Data Vampires: Sacrificing for AI (Episode 3)

[MASSIVE DEMAND]

SAM ALTMAN: 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 Altman, the CEO of OpenAI, speaking to Bloomberg back in January 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 power.

SAM ALTMAN: 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 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 — 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.

SAM ALTMAN: I still expect unfortunately the world is on a path where we’re going to have to do something dramatic with climate, like geoengineering as a band-aid, as a stop gap, 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.

[INTRODUCTION]

This is Data Vampires, a special four-part series from Tech Won’t Save Us, assembled by me, Paris Marx.

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 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/techwontsaveus so we can keep doing this important work. New episodes of Data Vampires will be published every Monday of October, but Patreon supporters can listen to the whole series today, then enjoy premium, full-length interviews with experts that will be published over the coming months. Become a supporter at patreon.com/techwontsaveus today!

So, with that said, let’s learn about these data vampires and by the end, maybe we’ll be closer to driving a stake through their hearts.

[COMPUTATIONALLY INTENSIVE AI]

Since the release of ChatGPT in November 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 a word to tell you it’s 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 Luccioni is the Climate Lead at Hugging Face, and I asked her why this new generative AI is so much more computationally intensive. This is what she told me.

SASHA LUCCIONI: If you compare a system that uses I guess extractive AI or good old fashioned AI to search the internet and find you an answer to your question, it’s essentially converting all these documents, all these like web pages from words to numbers. And when you’re searching for a query, like what’s the capital of Canada, it will also convert that query into numbers using the same system and then matching numbers is like super efficient. This stuff goes really, really fast. And it uses no compute at all. It can run on your laptop, it 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 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 the longer the sentence, the output, the more compute it uses. And when you think about it for tasks, especially like question answering, 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. So, I think fundamentally speaking, what bothers me is that 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.

SASHA LUCCIONI: When you’re switching between, a good old fashioned extractive AI model to a generative one, how many times more energy are you using? We found that, for example, for question answering, it was like 30 times more energy, for the same task for answering a question. And so what I really think about is the fact that so many tools are being switched out to generative AI. What kind of cost does that have? Someone recently was like: Oh, I don’t even use my calculator anymore. I just use ChatGPT. And I’m like: Well, that’s probably like 50,000 times more energy! I don’t have the actual number, but a solar powered calculator versus this huge large language model. Nowadays people are like: I’m not even gonna search the web, I’m going to ask ChatGPT; I’m not going to use a calculator, 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 ChatGPT and these 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 generative 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 Rikap, the University College London professor from the first episode in this series, explaining how that works.

CECILIA RIKAP: In 2019, Microsoft decided to invest $1 billion in OpenAI. 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 OpenAI is developing technology, what technology OpenAI 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 OpenAI, 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 OpenAI, but Semafor 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 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-pocketed benefactors like Microsoft. On top of that, Microsoft, Amazon, and Google have effectively raided 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, but 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.

CECILIA RIKAP: 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 profit, 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 forwarded something that was cooked from way before.

So, in short, the AI boom isn’t just creating this stock market bubble and allowing companies like OpenAI to rise up the ranks — 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 buildout of major data centers across the world. And, as we’ve touched on in previous episode, all of that comes with a massive environmental impact.

[CLIMATE IMPACTS]

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, 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.

BRAD SMITH: The most ambitious thing we’re saying today is, as you just mentioned, we are going to be carbon negative as a company by 2030 — not just for our company, but for our supply chain, for our 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 2020. Microsoft 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 become the next big thing 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 2024.

BRAD SMITH: 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 emissions were nowhere near going negative; they’d actually soared by 30 percent, 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 earlier this year that its emissions were up 48 percent over just five years, once again fueled by data centers.

SASHA LUCCIONI: 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. That was a lot more carbon than we expected. And I mean, historically as a species, we have a tendency to that, to 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, 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 controlled by Microsoft, Google, Meta, and Apple were 662 percent higher than 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 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 biked 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.

[HARMS OF AI]

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 Alkhatib. 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 permutations over the years.

ALI ALKHATIB: 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 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.

DAN MCQUILLAN: AI is a specific, very in our faces example of a general technological phenomenon which claims to solve things technically. And you know, we see that across the board from tricky social issues all the way up to the climate crisis. But I think that sort of divergent 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 or even semi-successfully diverse from those things is extremely toxic. So I’m really talking about there AI as a narrative, AI as an idea, AI as 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 hole 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 infrastructure that drives 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 healthcare, 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 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 technofixes so they can deliver us a digital utopia — which, it probably doesn’t need to be said, 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 deepfake 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, and which Alex Hanna, the Director of Research at the Distributed AI Research Institute, outlined in a bit more detail when I spoke with her.

ALEX HANNA: The other harms that we see of are these things replacing social services and becoming very, 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 any nursing that has to do with checking up on patients, doing follow ups, to be replaced by an automated agent. We’re seeing this in the replacement for lawyering services, and the ways in which people that don’t have means are going to have these things possibly foisted 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 Sachs 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 companies 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.

ALI ALKHATIB: If people are designing these systems to cause harm fundamentally, then there 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 absorb those costs into their business model. One example that I think I’ve given recently in 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 should be off limits become much 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.

ALEX HANNA: The data centers are not going to be decommissioned. They’re this huge capital expenditure. It’s a fixed asset. They’re going to try to do something with them. Data centers are not going to go to way of malls, which, like malls, are now just skeletons of their former selves. There’s going to be other way 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 whatever will expand corporate power and further increase the profits of Amazon, Microsoft, and Google.

[IDEOLOGY]

We started this episode with an honest, but troubling statement from Sam Altman: that the future he imagines where generative AI is integrated throughout 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 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 want it 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.

DAN MCQUILLAN: 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 an 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 wider global arrangements. Neoliberalism has kind of run out of steam. It’s fracturing. There’s a need for restructuring. There’s no desire to involve any kind of social justice or redistribution in that restructuring. So 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.

[OUTRO]

Data Vampires is a special four-part series from Tech Won’t Save Us, hosted by me, Paris Marx. Tech Won’t Save Us is produced by Eric Wickham and our transcripts are by Brigitte Pawliw-Fry. This series was made possible through support from our listeners at patreon.com/techwontsaveus. In the coming weeks, we’ll also be uploading the uncut interviews with some of the guests I spoke to for this series, exclusively for Patreon supporters. So make sure to go to patreon.com/techwontsaveus to support the show and listen to the rest of the series, or come back next week for episode 4 of Data Vampires.