impactful humans in the Python ecosystem. (laughs) Uh, 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-

Peter Wang 0:49 (laughs)

Lex Fridman 0:49 ... what to you is the best or maybe the most beautiful feature of Python, or maybe the thing that made you fall in love or stay in love with Python?

Peter Wang 0:59 Well, those are three different things. Uh, what I think-

Lex Fridman 1:01 (laughs)

Peter Wang 1:01 ... 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.

Lex Fridman 1:10 In the '90s?

Peter Wang 1:10 In, yeah, late '90s. And that was my first job out of college. Um, and we kept trying to do more and more, uh, like abstract and higher order programming in C++, which at the time was quite difficult, um, with templates, the, 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 a really productive scripting language. I mean, I, I knew Perl, I knew Bash. I was decent at both, but Python just made everything, it, it made the whole world accessible, right? I could script this and that and the other network things, you know, little hard drive utilities. I could write all these things in the space of an afternoon.

Lex Fridman 2:06 Mm-hmm.

Peter Wang 2:06 And that was really, really cool. So that's what made me fall in love.

Lex Fridman 2:08 Is there something specific you can put your finger on that you're not programming in Perl today?

Peter Wang 2:13 (laughs)

Lex Fridman 2:14 Like why, why Python for scripting?

Peter Wang 2:17 Oh, I think there's not a specific thing as much as the design motif of both the, the creator of the language and the core, uh, group of people that built the standard library around him. Um, there was definitely, there was a taste to it. I mean, Steve Jobs, you know, used that term, you know, in somewhat of a arrogant way, but I think it's a real thing, that it was designed to fit. Uh, a friend of mine actually expressed this really well. He said, "Python just fits in my head," and there's nothing better to, to say than that. Now, now people might argue modern Python, there's a lot more complexity, but certainly as of version 5, 1.5.2 I think was my first version, um, that fitted my head very easily.

Lex Fridman 2:54 Mm-hmm.

Peter Wang 2:54 So that's what made me fall in love with it.

Lex Fridman 2:56 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, you return to often as, as a thing that just brings you a smile?

Peter Wang 3:11 I really still like the, um, the ability to play with meta classes and express higher order things. When I have to create some new object model to, to model something, right, it's easy for me 'cause I'm, 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, that to me, I would say that's tied with the NumPy and vectorization, uh, capabilities. I love thinking in terms of the matrices and the vectors and these kind of, um, uh, data structures. So, uh, I would say those two are kind of, uh, uh, tied for me.

Lex Fridman 3:49 So the elegance of the NumPy data structure, like slicing through the different multi-dimensional arrays.

Peter Wang 3:54 Yeah, there's just enough things there, it's like a very, it's a very simple, comfortable tool. Just it's easy to reason about what it does when you don't stray too far afield.

Lex Fridman 4:05 Can you, uh, 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, uh, something you have to kind of write out on paper, look and, and say it's just right? Is it a taste thing or is there a systematic process? What's your sense?

Peter Wang 4:29 I think it's more of a taste thing, but one, one thing that should be said is that you have to pick your audience, right? 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, right, a compact projection for their needs. The more diverse the user base-

Lex Fridman 4:52 (laughs) Yeah.

Peter Wang 4:53 ... um, the harder that is.

Lex Fridman 4:54 Yeah.

Peter Wang 4:54 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, that's the downside of popularity. It's almost an intrinsic aspect of the complexity of the problem.

Lex Fridman 5:13 Well, at the very beginning, aren't you an audience of one? Isn't ultimately, aren't all the greatest projects in history-

Peter Wang 5:19 (laughs)

Lex Fridman 5:19 ... were just solving a problem that you yourself had?

Peter Wang 5:22 Well, so Clay Shirky in his, um, book on crowdsourcing or in his kind of thoughts on crowdsourcing, he identifies the first step of crowdsourcing is me-first collaboration.

Lex Fridman 5:31 (laughs)

Peter Wang 5:31 You first have to make something that works well for yourself.

Lex Fridman 5:33 Yeah.

Peter Wang 5:34 It's very telling that when you look at all of the impactful big proj- well, they're fundamental projects now in the SciPy and PyData ecosystem, they all started with the, uh, 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, you know, 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.... right? So it's almost like a, it's a, a necessity is the mother of invention aspect, and also it was a really harsh filter for utility and compactness and expressiveness. Like, it was too hard to use, then they wouldn't have built it, 'cause that was just too much trouble.

Lex Fridman 6:24 Mm-hmm.

Peter Wang 6:24 Right? It was a side project for them.

Lex Fridman 6:26 And also necessity creates a kind of deadline. It seems like a lot of these projects are quickly thrown together in the f- in the first step, and that, even though it's flawed, that just seems to work well for software projects.

Peter Wang 6:39 Well, it does work well for software projects in general, and in this particular space, um, uh, one, one of my colleagues, uh, Stan Seabert identified this, that all the projects in the SciPy ecosystem, um, 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, um, but NumPy coming from Numerica and Numery, these are different people. And then you've got Pandas, you've got Jupyter, uh, or IPython. There's, um, there's Matplotlib. There's just so many others that I'm, you know, not gonna do justice if I tried naming them all, but all of them are actually different people.

Lex Fridman 7:12 Mm-hmm.

Peter Wang 7:12 And as they rolled out their projects, the fact that they had limited resources meant that they were humble about scope.

Lex Fridman 7:19 Mm-hmm.

Peter Wang 7:20 Um, uh, a- a- a great, um, famous hacker, Jamie Zawiskie, once said that every, every geek's dream is to build the, uh, the ultimate middleware, right? And the, the thing is, with these scientist-turned-programmers, they had no such dream. They were just trying to write something that was a little bit better for what they needed than MATLAB, and they were gonna 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.

Lex Fridman 7:50 If you look at the whole human story-

Peter Wang 7:52 Mm-hmm.

Lex Fridman 7:52 ... how much of a leap is it? We've, uh, developed all kinds of languages, all kinds of methodologies for communication, and just kinda like grew this collective intelligence. The civilization grew. It expanded.

Peter Wang 8:02 Mm-hmm.

Lex Fridman 8:03 We wrote a bunch of books, and now we tweet. Uh, how big of a leap is programming if programming's yet another language? Is it just a nice little trick that's temporary in our human history, or is it like a s- a big leap in the, um, almost us becoming, uh, another organism at a higher level of abstraction, something else?

Peter Wang 8:26 I think the act of programming or, uh, 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, I'll first say it and then I'll tell y- tell you why it's not true.

Lex Fridman 8:55 (laughs)

Peter Wang 8:55 But for the most part, we can think of computers as being these iterated systems.

Lex Fridman 8:58 Yeah.

Peter Wang 8:59 So when we program, we're, uh, giving very precise instructions to, uh, iterated systems that then run at, um, incomprehensible speed (laughs) and run those instructions. In my experience, some people are just better equipped to model systematic iterated systems, uh, well, whatever, iterated systems in their head.

Lex Fridman 9:18 Mm-hmm.

Peter Wang 9:20 Um, some people are really good at that, and other people are not. Um, and so when you have, like for instance sometimes people have tried to build systems that, uh, 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, right? So handling iterated logic, um, 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, te- data transformation-oriented programming system. Um, and there's actually not an accident that that system is the most popular programming system, because it's so accessible to a much bro- a much broader group of people. I do think as, um, we build future computing systems, we're actually already seeing this a little bit, it's much more about composition of modular blocks. They themselves, um, 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, uh, Zapier or any of these kind of, you know, uh, I would say compositional scripting kinds of things, um, I mean, HyperCard was also a little bit in this vein, um, that's much more accessible to most people. It's, it's really that implicit state that's so hard for people to track.

Lex Fridman 10:52 Yeah, okay, so that's modular stuff, but there's also an aspect where you're standing on the shoulders of giants. So you're building like, uh, higher and higher levels of abstraction.

Peter Wang 10:59 Mm-hmm.

Lex Fridman 11:00 But you, you do that a little bit with language. So with language you develop sort of ideas, philosophies from Plato and so on.

Peter Wang 11:07 Mm-hmm.

Lex Fridman 11:07 And then you kind of leverage those philosophies as you try to have m- 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, um, expressions-

Peter Wang 11:27 Hmm.

Lex Fridman 11:27 ... uh, ability to express ideas in a s-

Peter Wang 11:30 (laughs) .

Lex Fridman 11:30 ... in, in a computational space.

Peter Wang 11:31 I think it's worth pondering the difference here between complexity and complication. Uh-

Lex Fridman 11:40 Sure. (laughs)

Peter Wang 11:40 Okay, right?

Lex Fridman 11:41 Back to Excel. (laughs)

Peter Wang 11:42 Uh, well, not quite back to Excel, but, but the, the idea is, um, you know, when we have a human conversation, all languages, uh, for humans emerged to support, um, human, uh, 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, um, 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 (laughs) to the computer until the one time we expressed ourselves right. Then we kind of put it in production and then discover that it's still wrong, you know, a few days down the road. So I think the- 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 im- to increase the system's capability, um, for someone else to then interface with. And we happen to use the same language for both of those things, and us- u- in most cases, but it doesn't have to be that, and Excel is actually a great example of this, uh, of kind of a counterpoint to that.

Lex Fridman 12:56 Okay, so what about the idea of, you said messiness.

Peter Wang 13:00 Mm-hmm.

Lex Fridman 13:01 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, uh, building on levels of abstraction that don't have messiness in them, that, uh, at the operating system level, then there's Python, the programming languages that have more and more power, but then finally, there's, uh, 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.

Peter Wang 13:42 Mm-hmm.

Lex Fridman 13:42 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.

Peter Wang 13:50 Mm-hmm.

Lex Fridman 13:51 So back to Excel. 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 s- the same kind of messiness that human communication allows.

Peter Wang 14:04 It does, but, you know, my background is in physics. I took, like, two CS courses in college. So I don't have, um... now, I did cram a bunch of CS, uh, in prep when I applied for grad school, but, um, but still, I- I don't have a formal background in computer science. Um, but what I have observed in studying programming languages and programming systems and- and things like that is that there seems to be this- this- this triangle. It's one of these beautiful little iron triangles, uh, in, uh, 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.

Lex Fridman 14:45 Mm-hmm.

Peter Wang 14:45 So there's the algorithms that you wanna, you know, apply, um, there's what the bits that are stored on whatever media actually represent, so the semantics of the data, you know, within the representation, and then there's what the computer can actually do. Um, in every programming system, every information system ultimately finds some spot in the middle of this little triangle.

Lex Fridman 15:06 Mm-hmm.

Peter Wang 15:07 Sometimes some systems collapse them into just one edge.

Lex Fridman 15:11 Are we- are we including humans as a system in this?

Peter Wang 15:13 Um, no, no, I'm just thinking about computing systems here.

Lex Fridman 15:15 Okay.

Peter Wang 15:15 Okay? And the reason I bring this up is because I- I believe there's no free lunch around this stuff. So if we build- 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, right, with hyper- hyperparameters we can tune kind of how we want the performance boundary and SLA to- to- to look like for transforming some set of inputs into certain kinds of outputs.

Lex Fridman 15:38 Mm-hmm.

Peter Wang 15:39 That training process itself is intrinsically sensitive to the kinds of inputs we put into it. It's, and 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, um, 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 this- in the view of these live data systems, how to better encode the semantics of the world for those things. There'll be less about the details of how we write a particular SQL query.

Lex Fridman 16:33 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?

Peter Wang 16:40 Mm-hmm. 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, 'cause for the last 40 years or so, software correctness was really defined, uh, 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, well, I mean, in fact I think the bright line in the sand between machine learning systems or modern data-driven systems versus soft- 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-

Lex Fridman 17:23 What's SLA?

Peter Wang 17:23 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, right? Um, 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- that question of correctness is becoming a bigger and bigger question.

Lex Fridman 17:45 Where does that map to the end of software?

Peter Wang 17:48 We've thought about software as just this thing that you can do in isolation with some, you know, test trial inputs and in a very, you know, um-... very sort of sandboxed environment and we can quantify how does it scale, how does it, you know, 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're gonna wanna spend a little more time thinking about. You're gonna find inputs that are not, it's not clear what you should do, right? So then the software has a varying amount of runtime, uh, and correctness with regard to input. And that is a different kind of system altogether. Now it's a full-on cybernetics system, it's a next-generation information system that is not like traditional software systems.

Lex Fridman 18:30 Can you maybe describe what is a cybernetics system? Do you include humans in that picture? So is it, is it human in the loop kind of complex mess of the whole kind of interactivity of software with the real world or, or is it, uh, something more concrete?

Peter Wang 18:44 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, is the fact what, for me, uh, makes it a cybernetics system. And-

Lex Fridman 18:58 But humans are out of that loop, they-

Peter Wang 19:01 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 cybernetics system. So we're just at the dawn of this, right? I think everyone talking about MLAI, it's, it's, it's, 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 all correctness and all these things, they really are the most important questions.

Lex Fridman 19:27 Okay. Can we just linger on this?

Peter Wang 19:28 Sure.

Lex Fridman 19:28 What does it mean for the human to be out of the loop in a cybernetics system? Because isn't the cybernetics system that's ultimately accomplishes some kind of purpose that at the, at the bottom, you know, the, the turtles all the way down, at the bottom turtle is a human. It's impossible to s-

Peter Wang 19:44 Well, the human may have set some criteria, but the human wasn't precise. So for instance, I just read the other day that, um, earlier this year, or maybe it was last year at some point, the, um, Libyan army, I think, um, sent out some automated killer drones with explosives. Um, and there was no human in the loop at that point. They basically put 'em in a geo-fenced area, said find any moving target like a truck or vehicle that looks like this, and boom. Um, that's not a human in the loop, right?

Lex Fridman 20:09 So increasingly, the less human there is in the loop, the more concerned you are about these kinds of systems because, uh, there's unintended consequences. Like, less the original designer and engineer of the system is able to predict, uh, even one with good intent is able to predict the consequences of such a system. Is that a-

Peter Wang 20:27 That's right. There are some software systems, right, 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, um, you know, in the, in the heyday of, uh, high frequency trading, there was a lot of market microstructure, people doing all sorts of weird stuff that the market, um, designers had never really thought about, contemplated, or intended. So when we run these full-on systems with these automated trading bots, um, now they become automated, you know, killer drones and then all sorts of other stuff. We, we are... 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.

Lex Fridman 21:03 Are you more concerned if you're thinking about cybernetics systems or even like self-replicating systems, so, so systems that aren't just doing a particular task but are able to sort of multiply and scale in-

Peter Wang 21:15 Mm-hmm.

Lex Fridman 21:15 ... some dimension-

Peter Wang 21:16 Mm-hmm.

Lex Fridman 21:17 ... in the digital or even the physical world. Are you more concerned about, uh, like, the lobster being boiled, so a gradual, with us not noticing, um, collapse of civilization, or a big explosion, uh, like, oops, kind of a, a big thing where everyone notices but it's too late?

Peter Wang 21:40 I think that it will be a different experience for different people. (laughs)

Lex Fridman 21:46 Um, I do, I do, um, share a common point of view with some of the climate, um, you know, people who are concerned about climate change and, and just the, uh, this, uh, the, the big existential risks that we have. But unlike a lot of people who are, 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, um, 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, and the, um, flaming torches and everything. It's interesting 'cause, uh, so my specific question wasn't as general. My question was more about cybernetics systems-

Peter Wang 22:41 (laughs)

Lex Fridman 22:41 ... or software.

Peter Wang 22:42 Okay.

Lex Fridman 22:43 Uh, I, it's interesting but it, it would nevertheless perhaps be about class. So the effect of algorithms might affect certain classes more than others.

Peter Wang 22:50 Absolutely.

Lex Fridman 22:51 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-

Peter Wang 23:05 Yeah.

Lex Fridman 23:05 ... or, or is it something truly dramatic where there's, you know, like, um, a mel- meltdown of a nuclear reactor kind of thing, Chernobyl-like, uh, catastrophic events that, um, are almost bugs in a program that scaled itself too quickly?

Peter Wang 23:21 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, you know, at least with nuclear weapons, when a nuke goes off, you can see it and then you're like, "Wow, that's really... Wow, that's kind of bad." Right? I mean, uh, Oppenheimer was reciting the Bhagavad Gita, right? When he saw one of those things go off. So we can see nukes are really bad. But socially-

Lex Fridman 23:45 He's not reciting anything about Twitter. (laughs)

Peter Wang 23:48 (laughs) Well, but right, but then s- when, when you have social media, when you have, um, uh, all these different things that conspire to create a layer of virtual experience for people that alienates them from, you know, e- reality and from each other, that's very pernicious. It's impossible to see, right? And it, it, it kinda slowly gets in there. So-

Lex Fridman 24:07 You've written about this idea of virtuality-

Peter Wang 24:09 Mm-hmm.

Lex Fridman 24:09 ... 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 a simulacrum." So let me ask, uh, what is real?

Peter Wang 24:26 (laughs)

Lex Fridman 24:26 Is there a hard line between reality and virtuality? Like, perception drifts from some kind of physical reality.

Peter Wang 24:33 Mm-hmm.

Lex Fridman 24:33 We have to kind of have a sense of what is the line as to we've gone too far?

Peter Wang 24:37 Right, right. For me, it's not about, uh, any hard line about physical reality as much as, um, a simple question of, um, 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, um, always some things are lost in translation. Usually, m- many, many dimensions are lost in translation, right? We ha- we're now coming to, uh, 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 s- I've seen you on YouTube lots, right?

Lex Fridman 25:21 Mm-hmm.

Peter Wang 25:22 Um, but then seeing you in 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, uh, embodied experience and participatory interaction that is lost. Um, and it's very hard to put your finger on exactly what it is. It's hard to say, "Oh, we're gonna spend $100 million building a new system that captures this five dem- five, 5% better higher fidelity human expression." No one's gonna pay for that, right? So when we rush madly into a world of simulacrum and- and virtuality, um, you know, the things that are lost are, it- it's difficult. Once everyone moves there, it's, it can be hard to look back and see what we've- what we've lost.

Lex Fridman 26:08 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.

Peter Wang 26:22 Mm-hmm.

Lex Fridman 26:22 And can bring joy to a lot of people and can connect-

Peter Wang 26:25 Right.

Lex Fridman 26:25 ... a lot of people, uh, and can get people to talk a lot of trash.

Peter Wang 26:29 (laughs)

Lex Fridman 26:29 Uh, so it 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?

Peter Wang 26:38 It is. I mean, it's possible to have that in the- in the current world. But, um, 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, uh, in the limbic system to create these things into id machines versus connection machines, then, um, then the- th- those good things don't stand a chance.

Lex Fridman 27:03 Can you make a lot of money by building connection machines? Is y- uh, is it possible, do you think, to bring out the best in human nature to, uh, create fulfilling connections and relationships in the digital world and make a shit ton of money?

Peter Wang 27:18 Um, if I figure it out, I'll let you know. (laughs)

Lex Fridman 27:21 But what's your intuition without concretely knowing-

Peter Wang 27:23 My-

Lex Fridman 27:23 ... what's s- s- the solution?

Peter Wang 27:24 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- it's sort of like sugary drinks. As people have more sugary drinks, they get- they need more sugary drinks to get that same hit, right? So with these virtual things, um, and with TV, uh, and fast cuts, and, you know, 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- on slow things and actually feel that embodied experience, right? So mindfulness now more than ever is so important in schools and, um, as a therapy technique for people because our environment has been accelerated. And- and McLuhan actually talks about this in the electric environment of the television, and that was before TikTok and before-

Lex Fridman 28:21 Mm-hmm.

Peter Wang 28:21 ... front-facing cameras. So I think for me the- 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.

Lex Fridman 28:39 Yeah, but humans are intelligent, and they're, uh, introspective, and they can reflect on the experiences of their life.

Peter Wang 28:46 Mm-hmm.

Lex Fridman 28:46 So for example, there's been many years in my life where I- I ate an excessive amount of sugar.

Peter Wang 28:50 Mm-hmm.

Lex Fridman 28:50 And then a certain moment, I woke up and said, uh, "Why do I keep doing this? This doesn't feel good-"

Peter Wang 28:57 Mm-hmm.

Lex Fridman 28:57 "... like, long term." And I think, uh, so going through the TikTok process of realizing, "Okay, when I shorten my attention span, actually the- that does not make me feel good longer term."

Peter Wang 29:10 Mm-hmm.

Lex Fridman 29:10 And realizing that and then going to platforms-

Peter Wang 29:13 Mm-hmm.

Lex Fridman 29:13 ... going to places that, um, are- are away from the sugar.

Peter Wang 29:17 Mm-hmm.

Lex Fridman 29:18 So- so, and- and in 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-

Peter Wang 29:26 Right.

Lex Fridman 29:26 ... develop, grow as human beings, and it just feels like humans are more intelligent than, uh, mice looking for cheese.

Peter Wang 29:34 (laughs)

Lex Fridman 29:35 They're able to sort of think, I mean, we can think, we can contemplate our own mortality.

Peter Wang 29:39 Right.

Lex Fridman 29:39 We can contemplate things like long term love, and we can have a long term fear of certain things like mortality. We can contemplate whether the experiences, the sort of the drugs of daily life that we've been partaking in is making us happier or better people.

Peter Wang 29:58 Mm-hmm.

Lex Fridman 29:58 And then once we contemplate that we can make financial decisions in- in using services and paying for services that are making us better people. So it just seems that w-... were in the very first stages of, uh, social networks that just were able to make a lot of money really quickly.

Peter Wang 30:16 Mm-hmm.

Lex Fridman 30:16 But in bringing out sometimes the bad parts of human nature, they didn't destroy humans, they just, they just fed everybody a lot of sugar. And now everyone's gonna wake up and say, "Hey, we're gonna start having, like, sugar-free social media."

Peter Wang 30:31 Right.

Lex Fridman 30:32 (laughs)

Peter Wang 30:32 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."

Lex Fridman 30:43 Yeah.

Peter Wang 30:44 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 (laughs) -

Lex Fridman 31:01 Slow down, let's slow down.

Peter Wang 31:02 (laughs)

Lex Fridman 31:02 Now you're, um, now a lot of people would argue with you on that one, but yes.

Peter Wang 31:06 Well, we're more than just that, but we're at least that, and we're-

Lex Fridman 31:09 Okay.

Peter Wang 31:09 ... very, very seldom not that. So, um, my, uh, 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 of-

Lex Fridman 31:21 Noping.

Peter Wang 31:21 Yeah.

Lex Fridman 31:21 That's an awesome verb. (laughs)

Peter Wang 31:22 It's a great term. Yeah. I love it. I use it all the time. (laughs) You're welcome, Mike.

Lex Fridman 31:26 I'm gonna have to nope out of that. Thank you.

Peter Wang 31:27 I'm gonna nope out of that, right? That's gonna be a hard pass. And, 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 gonna do that. I'm gonna go and spend time on long form reads. I'm gonna spend time managing my attention. I'm gonna 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 gonna do? You're gonna 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 gonna 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, 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, you know, sacrosanct in 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's making their own decision, but together, the, the bigger system is causing them to act with a group, um, 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 be, to have an, a high-trust connection, uh, as part of a collective, and for that collective to have its right to coherency and agency. That's something, like when, when a social media app causes a family to break apart, it's done harm to more than just individuals, right? 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, we're vaporizing molecules into atomic units, and then we're hitting all the atoms with certain things that it's like, "Yeah, well, that person chose to look at my app."

Lex Fridman 33:38 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.

Peter Wang 33:47 And these apps do as well.

Lex Fridman 33:48 And, 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.

Peter Wang 33:58 I would say multi-tiered.

Lex Fridman 33:59 Multi-tiered.

Peter Wang 34:00 Multi-tiered.

Lex Fridman 34:00 So individual as well, just not all-

Peter Wang 34:01 Individuals, family units, social collectives, um, and, and all the way up-

Lex Fridman 34:06 Okay.

Peter Wang 34:06 ... to-

Lex Fridman 34:07 So you've said that individual humans are multilayered, 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, that is a human being?

Peter Wang 34:25 Mm-hmm.

Lex Fridman 34:25 And maybe the human collective, human society?

Peter Wang 34:29 So I'm just stealing wholesale here from Robert Pirsig, um, who is the author of Zen and the Art of Motorcycle Maintenance, and in his, um, follow-on book, uh, is a sequel to it called Lila. He goes into this in, in a little more detail, but, um, it's, it's a, 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, is your mind, uh, you know, your consciousness, is that, 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." Right? And then, uh, and I'm not one of those people, right? I think that we don't have to draw a line between our things only this or only that. Collectives of things can emerge structures and patterns that are just as real as the underlying pieces, but, you know, they're transcendent, but they're still of the underlying pieces. So your body is this way. I mean, we just know physically you're, you consist of atoms and, uh, and, and, 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 call 'em cells, and those cells form, you know, sort of biological structures.

Lex Fridman 35:44 Mm-hmm.

Peter Wang 35:44 Those biological structures give your body its physical ability and its biological ability to consume energy and to maintain homeostasis, but humans are social animals. I mean, human by themselves is, is not very long for the world. So we also, part of our biology is wired to connect to other people, to, you know, from the mirror neurons to our language, uh, 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 and laughing, we're gonna feel better, right? And they didn't, 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, um, uh, Pirsig puts the intellectual level kind of one level higher than social. I think they're actually more intertwined than, than that, but the intellectual level is the, 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, um, 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, and we can't even, um, say what the dynamics of your emotion will be unless we analyze all four of these layers, right? Um, if it's, if it's, if you're, if you're Muslim at a certain time of day, guess what? You're gonna 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-layer stack, um, is, is all about. It's that a person's 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.

Lex Fridman 37:37 S- so no layer is, uh, special?

Peter Wang 37:40 Um, uh, not so much no layer is special. Each layer is just different.

Lex Fridman 37:43 (laughs)

Peter Wang 37:44 Um, but we are, but-

Lex Fridman 37:45 (laughs) Each layer gets their participation trophy. (laughs)

Peter Wang 37:48 E- each layer, yeah, each layer is a part of what you are. You are a layered cake, right? Of all these things. And if we try to deny, right, so many philosophies do try to deny the reality of some of these things, right? 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 it's not, they're not the same thing, even though it's the same atoms.

Lex Fridman 38:09 Mm-hmm.

Peter Wang 38:09 So I think the, the order and the patterns that emerge within humans, um, to understand, to really think about what a next generation 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 in nature, we really need to appreciate these, that human, what human beings actually are-

Lex Fridman 38:31 Mm-hmm.

Peter Wang 38:32 ... is the superposition of these different layers.

Lex Fridman 38:34 You mentioned consciousness. Are each of these layers of cake conscious? Is consciousness a particular quality of, of one of the layers? Is there, like, a spike if you have a consciousness detector at, at these layers-

Peter Wang 38:47 Mm-hmm.

Lex Fridman 38:48 ... or it's something that just permeates all of these layers and just takes different form?

Peter Wang 38:52 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. You know, it's, it's, it would be odd to say a proton is alive, right? It'd be odd to say, like, this particular atom or molecule of, of hydrogen gas is alive, but there's certainly something we can make assemblages of these things that cr- that cr- that aut- that have autopoeitic aspects to them, that will create structures that will, you know, crystalline solids will form very interesting and beautiful structures. Um, this gets kind of into weird mathematical territories. You start thinking about Penrose and Game of Life stuff, uh, about the generativity of math itself, like, the hyperreal numbers, things like that. But, um, 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. Uh, it's a concept of a externalized extended phenotype or niche construction. So, um, 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 ladder of life. So what we experience of, as consciousness, no, I don't think cells are conscious at that level. But is there something beyond mere equilibrium state biology and, and chemistry and biochemistry that drives what makes things work? I think there is. Um, uh, so Adrian Bejan has this constructal law. There's other things you look at. When you look at the life sciences and you look at, um, any kind of, uh, statistical physics and statistical mechanics, when you look at things far out of equilibrium, uh, when you have excess energy, what happens then? Life doesn't just make a hotter soup. It starts making structure. There's something there.

Lex Fridman 40:48 The poetry of reaches for order when there's an excess of energy.

Peter Wang 40:53 Mm-hmm.

Lex Fridman 40:54 Because you brought up Game of Life.

Peter Wang 40:55 (laughs)

Lex Fridman 40:57 (laughs) You did it, not me. My f- I love cellular automata, so I have to sort of linger on that for, for a little bit.

Lex Fridman 41:06 (exhales) So cellular automata, I guess, is, uh, 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, uh, w- within, like, this, this explosion of just turmoil somehow trying to construct structures-

Peter Wang 41:25 Mm-hmm.

Lex Fridman 41:25 ... and in so doing, uh, create very elaborate organism-looking type things.

Peter Wang 41:31 Mm-hmm.

Lex Fridman 41:32 What intuition do you draw from this simple, uh, mechanism?

Peter Wang 41:35 Well, I, I like to turn that around on its head and, um, and look at it as, what if every single one of the patterns created life or created, you know, not life, but created interesting patterns? 'Cause, you know, some of them don't, and sometimes you make cool gliders, and other times, you know, you start with certain things and you make gliders and other things that then construct, like, you know, AND gates and NOT gates, right? And you build computers on them. Um, 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?

Lex Fridman 42:10 Mm-hmm.

Peter Wang 42:10 You know, what if some of the things that we think are random are actually not that random? We're simply not integrating at a fine enough level across a broad enough time horizon. Um, and this is, again, where I said we go down the rabbit hole of some of the Penrose stuff or, like, Wolfram's explorations on these things. Um, there is something deep and beautiful in the mathematics of all this that is hopefully one day I'll have enough money to where I can retire and just ponder those, (laughs) those questions, but there's something there.

Lex Fridman 42:34 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.

Peter Wang 42:46 Yeah.

Lex Fridman 42:46 So, and, uh, maybe mathematics extends your perception capabilities, but it's still, it's still finite. It's, it's just like-

Peter Wang 42:55 Yeah. The, the mathematics we use is the mathematics that can fit in our head.

Lex Fridman 42:59 Yeah.

Peter Wang 43:00 You know, did God really create the integers? (laughs) Or did Cr- God create all of it and we just happen, at this point in time, to be able to perceive integers?

Lex Fridman 43:07 Well, he just did the, the positive inte- int- uh...

Peter Wang 43:09 She, I should say.

Lex Fridman 43:10 She, she. (laughs)

Peter Wang 43:10 Did she create all the integers? And then we-

Lex Fridman 43:13 Um, did, she, she, she just created the natural numbers and then we screwed it all up with zero, and then I guess, okay. Uh-

Peter Wang 43:18 But we did, we created mathematical, uh, operations so that we can have iterated steps-

Lex Fridman 43:23 Mm-hmm.

Peter Wang 43:23 ... to approach bigger problems, right?

Lex Fridman 43:26 Yeah.

Peter Wang 43:26 I mean, the entire p- the entire point of the Arabic numeral system, and it's a rubric for mapping a certain set of operations and 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, right?

Lex Fridman 43:41 The thing that worries me the most about aliens and humans is that there are aliens that are all around us, and we're too dumb-

Peter Wang 43:51 Yeah.

Lex Fridman 43:51 ... to see them.

Peter Wang 43:51 Oh, certainly, yeah.

Lex Fridman 43:52 Or life, let's say just life, life of all kinds of forms or organisms. You know what? Just even the intelligence of organisms-

Peter Wang 44:01 Mm-hmm.

Lex Fridman 44:02 ... is, uh, imperceptible to us because we're too dumb and self-centered. That worries me.

Peter Wang 44:07 Well, we're looking for a particular kind of thing.

Lex Fridman 44:09 Yeah.

Peter Wang 44:09 Um, my, uh, when I was at Cornell, I had a lovely professor of Asian religions, Jane Marie Law, and she would tell this, um, story about a musical, a musician, a Western musician who went to Japan and he had taught, you know, classical music, and could play all, you know, all sorts of instruments. And he went to Japan, um, and he would ask people, you know, he would basically be looking for things in the style of w- Western, you know, uh, chromatic scale and these kinds of things. And then finding none of it, he would say, "Well, there's really no music in Japan."

Lex Fridman 44:37 (laughs)

Peter Wang 44:37 But they're using a different scale.

Lex Fridman 44:38 Yeah.

Peter Wang 44:38 They're playing different kinds of instruments, right? The same thing, she was using it as a, sort of a, a metaphor for religion as well. In the West, we center a lot of religion, certainly the, 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, um, life, we, I think so many people are so fixated on certain aspects of self-replication or, you know, 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, um, allows them... You know, the, the invention of death is an interesting thing. There are some organisms on Earth that are thousands of years old-

Lex Fridman 45:22 Mm-hmm.

Peter Wang 45:23 ... 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, um, death was invented, you know, uh, somewhere along the eukaryotic scale. I mean, even the protists, right? There's death. And why is that? Along with the, um, 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.

Lex Fridman 45:56 You know, human historians seem to write about wheels and, and fire as the greatest inventions, but it seems like death and sex are pretty good.

Peter Wang 46:04 (laughs)

Lex Fridman 46:04 And they're, they're kind of essential inventions at the very beginning.

Peter Wang 46:07 At the very beginning, yeah. Well, we didn't invent them, right? They-

Lex Fridman 46:10 Well, broad we.

Peter Wang 46:11 (laughs)

Lex Fridman 46:12 You didn't invent them.

Peter Wang 46:12 Life, life-

Lex Fridman 46:13 I see us as one, uh, you particular, homo sapien, did not invent them, but, uh, we together, it's, it's a team project, just like you're saying.

Peter Wang 46:21 I think the greatest homo sapien invention is collaboration.

Lex Fridman 46:25 So when you say collaboration-

Peter Wang 46:27 Mm-hmm.

Lex Fridman 46:28 (laughs)

Lex Fridman 46:29 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?

Peter Wang 46:40 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?

Lex Fridman 46:46 No.

Peter Wang 46:47 It's a really fascinating thesis, which is that humans are the only conspecific, the o- the, uh, the, the only species that can kill other members of the species from range.

Lex Fridman 46:57 Mm-hmm.

Peter Wang 46:58 A- 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, uh, lion and the beta lion, and they take each other down.

Lex Fridman 47:07 Mm-hmm.

Peter Wang 47:07 Humans, we developed the ability to chuck rocks at each other, and, well, at prey, but also at each other. And that means the beta male can chunk a rock at the alpha male and take them down. And w- with ver- and he, he can throw a lot of rocks, actually, miss a bunch of times, but just hit once and be good.

Lex Fridman 47:22 Mm-hmm.

Peter Wang 47:22 So, um, this ability to actually kill members of our own species from range, without a threat of harm to ourselves, created essentially mutually assured destruction where we had to evolve cooperation. If we didn't, then if we just continued to try to do, like, "I'm the, the biggest monkey in the tribe and I'm gonna, you know, own this tribe, and you have to go," th- if we do it that way, then those tribes basically failed. And the tribes that s- 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 a range, uh, without har- 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 differences out." (laughs)

Lex Fridman 48:08 So violence is also part of collaboration?

Peter Wang 48:10 The threat of violence, let's say. (laughs) Well, the recognition, I would s- maybe the, 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.

Lex Fridman 48:23 (laughs) So, uh, mutually assured destruction in all its forms is part of this idea of collaboration.

Peter Wang 48:28 Well, and Eric Weinstein talks about our nuclear peace, right? I mean, it kind of sucks that we have thousands of warheads aimed at each other, you know, in Russia 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 III of, like, hundreds of millions of people dying to, like, machine gun fire and, and, you know, giant, you know, guided missiles.

Lex Fridman 48:47 So the original nuclear weapon is, is a rock that we learned how to throw, essentially?

Peter Wang 48:52 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. (laughs)

Lex Fridman 49:02 Eric Weinstein speaks very highly of you, which is very surprising to me at first 'cause I didn't know there's this depth to you, 'cause I knew you as a- as a- as an- an amazing leader of engineers and an engineer yourself, and so on. So it's fascinating.

Peter Wang 49:18 (laughs)

Lex Fridman 49:18 Maybe just as a comment, a- a side tangent that we can take, uh, what's your, nature of your friendship with Eric Weinstein? How did the two-

Peter Wang 49:28 (laughs)

Lex Fridman 49:28 ... 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?

Peter Wang 49:37 Yeah. Uh-

Lex Fridman 49:37 What is it?

Peter Wang 49:37 It's actually, it's very random. It's, uh, Eric found me. Um, he actually found Travis, uh, and- and I. Um-

Lex Fridman 49:43 Travis Oliphant.

Peter Wang 49:44 ... Oliphant, yeah. We were both working at a company called Enthought, uh, back in the mid 2000s, and were doing, um, a lot of consulting around scientific Python. Um, and we'd made some- some tools, and, uh, Eric was trying to use some of these Python tools to visualize, that he had a fiber bundle approach to, uh, modeling certain aspects of economics. He was doing this and that, and that's how he kind of got in touch with us. And so, um-

Lex Fridman 50:07 This was in the early aughts, you said?

Peter Wang 50:08 This was in the mid- mid 2000s. Uh-

Lex Fridman 50:09 Mid 2000s.

Peter Wang 50:10 Uh, uh, '07 timeframe, '06, '07 timeframe.

Lex Fridman 50:13 Eric Weinstein trying to use Python-

Peter Wang 50:16 Right.

Lex Fridman 50:16 ... to visualize fiber bundles.

Peter Wang 50:17 To visualize fiber bundles, uh, using some of the tools that were, that we had built in the open source. (laughs)

Lex Fridman 50:21 That's somehow entertaining to me, the thought of that.

Peter Wang 50:23 (laughs) It's pretty funny. It was pretty funny. But then, um, you know, we met with him a couple times, really interesting guy, and then in the wake of the '07, '08 kind of financial collapse, he, uh, helped organize with Lee Smolin, um, a symposium at the Perimeter Institute about, um, okay, well clearly, you know, big finance can't be trusted, government's in its pockets with regulatory capture, what the F do we do? Um, and all sorts of people, Nassim Taleb was there, and, uh, Andy Lo from MIT was there, and, you know, uh, Bill Janeway. I mean, just a lot of, you know, top billing people were there, and he invited me and, uh, Travis and, uh, uh, another one of our coworkers, uh, Robert Kern, uh, who is a, anyone in the SciPy, NumPy community knows Robert. Um, really great guy. So the three of us also got invited to go to this thing, and that's where I met Bret Weinstein for the first time as well. Yeah, I knew him before he got all famous (laughs) for unfortunate reasons, I guess. But, um, but- but anyway, we, um, so we met then and kind of had a friendship, um, you know, throughout, since- since then.

Lex Fridman 51:21 You have a depth of thinking that, uh, 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.

Peter Wang 51:33 (laughs)

Lex Fridman 51:33 I actually have never, um, you know, he'll bring up programming to me-

Peter Wang 51:38 Mm-hmm.

Lex Fridman 51:38 ... quite a bit as a metaphor for stuff.

Peter Wang 51:40 Right.

Lex Fridman 51:41 But I never kind of pushed the point of like, what's the nature of your interest in programming? I think he saw it probably as a tool-

Peter Wang 51:48 Yeah, absolutely.

Lex Fridman 51:49 ... uh, that to visualize, to explore mathematics and explore physics, but-

Peter Wang 51:53 Yep.

Lex Fridman 51:53 And I was wondering like, what's the, his, uh, depth of interest and also his, uh, vision for what programming would look like in the future. Have you- have you had interaction with him, like discussion in the space of Python or programming?

Peter Wang 52:09 No. Well, um, in the sense of sometimes he asks me, "Why is this stuff still so hard?" (laughs)

Lex Fridman 52:14 (laughs)

Peter Wang 52:15 Um, uh, yeah, you know, everybody's a critic, but, uh, but actually no Eric, uh-

Lex Fridman 52:20 Programming you mean, like in general?

Peter Wang 52:21 Yes, yes. Well pro- not programming in general, but certain things in the Python ecosystem. (laughs) But he, uh, but he actually, I think what I find in listening to some of his stuff is that he does, uh, 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, uh, discourse. Um, I haven't pair programmed with him in a very long time, if ever.

Lex Fridman 52:46 You've- you've previously pair coded with Eric Weinstein?

Peter Wang 52:47 Well, I mean, like, looked at his code, tried to- tried to help, like, put together some of the visualizations around these things, but it's been a very, not really pair program, but like-

Lex Fridman 52:54 Yeah.

Peter Wang 52:54 ... even looked at his code, right? I mean-

Lex Fridman 52:55 How legendary would it be is that like, uh, git repo (laughs) w- with Peter Wang and Eric Weinstein collab-

Peter Wang 53:02 Well honestly, honestly, Robert Kern did all the heavy lifting, so I have to give credit where credit is due.

Lex Fridman 53:06 Oh, yeah.

Peter Wang 53:06 Robert is- is the silent but incredibly deep, um, quiet, not silent, but quiet, but incredibly deep individual at the heart of a lot of those things that Eric was trying to do. Um, but we did have, you know, in the, as Travis and I were starting our company in, um, 2012 timeframe, we went to New York. Eric was still in New York at the time. He hadn't moved to, this was before he joined Thiel Capital. We just had like a steak dinner somewhere, maybe it was Keens, I don't know, somewhere in New York. So it was me, Travis, Eric, and then Wes McKinney, the creator of pandas, and then Wes's, um, then business partner, Adam. The five of us sat around having this just a hilarious time, amazing dinner. Um, I forget what all we talked about, but it was, it was one of those conversations which I wish, um, as soon as COVID is over, maybe Eric and I can sit down-

Lex Fridman 53:53 Recreate. (laughs)

Peter Wang 53:53 ... recreate it somewhere in, uh, in LA or maybe he comes here, 'cause a lot of cool people are here in Austin, right?

Lex Fridman 53:58 Exactly.

Peter Wang 53:58 Yeah, we're all here. He should come here.

Lex Fridman 54:00 Come here.

Peter Wang 54:00 Yeah.

Lex Fridman 54:01 So he uses, uh, the metaphor source code sometimes to talk about physics.

Peter Wang 54:05 Right.

Lex Fridman 54:05 We figure out our own source code. So you with a physics background, um, and, uh, 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 of reality?

Peter Wang 54:20 Well, I think we're constantly working on that problem. I mean, I think we'll- we'll make more and more progress. For me, there's some things I don't really doubt too much. Like, I don't really doubt that one day we will create, um, a synthetic, maybe not f- maybe not fully in silicon, but a synthetic approach to, um, cognition that rivals, uh, the biological 20 watt computers in our heads.

Lex Fridman 54:44 What's cognition here? What- what-

Peter Wang 54:46 Cognition-

Lex Fridman 54:46 Which- which aspect?

Peter Wang 54:47 Perception, attention, memory, recall, asking better questions.... that, for me, is the measure of intelligence.

Lex Fridman 54:53 Doesn't Roomba vacuum cleaner already do that? Or do you mean... Oh, it doesn't ask questions.

Peter Wang 54:57 No. I mean, I'm, no, it d- it's (laughs) . So, I mean, I have a Roomba, but it's-

Lex Fridman 55:01 Well, it asks questions.

Peter Wang 55:01 ... it's not even as smart as my cat, right? So-

Lex Fridman 55:03 Yeah. But it asks questions about, "What is this wall?" It, now, a new feature asks, "Is this poop or not?" Apparently.

Peter Wang 55:09 Yes. A lot of our current cybernetic sys- it's a cybernetic system. It will go and it'll happily vacuum up some poop, right? Uh, the older generations would. Um-

Lex Fridman 55:16 A new one just released.

Peter Wang 55:18 (laughs)

Lex Fridman 55:18 Does not vacuum up the poop.

Peter Wang 55:19 Okay, good. So-

Lex Fridman 55:20 This is a commercial for the Roomba.

Peter Wang 55:20 I wonder if it still gets stuck under my first rung of my stair. Um, in any case, I- I- these cybernetic systems we have, they are mold, th- 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, you know, 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, um, again, my cat, I think can do a better job (laughs) of, I have 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 question at all that we will be able to create synthetic systems that are able to do this, um, better than the hu- at an equal level or better than the human mind. It's also, for me, not a question that we will be able to, um, 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, right?

Lex Fridman 56:45 So what, uh, so who is in the sidecar in that picture of the future exactly? Is it a human?

Peter Wang 56:50 The- the ba- the baby version of our immortal selves.

Lex Fridman 56:53 Okay, so once the baby grows up, is there any use for humans?

Peter Wang 56:58 I think so. I think that out of hu- 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.

Lex Fridman 57:10 Out of epistemic humility. What- what's the nature of the humility that...

Peter Wang 57:13 That we don't know what we don't know.

Lex Fridman 57:16 So we don't... (laughs) .

Peter Wang 57:19 Right?

Lex Fridman 57:19 So we don't know if I could just-

Peter Wang 57:19 First, I mean, first, we have to build systems that- 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. We could alwa- you know, n- c- 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-

Lex Fridman 57:46 Yeah.

Peter Wang 57:46 ... um, you know, I think that's- that's i- for me, these are- these seem like things that are going to happen. Like, I- I don't really question that, that they're gonna happen, assuming we don't completely, you know, destroy ourselves.

Lex Fridman 57:58 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?

Peter Wang 58:10 I would hope that that is the design criteria for any of these systems.

Lex Fridman 58:14 I-

Peter Wang 58:14 If we cannot have a meaningful relationship with it, then it's still just a chunk of silicon.

Lex Fridman 58:20 So then what is meaningful? Because, um, back to Sugar.

Peter Wang 58:23 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 gonna take a page from Alain de Botton, love means that it wants to help us become the best version of ourselves.

Lex Fridman 58:36 Yes.

Peter Wang 58:37 Um...

Lex Fridman 58:37 That's- that's beautiful. I, that's a beautiful definition of love. So what- what role does love play in the human condition at the individual level and at the group level? 'Cause you were kind of saying that humans, we should really consider humans both at the individual and the group and the societal level.

Peter Wang 58:55 Mm-hmm.

Lex Fridman 58:55 What's the role of love in this whole thing? We talked about sex, we talked about death-

Peter Wang 58:59 (laughs) .

Lex Fridman 58:59 ... thanks to the bacteria that invented it. At which point did we invent love, by the way? I mean, is that- is that also...

Peter Wang 59:05 No, I think- I think love is- is the- the start of it all. And the feelings of, and this gets- this is sort of beyond, uh, just, you know, 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- than they are and more than just what we, you know, a- a pigeonhole we might stick them in. We see, I mean, I think there's, in any religious text, you'll find, um, voiced some concept of this, that you should see the grace of God in the other person-

Lex Fridman 59:50 Mm-hmm.

Peter Wang 59:50 ... right? You, th- they're- they're made in the spirit of- of what, you know, th- the love that God feels for his creation or her creation. And so I think this thing is actually the root of it. So I would say before, I- I don't think- I don't think molecules of water feel consciousness or have consciousness, but there is some proto micro quantum thing of love that's the generativity, um, when there's more energy than what they need to maintain equilibrium. And that, when you sum it all up, is something that leads to... I mean, I- I had my mind blown one day a- as an undergrad at the physics computer lab. I logged in and, you know, uh, when you log into 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." And I was logging in to work on some, you know, problem set, and I logged in and I saw that and I just said, "Son of a bitch." You know, I just, I logged out-

Lex Fridman 1:00:42 Ugh.

Peter Wang 1:00:43 ... I went to the coffee shop (laughs) and I got a coffee and I sat there on the quad and like, "You know-"

Lex Fridman 1:00:47 (laughs) .

Peter Wang 1:00:48 "... it's not wrong, and yet, WTF, right?" (laughs) Um, so when you look at it that way, it's like, yeah, okay, non-equilibrium physics is a thing.Um, and so when we think about love, when we think about these kinds of things, um, uh, 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 a- and that's very Hegelian, it's very kind of following from the Western philosophy of- of the- the individual as- as sacrosanct, but it's not really couched in, I think, the- 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. Um, but that- that love being at the center of it, I think when we design systems for cognition, um, it- it should absolutely be built that way. I think if we simply focus on efficiency and productivity, these kind of very, uh, industrial era, um, you know, all- all the things that Marx had issues with, right? Those, that's- that's a way to go and- and really, I think, go off the deep end in the wrong way.

Lex Fridman 1:02:13 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's societies, is, um, I believe that you believe that corporations are people.

Peter Wang 1:02:30 (laughs)

Lex Fridman 1:02:31 So this is a, 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-

Peter Wang 1:02:40 Mm-hmm.

Lex Fridman 1:02:41 ... in which sense do you believe that corporations are people?

Peter Wang 1:02:44 So, um-

Lex Fridman 1:02:46 And how does love connect to that?

Peter Wang 1:02:47 Right. (laughs) So the belief is that groups of people have some kind of higher level, I would say, mesoscopic claim to agency. I- I, you know, so- so where do I, you know, let's- let's start with this. Most people would say, okay, individuals have claims to agency and sovereignty. Nations, we certainly act as if nations, so at a very large, large scale, nations have rights to sovereignty and agency. Like, everyone plays the game of modernity as if that's true, right? 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, um, like a family unit is a thing. Well, in our law, in our laws, we actually do en- encode this concept. Uh, I believe that, um, in a relationship, in a marriage, right, one partner can sue for loss of consortium, right? If someone breaks up the- the marriage or- 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 right to... a- a claim to rights and- and sovereignty of some degree. I mean, we, and we- we, uh, l- look at our clubs, we look at churches. These are, we- we talk about these collectives of people as if they have a- a real agency to them. And- and they do. But I think if we take that one step further and say, okay, they can accrue resources, well, yes, check, you know, and by law they can, um, they can own land, they can, uh, engage in contracts, they can do all these different kinds of things. So we, in legal terms, uh, support this idea that groups of people have rights. Um, 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, right? So, um, I don't believe that's correct. I don't think it's good that they, th- you know, 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-

Lex Fridman 1:05:12 Agency.

Peter Wang 1:05:12 ... some agency and some- some mass, um, at a mesoscopic level, I think that's an important thing, because one- 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- the, uh, you know, father, mother, older, and then, you know, one of their children and their, uh, spouse-

Lex Fridman 1:05:50 Mm-hmm.

Peter Wang 1:05:51 ... that- 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.

Lex Fridman 1:06:04 Mm-hmm.

Peter Wang 1:06:04 Um, it's certainly not something that is, I think, taken into account very well when we design these kinds of things. So I think, um, 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, you know, around Dunbar number, you know, half to 5X Dunbar, and that's very different than right now where we, um, defer sense making to massive aging zombie institutions, um, or we just do it ourselves, we go it alone, go into the dark forest of the internet by ourselves.

Lex Fridman 1:06:41 So- so that's really interesting. So you've- 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, you know. There's a lot of people that argue the fiction of, of nation is a bad idea. One of them lives two doors down from me, Michael Malice, he's an anarchist.

Peter Wang 1:07:15 Mm-hmm.

Lex Fridman 1:07:16 You know, 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, uh, troublesome as well.

Peter Wang 1:07:26 Mm-hmm.

Lex Fridman 1:07:26 We need to let go of that in order to truly, like, to transcend, I don't know, I don't know what, what words you want to use, but suffering or to, uh, 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, uh, the, uh, half of Dunbar's number or five x Dunbar's number.

Peter Wang 1:07:57 Yeah, something on that order. And we call them fictions, but really they're rules of the game, right? Rules that we, we, we feel are fair or rules that we consent to.

Lex Fridman 1:08:05 Yeah, I always question the rules when I lose, like at Monopoly.

Peter Wang 1:08:08 (laughs)

Lex Fridman 1:08:08 That's when I usually question the r- when I'm winning, I don't question the rules.

Peter Wang 1:08:11 We should play a game of Monopoly someday. There's a trippy version of it that we could do.

Lex Fridman 1:08:15 What, what kind of, what-

Peter Wang 1:08:16 Uh, contract Monopoly. It was introduced by a friend of mine to me, where you can write contracts on, uh, future earnings or landing on various things and you can hand out, like, you know, you can land first three times, you land on Park Place, it's free or whatever, just, and then you can start trading those contracts for money.

Lex Fridman 1:08:33 (laughs) And then you create human civilization.

Peter Wang 1:08:35 (laughs)

Lex Fridman 1:08:36 Uh, and somehow bitcoin comes into it. Okay, uh, but some of these-

Peter Wang 1:08:40 Actually, I, 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.

Lex Fridman 1:08:49 Yes.

Peter Wang 1:08:49 I would never do it.

Lex Fridman 1:08:50 Yeah.

Peter Wang 1:08:50 But I bet we could actually sell the NFTs around.

Lex Fridman 1:08:53 I have other ideas to make money that, that I could tell you, and they're all terrible ideas, so (laughs) , um, including cat videos on the internet.

Peter Wang 1:09:01 (laughs)

Lex Fridman 1:09:02 Okay, but some of these rules of the game, some of these fictions are, it seems like they're better than others.

Peter Wang 1:09:09 They've worked this far to cohere, um, human, to organize human collective action, let me put it that way.

Lex Fridman 1:09:14 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 you select a 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.

Peter Wang 1:09:45 Mm-hmm.

Lex Fridman 1:09:45 It feels like if I have the right tools, I can deeply connect with a large number, larger number of people.

Peter Wang 1:09:51 Mm-hmm.

Lex Fridman 1:09:51 It just feels like, uh, there's a huge value to interacting just in person-

Peter Wang 1:09:58 Mm-hmm.

Lex Fridman 1:09:58 ... getting to share traumatic experiences together, beautiful experiences together. There's other experiences like, um, that, in the digital space that you can share. It just feels like Dunbar's number could be expanded significantly, perhaps not to, to the level of millions and billions-

Peter Wang 1:10:15 Mm-hmm.

Lex Fridman 1:10:15 ... but it feels like it could be expanded. So how-

Peter Wang 1:10:16 Yeah.

Lex Fridman 1:10:16 ... how, how do we find the right interface, you think, um, for, uh, having a little bit of a collective here that has agency?

Peter Wang 1:10:26 You're right, that there's many different ways that we can build trust with each other.

Lex Fridman 1:10:30 Yeah.

Peter Wang 1:10:30 Um, my friend Joe Edelman talks about a few different ways that, um, you know, mutual appreciation, trustful conflict, um, just experiencing something, like, you know, there's a variety of different things that we can do. But, um, 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, the less connection you build. So I think there is sort of a human capacity issue around some of these things. Now, that being said, if we can use certain technologies, so 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-

Lex Fridman 1:11:18 Mm-hmm.

Peter Wang 1:11:18 ... you know, 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. And we can then, um, you know, 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, um, lack of neuroticism, right? If you want to use like the, 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. Like, there's just a lot of the stuff that you have to deal with if you are not so well balanced, right? So yes, we can help people get better to where they can develop more relationships faster, and then you can maybe expand Dunbar number by quite a bit, but you're not gonna do it... I think it's gonna be hard to get it beyond 10 x, kind of the rough swag of what it is, you know.

Lex Fridman 1:12:16 Well, don't you think that AI systems could be an addition to Dunbar's number? So like, why-

Peter Wang 1:12:23 Do you count as one system or multiple AI systems?

Lex Fridman 1:12:25 Multiple AI systems.

Peter Wang 1:12:26 Mm-hmm.

Lex Fridman 1:12:26 So I do believe that AI systems, for them to integrate into human society as it is now, have to have a sense of agency. So there has to be a-

Peter Wang 1:12:33 Mm-hmm.

Lex Fridman 1:12:34 ... an individual, because otherwise we wouldn't relate to them.

Peter Wang 1:12:37 We could engage certain kinds of individuals to make sense of them for us and be almost like, did you ever watch, uh, Star Trek? Uh, like Voyager, like there's the Volta who were like the interfaces, the, the ambassadors for the Dominion. Um, we may have, uh, ambassadors that speak on behalf of these systems. They're like the, the Mentats of Dune maybe, or something like this. I mean, we already have this to some extent-... if you look at the biggest sort of, I wouldn't say AI system, but the biggest cybernetic system in the world is the financial markets. It runs outside of any individual's control and 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?" You know, we got Jim Cramer, like, running around and yelling stuff. Like, all of these people are part of that lowering of the complexity there to meet sense, you know, to help do sense-making for people at whatever capacity they're at. And I don't see this changing with AI systems. I- I think you would have ringside commentators talking about all this stuff that this AI system is trying to do over here and over here, 'cause it's a, it's actually a super intelligence. So if you want to talk about humans interfacing, making first contact with a super intelligence, we're already there. We do it pretty poorly, and if you look at the gradient of power and money, what happens is the people closest to it will absolutely exploit their distance for, um, personal financial gain.

Lex Fridman 1:13:54 Mm-hmm.

Peter Wang 1:13:54 So w- we should look at that and be like, "Oh, well, that's probably what the future will look like as well." But, um, but nonetheless, I mean, we're already doing this kind of thing, so in the future, we can have AI systems, but you're still gonna have to trust people to bridge the sense-making gap to them.

Lex Fridman 1:14:08 See, I don't... I- I just feel like there could be a, like millions of AI systems that have, um, have agency. So you have...

Peter Wang 1:14:17 Mm-hmm.

Lex Fridman 1:14:17 When you say one super intelligence, super intelligence in that context means it's able to, uh, 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 sort of weather prediction systems or, I don't know, logistics, optimization. I'm more referring to things that you interact with on the intellectual level.

Peter Wang 1:14:44 Yeah.

Lex Fridman 1:14:45 And that, I think, requires... There has to back story. There has to be a personality. I believe it has to fear its own mortality in a genuine way. Like, there has to be all... S- many of the elements that we humans experience that are fundamental to the human condition, 'cause otherwise we would not have a deep connection with it.

Peter Wang 1:15:05 Mm-hmm. 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 to it.

Lex Fridman 1:15:12 Sure.

Peter Wang 1:15:13 Um, so another, now the other kind of alien intelligence on this planet is the octopuses or oco- octopodes or whatever you want to call them.

Lex Fridman 1:15:20 Octopi.

Peter Wang 1:15:20 Octopi. Yeah. There's a, there's a little controversy as to what the plural is, I guess. But, um-

Lex Fridman 1:15:25 I- I- I look forward to your letters.

Peter Wang 1:15:27 An octopus... (laughs) An octopus, um, you know, it really acts as a collective intelligence of eight intelligent arms, right? Its arms have a tremendous amount of neural density to them, and I see, if we can build... I mean, just let- let's go with what you're saying. If we build a- a singular intelligence that interfaces with humans that has a sense of agency so it can run the cybernetic loop and develop its own theory of mind as well as it's o- theory of action, all these... I agree with you that that's the necessary components to build a real intelligence, right? There's gotta be something at stake. It's gotta make a decision. It's gotta 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 build five million of them, and if their cognitive systems are already digitized and already kind of there-

Lex Fridman 1:16:12 Mm-hmm.

Peter Wang 1:16:12 ... we stick an 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 neural, neuronal input at a much