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June 9, 2026

David Edgerton on Historical Analogies, Technology, and AI

David Edgerton is Professor of History at Kings College London. His book Shock of the Old challenged the way we think about innovation, arguing that we systematically overvalue the new and ignore the old (including maintenance). He has also written importantly on the British ‘warfare state’ and his book The Rise and Fall of the British Nation argues Britain became a fundamentally national economy from 1940-1985.In this conversation we discussed the uses of history in understanding AI and society, why the word ‘technology’ might mislead more than clarify and what a detailed material understanding of our world might look like.Philip Bell: David, you are a professor of history at King’s, and I think I’m right in saying you founded the Centre for the History of Science, Technology and Medicine at Imperial College London. In the foreword to Jean-Baptiste Fressoz’s new book, More and More and More, he describes it as coming out of the intellectual environment at Imperial, because I think he was a researcher there as well. It’s an absolute pleasure to have you on the podcast. Thank you for joining.David Edgerton: My pleasure.Philip Bell: The genesis of this conversation was the use of history and historical analogy in understanding what might inadequately be called technology. One thing I’ve found quite interesting about AI is that there seems to be a geopolitical divide in how it’s considered in historical context. The US, and the West generally, tends to compare AI to the atom bomb or the Manhattan Project — an existential threat to be contained. Whereas Chinese companies and policymakers, if you look at the “AI Plus” strategy in China, tend to compare AI to electricity — more of a utility to be diffused. Do you think analogies are helpful in understanding technologies or technical change, or do historical analogies trap us into particular responses?David Edgerton: Clearly some analogies will be helpful and others won’t be. What’s interesting about analogies in this strange area — discussion of technology, where we don’t really know what it is — is that they are often very predictable. Comparing AI with the atomic bomb is just a replica of comparing the Human Genome Project with the Manhattan Project. Desperately unoriginal comparisons. Comparing things with electricity is a bit richer and not quite so common. Suggesting that artificial intelligence will cause an industrial revolution just like the Industrial Revolution of the eighteenth century — that’s a very, very common argument. Not a very helpful one. So we need to beware the analogies. We need to wise up to the small range of analogies that are in play, and to understand why they’re being used and what they’re being used for.Philip Bell: That’s an interesting point about the predictability of the analogies. I’ve looked up a list of different analogies that have been used with regard to AI. Sundar Pichai, the CEO of Google, described AI as “more profound than electricity or fire.” Yann LeCun, until recently head of AI at Meta, said the invention of the printing press is the closest historical parallel to AI. Alex Karp, CEO of Palantir, compared AI to the Manhattan Project. It’s interesting because technologists seem very interested in talking about history. It reminds me of the famous Keynes quote: “Practical men who believe themselves to be quite exempt from any intellectual influences are usually the slaves of some defunct economist.” How would a technologist go about getting a better historical framing?David Edgerton: Well, you say technologists — who are these people? They’re not necessarily technologists. They are figures associated with great enterprises. I think that’s an important distinction to make. Yes, there’s a nice little list of comparisons. It’s always the big ones, isn’t it? They could have said agriculture. They could have said the Neolithic Revolution, but that wouldn’t sound quite right, I guess. They are looking for big ones. And that’s all they’re trying to say: this is going to be big. So they search back — probably just Google — for other big changes in the past. They could have come up with the wheel, actually. Why is it fire and not the wheel? Why is it the Manhattan Project and not the project to build the B-29 bomber? It doesn’t mean anything. It’s not the product of research. It’s just a PR gambit. It’s not to be taken seriously; it’s just propaganda.Now, if one actually wanted to make a historical comparison, I guess first of all we wouldn’t, because AI hasn’t had a major impact yet. We have to start with a proper definition of what it is in the present that we want to compare with the past. Another way of looking at it: AI is, at the moment, very largely hype. So let’s look back at other cases of hype. Nuclear would be a very good example. The claim that nuclear would transform the world — that electricity produced by nuclear reactors would be “too cheap to meter.” Well, that didn’t happen.In fact, the Manhattan Project as a comparator is a very strange one, because if you take the Manhattan Project seriously — beyond just a kind of reference to big bangs and lots of money — we find that atomic bombs have not been used in war since 1945, and that the generation of electricity through atomic power has not in fact been that significant. We could easily have lived without nuclear power in the world. What is the story that’s been told?These aren’t proper histories; they’re other kinds of hype. Airplanes would bring world peace. Dynamite would lead to the end of war. There are lots of these. This is a very, very old way of thinking about this mysterious thing, “tech.” You’d have thought we’d get over it. But in some ways AI itself makes it worse, because if you were to ask AI — as the Tony Blair Institute did — what is the future of AI, it will just trawl through all the rubbish that’s online and tell you it’s going to be like the atomic bomb, or like fire, or like the Industrial Revolution. We are living in a kind of miasma of very bad, very cheap knowledge about these things. My immediate reaction is that none of this is to be taken seriously except as PR, except as lobbying. There’s no analytical intelligence behind any of these claims; there’s a political intelligence behind them.Philip Bell: That’s an interesting point about using AI to regurgitate historical analogies. I think Adam Tooze has described AI as a kind of “technology of technology,” which I thought was interesting. But in your book The Shock of the Old, I think you describe historians as the true experts of the future — please correct me if I’m wrong. In my experience studying history as an undergraduate, I took away two principles: the contingent nature of society, the fact that the past was continually surprising, which makes me think the future is likely to be surprising too; and also a kind of humility about the fact that people got lots of things wrong. Do you still believe that historians are the true experts of the future, and why?David Edgerton: Yes, I do. And you’ve already explained it. We’re the experts on the future because we have to train ourselves to remember the future isn’t here yet and we don’t know what it is. We have to train ourselves because, of course, we’re looking at the past and we know what’s going to happen next. So we’re always going to beware the idea that war was inevitable in 1939 or that India would get its independence in 1947. We are very used to the idea — or should be — that the future is not completely open-ended, but as you say, will very likely be surprising. Anyone who claims they know what the future is doesn’t know anything about the future for sure, and doesn’t know anything about history either.Philip Bell: I was thinking that in a way, then, it’s useful not to look too deeply into the past. If you have a particular functional reason for acting — let’s say you want to raise lots of capital in venture capitalism — maybe there’s a balance. If history does teach us to be slightly more hesitant about our predictions, that might be less useful for someone trying to raise billions of dollars. So maybe there’s a slight paradox where it’s functionally not helpful for some people to carefully examine the past?David Edgerton: No, exactly. But nobody is carefully examining the past. As I say, this isn’t serious. What is serious is that it’s PR — PR to investors in particular, PR to governments who have to support these new technologies. If you are a serious investor, would you look at historical analogies? Well, no — unless you’re doing it really, really seriously. You’ve got to look at the present, you’ve got to look at the technology, you’ve got to make guesses as to what is going to happen. I think the one lesson to take from history is that predictions are likely to be wrong.One very important reason is that new techniques aren’t exactly the same as old techniques. In what sense is AI like a steam engine or like electricity? You mentioned Adam Tooze referring to AI as a “technology of technology.” So it’s different in that sense. Now, I wouldn’t say it’s necessarily the first technology of technology. The very word “technology” means the study of the technical arts. It is a way of understanding that allows you to change the techniques we have — a generalised view of the capabilities of machines, processes, whatever. So that’s not new. And the processing of information is not new either — that’s been central to our lives for centuries. The capabilities change, of course; the kinds of sensors change. But every new technique is by definition new, so you’re not going to find an exact parallel in the past.Philip Bell: That reminds me — I was listening to Andrej Karpathy, who used to be head of AI at Tesla, describing how AI coding feels more like a continuous evolution rather than a massive break with previous tools that supported coding and programming, in terms of the integrated development environment, which already auto-completed bits of code.David Edgerton: But look, why are we talking about AI? Why is that the one new technique that everybody is on about? Is there nothing else changing in the world of technique, the world of engineering, the world of chemistry? Clearly lots of things are changing, but we’re saturated with this PR about AI. And I’m not an expert on AI. Most people talking about it are not experts on AI. We’re just being deluged with nonsense. Now, that’s not to say AI might not be important — it’s important to stress that — but the talk we have represents, I think, a deliberate attempt to stop us thinking seriously about how the world is changing and how it might change. It is utterly self-interested.One reason we perhaps don’t see that is that part of the self-interested discourse talks about the downsides. “It could be dangerous, the machines might take over and kill us all.” Look, a critical voice. Well, it’s not a critical voice. It’s just a voice telling us that AI is really, really powerful by telling us it could do some very damaging things. That’s not new. It’s true of arguments about dynamite: dynamite would either bring world peace or destroy the world. Airplanes, same: they’ll bring us all together or they’ll bomb us all to smithereens. Either way, it’s an argument for the power of the airplane. And we have a prior question: what exactly is the airplane? What exactly might its powers be? That is the question we need to ask.Philip Bell: That’s a really good point. A common theme of your work is what makes our thinking visible or invisible. I’m thinking about The Rise and Fall of the British Nation, where you talk about the historical narrative of the welfare state needing to be quite dominant in our thinking in order to stimulate support for it, whereas other narratives didn’t need to be as obvious. And in The Shock of the Old, the hidden counterfactual alternatives to technologies — we think about technologies compared to nothing, rather than compared to actual alternatives. And you talk about the term “technology” itself as potentially confusing us. What do you think is the allure of understanding the history of technology as a sequence of ages marked by a particular technology?David Edgerton: I try to avoid using the term “technology” because it rots the brain. If we talk about cars or airplanes or knives and forks or spades, we have sensible, grown-up things to say. As soon as we use the word “technology,” our brains turn to mush and we start thinking in very Manichaean terms. The reality is that we don’t have a decent account of what I like to call the material constitution of our world — the machines, the processes. And we cover this up with this concept of technology.We think we understand something called technology, but if you actually take a look at what people mean by “technology” in history, they don’t actually mean what we use to live or to fight or whatever it might be. What they mean is the early history of some techniques which have been taken to be important for reasons that aren’t usually analysed. So what you have in effect is the early history of those techniques which have been thoroughly propagandised, which have been very visible. Atomic bombs are going to be there because people have been talking about them for eighty years. They make a big bang. You can see them on screen. Airplanes — a very visible form of transportation.What do you not talk about? The great mass of things. You don’t talk about ships in the twentieth century. Ships are much more important in terms of carrying trade than airplanes, but they’re boring, they’re old — who cares?So you start with this account of this weird concept, “technology,” which is the early history of things deemed important for probably the wrong reason. And then to create a nice narrative, you assume that in any particular era there are one, two, or three key technologies. Then you assume these emerge in clusters and you call these clusters “industrial revolutions.” The first Industrial Revolution in the UK in the late eighteenth century; the second in Germany and the United States, about electricity and chemicals; then a third, possibly in the 1960s; and in the most common variant, a fourth industrial revolution, which is something we’ve been living through maybe since 2010. Some people have five, some have six, some have two. It’s basically all made up. There’s no evidence behind any of this, but it’s a nice story. No politician or indeed historian has been booed off the stage for talking about a second, third, or fourth industrial revolution. You can talk any old rubbish if you start talking about technology and the fourth industrial revolution.Philip Bell: It reminds me of Fressoz, who describes our tendency to think about energy as defined by particular technologies that transition from one to another — and he undermines that argument. To give one example, he argues the so-called “age of coal” was also an age where wood was very important, because wood was needed to build coal mines, and wooden sleepers were used on train tracks to enable the transport of coal. I was also reading an article by David Graeber about whether animals play. He argued that, given the nature of our society and current political system, we tend to consider competition over cooperation — and that there’s no reason to think animals might not play and cooperate with each other, but our dominant metaphors lead us to consider animals as competing. Do you think that has anything to do with our narrative of technological change as a series of phases?David Edgerton: There is certainly the sense that the new obliterates the old — that is, if you like, a competition. But I don’t think that kind of thinking is the source of these stage models of energy or material development more generally. Jean-Baptiste Fressoz’s point is that it’s not we who think like this. We’ve been trained to think like this. We’ve been subjected to very particular models of stages and transitions in energy by interested people. He goes back, for example, to nuclear scientists of the 1940s, ‘50s, and ‘60s who wanted to say that we would transition to nuclear. They produced diagrams that purported to show transitions as the share of nuclear went up. That kind of thinking has become deeply ingrained in professional and academic circles, and it diffuses out from there. It’s not a natural condition of the human mind to think in those ways.If you look out the window here in London, it’s very obvious that the past is still with us — the layout of streets is old, a lot of the buildings are old. We wouldn’t immediately conclude that the world is completely renewed every supposed industrial revolution. That would be an absurdity. And yet, even sitting here in London in that material environment, people do go on about energy transitions and industrial revolutions. That’s because that’s the mode that has become established for talking about both of those things. But the point is it’s got nothing, or very little, to do with either present reality or historical reality. This is just a way of talking. This is stuff that’s in books and on the internet. It’s not what actual engineers work on or do. It’s a fantastical world.And it’s all to do with convincing us to buy these new things, and above all convincing government. This is why some things are public. The welfare state has to be public because it’s about government money, about politics. Most chemical production doesn’t have to be public because chemical companies just get on with it. But AI has to be public because we’ve got to pay for the energy, we’ve got to pay for the water, we’ve got to buy the things on offer to supposedly, for example, get the National Health Service working properly. This isn’t a disinterested analysis; this is a sales operation on a major, major scale — part and parcel of huge investments that have been made by very powerful people.Philip Bell: Do you think there’s some degree of legitimate, necessary simplification of the history of technology? Sometimes people talk about how, when we’re teaching students history, we wouldn’t necessarily teach them the way we’d teach a PhD student. Is there some argument that at different levels of expertise, we can have different levels of simplification?David Edgerton: Of course, self-evidently, we need to have that, and we do. But that’s not the point. The point is: what simplification? If you’re saying to me that for the grown-ups we should have proper history and for the plebs we should have any old rubbish, I’d say no. And the stuff we get in these histories is not just in “history for the plebs” — it’s in histories for the elite. It’s about this mysterious thing, technology, and it’s rubbish. The question is: why is it rubbish? Not why is it simplified or dumbed down — why is it wrong?To be sure, we need to simplify the histories of the material. What those simplified histories should be is an open question. Some people have some fun with this — what is the most important innovation of the twentieth century? And some people will say the washing machine. It’s not an uninteresting case. I think we can have lots of interesting simplified histories which would help us think. What we have is a lot of simplified histories which are stopping us thinking.It’s always the defence: “Oh, we’ve got to have a simple narrative, a story we understand. That’s why we talk about industrial revolutions.” It’s a very, very common defence. My critics try to say that my criticisms are intellectual criticisms just for the elite. No — my criticisms are precisely about the kind of stuff that has been fed to people. And actually, I would say there are much more interesting stories to be told than the ones we’re routinely fed.Philip Bell: I think that point about starting with the question is maybe counterintuitive but naturally quite engaging. I can imagine that being a really good way of teaching the history of technology in the twentieth century — using “what was the most important technology?” as a starting question. The fact that it’s contentious would actually make it engaging.David Edgerton: I’d avoid the use of the word “technology” if I could. What is the most important process? What’s the most important machine? What’s the most important technique? People are really interested in that, because they start thinking about alternatives — what we use things for, what sort of use is more important than some other. And crucially, you ask people to reflect on their own historical knowledge. It’s not the case that people don’t know what machines were used in the past. They do know, from all sorts of sources. So you’re getting people to think on the basis of their own knowledge, their own experience indeed sometimes.Philip Bell: In The Shock of the Old, you present a much more chronologically jumbled picture than is commonly presented. Some examples: electric vehicles were more common in the 1900s than petrol cars; the German army in 1941 invaded Russia with more horses than Napoleon did in 1812; small modular reactors are now coming back into fashion even though they were invented in the 1950s, and many people in the AI world are talking about them as the future of nuclear energy. The electricity grid is becoming incredibly important for emerging technologies, which is not a new technology in itself. Do you think other domains of study are less teleological? Are we able to understand regression better in economics or other aspects of society?David Edgerton: It’s teleological, yes — particular models of replacement of the old by the new. Are we better in other areas? I guess we are, in that we are more likely to want to find continuities in history in other areas — maybe in political history, for example. One wouldn’t imagine that politics was invented yesterday, or that the particular kind of politics we have today was invented yesterday.On the other hand, our accounts of the modern world are profoundly influenced by the stage theories of the material. Even political or cultural historians, who might well want to stress continuity, when it comes to the material will also tell these stories of radical discontinuity. These stories have a very particular influence, because we do believe — or most academics at least in the West believe — that we live in an industrial age, or a post-industrial age, a scientific age, a technological age, an age of mass production, an age of post-Fordism. These are all concepts about the material which are taken to represent profoundly important realities.Some people would say that anthropologists — and Bruno Latour would say anthropologists looking outside the West — have a kind of straightforward empirical view of the complexities of the interactions of nature and culture, and we should apply that insight to the West. But I think it’s not so easy, precisely because we understand the West in these very particular terms.It’s not difficult for non-academics, though. We just look around. It’s no surprise to us that our world is full of machines of different vintages. It’s kind of obvious. It’s just not obvious once we get into the realm of books and the word.You have to look around. I’m sitting in a room here. I’ve got computers in front of me, obviously, but I’ve got copper cable. I can see a wooden window. I can see glass, plastic — all sorts of stuff. And most people will have a sense of how old all this stuff is.Philip Bell: That’s a really interesting point — it’s the word, once we get into the realm of thinking about words, that can cause us to think differently.David Edgerton: Yes, and we think more intelligently about steel than we do about technology, or indeed science.Philip Bell: This question might be confusing the matter further, but do you think our society is becoming more techno-deterministic?David Edgerton: No, because technology isn’t determining. You’ve got to ask: what is the “techno” bit? What is the technology? And what people usually mean by that is innovation. Innovations can’t determine anything. Innovation is just the first use of something. It doesn’t have to be used again.This whole technological determinism thesis is itself nonsense, because it’s usually interpreted to mean that selected innovations drive history. Some foolish people say or imply that. But if you were to ask the question — is the primary shaper of our range of possibility the material constitution of our world? — then you might start thinking, well, that’s an interesting question. How might I answer that? And you’d have to work out what the material constitution of the world was before you could start making comparisons between that material constitution and possibilities. We’re nowhere near there. We don’t have the means to answer that question. The idea that the world is more technically determined now than before is simply unanswerable.Philip Bell: I was more asking whether our society thinks of technology as becoming more deterministic. When I watch the news and hear people talking about new technologies, it feels to me as if they’re spoken about as self-determining natural forces. Is that a new phenomenon, or have people always thought about technology in this deterministic way?David Edgerton: First of all, people aren’t thinking about technology in any broad sense in that deterministic way. They’re making claims for very particular innovations. It’s not “technology” — it’s the particular innovation. And yes, people write as if AI had its own logic and was imposing itself on the world, and we can’t resist it, we just have to adapt. Very common argument. It’s meant to stop us asking: who’s behind this? Who’s telling me this? Is this new? No — it’s as old as the hills. Or I should correct myself, especially as a historian: it is certainly more than 150 years old.Philip Bell: What do you think the determining factors are?David Edgerton: What is driving change in the world? There are actually lots of different answers to that question. If you ask experts, they might say forces of production, or science and technology, or innovation. But the serious ones would also want to discuss the concept of capitalism. Capitalism is the main driving force in the world and might indeed be driving innovation, driving the development of the forces of production.Other people might say something like industrialism, as opposed to capitalism. Some might say competition — once you release people’s innate capacity to want to do new things by opening up markets, you get change. People who are religious believers might give you different sorts of answers.I often find that people react to my casting doubt on technology as the driving force by implying that leaves us with nothing. But there are lots of other explanations of change, which we in fact routinely use in political discourse, in academic discourse, in daily life. We don’t go around saying our lives have got worse or better because the internet came along — unless you’re having a conversation about the internet. We talk about wages, prices, crime, the weather. We are much more capable of having serious discussions about our world than the discourse on technology and science suggests. We can have a much more intelligent conversation about the washing machine than about AI or technology in general.Philip Bell: Roberto Unger, the philosopher, in a 2007 book — I think it’s The Knowledge of Society — asked the question: who gets to innovate? Evgeny Morozov has also explored this question. Do you think this is useful?David Edgerton: Absolutely. Who gets to innovate? Nowadays, we’re all supposed to be innovators. We’re all creative. We apply for jobs: “I’m an innovator. I don’t teach like anybody else. I don’t manage my time like anybody else. I’ve innovated.” In fact, by using that language of innovation, you’re just showing that we are all very imitative. We’re expected to say we’re innovators.But the reality is that the capacity to innovate is highly restricted to very particular institutions. Large firms. New firms which have a lot of money behind them. These are the institutions which are innovating, and we need to understand them if we want to understand innovation. States innovate in certain ways as well. Why do they do it? Why do they innovate in this area rather than that area? These are absolutely the key questions.Philip Bell: Would you say in The Shock of the Old that the true heroes of innovation are the users?David Edgerton: No, I wouldn’t say that. I don’t think there are any heroes in Shock of the Old. But people have wanted to see my story as one that says ordinary people are innovators, and I’ve reacted against that a little bit.Yes, we all change things; we all have some agency. But when it comes to inventing really big things, we don’t have much agency. In fact, systems are there to make sure that we don’t have any agency. My book is a plea to understand where technical change comes from — not to talk ourselves into a world in which we believe we are all contributing to the big changes. We’re not.We live in this very weird culture where we are expected to say that we are all creative and innovators. Actually, we live in the greatest age of imitation.Philip Bell: Would you say it’s important that innovation should be, in an ideal scenario, somewhat inclusive or democratic — so that if there was some innovation being foisted on people that materially made their lives worse, they could have some say in it?David Edgerton: Well, sure, of course. But just believing that we’re all innovators and creative isn’t going to help us with that. On the contrary, it stops us seeing where the innovations are actually coming from and what it would take to control them. And what it takes is not having some discussion about research policy or having people involved in discussions with scientists. No — because the scientists and the universities aren’t deciding on these things. It’s corporations and investors.If you’re saying we should have democratic economic planning, I may well agree, but achieving that would be extremely difficult because it would meet enormous resistance from the very people who are controlling innovation. We can’t talk our way into controlling technology by believing that we are innovators, or that as users we necessarily have the power to change things.We do have some power. One important thing to remember is that we have to reject most of the new things we’re offered. “We” here is not just ordinary people, but also investors or the management of firms. They will have to choose between different research projects, between different ideas to pursue. We as consumers have to choose which phone we’re going to buy. We can’t buy every phone. But if we turn down the Samsung, people don’t say, “You’re a Luddite — you’ve turned down the Samsung.” That would be absurd. But when we talk in general — “I don’t quite like this new machine that we’ve been offered” — suddenly it’s “You’re a Luddite, you’re stopping progress.” No, I just don’t like this particular machine. Come up with a better one. Our language is deeply loaded and profoundly unhelpful.Philip Bell: I sometimes wonder if language is a technique that’s quite democratically shaped. Northern Irish people — I’m from Northern Ireland — don’t speak in exactly the same way as English people or Scottish people. We have some of our own words, and we have Irish, which is a separate language. I wonder whether that is one example where there’s some level of democratic input — where the only source of power an individual has isn’t just rejection, but also some generative power.David Edgerton: Sure, of course. But when it comes to machinery and things, where does one find this kind of thing at the political level? It’s very hard. It is different, and I hope we will be able to find this sort of language. I hope we’ll have a richer, more open, more democratic understanding of — I come back to this phrase — the material constitution of our world.If you ask the question, what are the most produced materials in the world, most of us wouldn’t have a clue. But up there are things like water, sand, gravel, pig iron, steel, chlorine, ethylene, salt. We don’t see these things, partly because some of them don’t come out of factories, partly because they’re extremely cheap, so they don’t register very heavily in economic statistics.They make up our world. Steel is so cheap that it’s everywhere, in more massive quantities than ever before. If you want to think about climate change — which is maybe one area where we have a kind of democratic politics about the material constitution of the world — we need to absolutely understand all these things. If we are going to reduce CO₂ emissions, we’ve got to change the nature of our transport vehicles. If we’re going to keep petrol, they’ve got to stop getting bigger. If we’re going to use steel, we’ll probably have to make it more expensively. These are very big issues which are going to require democratic consent. This isn’t just an academic discussion — it’s really serious. How do we change the material constitution of the world, which we need to do, without a good elite understanding of what that is — which is our major first problem — and without popular understanding?Philip Bell: That’s a really interesting point. I’ve heard climate breakdown being described as one issue where we’re all perpetrators and victims at the same time, although some obviously more victims and some more perpetrators. The question about NIMBYism — whether people are willing to have a solar farm in their community, or offshore wind farms — I know some people who’ve asked me to sign petitions trying to stop wind farms in their area. That’s a good point about the democratic aspect: thinking about how the benefits of wind farms or solar panels could be put back into those communities.David Edgerton: Yes, but Fressoz’s brilliant book makes the fundamental point that you can’t just think about climate in terms of wind turbines and electric cars. You’ve got to think about the whole world of production and carbon. And to do that, you need to understand where the carbon dioxide comes from. It’s not just electricity generation. It’s not just cars. Making wind turbines uses coal, which produces carbon dioxide. It’s a really complicated issue. And any serious politics of decarbonisation has got to go very much further than asking whether windmills are a good thing — and very much further than saying AI will help us optimise our emission profile.Philip Bell: One thing I took from Fressoz’s book was that he wasn’t saying the transition or a more sustainable future isn’t possible. He was just saying it’s very difficult and we need to be serious about it.I wanted to link your work in The Shock of the Old to the idea of “temporal claustrophobia,” which Jonathan White takes from the historian Chris Clark. Jonathan White talks about our current political situation being narrated as an emergency, which reduces our ability to think in the medium and long term. Do you think that short-termism and our obsession with the new are related? Are we addicted to the new in part because we’ve lost the ability to value longer-term qualities like repair, maintenance, stability?David Edgerton: Who’s the “we” here? I don’t know the answer to that question. But I would say that all this emphasis on the new is nowhere near as convincing as it used to be. We’ve had this very old rhetoric about novelty and change. But we’re less inclined to believe it, and we’re less inclined to believe it because economies have been stagnating, because living standards haven’t been going up.In the 1950s and ‘60s in the UK, being told that the future was going to be better than the present was a plausible argument, given that people’s standard of living was rising pretty rapidly. It’s not so plausible now. So I don’t think we are as gullible, as receptive to claims for innovation as we might think. Politics has changed in all sorts of ways, and it certainly can’t be reduced to a cult of novelty.Philip Bell: Thank you so much. I really appreciate your time.David Edgerton: Thank you. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://genfutures.substack.com?utm_medium=podcast&utm_campaign=CTA_1">genfutures.substack.com</a>

April 1, 2026

How is AI changing our experience of Time?

The rhythm of social media is frenetic. But what if AI can give us more choice about the speed at which we live our lives?In his article <a target="_blank" href="https://www.aipolicyperspectives.com/p/time-machines">‘Time Machines’</a> Nicklas Berild Lundblad outlines the thesis that AI has the potential to be a ‘temporal mediator’, enabling a decoupling of computational and biological time. Nicklas is a writer, investor, and formerly Head of Global Policy and Public Affairs at DeepMind.Philip Bell: You’re an investor, advisor, writer and researcher working across a number of different think tanks and writing a blog which I’m a big fan of—which is why I asked you to come on today. I previously worked at DeepMind on policy, so I loved your article “Time Machines” and it was really thought-provoking. It really made me consider some of the possibilities of how an AI-influenced world might sort of play out. So that’s why I was really keen to talk to you.My first question would be: how will AI influence our experience of time?NBL: The article has this fundamental idea that there are two kinds of time. There’s biological time and computational time. Everything that we’ve done as human beings—our relationships, our institutions, our society, our economies—they all evolved to run in biological time, in our time. Biological time is evolutionary time. It’s the kind of time of seasons. It has this particular pace.But as we developed computers and machines, we were able to create this other pace layer—to use a term from the literature—this computational time that’s much, much faster. We can now do calculations at a speed that no human being could do. We could play a million chess games in a very short time.So we have developed this computational layer of time, and there’s a tension between the two. Many of the institutions that we have developed are not necessarily built to run in computational time. Markets, for example, for a long time struggled with figuring out how to deal with high-frequency trading.My idea in the article is essentially this: maybe we can use artificial intelligence as this mediator that understands biological time because it can communicate with us, but also can operate in computational time. So it can help us prioritize across the many different things that happen in computational time and give us an effective interface. Artificial intelligence becomes a temporal interface between these two different pace layers.That’s the basic idea of the article. Not revolutionary in any sense, but I think it addresses this notion that we have of everything accelerating, everything speeding up, and at the same time, we have this upper limit beyond which we cannot speed up. We have 24 hours a day of attention and that’s it. That’s what we can spend.Philip Bell: One of the really interesting ideas in the article was you put it that perhaps most significantly this bifurcation will enable individualized relationships with time itself. That made me think: if people are able to have individualized relationships with time, that might influence identities themselves.Many scholars have talked about how the experience of time has helped form identities. For example, Benedict Anderson in his famous book about the origins of nationalism describes people reading a newspaper across different parts of—let’s say Germany—for the first time in the late 19th century, which created a kind of simultaneous feeling that you’re inhabiting the same time because everyone would read the newspaper at the same time in the morning. He described this as “national time.”Could this new capability—if people are able to have their own individualized relationship with time—influence identities? Do you think that could allow small communities to have their own identities based around time? How do you see that influencing culture and identity?NBL: That’s a great question. The way to think about this is to think about the way that we constitute our “now.” Essentially what Benedict Anderson is talking about is this notion of a national now, national time, national rhythm.There’s plenty to be learned about how we constitute a now even just looking at our own nervous system, because whatever you’re experiencing is in the past. You’re not experiencing the now directly because your nervous system needs to collate all of the signals from your body, all of the impressions and perceptions that you have into something that can be a single, coherent now.That’s why, paradoxically, if you—God forbid—were to be shot in the head, you wouldn’t experience it. Because what would actually happen is that before you can constitute the now of that moment, everything would go black. Just like—spoiler alert—in the last episode of The Sopranos.One of the things that I think is interesting is to think about: how do we then constitute now across different groups? You said nations, communities can constitute their own nows. And yes, I do believe that artificial intelligence could be a core part of constituting that now. But we should also remember that there’s a really interesting question here about how artificial intelligence constitutes its own now.Because now we’re talking about multi-agent systems. They need to coordinate with each other, they need to find a pulse, a sync, so that they can start to figure out what a now is. I think a little bit about this like the Empire of Rome. The Emperor of Rome had a now, but the pace of that was essentially what it took to ride from one part of the Roman Empire to the center. So that was the fastest possible now that Rome as an empire could experience.I think this is true also for technology and human beings: the fastest possible now we can experience is the least common denominator when it comes to speed. So in hybrid communities, we will still be limited by biological time. But you’re right—the pace, the time, the temporal experience, the constitution of the now is essential to identity.You can also probably say that you have different identities in which you constitute your now at different pace layers. You have one identity which is your personal experience of the world around you. And then you have a communal identity—you get together with friends and you update each other. That’s the first thing you do, right? “What’s up? What’s been happening lately?” That’s you building your now together. So there are all of these different nows that you operate through and in.Philip Bell: That’s a really interesting point. Going back to the comparison with what does the now mean for AI—I think with current large language models, one difference between the way humans think and the way large language models process information is that they can’t really dwell on anything for a particular period of time. Information is processed between layers at a fixed time.I actually read about a new paper in Europe by a company called Sakana—they created this idea called “continuous thought machines” where they basically introduce time into computations so that there is the ability for the model to dwell on different pieces of information according to different time speeds. That is an interesting difference between humans and AI currently—time, in a sense, is fixed in how AI processes information.Historically some scholars have said reading was quite important for human culture in that it allowed for asynchronous thinking. Whereas dialogue—one has to think on the spot, in the moment—reading allowed for asynchronous thought. You could say chain-of-thought in large language models is sort of allowing for some element of that, buying time to some degree. But that is a sort of difference. How do you see that playing out?NBL: You can turn the question on its head to some degree. What you can say is: how do we build things like dwelling machines, a machine that can dwell on something?One of the things we often do is that we try to say, “This is how the machine thinks, this is how the human being thinks, and here’s the difference between them.” A much more interesting approach, I find, is constructive. To say, “Okay, how do we build a dwelling machine? How do we do it? How do we build a machine that can be filled with regret?”All of these are temporal feelings. They all have to do with how we interact with time. So if we accept that it’s like an architectural construction problem, then we have to ask some really hard questions about: how would we model regret? How do we model dwelling on something?Dwelling on something sounds a little bit like a loop, right? So I come back to this thing again and again. I’m not coming back to it necessarily in order to resolve it. It’s not like I’m doing a loop until I can finish the calculation. I’m coming back to it because I believe that the change in me when I come back to it will be meaningful for how I can approach the thing I’m working on.For example, in art, I might be able to do something really quickly and just write it once and be done with it. But that’s not what poets do. Poets write a poem, they come back to it. They feel it because they change in between interacting with the thing. So there is this question: Okay, I’m building dwelling. I know it’s a loop, but it’s a loop where the object of my dwelling is not changing as much as I am in between the loops, in between the cycles. So what is that structure in me that then needs to change? How do I capture that?What all of this teaches us is that temporal architectures are really complex, but they’re probably also a really core part of what it means to be intelligent. And of course, you also have—and I’ve written about this on the blog—you also have the ultimate temporal horizons. A lot of our intelligence is structured the way it’s structured because we die. We are finite beings. Being a finite being means you have to structure your experience in a certain way. You can’t spend infinite amounts evaluating two almost equal options. You just have to pick. So that means that death is the great tiebreaker. It constantly forces you from afar to make decisions.That’s another temporal architecture and intelligence that’s deeply embedded in everything that we do. I think that the more we think about temporal architectures and intelligence, the more we realize that there is a lot here that actually is key to human intelligence. And that’s not replicated in what is sometimes a more atemporal model of artificial intelligence. Not saying that you can’t—I think you really can. But I think we will see—I could easily imagine that a specialization in AI research in the next 10 years or so will be the design of temporal architectures for intelligence, like thought machines or what we talked about before: context, chain-of-thought, all of those different things. Memory.We speak about memory as if it was something we could add on. We need memory and planning, we say, when we’re building the next generation AI on the path to AGI. And I think that’s a simplification because memory comes in so many different forms. If you read the works of Paul Ricœur, for example, you realize that memory, history and forgetting are super complex—not even individual mechanisms, but they’re deeply embedded in our social interaction with each other. The way we produce history, forgetting, memory—it has to do with how we interact.And this is another thing that I think is sometimes lost in the discussion: time exists between people. It’s a relational property of a system. It’s not an individual characteristic. An individual doesn’t have time. It experiences time vis-à-vis others. And modeling that relationship, I think, is a really interesting philosophical and probably also architectural question.Philip Bell: That’s really interesting. Is that why—because you mentioned previously about multi-agent systems—is that why that introduces a new kind of temporal element, because there’s the temporal relation between the different agents?NBL: And you can pace them in different ways. You can almost imagine orchestrating basically on temporal aspects. Here’s a slow agent that comes in once every 10 cycles and asks the same question. Here’s a very fast agent that collects a lot of information, synthesizes it, produces outcomes. And then as you design or orchestrate your multi-agent framework, you can use time as a key variable in how you design it. There are probably also safety and security measures that you can implement that have to do with the way you design temporal interaction in multi-agent frameworks.Philip Bell: Yeah, that’s really interesting. Going back to the point you made previously about death—to bring it back to death...NBL: Yes, let’s go back to death. More death.Philip Bell: Isn’t that what Heidegger said? We should spend more time thinking about death. But no, I believe I watched a lecture by Daniel Dennett where he was saying that one of the differences between AI and humans is that humans have more skin in the game because we’re mortal, because we die. So I suppose that’s an interesting point about how that structures our temporal relationships with one another and because our agency is more or less, in a subconscious way, related to the fact that we’re going to die.But one thing I wanted to ask is: I think the vision that you paint in the article is really positive. You say that, for example, AI systems adapt to us while we adapted to computers. I personally feel like sometimes social media kind of speeds up my life in a way that isn’t necessarily great for me. So I think that’s really interesting that you paint such a positive vision of AI systems adapting to us rather than us adapting to them.How likely do you think is this scenario to play out—that AI systems adapt to us? Are there other possible scenarios? And is it partly our choice as to how these different scenarios play out?NBL: It’s definitely our choice. It’s our choice also how we interact with social media. The fact is that we have agency and we can choose to deploy it or not. And I think sometimes it’s hard because architectures can capture us in ways that make it harder for us to exercise our agency. It’s an old problem.Both Plato and Aristotle spoke about something called akrasia, which is weakness of will. Now Plato was quite rough about it. He was like, “There’s no weakness of will. If you realize what’s right, then you do what’s right. So it’s just a question of you not knowing what’s right. That’s why you spend too much time on Facebook.” Whereas Aristotle was like, “Well, hang on, let’s give people a little bit of benefit of the doubt. Some people may do something that they know is not actually good for them, because we have weakness of will. It really exists. It’s not just a lack of knowledge.”And I think that one really interesting question that we will have to address is: how do we build architectures that augment our autonomy in different ways? And I think this is the natural evolution of the privacy debate. The privacy debates have been a lot about how do we make sure that information about me is not leaked and used against me in ways that reduce my ability to do things. And I think the natural extension of that is to say: how do we design not privacy-enhancing technology, but autonomy-enhancing technologies? Technologies that allow us to deal with and eliminate akrasia wherever it comes up.So it’s partly an architectural problem, partly an agency problem. You have to choose it. You have to want to have stronger agency. And then the other thing that we have to do is to figure out: are there really good ways in which we can build artificial intelligence to be autonomy-augmenting rather than akrasia-augmenting, for lack of a better term, feeding on our will in different ways?And this is philosophically a really difficult question because if I design a technology to strengthen my will, is my will then stronger or weaker because I needed a technology? And now I’m reliant on this will-amplifying technology. Does that mean that my will has actually weakened? And it’s like the question of: should I really ever use a car because then I will not be as fit as if I run everywhere?And we have to decide how we think about ourselves and what we think is reasonable to include in our identities when it comes to different kinds of technologies. The car is reasonable because it’s such a great advantage over just running everywhere. And we will probably find equivalents of that with certain artificial intelligence technologies that are just so good that they help us actually do more of what we want, but they remain silent on the question of what we choose.That’s going to be a really interesting design problem: how do you build a technology that amplifies your will without directing it in different ways? And in some cases we want our will to be directed too. I have a personal trainer. He’s great. He’s not necessary. I mean, my will is not necessarily to turn up at 6 a.m. in the morning, but I use him as a will-amplifying, augmenting technology in order to get into shape.So this question of how you build autonomy into a system is really important. And I’m an optimist. The reason I have this bright perspective in the essay is I think we have the ability and will to do this and we need to choose it. And I think we can if we want to. I don’t think there’s any technological determinism that leads to an outcome in which we’re necessarily the slaves of the machine.Philip Bell: I totally agree that a lot of the time it feels techno-deterministic the way people talk about technologies, and I think that’s also an interesting point in itself. But in terms of supporting agency and autonomy, it’s interesting because, as you said, it feels like that isn’t something that’s commonly talked about—at least in the debates I hear in the mainstream about utilizing new technologies such as AI.Do you think that there is a need for a new kind of vocabulary and intellectual scaffold for thinking about and defining what agency might be? Because I was sort of thinking of Amartya Sen’s work on capabilities. Or do you think we already have it? Why is it that we talk a lot about privacy, but we don’t think about autonomy and agency in terms of introducing AI currently? Is it because there hasn’t been enough work on building an intellectual scaffold around how to define these things, or is there another reason?NBL: No, I think you’re on to something. I think in many ways, artificial intelligence is this computational science giant on philosophical clay feet. It needs to figure out how to deal with a lot of the philosophical concepts that go into things like intelligence, agency, autonomy—all of these different things. The integration of time into all these perspectives. But I think it’s happening. There are some really good people out there thinking about it. Some strong philosophers really trying to understand this. There’s plenty of my former colleagues at DeepMind who are brilliant at this stuff.And to some degree, I think what we need to do is to find a way to perhaps frame the discussion about artificial intelligence in these terms: how do we want artificial intelligence to work? We should also focus on risks. We do, there are lots of risks. But we should have more of a discussion around who do we want to be with a machine, rather than what will the future look like with the machine. So it’s a question of how you frame your future perspectives to some degree.I’m worried that often we slip into techno-determinist thinking when we talk about whatever technology it might be. “This technology enables this, thus this will happen.” Well, it also enables all these other things that we can make happen if we want to. And the challenge of course is that we have to figure out what it is that we collectively want.Philip Bell: Yeah, that’s a really interesting point. I wonder because in the article you described that technology can speed some things up, but then there are other areas of life where you can’t really just speed things up due to using technology, like biological processes, for example.NBL: Or art. One example in the article is that you can imagine playing all of Bach’s work in a microsecond, just executing the notes, but then you won’t really have played Bach’s work. So I think there are some things that only exist if they’re performed temporally in a certain way.Philip Bell: Exactly. I sometimes listen on 2x speed to audiobooks and sometimes my girlfriend looks at me like, “Why are you doing that? You shouldn’t listen on 2x speed.”NBL: Yes. But it’s a bit slow, I do that too. I never listen at speed to Bach though.Philip Bell: Yeah, true. I think listening to music is a whole different thing.NBL: If you find somebody doing that, you would have found somebody who has a really different attitude to music, where the point of the music was the informational content, not necessarily... I can imagine that maybe there’s a professional musician that does that, listens to music sped up. But it’s a really interesting thought experiment: who would actually be listening to classical music at 2x, 3x pace because they just wanted to consume it faster? That’s interesting to me. I would love to know if there’s someone out there doing that.Philip Bell: Yeah, you could listen to multiple albums in an evening. I guess if you’re trying to experiment and think about new types of music, maybe you might just warp music in lots of different ways including speeding it up and slowing it down. But it wouldn’t be, as you said, in that scenario it wouldn’t be just trying to get the information content of the music.One thing I was going to ask about this is: Mancur Olson’s work on building coalitions is interesting in this regard because he argues that coalition building is necessarily a very long-term endeavor. And I’ve been really interested recently in the argument by Henry Farrell—he’s been arguing that AI should be considered a cultural technology that’s equivalent to the market or equivalent to democracy in that it’s basically a coordinating technology because it can classify at new speeds.And I guess what I was wondering was: building institutions, how is that time-bottlenecked? Is that something that could be sped up using AI, do you think? Or is that one of the things where it’s got to find its time speed?NBL: Yeah. No, I think it’s hard to speed up building institutions, partly because institutions are the result of repeated interactions. Now you could imagine artificial intelligences building institutions of their own much faster, because they can have repeated interactions much faster than we can as human beings. So there is this world in which you have a set of institutions that are solely, exclusively catering to different kinds of agents.So these institutions would then be different contracts, say, between agents that they all agree to, standardized ways of thinking about delivery of goods, those kinds of things. And maybe you could get a Lex Agentia, which would be like the Lex Mercatoria between traders back in the day. And they would have their own law, they would have their own institutions, their own way of resolving disputes.But if you think about any institution that contains a human being, that human being needs to repeatedly be able to interact with someone for the institution to arise. And then they need to invest their intentionality in something. John Searle famously said that institutions are the product of collective intentionality. And I think there is a lot of truth to that, which means that the pace at which you build institutions is the pace of intention. So that’s where you sort of end up.And I think, again, when it comes to hybrid institutions where we have both agents—artificial agents and people—the lowest common denominator holds. We need to feel that we interact with the technology in such a way that we over time come to trust it.Philip Bell: That’s really interesting. And I guess going back to your previous point about supporting agency, I wonder whether AI could help build collective agency as well as individual ones. I know that the Collective Intelligence Project have been doing work on getting AI to quickly find the commonalities between 30 people in a room, for example, which would be hard for a human to do in real time, but would facilitate a real-time conversation. I think that’s an interesting, also potentially positive vision of use of AI—supporting collective agency as well.NBL: Yeah. And you can almost turn it around. You could say: I think that agency primarily is collective. The notion of individual agency flows only from this idea that we together create agency. If you had one single person in the universe, would they then have agency? Would it be possible to have agency then? Whereas agency is actually dependent on being a part of a group where you can direct your will in certain ways and others can either join with you or oppose you.So I think that a lot of the agency we have is collective first and individual agency is a result of that. It’s just like individuality. We have this idea that we are born with an inner individuality and then that individuality expresses itself and we are who we are. But in reality, to go back to your quote from Heidegger, Heidegger notes I think somewhere that we’re “strewn in the eyes of others.” So identity is constructed by the eyes of others. And if that is the case, then I think agency might well be the same.Identity and agency might both be collective phenomena before they become individual, because identity and agency are tightly coupled. So maybe the question then is: what happens to this collective formation of agency first when it also has artificial content? Can artificial agents be a part of forming our agency so that we actually want new things when there are artificial agents in the collective that shapes our agency in the first place?And I do think that’s true. I think we’ll see certain very simple effects of that. Not wanting necessarily the same as the machine wants, but wanting in ways that are reminiscent of what the machine wants. Framing the world in ways that are reminiscent of the way that machine frames the world. And there’s this constant interplay between us and the machines that we use. Just like Heidegger—what are you? There’s the constant interplay between us and the tool or Zeug that we use in practical—say carpentry or something like that.Philip Bell: That’s really interesting. That was just making me think, because I think one thing that’s important in my identity is memory. I know you were speaking about memory earlier, and I’m now interested to read Paul Ricœur’s work. One thing that I think is sort of an interesting possible use of AI is in supporting human memory.We tend to think that as a society, relative to past societies, we’ve kind of forgotten the importance of memory to a large degree and our information systems are quite sparse and don’t support memory. Whereas in the past—this is slightly generalized—but in the past societies tended to have much more cyclical and thick knowledge environments which enabled people to retain information better. But in an AI world, I think it’s possible that AI could enable people to remember roughly whatever they want. Whereas today, most people kind of remember things based on chance. And I know...NBL: But is that true though? I mean, I wonder if you remember based on chance because my pushback will be: I think you remember on the basis of who you are, the stories you tell yourself, the narrative structures you put into your life, and those have a selection mechanism built into them so that some memories surface. You come back to some memories because they’re constitutive of the story you tell yourself.So there’s this distinction in some philosophy between history and memory where history is the things that happened and memory is the way you structure them and forget. And I think that there is a lot going on—consciously, or maybe not consciously, but going on in memory that is not random. I don’t think that what you remember is random.It’s not as if you wake up one day and then you remember, “Oh, I had a glass of orange juice on the 4th of February in 1994.” If that were to happen, you would probably be quite surprised and you would worry. And your first question would actually be this: “What does that mean? Why do I now remember that glass of orange juice in February 1994?” That must mean something. And that sense of meaning is deeply tied with a sense of memory because it’s tied to the sense of story.So we remember what we want to remember in the story that we tell about ourselves. And that’s not necessarily the historical events. And I do agree with you that AI could change that. It could make retrieval of historical facts easier, which might actually allow people to rewrite themselves. Because currently you strengthen the memory that’s in the story you tell about yourself. But the things that could tell a different story are forgotten. Your forgetting is also quite strategic. You forget who you do not want to be.So to some degree, you could then imagine an AI that could retrieve what was recorded—not remembered, but recorded—and allow you to remember it, to make it a part of your memory and then rewrite yourself.Philip Bell: That is really interesting. So it’s kind of a corrective almost—or well, corrective’s maybe a normative term—but yeah, it provides maybe a counterweight to the narrative you’ve built up. Now that’s a really good point actually. I mean, I think you’re right that maybe it’s not totally random. I guess that it doesn’t feel to me to be totally deliberate either. There are lots of things that I would like to remember that I can’t remember. I would say I forget most of the things that I read about and I don’t want to forget most of the things that I read about. And I guess that’s what I think is to me quite exciting about AI—it seems to provide the possibility that I will be able to remember more of the things that I want to be able to remember.NBL: I think you want to make a distinction between retrieve and remember, because the AI might—you might talk to it about a book you read. This I recommend actually, I actually might do this when I read a paper or book: I speak to Claude and then I make sure that there’s a small note of it so I can come back to it and I can retrieve that conversation. The reason I want to retrieve the conversation rather than what was written in the book is I want to retrieve how I read the book because my reading of the book is how I want to remember it. The retrieval I want is the retrieval of the remembering, if that makes sense.So to some degree, I think computational technology already allowed you to search, right? You had information retrieval technologies—they had nothing to do with AI. And information retrieval is different from remembering. But how we then retrieve remembrances is an interesting question.And I think this goes to the point we made earlier that all of these mechanisms are much more layered, complex, interconnected and messy than we would like for them to be. The idea we think about memory in a von Neumann machine is clean. There’s this random-access memory, there’s this long-term memory and then you retrieve things from it and you put it into the computation. That’s not how we work. The narrative we have structures the remembering we do. Some things we want to be able to retain—like information, you retain information rather than remember it. And then you want to be able to retrieve that information later at some point. But if you do, is that really remembering or is that just retrieval?There are all these nuances in language that we have evolved over time. And if we pay attention to the way we talk about it, we realize that these are different mechanisms, still reproducible I think. And I think it still would be really valuable to build an AI that can make a difference between remembering something and retrieving something. But it would be very different from just boiling it all down to the simple model where there’s memory and processing.Philip Bell: That’s a really good point. I wonder also whether—because in some sense AI recalls older attempts to unify human experience through knowledge. I was really interested by an article by Patrick Hutton I read recently where he talks about different mnemonic systems of a time, including Renaissance mnemonic systems. They had memory palaces and Camillo built a memory theater and there was a memory theater in London built by Fludd. And he even talks about how Freud in some ways thought that you could get to some sort of unifying human experience through memory. But he argues that since roughly the ‘60s and post-structuralism we have actually lost that aim of trying to unify human experience through connected knowledge.And I suppose that’s something that AI can, just by its affordances, support—connecting knowledge and enabling someone to reach into a common thread of human experience through that knowledge.One thing I was going to ask you actually is: I know in one of your other articles you talk about how sensors might become more important in an AI age partly because of the ability to compute lots more information. And I wonder how that might interact with human memory and identity. I suppose if I’m able to actually get much more empirical data about the world... I don’t know if that’s how you were thinking about it, but can you foresee a world where I’m able to get much more empirical data about the world through other systems as well?NBL: Yeah, no, you can definitely get the data, but then you have to translate it into your own sensorium. So you’re limited by the senses that evolution deigned to give you. You’re going to interpret it through your own sensorium anyway. But it’s such a good question because it gives us the opportunity to rephrase a paper by Thomas Nagel. Thomas Nagel once wrote a paper that was called “What Is It Like to Be a Bat?” He essentially asked this question very simply: because a bat has echolocation and we don’t, can we imagine bat consciousness with echolocation? Because the senses actually structure consciousness differently.And going back to our discussion, then you can now actually ask the question: what is it to remember like a bat? What is it when a bat remembers and how is that different from how we remember? It’s a really interesting question because bats probably remember other things than we remember in a structure that we can’t replicate.The reason I wrote the essay about sensors was I was fascinated by the fact that an AI essentially could collect any kind of physical measurement data. Anything you physically can measure can become a sensor. You can imagine that you track all of these different senses—and there could be millions of them—and then you could structure an understanding of the world from all of these different senses. And that in itself would give the AI an ability to understand our world in a very different way than we do. And you would have to ask the Nagel question: what is it like to be a multi-sensorial AI? And what is it like to remember like one?And you can sort of go on and you would probably end up in worlds where the amount of senses... Say for the sake of argument that you had an AI that had a million senses—a million different physical measurements fed into a massive AI that in some way construes its understanding of the world from that, finds patterns, interactions, correlations—you can find different kinds of affordances in that world that we are not even aware exist between different kinds of physical quantities that we hardly notice. That kind of AI would have a very different consciousness, a very different intelligence from ours, and it may well end up being completely impossible to communicate with, because communication presupposes some overlap in sensoria, in the senses we have.Evolution was extraordinarily parsimonious when it gave us our senses. We have, give or take, 6 to 11 depending on which biologist you talk to. And those senses are what we make do with. But if you look at the amount of possible senses that you could provide an AI with, that’s just a tiny, tiny fraction. The human world we inhabit is very small. AIs could essentially be sent out on missions to understand the world across sensoria that would be enormously different from ours. And I find that fascinating because I do think that if this were to happen, it would probably teach us a lot about the world if we could only translate it. And the translation problem interests me.Philip Bell: That is so interesting actually. Yeah, and I guess it also would get us back into old philosophical questions about empirical information versus other sorts of information. And yeah, that is an interesting point about: even if the data exists, how do we translate it into information that we can understand ourselves?NBL: Yeah. And we think about that with echolocation and bats too. When we think about how a bat thinks, we think that it thinks with this... we do our own voice, we’re like... I mean, we think it gets an echo and that’s roughly how it has to be. Now we know it’s not exactly like that because we know that their echolocation sense is much more advanced, but we have to translate it down to our own senses. So we translate it down to our own hearing. So we think it’s like hearing, sort of kind of, and then we translate it into our sensorium.And I think that’s what we’d have to do with an AI that was able to sense different kinds of quantum fluctuations or different kinds of radiation that we can’t detect or multiple other things that we can’t understand. And we would have to figure out analogies or metaphors for what that is for us. Maybe quantum fluctuation is a little bit like taste—it tastes metallic. But I don’t know. So you would have to find these analogs in order to translate and understand. And in many cases you might accept that you cannot.I mean, what if we get an AI—and this is the funny thing—what if you get an AI that has a million senses, it discovers a set of really interesting natural laws that we have no access to, but they can be translated into super cool machines that can be really good at stuff. And so we get these machines, we have no idea of how they work, because they work on the basis of data that’s been collected that’s way beyond what we can sense. We can get some kind of theoretical description of it, but we will essentially literally have built black boxes from these different sense datasets that we’ve been working with. And that’s not entirely out of the question that something like that could happen.In fact, I mean, that’s sort of how evolution works. We’re just discovering now that evolution is a-theoretical. For the longest time, I stupidly thought that evolution would have to take into account how much we have understood by physics. So of course, it would just use Newtonian or Einsteinian physical phenomena. But now we’re finding out that evolution has been using quantum phenomena for a long time in photosynthesis, possibly in our ability to smell or in the ability of birds to navigate. We have this growing field of quantum biology.And you have this a-theoretical force—evolution—that takes every small advantage that is physically detectable or sensible and turns it into fitness. And so we shouldn’t be surprised that there are quantum effects being utilized if those can lead to fitness. Now you can build an AI that has the same ability to become almost a-theoretical in the way it explores the world because it just adds senses and can measure all of the physical things that exist within its environment, and it can have that same a-theoretical exploration capacity.Philip Bell: Wow. That is really interesting. And I guess that is quite post-human in having this a-theoretical... or revolutionary. But it makes me think: maybe that’s how we become more ecological in some sense. Or more... I don’t know, I mean... That’s a really interesting idea also, that through collecting information that our senses wouldn’t otherwise be able to pick up, we could actually understand what it means to be—well, maybe we can never understand, but we can maybe appreciate better what it means to be a bat or a tree or something.NBL: We can be together with a bat, right? Our own “we” can slightly expand to accommodate the bat as well. And then some of the experience of the bat-likeness is in the bat and some of the experience of it is in us. So it’s this question of identity again that you raised in the beginning. What if this notion of individuality and identity is not necessarily as robust as we think it is?Philip Bell: Yes, and I guess we probably, in most cases, we can’t be with them on a temporal level. I mean, I would love to be on the time scale of a tree, but I don’t think it’s going to happen anytime soon.NBL: No, I think that’s right. And trees are interesting, temporally super interesting biological organisms. And as are mushrooms, for example. So there are tons of really interesting examples in nature of things that exist in different temporal dimensions, which is sort of what we’re saying that we’re building here. First wave of evolution was biological and then evolution could easily be machine-driven. A-theoretical, but at a different level with different capacities.Philip Bell: Yes, and we didn’t get into religion. I suppose that is another interesting area that could be influenced by this. I just had one more question: you’ve already given me the Paul Ricœur suggestion. Did you have any other book recommendations?NBL: Yeah, yeah, yeah. There’s actually a really good... if you like identity you should really read Oneself as Another, which is a great book by Paul Ricœur.Plenty. So I mean, I think when we’re talking about these things, you mentioned Heidegger—Being and Time is a heavy read, but there is plenty of good commentary. There’s a lot of good stuff in there. I really like, in terms of how one approaches issues methodologically, I quite like Philosophical Investigations, Wittgenstein. I think it’s a really good book. I think it’s, I really think it’s readable. And a lot of people go like, “Wittgenstein, that’s really hard.” No, I think it’s actually just very interesting to dip in and out of books like that and sort of try to figure out how people are thinking. So that’s another one. Those are classics, so they’re obvious.Ricœur I think is really good. There is a guy called—if I remember correctly, I don’t want to mess this up—it’s Pierre Hadot I think, who writes a lot about philosophy and identity. He was the inspiration for a lot of the work that Foucault did. Really good.You’ve already mentioned Dennett. His From Bacteria to Bach and Back is an excellent book. Dennett generally is just very readable, and especially when you don’t agree with him, because then you really have to do hard work because he’s so robust in the way that he argues.So I think those are some of the books, but there’s so much out there. Generally, I think one of the things we should do is to read more of the classics when it comes to artificial intelligence. And I think it’s good to go back to Turing’s original paper and just read it properly from the beginning. It’s good to go back to the early stuff from Minsky, especially now in the time of agents. Society of Mind is underestimated. It’s really good to look at things like what Margaret Boden did early on.Or if you want to take the other tack and look at it from the human side, you should look at Hannah Arendt and The Human Condition or Simone Weil and The Need for Roots. Great books that discuss what it means to be human in the nature of machines. So I think that there is no shortage of reading. Don’t get me started.Philip Bell: Awesome. Thank you so much. This has been so enjoyable and so... Yeah, it’s really helped me think about these things. So thanks so much and thanks for your article.NBL: Thank you so much for having me and good luck with the podcast. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://genfutures.substack.com?utm_medium=podcast&utm_campaign=CTA_1">genfutures.substack.com</a>

February 12, 2026

Can Europe Catch Up on AI? with Carl Frey

I spoke to Carl Benedikt Frey, Professor at the Oxford Internet Institute and one of the most cited scholars on the political economy of technology. His new book How Progress Ends argues that technological progress is far from inevitable. In this conversation, we discuss the delicate choreography between exploration and implementation, why different institutions suit different phases of the technology lifecycle, why Europe caught up in mass production but has failed in digital, and what this means for AI.Philip Bell: To start off, could you outline the key arguments in your book, How Progress Ends?Carl Benedikt Frey: The purpose of the book is to push back against the narrative that technological progress is inevitable. If progress was inevitable, it wouldn’t have taken humanity 200,000 years to have an industrial revolution. If progress was inevitable, most places around the world would be rich and prosperous today, and we wouldn’t see places that once prospered stagnating or declining or collapsing.I think the reason that progress isn’t inevitable is that as technology moves on, institutions need to adjust as well. Different institutional settings are more conducive to different stages of the technology lifecycle. Early on, when you’re exploring, you don’t know if something is going to catch on or not, so you’re better off having a system where people take different bets and then you see what works out.The Soviet Union was the most centralised economy the world had ever seen. If you were an aircraft engineer in the Soviet Union, you could develop a new engine and go to the Red Army to ask for funding. If they declined, maybe you had two or three other options. If they all declined, your idea would die with you. That’s quite different from the US system, where Bessemer Ventures famously declined to invest in Google back in 1999. They probably regret it today, but it also underlines that Google wasn’t a safe bet at the time—AltaVista and Yahoo were dominating search. To know if something will catch on, someone needs to take the risk and invest.On the other hand, when technology is more mature, it’s more conducive to planning. Airplane technology was quite well established when Europe set up Airbus as a competitor to Boeing. The jet engine—the last significant innovation—had already been invented. You were catching up to a static target, not trying to catch a moving one. When that’s the case, it’s much easier to plan, coordinate, and scale.The implication is that you need to move between these two phases. You explore, you get a prototype, you scale—and sometimes that runs into diminishing returns, so you need to move on to something new. That requires more decentralised structures. Some things are conducive to both exploration and scaling, though: having a large connected marketplace where people can move around and spread ideas, low transportation costs, good distribution networks. That helps both innovation and scaling.Philip Bell: I really liked that argument—the idea that you need the push of exploitation and the pull of exploration. You illustrated it through the tinkerers and inventors of 18th and 19th century Britain, and then the centralised bureaucracy of Bismarck’s Germany and Meiji Japan. One term you used which I found evocative was the idea of adapting your “ecological niche.” Is it easy for contemporaries to understand what ecological niche they’re in? Is it obvious at the time, or is there also luck involved?Carl Benedikt Frey: I think the big question is “make or buy.” Businesses do this all the time: do we invent something internally from scratch, or do we take existing technology, tweak it, and scale it?When the Industrial Revolution took off in Britain, I think it was a fairly straightforward decision for many firms and states on the continent to buy rather than make—although there was a bit of both. States pursued various tactics to attract talent from Britain who could make things in Germany and France. It was more a question of catching up to the technological frontier than pushing it forward. Germany did come to push the frontiers, particularly during the Second Industrial Revolution. But the key question is: are you lagging behind or are you at the frontier? That determines which path you choose.Philip Bell: That’s interesting for the current question about geopolitical sovereignty in the age of AI. Canada has championed Cohere, France has championed Mistral—even though those models don’t benchmark as well as Google, OpenAI, or Anthropic. Do you think the make-or-buy question is relevant today for AI specifically?Carl Benedikt Frey: Most certainly. The question is how easy it is to access the technology you need from abroad. During the post-war period, American technology and know-how was readily available through Marshall Aid, largely because of concerns about Soviet influence on Europe. America wanted Europe as a buffer, and that paved the way for much of the technology transfer we saw. It’s less clear today that Europe will have access to some of the technology being developed in the United States.One of the puzzles in my mind is that for the past couple of decades—this predates the Trump administration—Europe has been less dynamic, which is maybe not surprising. But it has also not caught up in digital. Europe managed to catch up in mass production in the post-war period but failed to do the same in digital. Why?I think a big part of the answer is that the single market is much more harmonised for goods than for services. The IMF estimates that if you take all barriers to trade inside the European Union and add them up, they amount to something like a 110% tariff. Trump Liberation Day tariffs, self-imposed inside the European Union. That obviously caps the return to investment in digital in Europe. What both China and the United States have in common is large domestic markets that firms can scale into. For Europe, a key priority needs to be harmonising the single market for services.Philip Bell: I’d never thought about it that way—that Europe hasn’t caught up in digital. There are European tech companies like Spotify and Klarna, but they’re few and far between. And it’s not a reflection of talent, because major tech companies open large offices in London to hire talented individuals at cheaper prices than in the US.In the book, it seems like the nature of the specific technology has some bearing on what institutions are most fruitful. You argue that centralised Soviet bureaucracy was useful for building heavy industry, but not for digital. Is there a specific institutional arrangement that will particularly help in the age of AI?Carl Benedikt Frey: I don’t know is the honest answer. With regard to the Soviet Union, they did well in heavy industry because they prioritised it—it aided the military-industrial complex, and defence was the key priority. As long as technology was static, Soviet elites could benchmark factory performance and hold managers accountable. The problem was that when mass production petered out, something new was needed for growth. That new thing was the computer revolution, to which Soviet contributions were essentially none.Part of the reason is that when you introduce a new technology in production, you can’t really benchmark. It becomes much harder to monitor performance. And unlike China, which is much more regionally decentralised, there was very little space to experiment in the Soviet Union without pulling the rug on the entire system.I don’t think AI is that different in that regard. AI development is quite concentrated in a few places. When technologies are implemented, they proliferate more rapidly than manufacturing industry did. And you see work beginning to migrate abroad as well—law firms reducing headcount in London, hiring more in Poland and India to save labour costs. AI aids that process because it reduces the productivity differential between a worker in London and a worker in India.There are probably things that will be different with AI, but it’s too early to say what institutional arrangements will be needed. What we can say is that certain things matter regardless of technology: barriers to entry are important for innovation, and having a large homogeneous market helps scaling.Philip Bell: Do you think AI will change the cost of exploration differently from how it changes the cost of exploitation? I’m building a startup, and AI has accelerated our implementation to an extreme degree. But the rate of exploration has increased much less—the people using our app still use it at a similar rate. We can build things quicker, but do you think AI will change the relationship between exploration and exploitation?Carl Benedikt Frey: I think yes, but probably in similar ways to the internet and the personal computer. When I have an idea, I can check fairly quickly using my preferred AI tool whether somebody else had the same idea. But the internet did that too. AI will reduce the cost of exploration further.The surprising thing is that despite the cost of setting up a company and exploring going down so much because of technology—the cloud has been enormously helpful to smaller firms—we’re seeing business dynamism in decline. Something is pushing in the other direction and has more than offset the advantages created by ICT and arguably AI.What is that? I’m at the university, and we see new rules and regulations and forms almost every week. That might be extreme, but I do think regulation has something to do with increasing costs. If you take an extreme example: most people believe AI will have a material impact on medical discovery. Even if that’s true, you still have to go through clinical trials, which is tremendously expensive. You probably need to partner with a large pharmaceutical company. I’m not suggesting we get rid of clinical trials—they fulfil a purpose. But those safety measures do come at a cost, primarily prohibitive for solo inventors and smaller firms.Another relevant aspect is what we incentivise. In academia, it’s publish or perish. When a new productivity tool arrives, we can do one of two things: use it to drill more holes, or use it to dig deeper and focus on one thing. It seems we’re incentivising people to do many things at any given point in time, which means attention is spread more thinly across multiple projects. We have research showing that the more projects you pursue at any given point in time, the less likely you are to make a real breakthrough. Those incentive structures might be another reason we haven’t seen a real upsurge in breakthrough innovation despite having tools that clearly aid exploration.Philip Bell: That reminds me of Marconi, who developed the radio on his estate near Bologna. He probably didn’t have loads of distractions on his phone and could concentrate. It feels like today it would be harder to do that. Do you think AI could facilitate more of that tinkerer culture coming back?Carl Benedikt Frey: Marconi had some help from the family butler too, so most people don’t have that. It’s entirely plausible that AI will democratise innovation. If everybody has access to their army of personal tutors and research assistants, that should tremendously aid people with good ideas. We could see an era of more decentralised exploration, unlike what we saw in the 19th century.What makes me think that’s probably not going to happen is the experience of the past two decades. ICT has already provided many tools that should make exploration cheaper and easier, and we haven’t seen the re-emergence of the solo inventor to the same degree as in the 19th century.It is true that the computer revolution in the United States was driven by new firms—Apple, Microsoft, Google, Amazon. But since the 2000s, we have a trend where new firms exit by acquisition rather than IPO. WhatsApp, Instagram, YouTube didn’t become firms in their own right. That’s not necessarily bad, but the trend towards killer acquisitions—where incumbents buy smaller firms just to shut them down and avoid competition—is concerning. The dynamism we saw during the early days of the computer revolution is no longer quite there, despite computers being much better today than in 1980.Philip Bell: How useful is it to use historical analogies to understand AI? Sundar Pichai compared AI to fire, Demis Hassabis compared it to the Industrial Revolution, Alex Karp compared building AI to the Manhattan Project. China’s AI Plus plan compares it to the internet or electricity. Is it useful to think about AI in comparison to analogous technologies, or should we take it on its own terms?Carl Benedikt Frey: It depends on the point you want to make. Going back to my previous book, The Technology Trap, I think the framework of replacing technologies that automate existing work versus enabling technologies that create new types of tasks—developed by Daron Acemoglu and Pascual Restrepo—sheds light on AI as well.If AI is just used for automation, we’re more likely to see something similar to the First Industrial Revolution, where wages were stagnant and probably even falling at the lower end of the income distribution. But if AI is used to create new products and industries, we’ll see something more similar to the Second Industrial Revolution, where the automobile industry emerged as the largest industrial undertaking the world had ever seen, along with supplier industries, electrical industries, and essentially every household appliance you have in your kitchen.But to say AI is inevitably going to reproduce the 1990s or 20th century pattern—I think that’s probably not right.Philip Bell: In The Technology Trap, you describe how technologies have sometimes caused short-term disruption but enabled long-term progress, partly determined by political impact. Do you think society has begun to perceive technology as more deterministic? When Dario Amodei says 20% of white-collar workers will be automated, it feels quite techno-determinist. Does that change how institutions, governments, and populations react to technology?Carl Benedikt Frey: I’m not sure if people have become more techno-determinist. Generally speaking, the further back in time you go, the smaller the cross-section of society you get on record. If you go back to the 18th and 19th century, Marx, Malthus, Ricardo—they all believed in some form of iron law of wages. They didn’t think technology could improve standards of living over the long run.In the mid-20th century, there was enormous optimism about electricity and personalised travel. Today there’s a lot of anxiety over AI, and a lot of people saying, “It’s the way it is, let’s hope for the best and maybe plan for the worst.” I think there have always been some of these impulses, but I may be wrong.Philip Bell: I’ve seen a debate between Arvind Narayanan, who argues AI is a normal technology, and AI 2027’s Daniel Kokotajlo, who argues AI is completely different because it will be self-replicating. Henry Farrell recently argued we should think of AI as a cultural technology, similar to the market or bureaucracy or democracy, because it allows classification on a large scale, which has never been possible before. Do you think it’s too early to make that classification?Carl Benedikt Frey: I haven’t read those articles, but I don’t think AI is there yet. AI is not going to replace an organisation or a firm anytime soon. Anthropic did an experiment recently where they had a vending machine being run by Claude—stocking it and selling products, a relatively straightforward operation. But Anthropic employees are a bit more experimental, and they managed to trick it into selling everything at 25% discounts and stocking the machine with metal cubes.This is a relatively straightforward operation, and I wouldn’t want AI running anything too complex right now. When you apply AI for fairly well-defined tasks with a lot of precedent, it does pretty well. But the messier something gets, the more moving parts you have that affect each other, the more unpredictable it becomes. AI generally doesn’t do very well at generalising to situations it hasn’t seen before in training.Advances will likely emerge in the next few years. Time will tell. Right now, I think it’s best to think about the AI we have as a tool, similar to the internet or the personal computer. In the future, maybe—but I’m not sure.Philip Bell: Do you think historical research will change in the age of AI? I saw an economist recently write a paper about how to use AI agents for economics. Are there going to be huge teams of AI agents going through archives?Carl Benedikt Frey: I’m not a historian, I’m an economist, so I’m not sure I’m best placed to answer. But speaking to historian colleagues, the challenge is that for AI to be of much use, things need to be digitised. AI might help that process, but that’s the first step. I suspect historians who spend much of their time in the archives will be some of the last researchers to be replaced—but I may be wrong.Philip Bell: That’s a good point. A lot of things aren’t digitised, and that information will become increasingly precious because AI companies want very specific data involving lots of reasoning. Do you personally use AI much in your work?Carl Benedikt Frey: I use it a bit. Partly because I write about it, I feel like I have to. I find it quite useful for some tasks, less useful for others. But the only way of knowing is trying it. So yes, I use it on a daily basis now.Philip Bell: One last question: do you have any book or article recommendations? It could be about the political economy of technology or anything else.Carl Benedikt Frey: The book that made me want to become an academic was Joel Mokyr’s The Lever of Riches, so I have to plug that one. I really like Doug Irwin’s Clashing Over Commerce—it’s a wonderful economic history of the United States in general, and a wonderful history of trade policy in particular. And Adam Tooze’s The Wages of Destruction: The Making and Breaking of the Nazi Economy is a book that’s beautifully executed and a pleasure to read.Philip Bell: Thank you so much, Carl. It’s been such a pleasure to speak to you.Carl Benedikt Frey: Thank you. It’s been a pleasure, enjoyed it myself. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://genfutures.substack.com?utm_medium=podcast&utm_campaign=CTA_1">genfutures.substack.com</a>

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