Why Scientists Can't Rebuild a Polaroid Camera [César Hidalgo]

I'm Cesar Hidalgo. I'm the director of the Center for Collective Learning and I recently completed this book, The Infinite Alphabet and the Loss of Knowledge. The book has two ambitions. The scientific ambition is to establish the scientific study of knowledge by showing that actually it can be organized around three laws: A law governing how knowledge grows in time, a law governing how knowledge diffuses across the space and activity, and a law showing us how we can estimate its value. But it also has a policy ambition which is that when we try to develop the knowledge sectors of our economy, we have to make sure that we incorporate these laws into our policy strategy and design. If we don't do it, we're going to have failed development efforts. So the book also tells a number of stories of failed development attempts that defy the laws of knowledge and that I equate to trying to build a rocket without respecting the law of gravity or understanding chemistry or aerodynamics.

We're going to try to understand how knowledge es and flows and what are the speeds and rates at which that happens. What are the functional forms that govern the growth of knowledge over time and how the growth of knowledge over time changes when you move from the scale of individuals and teams to those of industries. We're also going to look at how knowledge crosses mountains and oceans and how it moves between activities and how those movements also satisfy certain laws and principles. And we're going to then try to figure out how we can count knowledge in a world in which the idea of 1 plus one knowledge equal two knowledges doesn't make a lot of sense because knowledge is nonfible. It's made of a lot of unique components and we need to find ways to score them that can help us understand the potential of economies.

Many economists try to develop by creating cities of knowledge or science parks and so forth. And usually when they do that, they do it in ways that we could say are boneheaded because they defy these principles and those projects involve vast amounts of money and they end up in failure. So the idea is that well if we understand that knowledge follows principles like other quantities that we have learned to understand in the past like temperature or other things and now we can think of policy h in light of these principles and try to create policies that do not contradict them so that we can develop knowledge in ways that are compatible with its nature. Intelligence is about the efficient acquisition of coarse grained knowledge and you you develop this idea that knowledge is incredibly important and we've become obsessed with it. So we've been thinking well what does it mean to understand something and and in a way we we've developed this incredibly abstract view of knowledge you know almost like it's a probabilistic graphical model or it's a symbolic expression or you know maybe you know maybe the types of things in in a neural network is knowledge. But this this idea that that knowledge as a quantity is really important is something that has been impressed on us.

[Speaker Name]: Yeah. And I think that's something that you have in common with other disciplines. In my case, I'm coming more from the perspective of economics and social psychology in which we look at knowledge as this sort of quantity that is essential to explain economic growth and the wealth of nations. And this is something that has led to a couple of Nobel prizes. 2018 you know Paul Romer got the Nobel prize for endogenous growth theory. this year you know Aion and how it also got the Nobel Prize and the idea of Romer in particular is the one that is interesting and I think might be different from the way in which maybe a computer scientist thinks about knowledge but it's the following so when you're trying to explain economic growth you're trying to explain output you know so imagine you have 10 carpenters that have access to hammers and nails and boards of wood and they have to produce birdhouses and these 10 carpenters produce 10 birds per hour now you know let's Say that you want to produce 20 bird houses an hour. Well, you might need to double the number of carpenters because if they're doing the same thing, you know, and you want to do more of them, you're going to have to have more compressors, more nails, you know, more hammers and so forth.

So, that tells you that labor and capital arrival inputs. And if you want to increase output, you don't increase output in per capita terms. The bird houses per carpenter remain the same. Imagine now one of the carpenters figures out how to build a nail gun that embodies knowledge or figures out a technique to maybe organize the workshop differently that saves them some time and they're now producing, you know, 12 bird houses an hour instead of 10. Well, knowledge has this property of being non-rival that can be shared without being depleted. I can teach you a song, but I still would know the song. If I give you a hammer, I cannot use the hammer while you're using it because it's rival. So what economists figured out in the 80s and in the 90s is that if you wanted to explain economic growth, which happens in per capita terms, the only way that you could do that is by assuming that growth was a consequence of a non-rival quantity, something that could be copied without being depleted. And that was ideas or knowledge. And that became a big revolution in the 90s. In the 90s, everybody was talking about the knowledge economy and the idea that knowledge is the secret to the wealth of nations.

But what my book tries to do is to bring that to the next level because in that interpretation from Romer and other people in the 90s, knowledge is still some sort of quantity that you can accumulate in a barrel. It's undifferiated. So my book focuses a lot on the fact that knowledge has another property which is that it is nonfgeible, not also non-rival. And that nonfgeibility is the one that makes it interesting to study because it has all of this categorical you know differentiation that um requires you to use a math and a set of representations that are more similar to the ones that use in machine learning which also deals with nonfible things like language words are nonfible this nonfgeibility thing is is fascinating so all of us have this intuition right um you you hire someone and I'm a big believer that knowledge is situated And there is this fanciful idea about knowledge that it's a completely abstract thing that you read a book and you acquire the knowledge and it can just be copied any amount of times. In fact, that's the argument that AI existential risk people make. You know, you can just copy the language model and now you've got a thousand um you know Einsteins instead of one.

What we find in practice is that it's quite difficult to exchange knowledge. Why is that? there is a tacit and implicit idea there that knowledge is something that something can have while my view is that knowledge is a much more collective phenomenon. Okay. So and it's not something also that you can put in something like a book. In my opinion the book doesn't have knowledge. The book is an archival record of some ideas that I was able you know to put together in a nice structure. But you cannot have a conversation with the book in the way that you can have a conversation with me in which I can tell you the story of Jachai or the story of Sam Slater or the story of how Sony got started based on you know what we're talking about and and have that dynamic response. So knowledge only can go to work when it's embodied. You cannot throw like you know a bunch of engineering manuals and cement into a gorge and expect to get a bridge because the books don't have knowledge, teams have knowledge, organizations have knowledge and all of that. Now that diffusion of knowledge is something that is is hard and and that's something that we have established really well.

What is interesting to me also about this field of study is that people say well there are no really laws in economics. But when it comes to economic geography there are a lot of things that are very well established and that are lawike. For example, the fact that knowledge diffuses more effectively at shorter distance and that that short distance diffusion is explained by social networks has been established not by one or two papers but by dozens of studies if not maybe hundreds of studies that have verified you know those effects. You know the idea that knowledge moves more easily among related activities and that that can come from the complimentarity of the inputs that are required to develop each one of those activities is something that also we call the principle of relatedness and that has been documented by hundreds of studies. So we do have lawike behavior for the growth, diffusion and value of knowledge uh that we're starting to understand and some of that low-ike behavior goes into your question which is that of why it is difficult to diffuse knowledge. Just the words in a paper or in a book they are completely different to the actual physical embodied process.

[Speaker Name]: But is there a middle way? But are you saying only the physical embodied process is a form of knowledge and understanding or do you think there exists any type of model or representation which we could say is a form of understanding?

[Speaker Name]: The problem of talking about knowledge and that's something that I addressed in the introduction of the book is that it's a word that we use to mean vastly different things you know and and we we kind of like understand that from context but it's good to classify you know those different ideas and uh there's people that have done that. So one of the ways that you can understand um different types of knowledge is by thinking of a detective novel. So a detective novel or a detective TV show usually starts in a murder scene, you know, and ends in an arrest. And it's beautiful because the writer just needs to fill the space in between. And it starts usually at that murder scene when the detectives come in and they start collecting what we call factual knowledge. Yeah. There is a bullet hole in the wall. There was a call at 700 p.m. last evening. And those facts don't tell you about, you know, the motive of the murder or who was involved, you know, any of that stuff. And factual knowledge is knowledge that is very easy to diffuse. You know, I can tell you that Santiago is the capital of Chile and you can remember that you can transmit that information or or that little piece of factual knowledge very easily to someone else and so forth.

Then you have conceptual knowledge which is what usually the the hero of the detective novel would do which is putting everything together in a story where all of the facts are you know little anchors that can be used to validate that story. Okay. So that's story. Okay. You know, the bullet hole was there because when you know the the murderer tried to shoot the this person moved to the side and and and the phone call was placed because someone was trying to warn him and they kind of like figure out the entire story. Now to validate that story, what they need to do is sometimes they need to collect additional evidence that is not factual knowledge but that is collected through procedural knowledge. So they maybe have a little bit of blood and they need to send that to a DNA lab for sequencing. Now the DNA lab has procedural knowledge because it understands how to perform that procedure to sequence DNA and that produce another facts that get put into the concept. So when we talk about knowledge we talk about all of these different things.

Now there is another distinction that is extremely important and that I use a story at the beginning of the book to illustrate is that also especially among academics or people that are highly educated we talk about knowledge as these sort of truths that have been validated by the scientific method and so forth. And the book is not about that knowledge. And in economics, knowledge is not just about validated truths that have come, you know, from universities, scientists or or researchers. But there's knowledge in a lot of different things that is much more pedestrian and common. So a car mechanic has knowledge. A baker that has been producing different types of pastries and and and breads, you know, would have knowledge. Everyone has knowledge. And knowledge is highly specific. is not necessarily things that are 100% guaranteed to be true because of the scientific method, but are all of this experience and received wisdom that people have and that allows the world to work because the world works not because everybody's operating according to a scientific theory, but because you know car mechanics know what they're doing, because gardeners know what they're doing, because the guy that comes and clean the pool know what they're doing and they have their own experience. Maybe you know it includes even knowing how to deal with pesky dogs. If you're a pool cleaning guy, they have knowledge on how to deal with that. that comes from experience and it's not what you would find in a book. So it's about that more democratic definition of knowledge.

[Speaker Name]: Yeah. I mean I I do agree that we should have a a notion of knowledge which doesn't completely depend on humans in the most abstract sense. We might say that it's a form of modeling. So there are certain types of systems which we might say are alive and part of the process of staying alive and you know sort of like minimizing this free free energy you might say um is being able to model the world. Now the only reason I'm bringing this up is is you said okay there are facts which is like you know this is what the state of the world is there's procedural knowledge this is what we what we can do there's conceptual knowledge this is how to think you're talking about this detective film so he he was doing this conceptual type of reasoning where he was imagining possible worlds so he was saying oh you know what if this person um you know killed the person what if this person and that kind of imagination is simulating without direct physical experience so this person had knowledge and what they did was like a jigsaw puzzle. They were just trying different configurations of counterfactual futures. They found one which was plausible and then they generated a hypothesis. So that is a form not necessarily of it being a physical embodied process but also a form of like mentalized internal thinking.

[Speaker Name]: Yeah. And and I think that's correct. And the reason I think why that is correct is because it involves knowledge that is simple enough that fits within an individual, you know. But the thing about knowledge is that knowledge um can be such that you need multiple individuals to hold it. So yes, there could be a detective that can put all of the pieces together, can generate multiple representations, multiple alternative stories and use facts, evidence uh to decide among those alternative stories. Now, um, when we talk about economic growth and development, we're talking about knowledge that tends to be procedural and that tends to produce products or services that can improve the standards of living of people. So, for instance, manufacturing an aircraft, you know, manufacturing an aircraft is an operation that simply cannot be done by a single individual. No individual has all of the knowledge needed to manufacture a large, you know, jet passenger aircraft. And that knowledge tends to be distributed and embodied in this case in networks that involve humans and machines. They include whether it is printed material from manuals, whether it is an LLM that is helping now retrieve some information, you know, that comes from those manuals, whether it is the experience of people that have worked on that same model in the past and so forth.

So the book focuses a lot on knowledge at that collective level. you know, I I run a center that's called the center for collective learning for a reason because I think, you know, of learning and knowledge at um at that scale, you know, and I think that's a very different story than that of sort of like figuring out the right theory in the detective novel, you know, it's a story that I think it's it's not so logical. It's much more experiential and that we do have models for. So, the model that I love in that space, there's this model by Linda Argot. She's a professor at CMU and she says an organization is a network that connects three types of nodes like people you know things and let's say ideas, concepts, procedures and like something you know more intangible. And at any point in time an organization is a network in which some people are working with some other people. Some people are using some tools to produce some goals and so forth. And an organization learns not only by the learning of people. It also learns as that network reconfigures which is kind of interesting because it's sort of like a parallel to like the deep learning type of idea that you're adjusting weights and in an organization we're also adjusting weight.

So we discovered that team maybe that's like working with Robert and they don't get along they compete whatever. So maybe he's going to work better with Charles and the moment that maybe management or maybe organically Tim starts working with Charles the organization learns something and maybe maybe Tim was working in marketing but he hates marketing so maybe team wanted to work in engineering and if we assign team now to this different activity or to this different tool then there is learning and there is organizational learning that happens only by the reconfiguring of the same parts in a system that's a model of learning that goes beyond the individual and that has an analog to the types of learning that we're trying to reproduce I think in silicon right now. But still I would say the incilico models are still individual learning you know systems. They're not so much collective learning systems that involve all of these other social relationships and complexities.

[Speaker Name]: I absolutely love that. I mean, there was a wonderful example in your book. You you're talking about um Barnes & Noble and they're over there in Seattle and they they they went up against Jeff Bezos and they said, "Well, you know, Jeff, we've just launched a website and and we think we can do what you do better than they do." And they, you know, Jeff said, "I don't think so. You know, you might have a website, but you're a completely different type of business to us. We are geared up. We have the logistics. We can send individual things anywhere in in, you know, in in America. They were set up for wholesale and and retail." But but the thing is so you know they they could do the same thing but they were wired differently. So that brings in the idea of architectural innovation architectural knowledge. So it's a very interesting concept that was introduced by Rebecca Henderson from HBS and the idea is that when you innovate uh often you have what would be called gradual innovation in which you are changing a component.

So for instance one of the classic examples in this literature is um the manufacturer of aircraft. So, if you had propeller aircrafts that had combustion engines, changing one engine for a more powerful engine or a newer engine model was something that you could do relatively easily because you didn't have to redesign the entire airframe. You just brought, you know, the new engine, replace, you know, the the old one with a new one and you were done. When jet engines were invented, uh you needed to redesign the entire airframe, you know, to be able to produce an aircraft. So the companies that were operating with combustion engines, they went bust most of them. And there was a new wave of companies like Boeing, you know, that were newcomers at that time that were specialized on jet engines because they were designing the entire airframe around the new engine. Now in the case of Blockbuster and Amazon, those are very good examples of architectural innovation because you might think that well Barnes & Noble is able to ship you know millions of book to all of these stores. you know, it has thousands, if not maybe tens of thousands of employees that are experts, you know, on the business of books and dealing with clients.

And the idea of shipping the book directly to a consumer might look like a small incremental innovation, but in reality was an architectural innovation. And and when I do talks and I present this with slides, what I do is I show the picture of a Barnes & Noble and then next to that I show a picture of an Amazon fulfillment center which looks like kind of like this part of the airport that is you know sorting all of the different luggage and that shows that no that little idea of just shipping directly to consumer require a completely different organizational design and the distance between the Barnes & Noble organization in this network that we were describing before in that model with the to the Amazon model was enormous in reality just because of that change.

[Speaker Name]: Exactly. And and this is the reason why in my opinion LLMs are not intelligent because they don't have this coarse grained dynamic adaptation of their architecture. But we're getting ahead of ourselves a little bit. So at the beginning of the book you spoke about this concept of a person bite which is roughly how much can one person know and we're a collective intelligence. we work together and you you spoke about this kind of power law learning curve which is basically at what point does our learning as and and and we'll get to that as well but there was one fascinating example you you gave um you're talking about this this ship building company and over the course of I think was it the second world war the first world war they became much more efficient at building ships and was that because of experience or was it because of process

[Speaker Name]: The first law of knowledge the law of time is divided into several subprinciples and the first one is about the growth of knowledge in individuals and teams. That's a story that starts with Leon Thirststone. He was the first one to, in my opinion, map like a really good learning curve in 1916. Funnily enough, he started as an engineer and then, you know, he, you know, actually produced a camera that um got him an interview with Thomas Alba Edison. He decided not to work with Edison and go to teach at the University of Minnesota. Instead, he becomes frustrated that he's really good at math and engineering and it's hard to teach it to students. So, he becomes interested in learning. He goes to Chicago. He enrolls in the PhD in education and after a year he switches to the program in psychology and there he gets access to a data set that was being collected at the DAFF College of Business in Pittsburgh in which h you had records of how well people learn how to type. So imagine you have a mechanography class you know people are learning how to type. These are 18, 19 year olds that are typing on a typewriter for the first time. And you see every week how many words they're able to type per minute, every four minutes actually, you know, and then you see how many pages they've written throughout the semester.

And when you put those two things together, you get a very neat, you know, learning curve that follows this sort of power like imagine like a square root type of shape, you know, uh, in which learning is really fast at the beginning and then it peters out. Then in 1936, you know, that's about 20 years later, Theodor Wright, which is an aircraft engineer in the United States. he was actually important enough to be in charge of aircraft manufacturing for all of the United States at the end of second world war publishes a paper in which he looks at the cost of producing an aircraft you know uh he's very smart he looks at the cost of the last aircraft produced in a batch because aircrafts are produced in batches and he finds also that the number of man-hours as a function of the number of aircrafts in the batch decreases as a power law okay so it's the same result that thirststone got in one case you can look at capacity in another case you can look cost. And then in 1965, Leonard Rapping, an economist, grabs data from the liberty ships. The United States was producing during the Second World War, an insane amount of liberty ships in multiple shipyards.

So, he could use the fact that shipyards started at different times to have like a more causal story. You know, economists love kind of like having that extra little hint. And uh he was able to show that this learning that was observed the fact that the man-hour needed to complete a ship were decreasing over time was not a consequence of changes in technology or increase in capital expendure or increase in labor that basically more people was working on the ships but it was a function of experience. So how many ships your shipyard had already built. So that provides evidence of learning. Now what happens is that that phenomena is true only at the level of individuals, teams, firms and so forth. And once you transition to the industry level, you get to Morse curve which is very different. It's qualitatively different is exponential. And part of the book focus on explaining the connection between the two.

[Speaker Name]: We we know from experience, right, that when we have experience, we get better at things. And we have this this weird I don't know whether it's an illusion that all we need to do is just write down our understanding into a into a wiki document. It's the same thing with what I do on MLST. So I I've I've tried to write down how I edit the videos and how I do the sound design and the video and so on. And it just became more and more and more content. I could probably write an encyclopedia about it at this point. And I realized at some point that a lot of it is tacit and it's very very difficult to transfer to any new staff that that come on. It it is simply just a function of experience and this is really depressing to anyone who wants to start an enterprise or a business because the biggest problem is this knowledge transfer bottleneck. Um are you saying that that cannot be overcome in any other way than just having lots of experience and lots of people working?

[Speaker Name]: No, I think exponential learning is important to transmit that tacid knowledge. And I think you have a stories of people that have that intuition and that have been successful because they have developed careers following that intuition. One example that comes to mind, you know, is did you watch Arnold's documentary? Uh Arnold Schwarzenegger had a fantastic documentary on Netflix like

[Speaker Name]: No, no, no, no. A three-part documentary that basically goes through his entire life. So the first part is about him as a bodybuilder, the second part as him as a movie actor and the third part as a politician. And there's a constant in the documentary says like look, I wanted to become the best bodybuilder in the world. If you want to become the best bodybuilder in the world, you have to be with the best. So I figured out that the best were in California. So I moved to California and I became the best. Then you know I wanted to become the best paid actor in the history of Hollywood. So you have to work with the best. So, you know, like I I had money that I had saved from my bodybuilding activities. I had real estate that could keep me alive so I could, you know, be picky about the roles and I wanted to work with the best. And eventually 10 years later, he or 15 years later, he becomes the best paid actor in Hollywood. And he accomplishes everything that he has set his mind to. But he's very conscious that the only way to do there is not by figuring it out on his own on a quiet room on the back of his house. It's by trying to make sure that he's with the best. And when it comes to politics, he was part of the Kennedy family. You know, he he marries Maria Shriver like early on and he learns from the Kennedys for, you know, more than a decade before he decides to run for governor of California. So again, you know, you learn from the best.

The example I have in the book is that of Samuel Slater, which is a local lad, you know, and it's truly a hero. H and this is a a guy that is born in the Midlands at the time that the Midlands were the place that had for the first time figured out how to do water powered cotton spinning. That was a devilishly difficult technology. The first patents for water power cotton spinning are from the 1730s. They tried to build a mill in Birmingham. It doesn't work. Another in Northampton doesn't work either. about 50 years have to pass after that for people like um Arkrite and Strat to develop water power spinning in you know first in Kroford which is a very small town but that had you know water power and then eventually they created mills in Derby and Belper and so forth. Now Samuel Slater you know is born at the time that these mills are first being erected and he joins one of a strat mills at the age of 14. He's very smart, becomes an overseer, and at the age of 21, he says, "Okay, you know, I I know this business, and I know that I'm not going to make it in this business because this technology is just spreading like wildfire. Everybody figured out how to build these meals, you know, but in the US, they have not figured that out.

So, uh, he escapes, you know, Belper in the middle of the night without telling a soul. He goes into London. in London. He boards a ship pretending to be a farmer and he lands, you know, in h New York 66 days later. He goes into a mill that was in Manhattan. He immediately sees that the machines were no good. They had no water power. So, he quits, you know, after 4 days. And then he learns from us loop captain that there was a man in Pucket that was trying to develop you know water power cotton spinning technology but they were not able to produce you know yarn of good enough um finess strength. You know the thing about cotton yarn like the one that we have in our jeans is that to resist the tension of the loom it has to be very well spun. And if you manually spin uh cotton, you cannot produce jeans or or fabric of that type because it would just snap under the tension of the loom. No. So he moves to patake it, you know, and eventually, you know, develop the first water powered cotton spinning in the United States within a period of about a year and it starts the American industrial revolution and mills start to spread there just like before. So it's a very good example of that embodiment of knowledge like the the people in Potake had tried to develop water power cotton spinning based on hearsay.

There is this story you know from from the book where I where I got that story that there were some Scotsman that had seen one of Arcright's meals and they had told these other guys how they worked but based on that hearsay they were not able to develop it. You had to have someone that had that experiential knowledge, you know, that had worked with the best with Arrite and Strat to come all the way to the US in an act of treason because it was a punishable act of treason to bring that technology to America to eventually be able to build that capability.

[Speaker Name]: You're strongly asserting that there is a huge physical component that there's an embodiment to the propagation of knowledge. the way it flows es and decays you know everyone can access GitHub people the other side of the world can start playing with software so do you think at some point at least the propagation of knowledge becomes more virtual

[Speaker Name]: I think there's two things one thing is um to be precise about what we mean by physical and everything has to be physical because even GitHub you know has to store its data in some sort of hard drive or magnetic field or whatever technology but it's not storing it in nothingness you know so so knowledge Knowledge information always has this form of physical embodiment. Now I think we tend to think about it as non-physical uh because it is a thing that is not a thing which is uh the same as temperature. Yeah. So in in the book I have a a chapter in which I tell the history of temperature. Temperature is kind of funny because today you wake up, you look at your phone and you see the temperature and you decide how you're going to dress and nobody has any doubt that temperature is something that can be measured. But it took about like 2,000 years for us, you know, as a species to figure out, you know, what temperature was and the fact that it could be measured. And there were two fundamental difficulties that I would say made it difficult for us to understand, you know, uh temperature. The first one is that first people thought that hot and cold were two separate things.

Okay? So that temperature was like a mixture of the two like when you make green out of blue and yellow. Okay? And it took a while for people to understand that cold was the absence of heat and not that cold and heat were two different quantities that were tempered together. They were mixed. So temperature actually mix means mixture not you know like what we now mean by temperature. The other thing that was very difficult to understand is that people thought that temperature was a thing was some sort of fluid that grabbed onto things. So let's say if you had a steel uh rod that is hot is that steel rod kind of like has this sort of invisible fluid that is heat and they had good reasons to believe that it was an invisible fluid because it could flow. Let's say you could connect that rod to something that was cold and that cold thing was going to warm up because that fluid was going to be flowing in that direction and so forth. So they thought that it had a physicality as a thing. um a brilliant Englishman Jou basically figures out that that is not the case that you know temperature is not a thing and the way that they do it is through this observation in which I don't know if you know how cannons used to be built you know so if you just grab a piece of sheet metal and you make it into a cylinder and you try to make a cannon out of that the moment exactly that you that you shoot the cannon that's going to open up like a flower in a cartoon you know like like you know like a Looney Tunes type of situation So what they would do is they would make these solid, you know, cylinders of metal and they would bore a hole in it, you know, to create the cannons and boring those holes released an enormous amount of heat.

So J thought, well, how come all of that heat is there? It's like an infinite amount of heat. If I continue to bore a hole in a piece of metal for an infinite amount of time, I'm going to it cannot be a thing then. And that you know leads him to realize that temperature is actually something that has to live in things but it's not a thing itself is related to the kinetic energy of the particles in the thing but it's not a thing itself. It doesn't have its own particle. There isn't kind of like a temperature particle. Temperature is kind of like a property that matter has and that holds on to things. Knowledge is similar, you know, in that it holds on to you and to me, you know, and and and to the collective to exist, but it doesn't have kind of like a physicality in itself, but it always exists in some sort of physical medium or substrate. So, in that sense, it's always going to be physical. No matter how virtual it gets, it has maybe a different type of physicality. But even electromagnetic waves that are transmitting, you know, data from your Wi-Fi router to your laptop are technically a physical embodiment.

[Speaker Name]: There's um an interesting perspective here. So David Krakow, he says that temperature is an intensive property of matter. Yeah. And he says intelligence is an extensive property, you know, and and I think when he says intelligence, he's actually talking about knowledge. So he says that when we look at these complex adaptive systems, he says there's two types of systems in the world. There are the sort of the Roger Penrose symmetry dominated systems and then there's the systems that break symmetries which are these kind of complex systems which are you know things like life and and evolution and um so yeah the the these systems the amount of information they've accumulated in their lifetimes is a good proxy for the amount of intelligence that that they've had and and you're basically saying that that accumulation of of information you know roughly as as a physical property of matter is how we should think of knowledge.

[Speaker Name]: I' I've thought a lot about whether knowledge is intensive or extensive or whether complexity as you know is what we measure in in the technical literature is intensive or extensive and the key insight is is the following is that uh let's say you are wondering whether putting together two countries would lead to having more knowledge h than having those two countries separately. Okay. and you're going to proxy knowledge by the specialization that these countries have on the activities that they perform. So now when you put those two countries together, if let's say there are two developing countries in Africa that are specialized in a few activities, they're going to be specialized mostly on activities that have low knowledge intensities and a few that have high knowledge intensities. And when you put them together, you're going to realize that the high knowledge intensity activities, which are the ones that are not in common, maybe get subtracted because now they're