The 3 Laws of Knowledge [César Hidalgo]

be organized around three laws. A law governing how knowledge grows in time, a law governing how knowledge diffuses across 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, uh, strategy and design. If we don't do it, we're gonna have failed development efforts. So the book also tells a number of stories of failed development efforts. development attempts that defy the loss 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 gonna

Try to understand how knowledge ebbs and flows and what are the speeds and rates at which that happens. You know, 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 gonna 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 now we can think of policy uh, 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.

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, th maybe the types of things in in a neural network is knowledge. But

You know, Paul Romer got the Nobel Prize for endogenous growth theory. This year, you know, Agion and Howard 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 uh when you're trying to explain economic growth, you're trying to explain output. You know, so imagine you have

Ten carpenters that have access to hammers and nails and boards of wood, and they have to produce bird houses. And these ten carpenters produce ten birdhouses per hour. Now, you know, let's say that you want to produce twenty 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 wanna do more of them, you're gonna have to have more carpenters, more nails, you know, more hammers and so forth. So that tells you that labor and capital are rival inputs

And if you wanna increase output, you don't increase output in per capita terms. The bird houses per carpenter remain the same. Now 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, twelve bird houses an hour instead of ten.

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 are using it because it's rival.

So what economists figured out in the eighties and in the nineties 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 nineties. In the nineties 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 Rohmer and other people in the nineties, knowledge is still some sort of quantity that you can accumulate in a barrel. It's undifferentiated.

So my book focuses a lot on the fact that knowledge has another property which is that it is non fungible, not also non rival. And that non fungibility is the one that makes it interesting to study because it has all of this categorical, you know, uh differentiation that um it Requires you to use a math and a set of representations that are more similar to the ones that are used in machine learning, which also deals with non-fungible things like language. Words are non-fungible.

Copy the language model and now you've got a thousand um, you know, Einsteins in instead of one. What we find in practice is that it's quite difficult to exchange knowledge. Why is that?

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 Yachai or the story of Sam Slater or the story of how Sonny 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 uh 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 lawli.

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

So we do have law like behavior for the growth, diffusion and value of knowledge uh that we're starting to understand. And some of that law like behavior goes into your question, which is that of why it is difficult to diffuse knowledge.

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 seven PM 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.

Eh, 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 on.

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 uh can be used to validate that story.

Okay. So that 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 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 this 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 and breads, you know, would have knowledge. Everyone has knowledge and knowledge is highly specific. It's not necessarily things that are a hundred percent guaranteed to be true because of the scientific method, but are all of this experienced 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 cleaned 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.

um is being able to model the world. Now the 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.

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.

uh 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 L L M that is helping now retrieve some information you know, that uh 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 uh in that space, there's this model by Linda Argood. 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 s 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 are adjusting weights.

And in an organization we're also adjusting weight. So we discovered that team, maybe that's a like working with Robert and they don't get along, they compete, whatever. So maybe he's gonna work better with Charles and the moment that maybe management or maybe organically team starts working with Charles, the organization learns something.

And maybe maybe team 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 analogue to the types of learning that we're trying to reproduce, I think, in silico right now. But still I would say the in silico 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.

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.

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 manufacture of aircraft. So if you had propeller aircraft 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 the 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 uh you might think that, well, Barnes and Noble is able to ship, you know, millions of books 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 it 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 and 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 and 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.

And you you spoke about this kind of power law learning curve, which is basically at what point does our learning acetate? And and and we'll get to that as well. But there was one fascinating example. You you gave um you're talking about the sh the shipbuilding company. And over the course of I think it was it the Second World War or the First World War, they became much more efficient at building ships. And was that because of experience or was it because of process?

And then, you know, he you know actually produced a 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 dataset that was being collected at the DAF College of Business in Pittsburgh, in which uh 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.

Then in nineteen thirty six, you know, that's about twenty years later, Theodore 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? He's very smart. He looks at the cost of the last aircraft produced in a batch because aircrafts are producing 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 load.

Okay, so it's the same result that Thurstone got in one case you can look at capacity, in other case you can look at cost. And then in nineteen sixty five, Leonard Rapping, an economist, graphs 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 hours needed to complete a ship were decreasing over time. was not a consequence of changes in technology or increase in capital expenditure or increase in labor that basically more people was working on the ship.

but it was a function of experience of 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 Moore's curve, which is very different. It's qualitative, it's exponential. And part of the book focused on explaining the connection between the two.

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 realised at some point that

It 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?

Uh Arnold Schwarzenegger had a fantastic documentary on Netflix. A three-part documentary that basically goes through his entire life. So the first part is about him as a bodybuilder, the second part uh as him as a movie actor and the third part as a politician. And there's a constant in the documentary that says like look, I wanna become the best bodybuilder in the world. If you wanna 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 had 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 peaky about the roles and I wanted to work with the best. And eventually ten years later

he or fifteen 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 Schreiber 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. 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

figure out how to do water powered cotton spinning. That was a devilishly difficult technology. The first patents for water powered cotton spinning are from the seventeen thirties. They tried to build a mill in Birmingham, it doesn't work. Another in Northampton, it doesn't work either.

about fifty years have to pass after that for people like um Arkwright and Strutt to develop water power cotton spinning in, you know, first in Cromford, 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 Stratz mills at the age of fourteen.

He's very smart, becomes an overseer, and at the age of twenty one, He says, okay, you know, I I know this business and I know that I'm not gonna 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 in 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 four days. And then he learns from a sloop captain that there was a man in Potaket that was trying to develop, you know, water powered cota spinning technology, but they were not able to produce

You know, yarn of uh good enough um fineness and strength. You know, the thing about uh 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 nap under the tension of the loom.

So he moves to Pataket, you know, and eventually, you know, develop the first water powered cotton spinning in the United States within a period of of about a year and 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 Potaket 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.

Uh that there were some Scotsman that had seen one of Arkwright'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 Arkwright and 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 uh eventually be able to build that capability.

has to store its data in some sort of hard drive or magnetic field or whatever technology, but it is not storing it in in nothingness. You know? So so 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.

So in in the book I have a a chapter in which I tell the history of temperature. Temperature is kinda funny because today you wake up, you look at your phone and you see the temperature and you decide how you're gonna dress and nobody has any doubt that temperature is something that can be measured.

But it took about like two thousand 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.

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 kinda 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 gonna warm up because that fluid was gonna be flowing in that direction and so forth. So they thought that it had a physicality as a thing.

a brilliant Englishman, Jowl, basically f 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 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 gonna 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, uh cylinders of metal and they would bore a hole in it, you know, uh to create the cannons. And boring those holes released an enormous amount of heat. So Jowell 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 not gonna 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 kinda like a temperature particle. Temperature is kinda like a property that matter has and that holds onto 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 gonna 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 eh from your wifi router to your laptop are technically a physical embodiment.

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.

So now when you put those two countries together, if let's say there are two developing countries in Africa that are specializing a few activities, they're gonna be specialized mostly on activities that have low

uh knowledge intensities and a few that have high knowledge intensities. And when you put them together you're gonna realize that the high knowledge intensity activities, which are the ones that are not in common, maybe get subtracted because now they're not a specialized, they're not producing enough to justify the now combined, you know, uh area.

And therefore, you know, the complexity of those economies when they're put together doesn't go up. So an intensive quantity is one that when you put two units together, it averages out. And an extensive quantity is one when you put two units together, you add it up. You know, and knowledge has kind of like a little bit of that intensivity that depends on complementarities, you know. So if you put

two people together that know the same thing, you don't get twice the knowledge. Then in that case it's clearly not intensive because it would r be redundant. If you put two people together that know the same thing And one is really dumb and the other one is okay, you don't get a super smart person as a result. You get kind of like a half dumb person, maybe, you know, you you average them out, you know?

So for that extensivity to kick in, you need to have, you know, a good level of performance but also complementarities. You need to be able to put things together. that when they're put together they're more than the sum of the parts. And that's not always guaranteed. You have a lot of examples in which actually putting things together is gonna give you like an an intensive result or property. You need kind of like that diversity and those complementarities.

with people here. And it connects to what you're saying because um after you know telling some stories from that chapter, I had a colleague, you know, um from from here from the UK that sends me an email. He says, this reminds me of the Isse Temple story. So okay, what's this temple? So I looked it and I I read it up, and there is a temple in Japan that they rebuild every 20 years.

Okay? And by rebuilding the temple every twenty years, they basically train the new generation of people that are gonna know how to build a temple and is gonna have to train the next generation of people in twenty more years. So if you think about it you can think of a temple as, you know, a structure uh and you might wanna preserve that structure. Like here in Europe we love this historical heritage and so forth.

But in this particular example they're deciding not to preserve the structure. It's not that this beam here is three hundred years old or five hundred years old or no. What they're preserving is the knowledge on how to rebuild the structure by keeping the muscle, you know, uh fit, you know, by by continuing to doing the activity over and over again.

Was estimated to decay about three percent to six percent per month. You might think three percent sounds like a little bit, but it's fifty percent a year, meaning that if an organization were to stop working one day and wanted to resume after a year, they would have lost fifty percent of the knowledge. So knowledge decays really fast. And the reason why we don't observe that decay as frequently is because, of course, knowledge is being accumulated in a way that offset

you know, those decays. But you have very good examples of knowledge decaying. One of them is the history of Polaroid. You know, so Polaroid was an amazing company created by a quintessential Harvard dropout and entrepreneur, Edwin Land. You know uh it's called Polaroid because what they did was uh polarized film. They didn't start with instant photography, they started doing polarized film like basically land.

figure out a way to create, you know, uh polarized filters that were l large enough to have an industrial use and application by stretching kind of, you know, like this this goo with this, you know, material in a magnetic field so that you could create the polarized filters. And then after the Second World War, They need to find a new business model because you know, polarized filters were usually sold to the military.

So they come up with this idea of doing instance photography which was devilishly difficult. Involved tons of patents because the chemicals that you use to to develop a photograph are are extremely reactive. So if you put them on an envelope that people are gonna be shaking around and and you're gonna be transporting around the country Most likely than not, you know, they're gonna react at some point they they might mix and you know your your envelope uh becomes worse.

And they developed extremely high quality instant fotography so that fotografers like Ansel Adams and so forth they would use, you know, uh uh Polaroid cameras. Υπότιτλοι AUTHORWAVE Πολαρδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδοδο. And there's a man in Vienna that has a...

shop online that sells kind of like this vintage film and so forth. And when he finds out that this plant is gonna close, you know, he flies into the Netherlands and he gets kind of like a group of people together to acquire, you know, the plant because Polaroid was just simply gonna close the plant and say, why don't instead of closing it

selling it to ourselves. They they they sold him the plant, you know, and they tried to restart the production of the technology. Now they had access to the factory. They had access to all of the original equipment because they they didn't dismantle the factory. And I interviewed him when I visited Vienna a couple of years ago and I asked him, Well, the people that you hired to work on the plant.

You know, did you did you get your pick or did you have to deal with whoever won it? And say, No, no, no. We had the A team. It was the star team. You know, we d we couldn't hire everyone back, but for each machine we hired the top player that we had. And still, you know. Um after they ran out of the stock of film that had been produced previously and they had to start selling their own film, it's black and white film. It takes thirty to forty minutes to develop. It often has aberrations.

took, you know, this impossible project, you know, several years to start producing film of any quality and maybe only like about a decade later they start producing you know film that is of a quality that maybe could be considered comparable to the one that Polaroid was producing in the 70s So knowledge can really disappear rather fast, you know, if we stop exercising it. If you don't use it You lose it when it comes to knowledge.

You you you use this um wonderful term um absorbative capacity. There was that was the story of um, you know, the the the US after the Second World War, that they were in Japan. and there was this inventor in Japan and he studied one of these tape players in great detail and he could go away and he could create a prototype just using like, you know, f a frying pan and like you know, like this kind of head

Just in terms of this knowledge decay thing, isn't isn't it amazing that we think that we are at the pinnacle? We think that we've accumulated so much and we haven't lost anything. And there are so many examples like Concorde is is is a great one. What if we wanted to build Concorde again?

Would we just be able to, thirty years later, pull up the blueprints and just kind of stitch this aircraft together and it would be just like Concorde was thirty years ago? Probably not. A another example is in software engineering. There are famous examples of I think IBM publishing their source code online.

And everyone in the company said, No, no, you can't publish the source code online. This is all of our knowledge. If someone gets hold of this source code, they're just going to be able to recreate everything that we've done and they'll know it they'll know everything. That's not true.

Because the actual knowledge is just in the minds, the interactions, the the the the the sort of the the ecosystem, the organism of the company. And you also said in the book which I thought was fascinating, which is that the purpose of an organisation is to retain as much knowledge as possible.

today in the context of the conflict between Russia and Ukraine. But Donetsk is a city that was actually created by a Welshman by the name of John Hughes. So John Hughes was a Welshman, he was a successful entrepreneur here in the UK, he was in ironworks. And he was in the business of iron cladding ships. So you have to remember that in the nineteenth century, you know, wooden ships were being ironcladded so that they would be more resistant to damage, you know, when when you had sea battle.

And uh later in life, you know, this is not a story of a of a twenty one year old like Sam Slater. Later in life, like when he was in his fifties, he goes, you know, to iron clad a ship, you know, for the Russian Empire.

They unload and then they drag this thing through the mud into what later is gonna become Donetsk. They set up a camp and they start building, you know, their ironworks from scratch in that location. and in about three years they were producing pig iron and then, you know, they start, you know, developing what eventually becomes one of the main, you know, uh iron and still producing regions of the Soviet

So actually there were some people that when the conflict started they say we should secede back to Britain because we are British. You know, this was a city that was created by British. But but the whole point is that you see Hughes understood that if he went only with the knowledge that he had to that area and he tried to set up, you know, iron works on his own, you know.

He was not going to succeed because the knowledge had to be embodied on a much larger crew. You know, a crew that required seven ships to be taken from England to the Ukraine.

But then um in the next chapter you went on to talk about disruptive technology. So you gave a great example of steel. So, you know, like it's a manufacture steel, there are roughly four or five tiers of ske uh of steel And what happened was that as companies started to develop higher grades of steel, entrants would come in and develop lower tiers of steel that were kind of crap but they had more upwards trajectory.

And that would create this disruptive cycle, which wasn't this limiting curve, but was actually a bit more like Moore's law. It was like an exponential curve. Explain that.

uh first that are a little bit like a square root, you know that they grow fast in the beginning and then they they slow down and picture out. And then you have Moore's curve, which is an exponential that grows over several orders of magnitude. And you have to reconcile the two.

Now the way that you reconcile the two is that you realize that when you're operating at the industry level or or at a geographic level, you know, like at a country scale and over long periods of time, you don't have the development of a technology that involves a single learning curve. But a collection of generations of technology that have multiple learning curves and that Moore's Law is like that envelope that uh

porque estás en el principio. No. So when the new technologies are introduced, they are worse than the incumbent technologies. Like when digital photography was introduced, it was much worse than chemical.

Instant photography was also laughed at at some point because it was worse. But then eventually that curve keeps on growing and it gets to like a plateau that goes even higher. And then a new technology comes along and moves into a plateau that is higher. So every time You have that intersection coming, you have a window of opportunity. Because in that window of opportunity you have a technology that is worse.

you know, that uh the incumbents don't take seriously, you know, and that it's gonna be able to surpass them. And the moment that those two curves cross, you know, is when the incumbents get, you know, desperate. and they think that they're gonna be able to get there, but usually those changes also involve architectural innovation which is what we discover uh discussed earlier. So moving from, let's say, chemical photography

to digital photography would have required completely redoing and restructuring the entire operation of Polaroid because they were not set up to do that. They were like a chemical company that started with doing polarizing film. They were heavy into chemistry, not into electronics.

So they would have like the little pocket radio that was low quality. And then transistors eventually became good enough and and nowadays we can get amazing sound quality from transistors. Yeah.

Um, it's interesting that Moore's Law has kept on and and you said that the reason for that is they have all of this disruptive technology, they've got larger and larger teams, better and better processes, and they can just keep innovating. How long will that go on? And also w what what is the reason why we can have these disruptive cycles? Because what we were saying before about Barnes and Noble is that

It's really, really difficult for an organization to do this architectural rewiring. So the way that disruptive innovation happens is that Usually you have an independent thread. So somewhere else in the epistemic phylogeny, you have different people with different ideas and different objectives. And they come up with a completely different architecture and they innovate. But now we're in this domain where we have these large big technology companies and they might acquire any new startup.

And that means the new startups will be contaminated with all of the cultural knowledge of the of the incumbent. So don't you need to have like independence, diversity, preservation and competition to keep on this exponential curve?

have arguments against, you know, his evidence and so forth. But but I think it's uh the right question to ask because, you know, definitely

uh i there it looks like there's something in in in that direction. And you can see it in the history of the transistors because the first transistors uh were developed by a team of three people, you know, originally actually a team of two. It was uh Brattain and Bardeen and then Shockley got jealous and over Christmas developed a transistor design that replaced the first transistor that was developed by

his uh lab assistants with Rebrattain and Bardeen and that became the point contract transistor in in nineteen forty eight. But then you had Several different transistor designs. For example, Shockley semiconductor company was not successful at producing transistors, but when Moore and Noyce moved to Fairchild,

Then they produced the Mesa Transistor I think in nineteen fifty eight. And then they produced a planner design in nineteen fifty nine. And then they produced an integrated circuit in nineteen fifty nine. Uh there was also another team in Texas Instrument that produces you know an integrated circuit jack kilby jack kilby was someone that had just joined the company

And because he has just joined the company, the company was kinda like empty. So during the summer he had nothing to do and he created the integrated circuit as kind of like an experiment on his own. But you see it's a team of one. Nowadays to

produce the next generation of NVIDIA CPUs or, you know, Intel CPUs or or whoever, you probably have enormous teams involved in the design, in the manufacturing. So I think that might be at some point the limiting factor, which is uh a at some point maybe, you know, to double again, the teams are gonna get larger than what we're able to coordinate.

And if that coordination capacity doesn't get to scale to the amount of knowledge that we would need to generate another doubling, you know, we might see, you know, this curve petering out. Now, is that close to happening? I I don't know.

quite toxic for progress because creativity is about following your your own gradient of interest and and basically preserving diversity and having new ideas about things. So yes, we have a new generation of these large language models. They're getting, you know, they're ten times bigger every few years.

And the the bullish people say, Oh, we're on an exponential curve and it's just going to keep going up. But I think that because there is so much groupthink and it's fundamentally the same technology and the same people and there's no fresh new ideas that in a sense it's converged and it's not disruptive anymore.

Those are not the teams that you're talking about. Those are the teams that are now flying under the radar. Those are the Edwin Lands before people know about, you know, uh instant photography. uh those are you know mm the Ibucas before Sony makes it big with you know the transistor radio and so forth. So yes I agree that there might be incumbents that are really big. Those are like the Barnes and Nobles and the Walmarts and you know off

uh the tech industry and there might be disruption that comes from teams that maybe have thought of a different way of creating, you know, a artificial intelligence that might overtake those incumbents and that might be disruptive because maybe require doing things in a different way. we're not gonna know until until those curves cross. You know, usually it's very hard to see those disruptors early on simply because they're not yet getting the attention.

uh went into boats and went into the ocean there, you know, and they needed to be relocated into the United States. These were people that had cooperated with the United States and and now they were looking for asylum. So Uh the United States had to relocate, you know, hundreds of thousands of people, you know, uh within the period of a year. And that relocation can be considered to be quite random or exogenous because it wasn't that

Where do you wanna go? Would you prefer to go to New York or to San Francisco? No, it was like we have, you know, uh a small town in Iowa that has a church that is willing to take ten people. Okay, here we have two families of four and this couple, boom, Iowa. You know, and they would be relocating people at an enormous speed because, you know, this was a humanitarian crisis that they were trying to solve, you know, and there were different takers and they were being allocated like that.

Uh after the war is over also what happens is that the United States uh imposes an embargo on Vietnam so they cannot trade. So what this economist Parsons and Vecina did is then they look at the stock of Vietnamese people that was exogenously allocated through this process. And then they look at trade data starting in the year nineteen ninety five, when the embargo is lifted. And they say, Well, you know, if this estate got more Vietnamese people because of this exogenous allocation, did they

trade more with Vietnam after the embargo is lifted? And the answer is yes, you know, there is an effect there. So they show that in this case, you know, uh the relocation of these people brought knowledge on how to uh trade with Vietnam and they had relationships that they could use to develop that that commerce.

The economy involves multiple activities, like multiple industries or multiple products, and you can think of each one of those activities as like a tree in a forest. Okay? So you have multiple trees in a forest. So let's say this is a tree that represents shirts. And nearby you have a tree that represents, you know, blouses. And those are very nearby trees because shirts and blouses are are are similar products. So it's if you produce one you can produce the other.

And then maybe far away there you have a tree that represents, you know, natural gas. And then on the other side you have a tree that represents, you know, tractors or combustion engines and so forth. And you have kind of a geometry or a geography of knowledge itself. So in that representation, if knowledge or is represented by the activities that you know an economy can produce and these activities are trees, countries are collection of firms

Which are collections of monkeys that live on this tree. So let's say you have a garment company and all of your monkeys, you know, all of your knowledge is being harvested from the uh fruit that is being grown in the in the tree of blouses, in the tree of shirts, in the tree of linens, in the tree of you know socks and so forth. You know, you have an electronics company, you are in a different part of the product space.

And economic development is the process by which countries move into new trees. But the ability of monkeys to jump from a tree to another depends on their distance. So what we have uh shown and uh after we did it uh it has been shown by hundreds of papers, is that the probability that you would enter an activity

uh that you were not specialized in in the past depends on how many monkeys you have around in other trees. You know? This geometry really matters. And and the story that I use to illustrate that, which I think is the most um telling, is the story of Vespa. So everybody knows Vespa, you know, it was even in a recent Disney movie. It's an Italian scooter uh that is iconic, but people don't know that it was created not by a motorcycle engineer, but by an aircraft engineer.

His name was Corradino Dascanio. Corradino is the equivalent of Theodore Wright, but for Italy, he was in charge of the production of aircraft for Italy during the Second World War as well. And he was working in a factory that was uh from the Piaggio family that was specialized also in the manufacturing of aircraft. Now.

When the war is over, three things happen that change the destiny of the Piaggios and of Corradino. The first thing is that Italy is no longer allowed to manufacture aircraft. Okay? That's forbidden. The second thing is that the factories had been bombed. You know, uh aircraft factories are primary military targets. They're not hospitals, they're not schools, they're you know, they're weapon factories. So they got bombed, so all of that capital let's say was destroyed or or uh or or what

The third thing is that the bridges, you know, the roads had also been bombed and destroyed. You know, th the i th i i i i it was a war zone, you know. So people in Italy needed a way to move around. They they wanted to arrive at work without being all muddy, you know. uh and in that process, you know, people start thinking about

vehicles that they could uh create to satisfy that need. So first Corradino goes to work with Inocente, was a competitor, the Inocente wanted to do a a motorcycle based on on tubular, you know uh Uh Corradino wanted to do shit metal so they they they don't get along, they had a falling out, and he goes back to the Piaggios.

And then they create a motorcycle in which the mat guard, you know, is the body. The engine goes in the back so you can sit with your feet together, you know, and the wheel in the front comes on and off the same way as the it would come on and off in a helicopter.

Now you might think that's a nice anecdote. Look, these guys were manufacturing aircraft. Now they cannot manufacture aircraft anymore. They do motorcycles. But if you go to Japan or if you go to Germany, you see the same example, like, you know Uh miles and miles away, you see companies like Kawanishi going into light vehicle manufacturing after not being able to produce.

jet uh sorry uh fighter aircrafts anymore. Heinkel in Germany, the same story they produce the Heinkel tourists when they're not able to produce you know, in in their case they they were even able to produce jet aircraft during, you know, the war. So what that tells you is when push comes to shove

And you have to get out of your industry because in this case these guys had no option. They could not stay aircraft manufacturers. And they have to jump into something else. They all jump into something similar. So if they're all jumping to something similar, well it means that they're moving along the same map.

You know, and it's a map in which motorcycles were close to aircraft and aircraft maybe was not close to blouse manufacturing. So they all ended up in the same parts because the diffusion of knowledge is also constrained by the geography of knowledge itself.

So YouTube started as a video dating website and, you know, like the way microwaves are invented to you know, took quite a divergent and weird and wonderful path. And we might we might um conclude from that, oh, it's just a random walk through in you know, epistemic space. And that's not true at all.

um there are like these analogical jumps that that you can make when you perform creative actions. And I think the examples you gave just kind of pointed to that. So the jumps are still very much dependent on the history, but they are kind of like obvious stepping stones that can be taken from the the previous position. Does that make sense?

are better at developing unrelated activities while locals are better at entrepreneurial activities that are related to the current you know economic structure. So we even kind of have a ways to d connect these two stories. You know, migration It's gonna be something that is gonna help you jump far in the product space. Like the the migrant monkeys might land in a tree that is not near your trees and might help you develop a new area.

Th the the the outer organism. They were almost so disconnected that no migrants would want to go there and it can't actually be self sustaining in terms of the creation of of new knowledge.

Seventy percent of them, you know, were born outside of the country or or or they migrated at some point in their lives. There's uh something like that. Uh the Yeah, the the higher the level of education, the the the higher the propensity to migrate, you know. Uh that's something that we know for a fact. But that migration

It's not random. It's these are people moving to centers of knowledge where they know that they're gonna find the complementaries that they need to develop the things that they're looking to, you know, develop.

So being attractive to high skill migrants, it's a very good signal for an economy. I I was talking with people here in the Innovation Agency of the UK in Nesta the other day and and one of the things that was said like look, if you wanna look at the impact of you know migration in innovation in the UK You what you need to count is the number of superstars that you attract because at the end you worry about kind of like that tail end of the distribution.

And uh you know, I know migration is a topic that is quite um controversial right now around the world. And I I don't do in my book uh a call for a single way of thinking about it, but I do think that I show evidence that um migration is not all equal. That if you're able to attract people that have a high level of skill and talent

the probability that they would be net contributors to your economy and to your society is gonna be much higher. So it's not kind of like a let them all in and we'll figure it out later. But you know, how can you become attractive You know, so that the best people in the world are fighting to be, you know, working in your cities, in your universities, in your companies.

There was this really interesting concept uh and by the way, as a point on what you just said, I think there was a statistic that something like fifty percent of the large companies in in the US were started by migrants. I can't remember the the exact statistic, but as you say, when you have skilled migrants it should be obvious. that you have this kind of diversity um acquisition.

And you need to have diversity to try new and interesting things, to not get caught in the basin of attraction that you're currently in. This seems obvious to me. There is a lot of like nasty talk about migration at the moment. But I mean th there is migration is clearly a very, very good thing. Uh maybe you want to comment on that first.

that uh focuses on very high skill individuals. So they're looking at migration by looking at people that patent. What fraction of the population patents? No, or people that publish papers. That's a larger group, but still it's a very elite

in in the context of Africa because I was in in Rwanda a couple of weeks ago actually. You know, it's a fantastic country. Honestly like the like I was very impressed of what they've been able to accomplish. the country is clean, people you know are are you know uh nice and respectful and they're really pushing forward and so forth. But one of the things that you notice when you go to a developing country, I I do a lot of development work and I travel around the world quite a lot.

Is that there's many people doing the same thing. So for example in Rwanda taxis are mostly motorcycles. Okay? They're moto taxis. Okay, so th these guys are going around a motorcycle, they're carrying a couple of helmets, and you put on a helmet, you sit on the back, and that's your taxi. And you have parts of Kigali that you have

Tons of motor taxis. So it's a lot of people. But if you were to add, let's say, the diversity of that knowledge based on the different activities that they do, you wouldn't add that much because, you know, it's the same activity. And then when you go to other places

that are knowledge intense, what you have is that everyone is specialized on something different and they tend to be complementary. So it goes again into this idea of whether knowledge is extensive or intensive. You put a hundred moto taxi guys, you know, the amount of knowledge that you add From one to one hundred is not that much.

But if you then, you know, put a hundred people that do different things and that are complementary, you could get an amazing amount of knowledge. Like whether it is extensive, whether it adds up, depends on whether there are complementarities and difference.

And you know, the World Bank starts lending money to the Netherlands, to, you know, France, to different countries. Those development efforts do really well because the money that comes into Europe at that moment, you know, translates into investment that helps...

rebuild Europe and Europe starts to pick up, it starts to grow, it starts to rebuild and so forth. And then people say, Wow, you know, development is a really easy business, you know. You just throw money, you release the constraint of finance.

And it just happens. Now the thing is that Europe is extremely knowledge rich. So they were, you know, uh throwing money in a continent that was extremely knowledge rich. So sure, the bridge was not there, the hospital had been maybe, you know, partly destroyed and so forth. But the knowledge was still around, so if you liberated the financial constraint, you were gonna get that rebuilding happening, you know, and the GDP was gonna, you know, start to return to what it was supposed to be.

Then they started to do the same, thinking that you could develop other regions of the world through a similar model. And the results were not the same. No. So many things happen. On the one hand, people start to think, well, maybe it is about, you know, institutions and so forth, and there were lots of efforts to try to now

financial support with institutional reforms. So okay, we're gonna make you this loan, but you're gonna have to do all of these reforms within your country. And what happened is that a lot of countries would do those reforms

And the medicine still did not take, you know. Uh now there's people that have said that well, the thing is they would do these reforms only in form. They would not be real reforms. It would be kind of like a mimicry of the reform. And therefore that's why it it it doesn't take. You know. Uh but also, you know, there uh is people that think that in reality it's not just about institutions

a knowledge also plays a role because the demand for those institutions that you need to develop comes from the more knowledge-intense members of your society. You know, so what I do in in that chapter is Is is build a little bit of a parallel because when it comes to economic development, I think the two dominant ideas are that it's about knowledge or it's about institutions.

And I'm happy to believe that both play a role and that sometimes one is the horse and the other one is the carriage. But there have been periods in which you can clearly see a a change between the two. So I tell a lot of stories about China and how knowledge intense workers in China help demand institutions of, you know, uh intellectual freedom and and entrepreneurship that helped develop Zhongwan Jung, the main innovation district of Beijing.

I tell the story of the printing press, which is an excellent example of a technology that enabled institutional change, you know, about sixty years later with the reformation.

that had invented tokamak technology, this idea, you know, this sci-fi idea of confining plasma in a magnetic field, you know? That was the Soviet technology that he had learned during, you know, his graduate studies. he brought to China and he built, you know, a fusion reactor. So you cannot say that Cheng Shenxian was, you know, a a low knowledge intense worker. You know, building a fusion reactor probably is a little bit difficult. Yeah. Anyway, as a

Uh as a consequence of that, he gets invited to be part of a committee that goes to the United States to see how the Americans were building the fusion reactor. So he go to Princeton, he goes to Boston, he goes to Stanford, so forth. And

And he was expecting that the Americans had these huge factories where they were building the components for their fusion, you know, reactors. And And what he realizes is that they didn't have these huge companies that even though the US was so much more advanced than them, what they had what these small companies of professor entrepreneurs. So the same professors or professors that were adjacent to these universities.

We would have a company and the company would be like ten, twelve people that would be building some components, some electronic device, you know, some something that would then be sold to the plasma fusion lab. So he goes back to China and he starts advocating for that. He goes back to the US to learn more about this because he becomes obsessed with this idea that professors

People, for example, the ones that did Lenovo or or you know others were watching that because if he did not succeed, why bother to try? You know, I like they cut his head, they're gonna cut my neck. You know, and eventually through a s set of serendipitous you know e encounters, you know, Chen Chun Chan is able to succeed. Like one of the persons that was championing him, a very smart woman is married to a journalist that would write a briefing for the Politburo.

from the middle management that was oppressing him, and when Chen Junshan survives that, then there is this wave of entrepreneurship that gets released. But the point of the story is that the guys that are demanding those institutions of entrepreneurship are not guys that are selling oranges in the red light, okay? These are guys that are building plasma fusion reactors. So

So that demand for institutions also needs to come from somewhere. And this is a very good example that that demand in this case is coming from knowledge intense workers. So knowledge also generates a demand side for the institutions that you might need to continue to develop that knowledge.

And and possibly go going forwards they're going to become the innovation center o of the world. But what would you say to people that have that kind of common perspective of China?

You know, and therefore the diversity that you have internally it's enormous in terms of like creativity, innovation and capacity and so forth. But there's a lot of skepticism and I think kind of like uh like uh um you know I wouldn't maybe say xenophobia in an industrial context, which I think is is unjustified.

put a foot on China or sometimes outside of the continent of Europe themselves. You know, so they they they might not, you know, have the experience that, you know, would have helped them open their eyes to these other countries and how they work. uh and I I think China it's you know a country that Eh for better or for worse, you know, eh it's here to stay, it's growing faster than everyone else for a long time, has taken so many people out of poverty.

and and it's a force to reckon with that you wanna be friends with, you know, like uh d so so that geopolitical side of like trying to be too defensive about it. I I I don't think it's conducive to the growth of knowledge at the global scale.

Um I mean th th th this goes to my theory of creativity. It it it's not possible to create something unless you're in possession of the entire generative process. I mean understanding is is the generative process of of new knowledge and new artifacts. And if you're not in possession of that then you can't create anything. So in a sense it's doing them a disservice if if they are creating things that that work.

But I w I wanted to get we just touched on this a little while ago, which is that um you know, certainly during your PhD you were doing all of this sophisticated graph analysis. You might have been using something like Gephy, so you were looking at all sorts of

Well, eventually, you know, it's learning how to type. You you don't have knowledge about different things. It's about one thing. You're learning the same thing. When you look at Moore's curve, Well it's transistors, like there's nothing there about other technologies or you might have the same law in other domains, but it's a within knowledge or within domain law, you know.

When we look at relatedness, the story of the monkeys and the trees, now we have some specificity of the knowledge. You know, you you basically the monkey is in this tree and this tree is close to these other trees and not those other trees. So non-fungibility starts playing a role.

So then you have another question which is well, if knowledge is non fungible, if it's kinda like the letters of an ever growing alphabet, eh, you need to maybe figure out a way to count the letters that a country or a city might have, you know, from that alphabet as a way to score

And how do you do that? You know, should all letters count the same? In a scrabble, they have different values, yes, like a Q gets more points because it's harder to use, you know, and and and and and you know, being more rare, you know, makes it more valuable and so forth. So how do you do this? And when what I did is and and I think uh originally it was it was a little bit serendipitously, but then eventually we developed all of the formal models to validate it and prove it.

is that uh there's a smart way to estimate the number of letters that a country will have in the alphabet by extracting eigenvectors of matrices of specialization that are carefully normalized. So if you grab a matrix that tells you which country exports which product And you normalize it in such a way that you take away the effects of country size because larger countries, well, you know, there is some extensivity to things.

You know, uh products that have larger markets, yes, you know, y y the that that column is gonna be very heavy, it's gonna have a lot of big numbers. You have to take that into account, you know, because there's a lot of heterogeneity in geography. And if you do that, you normalize things properly and everything, you can then extract a vector that we can show through a deductive model, it's a monotonic estimate.

of the number of different letters that a country would have, you know, of the infinite alphabet. And what is interesting about that vector, that once you estimate that eigenvector, that eigenvector is very good at explaining future economic growth.

So it tells you ah the countries that have more letters than you would expect based on their income are gonna grow. So for example, when you run that model right now, what it tells you is that the country to bet on right now is not China because China is already quite rich. So, you know, the the the the room that they have to grow is is less. They're slowing down, they're gonna be growing at four percent. But India should be the next rocket and Indonesia and Philippine are the next rocket.

but not other countries that are similarly poor in other parts of the world, because for other countries we don't expect that growth, we don't observe the letters based on the products that they export or the industries that employ individuals and so forth.

Um in in economics you have this idea of convergence clubs, you know. That it's not that economies simply converge because they're poorer and therefore, you know, capital is gonna generate larger returns and and these economies are gonna grow faster. But

that that convergence is gonna be conditional on meeting some conditions. In this case, you know, we can show actually that there are convergence clubs associated to same levels of complexity. So when you look at India according to this measure, you say, well, India In terms of income is here, but in terms of complexity, it's at the same level as Turkey. So India should be able to converge.

to the level of income of Turkey, you know, and it's within that club, you know. But when you look at Liberia, Liberia is very poor, but in its club there's n there are not a lot of rich countries that there are none actually, you know? So therefore

its complexity it's in equilibrium with its income at that moment. And when you look at a country like Qatar, you get the opposite. It's like, okay, their income is extremely high given their complexity. So if they were to run out of petroleum gas then you know their income would have to end come down because there would be an equilibrium with countries that are more middle income, maybe like with you know uh countries that are about seven, ten, twelve K of GDP per capita.

creating something similar to embeddings based on um, you know, different types of products being sold in in the same location and economic space. But the point I was making though is that you could just argue that more complexity good. In a way what that's saying is that there are more paths, so there are more parts in innovation space which are reachable through combinations of the letters that exist. But can too many paths be a bad thing?

then there are things that are simply not a choice for you. So i in the case of a developing country with low complexity, there are a lot of paths that are not a choice for them. Even if they would wanna go there, the probability of success is is extremely low and they need to gradually get to a point in which those paths open to them. So I would say in general, yeah, having, you know, more letters of the infinite alphabet is is a

uh they have a wide set of options, you know, and that doesn't mean that they're exploring all of them. There might be a lot of bad options to explore. Like you you you let's say breaking bad is a business model. There's a bad option, you know, you can you can use your skills as a chemist you know, to go down that path, you know. But the same skills that Walter White had as a chemist can be used also to, you know, find cures for diseases, which is, you know

a another application. So the the options I think are there and the choices are the ones that can be good or bad. But not having chemists in your country, I think, you know, it's a really constraining, you know, capacity.

you know uh is the calculating pi using polygons or calculating pi using Newton's serious formula and and you know the idea of calculating pi using polygons was an idea that that people use for more than a thousand years and the last person to use it uh extensively was uh um I think a Dutch mathematician that dedicated his life to calculate pi to like about thirty two digits.

And it took him twenty years to do so. And then Newton was able to do that, you know, in a matter of days using this formula. Uh so therefore you had kind of like now disruptive innovation. uh in an example in which the new curve didn't even start worse. It already started better and and and and soared to enormous, you know, height. And in that context I think it's fine that we forget how to calculate, you know, pi using, you know, a polygon with a million sides inscribed into a circle because

it is useless. It is contained within a better way of doing things, you know. So so we have a superior knowledge uh representation that replace, you know, that that that inferior one.

So I do think that L LMs, you know, contribute to this ecosystem of knowledge. Some people would have said in the past, I have all of the knowledge in the world in my library, you know. I I I would say books do not have knowledge, but books contribute to that collective capacity to know and to learn and to communicate knowledge and so forth. So if knowledge is a collective phenomena, the question is not whether L LMs

No, have knowledge or not, because none of us have. Knowledge only exists in that larger context. The question is, well, if LLMs within that larger ecosystem are increasing our collective intelligence or our capacity for collective learning.

that I can more easily consult using an LLM than a Google search. You know, so I wanna know about, you know uh tax uh law, you know, in France. You know, there's a lot of details. Well, I explain my situation, I get those answers, and then I have an accountant and when I meet with my accountant to discuss that, I'm much better informed and I can make better questions. So

I think I'm becoming smarter through those conversations because I'm learning faster thanks to those interactions. Is it because D L M has knowledge? Is it because I have knowledge? Or is it because we are wiser when we're together?

In the afterword you said that I think it was about ten years ago you you wrote a book, I think it was called Why Information Grows and you know, you'd done your PhD and you were using very abstract thought experiments and now you've had time to think about it and you've told it using um stories, you know, like um you you found really interesting examples that exemplify all the different ideas and it makes it so much easier to understand. Tell me about that journey.

your book is about the fact that knowledge is extremely specific. The book starts with the story of Charlie, which we haven't told, but it's a story that shows that knowledge can get extremely nuanced. But then you go and communicate all of your ideas about the principles that govern the growth, diffusion and value of knowledge using these very nuanced ideas. But these nuanced ideas in this context are not just decoration.

They help exemplify that exact point. That knowledge is extremely nuanced, that the story of Ibuka absorbing knowledge on how to produce magnetic tape and the story of, you know, some we're slater bringing cotton spinning manufacturing to the United States are full of details and that in those details, you know,

uh is where knowledge hides, you know. Uh so that's something that I I thought it was quite interesting because uh Honestly I had not realized it uh un until she mentioned it that way that that the book made that point about specificity and that therefore the specificity of the story like that detail was not a simple literary resource. but was a way to hammer on on that point.