I've Got Questions with Sinead Bovell - Leading AI Professor: We Must Address AI's Climate Impact Before It’s Too Late | Kate Crawford

Episode Date: October 16, 2025

In this episode of I’ve Got Questions, I sit down with leading AI scholar Kate Crawford, professor at USC, and author of award-winning Atlas of AI to examine the environmental, social, and ethical costs of artificial intelligence. We break down how AI is an extension of earth’s resources with giant data centers that use huge amounts of electricity, water, and minerals. We discuss how this is putting pressure on the planet and on communities that often go unseen. Kate breaks down how the global “AI race” between countries is creating new risks and what it will take from us to build technology that helps both people and the planet. 0:00 – Introduction 1:00 – Why Kate Crawford Says AI Isn’t “Virtual” — It’s Industrial 2:30 – The Hidden Environmental Cost of AI Models 4:05 – How AI Competes with Humans for Survival Resources 6:00 – Data Centers, Fossil Fuels, and Real-World Harm to Communities 8:00 – The Illusion of “Efficiency” and the Jevons Paradox 10:30 – How Behavioral Change Fuels the AI Explosion 12:00 – What Happens If We Don’t Change How We Build AI 14:00 – Why China’s Energy Strategy Is Beating the U.S. 16:00 – The False Choice: National Security or the Planet 18:00 – Shifting Responsibility from Individuals to Systems 20:00 – The Structural Design Failure Behind AI’s Carbon Footprint 22:00 – How Race Dynamics and Geopolitics Drive Destructive AI Growth 24:00 – Why Concentrated AI Power Threatens Democracy 26:00 – Nationalism, Cooperation, and the Future of Global AI Policy 28:00 – Why Technology Is Never Neutral 30:00 – The Myths and Realities of “AI for Climate” 32:00 – What Smaller, Efficient AI Could Look Like 33:00 – The Billionaire Empire Behind the AI Revolution 35:00 – AI as the Continuation of Historical Power Structures 38:00 – The Long Arc of Empires and Industrial Transformations 39:30 – When Private Tech Companies Rival Nation States 41:00 – The Next Three Years: The Most Consequential in AI History 43:00 – The Rising Threat of AI-Driven Misinformation 44:00 – The Dangerous Defunding of Science and Research 46:00 – Could a Brain Drain Lead to New Global Research Centers? 47:30 – What Individuals Can Do to Shape the AI Future 48:30 – Closing Reflections Follow my work here: Website: https://www.sineadbovell.com Substack: https://sineadbovell.substack.com Instagram: https://www.instagram.com/sineadbovell LinkedIn: https://www.linkedin.com/in/sineadbovell Twitter / X: https://twitter.com/SineadBovell YouTube: https://www.youtube.com/Sineadbovell TikTok: https://www.tiktok.com/@sineadbovell

If we don't change how we're building artificial intelligence, what will be the overall cost to the planet? If we're racing towards a climate disaster, if we're racing towards a real collapse in the labor market, we might want to be asking harder questions about how we build and use these technologies. We tend to think about these technologies as an extension of the brain, but it's an extension of Earth. Generative AI will be using the same amount of energy as India by 2030. The narrative right now is that it is AI and national security. or the planet. And that lets tech companies get off easy because any decision maker or world leader is going to choose national security in this moment. We're looking at eight billionaires who are

having extraordinary power over the lives of eight billion people. This has to be a public conversation. This is one of the last moments where you can really start to shift what these infrastructures are going to look like. That to me is the real urgency. Professor Kate Crawford is one of the leading voices examining the environmental, social, and ethical costs of AI and emerging technologies. She's a professor at USC in Los Angeles. She advises world leaders and policymakers. And today, we're going to pull back the veil on the planetary costs of artificial intelligence. I'm Senebeauvel and this is I've got questions. Professor Kate Crawford, what do you think is one of the biggest illusions about artificial intelligence?

Oh, that's a good one. There are so many illusions at the moment, but I think perhaps the biggest one is this idea that AI is immaterial. It's a magic trick. It's code in the cloud. In actual fact, where in the middle of the biggest industrial transformation in a century, in fact, it's been called the biggest infrastructure project of all time, we're seeing massive data centers sprawling across the U.S. and across the world. that are predicted to be using as much electricity as Japan by next year. So this is an enormous energy, water, and resource-draining infrastructure that's being built so rapidly that some of the new data centers are being put in tents

because they can't build the buildings fast enough. So, I mean, I think at this point we have to start looking at AI as a massive industrial infrastructure that comes with real costs, particularly if we're not thinking ahead in terms of how to make this sustainable. And what are some of those costs? So for somebody who is not familiar with how AI is built, how these systems are run, where does the environmental toll happen? And is it worse when AI systems are being built or when we're interacting with them?

Well, I mean, if we really break it down, essentially AI models run on semiconductor chips. So these chips, they're called things like the A100 or the H100. these are enormously energy-hungry, and they also produce a lot of heat. So in addition to needing a lot of electricity, they also need to be cooled down, often by using fresh water that's coming from our reserves. And in many ways, these giant infrastructures can have somewhere from, you know, 10 to 20 to 30,000 of these chips running 24-7. Now, the way that AI models are trained, they actually go through this very large process,

are being trained on a data set that can be as large as the web itself. I mean, these are enormous datasets, often not very well curated either. So you're sort of throwing everything in. And that training process can take, you know, a full year or in some cases even longer. That is obviously a very resource intensive period for energy and water. But then there's this second stage, which is called inference. That's when you and I are asking questions of, say, chat GPT. Every time we do that, it's depending how you count somewhere between five to ten times more energy intensive than an ordinary search.

Now, a few times that by the number of queries that are happening every day. Hard to get that number, but Open AI says they're getting around 2.5 billion queries a day. That's a lot of energy and that's a lot of water. Then, of course, there's the mineral layer. To build all of these things actually requires mind. everything from rare earth to lithium to cobalt. You know, in my work, I've done a lot of field studies going to these sites, you know, visiting the mines, seeing what happens environmentally, but also in terms of the labour. This is, you know, some really backbreaking work in these mines, often exploiting child labour, depending where you are in the world.

And then, of course, we're seeing enormous pressure being built and basically being put on these mines to produce more and more and more to build all of these new AI. hyperscale data centers. So at every level, if you're talking about minerals, water, energy, all of our data, all of our work over the years, these are becoming the resources that are driving this AI revolution. So in many ways, AI in its current form is competing with humans for resources of survival. Exactly right. And in this sense, you know, it's not artificial intelligence, it's massive industrial scale synthesis. It's inference engines that are drawing so much more power than most people even realize. In fact, some analysts predict that generative AI will be using the same amount of energy as India by 2030. It's like bolting a whole new

industrial nation onto the grid. And of course, you know, I think if you listen to industrial leaders, they know this. They're saying that right now, data centers are being planned that are 10 gigawatts. You know, that's a lot when we realize that a nuclear power station, for example, is one gigawatt. So really these AI data centers are going to be using as much power as entire cities. So that comes at a real cost, particularly because this is not all alternative energy. A lot of this is using gas, fossil fuels. Which is the big part of it. It's the choices that are being made. And so are there examples of some of the harm that's happening right now to communities in real time because of these data centers? Because like you had mentioned, we think of the cloud

as this thing that isn't really real. It just floats around. But it's not exactly that. And a framing that I loved, a point you made in your book, Alice of AI, is that we tend to think about these technologies, especially one like AI. And we hear this metaphor all the time as an extension of the brain. But it's an extension of Earth and the planet, not us.

Exactly, right. And you asked this really good question of, you know, what are the lived impacts for communities right now? And perhaps one of the most dramatic is playing out in South Memphis, where Elon Musk has built what he claims is the current biggest data center. He calls it Colossus. That's a whole other conversation in part two. Yeah, part two. But, you know, Colossus requires far more energy than the grid is able to provide. So they started to use a whole of methane gas generators to power this, you know, enormous structure.

That releases quite toxic nitrogen oxides and formaldehyde into the air. And this is affecting a historically black community that already has some of the highest asthma rates in the country. So we're talking about local communities really experiencing very real downsides. That's before we even talk about the cost. I mean, we're seeing the cost of electricity be passed onto consumers. In some cases, it's doubling in some regions. And if you look at a state like Virginia, right now, 25% of the electricity of Virginia is being spent in data centers. That's going to go up to 50% in a few years.

This has real implications all the way down for people and communities, but also for our environment and also for the kind of world that we're trying to build. But if you look at some of the more recent numbers, right? So you had made a point at a talk that we were at in February about deep seek. So this is a Chinese AI model that came on the state. and it made a lot of headlines, not just because of some of the efficiency gains, but because it showed America, they're not the only game in town.

But it showed that you could build an AI system with one-tenth of the computing power than some of its competing models. And then 80% of the energy tends to happen in inference, so when we're chatting with these AI systems or when we're making images. And some of the recent gains, I mean, there was a study from Google

that showed in just 12 months, they've reduced their carbon emissions and inference by about 98%. So things are somewhat trending in the right direction, but is this not the whole story? Is that enough that we're just seeing a downward pressure on emissions in a good way? Well, I mean, let's talk about that Google report. I think it's, first of all, fantastic that they're finally releasing some numbers. Researchers like myself and many others have been pushing for much more granular data.

So that's fantastic. And of course, Google is saying that they've increased their efficiency really quite dramatically in the last couple of years. And if you really dig into that report, there's a couple of things that are worth noting. One is that they really only look at the numbers for text generations. So if you're, you know, generating an email, for example. They don't look at images or video that are far more energy intensive. Another thing here is to think about what efficiency really means. It means that effectively you're lowering the cost of every query.

Now, this is really interesting because it takes us back to a well-known paradox in the economic literature. It's called Jevons Paradox. This was a, basically an idea that was discovered by an economist in the 19th century called William Stanley Jevons. And he was looking at coal production. He found that when we made coal production more efficient, rather than people actually using less coal, they used more. AI is like that problem on steroids. Because even if you make every query more efficient, then we start to see bigger and bigger. models being built with more and more hyperparameters from billions to trillions of hyperparameters. So even if you have more efficiency, the increased use at the inference layer that you just mentioned

actually means that overall, more energy is actually needed. But there's something else going on too, and I just wrote a paper with two of my colleagues, Sasha Luchione and Emmys True Bell, where we really dig into Jevin's paradox. And what's interesting is it's not just a question of energy. It's a question of our behavior. It's the fact that we're getting so used to AI being built into everything that we use every day. Even if you're not thinking about it, a chatbot will suddenly appear and say, you know, can I help you? Even if you're not intending to use Gemini, you'll be getting an AI recommendation. So it's starting to infiltrate at every level which is changing us behaviorally.

So we have to start to look at a much bigger social analysis of what AI is doing. And I think that's really been the focus of our research. research recently. Right. And it's an amazing point because the more efficient the technology becomes, the more we're going to use it. The more progress we see in the technology, I mean, it's the worst it will ever be. Even if we don't see much more progress, we'll figure out new ways to use it. And I guess AI being a general purpose technology, that is the path that it is on. It is going to become infrastructure. And some of the things I mean, I find funny too when I read these reports is that they often make comparisons to a Google prompt or a Google search in

in 2008 or streaming Netflix. Now we're down to a few questions with an AI system as equivalent to maybe eight to 10 seconds of Netflix. But that doesn't actually make me feel any better because we're still using the wrong energy sources because not all energy is equal on the planet. So it's still, if we zoom out, how are we actually living in our behavior and the technological infrastructure more broadly? If we don't change how we're building artificial intelligence, what will be the overall

cost to the planet? What timeline are we on? Well, this is a really hard question to answer, again, because of the difficulty in getting the real picture in terms of the data out of all of the major companies. But what we do know is that the current predictions for, say, how much electricity AI is going to need are shifting from between 30 to 90 percent in the next 10 years. That is a staggering figure. Obviously, if we keep on this path of using a lot of fossil fuels in the mix, then we're accelerating a really dangerous trajectory. We're already in a perilous position with climate change. The new reports, frankly, are keeping me up at night.

I know this is affecting you as well. So in that sense, we just don't have time to be building these highly inefficient fossil fuel-driven data centers. And there are some really good examples of making different choices. You can build AI differently. And you can think about it down the stack. let's think first of all about where you're getting your energy from. Now, China, for example, has got something like 900 gigawatts of alternative renewable energy in their grid. They just

added another 300 gigawatts of solar just last year. That means the marginal cost of your AI queries is approaching zero from an energy perspective. That's something that the US could and I think should be doing. And in fact, you know, a lot of us, again, have been pushing from a policy perspective to go in that direction. But in fact, we're seeing the opposite. We're seeing a sort of a doubling down on fossil fuels and a de-incentivization for following renewables trajectory. So I think this is a really urgent issue that we need to look at. And again, to think about what this is going to do in terms of the world that we're going to be living in just 10 years' time. If we go down the stack again, though, there are all sorts of other things you can do. For example, you could start to use far more tailored, curated data sets instead of these huge internet scale data sets. That's less energy. You could also use something like a mix of experts approach. That's, of course, what Deepseek used.

It's part of the reason that their energy demand was much lower in terms of training that model. That means instead of spinning up a gigantic general purpose model, you're actually just using small components of a model at any one time. And also just generally speaking, people are deploying huge large language models for customer service where you could just create a customer service spot for a hundred times less energy. So these are the kinds of decisions that I think we could be making. Part of the reason that's not happening is that we're in a race. And in a race dynamic, everyone feels like bigger is better, as fast as you can, get your model out before the next group does. But the question that's not being asked is, what are we racing towards? And if we're racing

towards a climate disaster, if we're racing towards a real collapse in the labor market, particularly for younger people, if we're racing towards a system where we're outsourcing our own cognition and creativity, we might want to be asking harder questions about how we build and use these technologies. Right. Where are we even going? And I think it's really important that you brought up China because the race, I mean, America has named China as its competitor in chief in this AI race. Right. And there is a lot of truth

to the national security implications of this technology and the geopolitical backdrop that it's coming online in. But I think that there is a narrative that should be corrected because the narrative right now is that it is AI and national security

or the planet, that it's this zero-sum game. And that lets tech companies get off easy because, of course, any decision-maker or world leader is going to choose national security in this moment. But that's not true. It is not zero-sum.

There are different choices in how to build or how to power AI systems, and America's biggest competitor is making those choices. So do you think it's time to change the conversation around AI and clean energy? It's not just for a moral imperative reason. It's in the name of national competitiveness. I mean, even when you look at China, I'm sure some of it has to do with climate. but it more has to do, I'm sure, with national security. I mean, they were the largest energy importer, one of them in the world.

They were very dependent on global markets and geopolitics. And they recognized that in 2010 and said, nope, we have to cut that off. So isn't, I mean, if anything, the national security argument is really strong for clean energy. So what is the U.S. not getting that China is? Honestly, I think it's ideological. I don't think we're looking at a set of rational choices. around the energy mix for the United States at this moment. And that's a real shame because we happen to be at the moment historically

where we have to make the most dramatic decisions around how we're going to be generating electricity going forward. So I think if you really talk to people in the industry, you know, off the record, they'll tell you the same thing. They know that clean energy and renewables is the only way forward, not just in a sense of virtue signaling, but in a sense of actually lowering the real cost. even for people who don't care about climate change, I don't understand how they can feel that way in this day and age, but they just look at the cost of running these models.

If you are moving towards solar, for example, that's raining down from above. That is a much easier option for you. So we could get into the weeds. There are some really interesting numbers and facts and figures around what you can do in terms of building different infrastructures. But perhaps the biggest takeaway here is there is another way. And if AI is going to produce important benefits to society, it can't be doing so at the cost of our ability to live on this planet. I would 100% agree. Even if you want to put the ideology of climate aside for people who call it that, in the name of capitalism and the name of the bottom line and in the name of national security, clean energy aligned.

I mean, this is technically probably the first time in history where national security needs, energy needs, and the needs of companies and for the planet are. are all in alignment. And these are choices that are being made to not go that route. And I think the challenges, too, with this zero sum, it's planet or AI national security is that it put individuals in an impossible position. Because now there's a growing number of people who are opting out of using AI for the sake of the planet. And that can come at a steep cost, right?

You're not learning the skills. So you're risking future employability. You could be having productivity gained in your own life. And then it's the same offloading of systemic responsibility onto the individual. It's the don't use your plastic straw and please carry your paper bag to work. And it's not reasonable to make people choose between employability and clean air because it's not a personal consumption issue. It's a structural design failure.

And these are two very different things. I couldn't agree more. And we've been here before, of course. The whole idea of the individual carbon footprint was invented by the oil industry to try and put all of that emphasis of behavioral change onto the individual. We're seeing the same happen with AI. To be super clear, I don't think this is down to individuals to try and remove their carbon footprint from the AI mix. It's simply impossible given the fact that you can't even elect whether you're using some of these systems. They're being built into

everything you use from your phone to your browser. The bigger question is, as you say, a structural one. And it really means there needs to be a lot of pressure on this industry to move towards renewables and also to start designing AI for the long term, for its sustainability, for human flourishing. And that actually means making different choices than a race against China where apparently everything is on the table and everything becomes fair game. It's a race to the bottom, effectively. So you're exactly right that what we need to look at is a much more, I think, grounded, research-led view of how AI can function. And in many ways, looking at the bigger picture, who is this designed to serve and who is bearing the costs right now? Because we're seeing in many places, not just in the United States, across Europe, India, you know, you name it. China's the same. It's actually marginalized communities who are bearing the greatest downsides of these new AI,

infrastructures. So it's going to affect individuals. It will affect communities. It'll affect politics. It'll affect the environment. So that's why I think this really is a societal set of questions, which means it has to be pursued democratically. And I'm not understanding even the lack of foresight there because it's harming the community and eventually will come back to haunt the plants there too. Right. So I mean, I think in many ways this is the problem of a race dynamic. People will think short-term and they're not thinking big picture. And in some ways, I think that's what communities are really asking for now, as they're saying, we care about where we live, we care about our community, we need our water supplies. We don't want to be paying

10x, what we currently pay for electricity. All of these questions, economic, environmental, individual are coming to a head. And I think that is pushing a different conversation about AI that is well over the year. And I think it's really important that you did mention there are choices that can be made. So again, there are different futures that can be pursued. It is absolutely not zero some from where these data centers are located, but start with maybe not in deserts, the architecture of chips, the energy choices. I mean, had the U.S. perhaps not shut down so many nuclear sites and had renewables been a bigger investment and had data centers been placed in non-water scarce areas, It is possible we wouldn't even need to be having this conversation or it would be very different.

We would have a different focus. But how do you look at the race? I think any time that there is a race that some people end up getting harmed. But the reality is that geopolitics are real in this moment. There are allies becoming adversaries. It is a stunning moment to be bringing on one of the most consequential technologies of all time in this era of distrust. So how do you even share the narrative that the race is hurting us when many people will view the outcomes of the race as existential depending on who wins? And I think that is already part of the problem of the framing.

Who wins? Who wins what? I think that really has to be asked in a much more honest way. And I think this geopolitical framing is being abused in many ways. Again, as part of my research, you know, I've traveled to a lot of labs where people are making models. I've traveled to the data centers and talk to people who are sort of installing these systems around the world. People feel as though they have to be in this highly competitive framework. But we're going to need to find pathways where people can actually share information, collaborate.

We're seeing a whole new conversation spin up around open sourcing and models. There is a desire for people. particularly in the scientific community, to think about how do we build these for everybody to be better, to be more sustainable, to be less socially harmful. Those conversations are getting shut down by the emphasis on national security.

So I think you're starting to see, particularly right now, researchers try to have a different conversation because they're seeing the real downside of this all-or-nothing thinking. And what's fascinating is that in many ways, AI systems that don't work properly for people, or a planet that continues to burn isn't of itself a national security crisis.

Exactly. Most militaries have listed climate change as a massive threat or a threat multiplier from their drones don't take off or they crash or they can't control them. They've had to move bases. So all of these things still, even if you can only look through this unilateral lens of national security, this will come back to haunt you. And if you have models that don't work well for certain communities, I mean, whichever country builds the models that do and are less harmful, that is going to be, they'll have a bigger market share. So it's this kind of roundabout cycle and it's really hard to find the logic in this moment. Well, I think it's because it's coming at a time where the sort of political thinking is really moving towards technological sovereignty and almost autarky, the idea that you can be completely. independent nation. Now, that's in complete contradiction with the world that we live in, which is

extremely interdependent, where supply chains trace around the world and invoke so many parts of every continent in order to produce something as complex as a semiconductor chip. We are living in a world that is highly interdependent, and that means this idea that you can make everything yourself, that you can be absolutely number one, and you know, you can be the tech dominant power, which is, of course, the language we're hearing, I think is really missing the sort of the deeper underlying realities. So that's the question that we need to be posing is, what is the political vision that in many cases is driving this era in AI? And how does that change? I mean, for somebody that sits in industry and academia,

and you also advise world leaders and politicians, is there overlap in the visions, or how do you see the different visions? Is there any alignment? Or do people secretly have certain conversations? But then publicly everybody feels like they have to fall in line. Well, I think we saw this really play out at President Macron's summit, you know, the big AI summit that, you know, was designed to be the place where countries come together

and talk about the shared risks as well as the shared opportunities around artificial intelligence. What we saw in a game theoretic framework, if you will, was something more like defection instead of collaboration. We saw countries really just focus on their own gathering of private investment to build their own AI infrastructures. We saw a really dramatic unwillingness to sign a shared document around what is the vision, around how do we protect communities and the planet during this enormous industrial, transformation. So I think we're at a low point in terms of real international diplomacy rather than a sense of a shared vision. And this is something we urgently need. Let's be

very clear. It couldn't be a more catastrophic moment for institutions like the UN to have, you know, such low levels of respect from certain world governments and by being constantly, I think in many ways, undermined by some political leaders. leaders. So this is the conversation that people want to be having, particularly in policy circles, is what can be done? And you're starting to see, I think, perhaps a more nationalistic focus. If we're not going to see an international conversation, how can individual nations look out for their own communities, their own environments, their own longevity over time? I think that's necessary, but not sufficient. So part of reigniting that international conversation is, I think,

really one of the most important challenges. And this is why people often say technology is neutral. And it is not. It is absolutely a product of the political, cultural, and economic environment it was born into. So AI is completely being framed through this national security lens. And that is impacting design choices, algorithm choices, energy choices, everything. Technology is never, it doesn't have to be inherently a bad thing, but it is never, ever neutral. It's really not.

And this was very much part of my motivation in writing Atlas of AI was putting these forms of technological development in a deep social and political context. And ultimately technology is politics made solid. All of these decisions are coming from political and economic priorities. So I've always said in many ways that these are fundamentally. expressions of social and political views. And that means that they can change. Technology is neither inevitable nor neutral. That means this should be a conversation that we're having much more widely. This should be a social conversation around how should these technologies be serving us and not us serving them. I completely agree. And if you're to think about why AI is

a technology that is worth fighting for. Because there are some big wins on the climate side that are happening that I think people aren't aware of. Because if you were to power AI correctly, and if we weren't so extractive with minerals, and if the data was sourced in an ethical way and applied to use cases and problems that we actually wanted to solve, that is pretty much a win-win scenario. And it is possible it's going to take a lot of different decisions. But if we were to zoom in just on some of the the winds that AI has had already in climate? Have you been seen some? Well, this is interesting. So again, in this recent paper with Sasha and Emma, we look at so many of the claims that AI will solve climate change. And it's interesting because we hear this a lot, but actually

the evidence is still very speculative. There's some interesting potential. Obviously, we're seeing AI be used to try and make electrical grids more efficient. We're seeing AI being used in satellite imagery to track where deforestation is happening. You know, these are important tools. In fact, in many ways, AI is the reason that we know that climate change is happening at all. But there's a gap between what the technology can show us and what can be implemented. What is the action that will be taken? Again, echoes of climate change.

we know what's going on. That's not the problem. It's about a political will and a social reality around how these issues are being framed. So again, I think with AI, it could be used in so many ways that I think would be profoundly helpful. But is the incentive there? Are we seeing the economic force moving companies into that direction? Or are they being incentivized to do something very different? And I think this is why you have to look at the economic realities of how these companies are being funded and what they're being incentivized to do, which at the moment is to create highly generalized models that claim that they can do everything and that in many cases are just the most resource intensive things that we have created as a species. That is the wrong set of incentives in terms of if you wanted to create small energy efficient models that solved. discrete problems. But when you look at also the dynamics of markets and economics, that to me is a gap that a startup, a nimble startup should be and probably will come in and fill. So if you're to design a really efficient system that you said can solve discrete problems, and so maybe

you're not bringing 17 Nobel Prize-winning artificial brains, if we can even call it that, to the table, you're bringing something very specific. Then, and it's maybe an on-device AI system that can solve specific problems, isn't that also a potential market winner in the future? So again, can't some of these, the question of bottom line and profit, it can also tie back to making the right choice, but it's going to take some challenge. I don't, I'm just not seeing what companies aren't fully seen in that. I think you're right. There's a lot of things that could happen. And obviously, there's still, you know, a lot of potential for alternative means of entering this market of making AI differently. But I think we also have to look at the

very real concentration of power that is going on here. We're looking at really, depending how you count, between five to ten companies that can really produce AI at scale. You're looking at really eight billionaires who are having extraordinary power over the lives of 8 billion people. That kind of concentration of power is historically, extremely rare. You have to go really quite far back to see something similar. And what you see really is forms of monarchy, forms of highly concentrated power in feudal structures. These are in every sense of the word, empires. So do you think AI is a continuation of existing power structures and empires, or it's the making of a new one?

I think it's absolutely a continuation in the sense that you can see the dynamics of companies that really have sort of shifted from being part of the personal computer revolution into being part of the internet revolution and now the AI revolution. You can see the cluster of companies actually decrease over that 30-year period. That again points to these more historical dynamics of how empires begin to acquire more more and more capacity and using technology to do so. Part of the reason that I think we need to take a much longer view on understanding artificial intelligence is that there's a tendency to get trapped in a technological presentism.

The idea that this is unlike anything that's ever happened before, that it is a completely new technology. AI is not new. There's many of the ideas behind neural nets are in fact 60 years old. And these industrial formations are also not new. So I think in some ways, you know, in recent work, we've been looking at, you know, a hundred years that led us to this position in terms of all of the industrial formations of power, the legal structures that have allowed for this sort of concentration to happen. Even that's not enough. I think you need to go three, four, five hundred years back to see how did capitalism itself emerge to create this type of winner-takes-all?

structure. And again, I think that's part of what's driving this AI race as well, is this idea that there can only be one winner. And you've done this work. So you are, you wear many hats, and one of them is artists, and you are an award-winning artist in one of your most recent installations, calculating empires, just won the Silver Lion in Venice. So congratulations. Thank you so much. Can you put into context then the moment that we're in? Are we in an inflection point, or does history show us that this is a continuation of something bigger? Actually, both of those things can be true. I think we are at an inflection point, but we've had many inflection points over the last 500 years.

So calculating empires is a very large scale installation that in some ways looks like a fresco. We sort of think historically about the ways in which humans have used massive frescoes to illustrate forms of change over time. The story of calculating empires is a genealogical. one. It's looking at that history of how empires have used technology. And, you know, we really go back to the 1500s where you see the emergence of three really important phenomena. One is the rise of capitalism. The second is the rise of European colonization with technologies like long-range shipping that was being built in places like, you know, the Venetian arsenal. These ships allowed for much longer journeys and the capture of lands and

of course, the subjugation of indigenous people. And then, of course, the creation of the world's first global information networks with books and the printing press. So these three phenomena sort of happen, you know, around about a sort of 80-year period, around 1,500. What we start to see there is a very important set of shifts that I think we're living through today. Those trajectories have continued to shift. We see it happening in the 1800s with the mass cliques. classification of humans, animals, plants, and the planet. We see it shift again with the emergence of the Industrial Revolution. Again, an important pivot point, but one that really changed how

societies were organized, even how we thought about the structure of time, moving from agrarian time to factory time. And now, of course, we're seeing a completely different sense of time with the emergence of AI. We have this set of industrial moves that would only have been possible if you are building on already a fairly concentrated infrastructure. This is why chips and giant data centers are actually now really in the hands of so few companies because they're so expensive. We're looking at a trillion dollars being spent in the next couple of years just on AI infrastructure alone. So that echoes these big industrial transformations over the last 500 years.

I think by looking at these echoes of colloquies. of colonialism, of capitalism, of militarization, that we can start to see where those patterns are being entrenched, but also where they can be challenged and where they can be unwound. And when you zoom into history, because something else happened where the private sector challenged state power at some points. So I think the East India Tea Company is an example of that, and they had their own Navy. they established their own laws in countries, and then it became a threat to Great Britain at the time. Is there a possibility that AI companies will soon threaten state power and the way we are all lining up and trying to protest

and resist certain aspects of this development, states and countries may find themselves in that line at some point, too? I think that's already happened. AI companies in many ways are already more powerful than nation states. In fact, some scholars call this the idea of the para state. These companies that in many ways are transnational in nature, are enormously capitalized, and have extraordinary power to shift policies and politics in any individual nation where they happen to have a very strong base.

Now, does that mean that people no longer have the ability? to have a say, not at all. I think we're at a really important moment right now where this has to be a public conversation. People have to realize that this is one of the last moments where you can really start to shift what these infrastructures are going to look like. So that to me is the real urgency. And so do you think for people it's as much as you can learning about what's happening, learning about this dynamic, I mean, I was really surprised that AI didn't really play a role. in the last election on many fronts, on the voter front, on the political front. I didn't really hear it mentioned from anybody.

But going forward, who we elect into office, these are really consequential decisions. And if these general purpose technologies, they change the trajectory of history. So we're making some very permanent decisions or at least semi-perman decisions in the next couple years. I mean, would you say the next three years or maybe some of the most consequential years this century? I don't like to make predictions, particularly given how quickly everything is changing right now. As an academic and a researcher, I like to do the work and then, you know, say this is what we know. But based on certainly what we've seen, do I think the next three to five years

are going to be extraordinarily significant and dramatic in terms of what's happening with infrastructure, with the centralization of power and with political shifts, absolutely. I think we're already in that moment. It really is an extraordinary period. And perhaps what worries me most around those forthcoming elections is people are getting their information more and more from AI engines, not from newspapers, not from the web. And that means that the small collection of men who can actually change what those AI engines are saying what results they're producing can shift political thinking. I mean,

we've seen Elon Musk already ask for certain changes in terms of what GROC is producing in terms of its answers around political questions. Again, we saw a huge story emerge around the way that GROC is referring to a South African genocide of particularly white South Africans. that was a political change that was made to a large language model that is producing what it claims are facts. That's just the beginning. If we're starting to see engines respond to their owners and masters by telling them this is the political reality that we want to promote, even if it's running completely against a factual basis, how are people going to know what they can trust? that represents, I think, an epistemological threat far greater than anything that we've seen from forms of propaganda in the past. That really is a type of psychological and truth-bearing power that AI companies have that people might want to really begin to question before it's too late.

And as an academic, I mean, isn't this the moment that academia shines? This is where we need the deep thinkers to come through. And I mean, what is the pathway through that? What do you think is a solution there? Well, Sheney, I think you're exactly right, that this is the moment for research, for science, for the sort of work that would actually shine a light on how we can make AI systems more sustainable, safer, and in many ways actually support communities as we go through this very dramatic period in time. The opposite is happening. We're seeing an extraordinary and I think

very dangerous defunding of science and of research, particularly the research that looks at AI's impacts. So we're seeing a moment where what we most need is actually what's being cut off in terms of the, you know, scientific mix. That is something that I think we are going to be living the consequences of for 20 to 30 years. Labs are shutting down. People are leaving the United States. And that has global consequences too. You know, in many ways, the United States has been at the forefront of funding AI research in many disciplines. So how that will impact us? I think that's the big question. And what would you say to areas like regions like Europe or Canada or just other parts of the world, the global south?

I mean, isn't this a moment where maybe new voices are going to shine or hopefully continue to shine or we can pass the baton? Because America did subsidize research for the world in many ways. And it was incredible. Research fuels the private sector. And I think maybe a lot of people don't know that. GPS, the internet, Siri, driverless vehicles, all of these go back to the U.S. government as a venture capitalists and taking bets on science and taking bets on researchers and not asking for a return on investment the way a traditional VC does. And that has been cut at the not everywhere.

So maybe there are other regions of the world where this is their moment. There's two really important things in what you're saying. The first is this reminder that technologies like AI are publicly funded. We have funded the creation of this technology that is now really, in many ways, making a very small number of humans extremely wealthy. So we have a stake in this. This really is our technology as much as anybody else's. The second point is, are we seeing a moment where perhaps we'll see a growth in research outside of the United States? Are we going to see this brain drain, which has really favored the US reverse and go the other way. We'll see Europe, Canada, Australia, India, China all start

to benefit from this influx of scientists and researchers. That's entirely possible. And I really hope we start to see, again, new voices, new research paradigms start to emerge from this sort of fracturing from a unipolar world to a multipolar world. That in many ways is perhaps the most optimistic analysis. And if you were to give people a few pieces of advice, what they can do in this moment, to increase those odds of moving towards futures that, again, I believe are possible. I don't call myself an optimist or a pessimist, because I don't think it's helpful for the work that I do, but I would not be able to wake up and do what I do if I did not think the optimistic scenarios were possible.

Right. So if there were a few things people can do in this moment, what would you recommend? I'm with you. First of all, I think the optimism, versus pessimism debate is so tired, it's so over. I think of myself as an AI realist, and I come to that position by doing the research, by being on the ground, by meeting people, by going to the sites where these systems are being built, by visiting the mines, by going to the data centers, by really finding out what is the full map of what's happening here. And I think when I look at those experiences and those journeys, what gives me hope is the fact that everything can change. Nothing that is being built right now is going to look the same way in 10 years' time.

Nothing is inevitable. And people have much more power than they think in saying this is the kind of world that we want to live in. This is how we want our community to flourish. This is what we want to happen to our own resources, to our own resources, to our own environments. And I hope that what we'll see is political leaders that will start listening to that form of power that is really in the hands of people. It's going to be a shift where obviously in an extremely difficult moment politically. But my hope is that that is the direction that people will start to take, that they will start to realize that the power really is in

their hands. And that means they can decide their political leaders for now. And that's a power that we need to use. So it has been an absolute pleasure. As I told you when you came in, this is going to be one of many because we just scratched the surface of the things that you research. So Professor Crawford, thank you so much. And we'll hopefully see you soon. It's been such a pleasure. See you soon. Thank you. Thanks so much for joining us for this episode of I've got questions. If you've got questions, we would love to hear them. Send us a message or a voice note on our website, IGQ with shenadeauvel.com. And if you've enjoyed this episode,

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