382. Empire of AI: Karen Hao On Today’s Hidden Colonialism (Ep 2)

Are modern AI companies the new empires? What is the hidden human cost behind chatbots like ChatGPT? How does Silicon Valley’s rhetoric echo the East India Company? In this episode, Anita is joined by award-winning tech journalist Karen Hao, author of Empire of AI, to explore the hidden colonialism behind the artificial intelligence boom. They discuss the rise of Sam Altman, the industry’s reliance on precarious workers in the Global South, and the similarities between the East India Company and OpenAI. Summer sale is here: get an annual Empire Club membership for an extra 20% off with code SUMMER26. That's ad-free listening, early-access, every bonus episode, and full access to our exclusive members' series. Sale ends August 31st, so grab it before summer's over. For more Goalhanger Podcasts, head to www.goalhanger.com. Email: [email protected] Instagram: @empirepoduk Blue Sky: @empirepoduk X: @empirepoduk Assistant Producer: Imogen Marriott Editor: Bruno Di Castri Social Producer: Charlie Johnson Producer: Anouska Lewis Executive Producer: Dom Johnson Learn more about your ad choices. Visit podcastchoices.com/adchoices

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  So we've got some very exciting news for you. We've sold out our first show, but we've got a second show at the Rest Is Fest in September.

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  And we have a brilliant lineup, incredibly topical one, too. We're calling it Three Ayatollahs and Iranian Dynasty.

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  Delighted to say we're going to be joined by a great friend of the show, Ali Ansari, one of Britain's foremost experts on Iranian history. He is an excellent storyteller. Ali, just tell us, what are we going to be chatting about?

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  Well, we will be exploring big questions like how is the influence of the Ayatollahs shaped Iran since the 1979 revolution? Does the Islamic Republic's grip appear to be fracturing? And what role have other nations played in shaping Iran's fate?

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  And we're going to unpack one of the most fascinating and urgent stories in the world right now, as the regime faces economic pressures, regional setbacks, and a disillusioned younger generation, but in many ways, a revitalized revolutionary guard's call. What comes next for a great civilization that has outlasted many, many empires?

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  And I can't wait to get into the discussion.

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  You can expect sharp analysis, you can expect vivid storytelling, and of course, you can expect the usual chaos that comes about when we're on stage chatting to each other.

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  The Rest Is Fest runs from the 4th to the 6th of September at London's South Bank Centre.

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• 2:43Speaker 00

  Hello and welcome to Empire with me, Anita Arndt. Now, over the last couple of episodes, you know, William and I have been telling the story of the modern day empire. It's always the one I cite as the one I'm most worried about when we do tours of empire. And we've been telling the story of technology companies like X, Amazon, Palantir, and how they really have such strong echoes with empires that we know very, very well. Particularly, the one that keeps coming into our mind is the East India Company. And, and you know, again and again, when we're talking about tech, we keep running into this same word, empire. But you know what? Someone got there before we did. Much to my chagrin. I thought this is my original thought, but no. Our guest today is the brilliant journalist Karen Howe.

• 3:27Speaker 00

  She is the author of this fantastic, fantastic book, Empire of AI. It's right on the cover there. And you know what, Karen, you've been sort of warning about this Cassandra-like, because I feel like people weren't really listening for a very long time. You were the first reporter ever allowed inside OpenAI. I'm right, that's the company behind ChatGPT?

• 3:48Speaker 05

  Deeply embedded within OpenAI. They had let in other reporters before, but it gave me more extensive access. Yeah.

• 3:55Speaker 00

  Right, okay. So lots to talk about. And it's seven years in the writing of this book, I understand?

• 4:00Speaker 05

  Yeah, it's been based on around that many years of research, although the actual book process was a very compressed one-year writing sprint.

• 4:10Speaker 00

  Right, okay. Because everyone, all your publishers say, we want it, we want it now. We don't understand what is happening here. Look, first of all, I think what we should do, because we shouldn't take it for granted that everybody lives in the world of tech and understands what AI is. So how is AI different to the kind of computer interface that we've had before that people who did the Google, would have, you know, Googling things on a daily basis, will understand? So just in a nutshell, for a very, you know, sort of, let's say uninformed audience, like my mum, maybe, what is AI that's different?

• 4:44Speaker 05

  Yeah, so I often like to say that the word AI is like the word transportation, and that it refers to a collection of technologies that can be vastly different, the way that bicycles and rockets are very, very different. And there are different design intentions behind each type of AI system. They do different things. They have different cost benefit trade-offs as well. When you used to Google search, you were actually using an older form of AI, where it was processing your query by extracting the language patterns in what you were typing into the search box, and then it was extracting the language patterns in webpages and matching them. And that's how you got the 10 blue links, and hopefully

• 5:27Speaker 05

  they were pretty relevant to what you were actually searching. Now, what we have is large language model systems, which is a different type of AI system, that is based on processing all of the language data on the internet and on books and other types of media, before you even enter a search query. And when you are talking with something like ChatGPT, with Claude, with Gemini, you are actually interfacing with the AI model directly. It's not the AI model use, like, it's not the AI model trying to surface relevant information on webpages. It's generating the answers to your query in real time based on the language patterns.

• 6:11Speaker 00

  Right. So I mean, what would I try to, would I explain it to my mum? And mum, if you're listening, I'm not using you as the archetype of people who don't know anything. But you know, the way I see, say it is that before you would have a little librarian who would run as fast as its feet would carry it to go and find you the books that existed out there. But now you've got a scribe that sits down with AI and writes it for you, depending on what it thinks is important. So you don't have the direct interface with stuff that is out there, that is proven, is removed. You're, you're kind of relying on a, on a good agent to come and write it for you. And sometimes that stuff, you know, we've heard the terms hallucinate, you know, it can make stuff up, but it's getting better at doing the job, would you say?

• 6:57Speaker 05

  I

• 6:58Speaker 00

  No, you would say no. You would say it is not, Karen. I can tell. Yeah.

• 7:03Speaker 05

  So I love your analogy. The way that I would change it is that it, there's not, it's not a scribe that's writing based on what it thinks. It's as if the scribe were just a corporate automaton and it's writing based on what the corporation

• 7:18Speaker 00

  Okay.

• 7:18Speaker 05

  The types of values that the corporation wants the scribe to write in, which is an important part of the, then why we call these, these companies empires and, and why that's a relevant analogy. And one of the problems is that the way that these technologies are designed, there, it's actually fundamentally impossible to eliminate hallucinations. And the reason is because the way that it generates text is through calculations on statistics in language, like it's, it's calculating the patterns in language and then reconstructing sentences based on those patterns. And that can sometimes lead to accurate answers, but it's actually rather a coincidence when it's accurate. And other times, you know, because

• 8:03Speaker 05

  it's not constructing things based on discrete pieces of information, it's not just constructing it based on actual understanding of meaning. And so if you have lots of patterns in your data that happen to say, you know, dogs have ears, then yes, your automaton, corporate automaton is going to generate out dogs have ears. But if for whatever reason, your data says that dogs can fly lots and lots of times, then maybe sometimes it's also going to say dogs can fly.

• 8:30Speaker 00

  Right. So I mean, that's, that's supremely important. I'm glad you did the correction because whoever owns the AI agent also owns the flavour of information that then is out there and builds up, you know, sort of, the more it's replicated, the more it's out there, the more it's going to be used and searched for. If you're not having a human running around finding you an original book, it's going to use the quantum of information that's out there. And if there is, you know, bad actor or somebody who wants to lead you a certain way, they can do it. They've got more power to do it. So I get the transportation model works perfectly. Can we talk about the empire word as well? Because, you know, you, you don't use corporation, you deliberately use empire. And tell me when that word came

• 9:14Speaker 00

  to you and why it's stuck so firmly?

• 9:16Speaker 05

  Yeah, it was not my original thinking. I first started thinking about this parallel in 2019 because I came across the work of some scholars who were already drawing parallels between the AI industry and colonialism. One book in particular was really influential, it's called The Costs of Connection. And then there was another paper, research paper, written by researchers at the University of Oxford in DeepMind called Decolonial AI. And they were highlighting the fact that, and this is pre-ChatGPT, many of the patterns that we were seeing in the development and application of AI systems was replicating these historic patterns. And I then started using the term empire when I was thinking

• 10:00Speaker 05

  about writing this into a book. And I was talking with my book editor about this analogy, and he refined it further and said, it's not like, how can we illustrate the entity that is acting or creating the colonial world order? And originally, I was thinking of the entire AI industry as one single empire, but he then pointed out to me, each company is a different empire, because an important aspect of the empire construction is that there has to be competition between empires, and they use that competition to justify why they have to continue each individually exploiting an extraordinary amount of resources

• 10:40Speaker 05

  and lands and labor. And so it was essentially, once I started reporting out the story through OpenAI's perspective, that I then identified that OpenAI, Anthropic, Google, each individual company is an empire of AI.

• 10:56Speaker 00

  Right, okay. So I mean, because I guess some people might think, you know, this sounds terrible, you know, concentrating wealth and power in the hands of a small elite, and, you know, they happen to be concentrated in one place, Silicon Valley. But how is that different from capitalist corporations of the past? And you think it is very different. And tell us why you think it's different.

• 11:18Speaker 05

  Yeah, so there are four parallels that I draw upon in the book between the empires of AI and the empires of old. The first is that they lay claim to resources that are not their own. And you could say that some corporations outside of the AI industry do that as well. But we'll get through all of the features that are unique in constellation to the empires of AI. So they take data from individuals, they take intellectual property from artists, writers, and creators. They do not ask for consent. They do not give those individuals credit. Second feature is that they exploit an extraordinary amount of labor that refers to both the workers that are part of the production of these technologies and essential to the production of these technologies, but are paid a pittance

• 11:59Speaker 05

  and rarely see any proportional value that they create for the empire. It also refers to the ways that when these AI systems are deployed, it then displaces the worker on the other end of the supply chain, and it erodes labor rights for workers across different sectors, particularly knowledge workers. The third parallel is that they control information flows in society. And this is why I was mentioning that we should think of the scribe as a corporate automaton that's actually generating text based on the corporations or the empire's values, because the companies control information both in the most fundamental levels, where over the last decade, the AI industry has become the largest bankroller of AI research

• 12:44Speaker 05

  in the world. And you could imagine if most climate scientists were bankrolled by the fossil fuel industry, we wouldn't get a clear picture of the climate crisis. And in the same way, we don't get a clear picture of the true limitations and capabilities and nature of the AI technologies they produce. So they're literally censoring and controlling the, one of the most basic layers of knowledge in our society. And then they're producing this AI technology that they seek to make the portal through which people engage with the world. So they're adding all of these value judgments, they're adding sort of a filter to people's eyes for how they want them to see the world. And the last and final

• 13:24Speaker 05

  parallel is that they engage in this narrative that they are in this existential race against an evil empire. So these companies are the good empire on a civilizing mission to bring progress and modernity to all of humanity. And if they don't exploit all this labor and extract all these resources, and they lose to the evil empire, instead of going to heaven, humanity goes to hell.

• 13:49Speaker 00

  Hmm. You see, what's interesting to me is that you use the word control a lot. And the companies themselves say, you know what, actually, look, it's on the label, OpenAI. We're fighting to make sort of information open to everybody. But what you're saying is actually, it's the complete reverse of that. Can we turn to, I mean, OpenAI in particular, and we'll talk about others as well. It is the company behind ChatGPT. You've got quite the history with the founder of OpenAI, Sam Altman, because it was back in 2019, I think you did your first kind of musings and writings about him. What was the reaction to that? First of all, tell us about the article. Was it immediately critical? Did you see the warnings? And what was the repercussion of that?

• 14:31Speaker 05

  Yeah, so I was a journalist at MIT Technology Review. I was reporting on AI for them, and I decided to do a profile of OpenAI under the advice of my editor. And so I embedded within the company for three days in 2019. And I actually didn't meet Sam Altman, because he was not that important to the company at the time. So I met the executives that were important to the company and running the day to day, and that included Greg Brockman, the chief technology officer, Ilya Sutskever, the chief scientist, and Dario Amade, who's now the CEO of Anthropic, but at the time was a director of research at OpenAI. And

• 15:10Speaker 05

  what I realized, I came into the offices taking their mission statement at face value. We're trying to ensure AGI benefits all of humanity. And I just started asking pretty simple questions in the very first interview that I did with Greg and Ilya, where I asked, what is AGI? And why are you focused on AGI?

• 15:31Speaker 00

  And AGI, AGI stands for, just remind people?

• 15:35Speaker 05

  AGI stands for Artificial General Intelligence, and it is a term that was popularized by OpenAI to refer to a theoretical AI system that could one day reach human intelligence and do everything that humans can do. And the problem with this term is it's incredibly ill-defined, because we have no scientific consensus around what human intelligence is. And we have no scientific consensus around whether or not machines could ever reach this nebulous thing that we call human intelligence. But OpenAI in its early days may put the stake in the ground and said, we think that we can build artificial general intelligence.

• 16:17Speaker 05

  And not only that, we think that we can build it within the next few years, and we are going to race to get there. And at the time, actually, it made them a laughingstock of the AI field, because a lot of AI researchers said, no serious scientist would make these outlandish claims. But then OpenAI pumped an extraordinary amount of money into this endeavor, and irrespective of whether or not you think that they are actually reaching this goal, they have certainly made being in the AI industry incredibly sexy and exciting and lucrative. And so now AI researchers are no longer laughing at them.

• 16:57Speaker 00

  So you wrote your piece, and then you got the taste of something that is far from open, like a slamdown. I mean, what happened when you came out with your article, warning people that, you know what, there needs to be, there needs to be a check, there needs to be a balance. This is a lot of power that we are imbuing one corporate entity with. What was the result of that?

• 17:18Speaker 05

  Well, at the time, the main thing that I was criticizing OpenAI for was that they just were not being transparent with the public. They had this whole narrative that they're a nonprofit, they're not advancing any commercial interests. And I discovered through my reporting that they actually had secret plans for commercialization, but they just weren't telling anyone about it. They said that they would publish all of their research and open source all of their code so that they could bring the public along in the decision-making for this transformative technology. And I found that, once again, they were not actually doing that. They were hiding a lot of their research, and they were planning on actually increasing the amount of research that they would hide. And I didn't think at the time that they would become such an important company. I did not,

• 18:03Speaker 05

  it wasn't, I didn't identify OpenAI to profile it because I thought, oh, one day it's going to launch this chatbot-like system and become, you know, the most famous company in the world.

• 18:14Speaker 00

  You're basically telling us you tripped over one of the biggest stories of the century.

• 18:17Speaker 05

  Yeah, I truly did. I truly did. And, you know, I literally, I was covering it and profiling it because it was important enough as a, one of the AI research labs to profile it, but it was not important enough to give it to the senior AI reporter at MIT Tech Review. I was the junior AI reporter. So that's how I landed the story. But I, so at the time, my profile wasn't saying there's, this is a massive amount of power concentrated in this entity. It was actually just saying, OpenAI has made some waves with some big names attached to say that it is going to usher in this transformation with the benefit of all humanity, transparently, democratically, you know, in the public interest. And I was

• 19:00Speaker 05

  like, we should seriously be scrutinizing all of these claims, because it seems that many of them are not actually true, once you dig into the internal conversations and documents.

• 19:13Speaker 00

  So when that came out, what happened?

• 19:15Speaker 05

  Yeah, so OpenAI decided that I was public enemy number one. They got, they were really unhappy, and they decided to never speak to me again, which, you know, they then changed their position on for a while, and they spoke to me again three years later. But they iced me out for three years, and wouldn't allow me to, you know, interview anyone within the organization, wouldn't allow me to get any more access.

• 19:43Speaker 05

  And I then thought, you know, I can no longer cover this company because I thought that I would need that access to continue covering it. And I, so I, I, I paused my coverage for around three years and then I went to the Wall Street Journal, became a reporter there. And then ChatGPT came out on the Wall Street Journal, said to me, you know, you've been covering AI for years, so we really need you to start covering OpenAI again. And so then that kind of thawed the relationship with OpenAI a little bit because they had to speak to a Wall Street Journal reporter.

• 20:17Speaker 00

  Right, right, because if you, if you block out a newspaper, then that's, that in itself is a, is a story. Okay, so, so just, I mean, you didn't talk to Sam Altman himself, he never sort of volunteered himself for an interview.

• 20:27Speaker 05

  He never accepted any of my interview requests.

• 20:30Speaker 00

  But 260 odd other people around him, you spoke to, uh, about him. Um, and some, I, I take it quite close to the man himself. Just paint us a picture of Sam Altman. We like to do the superhero or stroke supervillain origin story, whatever, where you want to take that. But, but tell us about what makes Sam Altman tick. Who is he?

• 20:50Speaker 05

  Sam Altman's a very controversial figure. He is lauded by some people as the greatest tech leader of our generation and criticized by other people as a great manipulator and abuser and pathological liar. And really, his career started in Silicon Valley. He, he is a creature of Silicon Valley. He's been there from the very beginning. He dropped out of Stanford University to found his first startup, which was called Looped. It was a mobile-based social media platform. It didn't do very well, but one of the features of his career is that at every stage of it, Altman is really remarkable in his ability to ingratiate himself within the centers of power.

• 21:30Speaker 05

  So despite the company not doing that well, he then found himself very strategically placed within Y Combinator, which was a startup accelerator that's still quite prestigious in the valley. And he quickly catapulted himself to running the institution and then became part of the center of mass of how Silicon Valley operates and develops this very vast network of people within tech, people that wanted access to tech, including policymakers. And the reason why he decided to co-found OpenAI was actually not necessarily because he identified AI uniquely as a next big wave of technological transformation.

• 22:14Speaker 05

  He was spraying investments across all different kinds of technologies. He was investing in nuclear fusion, in self-driving cars, in quantum and, and also AI. And when it seemed that AI was the next big rocket ship, I think that's when he sort of dedicated himself to becoming the CEO officially of this organization that he co-founded.

• 22:38Speaker 00

  Well, I mean, one of the things that is a, is a common thread is that when you read about these people in the press, there were certain stories that are, you know, very homespun, you know, like very close family network, sitting around a table, you know, busy, you know, sort of after school, parents who are very involved and then they sort of retreat into a garage. And, and you know, you've got some shades of darkness and stroke weirdness that are, that are evident. I mean, the, the weirdness is, I know his father died of a heart attack when he was very young, you know, sort of, it, it just, I think 68 odd he was. And Sam decides to turn his father's ashes into diamonds. Now, that may be a story that people are familiar with. And, and for him, that's a symbol of, you know, sort of an enormous closeness, a way

• 23:22Speaker 00

  to process loss and grief and how, you know, much, but there is a dark story running and, you know, um, we, we should be careful about it. But his younger sister has accused him of abuse, you know, just saying that he was, he's an abusive man. He's counter-suing, so we maybe leave it at that. But it, it does sort of go along with the fact that all of these people, all of these sort of emperors of tech, you know, the broligarchy, they are very anxious to put out very wholesome stories about themselves, whereas, you know, things are complicated. And in this case, in this story, very complicated.

• 23:55Speaker 05

  Yeah, I, I mean, one thing that's so interesting about Altman in particular, because I've covered many other tech leaders and what's distinct about him is that even people that have worked with him for a very long time, very closely, struggle to articulate what he actually stands for. Like what does he truly believe? They're not really sure. And I think this goes to why he has such a split, you know, some people think he's amazing and some people think he's the devil, is because he has this remarkable ability to understand people and to persuade them to lend their talent or their money to a particular endeavor.

• 24:38Speaker 05

  So he's very good at turning his vision of the future into reality. But some people who do not actually agree with that vision of the future begin to feel that he, he is manipulating them and tricking them along the way into serving a cause that they actually don't agree with. This is the case of Dario Amade, where Dario left to found Anthropic and to be a competitor of OpenAI because he felt like Altman had somehow turned his intelligence, Dario's intelligence, against the cause that he actually wanted to participate in.

• 25:17Speaker 00

  Right, and Anthropic, we should say for anyone who is going, well, who are they? I mean, they're, they're behind things like Claude, for example.

• 25:24Speaker 05

  Yeah, that's right.

• 25:25Speaker 00

  Sort of enormously successful AI that's out there. And the story of Silicon Valley is built on, you know, disgruntled workers walking out and making their own thing.

• 25:34Speaker 05

  100%.

• 25:35Speaker 00

  Okay, let's just take a break there, Karen. And when we come back, I'd really love to talk to you about the parallels of quasi-religious missions, if I can put it that way, of AI companies and how they echo the civilizing missions of historical empires.

• 25:59Speaker 06

  Everyone's got a hard question about AI. Your job, your kids, where it's going. Anthropic was built to surface those questions and share what it finds along the way because there's hope in hard questions. Ask yours at claude.ai/spotify and keep thinking. That's claude.ai/spotify.

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• 27:26Speaker 00

  Welcome back, Karen. So your, your book opens with a line from Sam Altman. Um, and it says, the best way to start a religion is to start a company. Now, was he joking or is this the truest thing he's ever said?

• 27:41Speaker 05

  I don't think he was joking at all. I think that, I mean, so this is, this is from a blog post that he wrote and Altman was a very prolific blogger in the early days of his career. And so it is quite a treasure trove of the things that he was thinking about. I, it's not 100%, you know, when you read the blog post, you should understand that Altman communicates to the public very carefully. He's always thinking about how he needs to motivate the audiences that he's speaking to. So it's not a direct correlation with what he's necessarily thinking or processing at the time. But I think this particular line where he's reflecting on the necessity of building

• 28:20Speaker 05

  a religion to mobilize the most people around a cause was him hitting upon something that he then used through the rest of his career, which is that you can get people to, to have a lot more conviction around a particular technology and to mobilize more money and more resources than ever before, if you can get them to believe in a myth first and foremost, and a mission.

• 28:43Speaker 00

  A mission. So that's, that's such an important word because again, It set bells ringing in my my head because I thought actually that is exactly East India Company, VOC, you know, all these, the Belgians, for example. They start off with, you know, it's a it's a purely economic venture, you know, they're going out, they're exploring, they're taking land, they're making money, they're sending it back. And then it becomes a civilizing mission.

• 29:06Speaker 05

  Yes.

• 29:06Speaker 00

  You know, whether it's a mission to spread, you know, Christianity or civil, just civilization. And you're saying that is exactly the thing. And that doesn't, that's not just OpenAI. I mean, that that seems to be something I hear from others too.

• 29:19Speaker 05

  Yeah, absolutely. It's not just OpenAI. I I mean, I don't think Altman was particularly clever in in in hitting upon this. This idea, I think a lot of the Silicon Valley elite had all hit upon similar ideas. Actually, in my book, I I detail some emails that were exchanged between Elon Musk, Sam Altman and Greg Brockman in the early days when they were thinking about. Co-founding OpenAI and Musk explicitly articulates in these emails, we need to think carefully about how we craft our mission statement. Because that is what's going to give us permission in the public to do all the things that we want to do. So the mission statement is both relevant for people in the public and also for then mobilizing employees.

• 30:01Speaker 05

  So it's an it's an internal and outward facing mission. And yeah, that's how Silicon Valley operates and that's how all of the empires of AI operate. They each have this civilizing mission and they each claim that they are the good guys and everyone else is the bad guy.

• 30:18Speaker 00

  Well, yeah, I know. They're they're saying they're doing the same thing, but they're at war with each other and the war isn't just technological. I mean, it's it's now very nakedly political because you've got these two massive AI giants in the form of Anthropic and OpenAI. Who are pumping tens of millions of dollars into super PACs.

• 30:38Speaker 05

  Hundreds of millions.

• 30:39Speaker 00

  Hundreds of millions, hundreds of millions and and escalating. So, um, you know, super PACs are these. Organizations which are not meant to be connected to candidates. A candidate has no say on how they spend the money that they are putting into a campaign, but they they inject money. So when it comes to these super PACs, I mean, how much money are we talking about, Karen?

• 30:58Speaker 05

  There are, by my count, four super PACs that the AI industry has set up. One is very deeply connected to OpenAI, although OpenAI denies it. One is deeply connected to Anthropic and then there are two connected to Meta. And collectively, they have raised um over two hundred million, maybe close to three hundred million now. To pump into the midterm elections.

• 31:24Speaker 00

  Right.

• 31:25Speaker 05

  The way that these super PACs works is actually modeled after a previous set of super PACs that were launched by the crypto industry.

• 31:31Speaker 00

  And crypto industry just, you know, you might have had a Bitcoin, other coins do exist, but this is sort of, it's money that you can't hold in your hand and put in your pocket. It exists in a digital form, just just to make that clear.

• 31:42Speaker 05

  Yes, exactly. And a lot of the characters that are embedded within the crypto industry are the same characters embedded within the AI industry. So suffice to say that they are both a collection of Silicon Valley elites that have extraordinary amounts of money. And then seek to deploy that money in various ways to bend society to their will. And the crypto industry was the the first faction of Silicon Valley that decided to much more explicitly play politics. And deploy their financial might in elections. And they spun up a a um a super PAC that similarly deployed around two hundred million dollars into the elections. And they were very naked about how they were going to use this money. It was if a candidate is pro crypto, we give them money.

• 32:27Speaker 05

  If a candidate is anti crypto, we use money to block their candidacy. And this was the brainchild of a man named Chris Lehane, who is now the head of global public policy at OpenAI. And he's considered the master of disaster because he was formerly working for the Clintons and helped usher the Clintons through a series of scandals. And then he hopped over to Silicon Valley, joined Airbnb, then joined the crypto industry and then joined OpenAI. And so because the crypto super PACs, they claim were incredibly successful at getting a slate of pro crypto legislators into office. And then in passing pro crypto legislation at the federal level.

• 33:12Speaker 05

  The AI industry is now taking that exact playbook. And in fact, one of the main super PACs, which is called Leading the Future, which is the one that is deeply intertwined with OpenAI. It has funding from Greg Brockman, the president, Chris Lehane was also behind it. Literally has the same cast of people running the super PAC as the former crypto super PAC. So all of these AI super PACs have have have stated that the game is they're going to find the pro AI candidates. And the anti AI candidates and boost or tank their campaigns, except for Anthropic's super PAC, which interestingly.

• 33:53Speaker 05

  Is trying to play this counter narrative game where they are deploying a significant amount of money to, they say, to boost uh people. Candidates that actually want to regulate AI because Anthropic's whole narrative is that they're pro AI regulation. But this is a narrative and it should be deeply scrutinized because at the end of the day, the way that Anthropic deploys its money is exactly the same as OpenAI. And that they're ultimately supporting the candidates that align with their agenda.

• 34:22Speaker 00

  Well, so, I mean, again, this is something that is truly an echo of of what we've seen go before in in a nation empire. So with the East India Company, you had people who went and made a lot of money through the company overseas and came back and bought themselves, you know, um, positions. Political positions, you know, rotten boroughs, you know, they could become MPs, they could. And then you have a full chorus of people who are lording. Rather than questioning the behavior of a corporate entity. And and that feels like what you're saying is happening with OpenAI, although, you know, they're fighting each other. They're also fighting for territory, political territory, as well as, you know, sort of technological and and financial territory.

• 35:02Speaker 00

  Can we talk about the, you know, if we're going to carry on this this empire analogy, I want I want to know about the people at the other end. Because you, very interestingly, haven't just haven't looked at the top of the pyramid, but you've looked at the workers in places like Kenya. Who make these systems usable. Now, first of all, that's going to surprise people that, you know, these are. These are organizations that have workers in, um, you know, the developing world or the global south who are. I mean, are they benefiting? What kind of work are they doing? Tell us a little bit about that, Karen. How are they? How are they associated?

• 35:35Speaker 05

  Yeah, one of the myths of the AI industry that allows them to perpetuate this idea that AI is this magical technology is they. They say that these systems just learn and do everything that they can by themselves. But actually, anything that an AI system can do was taught to it by humans, by data workers. Who are manually cleaning, preparing and content moderating the data that is used to train these models. And, you know, some of that, you know, the fact that ChatGPT can chat is because there were tens of thousands of workers that were prompting. A raw large language model right after it was trained on just a bunch of data.

• 36:17Speaker 05

  And getting the model to start speaking more conversationally. Based on examples of this is what human conversation looks like. Speaker A talks, then Speaker B, then Speaker A again.

• 36:30Speaker 00

  So, I mean, does that, what what does that actually look like? Is that sort of like, you know, you'll have a big warehouse full of people on terminals saying. You said this, but actually this is more human. If you're going to do this, do it this way. Is that the kind, I mean, I'm just trying to picture it in my head and I'm sure people want to picture it.

• 36:44Speaker 05

  Yeah, yeah. So so if it comes in different forms, some sometimes these workers are working in offices, but most of the time they're actually working from home. And they are remote workers that are distributed in largely impoverished communities. And they're essentially just given tasks from, they they often don't know which company that they're working for. They're just given a task by a platform that's sort of like an Uber for data work. And the task will just give them very detailed instructions saying, we would like you to write a transcript of dialogue between a father and a son. Like, imagine, imagine what a conversation would be like. And then they'd literally write, almost as if they're a playwright, they write some dialogue.

• 37:28Speaker 05

  And then it's just sent into the ether and they have no idea what that was for. But the company is then receiving all of this stuff. And then they're putting it into the models, saying, this is examples of dialogue, this is another example of dialogue, this is another. And then there are other workers that they'll send a task and the worker will just get instructions that says, write a question that you have on your mind. And you will receive an answer and you should upvote or downvote the answer based on whether you think it was a good answer. And you know, when, when, before ChatGPT came out, like, the workers who were doing this had no idea that they were doing this for an AI model. There was no conception of having a conversation with a computer. And so the instructions that they received

• 38:10Speaker 05

  were just very vague and they had no idea what they were actually contributing to.

• 38:15Speaker 00

  And may even have sounded ludicrous, like, have a, have an argument with your girlfriend, tell me how that would sound, you know, that kind of thing, you know?

• 38:21Speaker 05

  Yeah, yeah, exactly.

• 38:22Speaker 00

  But I suppose somebody listening to this might say, okay, that's fine, but that's employing people, you know, that's, that's making people wealthy. There's so many people, if there are so many people that are involved in this, particularly from, and I think the, the places that you've talked about in the book, Chile and, um, you know, Kenya, for example. They're making loads of money. This is great. You know, what's to complain about? Are they making lots of money?

• 38:42Speaker 05

  They're not making loads of money. But before we get to that, I mean, I, I've been covering these workers for seven years and the way that the work is structured makes it impossible for these workers to actually live a normal life because they just have accounts on these platforms and the platforms will send them work whenever. It's not a nine to five job. You don't know when the work is going to arrive. And in fact, you compete with the other workers on the platform to claim a task, to even get the opportunity to do that task. So I tell the story of this one woman, Oscarina Fuentes Zanaya, and she's a Venezuelan refugee based in Colombia. And she was the first worker to ever show

• 39:23Speaker 05

  me how this structure completely disrupted the rhythms of her life. Because she didn't know when the work would arrive, and then she had to snatch the work the moment that it arrived. She was tethered to her computer, day in and day out. There was one time she took a walk outside and she got a notification on her phone that a task arrived. She sprinted back to her apartment in the hopes that she would be able to claim the task before another worker did. And she got home too late. And that task could have paid her enough for her groceries for the week. And she vowed after that to never go on a walk again.

• 39:59Speaker 00

  Oh, God, really?

• 40:00Speaker 05

  Yeah. And, and she eventually figured out that the tasks only arrived during the weekdays, so she went on walks, thirty minute walks on the weekends. But I mean, this is deeply, deeply controlling work where you cannot, you know, I, I spoke with another worker in Kenya, her name is Winnie, and she would work for twenty-two hours straight once she received work. Because she thought, if I go to sleep and I wake up, the work might be gone tomorrow. And then I would have missed the opportunity to earn just a few extra dollars to put my kids on the bus for school.

• 40:37Speaker 00

  Few extra dollars. Okay. I mean, just let, let's talk about that for a bit because, you know, these are, these are enormously wealthy companies. I mean, they're, you know, richer than creases, eye-watering amounts, that they can put hundreds of millions into, into their political, um, you know, uh, persuasions as well. You know, how much is Winnie getting paid? How much is your Venezuelan refugee? How, how much are they making for this chunk of their life?

• 41:02Speaker 05

  It dramatically varies. I mean, sometimes they're making just two dollars an hour. Sometimes they're actually making, you know, a hundred dollars for the day. But the, this is kind of the, the challenge with this work is that you don't know, you just don't know, because it's dictated by the company that gave the task and also the platform, the Uber-like platform that's distributing the task. The price is set based on what they think is the, is the willingness of the worker to do the work. Um, and so that becomes a problem because they're seeking the most impoverished and most desperate parts of the world to do this work. So they can squeeze the worker by paying abysmal amounts of money

• 41:47Speaker 05

  sometimes. But it's, you know, it's ultimately not just about how much they earn. It is, it is about just the complete lack of ability for people to do any kind of financial planning, to actually know how much they will end up with at the end of the month. Because one of the things that these companies will use as a defense is that now increasingly they are employing workers in developed contexts. So the US, data annotation has become the fourth fastest growing job in the US this year. And they're also employing people in the UK, across Europe. And these companies will say that we're actually now paying, you know, fifty dollars an hour for these workers, or sometimes two hundred dollars an hour

• 42:28Speaker 05

  for these workers. And still, when you speak with those workers, and there was a survey that was conducted of workers in North America that are doing this kind of work, an extraordinary share of them have dealt with homelessness, are chronically ill, and default on their bills at the end of the month. Because even if they're being paid, it seems like a flashy number and an hourly wage, they don't know when it's going to arrive. They end up working very, very, very long hours to just cobble together enough. And at the end, with taxes and with their medical bills and with all these other things, they end up still not

• 43:08Speaker 05

  having the basic living wage left over. So there is both an extortion that happens with the pittance that sometimes workers are paid, but even when they're paid supposedly well, it still does not add up to something livable.

• 43:24Speaker 00

  So the, the other thing that goes hand in hand with empires of yore, particularly the ones that we talk about, is acquisition of land and extraction of, you know, sort of power, water, you know, whatever the natural resources are. Now, is that happening as well? And I know sometimes it's, you, you yourself have had some difficulty in this arena of, you know, sort of pegging numbers to this. But I mean, do we have an idea of, for example, freshwater? Let's talk about the most basic commodity. Are these things, are they thirsty, these data centers that are needed to run these, these corporations?

• 43:58Speaker 05

  Yeah. So the thing about the environmental impact is it stems primarily from the vast computing infrastructures that these companies seek to build. The reason why they build such vast computing infrastructures is because they base the advancement of their AI models on needing to train it on ever, ever more computing power. And they, they try to multiply the amount of computing power that they use for each subsequent generation of a model by, you know, an order of magnitude each time. And so they are acquiring vast tracts of land to build these facilities. And they need an extraordinary amount of many different

• 44:38Speaker 05

  types of resources to power and to cool these facilities. So when it comes to freshwater, the reason why these facilities, many of these facilities need freshwater is twofold. One is because of the sheer amount of energy that they, that these facilities are using. A lot of energy is produced through the consumption of fresh water. Um, this is true in the US, it's true in many parts of the world. There is a relationship between energy and water. And the second thing that these facilities need is to cool themselves down. And many facilities also use freshwater to cool. And the reason why it has to be fresh is because the facilities need the water to be clean enough to not leave behind residue that then

• 45:20Speaker 05

  corrodes the equipment over time and makes it not functional. The honest answer is we don't have good numbers on what, how much freshwater these, these data centers use because these companies are completely untransparent about those numbers. In fact, there was recently reporting out of Europe, where investigative journalists from Investigate Europe found that the EU Commission passed a directive to preserve the secrecy of these, the environmental impact of these data center facilities, because companies like Microsoft and other big tech giants lobbied the EU Commission and essentially got the EU Commission

• 46:03Speaker 05

  to draft this directive almost word for word, based on the company's interest, saying, we can't release these numbers at a data center level because it is

• 46:13Speaker 00

  proprietary information. Yeah.

• 46:15Speaker 05

  Yeah, proprietary information.

• 46:17Speaker 00

  Yeah, yeah.

• 46:17Speaker 05

  And so, you know, in, in my book, I was able to obtain some numbers for some facilities. Um, but they were based on projections of reports that the company gave, filed in, in government filings, saying like, we believe that we are going to use this amount. And oftentimes, these projections are not even accurate, once the facility is actually built and starts operating. There was a case in, um, Aragon, Spain, where The Guardian found through internal document leaks, that Amazon was actively trying to hide the amount of water use of that facility from the public as they sought to also significantly increase it over time.

• 46:59Speaker 00

  We've got, you know, unfortunately, finite amount of time. There's so many things I want to ask you. Uh, uh, like, we could talk for ages. I, I find this completely fascinating. Can we talk about something that's, you know, a, a, a physical object that is sort of at the heart of, of, of tech? And that's the, the microchip. And this is where geopolitics, I find, really interesting, because ninety percent of the world's most advanced chips are manufactured in Taiwan.

• 47:23Speaker 05

  Yeah.

• 47:24Speaker 00

  Right? Now, Taiwan itself is in the crosshairs, because China says it belongs to them, America says, hands off, it does not belong to you. It is a, a flashpoint. Every time there is a, a movement in the South China Seas, everybody holds their breaths, because could this be the thing that triggers us or tips us into a world, international conflict? So I mean, just how is it that, first of all, 90% of the world's most advanced chips are made in this one island? I mean, how did we come to this?

• 47:53Speaker 05

  Semiconductors are so incredibly complex. They are honestly an extraordinary technology. Because they're so complex and because of globalization, the supply chain for producing these things is all over the map. And it's not just the fact that Taiwan produces most of the chips in what they call fabs, or like the factories for these chips. Also, there's a Dutch company called ASML that is the only company in the world that makes a particular kind of machine that then the Taiwanese semiconductor manufacturing company needs to acquire to produce the chips. So there's actually many bottlenecks on the supply chain where there's just one company or one geography that actually has this outsized share of

• 48:36Speaker 05

  the piece for that particular part. And Taiwanese Semiconductor Manufacturing Company is, it's a, it's a company that was started many decades ago to produce these technologies. They were able to advance through a lot of R and D, their ability to, in particular, etch the wafers. So when you build a microchip, you have these silicon wafers, these incredibly thin sheets of metal that you are etching to get the chip to have all of the processing power that it can do. And the reason why our computers have gotten smaller and smaller is because the ability to etch these things has become more and more precise and more

• 49:18Speaker 05

  and more delicate. And literally, the, some of the transistors that are on these wafers are now three nanometers apart. So that's, I don't know how much a hair is. It's probably less than a hair of, of distance. And TSMC, this company, just became particularly good at that. And other companies that produce these chips are just not as good at that. So that's why this particular company, and therefore the island of Taiwan, has this, this outsized influence on the most advanced computer chips.

• 49:55Speaker 00

  I have an answer for you. Just, I've just looked it up. A human hair is approximately 80,000 to 100,000 nanometers. So if you're saying, you know, now there is a position.

• 50:04Speaker 05

  I didn't even realize that.

• 50:07Speaker 00

  Yes, you should, you should know. But, but you know, this idea of sort of all being concentrated in one place has all of the technology that these huge state actors are interested in. Again, that just makes me think of, we very recently did a series on Nutmeg. And, you know, the kind of, you know, it was in one place that you could get Nutmeg. And so you have these huge, the Dutch are after it, the British are after it. It feels like these things echo so, so distinctly. Just as we come to the end of this, there are going to be people who are saying, you know what, you lot are so miserable and you don't see that this is the next stage in evolution. You're a bunch of luddites, you're being really stupid about this. AI is going to make everybody's life better. And these

• 50:49Speaker 00

  are the people who took the risk. These are the innovators. They're the ones who put their, you know, homes on the line. They put everything on the line. And this is the next leap forward. What about the happily ever after in this? Why, why are you convinced? And I can see from your smile, you're not convinced. Why can this not lead to a happily ever after?

• 51:08Speaker 05

  Well, what I will say, so going back to this analogy that AI is like transportation, I am actually quite bullish about certain types of AI technologies. What we are critiquing and analogizing to Empire is a very particular type of AI system and the political economy that it has birthed to produce this system. And that is, you know, that, those are the large language models. Those are the corporations that are trying to accelerate these large language models and scale them ever, ever more. But there are other types of AI systems, which I call the bicycles of AI, that I want more of. I want more specialized, efficient, sustainable, democratically governed AI systems that can help advance healthcare, educational

• 51:52Speaker 05

  outcomes, actually mitigate climate change instead of accelerate climate change. So I do actually believe in the power of technology to bring us to a better world. But you, we have to ask which kinds of technologies and who controls those technologies. And ultimately, what I think will unlock broad-based benefit out of AI is if we pick the bicycles of AI instead of the rockets, so much more cost efficient and much more sustainable systems. And we also design governance structures where anyone who is impacted by a particular AI system actually has a seat at the table in holding the developers of that system accountable to their interests. That's just not what we have right now. We have a governance

• 52:37Speaker 05

  structure that echoes Empire, where just a few people get to make the decisions about how these systems are constructed and deployed in the world.

• 52:46Speaker 00

  I promise you, this is my final question, although I promised you I could ask you a million more. With the case of the East India Company, when the mutiny happened in 1857, it was deemed it was too big to fail.

• 52:57Speaker 05

  Yeah.

• 52:57Speaker 00

  And you had the government step in. Now, is this sort of landscape that we're in, where AI is the new empire, or different empires in that landscape, are they too big to fail now?

• 53:11Speaker 05

  I love this analogy because it also has helped me think about what could happen next. And I think these companies try, are trying to make themselves too big to fail. Because in the same way that the East India Company was actually deeply vulnerable, even as it seemed at the height of its power, I think these companies recognize that they are deeply vulnerable. And they do not have actually a, a long-term ability to sustain the sheer amount of spending.

• 53:37Speaker 00

  Vulnerable how? I mean, what are the vulnerabilities that you identify?

• 53:40Speaker 05

  So OpenAI has committed over a trillion dollars of spending to building out the computing infrastructures that they believe they need to train the next generations of their models. They have only been able to generate tens of billions in revenue. That is an extraordinary balance sheet, and it is not unique to OpenAI. All the AI, major AI players have this kind of balance sheet when it comes to their AI development. They do not have a business model. And so they have been trying to raise money to plug this hole while they figure out a business model from wherever they can. That's why, you know, you see extraordinary numbers about OpenAI and Anthropic raising massive, massive amounts of capital from

• 54:25Speaker 05

  private markets. That's why many of them are now preparing to go to IPO. I mean, SpaceX already went to IPO, but OpenAI and Anthropic also have announced their intentions to go to IPO because they've tapped out private markets and now they need to get money from public markets. Meta uses creative financing methods to try and get out an extraordinary amount of debt, and also then creative accounting methods to try to hide that debt on their balance sheets as a public company. So, so all of these companies are unsustainable in, in the financial sense of the word, and are trying to figure out how to make themselves sustainable financially before it's too late. But they recognize that if they don't figure

• 55:08Speaker 05

  that out, the entire company falls apart. The entire AI industry falls apart in the same way as the British East India Company. And so this is why I think these companies are dealing in more and more circular financing. They are intertwining their fortunes with one another because they're trying to become too big to fail such, such that when they potentially wobble financially, the government does step in to bail them out.

• 55:41Speaker 00

  Well, look, this has been quite the ride. Thank you, Karen. I really appreciate it. Karen How's book, yes, I mean, you're not going to sleep very well after it, but you know, it's a great read. The book is Empire of AI. Really delighted to have you on. Thanks very much for listening to Empire. If you'd like early access, ad-free episodes, or our full reading list for this episode, just join the Empire Club at empirepoduk.com. That's empirepoduk.com. Next time on Empire, we're going in a very different direction. Our series takes us to ancient Ireland and ancient Scotland at the time of Picts and Druids and Vikings. Till the next time we meet, it's goodbye.

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