Silicon Valley Insider EXPOSES Cult-Like AI Companies | Aaron Bastani Meets Karen Hao

I went to school with a lot of the people that now build these technologies. I went to school with some of the executives at OpenAI. I don't find these figures to be towering or magical. Like I remember when we were walking around dorm rooms together in our pajamas, and it instilled in me this understanding that technology is always a product of human choices. And different humans will have different blind spots. And if you give a small group of those people too much power to develop technologies that will affect billions of people's lives, inevitably that is structurally unsound.

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Artificial intelligence is the backbone of some of the biggest companies in the world right now. Multi-trillion dollar companies can talk about nothing else but AI. And of course, whenever it's discussed in the media by politicians and civil society, it's compared invariably to the steam engine. It is going to be the backbone for a new machine age. Some people are really optimistic about the possibilities it will bring. They are the boosters, the techno-optimists, the technoutopians. Others are doomers. They're down on AI. AGI, artificial general intelligence, it's never going to happen. And if it does, well, it's going to look like The Matrix or maybe even the Terminator and Skynet. We don't want that, do we?

Today's guest, however, is not speculating about the future. Instead, they're very much immersed in the present and indeed the recent past of the artificial intelligence industry. Karen How went to MIT. She studied mechanical engineering. She knows the STEM game inside out. But she made a choice to go into journalism and media to talk about these issues with a fluency and a knowledge that very few people have. Rather than speculate, what Karen has done with this book is talk to people in the field. 300 interviews with 260 people in the industry, 150 interviews with 90 employees of OpenAI, both past and present. She has access to emails, company Slack channels, the works. This is the inside account of OpenAI and the perils of artificial intelligence, big tech, and big money coming after, well, pretty much everything. It's an amazing story told incredibly well. I hope you enjoy this interview.

Karen, how welcome to Downstream.

Thank you so much for having me, Aaron.

It's a real pleasure to have you on.

Right. We say we say that to everybody, every guest. Uh, but I have to say, and this has had rave reviews, even though I've lost the uh the dust jacket, Empire of AI with this huge poem you've written, 421 pages, I think, not including the acknowledgements. Really, really interesting book. It's about AI, this burgeoning industry in the United States around artificial intelligence. That word has been in circulation since the 1950s, I believe.

Yeah.

Before we drill down into your book, what is AI and what do people mean by AI when they talk about it in 2025 in Silicon Valley? This is you would think this is the easiest question, but this is always the hardest question that I get because artificial intelligence is quite poorly defined. We'll go back first to 1956 because I feel like it helps understand a little bit about why it's so poorly defined today. But the term was originally coined in 1956 by this Dartmouth professor, assistant professor John McCarthy. And he coined it to draw more attention and more money to research that he was originally doing under a different name. And that was something he has explicitly said a few decades later. He said, "I invented the term artificial intelligence to get money for a summer study." And that kind of that that marketing route to the phrase is part of why it's really difficult to pin down a specific definition today. The other reason is because generally people say that AI refers to the concept of recreating human intelligence in computers. But we also don't have a scientific consensus around what human intelligence is. So quite literally when people say AI, they're referring to an an umbrella of all these different types of technologies that appear to simulate different human behaviors or human tasks. Um, but it really ranges from something like Siri on your iPhone all the way to chat GBT, which behind the scenes are actually really, really different ways of operating. They're totally different scales in terms of the consumption of the technologies. Um, and of course they they often have different use cases as well.

So right now when OpenAI, Meta, when they use those words AI in regards to their products specifically, what are they talking about? Most often they are now talking about what are called deep learning systems. So these are systems that train on loads of data and you have software that can statistically compute the patterns in that data and then that model is used to then make decisions or generate text or make predictions. So most modern-day AI systems built by companies like Meta, by OpenAI, by Google are now these deep learning systems. So deep learning is the is is the same as machine learning is the same as neural networks. Deep learning is a subcategory of machine learning. Machine learning refers to a specific branch of AI where you build software that calculates patterns in data. Deep learning is when you're specifically using neural networks to calculate those patterns. So you have a what I call um one of the founding fathers of AI used to call AI a suitcase word. So you because you can put whatever you want in the suitcase and suddenly it AI means something different. So we have this suitcase word of AI and then under that any data-driven AI techniques are called machine learning and then any neural network data-driven techniques are called deep learning. So it's the smallest circle within this broader suitcase word. So deep learning and neural networks are kind of interchangeable.

Not exactly in the sense that neural networks are referring to a piece of software and deep learning is referring to the process that the software is doing, right?

Yeah.

Do you get upset when when when politicians so in this country we have a prime minister called Karma, you know, and they say we think the NHS can save, you know, 20% by, you know, using AI applications, right? Do you sort of think my good like these people have no idea what they're talking about because that is such an expansive term. It can't really it's its political convenience is precisely doesn't mean anything.

It it does frustrate me a little bit. I so I often use the analogy that AI is like the word transportation. I mean if transportation can refer to bicycles or rockets or self-driving cars or gas-guzzling trucks, you know, like they're all different modes of transportation, serve different purposes, different cost-benefit analyses. And you would never have a politician say we need more transportation to mitigate climate change. You would be like, but what kind of trans like what are you talking about? Well yeah we need more transportation to stimulate the economy. I mean maybe in that case it's like it's just yeah like there is a vagueness around the AI discussion that is really unproductive and I think a lot of that leads to confusion where people think AI equals one thing and AI equals progress and so we should just have all of it but actually if we were to use the transportation analogy you know like having more bicycles having more public transit sounds great but if someone were actually referring to just like using rockets to commute from, you know, um, Dublin to to London and we were like, everyone should get a rocket now, like that's going to bring us more progress, you'd be like, what are you talking about? And that's effectively what these companies are doing with general intelligence. When you're giving people tools for free with regards to generative AI to just generate stupid images of nonsense, that's kind of what we're doing, right?

I I presume you would take that analogy to that level. It's like saying, "Let's use a rocket to get from Dublin to London to Paris."

Yeah, exactly. Like, it's not fit for the task. Um, and the the extraordinary amount of environmental costs for flying that rocket when you could have flown a much more efficient plane to do the same thing is like what are you doing, you know? Um, and that's some one of the things that people don't really realize about artificial or about generative AI is that the resource consumption required to develop these models and also use these models is quite extraordinary and often times people are using them for tasks that could be achieved with highly efficient different AI techniques and you're but because we use the sweeping term AI to mean anything then people just think, "Oh, yeah, right, right. I'm just going to use Chat GBT for my one-stop shop solution for anything AI related." So, right now, data centers globally, I think, are about 3 3.5% of CO2 emissions. I think the the data centers for AI are a tiny fraction of that, but obviously they're growing at an extraordinary pace.

Yeah.

Are there any numbers out there with regards to projected CO2 emissions of data centers globally 5, 10, 15 years from now or is that also it's so recent that we can't really speculate about the numbers involved?

There are numbers around the energy consumption which you could then use to kind of try and project uh project carbon emissions. So there was a McKenzie report that recently projected that based on the current pace of data center and supercomputer expansion for the development and deployment of AI technologies, we would need to add around half to 1.2 times the amount of energy consumed in the UK annually to the global grid in the next 5 years.

Wow.

Yeah. And most of that will be serviced by fossil fuels. This is something that Sam Altman actually even said in front of Senate the Senate a couple weeks ago. He said it will most probably be natural gas. So he actually picked the nicest fossil fuel. But we already seeing reports of coal plants having their lives extended. They were meant to be retired, but they're no longer being retired explicitly to power data center development. We're seeing reports of Elon Musk's XAI, the giant supercomputer that he built called Colossus in Memphis, Tennessee. It is being powered with around 35 unlicensed methane gas turbines that are pumping thousands of toxic air pollutants into the air into that community. So this data center acceleration is not just accelerating the climate crisis. It also is accelerating the public health crisis of people's ability to access clean air as well as clean water. So one of the aspects that's really undertalked about with this kind of AI development, the OpenAI's version of AI development is that these data data centers need fresh water to cool because if they used any other kind of water, it would erode corrode the equipment. It would lead to bacterial growth. And so most often these data centers actually use public drinking water because when they enter into a community that is the infrastructure that's already laid to deliver the fresh water to companies, to businesses, to residents. And so one of the things that I highlight in my book is there are many many communities that are already they do not have sufficient drinking water even for people. And I went to Monte Vido Uruguay to speak with people about a historic level of drought that they were experiencing where the Monte Vido government literally did not have enough water to put into the public drinking water supply. So they were mixing toxic waste water in just so people could have something come out of their taps when they opened them. And for people that were too poor to buy bottled water, that is what they were drinking. And women were having higher rates of miscarriages. uh elderly were having an exacerbation or inflammation of their chronic diseases. And in the middle of that, Google proposed to build a data center that would use more drinking water. This is called potable water, right? This is a potable water.

Yeah.

Exactly. You can't use sea water because of the saline aspect that you

Exactly.

Exactly. And Bloomberg recently had a story that said 2 thirds of the data centers now being built for AI development are in fact going into water-scarce areas.

You said a moment ago about um XAI unlicensed energy generation using methane gas. When you say unlicensed, what do you mean?

As in the company just decided to completely ignore existing environmental regulations when they installed those methane gas turbines. And this is actually a really one of the one of the things that I concluded by the end of my reporting was not only are these companies really corporate empires, but also that if we allow them to be unfettered in their access to resources and unfettered in their expansion, they will ultimately erode democracy. Like that is the greatest threat of their behaviors. And what XAI is doing is a perfect example of at the smallest level the they're enter these companies are entering into communities and completely hijacking existing laws, existing regulations, existing democratic processes to build the infrastructure for their expansion. And we're seeing this hijacking of the democratic process at every level, the smallest local levels all the way to the international level. It's kind of that that orthodoxy of seek permission after you do something is now I mean when you start applying this is business as usual for those companies that's part of their expansion strategy which we'll talk about and we're going to talk about um the sort of global colonial aspect as well with regards to resource consumption resource use just bring it back to the US again because at the top of this conversation I want to offer a bit of a primer to people out there who they maybe know what AI is they maybe have used chat GPT what are the major companies we're now talking about in this space particularly in the United States of America over the last 5 years who who are the people in this race to AGI

Mhm.

allegedly um artificial general intelligence something which you know either might be sentient probably not or capable of augmenting its own intelligence more plausible who are the major players in that field right now one caveat on AGI is that it's as ill-defined as the term AI um so I like to think of it as just a rebranding you know the the entire history of AI has just been been about rebranding and the term deep learning was also a rebranding so anyway but the players First, OpenAI of course they were the ones that fired the first shot with chat GBT anthropic major competitor Google Meta Microsoft they're the older uh internet giants that are now also racing to deploy these technologies um super safe super intelligence which spun out of also uh o an open AI splinter there are many openai splinters so this was founded very recently by the former chief scientist of OpenAI and Thinking Machines Lab founded very recently by a former chief technology officer of OpenAI and Amazon is now trying to get into the game as well. So basic and Apple is also trying to get in the game. So basically all the older generation tech giants as well as a new crop of AI players are all jostling in this space and that's just the US, right?

And that's just the US, right? So the the Chinese ecosystem is interesting because they're not so um they don't really use the term AGI like that this is like a very kind of unique thing about the US ecosystem is that there's a quasi-religious fervor around that underpins the construction of AI products and services whereas in China it's much more like these are businesses we're building products that users are going to use. So, if you're just looking at companies that are building chat bots that are sort of akin to chat GBT, then we're talking about Bite Dance, owner of Tik Tok. Um, Alibaba, the equivalent of Amazon, BYU, the equivalent of Google, Huawei, the equivalent of Apple, and uh, Tencent, the um, what is the equivalent of Tencent? I I guess Meta is the equivalent of Tencent. So, they're also building on these things. And there's similarly a crop of startups that are moving into the generative AI space. And in Europe, we've got the little tidlers like Mistral in France, you know, really not not at the races cuz we're Europe.

Um what's the business case for all this? Because obviously you've got massive companies often driven by maximizing shareholder value, multi-trillion dollar valuations. You do these things, you invest money to make money as a capitalist society. So what what is the business case made by say Microsoft when they have their shareholder meetings and they say we're going to allocate 40 50 billion dollars towards building data centers and so on.

So it's really it's interesting that you mentioned Microsoft because Microsoft has recently been pulling back their investments in data centers. They they went all in and now they're really rapidly starting to abandon data center projects. So to answer your question, it is really unclear what the business case is and Microsoft has been one of the first companies to start acknowledging that and Satya Nadella has come onto some podcasts recently where he actually stunned some people in the industry by being quite skeptical of whether or not this race to AGI was productive. Um but one of the things that I I really felt after reporting what is driving the fervor is you can't actually fully understand it as just a story about money. It has to also be understood as a story of ideology because when in the absence of a business case then you ask why are people still doing this? And the answer is there are people who genuinely fervently believe and they talk about it as a belief in this idea that we can fundamentally recreate human intelligence and that if we can do that there is no other more important thing in the world because what else like how else you should you be dedicating your time other than to bring about this civilizationally transformative technology. And so that's part of why what drives Open AI, what drives Anthropic, what drives safe super intelligence, these other smaller startups. And then the bigger giants which are more business focused and more classic companies that actually care about their bottom lines, they end up getting pressured because shareholders are seeing the enormous amounts of investment by these startups and they're seeing users start shifting from Google search to using chat GBT as search. Chat GBT should not be used as search but consumers think that it is. And then shareholders ask in Google's shareholder meetings, what are you doing with AI? What is your AI strategy? Why aren't you investing in this technology? And so then all of the other giants end up racing in the same direction.

What does Warren Buffett make of it? That's what I want to know. Is he sort of like you guys if he's like you guys are wasting your money?

He's like he's he's probably right. I have no idea.

Has he invested in AI?

No, I don't I don't think so. He just sticks to Coke and these sorts of things, doesn't he? I mean there's there's two rational. So I think one is like you say a quasi-religious fervor has inflected the investment decisions of some of the world's most um valuable companies which is just an extraordinary thing to even think about. I suppose the other one is that a lot of people in this space, as we'll talk about in a moment, are heavily influenced by people like Peter Thiel. And Peter Thiel's orthodoxy is that competition is for idiots, right? If you're going to start a business, it has to be a monopoly. And I can only presume that companies like Microsoft, etc., Although maybe that's not the best example now given recent events, but XAI, Open AI, Meta, the only reason you would invest ultimately hundreds of billions, trillions of dollars into this is because first-mover advantage gives you a monopoly on the most transformational technology since the steam engine.

Yeah.

I mean, that's the only way I can make sense of it, right?

Have Have they has anybody in that space kind of said that we want a we want the monopoly on AGI? We want to be the the Facebook of AGI.

Well, what what OpenAI often says to investors is if you make this seemingly fantastical bid into our technology, you could get the biggest returns you've ever seen in your life because we will then be able to use your funding to get to AGI first. So, it's still riding on this concept of the fact that there might be an AGI, which is high, it's not like rooted in scientific evidence. Um, and even if we fail, we will successfully be able to automate a lot of human tasks to the point where we can convince a lot of executives to hire our software instead of a labor force. So that in and of itself could potentially end up generating enough returns for you more than you've ever seen before. So that's usually the pitch that they make. But you know it is a huge risky bargain that these investors are actually pitching into. And and you know a lot of investors they they have a bandwagon mentality like they aren't necessarily doing their own analysis to say let me do this investment. They're just seeing everyone glom onto this thing and they're like well I don't want to miss out. Why don't we glom on as well? But you know, there are some investors that have actually recently reached out to me to be like, one of the most under-reported stories right now is the amount of risk that is not just being taken on by these VCs is actually being taken on by

The entire economy because the money that these investors are investing comes from like university endowments and things like that. So if the bubble pops, it doesn't just pop for Silicon Valley; it actually has will have ripple effects across the global economy.

I mean, when you look at the the the sort of e-commerce bubble in the late 90s, okay, it was a bubble, you know, pets.com or whatever. It was, you know, had these crazy valuations, but, you know, buying and selling goods and services offline and then taking that online. I mean, that makes sense. That's a that's a plausible sort of commercial model, but like you say, nobody's really done that with artificial intelligence. It does kind of feel like, you know, you read these stories about Tulip Mania in 17th-century Holland, and it does kind of feel very similar.

Um, you mentioned OpenAI, and we've talked about it many times, and of course OpenAI is is the central organization in this book. What's the big idea behind OpenAI? When it starts and when does it start? Let's let's end of 2015. 2015. So, it's 10 years old. What are the animating values that give birth to open AAI?

So OpenAI started as a nonprofit, which many people don't realize, based on the fact that it's one of the most capitalistic, if not the most capitalistic, organization in Silicon Valley today. But it was co-founded by Elon Musk and Sam Altman as a bid to try and create a fundamental AI research lab that could develop this transformative technology without any kind of commercial pressures. So they positioned themselves as the anti-Silicon Valley, the anti-Google, because Google at the time was the main driver of AI development. They had developed a monopoly on some top AI research scientists, and Musk in particular had this really great fear of not just Google but Google's DeepMind—uh, Google's acquisition of DeepMind—where he was very worried that this consolidation of some of the brightest minds would lead to the development of AI that would go very badly wrong. And what he meant by very badly wrong was it could one day develop sentience, consciousness, go rogue, and kill all humans on the planet.

And because of that fear, Altman and Musk then thought, we need to do a nonprofit, not have these profit-driven incentives. We're going to focus on being completely open, transparent, and also collaborative to the point of self-sacrificing if necessary. If another lab starts making faster progress than us on AI and on the quest to AGI, we will actually just join up with them. We will we will dissolve our own organization and join up with them. And uh, that didn't hold for very long.

So what's their theory behind that? Because you know, at that point Google is now about maybe 2015 is maybe the world's most valuable company. I don't know. Is certainly up there, and this is a nonprofit. Yeah. So how how are they going to achieve AGI before Google?

So initially, the bottleneck that they saw was talent, like right Google has this monopoly in talent. We need to chip away at that monopoly and get some of those Google researchers to come to us and also start acquiring PhD students that are just coming out of uni. And because of that, I have come to speculate—this is not based on any documents that I read or anything—I've come to speculate that part of the reason why they started as a nonprofit in the first place is because it was a great recruitment tool for getting at that bottleneck. They could not compete on salaries with Google, but they could compete on a sense of mission. And in fact, when Altman was recruiting the chief scientist, Ilya Sutskever, who was the critical first acquisition of talent, that then led to many other scientists being really interested in working for OpenAI. He appealed to Sutskever's sense of purpose, like do you want to do you want a big salary and just to work for a for-profit company, or do you want to take a pay cut and do something big with your life? And it was actually that reason that Sutskever said, you know what, you're right. I I do want to work for a nonprofit. And so that's how they initially conceived of competing with Google was we we're starting a little bit late to the game. How do we first get a bunch of really really smart people to join us? Let's create this really big sense of mission. And and the I open the book with two quotes in the epigraph, and one of them is from Sam Altman writing a blog post in 2013, and he quotes someone else that says, "Successful people build companies; more successful people build countries; the most successful people build religions," and then he reflects on this and says, "It seems to me that the most successful founders in the world don't actually set off to build a company. They set off to build a religion. And it turns out building a company is the easiest way to do so." And so, you know, it's not like 2013 and then 2015 he creates OpenAI as a nonprofit.

It's important to say as well, Sam Altman is not some sort of idealistic um, porpa, you know, he's working at Y Combinator. He is very much ensconced within the Silicon Valley elite.

Um, I suppose also there's tax as well, right? If you're a nonprofit, you've got the mission; you've also got a bunch of tax breaks which you don't have as a for-profit. So maybe there's a very cynical genesis there.

Um, but I suppose just reading your book and becoming more familiar with the arguments over time, you know, clearly the amount of compute you have is is was always going to be critical. And if you if you believe on the in the um neural network model, the deep learning model, the amount of compute you have is always going to be critical. And it just seems implausible that a nonprofit could ever have been able to compete with Google, for instance, ever. Like it seems implausible because you have to spend, as we now see, tens of billions, hundreds of billions of dollars on compute. Did nobody say that? Did nobody say, "Hey, you know, like the bottleneck isn't just talent acquisition. It's being able to spend hundreds of billions of dollars on these Nvidia GPUs."

It's so interesting because at the time the idea that you needed a lot of compute was actually neither very popular nor one that was seen as that scientifically rigorous. Right? So there were there were many different ideas of how to advance AI. One was we already actually have all the techniques that we need, and we just need to scale them. But that was considered a very extreme opinion. And then on the other extreme, it was we don't even have the techniques yet. And interestingly, recently there's a New York Times story that says why we likely won't get to AGI anytime soon by Cade Metz. And he cites this stat that 75% of the longest-standing, most respected AI researchers actually still think to this day we don't actually have the techniques to get to AGI, if we will ever. So, it's we're we're kind of coming full circle now, and it is starting to become unpopular again. This idea that you can just scale your way to so-called intelligence, but that was the research vibe when OpenAI started was we can actually maybe just innovate on techniques, right? And then very quickly, because Ilya Sutskever in particular was a scientist who anomalously did think that scaling was possible and because Altman loved the idea of adding zeros to things from his career in Silicon Valley and because Greg Brockman, the chief technology officer, also very Silicon Valley entrepreneur, liked that idea as well, then they identified why don't we go for scale because that is going to be the fastest way to see whether we can beat Google. And once they made that decision about less than a year in, roughly, is when they started actually talking about that, that's when they decided we actually need to convert into a for-profit because the bottleneck has shifted now from acquiring talent to acquiring capital. And that is also why Elon Musk and Sam Altman ended up having a falling out because when they started discussing a for-profit conversion, both Elon Musk and Sam Altman each wanted to be the CEO of that for-profit. And so they couldn't agree. And originally Ilya Sutskever and Greg Brockman chose Musk. They thought that Musk would be the better leader of OpenAI. But then Altman essentially, and this is something that is very classic, a very classic pattern in his career, became very persuasive to Brockman, who he had had a long-term relationship with, about why it could actually be dangerous to go with Musk and like like I would definitely be the more responsible leader, so on and so forth. And then Brockman convinces Sutskever, and the two chief scientist, chief technology officer pivot their decision, and they go with Altman, and then Musk leaves in a huff and says I don't want to be part of this anymore, which has become rather typical of the man, hasn't it, subsequently, but that is incredible really.

So by 2016, there's a recognition that in terms of capital investment they're going to have to go toe-to-toe with maybe at that point the world's biggest company, and they're a nonprofit. Yeah. I just find it weird that and but lots of people bought the propaganda that OpenAI was in some way open. Yeah. What did the open stand for by the way?

The "open" originally stood for open source, which in the first year of OpenAI they really did open-source things. They did research, and then they would put all their code online. So it it it really was like they did they did what they said, and then the moment that they realized we got to go for scale, then everything shifted.

It's such an amazing story and so emblematic of the 2010s that you have this organization which presents itself as effectively an extension of activism. Yeah. You know, ends up becoming today—some people value OpenAI at $300 billion. Yeah. Um, and it's doing all these terrible things which we're going to talk about.

Sam Altman specifically, who is he? What's his background? How does this guy who nobody's heard of become the CEO of a company which today is, you know, it's it's almost more valuable than any company in Europe, for instance?

Yeah. Altman is—he's spent his entire career in Silicon Valley. He was a first a founder, a startup founder himself, and he was part of the first batch of companies that joined Y Combinator, one of now today one of the most prestigious startup accelerators in Silicon Valley. But at the time he was he was the very first class, and no one really knew what YC was. He did that for seven years. He was running a company called Looped, which was a mobile-based social media platform, effectively a Foursquare competitor, but which actually started earlier than Foursquare. It didn't do very well. It was sold off for parts, and but what he did do very well during that time was ingratiate himself with very powerful networks in Silicon Valley. So, one of the first and longest mentors that he ended up having throughout his career is Paul Graham, the founder of Y Combinator, who then plucked Sam Altman to be his successor. And Sam Altman then at a very young age became president of YC. And then he ended up doing that for around 5 years. And during his tenure at YC, he dramatically expanded YC's portfolio of companies. He started investing not just in software companies but also pushing into quantum, into self-driving cars, into fusion, and really going for those hard tech engineering challenges. And if you look at how he ended up then as a CEO of OpenAI, I think that he basically was trying to figure out what is going to be the next big technology wave. Let me test out all of these different things. Position myself as involved in all of these different things. Um, so in addition to all his investments, he started cultivating this idea of AI also seems like maybe it'll be big. Let me start working on an idea for a fundamental AI research lab that becomes OpenAI. And once OpenAI started being the fastest one taking off, then Altman hops over and becomes CEO. He hops over.

So how does that happen? Where does he come from? Cuz like you say, originally it's got people like—he's there. Who's there first? Him or Ilya?

Technically, Altman recruited Sutskever, but Altman was only a a chairman; he he didn't take an executive role at OpenAI, even though he founded the company, right? And similarly with Musk—Musk didn't have an executive role; he was just a co-chairman, so it was just the two of them that were chairmen of the board, and Ilya Sutskever and Greg Brockman were the main people, the main executives that were actually running the company day-to-day in the beginning. I mean, I have to say, reading the book, Sam Altman, he comes across as a a master manipulator—like masterful manipulator and understander of human psychology. There's this great quote—let me get it up—uh, which you have. I think it's from Paul Graham—Sam Altman has it: "You could parachute him into an island full of cannibals and come back in 5 years, and he'd be the king." If you're Sam Altman, you don't have to be profitable to convey to investors that you will succeed with or without them. I mean, he just sounds—He's also described, by the way, as a once-in-a-generation fundraising talent. I think that's by you. Yeah.

Um, how how is he able to just basically come out of nowhere and compete with people like Elon Musk, Zuckerberg, as this kind of intellectual heavyweight in Silicon Valley in regards to one of the major growth technologies of our of our decade?

So, from the public's perspective, he came out of nowhere. But within the tech industry, everyone knew Sam Altman. You know, like I I as someone who worked in tech, like I knew Sam Altman ages ago because Y Combinator was just so important. It was as a CEO of a potential company that valuable. Was it always something that he might be in?

No, I don't think people ever thought that he would jump to become the CEO of a company because he has such an investor mindset, and his approach has always been to be involved in many many companies. I mean, he invested in hundreds of startups as both the president of YC and running some uh personal investment funds as well, but people—he was well respected within the valley. He was seen as a critical lynchpin of the entire startup ecosystem, and not just by people within the industry but by policy makers, which is key. He started cultivating relationships with politicians very very early on in his tenure as the president of YC. And for example, I talk in my book about how Ash Carter, the head of the Department of Defense under the Obama administration, came to Altman asking, "How can we get more young tech entrepreneurs to partner with the US government?" So, he was seen as a gateway into the valley. And obviously the valley isn't just made of of of startups. There's also the tech giants. But back then, like starting a startup was way cooler than working at a tech giant because Google, Microsoft, they were considered the older, safer options if you really wanted job security. But if you wanted to be an innovator, if you wanted to do breathtaking things, you would build a startup. And then that start your number one goal as a startup founder was to get into YC. So Altman was the pinnacle. He was he was a he was emblematic of the pinnacle of success in the valley. And he even if his net worth wasn't the same as other people in terms of his social capital, his networking, he understood early on that's where the real value lies.

Exactly. So interesting. I mean, some notes that I wrote down um cuz there are there are points where I'm thinking, why on earth is this gentleman the CEO of such a valuable company—he seems kind of useless—and the notes I had down were—um, people-pleaser, yes; liar; conflict-averse. How do you become the CEO of such a successful company—maybe you think that or don't think that—I don't know. I mean, at points it kind of comes across as almost psychotic, the capacity to to lie.

Here's an interesting question for me, and I don't know I don't know how comfortable you are with answering it. In writing this book, there's another alternative timeline where you basically write a hagiography of Sam and you leave all of that out, right? There are other writers out there, I won't name them, they sell a ton of books, and they write very positive, affirming um biographies of these visionary leaders, whether it's Elon Musk or Steve Jobs, etc. Why didn't you just write that book about Sam Altman? You know, you would have made a ton more money. Right. And I'm but I'm reading this stuff and I'm thinking, my good, this—and it's so deft and nuanced—your your portrait of Sam Altman. I just think the guy—I mean, this this is going to really hurt him when he reads this stuff. I imagine. Why didn't you do that? Take the easy route.

I don't know that that would have been the easy route. I mean, I just wrote the facts, and the facts come out that way, you know, like I interviewed over 260 people across 300 different interviews, and over 150 of those interviews were with people who either worked at the company or were close to Sam Altman. And that's just what they presented was all of the details that I ended up putting in. And one of the things that he that just came through again and again and again, well, two two things that came through again and again, no matter how long someone worked with him or how closely they worked with him, they would always say to me, at the end of the day, I don't know what Sam believes. So that's interesting. Mm. And then the other thing that came through was I would ask them, well, what did he say to you—he believed in this meeting at this point in time for why the company needed to do this XYZ thing—and the answer was he always said he believed what that person believed, except because I interviewed so many people who have very divergent beliefs, and I was like, wait a minute—he's saying that he believes what this person believes and then what that person believes, and they're literally diametrically opposite. So yeah, so I just I just ended up documenting all of those different details to illustrate how people feel about him. I mean, he's a polarizing figure, both extreme in the positive and negative direction. Some people feel he is the greatest tech um leader of our generation, and they but they don't say that he is honest when they say that. They just say that he's one of the most phenomenal assets for achieving a vision of the future that they really agree with. And then there are other people who hate his guts and say that he is the greatest threat ever. And it really also comes down to whether or not they agree with his vision, and they don't. And so then his persuasive powers suddenly become manipulative tactics.

Mm. I mean, if you compare him to somebody like Elon Musk as a CEO who is obviously far from perfect, but Elon Musk makes makes big bets. He has gut instincts. He's very happy to alienate people if he thinks he's right about something. And you know, obviously I don't agree with him on many many things, but that's that's quite a sort of—there's an archetype with regards to a business leader that that looks like that. And then you got somebody like Sam Altman. He's doing all of these things. Like I say, the people-pleasing, the conflict aversion, and yet he's managed to lead this company to essentially a third of a trillion valuation. He must obviously be doing something right as well. So what are his sort of comparative advantages as a business leader, cuz on paper I read all that stuff and I think the guy wouldn't be able to get up in the morning and make breakfast, and yet he's accomplished some extraordinary things.

Yeah, I think it really comes down to—he really he does understand human psychology very well, which not only is helpful in getting people to join in on his quest. So, he's great at at acquiring talent, and then he's said himself like I'm I'm a visionary leader; I'm not an operational leader, and my best skill is to acquire the best people that then operationalize the thing. So, he's he's good at persuading people into joining his quest. He's good at persuading whoever has access to whatever resource he needs to then give him that resource, whether it's capital, land, energy, water, laws, you know. Um, and then he is—people have said that he instills a very powerful sense of belief in his vision and in their ability to then do it. He's good—we say in English soccer, we would say good man-manager. He can inspire people. He inspires people to do things that they didn't think that they would be able to do. Yeah. Um, but yeah, but I mean, this is this is why there's so much controversy. He is such a polarizing figure because people who encounter—everyone has a very personalized encounter, relationship with him because he he often um he he does his best

Work in one-on-one meetings when he can say whatever he needs to say to get you to do, believe, achieve whatever it is that he needs you to do. And that's also part of the reason why there's so many diverging, like people that are like, "Oh, I think he believes this. I think he believes that." And they're like totally diverging. It's because he's he's having these very personalized conversations with people. Um, and so some people end up coming out of those personalized meetings feeling totally transformed in the positive direction, being like, "I feel superhuman. I can now do all these things, and it's in the direction that I want to go. It's I'm building the future that that he sees and I see. And we're like aligned." And then other people end up coming out of these meetings feeling like, "Was I played? You know, like, was this was he just telling me all these things to try and get me to do something that's actually fundamentally against my values?"

You said you spoke to 150 people who were connected with OpenAI, um, over 150 interviews.

Yeah.

Yeah. Sorry. 150 interviews, 250 interviews altogether. 27 people altogether. The the numbers were—

No, but it's absolutely incredible. I should have said this right at the start, really. What's your what's your personal sort of bio on all this stuff? Because, of course, when people out of journalism, media cover technology, the intersection of that with politics, we go, "Well, they don't really know what they're talking about. They're generalists because they come out of journalism." What's your background? Because it's quite particular.

I studied mechanical engineering at MIT for undergrad, and I went and worked in Silicon Valley because that's what I thought I wanted to do. I lasted a year before I realized it was absolutely not what I wanted to do. And then I went into journalism. And the reason why I had such a visceral reaction against Silicon Valley is because I was quite interested in sustainability and how to mitigate climate change. And the why I went to study engineering in the first place was I thought that technology could be a great tool for social change and shaping consumer behaviors to to prevent us from planetary disaster. And I realized that Silicon Valley's technology incentive structure, incentive structures for producing technology were not actually leading us to develop technologies in the public interest. And in fact, most often it was leading to technologies that were eroding the public interest. And the problems like mitigation of climate change that I was interested in were not profitable problems. But that is ultimately what Silicon Valley builds. They want to build profitable technologies. And so it just seemed to me that it didn't really make sense to try and continue doing what I wanted to do within a structure that didn't reward that.

Yeah. And then I thought, "Well, I've always liked writing. Maybe I can use writing as a tool for social change." So I switched to journalism.

You went to MIT Review, right?

And then I went to a few publications and then eventually MIT Technology Review to cover AI and then Wall Street Journal.

And then the Wall Street Journal. I mean, these are big—just just so people know there's real there's real credibility behind this. All these interviews, this CV. Um, and it's interesting as well you say, "I wouldn't write a hagiography, I just wrote what was there." I mean, maybe that's partly an an extension of your sort of STEM background, right? You know, rather than writing like propaganda on a puff piece, which let's be honest is is most coverage of of the sector. But it's true, right?

Well, you know, people often ask me this is like, "How much does did my engineering degree help me in reporting on this?" And I think it helps me in ways that are not what people would typically assume. I went to school with a lot of the people that now build these technologies. I went to school with some of the executives at OpenAI, you know, and so for me, I do not find there to be magic. I don't find these figures to be towering or magical. Like I remember when we were walking around dorm rooms together in our pajamas, and it it instilled in me this understanding that technology is always a product of human choices. And different humans will have different blind spots. And if you give a small group of those people too much power to develop technologies that will affect billions of people's lives, inevitably that is structurally unsound. You like, "We should not be allowing small groups of individuals to concentrate such profound influence on society when it is not—you cannot expect any individual to have such great visibility into everything that's happening in the world and perfectly understand how to craft a one-size-fits-all technology that ends up being profoundly beneficial for everyone." Like that it just doesn't make sense at all. Um, and I think the other thing that it really helps me with is it—Silicon Valley is an extremely elitist place, and it allows me to have an honest conversation with people faster because if they start stonewalling me or like trying to pretend that there's certain things that these technologies are capable of that they're not actually capable of, I will just slap my MIT degree down and be like, "Cut the bull crap," like, "Tell me what's actually happening." And it is a shortcut to getting them to just speak more honestly to me, but it's not actually because of what I studied. It's more just that it signals to them that they need to speed up their throat clearing.

That's really interesting though. Yeah, because I do I do feel like lots of coverage of this sector. I mean, I again I can only speak in regards to the UK, and we're a tiddler compared to to you guys, but at the intersection of particularly politics and technology, the coverage by political journalists at Westminster—you know, Keir Starmer and Rachel Reeves say, "We're going to build more data centers—isn't that fantastic"—actually, not necessarily—they're not going to create that many jobs once they're built, they can use a ton of energy, ton of water—what's the upside for the UK taxpayer? There is very little interrogation of just the press releases.

Yeah.

Um, and it's really interesting to me that you've come out of MIT and then you've taken this trajectory—is this stuff you just talked about, knowing these people, this tiny group of people whose decisions now affect billions already—is this stuff a—on the present trajectory—is it an existential challenge to democracy? And challenge is is speculative. Is it going to end democracy?

I think it is greatly threatening and increasing the likelihood of democracy's demise. But I I never make predictions of "this outcome will happen" because it makes it sound inevitable. And one of the reasons why I wrote the book is because I very much believe that we can change that and people can act now to shape the future so that we don't lose democracy. But on this trajectory, right, if the next 20 years, like the last 20 years, on this trajectory for sure, I think it will end democracy.

Yeah.

How quickly? We've really screwed up in the last 20 years, right? I wonder, you know, it's kind of—

Gosh.

Yeah. I'll give it maybe 20 years.

20 years.

Yeah.

Yeah. We used to have this thing called privacy, high streets, childhood, all gone. Um, you've said that um what OpenAI did in the last few years is they started blowing up the amount of data and the size of the computers that need to do this training in regards to the um in regards to the um deep learning. Give me a sense of the scale. We've talked a little bit about the data centers, but how much energy, land, water is being used to power OpenAI just specifically as one company.

Yeah. To power Open—that's really hard. Um, because they they don't actually tell us this. So we only have figures for the industry at large and the amount of data centers. So it's not in their annual reports, for instance.

No.

Well, they don't have annual reports because they're not a public company.

Of course.

Yeah. Huh. So that's, you know, one of the ways that—and and actually it doesn't matter if they're a public company because Google and Microsoft, they do have annual reports where they say how much capital they've spent on data center construction. They do not break down how much of those data centers are being used for AI. They also have sustainability reports where they talk about the water and carbon and things like that, but they do not break down how much of that is coming from AI either. And they also massage that data a lot to make it seem better than it actually is. But even with the massaging, there was that story 2 years ago or sorry, la—last year, 2024, where both Google and Microsoft reported I think it was a 30% and 50% jump in their carbon emissions.

Yeah.

Because largely driven by this data center development.

Yeah. And also the context here is over the last—it was one of the good news stories of the last sort of 10 to 15 years is that CO2 emissions per capita in the US has kind of plateaued, right? Across the west had kind of plateaued, and actually in the UK energy consumption dropped—I mean, we stopped making things, but still, you know, everything's made in East Asia now, but no, but it's it it was kind of a good story, and I kind of bought it, right? I thought that, you know, we'd have we'd kind of plateaued—obviously the global south would consume more energy—but we are as well.

Um, should we look at these companies as kind of analogous to the East India Company of the 19th century?

That is the analogy that I have increasingly started using, especially with the Trump administration in power because the British East India Company very much was a corporate empire and started off not very imperial. They just started off as a company, very small company based in London. And of course, through economic trade agreements with India gained significant economic power, political power, and eventually became the apex predator in that ecosystem, and that's when they started being very imperial in nature, and they were the entire time abetted by the British Empire, the nation-state empire. So you have a corporate empire, you have a nation-state empire, and I literally see that dynamic playing out now where the US government is also in its empire era. The Trump administration has quite literally used words to suggest that he wants to expand and fortify the American empire, and he sees these corporate empires like OpenAI as his empire-building assets. And so I think he is probably seeing it in the same way that the British crown saw the British East India Company of, "Let's just let this company acquire all these resources, do all these things, and then eventually we'll nationalize the company, and then India formally becomes a colony of the British Empire." So Trump—whatever the equivalent modern-day equivalent would be of nationalizing these these companies—is his endgame. Like he is helping them strike all these deals and installing all this American hardware and software all around the world with the hope that then those become national assets, and then—you know, there was actually just a recent op-ed in Financial Times from Marate Shake, one of the former EU par—parliamentarians who pointed out, like, "Isn't it so convenient for the US to get all of this American infrastructure installed everywhere around the world so that the US government could literally turn it off at any time?" I mean, if you want to talk about empire building, there's that. But at the same time, these corporate empires are also trying to use the American empire as an asset to their empire-building ambitions. So there's a very tenuous alliance between Silicon Valley and Washington right now in that each one is trying to use the other and ultimately trying to dominate the other. And there's a growing popularity in Silicon Valley of this idea of a politics of exit, this idea that democracy doesn't work anymore. We need to find other ways of organizing ourselves in society. And maybe the best way of organizing ourselves is actually a series of worked companies with CEOs at the top. So I don't ultimately know who's going to win—like the nation-state empire or the corporate empire. But either version is bad because all of the people in power now—both the business ex—executives and the politicians—do not actually care at all about preserving democracy.

I mean, the analogy of India is really interesting. So I think I might have my dates wrong. Um, East India Company is running things until 1857. You have the Indian mutiny, basically an uprising against the East India Company, and then of course that commercial endeavor has to be underpinned by the organized violence of the British imperial state. Um, and it does feel it does feel like that could be the next step of what happens with regards to US interests overseas. I suppose one retort would be, "Well, hold on—it sounds kind of good. I'm a I'm a socialist. I kind of like the idea of SpaceX being nationalized. I kind of like the idea of, you know, the federal government having a 51% stake in OpenAI and Tesla and Meta." What would you say to that?

I don't necessarily know if my critique is of the nationalization of the company more as like, "Why are they nationalizing these companies, and what are they—what," you know, like the—because of this endgame mentality of, "Let's just let these companies run rampant around the world so that ultimately whatever their assets are become our assets"—is leading the Trump administration to have a completely hands-off approach to AI regulation—they're quite literally—they proposed the big beautiful bill which passed the House and is now going up to the Senate with a clause that would, if implemented, put a 10-year moratorium on AI regulation at the state level, which is usually the state level is usually where regulation, sensible regulation happens in the US. So they're doing all of these actions now with wide-ranging repercussions that will be very difficult to unwind in the name of this idea that maybe if they just allow these companies to act with total impunity that it will ultimately benefit the nation-state.

How do people like Sam Altman look at the rest of the world outside the US? These kind of tech leaders and how do they look at Little Britain and Italy and how do they look at us? What do they think about us? You know, you've you've been inside their minds. It's it's—

Yeah, I mean, they see them as resources. They see different territories as different types of resources, which I mean is what older empires did. You know, they would look at a map and just draw out the resources that they could acquire in each geography. "We we're going to go here and acquire the labor. We're going to go here and acquire the lands. We're going to go here and acquire the minerals." I mean, that's literally how they talk. Like when I was talking with some OpenAI researchers about their data center expansion, you know, there was this one OpenAI employee who said, "We're running out of land and water," and he was just saying, "Yeah, we're just like trying to—we're just trying to look at the whole world and see where else we can place these things. Where what other geographies can we find all the conditions that we need to build more data centers? Land without earthquakes, without floods, without tornadoes, hurricanes, all these natural disasters and can deliver massive amounts of energy to a single point and can cool the systems." And they they are they they're looking at that level of abstraction to what are the different pieces of territory and resources that we need to acquire, and that includes other parts of the west.

Yeah.

That's not just the global south.

No, it includes other parts of the west as well.

Yeah. So there have been rapid data center expansion in rural communities in both the US and the UK, and they—it always ends up in economically vulnerable communities because those are the communities that often actually opt in to the data center development initially because they are not informed about what it will ultimately cost them and for how long. And so I spoke with this one Arizona legislator who said, "I didn't know it had to use fresh water." And for the UK audience, Arizona is a desert territory. There is no—there's there's a very very stringent budget on freshwater. And after that legislator found out, she was like, "I would have never voted for having this data center in." But the problem is that there are so few independent experts for these legislators, city council members to consult that the only people that they rely on for the information about what the impact of this is going to be are the companies. And all the companies ever say is, "We're going to invest millions of dollars. We're going to create a bunch of construction jobs up front, and it's going to be great for your economy."

Yeah.

I mean, that's all we hear about data centers in this country. And it's a great it's a great top line for the chancellor and the prime minister because they can say tens of billions of pounds worth of investment. Okay. But in terms of long-term jobs, how many? And also, by the way, for that rural community in God knows where, you know, the northeast of England or whatever.

Yeah.

You're not telling them that actually they can't use their hose pipes for 3 months a year because all the water is going to that local data center.

Exactly. And it's quite extraordinary. And and and the most scary thing about all of it is in the UK at least the politicians don't know any of that. I sincerely don't think the chancellor knows any of that. Uh, and there's no real—I mean, even if you use the prism of colonialism, imperialism with regards to exploitative economic relations between the United States and other parts of the world, they think you're a Trotskyist, right? That's that's the crazy things. They can't even look after their own people because if looking after your own people boils down to being too left-wing—well, I think part of it is also that they don't really realize that it's literally happening in the UK. So the so to connect it to the UK, data center development along the M4 corridor is has literally already led to a ban in construction of new housing in certain communities that desperately need more affordable housing. And it's because you cannot build new housing when you cannot guarantee deliveries of fresh water or electricity to that housing. And it was due to the massive electricity consumption of the data centers being built in that corridor that led to that ban.

That's nuts. I mean, that's the most valuable real estate for housing in in the country, the M4.

Yeah. And do you think UK politicians are are aware of that contradiction or is that just—that's—I mean, you know, I don't know if they are aware—if maybe they're they don't have awareness or maybe they are aware and they're also thinking of other tradeoffs. I mean, now in the UK and and in the EU at large there's just this huge conversation around data sovereignty and of course technology sovereignty—there's this whole concept of developing the EU stack and why is it that we don't have any of our tech giants—why don't we have any of this infrastructure—um, and like here Starmer just said this week during London Tech Week, "We want to be AI creators not AI consumers"—so I think in their minds maybe this is a viable trade-off. "We we skimp a little bit on housing for the ability to have more indigenous innovation." But I think the thing that is often left out of that conversation is this is a false trade-off. People think that you need colossal data centers to build AI systems. You actually do not. This is specifically the approach that OpenAI decided to take. But actually before OpenAI started building large language models and generative AI systems at these these colossal scales, the trend within the AI research community was going the opposite direction—towards tiny AI systems. And there was all this really interesting research looking into how small your data sets could be to create powerful AI models and how little computational resources you needed to create powerful AI models. So there were there were interesting papers that I wrote about where you could have a couple hundred images to create highly performant AI systems or uh you could have AI systems trained on your mobile device. That's like a sing—not even a one computer chip on—running on your mobile device. And OpenAI took an approach that is now using hundreds of thousands of computer chips to train a single system. And those hundreds of thousands of computer chips now are consuming, you know, city—city loads of energy. And so if we divorced the concept of AI progress with this scaling paradigm, you would realize then you can have housing and you can have AI innovation. But once again, there's not a lot of independent experts that are actually saying these things. Most AI experts today are employed by these companies. And this is basically the equivalent of if most climate scientists were being bankrolled by oil and gas companies. Like they would tell you things that are not in any sense of the word scientifically grounded, but just good for the company. I interviewed—

A great guy, um, twice actually. Now, a guy called Angus Hansen who's really just on it with regards to the exploitive nature of the increasingly exploitive nature of, um, uh, of the United States, um, economic relations with the UK. Just fascinating. Fascinating, uh, a book and man, and I just don't think it's cut through to our politicians here how bad it's getting. And you're saying about AI consumers or or or creators? I mean, ultimately you're talking about Meta, you're talking about Alphabet, you're talking about XAI, you're talking about OpenAI. We are consumers; we are dependent. It's a colonial, exploitative relationship with regards to big tech, and it has been for a really long time. Our smartest people, which the taxpayer trains here, go to the US. I think one of the top people at Slack is a UK national, Demis, you know, um, uh, DeepMind now working under the, you know, the sort of the the umbrella of Alphabet. And yeah, it just doesn't make it just doesn't make sense for me with regards to that formulation. They they simply don't get it, you know. Every I came here using my Mastercard; millions of Brits use Apple Pay and Google Pay and Mastercard and Visa, and every time we do, 0.1, 0.2%, 2% crosses the Atlantic, and and it's it just goes over the heads of, um, our political class, which is is very unnerving in regards to the efficiency of these, um, smaller systems.

Where does where does DeepSeek fit in all of this? Because, of course, the scaling laws at the heart of OpenAI, which is you get to AGI by more compute, more parameters, more data, is kind of untethered a bit by the arrival of DeepSeek. Yes. What? DeepSeek is such an interesting and complicated case because they basically they it was it's a Chinese AI model that was created by this company, HighfFlyer, and they were able to create a model that essentially matched and even exceeded some performance metrics of American models being developed by OpenAI and Anthropic with orders of magnitude less computational resources, less money. That said, it's not necessarily the perfect. It's like I I don't think the world should suddenly start using DeepSeek and saying DeepSeek solves all these problems because it's still engaged in a lot of data privacy problems, copyright exploitation, things like that. Um, and some people argue that ultimately they were distilling the models from that that were first developed through the scaling paradigm. So you first develop some of these colossal scaling models, and then you end up making them smaller and more efficient. So some people argue that you actually have to first do that scaling before you get the efficiency. But anyway, what it did show is you can get these capabilities with significantly less compute. And it also showed a complete unwillingness of American companies, now that they know that they can use these techniques to make their models more efficient. They're still not really doing it. Why do they do they like giving their money to Nvidia? What's the Because when you if you continue to pursue a scaling approach and you're the only one with all of the AI experts in the world, you persuade people into believing this is the only path and therefore you continue to monopolize this technology because it locks out anyone else from playing that game. And also because path dependence, like these companies are actually not that nimble, they end up the way that they they organize themselves. It it's not so easy for them to just like immediately swap to a different approach. They end up putting in motion all the resources, all of the training runs, so on and so forth, o o over the course of months, and then they just have to run with it.

So DeepSeek actually wasn't the first time that this happened. The first time that this happened was with image generators and Stable Diffusion. And Stable Diffusion was specifically developed by an academic in Europe who was really pissed that the AI companies like OpenAI were taking a scaling approach to image generation. He was like, "This is literally wholly unnecessary." And they're spending thousands of chips, all of this energy to produce DALL-E. And ultimately, he ended up producing Stable Diffusion with a couple hundred chips, using a new technique called latent diffusion, hence the name Stable Diffusion. And you know, arguably it was actually an even better model than DALL-E because users were saying that Stable Diffusion had even better image quality, better image generation, better ability to actually control the images than DALL-E. But even knowing that latent diffusion existed, OpenAI continued to develop DALL-E with these massive scaling approaches. And it wasn't until later that they then adopted the cheaper version. But it was it was just significantly delayed. And and I was asking OpenAI research like why that doesn't make any sense. Why did you do that? And they were like, well, once you set off on a path, it's kind of hard to pivot. Also, Jensen Huang, the the CEO of Nvidia is really charismatic, right? I mean, it's quite funny when cuz I'm I'm a Marxist. Just I'm going to make that confession. You have these big sort of structural, um, understandings of how of how history happens, and then you sort of realize actually this guy's really charismatic and this person's really manipulative, and all of a sudden the world's hyperpower is, you know, making these technological decisions. Okay. Uh, quite strange.

Um, we talked about data centers. We talked about earth, um, water, energy. I want to talk also about some of the more exploitative practices with regards to workers in the Global South. You use one really grueling example actually in Kenya. Can you talk about some of the research around that? Some of the people you met? Yeah. So I ended up interviewing workers in Kenya who were contracted by OpenAI to build a content moderation filter for the company. And at that point in the company's history, it was starting to think about commercialization after coming from its nonprofit fundamental AI research roots. And they realized if we're going to put a text generation model in the hands of millions of users, it is going to be a PR crisis if it starts spewing racist, toxic, hateful speech. In fact, in 2016, Microsoft infamously did exactly this. They developed a chatbot named Tay. They put it online without any content moderation, and then within hours it started saying awful things, and then they had to take it offline, and to this day, as evidenced by me bringing it up, it's still brought up as a horrible case study in corporate mismanagement. And so OpenAI thought, "We don't want to do that; we're going to create a filter that wraps around our models so that even if the models start generating this stuff, it never reaches the user because the filter then blocks it." In order to build that filter, what the Kenyan workers had to do was wade through reams of the worst text on the internet as well as AI-generated text on the internet where OpenAI was prompting its models to imagine the worst text on the internet. And the workers then had to go through all of this and put it into a detailed taxonomy: Is this hate speech? Is this harassment? Is this violent content? Is this sexual content? And the degree of hate speech, of violence, of sexual content. So it was they were asking workers to say, "Does it involve sexual abuse? Does it involve sexual abuse of children?" So on and so forth. And to this day, I believe if you look at OpenAI's content moderation filter documentation, it actually lists all of those categories. And this is one of the things that it offers to clients of their models, business clients of their models, that you can toggle on and off each of these filters. So that's why they had to put this into that taxonomy. The workers ended up suffering very many of the same symptoms of content moderators of the social media era. Absolutely traumatized by the work, completely changed their personalities, left them with PTSD. And I highlight the story of this man, Moffat, who is one of the workers that I interviewed who showed to me that it's not just individuals that break down; it's their families and communities because there are people who rely on these individuals. And so Moffat was on the sexual content team. His personality totally changed as he was reading child sexual abuse every day. And when he came home, he stopped playing with his stepdaughter. He stopped being intimate with his wife. And he also couldn't explain to them why he was changing because he didn't know how to say to them, "I read sex content all day." That doesn't sound like a real job. That sounds like a very shameful job. ChatGPT hadn't come out yet. So there was no conception of what does that even mean? And so one day his wife asks him for fish for dinner. He goes out, buys three fish, one for him, one for her, one for the stepdaughter. And by the time he comes home, all of their bags are packed and they're completely gone. And she texts him, "I don't know the man you've become anymore, and I'm never coming back."

You say that's the case with regards to text. Are people also having to engage with images as well? I mean, that was more of a social media thing. Is that here too? Yeah, they there were workers that they then so after this they contracted these Kenyan workers that contract actually was cancelled because there was a bunch of scrutiny on that company and there the the third-party company that they were contracting the workers through and huge scandal, um, this is Sama, right? Sama. Yeah, and there was a huge scandal around Sama, and then OpenAI ended up shifting to other contractors who were then involved in moderating images. And were they remunerated for the kind of work they were doing quite well? Or for the Kenyan workers, they were paid a few dollars an hour, right? Yeah. And then on the other side of the of the Atlantic, you talk about people in South America, um, doing effectively, you know, Mechanical Turk piecework for these companies as well. Can you talk about that a little bit? Yeah, so generative AI is not the only thing that leads to data annotation. This has actually been part of the AI industry for a very long time. And so I ended up years ago interviewing this woman in Colombia who was a Venezuelan refugee about the specific thing that happened to her country in the global AI supply chain. So when in 2016 when when the AI industry first started actually looking into the development of self-driving cars, there was a surge in demand for highly educated workers to do data annotation, labeling for helping self-driving cars navigate the road. You have to show self-driving cars, "This is a car, this is a tree, this is a bike, this is a pedestrian. This is how you avoid all of them. These are the lane markings. This is what the lane markings mean." And they're humans that do that. And it just so happened in 2016 when this demand was rising that Venezuela as a country was was dealing with the worst peacetime economic crisis in 50 years. So the economy bottomed out. A huge population of highly educated workers with great access to the internet suddenly were desperate to work at any price. And these became the three conditions that I call the crisis playbook in my book that companies started using to then scout out more workers that were extremely cheap for working for the AI industry. And so the woman that I met in Colombia, she was not just it she was working in a level of exploitation that was not based on the content that she was looking at. She was labeling self-driving cars and labeling, you know, retail platforms and things like that. The exploitation was structural to her job in that she was logging into a platform every day and looking at a queue that automatically populated with tasks that were being sent to her from Global North companies, and most of the time the tasks didn't appear, and when they did, she had to compete with other workers to claim the task first in order to do it at all. And because there were so many Venezuelans in crisis and so many of them were finding out about data annotation platforms, in the end there were more and more and more workers competing for smaller and smaller volumes of tasks. And so these tasks would come online and then disappear within seconds. And so one day she was out on a walk when a task appeared in her queue, and she sprinted to her apartment to try and claim the task before it went away. But by the time she got back, it was too late. And after that, she was like, "I never went on a walk during the weekday again." And on the weekends, which she discovered is less often less likely for companies to post tasks, she would only allow herself a 30-minute walk break because she was too afraid of that happening again. And did she did she detail about how that gave her sort of anxiety or insomnia or mental health kind of overheads? It's it it that sounds insane, sounds insane way to live. Um, it completely controlled her life. She didn't tell me about whether or not it gave her insomnia, but it completely controlled the rhythms of her life in that she had this plugin that she downloaded that would sound an alarm every time a task appeared so that she could, you know, cook or clean or whatever without literally just looking at the laptop the whole day. And she would turn it on to max volume in the middle of the night because sometimes tasks would arrive in the middle of the night, and if the alarm rang, she would wake up, sprint to her computer, claim the task, and then start tasking at like 3:00 a.m. in the morning. Um, and she had chronic illness. Um, one of the reasons why she was tethered to her apartment doing this online work in the first place was not just because she was a refugee, but also because she had severe diabetes. And it got to the point where she ended up in the hospital and was completely blind for a period of time. And the doctor said that if you had not come to the hospital when you did, you would have died. And so she was tethered to her home because she had to inject herself with insulin like five times a day. And it was this really complicated regime that didn't allow her to commute to a regular office, have a regular job. So she was doing all this extremely disruptive, disregulating work on top of just trying to manage extreme severe diabetes.

I mean, it's extraordinary you've managed to unveil those stories. I think I mean that's why the book is so interesting, fascinating for me. That's why it's got the plates it's got is that you're, you know, you're speaking to people who are on first-name terms as Sam Altman, then you're talking to Venezuelan refugees in Colombia. Um, and it's really important to say that this work is being done for multi-trillion-dollar companies. Yes. That's the other side of it, right? You're seeing Elon Musk worth 300 billion plus dollars, and then there are people that's where the value is being generated. Yeah. Exactly. And that's when the reason why I really wanted to highlight those stories is because that's where you really see the logic of Empire. There is no moral justification for why those workers whose contribution is critical to the functioning of these technologies and critical to the popularity of products like ChatGPT are paid pennies when the people working within the companies can easily get million-dollar compensation packages. The only justification is an ideological one, which is that there are some people born into this world superior and others who are inferior, and the superior people have a right to subjugate the inferior ones. My last question: What does the US public do about big tech if it wants to take on some of these issues—income inequality, regional inequality, global imperial overreach, etc.? A few proposals and which, you know, somebody can execute on. What would you suggest? Yeah, I wouldn't even say it's just the US public. I mean, anyone in the world can do something about it. And one of the remarkable things for me in reporting stories is people who felt like they had the least amount of agency in the world were actually the ones that put up the most aggressive fights and actually started gaining ground on these companies in taking resources from them. So, I talk about Chilean water activists who pushed back against a Google data center project for so long that they've stalled that project now for 5 years, and they forced Google to come to the table and the Chilean government to come to the table, and now these these residents are invited to comment every time there's a data center development proposal, which they then said is not it's not the end of the fight, like they still have to be vigilant, and at any moment if they blink, something could happen, but but anyone in the world I think has an active role to play in shaping the AI development trajectory, and the way that I think about it as the full supply chain of AI development. You have a bunch of resources that these companies need to develop their technologies: data, land, energy, water, and then you have a bunch of spaces that these companies need access to to deploy their technologies: schools, hospitals, offices, government agencies. All these resources and all these spaces are actually places of democratic contestation. They're collectively owned. They're publicly owned. So, we're already seeing artists and writers that are suing these companies, saying, "No, you cannot take our intellectual property." And that is them reclaiming ownership over a critical resource that these companies need. We're seeing people start exercising their data privacy rights. I mean, one of my favorite things about visiting the UK and EU as an American that has no federal data privacy law to protect me is to reject those cookies every single web page that I encounter. That is me reclaiming ownership over my data and not allowing those companies to then feed that into their models. We're seeing just like the Chilean water activists, hundreds of communities now rising up and pushing back against data center development. We're seeing teachers and students escalate the a public debate around, "Do we actually want AI in our schools? And if so, under what terms?" And many schools are now setting up governance committees to to to determine what their AI policy is so that ultimately AI can facilitate more curiosity and more critical thinking instead of just eroding it all away. The same thing, I'm sure wherever your audience is sitting right now. If they work for a company, that company is for sure discussing their AI policy. Put yourself on that committee for drafting that policy. Make sure that all the stakeholders in that office are at that table actively discussing when and under what conditions you would accept AI and from which vendors as well because again, not all AI models are created equal. So do your research on which AI technologies you want to use and which companies are providing them. And I think if we everyone can actually actively play a role in every single part of the supply chain that they interface with, which is quite a lot. Most people interface with the data part. Many people will now have data centers data centers popping up in a community near them. Everyone goes to school at some point. Everyone works in some kind of office or community at some point. If we do all of this push back a 100,000 times fold and democratically contest every stage of this AI development and deployment pipeline, I am very optimistic that we will reverse the imperial conquest of these companies and move towards a much more broadly beneficial trajectory for AI development.

Yeah, we've had we've had big tech, social media for the last 15-20 years, and I suppose the question is, is the same set of patterns going to apply to this stuff? And I I I think when you speak to someone like Jonathan Haidt when he talks about, um, young people and their consumption now of social media and mobile telephones, etc., his real worry is AI. Yeah. And if there is this laissez-faire attitude from policymakers and also, let's be honest, from civil service, civil society that there was over the last 15-20 years, I mean, he's terrified about the implications. So it's interesting to see that there's congruence between what you're saying, what Jonathan Haidt's saying. Can I ask you one more question? Have you ever read Dune by Frank Herbert? I've watched the movie, and it's sitting on my bedside table to actually read the original. And I'm so glad that you asked me this because this is an analogy that I use all the time now to describe the AI world. Yeah. But Larry and Jihad. So yeah. So one of the things that was so shocking to me because we already talked about this like quasi-religious fervor within the AI community, and I was interviewing people who one of the people that their voice was quivering when they were telling me about the profound cataclysmic changes on the horizon. Like these are very visceral reactions. These are true believers. And Dune strikes me as a really good analogy for understanding this ecosystem because Paul Atreides's mom in the story, she creates this myth to help position Paul as a supreme leader and to ultimately control the population. And the people who encounter this myth, they

Don't know that it's a creation. So, they're just true believers.

And at some point, Paul gets so wrapped up in this own mythology that he starts to forget that it was originally a creation. And this is essentially what I felt like I was—I was seeing with my interviews of people in the AI world because because I w—I had the opportunity to start interviewing people starting all the way back in 2019. You know, I interviewed some people who, for back then and for the book, to just map out their—their trajectory—and there were non-believers back then that are true believers now. Like, if they were able to stay long enough at that company, they all, in the end, become true believers in this AGI religion.

And so there's this vortex of—it's like a black hole, ideological black hole. I don't know how to explain it, but people, when they swim too long in the water, it just becomes them.

So what you're saying is Sam—Sam Altman is the Lisan Algib. That's the character, and—and Paul Graham maybe was the, you know, the—it would seem—it would seem like that would be the most appropriate character to assign to him.

Yeah. Wow, this has been fabulous. And I have to say, honestly, the book is really, really exceptional. Empire of AI. I read it so much that the dust jacket—I think my daughter actually ripped it off. But anyway, uh, it is a sensational book. Sensational journalism, fantastic journalist. We don't have enough of those in the world. Thank you. Um, real pleasure to meet you, Karen.

Thanks so much for joining us. It was great to meet you. [Music]