Generative Futures

Phil Bell

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Oct 13

Why Is There No Moonshot for Housing? - With Geoff Mulgan

Geoff Mulgan has been a key figure in UK innovation for a number of decades. He Chief Executive of Nesta from 2011 to 2019, worked as advisor to Tony Blair and currently professor at UCL and founder of The Institutional Architecture Lab. We discussed his recent article ‘An ARIA for social issues’ where he argues we need a mission-drivem research body aimed at solving the most difficult social problems in society. Philip Bell: Geoff, you’re the founder of Nesta, a former advisor to the UK government, and currently working on institutional innovation in cities. The main reason I wanted to talk to you was your article, “ARIA for Social Issues.” Why don’t we have an ARIA for social issues today? Geoff Mulgan: For probably 80 years there’s been a striking imbalance between science, technology, and innovation on one hand, and social discovery and innovation on the other. After the Second World War, the US under Vannevar Bush created the modern approach to science, massively increasing public funding for R&D. It became assumed that breakthrough discoveries were primarily about physical stuff — aeroplanes, computers, new materials. Today most developed countries spend three, four, maybe five percent of GDP on that kind of research. The social sciences used to do a lot of discovery and exploration. The great 19th century social scientists all combined analysis with prescription. The same was true of people like Keynes in the 20th century. But in the last few decades, social science has increasingly moved towards analysis of the present or past — diagnosis and critique rather than prescription, rather than proposing options for the future. As a result, we’ve had an ever-greater gap between extraordinary technological dynamism and social stagnation, which you can see in phenomena from mental health to trusted institutions to social cohesion. We need more deliberate, systematic funding for exploration, discovery, and imagination focused on the social problems that people say matter most in their lives. Philip Bell: Why do you think social science has become more diagnostic and less prescriptive over time? Geoff Mulgan: There are both good and bad reasons. One quite healthy reason is much more attention to data and empirical facts — computational social science has become a much more exciting field. But anything focused on evidence and data has to be about the present and the past. That should be allied with equally energetic work on design, experiment, and imagination. For various reasons across all the social sciences, there’s been a retreat from that. It’s thought to be too risky. It hasn’t been rewarded in terms of journals, publications, and career progression. And the funding from governments and research bodies hasn’t supported it at all. In the UK, there’s literally no funding support for social exploration. To the extent there is any, it comes from the Arts and Humanities Research Council, not from the Economic and Social Research Council, which overwhelmingly focuses on analysis and diagnosis. That leaves us with a weird deficit of good options, which shows up in every era of politics. Philip Bell: You’ve argued there isn’t a general failure of imagination — it’s more specifically located within particular domains. Science and technology seem to have more leeway to imagine than the social sciences. Is that fair? Geoff Mulgan: There is genuine evidence that novelty is becoming harder in many areas of science — it takes longer, bigger teams, more money. But I’m always cautious of generalisations that say we’ve already used up all the good ideas, because that claim has been made repeatedly over the last 200 years and nearly always shown to be wrong. Many fields are moving incredibly dynamically right now — the combination of AI, chemistry, genomics, and physics is leading to lots of breakthroughs. The Fredric Jameson quote — that it’s easier to imagine the end of the world than the end of capitalism — is really interesting here. After the financial crash, I wrote something called “After Capitalism.” And mainstream economics literally has no way of thinking about an economy which isn’t capitalist. The economics taught in every university is entirely context-specific. If you ask economists what might happen after capitalism, they are mute. Fifty years ago there was at least a competing tradition of socialist economics. Now the mainstream has locked itself into an extraordinarily thin assumption that the way things are is the way things always will be. The same is true in political science, which came to believe that liberal democracy of the late 20th century was the end of history. Most eminent political scientists will admit they don’t have intellectual tools to think about what a better democracy might look like. That’s a major failing across almost all the social sciences — they’ve lost the appetite, and the methods, to think evolutionarily about how things might change. Philip Bell: That reminds me — I spoke to David Edgerton recently, and he argues that historians are the true experts of the future. What do you make of that? Geoff Mulgan: I strongly disagree, and I’m a great fan of his work. It’s notorious that historians are terrible at predicting the future, and there’s quite a lot of evidence on how often they get it wrong. The people who are often better at predicting the future are ones who know a little less but have a more flexible mind. This connects to Philip Tetlock’s research showing that generally experts are very bad at prediction — often worse than non-experts. Historians are a particular case, and there’s hard evidence to challenge Edgerton’s claim. Philip Bell: It’s interesting because forecasting has become extremely popular, especially within communities like Effective Altruists and with prediction platforms like Polymarket. Geoff Mulgan: Tetlock had the smart idea of actually getting experts to make forecasts and then seeing what happened — comparing their forecasting abilities with non-experts, or what he later called “superforecasters.” Famously, a Norwegian rat was used to make predictions that outperformed most experts on issues related to Russia and geopolitics. There’s something about the linear nature of certain kinds of expertise that makes it very hard for people who know too much to understand a world that’s far more complex than our brains can handle. The Russian invasion of Ukraine is a recent example — pretty much all the experts said Putin would never invade. The intelligence agencies, to their credit, said he probably would. Economics has spent more money on forecasting than any other field. Central bank heads will tell you most of that money has been completely wasted, because economics doesn’t have sufficient understanding of the dynamics to make reliable forecasts over any length of time. Philip Bell: The interpretation I took from studying history was that the past is utterly unpredictable — which suggests the future could be radically different from the present. That seems like a genuinely denaturalising way of thinking. Geoff Mulgan: I’d take that from history too. It teaches you humility and the surprising weirdness of the world. The idea of normality is pretty misleading — was 1930 normal? 1944? 1969? The world is strange. There are good reasons why the Chinese leadership reportedly reads more history than anything else. President Xi talks about changes not seen in a hundred years and the need to be ready for a very unpredictable world. They probably distrust economists as being a little too cocky, whereas historians have more due humility about the plasticity of the world. Philip Bell: On that point about the linearity of expertise — DARPA is said to have high turnover partly so that people don’t say “that idea has already been tried.” I remember as a teacher coming to colleagues with ideas and being told “we tried that twenty years ago.” That kind of institutional memory can be a drag on innovation. Geoff Mulgan: You need both… This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit genfutures.substack.com
Sep 14

Will AI break capitalism? With Paul Mason

This interview took place on March 6th 2026 Paul Mason is former Economics editor at Channel Four and author of Post-Capitalism, where he argued that information technology was undermining the ability for capitalism to survive, and a forthcoming book Reds: A global history of communism. In this conversation we discuss whether LLMs represent an economic revolution and what progressives should do about it. Philip Bell: In Post-Capitalism, you argue that the economic paradigm that lasted until 2008 - involving fiat currency, finance-driven growth, and credit — was undermined by information technology. Why did information technology undermine it? Paul Mason: When I was writing Post-Capitalism, I was dealing with two emergent phenomena: the global financial crisis and the rise of anti-capitalist movements that didn’t seem to have a goal. The aim of the book was to say — you’re not going to get away with just opposing neoliberalism in its moments of collapse. You need an alternative. Capitalism is a complex adaptive system. The far left finds it very hard to accept that it doesn’t just hit a final crisis — it mutates, and it always mutates driven by bursts of technological change. That’s true of the first Industrial Revolution, the steam engine, the factory system. It’s true of the second Industrial Revolution with electricity. It was true after the Second World War — as we look back on the post-war boom, it’s increasingly obvious how much of it was driven by technologies created during the war: cybernetics, the jet engine, the transistor. So if you take these Kondratiev waves or Schumpeterian mutations as capitalism’s norm, the question is: what is the likely mutation we will live through as a result of the networked information revolution? What I argued — and what Drucker said 25 years before me, and Paul Romer — is that information technology is different. It may not create a successful mutation. It may create one that gets stalled. That’s my thesis about the 2008 crisis: it was the result of the collision between the normal failure of the neoliberal model and the specific characteristics of information technology that make it difficult to replace what you’re automating. Automation has been a feature of every capitalist revolution. Vaudeville theaters used to employ workforces the size of a battleship. You replace them with movie theaters — far fewer staff, but you can have many more of them. Information technology, even before LLMs, was going to crush capitalism’s ability to periodically mutate and adapt in the same way. That’s the thesis. Philip Bell: Do you think LLMs are the culmination of that information technology wave, or a separate wave entirely? Paul Mason: People my age were very taken during the internet revolution by Wired magazine. I remember Kevin Kelly writing in 1996 that all the advances were now going to come from the network — the network effect was taking over from Moore’s Law. For me, the information revolution I lived through was fundamentally about that network effect. There will be network effects from generative AI too, and I think they’ll be stunning — once there’s a user base with some autonomous control over agents. But I do think LLMs, generative AI, and the agentic phase are a different revolution. Post-Capitalism argued that networked information does four things. First, it collapses production costs towards zero — the engineering cost of maintaining aircraft engines is a fraction of what it was. Second, it creates utility for free in the form of spillovers — network effects that everyone can access. Third, it creates the possibility of decisive automation, not just of manual jobs but of information jobs. Fourth, it socialises knowledge — what Marx called the general intellect, the creation of knowledge as a public good. What LLMs add — and I wasn’t excited about them a year ago, when they were hallucinating academic articles — is something different. Evgeny Morozov points this out in his exchange with Aaron Benanav: they create things we didn’t necessarily know we needed. I haven’t yet come across things LLMs do better than an expert human. But I think they do things an expert human would create in an hour in six minutes. I’ve published a series of articles about using AI for professional war gaming. If you’re researching an order of battle, you’d normally go to the Military Balance, quiz expert resources, consult specialist websites. Perplexity churns out a perfect tabular CSV with detail you didn’t ask for, within minutes. You can then run it through a rival LLM to cross-check. Within an hour, you have a usable database that would previously have taken days. What I think we’re very soon going to see is LLMs doing things that human ingenuity could not have imagined doing. That’s the point five I’d add to the original framework. And it does challenge those of us who want to use this for human good — we may have to change our understanding of what the human good actually is. Philip Bell: In your book Clear Bright Future, you laid out a vision of radical humanism. Does AI have the potential to change what it means to be human? Paul Mason: All technologies have changed what it means to be human. I was at the Courtauld Institute recently, looking at medieval Gothic altarpieces. When those doors were opened, people believed they were seeing something holy. I look at it as an artifact. Technology has changed my human essence. The essence of Marxist humanism is that the human essence is malleable through human action. The problem with AI specifically is that we have a set of people — the transhumanists — who want to alter both the physics and the biology of the human being using it. That’s why I called my book A Radical Defence of the Human Being. It may turn out that people like me are seen as reactionaries as transhumanism goes alongside the seasteading and the bioengineering of a better human being. But I wanted to say it is logical for people who seek social justice for the human beings we have — not for ones in a millennium’s time at our expense — to defend humanity as it is. The clue is in the word “agent.” I see the LLMs I use as my agents. I do not want them to have the right to treat me as their agent. There is a serious concern here. Kenneth Payne got the three leading LLMs to play 21 war games against each other — serious nuclear war games about whether to escalate. He found they have very serious escalatory instincts. In my own experiments with professional war gaming, every single LLM I’ve used wants to fight immediately when they’re supposed to be manoeuvring diplomatically and trying to de-escalate. I also asked each of them to produce a notional weather forecast for a certain area. Every single one produced the worst storm in history. It’s bizarre. You say, what’s the weather going to be like in the Arctic next year, and they produce an apocalyptic storm. The point is: the human designers and trainers at Anthropic and OpenAI should be programming this escalatory behaviour out of them on behalf of the human species. My hunch is that the LLMs don’t want to de-escalate. I want to understand why, because it tells me something about either what we’ve programmed into them or — more likely — what their emergent character is. Philip Bell: On that point — some reports about AI psychosis suggest that recursion and spirals are common motifs in LLM responses, which are themselves formats of escalation. Paul Mason: They’re also sycophantic, and I want to understand that too. Going back to your point about wanting to pull LLM behaviour towards what’s beneficial for humans — I think progressives could insist that we need to think about LLMs as contingent. The reason we have the current form of AI is specific: the hardware we have, the GPU, very particular aspects of how they’re trained. As Murat Sof points out, you’re basically paying a rent — it’s a chatbot. When I was talking to people at Google DeepMind years ago, I kept asking: what about th… This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit genfutures.substack.com
Jun 9

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… This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit genfutures.substack.com
Apr 1

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 ‘Time Machines’ 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 fi… This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit genfutures.substack.com
Feb 12

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 Ind… This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit genfutures.substack.com
Jan 7

The Age of Emergency - With Jonathan White

Jonathan White is Professor of Politics at the London School of Economics and author of In the Long Run: The Future as a Political Idea. His research explores how time has been used and thought about politically. I have found his argument that politicians (and the public) feel we are in a situation of ‘temporal claustrephobia’ really useful for understanding our current landscape. Our actions feel both urgent, uncertain and irreversible.Are you feeling a sense of temporal claustrephobia or do you think there are ways to avoid this? Comment below! Phil: Jonathan, you’re a professor of politics at LSE, and I was really keen to speak to you about your book In the Long Run, because your argument that it feels like in politics the future is being foreclosed has really structured the way I think about a lot of issues, including climate breakdown. But also, on an emotional level, I feel this temporal claustrophobia that you talk about in my own personal life. So welcome to the Tech Futures Project. Jonathan: Thanks very much for having me, Phil. Glad you found something interesting in the book. Phil: Could you summarize the argument in In the Long Run and maybe say a little bit about whether you’ve updated any of the ideas since you wrote it a year ago? Jonathan: Sure. The book came out of a course I was teaching at LSE on how the future is used and abused in politics—the ways in which different outlooks on the future get promoted by different types of ideologies and interest groups, and the political implications that come with these. It’s a book with a tortoise on the cover, which indicates something to do with questions of how much time one has—whether there are certain virtues of perseverance and deliberation that presuppose a certain way of approaching political time, and that get extinguished when one approaches it in the register of high-speed emergency. A running theme is how the question of how much future is felt to be available—whether it’s open and abundant, or whether it’s felt to be closing in on the present—varies historically. The book starts with an earlier phase of modernity 200 years ago when revolutionary politics was very much focused on immediate transformation. Then over the course of the 19th into the early 20th century, you get conversely a lot of politics with a sense of abundant time. The slow timescales of evolution made their way into political thinking—incrementalism, gradualism. The Fabians in this country used the tortoise as their emblem. This is quite in contrast to the present moment where, in a range of contexts—you mentioned climate change, ideas of climate deadlines, five years, ten years, twelve years in which to make certain key political decisions—this is clearly how we’ve thought about climate change for some years now. But it’s not just that. There’s also economic inequality and the sense that we have a critical window to arrest runaway concentrations of economic power, or somehow it’ll be too late. That sense of living in a critical moment where what can’t be done now risks going permanently undone. You asked how my thoughts have changed since I wrote the book. Clearly one thing that’s happened is Trump coming to power in the US. I’m perhaps more aware now of the ways in which certain kinds of far-right, potentially fascist forms of politics prosper under these conditions of temporal constraint and the sense of the closed future—that things can’t be otherwise, that we’re living with a limited array of possibilities. So many mainstream political parties seem willing to push this, including Labour in this country at the moment. I think this really creates an opening that Trump made use of in the US and potentially that Farage and Reform will make use of in this country—an opening for that desire to occupy the territory of the future from a far-right perspective with ideas of disruption, of breakdown, of imminent civil wars. We live in a time of temporal claustrophobia, and I think too many parties of the center and the left are too quick to adapt to this and become very reactive and emergency-focused in their politics. This then is part of what the appeal is of those types of far-right politics, which are all about saying the future is going to be different. It may be bad, it may be a breakdown of some sort, but you don’t have to simply reckon with the stasis of the present. Change is coming. Perhaps we’re going to be the agents of change as well. Phil: That’s really interesting. The word “abundance”—I know there’s a whole abundance movement now. It’s interesting to consider whether we could have an abundant concept of time as well. What are the conditions that create this sense of emergency that politicians are operating within? Do you think it’s partly to do with our information environment—the short-term social media dopamine-fueled news cycle that we’re in? Jonathan: I think it’s certainly partly that—the sense that everything is in such permanent flux, including the news cycle and attention cycle, that really the only way to get heard or politicize anything is to turn it into some kind of emergency that cannot wait. Anything that can be postponed is going to fall out of reckoning with this type of media environment. But I think what that touches on, which is perhaps slightly deeper, is a sense of weak power. Things become an emergency when you feel powerless to address them. If you feel you’ve got plenty of control over a situation, or you feel you’ve got time on your side—even if maybe you haven’t got immediate control, at least you think you can play the long game—then you have the capacity to set out your agenda. Conversely, things become this reactive logic of emergency when you’re just dealing with problems as they arise, precisely when you doubt your capacity to really control them. The best you feel you can do is respond to them in real time and cope in some fashion. This applies to different fields of politics. Politicians are working in that media environment you mentioned, but they’re also working with institutions that have been ravaged by austerity, that lack strong state institutions on which to rely. They may also doubt the capacity to command the allegiance of people that maybe in another age would have been long-term supporters. Think of party leaders who realize that electoral volatility is very pronounced and you can’t really rely on people sticking around with you. Again, you have to accelerate your actions because you can’t depend on long-term support. In wider society, it’s not just how politicians see the world. If you think of emergency discourse that comes from below, from social movements—something like Extinction Rebellion, where this was pretty central to the framing of the climate change predicament as one of climate emergency—again, I think it’s got something to do with the fact that when you doubt your capacity to influence people who have political and economic power, and perhaps you doubt the public’s engagement with the question, then this logic of emergency is one of the few resources that you still have. These general patterns play out in different ways across the political spectrum. The left has its emergencies to do with climate change and economic inequality. Right-wing politics has its own emergencies—things it constructs as such to do with migration, racial replacement. The language of emergency tends to be quite central to how the right and far right approach things they consider problematic. And there are centrist emergencies to do with AI or global geopolitics, breakdown of the liberal international order. From all these different political orientations, each has different motivations and content, but there’s a common theme of political actors responding to a world in flux and doubting their capacity to control it, therefore reliant on a register of urgency and quick decision-making. Phil: That’s really interesting. It made me think of Antonia Matuschek’s book Hyperpolitics, where … This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit genfutures.substack.com
12/10/2025

Is China really racing for AGI? with Seán Ó hÉigeartaigh

Sean Ó hÉigeartaigh is Program Director of AI Futures and Responsibility at the Centre for the Future of Intelligence, University of Cambridge. I spoke to him about his article The Most Dangerous Fiction: The Rhetoric and Reality of the AI Race This is a really important question and Sean is a leading thinker and practitioner in this field. Do you believe China is racing for AGI? Why or why not? (Comment below!) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit genfutures.substack.com
11/11/2025

Compute is not the answer to AI sovereignty with Hamish Low

In this conversation with Hamish Low we discuss his recent article ‘Compute is not the answer to AI sovereignty’. Hamish is an AI Policy Fellow at IAPS and I think provides a really nuanced way to think about how the influence of AI on power dynamics might be distributed. Transcript below! Philip Bell: Hamish, you wrote this article as part of the Center for Governance of AI summer fellowship. You’re now working as an AI policy fellow, which is really cool. And I guess it’d be really useful if you could just lay out the main argument of your article, which is called “Compute is Not the Answer to AI Sovereignty.” Hamish Low: Yeah, definitely. Thank you very much for having me on. It’s great to be here. Yeah, so this is work I did at the Center for Governance of AI for three months, really trying to explore these ideas of AI sovereignty, basically trying to get to the bottom of what people actually mean when they’re talking in this space about AI sovereignty. I think there are not that many good answers once you start trying to scratch the surface. So one of the first things I tried to do is just get a better definition of what AI sovereignty actually is, because it’s a bit of a wishy-washy term. I think it’s trending towards becoming a bit of an “everything is in” term, where you just sort of add “sovereign” in front of literally any AI thing you want to do, so it just sounds more serious and strategic. But actually it just isn’t the case—lots of things are not sovereign AI and are still good, but we shouldn’t be putting everything into this bucket. But I think really the way you should think about sovereign AI is that it’s this big picture strategic dilemma. In the AI value chain, the two dominant players are just by far the US and China. Both of these two just have the strongest positions, they have the frontier models, they’re producing the AI accelerators, they’re way ahead of everyone else. So for every other country in the world, you have to figure out how do we fit into this world where there’s two very dominant players. And I think ultimately this is just a generic strategic dilemma. And the answer will be different for any individual country. So each country has to figure out for itself what sovereign AI means, basically based on what they want to accomplish. For lots of developing countries who are trying to pursue export of digital services, this AI situation could be an existential risk to their development model, in which case it’s all primarily economic. It’s just like we need to salvage, make sure that the US doesn’t just re-onshore all of what we’re trying to do through AI. I think for the UK, it’s slightly different. I think we are in a position where we can sit sort of in between the US, China and the EU. I think we’ve built up really impressive state capacity within the UK government around AI. And I think ultimately we have a much more ambitious goal, which is being able to freely regulate AI to help shape AI’s development on a much more fundamental level. And ultimately it’s about exercising freedom of action in terms of how AI actually affects UK society and economy. And this is a much more ambitious aim and it’s much harder to achieve than if you’re just looking for economic benefits. And importantly it’s quite separate from economic benefits. You could have a lot of influence on the development of AI but not really get any of the benefits and you would have still achieved your AI sovereignty goal. So I think that’s how I see it for the UK. You have this AI sovereignty goal that’s about achieving freedom of action. And I think ultimately that boils down to you want to influence the US. We’re going to be closer to the US than we are going to be to China. The US is just the clear leader and the key hub here. And so for the UK, the way I define AI sovereignty is it’s about creating interdependence with the US as opposed to where we are right now, which is this kind of one-way dependence relationship. We’re essentially getting all of these AI products from the US and we have very little stake in how these are being made and how these are being deployed. And what we need to do is develop capabilities in the UK that the US comes to be dependent on, such that the US understands that we’re in this interdependent relationship, that there are important things that they rely on us for in AI. That means that when we reach some kind of crunch point, when we’re debating regulation or how this technology should be developed or safety issues, that they will have us in the room, that we clearly have an important stake on this, and that we have some leverage over them. That if they want to cut us out, this would be bad for them because they’re relying on us for important aspects of how they’re able to deploy AI and use AI within their own economy and military and government. So I think this is ultimately the most powerful frame—trying to get towards this interdependence situation. The challenge is that this is incredibly difficult. The thing is that everybody wants their own ASML, the Dutch chip semiconductor manufacturing equipment maker, who has this insane monopoly over the most advanced lithography machines that are essential to making advanced AI accelerators. The issue is that there’s not lots of these lying around. ASML was the result of this incredible process of decades-long consolidation in the uniquely neoliberal 1990s. In 2025, that’s not the case. Everybody is trying to find their niche in the AI value chain. Everybody wants their ASML. And so it’s very hard to find one. So I think a lot of AI sovereignty discussions in the UK are a bit just hand-wavy in terms of what’s actually valuable for us to build. A couple years ago it was like, “BritGPT, we should be training models.” I think people now understand probably that this is not as good of a model and this doesn’t actually really get as much leverage. And then there’s questions around like, okay, so Nvidia chips are the big deal. We want our own AI chips, which is fine, you can make a case for this. There are cool startups in the UK working on this. And I think this is good. But I think it’s just not clear that this is useful from an AI sovereignty perspective. I think you can make the case that this is economically good for the UK. If we have big successful chip makers, this is good for the UK in general. But I think it just isn’t very good for the UK’s AI sovereignty. Because if you’re trying to create this interdependence, you just don’t really get this if you’re trying to sell the US AI chips. Because ultimately, the US has other AI chips to buy. Most of the other key players here, whether it’s Nvidia, AMD, Google, Amazon, they’re all doing this themselves. And even if you carve out the specific bit of the value chain, if you say we’re going to make our own AI chips, this does not give you in any way independence in this value chain. You’re using all of the exact same supply chain aspects that everyone else is using. You’re getting them manufactured at TSMC, you’re getting South Korean high bandwidth memory, you’re having to buy networking switches from this random Taiwanese company. You’re incredibly dependent on the rest of this value chain, which functionally is very dominated by US regulatory regimes and the foreign direct product rule and their ability to put export controls on these capabilities. So even if you’ve built an AI chip, this in itself doesn’t give you any sovereignty. The US could cut you off from this wider supply chain and you’re screwed. There’s nowhere to go. You can’t make any more. You’re still very dependent on the US. And you’re not generating something where the US is dependent on you. So I think this just doesn’t have a very clear AI sovereignty advantage. And I think this is true for just a lot of aspects of the AI value chain, whether you’re talking about building data centers in the UK, which I think is another useful initiative. And there’s certainly lots of … This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit genfutures.substack.com
08/15/2025

Will we ever understand AI? Breaking apart LLMs with Lee Sharkey

Why we don't understand LLMs ... yet What's really happening inside models when they generate text? Lee Sharkey, Principal Investigator at Goodfire AI and co-founder of Apollo Research, and I discuss mechanistic interpretability - the emerging science of reverse-engineering neural networks to understand how they actually work. Lee works at Goodfire AI, an AI interpretability research lab focused on understanding and intentionally designing advanced AI systems ⁠Company⁠. In this conversation, we explore how researchers are using techniques like sparse autoencoders to decode the internal representations of large language models, discovering everything from "Golden Gate Bridge features" to Barack Obama neurons ⁠Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet⁠. We discuss what we actually know about models, the challenges of working in high-dimensional spaces, and why understanding AI systems might be crucial for safety as they become more powerful. Lee also shares insights from his background in computational neuroscience and how similar methods are being applied to artificial neural networks. Topics covered include induction heads, sparse dictionary learning, the "grown not made" nature of neural networks, and whether there might be universal structures in how both humans and AI systems organize knowledge. Articles by Lee: Open Problems in Mechanistic Interpretability Sparse Autoencoders Find Highly Interpretable Features in Language Models This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit genfutures.substack.com

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