Tech Won't Save Us - Data Vampires Redux: Sacrificing for AI (Episode 3)

Episode Date: September 17, 2026

Paris’ new book Hyperscale: The Ambition and Excess of Data Empires is coming out in October. To celebrate, we’re revisiting the series that planted the seeds for his deep dive into hyperscale data centers, the communities they’re affecting, and the future Silicon Valley is trying to build. This is episode 3 of Data Vampires, a special four-part series from Tech Won’t Save Us.The show is hosted by Paris Marx. Original production on Data Vampires was by Eric Wickham. Current production is by Kyla Hewson. Support the show on Patreon.Also mentioned in this episode:Preorder Hyperscale to support Paris at hyperscalebook.com.Sustainable AI Group cofounder Sasha Luccioni, Associate Professor in Economics Cecilia Rikap, former head of the Center for Applied Data Ethics Ali Alkhatib, Goldsmiths University lecturer Dan McQuillan, and Director of Research at the Distributed AI Research Institute Alex Hanna were interviewed for this episode.Interviews with Sam Altman and Brad Smith were cited.Support the show

Hello and welcome to Tech Won't Save Us. I'm your host, Paris Marks. And this month, we are continuing our re-airing of the Data Vampire series that I made about this buildout of hyperscale data centers, the demands that they have on communities, and why the tech industry is actually doing this in the first place. What is really driving them? And of course, we're doing that to mark the release of my new book Hyperscale, which looks into those very same issues and comes out on October 20th, in the US, Canada, the UK, and more broadly in Europe. If you like this series, I think you are really going to like the book as well. And of course, you can find out more information

and pre-order a copy for yourself at hyperscalebook.com. Now, I think it's actually really interesting that this is the episode of Data Vampires that happens to coincide with this week because as I'm talking to you in 2026, for the past week or so, we've been hearing a lot about the threat of AI to humanity, basically.

You know, a former worker at Anthropic came out and warned against the risks that generative AI and the AI models that Anthropic is creating, the risk that they pose for humanity itself. And continuing this myth that we've been hearing for the past few years, that AI is getting closer and closer to being super intelligent and being able to take over the world and effectively control us humans, if not wipe us all out. Now, if you've been listening to the podcast for long enough, you will know that I think that is completely science fiction. but actually this episode of the series

digs into the wider costs of these generative AI technologies and of course the data centers that power them and how the real threats are not, you know, this notion of AGI artificial intelligence or even AI superintelligence, but the actual demands of the technology, what it means for the climate,

what it means for communities, as, you know, I was talking about last week on the show, and how these are the real problems, not these kind of fantastical, science fictional future-focused solutions, all that they do is distract us from the real harms that are being caused in the present that these companies really don't want us to be focused on and that they certainly don't want regulators to be focused on. And of course, that's not to mention how I would argue there are just a lot of people in this industry who are kind of diluted and who have collectively

diluted themselves into believing that they're building the science-fictional threats that you know, they've seen in movies and novels that they encountered as younger people. And that doesn't mean that we should believe everything that they tell us about the technologies that they're building. So that's all to say, I think that this episode of the series is very relevant to what we're seeing right now. And I think you're going to really enjoy it, whether you've never heard it before or even if you heard it a couple years ago, but maybe don't remember all the details that we included in this series. You know, the thing that it opens on is while enough on its own, and you'll hear that in just a minute, but then to think about the other statements that these companies and CEOs

and, you know, just workers are making about this technology, it's wild. And that that doesn't mean that we should believe exactly what they're saying. And of course, just a final note that if you do enjoy this series, I think you're really going to enjoy my book, Hyperscale, the Ambition and Excess of Big Text Data Empires, which, as I said, comes out on October 20th, and you can find more information at Hyperscalebook.com. So that's it, please enjoy this week's episode of Data Vampires. We do need way more energy in the world than I think we thought we needed before. And I think we still don't appreciate the energy needs of this technology. That's Sam Alvin, the CEO of OpenAI, speaking to Bloomberg in January 2024 at the World Economic Forum.

In that interview, he was lightly pressed on the climate cost of his generative AI vision. And he was remarkably honest. The future he wants to realize is one that will require an amount of energy that's hard to even fathom. And all that energy needs to come on stream in record time at the same moment. We're supposed to be phasing out fossil energy in favor of less emitting alternatives like solar, wind, hydro, or, in some people's minds, a ton of nuclear energy. The good news, to the degree there's good news, is there's no way to get there without a breakthrough. We need fusion or we need, like, radically cheaper solar plus storage or something at massive scale, like a scale that no one is really planning for. So we, it's totally fair to say that AI is going to need a lot of energy, but it will force us, I think, to invest more in the technologies that can deliver this, none of which are the ones that are burning the carbon.

The way Altman talks about the massive energy demands his AI ambitions are creating is typical of tech billionaires. The climate crisis is not a political problem, but simply a technological one. And we need not worry because our technocratic overlords will deliver a breakthrough in energy technology so they can, can continue doing whatever they want, regardless of whether it makes any real sense to do so. Even though Altman refers to this as good news, it's hard to see it that way. He's basically acknowledging that warming far beyond the 1.5 or 2 degrees Celsius limit we're supposed to be trying to keep to is essentially locked in because of industries like his own, unless they come up with a technological breakthrough in time. There's no guarantee that will happen,

and in fact, it's highly likely it won't. That's why so many of their scenarios assume we're going to overshoot on emissions, but hope we'll be able to use some future technology to pull all those greenhouse gases back out of the atmosphere. And again, another massive gamble with the planet and everything that lives on it. But in the interview, Altman wasn't just candid on how much energy the widespread rollout of generative AI will require, but also about that more grim scenario. I still expect, unfortunately, the world is on a path we're going to have to do something dramatic with climate, like geoengineering as a band-aid, as a stopgap. But I think we do now see a path to the long-term solution. Altman and his fellow AI boosters want us to gamble with the

climate to such a degree we have to try to play God with weather systems, all so they can have AI companions and imagine that one day they might be able to upload their brains onto computers. It's not only foolish, it verges on social suicide. And I don't think that's a trade-off that many people will openly accept. This is Data Vampires, a special four-part series from Tech Won't Save Us, assembled by me, Paris Marks. Over the course of this series, we'll learn more about data centers and the extreme vision of the future these powerful people in the tech industry are trying to foist on us, regardless of whether we want it, or whether it will even make the lives of most people any better.

In this week's episode, we'll be digging into how generative AI hype is accelerating the data center buildout and presenting a series of threats from worsening climate catastrophe to further social harms. That will only become more acute, the longer this is a little. allowed to continue. This series was made possible by our supporters over on Patreon, and if you learn something from it, I'd ask you to consider joining them at patreon.com slash tech won't save us, so we can keep doing this important work. Plus, enjoy premium full-length interviews with the experts I spoke to for the series, and I put them together in a special collection whose link you can find in the show notes. Become a supporter at patreon.com slash tech won't save us today. So with that said,

let's learn more about these data vampires, and by the end, maybe we'll be closer to driving a stake, through their hearts. Since the release of ChatGBTGBT in November of 2022, talk of artificial intelligence or AI has been everywhere. Let's be clear, AI is not a new thing. The term has been in use for decades and has referred to different things since then. When you type a message on your phone and the keyboard suggests the next word, or when you're putting together a document in Microsoft Word and a squiggly line appears beneath the word to tell you it spelled wrong, that's AI too. It's just not the same kind of AI as what powers the chatbots and image generators that are all the rage today. That's, generative AI, and it's what's fueling a lot of these problems.

Sasha Luchoni is the co-founder and chief scientific officer at Sustainable AI Group, and I asked her why this new generative AI is so much more computationally intensive. This is what she told me. If you compare a system that uses, I guess, extractive AI or good old-fashioned AI, to search the internet and if I do an answer to your question, it's essentially converting all these documents, all these webpages from words to numbers. And when you're searching for a query, like, I know, what's the capital of Canada, it will also convert that query into numbers using the same system.

And then matching numbers is super efficient. This stuff goes really, really fast. It uses no compute at all. It's like you can run on your laptop. You can run anywhere. But if you're using generative AI for that same task, instead of finding existing text numbers, it's actually generating the text from scratch.

And I guess the advantage, quote, unquote, is that instead of just getting Ottawa, you'll get like maybe a full sentence, like the capital of Canada is Ottawa. But on the flip side, the AI model is generating each one of these words sequentially. And so, like, the longer the sentence, the output, the more compute it uses. And, you know, when you think about it for tasks, especially like question answering, like finding information on the internet, you don't need to make stuff up from scratch. You don't need to

generate things. You need to extract things, right? So I think fundamentally speaking, what bothers me is that, like, we're switching from extractive to generative AI for tasks that are not meant for that. So basically, there's a lot more work that goes into generating text or images than simply trying to identify what you're looking for. These generative AI tools are built on general purpose models that were trained on almost any data these companies could get their hands on, often by taking it off the open web. That includes everything from Hollywood movies and published books to paintings and drawings made by all manner of artists, and even many of the things you or me have posted on social media and other parts of the web over the years. And the vast majority

of that data was taken without anyone's permission. Now, it forms the foundation of the AI tools and models that kicked off all this hype, and that have companies of all sorts rushing to adopt generative AI and push it onto regular users, regardless of whether it's really necessary for the task they're trying to accomplish. And that all comes with a cost. When you're switching between a good old-fashioned extractive AI model to a generative one, like how many times more energy are you using? We found that, for example, for question answering there's like 30 times more energy for the same task for like answering a question. And so what I really think about is like the fact that so many tools are being switched out to generative

AI, like what kind of cost does that have? Someone recently was like, oh, I don't even use my calculator anymore. I just use Chad GPT. And I'm like, well, that's probably like 50,000 times more energy. Like I don't have the actual number, but you know, like a solar powered calculator versus like this huge large language model. Nowadays people are like, I'm not even going to search the web. I'm going to ask Chad GPT. I'm not going to use a calculator, right? All of that, what the cost to the planet is. And for all that energy, there's no guarantee the outcome is even going to be better or more accurate. As Sasha explained to me, these tools operate not based on understanding, but probabilities. Again, think of when the keyboard on your phone is suggesting the next word.

It doesn't know what you're doing. It's using probabilities based on the data it has to see what word has the highest likelihood of coming after what you've already written. That's why we so often see examples of chatGBT and other chatbots generating completely incorrect outputs. There's no real understanding there. despite how often tech CEOs try to make us believe their large language models are on the cusp of sentience like a human being. But for those generative AI tools to work, they need a ton of computation, which is why Sam Altman says we either need a technological breakthrough in energy technology or to start geoengineering the planet. The notion of scaling the tech back is unacceptable.

But there are only a small number of massive companies that have access to nearly the amount of computation to properly compete in the generative AI game, which is why the massive tech companies, especially Microsoft and Google, have become so involved. They're not only providing the cloud infrastructure to power the janitor of AI hype, they're also making sure they have a lot of influence over the startups finding success in this financial cycle. Here's Cecilia Ricap, the University College London professor from the first episode in the series explaining how that works. In 2019, Microsoft decided to invest $1 billion in Open AI. Of course, Microsoft, with all the profits it makes annually, has a lot of liquidity and can decide to invest in

many different things. But Big Tech in particular, have decided to pour a lot of money into the startup world as corporate venture capitalists. So Microsoft did this with OpenAI, but the main motive is not financial. It's not that they want to make more money just like by investing in the company. But the way to make more money, it's actually about how Open AI is developing technology, what technology Open AI was working on and how Microsoft can steer that development. And by doing it, you can eventually get access to that technology earlier. So you can adopt it earlier, as Microsoft did with Open AI. But you eventually may also be able to make extra profits if the company you invested in is successful and starts developing a business.

In early 2023, Microsoft invested another 10 billion into Open AI. But Semaphore reported months later that a significant portion of that investment wasn't in cash, but credits for Microsoft's Azure cloud computing platform, what OpenAI needed to try. train its models and run its business. The company is reportedly losing $5 billion a year, but can continue to operate because of the support of powerful and deep pocket benefactors like Microsoft. On top of that, Microsoft, Amazon, and Google have effectively rated the talent at inflection AI, adept AI and character AI, respectively, to the degree that regulators are investigating them. Meanwhile, Amazon and Google have both put billions of dollars into Anthropic,

and Microsoft has an investment in Mistral AI. This ensures that on its face, the AI ecosystem looks like there are a bunch of new tech companies rising, but those companies are still completely dependent on the dominant players, not just for funding, but also for computation. There's one more angle of this. Cecilia pointed out to me, though. Yes, generative AI is dependent on the centralized computation of major cloud providers, by the hype around it and the perception that if companies adopt it, they'll see their share prices rise, has accelerated its adoption, and by extension the demand for computation and the energy and water needed to run all those data centers. Just because everyone is talking about AI these days, as a big company, you don't want to be left

out. Basically, what has happened is a much faster adoption, not only of generative AI, but widely of the cloud and widely of all the different forms of AI. And because behind all this, we have the power of Amazon, Microsoft, and Google, not only because of the cloud, but also because they have been investing as venture capitalists in pretty much every single AI startup in the world, they keep on expanding not only their profits, but also their control over capitalism at large. So in a way, it has its own specificities, but if we want to put it just in a nutshell, it has fast forward something that was cooked from way before it. So in short, the AI boom isn't just creating the stock market bubble and allowing companies like OpenAI to rise up the rank.

with the support of the existing dominant tech firms. The growth of generative AI isn't a challenge to companies like Amazon, Microsoft, and Google. It further cements their power, especially as other companies, non-tech companies, adopt it. Because every time they do so, they're becoming more dependent on the cloud businesses of those three dominant firms, further increasing their power, their scale, and driving a further build-out of major data centers across the world. And as we've touched on in the previous episode, all of that comes with a massive environmental impact. For quite some time, tech companies have wanted to be seen as green. In the picture they painted,

digital technology was clean and green, the sustainable alternative to the dirty, polluting industrialism of the past. That was always more marketing campaign than reality, though, as the internet doesn't emerge out of nowhere. All the technologies that underpin it have serious material consequences that create plenty of emissions and environmental damage of their own. But as efforts were ramping up to tackle the climate crisis, they wanted to keep that image alive. The most ambitious, the most thing we're saying today is, as you just mentioned, we will be carbon negative as a company by 2030, not just for our company, but for our supply chain, for our so-called value chain. And by 2050, we will remove from the environment all of the carbon that Microsoft has emitted either directly

or for electrical consumption since we were founded in the year 1975. That's Brad Smith. He's the president of Microsoft, and that clip is from an interview he gave to Bloomberg back in January of 2020. was rolling out a new climate pledge. It would not just achieve net zero emissions, but become carbon negative within a decade. The company called this a carbon moonshot, indicating it was ambitious, but a goal they thought they could achieve. Well, that was before generative AI became the next big thing of virtually everyone in Silicon Valley felt they had to chase, and that Microsoft saw could significantly expand its cloud business. Here's Brad Smith again in May 24 this time. You know, in 2020, we unveiled what we called our carbon moonshot, our goal of being carbon negative by 2030.

That was before the explosion in artificial intelligence. So in many ways, as I say across Microsoft, the moon has moved. It's more than five times as far away as it was in 2020. If you just think about our own forecast for the expansion of AI and its electrical needs. Yes, you heard that right. The moon had moved five times farther away in just a few years. That was a generous way of saying Microsoft's climate pledge was sacrificed on the altar of market ambition. Between 2020 and 2023, Microsoft's emissions were nowhere near going negative. They'd actually soared by 30%, in large part because of all the data centers it was building and continued to build through 2024. Google wasn't any better. Despite making a carbon neutrality pledge of its own, it announced in 2024 that its emissions were up four. 48% over just five years, once again fueled by data centers. I'm just worried that once all the dust settles, if the dust settles, if there's no new paradigm that gets invented in the meantime, that we're going to look back and be like,

oh, oops, like that was a lot more like carbon than we expected. And I mean, historically as a species, we have a tendency to do that, like retroactively look back and be like, oh, this was worse than for the planet than we expected. There are already signs Sasha's worries may be coming true. In September 2024, the Guardian looked over the emissions figures of the major tech companies and found what they were reporting didn't reflect what the numbers actually showed. The collective emissions of the data centers control by Microsoft, Google, Mehta, and Apple were 662% higher than what the companies claimed. When Amazon's data centers were included, the combined emissions of those five companies' facilities would make them the 33rd highest emitting country in the world, just ahead of Algeria. and that's just for their data centers that existed up to 2023.

But why can these companies claim to emit so much less than they really do? One expert the Guardian spoke to called it a form of creative accounting. Basically, they buy a bunch of offsets and act as though, having done so, means their emissions have been negated. Probably the most important of those tools are renewable energy certificates, which shows they've bought renewable energy that can be produced at another time of day or on the other side of the world. As long as it was generated somewhere, the companies use it to pretend they didn't actually

generate the emissions, they very much did add to the atmosphere. And some tech companies are lobbying hard to ensure the rules on carbon accounting are rewritten to make it look like they're emitting way less than they really are. According to reporting by the Financial Times, Amazon and Meta are leading the charge to ensure their deceptive accounting mechanisms are legitimized by the greenhouse gas protocol, which is an oversight body for carbon accounting. Google is pushing a competing proposal that would force companies to at least buy renewable certificates that are closer to where they're actually operating, but still relies on offsets at the end of the day. Companies like Amazon say even that would be too expensive.

Matthew Brander, a professor at the University of Edinburgh, who spoke to the Financial Times, gave a pretty good example to show why this is all so ridiculous. He said, allowing companies to buy renewable certificates is like if you paid a fitter colleague of yours for the right to say you bite to work when you really drove your gas-powered car. It's foolishness, but this is how they're planning to keep expanding their data center networks while claiming they're reducing, if not eliminating, their emissions. It's a recipe for disaster on a global scale.

We've talked a lot about why AI is using a ton of computation and further fueling the climate crisis, but what is all the compute we're putting into it really achieving? Maybe there's a world where all those resource demands are justified because the benefits are so great. And indeed, that's what tech CEOs like Sam Altman or supposed luminaries like Bill Gates would have us believe. But the truth is that the rollout of this technology only presents a further threat to much of the public. We're used to hearing about AI as forming the basis for a series of tools that can do all manner of tasks, but I was struck by how two of the people I spoke with described the broader project that AI seems to be part of when you consider who is developing it and how it's actually being deployed. Let's start with Ali Al-Katib. He used to be the head of the Center for Applied Data Ethics at the University of San Francisco.

When I asked him how he would describe AI, he began by noting how the term itself is decades old, but there was a troubling through line between its various permutative. over the years. I think the thing that we would all recognize all the way through continuously, like is the techno-political project of taking decisions away from people and putting consequential, life-changing decisions into a locus of power that is silicon or that is automated or something along those lines, and redistributing or shifting and allocating power away from collective and social systems and into technological or technocratic ones. And so this isn't really like a definition of AI that I think a lot

of computer science people would appreciate or agree with. But I think it's the only one that, again, if you were a time traveler, kind of like going back 20 years and then 20 more years and then 20 more years, you would see totally different methods, but I think you would see basically the same goals, basically the same project. Ali's description is unlike anything you'll hear from industry boosters who want you to see AI as a way to improve many aspects of human life or on the extreme end thinking it could end humanity if not done right. They don't want to talk about that more political angle, the way it's used to cement their power in a way that can be harder to immediately identify than, say, the outwardly authoritarian actions of a politician or leader. AI much more

quietly erodes the power of much of the public over their own lives, taking away their autonomy by shifting decisions to unaccountable technologies and the people who control them. This is something Dan McQuillan, a lecturer at Goldsmiths University, and author of resisting AI, identified too. AI is a specific in our faces example of a general technological phenomenon which claims to solve things technically. And we see that across the board from tricky social issues all the way up to the climate crisis. But I think that that sort of diversion aspect is really an important aspect of contemporary AI exactly because the issues are so urgent and other forms

of collective, social, grounded community action and worker action are so urgently needed, that's something that successfully, even semi-successfully diversers from those things is extremely toxic. So I'm really talking about their AI as a narrative, AI as an idea, AI is a real technology that appears to do certain things, you know, that can emulate certain things or synthesize certain things in a way that provides people with a plausibility argument that maybe this could fill the whole in health services or education or whatever. So that's the technology. In Dan's telling, AI isn't just a digital technology made up of complex algorithms and underpinned by the material computational infrastructures that drive it.

It's also a social technology, one that's deployed so the powerful can claim to be addressing what are very pressing problems in society, the lack of health care, inequitable access to education, growing poverty and inequality, not to mention the accelerating climate crisis, without having to actually take the extent of the difficult political measures that would really be necessary to tackle them. Measures, the elites in our society likely don't want to see taken in the first place, as it might erode their power and certainly require their wealth to be taxed at much higher rates. Instead, AI, like too many other digital technologies, can be presented as a seemingly apolitical

solution. It doesn't require a sacrifice and doesn't challenge the hierarchy of capitalist society. Indeed, if anything, it further solidifies it in place. And all we need to do as a public is have a little patience as our saviors in the tech industry perfect their techno fixes so they can deliver us a digital utopia, which it probably doesn't need to be said, it never actually arrives as those deeper issues just keep getting worse. There are many harms we can talk about with generative AI, and some of the more common forms of it, too. We could talk about how companies are stealing all this data and using it to harm the prospects of workers in different industries, like in visual media, writing, journalism, and more. Or we could talk about the waves of AI generated bullshit

flooding onto the web, some with malicious intent like non-consensual, deep fake, and AI nudes, but much more of it being made just to try to make a buck through social media engagement or tricking people into scams. Those things are important, but the deeper issue to me seems to be those that Ali and Dan are describing, in which Alex Hanna, the director of research at the Distributed AI Research Institute, outlined in a bit more detail when I spoke with her. The other harms that we see of are these things replacing social services and becoming very automated, whether that's kind of in terms of having medical services being replaced by generative AI tools. We've seen this with like Hippocratic AI and the way that they say they want to basically take nursing

that has to do kind of checking up on patients, doing follow-ups to be replaced by an automated agent. We're seeing this in kind of the replacement for lawyering services and the ways in which people that don't have means are going to have these things. things poised upon them. We're seeing more and more at the border intense amounts of AI and automated decision-making with biometrics that is not necessarily generative AI, but there are other kinds of things that could be looped in with generative AI, which are used at the border. Healthcare, education, legal access, virtually anything that happens on the border, and the list of all the places they're trying to falsely present AI as a solution goes on. Ultimately, generative AI is another one of the tech industry's financial bubbles, where its leading figures hype up

the next big thing to drive investment and boost share prices until reality starts to creep in and the crash begins. We saw it most recently with cryptocurrencies and NFTs, but there are already questions about how long the generative AI bubble is going to last with everyone from Goldman Sacks to Sequoia Capital, starting to join the existing chorus of critics in calling out the aspects of generative AI that are clearly inflated and poised to crash. Even after that crash, generative AI won't fully go away, just as other forms of AI have stuck around as well. It won't be everywhere, or have the widespread implementations the company's promised. But that doesn't mean there still won't be threats that emerge from its ongoing presence.

As Ali explained to me, we'd be foolish to think it can be seized and redirected to mostly positive ends. If people are designing these systems to cause harm fundamentally, then there kind of is no way to make a human-centered version of that sort of system. In the same way, legislation that makes it slightly more costly to do something harmful doesn't necessarily fix or even really discourage tech companies that find ways to amortize those costs or kind of absorb those costs into their business model. One example that I think I've given recently in like conversation was that there are all sorts of reasons or all sorts of powers

that cause us to behave differently when we're driving on the streets because as individual people, the costs of crashing into another car or of hitting a pedestrian or something like that are quite substantial for us as individuals. But if a tech company that's developing autonomous cars is going to put 100,000 or a million cars out onto the streets, it really behooves them to find a way to legislatively make it not their fault to hit a pedestrian, for instance. And so they find ways to sort of defer the responsibility for who ultimately caused that harm or who takes the responsibility for whatever kind of incident or whatever. And so that creates like these really wild perverse incentives to find ways to sort of consolidate and then offload responsibilities and consequences

for violence. And I just don't see a good way with design out of that or even with a lot of legislative solutions and everything else like that. When the harms are acknowledged, the discussion around AI is about how to properly regulate it. But even then, all too often, the conversations about those regulations are dominated by industry figures who shape the process and sometimes even present outlandish scenarios like AI presenting a threat to the human race itself to completely sidetrack the discussions. The idea that maybe some of these technologies shouldn't be rolled out at all, or that some use cases become off limits, become harder to contemplate because the narrative we have about digital technology is that once the tech is out in the world, it can never be reined in again,

a perspective that not only feels defeatist, but is clearly proliferated by the industry to serve its own interests and prevent any public discussion or democratic say over our collective technological future. In my view, that can't stand, either when it comes to AI or to data centers, because that's the other piece of this discussion about AI and the bubble currently fueling it. Once the crash comes, the generative AI might not fully go away, but neither will the infrastructure that's been built to support it, namely all those massive, hyperscale data centers. The data centers are not going be decommissioned. They're this huge capital expenditure. It's a fixed asset. They're going to try to do something with them. Data such that I'm not going to go away of malls, which like

malls are now just skeletons of the former cells. There's going to be a demand for computation, but maybe it's not AI. And that's going to have lasting environmental impacts. What uses will all that additional computation be put to? It's hard to say for now, but we can be pretty certain it won't be for the social good, but rather will expand corporate power and further increase the profits of Amazon, Microsoft, and Google. We started this episode with an honest but troubling statement from Sam Altman, that the future he imagines, where generative AI is integrated through society, regardless of whether it truly has a beneficial impact, will require an unimaginable amount

of energy, and that means we either find a breakthrough in energy generation, or we begin geoengineering the planet. The notion that maybe his vision for the future isn't the ideal one, or the one the rest of the public might not agree to, cannot be fathomed. This is the path that he and many of his powerful buddies in the tech industry want to put us on, and thus it must be pursued. The rest of us do not have a say. We must simply accept it and hope it works for us. But that's not good enough. Dan argues this isn't just about AI or data centers, but something greater, and I tend to agree with him. It's a remaking of society by the new dominant group, who not only wanted to function in a certain way, but also want to protect their privileges and are willing to deploy their vast

power and wealth to realize it. I have a feeling that this stuff, once the sort of inflationary effect does whatever it does, bursts or in some way or deflates in some way, will tend to condense around the things that were my original set of concerns, which were that AI is really a part of another restructuring, you know, in the same way that neoliberalism was a restructuring. I have a feeling that we're living through another phase of restructuring, driven by and attempt of the sort of hegemonic system at the moment that isn't currently, you know, reaping the most benefits out of this world, of our social structures and of the wider global arrangements, neoliberalism has kind of run out of steam, it's fracturing, there's a need for restructuring,

and there's no desire to involve any kind of social justice or redistribution in that restructuring. So, you know, we've got to find both a mechanism and a legitimation of what we're going to do instead, and AI is one of the candidates for that. And I think despite the fact that generative AI is demonstrably bullshit, it's still going to serve some kind of function in that, whether we like it or not. A restructuring sounds about right. And it's not just one that doesn't consider the broader concerns of the public. It's one we have little say in. The effort to roll out these AI technologies, ensure digital technology is at the core of everything we do,

and increase the amount of data collected and computation required is wrapped up in all of this, as are the social harms that are already emerging from it, and the broader climate threat presented by the intense energy demands. It's no wonder communities are pushing back locally, but stopping these data centers and the new world they're designed to fuel will require even more. Next week, we'll explore this ideology more deeply and what another path might look like. Data Vampires is a special four-part series from Tech Won't Save Us,

hosted by me, Paris Marks. This original series was produced by Eric Wickham, and updates were made by our producer, Kyla Hewson. This series was made possible through the support from our listeners at patreon.com slash tech won't save us. We've already uploaded the uncut interviews of some of the guests I spoke to for this series exclusively for Patreon supporters.

So make sure to go to patreon.com slash tech won't save us to support the show and make sure to order your copy of Hyperscale, the ambition and excess of big techs data empires. You can find more information about that at Hyperscalebook.com.