Podcast

AI Whistleblower- We Are Being Gaslit by the AI Companies! They’re Hiding the Truth About AI

The Diary Of A CEO with Steven Bartlett

Last highlighted March 30, 2026 Source ↗ ← All highlights

How A Climate Startup Pushed Karen Hao Into AI
- Karen Hao left engineering for journalism after a climate-focused startup fired its mission-driven CEO because the company was not profitable.
- That rupture made her question why innovation serves profit over public benefit and launched her AI reporting career. Transcript: Karen Hao Took a strange route into journalism. I studied mechanical engineering at MIT. And so when I graduated, I moved to San Francisco. I joined a tech startup. I became part of Silicon Valley. And I basically received an education in what Silicon Valley is about because a few months into joining a very mission-driven startup that was focused on building technologies that Would help facilitate the fight against climate change, the board fired the CEO because the company was not profitable. And this was, in hindsight, a very pivotal moment for me because I thought if this hub is ultimately geared towards building profitable technologies and many of the problems in the World that I think need solved are not profitable problems like climate change, then what are we actually doing here? Like what, how did we get to a point where innovation is not actually necessarily working in the public benefit and sometimes even undermining the public benefit in pursuit of profit. In that moment, I had a bit of a crisis where I thought, well, I just spent four years trying to set myself up for this career that I now don’t think I am cut out for. And I thought, well, I might as well just try something totally different. I’ve always liked writing. And that’s how after two years, I landed at a role at MIT Technology Review covering AI full time. And that gave me a space to then explore all of these questions of who gets to decide what technologies we build? How does money and ideology also drive the production of those technologies? And how do we ultimately make sure that we actually reimagine the innovation ecosystem to work for a broad base of people all around the world? And so that is kind of how I then set off on this journey of ultimately writing a book. (Time 0:03:45)
Why Karen Hao Calls AGI A Moving Target
- Karen Hao argues AGI has no stable definition because human intelligence itself lacks scientific consensus.
- She says OpenAI shifts AGI from curing cancer to a digital assistant to a $100 billion revenue threshold depending on audience. Transcript: Karen Hao I think we should start with when AI started as a field. So this was back in 1956 and there were a group of scientists that gathered at Dartmouth University to start a new discipline, a scientific discipline to try and chase an ambition. And specifically an assistant professor at Dartmouth University, John McCarthy, decided to name this discipline Artificial Intelligence. This was not the first name that he tried. The previous year, he tried to name it Automata Studies. And the reason why some of his colleagues were concerned about this name was because pegged the idea of this discipline to recreating human intelligence. And back then, as is true today, we have no scientific consensus around what human intelligence is. Biology, neurology. And in fact, every attempt in history to quantify and rank human intelligence has been driven by nefarious motives. It’s been driven by a desire to prove scientifically that certain groups of people are inferior to other groups of people. There are no goalposts for this field and there are no goalposts for the industry when they say that they are ultimately trying to recreate AI systems that would be as smart as humans. How do we even define what that means? And when are we going to get there if we don’t know how to define the destination? And what that effectively means is that these companies can just use the term artificial general intelligence, which is now the term to refer to this ambitious goal to recreate human Intelligence. They can use it however they want to, and they can define and redefine it based on what is convenient for them. OpenAI’s history, it has defined and redefined it many times. When Sam Altman is talking with Congress, AGI is a system that’s going to cure cancer, solve climate change, cure poverty. When he’s talking with consumers that he’s trying to sell his products to, it’s the most amazing digital assistant that you’re ever going to have. When he was talking with Microsoft, you know, in the deal that OpenAI and Microsoft struck, where Microsoft invested in the company, it was defined as a system that will generate $100 Billion of revenue. And on OpenAI’s own website, they define it as highly autonomous systems that outperform humans in most economically valuable work. This is like not a coherent vision of one technology. (Time 0:07:45)
How Sam Altman Won OpenAI Over Elon Musk
- Karen Hao says Sam Altman mirrored Elon Musk’s AI-doom rhetoric while recruiting Musk into OpenAI.
- She reports Altman later persuaded Greg Brockman that Musk would be dangerous as CEO, helping push Musk out when the for-profit entity formed. Transcript: Karen Hao In general, when Altman is writing for the public or speaking for the public, he does not just have the public as the audience in mind. There are other people that he is trying to motivate or mobilize when he says these things. And in that particular moment, Altman was trying to convince Elon Musk to join him on co-founding OpenAI. Was spending all of his time sounding the alarm on what he saw as a huge existential threat that AI could pose. And so in that blog post, if you look at the language that Altman uses side by side with the language that Musk was using at the time, it mirrors all the things that Musk was saying. Steven Bartlett It’s identical. I mean, 10 years ago, Musk was going on podcasts saying, tweeting, whatever, that the greatest existential risk to humanity was AI. Karen Hao Yeah. And so, you know, like his parenthetical, there are other things that we that might actually be more likely to happen, like engineered viruses. It’s because up until then, Altman had been talking just about engineered viruses. So now that he needs to pivot to speak to an audience of one, to Musk, he needs to kind of resolve the contradiction between what he’s now elevating as his new central fear to be the same As Musk’s new central fear with what he had previously been saying. So that’s why he’s like, I think this is now, even though before I said this. Steven Bartlett And are you saying that Sam Altman manipulated Musk? Because Elon did end up donating a huge amount of money to OpenAI and co-founding it, I believe, with Sam Altman. Karen Hao Elon Musk did end up co-founding it with Altman. And certainly from Musk’s perspective, he does feel manipulated because he feels like Altman was engineering his language in a way that would make Musk trust him as a partner in this Endeavor. And of course, then Musk leaves. And through some of the documents that came out during the lawsuit that Musk and Altman are engaged in now, it has become clear that there was a degree to which Musk was actually muscled Out a little bit. And so that’s why he’s left with this very intense personal vendetta against Altman, saying that somehow Altman tricked him into being part of this. Steven Bartlett So in 2015, Sam Altman is writing these blog posts saying this is, you know, one of the greatest existential threats. At the same time, in 2015, Musk is doing some very famous speeches at the time at MIT. He said that AI was the biggest existential threat and compared developing AI to summoning the demon. And what you’re saying here is you’re saying that Sam Altman was just mirroring the language that Elon was using to get Elon involved in OpenAI. And later it appears, and again, there’s a legal case taking place now, that Sam might have muscled Elon out in some capacity. Karen Hao Yeah. So we know from the lawsuit and the documents that have come out in the lawsuit that Ilya Stutzgever, who was the chief scientist of OpenAI at the time, and Greg Brockman, chief technology Officer at the time, when they were deciding whether or not to maintain OpenAI as a nonprofit, because it was originally found as a nonprofit, they decided, OK, we need to create a for-profit Entity. But the question was, who should be the CEO of this for-profit entity? Should it be Musk or should it be Altman? Because they were the two co-chairmen of the nonprofit. And in the emails, it became clear that Ilya and Greg first chose Musk to be the CEO. But through my reporting, I discovered that Altman then appealed personally to Greg Brockman, who was a friend of his that they’d known each other for many years through the Silicon Valley scene and said, don’t you think that it would be a little bit dangerous to have Musk be the CEO of this company, this new for-profit entity? Because, you know, he’s a famous guy. He has a lot of pressures in the world. He could be threatened. He could act erratically. He could be unpredictable. And do we really want a technology that could be super powerful in the future to end up in the hands of this man? And that convinced Greg. And Greg then convinced Ilya, you know, I think there’s a point here. Do we really want to give this much power to Musk? And that is why Musk then leaves, because then the two switch their allegiances. They say, actually, we want Altman to be the CEO. And then Musk is like, if I’m not CEO, I’m out. (Time 0:11:09)
Why Sam Altman Creates Extreme Reactions
- Karen Hao says people rarely feel neutral about Sam Altman because reactions depend on whether they share his vision of the future.
- Allies see a master storyteller and recruiter; critics feel manipulated into building a future they never wanted. Transcript: Karen Hao Think he’s a very controversial figure. Steven Bartlett You did an interesting pause. It’s a pause where someone tries to select their words. Karen Hao Well, this is what’s so interesting about those interviews is people are extremely polarized on Altman. No one has in-between feelings about him. Either they think he’s the greatest tech leader of this generation, akin to the Steve Jobs of the modern era, or they think that he’s really manipulative and an abuser and a liar. And what I realized, because I interviewed so many people, is it really comes down to what that person’s vision of the future is and what their goals are. So if you align with Altman’s vision of the future, you’re going to think he’s the greatest asset ever to have on your side because this man is really persuasive. He’s incredible at telling stories. He’s incredible at mobilizing capital, at recruiting talent, at getting all the inputs that you need to then make that future happen. But if you don’t agree with his vision of the future, then you begin to feel like you’re being manipulated by him to support his vision, even if you fundamentally don’t agree with it. And this is the story especially of Dario Amadei, CEO of Anthropic, who was originally an executive at OpenAI. Steven Bartlett So for people that don’t know, Dario now runs Anthropic, which is the maker of Claude. A lot of people probably are more familiar with Claude. Yeah. Karen Hao And it’s one of the biggest competitors to OpenAI. And Amadei, at the time when he was an executive at OpenAI, he thought that Altman was on the same page with him. And then over time began to feel that Altman was actually on exactly the opposite page of him and felt that Altman had used Amadei’s intelligence, capabilities, skills to build things And bring about a vision of the future that he actually fundamentally didn’t agree with. (Time 0:15:58)
The AI Race Runs On An Unproven Brain Theory
- Karen Hao says many AGI predictions rest on Ilya Sutskever and Geoffrey Hinton’s unproven belief that brains are giant statistical models.
- If that hypothesis is wrong, claims that scaling neural nets will surpass humans become far less certain. Transcript: Karen Hao One of the things that I feel like we should take a step back to examine is going back to this idea of what even is artificial intelligence and what do we mean by intelligence? And a huge part of the views of the different people and the quotes that you’re reading derives from a specific belief that they each have in this question of what is intelligence, what Constitutes intelligence. For Ilya, he has, throughout his research career, felt that ultimately our brains are giant statistical models. This is not something that, you know, we actually know, but this is his own hypothesis, also the hypothesis of his mentor, Jeffrey Hinton, who also was on this podcast. This is why they have such a strong conviction in the idea of building AI systems that are statistical models and that this particular approach is going to lead to intelligent systems As we are intelligent. It’s a hypothesis that they have. It’s not one that has been proven by science. And some people vehemently disagree with them on this particular thing. But if you step into their shoes and take on that hypothesis and assume that it’s true, that our brains are in fact statistical engines and that these systems that they’re building are Also statistical engines that they’re making bigger and bigger and bigger until they become the size of the human brain. That’s why they say that making this comparison where the system will become equal to human intelligence and then maybe exceed human intelligence is relevant in their framework. And Ilia gave a talk at one point at this really prominent AI research conference that happens every year called Neural Information Processing Systems. It’s a mouthful. But he gave this keynote where he shows this chart of the size of brains and the intelligence of a species. And it’s roughly linear. The bigger the size of the brain, the more intelligent the species. And so for him, he thinks he’s building a digital brain because he thinks brains are just statistical engines. So from that logic, it’s like, okay, if we then build a bigger statistical engine than the human brain, then based on this chart, it will be more intelligent and then we will be subjected To the same treatment that we’ve subjected animals. But it’s really important to understand that these are scientific hypotheses of specific individuals within the AI research community. And there’s a lot, a lot of debate about whether this is in fact the case. And some of the biggest critics say it’s very reductive to think of our brains as simply just statistical engines. (Time 0:21:10)
Why Building Human Replacements Is The Wrong Goal
- Karen Hao challenges the default premise that AI should recreate humans rather than augment them.
- She says medicine-focused systems like drug discovery tools could deliver value without building labor-replacing digital brains. Transcript: Karen Hao And no. So it matters because these companies, they are driving their future actions based on this hypothesis. So they have decided, we think that this hypothesis is true, like we should just continue building larger and larger statistical models in the pursuit of artificial general intelligence. And that’s then having global consequences. Like, in order to continue doing that, they’re hoovering up more and more data. They’re building more and more data centers. They are having, they’re, you know, exploiting more and more labor in order to continue on this path. Here’s a question that I think is important to ask is, why are we trying to build AI systems that are duplicative of humans? We’re kind of having this conversation right now where we’ve just taken the premise of this industry as a good thing. Like, they said that we should be building AGI, so we say that we should be building AGI. But I would like to ask, like, why are we doing that? Why is it that we are building a technology that is ultimately designed to replace and automate people away? That is not the enterprise of technology. Like, we should be building technology. And the purpose of technology throughout history has been to improve human flourishing, not to replace people. And so this is like a critical part of my critique of these companies and these scientists that have just adopted this goal and have relentlessly pursued it and have had enormous capital And enormous resources to pursue it. Is this the right goal? Like, why are we doing this? Why can’t we just build AI systems that do things like accelerate drug discovery and improve people’s healthcare outcomes, which are systems that have nothing to do with the statistical Engines that they’re trying to build to duplicate the human brain? (Time 0:24:18)
Why Karen Hao Calls AI Companies Empires
- Karen Hao calls major AI firms empires because they seize data, exploit labor, and control research that explains the technology.
- She points to Timnit Gebru’s firing after criticizing large language models as a case of silencing inconvenient science. Transcript: Karen Hao Think it’s because they’re driven by an imperial agenda. And that is why I call these companies empires of AI. Steven Bartlett What do you mean by an imperial agenda? What does that term mean? Karen Hao Empire is the only metaphor that I’ve ever found to fully encapsulate all of the dimensions of what these companies do and the scale that they operate and what motivates them to do what They do. And there are many parallels that you see between what I call the empires of AI and the empires of old. They lay claim to resources that are not their own in the pursuit of training these models. That’s the data of individuals, the intellectual property of artists, writers, and creators. They’re land grabbing in order to build these supercomputer facilities for training the next generation models. Second, they exploit an extraordinary amount of labor. They contract hundreds of thousands of workers all around the world, including in the U.S., to ultimately make these technologies. We can talk about that more. And they also design their tools to be labor automating so that when the technologies are deployed, it also affects labor rights because it erodes away labor rights. And this is a political choice that they have. Third, they monopolize knowledge production. So they project this idea that they’re the only ones that really understand how the technology works. And so if the public doesn’t like it, it’s because they don’t actually know enough about this technology. They do this to the public. They do this to policymakers. And they’ve also captured the majority of the scientists that are working on understanding the limitations and capabilities of AI. Steven Bartlett You think they’re gaslighting the public in a way? Karen Hao They are, yeah. So if most of the climate scientists in the world were bankrolled by fossil fuel companies, do you think we would get an accurate picture of the climate crisis? Steven Bartlett No. Karen Hao And in the same way, they employ and bankroll, the AI industry, employs and bankrolls most of the AI researchers in the world. So they set the agenda on AI research in soft ways, simply by funneling money to their priorities so that only certain types of AI research are produced. But they also will censor researchers when they do not like what the researcher has found. And so I talk about the case of Dr. Timnit Gebru in my book, who was the ethical AI team co-lead at Google. When she was literally hired to critique the types of AI systems that Google was building, she then co-wrote a critical research paper that was showing how large language models specifically Were leading to certain types of harmful outcomes. And in an attempt to try and stop this research from being published, Google ended up firing Gebru and then fired her other co-lead, Margaret Mitchell. (Time 0:26:26)
OpenAI Subpoenaed A Small Watchdog Group
- Karen Hao describes how OpenAI subpoenaed a small watchdog leader during the fight over its nonprofit-to-for-profit conversion.
- The papers demanded all communications involving Elon Musk, even though the group said Musk never funded or directed them. Transcript: Karen Hao Was opening. I started subpoenaing some of its critics. Yeah. As, as part of a, what’s what appears to be a campaign of intimidation, but also what appeared to be a campaign of phishing for more information to figure out, to map out the network of Critics further. But this was a man who runs a small watchdog nonprofit, and they had been doing a lot of work during that time to try and ask questions about OpenAI’s attempt to convert from a nonprofit To a for-profit. Ultimately, OpenAI was successful in that conversion. During the period where it was sort of existential for OpenAI to complete this conversion, there were a lot of civil society groups and watchdog groups like Midas who were trying to Prevent the process from happening in the dead of night. They were trying to get more transparency. They were trying to have more public debate about this because it’s unprecedented. And it was then that there was a knock on his door and he was served papers. Steven Bartlett What do the papers say? Karen Hao The papers asked him to reproduce every single piece of communication that he had had that might have involved Musk. So this was like the strange paranoia that opening I had that Musk was somehow funding these people to block the conversion. None of them were actually funded by Musk. (Time 0:29:43)
How AI Leaders Use The Good Empire Story
- Karen Hao says AI leaders justify concentration of power with a familiar empire story: a good empire must beat a bad one.
- OpenAI first cast Google as the threat; today China often plays that role to rally capital and deference. Transcript: Karen Hao And one of the other ones that’s really important to understand about the AI empires in particular is empires always have this narrative that they say to the public, like, we’re the Good empire. And we need to be an empire in the first place because there are also bad empires in the world. And if you allow us to take all the resources and use all of the labor, then we promise we will bring you progress and modernity for everyone. We will bring you to this utopic state akin to an AI heaven. But if the evil empire does it first, we will descend into a hell. And the evil empire being in this case? In this case, most often it’s China. But actually in the early days, OpenAI evoked Google as the evil empire. So all of their decisions were about, we need to do it first, because otherwise Google, this evil corporation that’s driven by profit, us as a benevolent nonprofit, like this is a critical Contest of who wins. Steven Bartlett Do you think the people building these AI companies believe that the outcome is going to be all good now? Do you think they think that it’s going to be, it’s going to serve everyone, it’s going to be the age of abundance, everything’s going to go well? What do you think they believe? What do you think Sam believes? Karen Hao So this is so funny, is such a core part of the mythology that they create around the AI industry includes the belief that it could go very badly. It goes hand in hand. Like they need that part of the myth in order to then say, and that’s why we need to be in control of the technology, because that’s the only way that it’s going to go really, really well. And Altman has said publicly, you know, the worst case lights out for everyone. But best case, we cure cancer, we solve climate change, and there’s abundance. And Dario Amede, same kind of rhetoric. It’s like, worst case, catastrophic or existential harm for humanity. Best case, mass human flourishing. So this is like two sides of the same coin. They have to use both of these narratives in order to continue justifying an extremely anti-democratic approach to AI development where there should not be broad participation in Developing this technology. (Time 0:31:28)
OpenAI Refused To Engage With Karen Hao’s Book
- Karen Hao says OpenAI first agreed to participate in her book, then cut off access after Sam Altman’s firing made the company more sensitive.
- She sent 40 pages of requests for comment over a month, and OpenAI never answered any of them. Transcript: Karen Hao Knew my book was coming out because I had contacted OpenAI from the very beginning of my process and said, I’m working on a book now. Will you participate in it? And actually, initially, they said yes, even though… So my history with OpenAI, I profiled the company for MIT Technology Review. I embedded within the office for three days in 2019. My profile comes out in 2020. The leadership are very unhappy. And in my book, I actually quote an email that I received that Sam Altman sent to the company about my profile saying, yeah, this is not great. And from then on, the company’s stance to me was, we are not going to participate in anything that you do. We are not going to respond to anything, any questions that you receive. And this was, you know, this was things that they explicitly articulated. It wasn’t like me inferring. So I had a colleague at MIT Technology Review that also covered AI. And at one point opening, I sent him this press release being like, we would love for you to cover this story. And he was like, I’m really busy. Will you send it to Karen? And they were like, oh, no, we have a history you understand. And so for three years, they refused to talk to me. But then I ended up at the Wall Street Journal, where if they felt a bit compelled, because it was the journal, to reopen the lines of communication. And so I started having, you know, more dialogue with them. Every time I wrote a piece, I would always send them, here’s my request for comment. I would always ask them, like, will you sit for interviews? And we did get to a more productive relationship. And then I embarked on the book. So I left the journal to focus on the book full time. And I told them right away, I’m working on this book. I want to continue this productive conversation where I make sure I reflect OpenAI’s perspective in the book. And so they were like, we can arrange interviews for you. You can come back to the office. We’ll set up some conversations. And then as we were going back and forth on this, the board fires Sam Altman. And that’s when things started going kind of south because the company started becoming very sensitive to scrutiny. And so then they started pushing, kicking the can down the road, down the road, down the road. And I kept saying, hey, when are we rescheduling this? What’s going on? And then I get an email saying, we are not going to participate at all. You are not coming to the office. You’re not doing interviews. And I had actually already booked my tickets. So I was already going to fly to San Francisco to have the interviews. And so then I told them, I was like, that’s fine. I will still engage in the process. We’ll give you extensive requests for comment. I’ll ask through my reporting. I’ll keep you updated on all the things that I’m finding so that you can choose to still comment. I gave them 40 pages of requests for comment. And I gave them over a month to respond to all of that. So this was when the tweet came out, was we were doing all this back and forth, trying to, and that’s when Altman tweeted this. Hmm. They never responded to a single one of the 40 pages. (Time 0:34:42)
How AI Companies Control Coverage With Access
- Karen Hao and Steven Bartlett say AI companies use access as leverage to shape coverage and discourage critics from being platformed.
- Hao says losing OpenAI access early freed her to report without playing the access game. Transcript: Karen Hao Mean, going back to what you were saying earlier, that with the way that OpenAI and these companies control research, you asked, do they also do this with journalists? I mean, yes, the answer is yes. And apparently they also do it with anyone who has, you know, a broad mass communications platform. It’s not just about the conversation that you’re going to have with them. It’s about who you also choose to platform. And there’s this huge problem in technology journalism where companies know that a really big carrot that they can give to technology journalists is access. Yeah, yeah, yeah. And they will withhold that access at the drop of a hat if they catch wind that you’re speaking to someone that they didn’t want you to speak to. Steven Bartlett This is so true. And I don’t think the average person really truly understands this. Yeah. So this kind of sounds like theory as you say it, but I’m not going to name names here because I don’t think it’s important. But there is a particular person in AI whose team have basically dangled the carrot of them coming here for like 18 months and I’m like you don’t you don’t have to dangle the carrot I’m Gonna speak to whoever I want to regardless of the carrot or not yeah and when this person comes if they want to come I’ll give them a fair shot I’ll ask them all genuinely curious questions About what they’re doing their incentives I won’t gotcha them I don’t have a history of ever gotchering anybody even if I disagree like even if I have a different of opinion I’ll ask the Question yeah but they dangle carrots and they say well if you know he’s thinking about it let’s think about a date and what the what the strategy is and i don’t think they they think most People don’t understand is if we just dangle it for long enough then they will um perform in the way that we want them to do and they’ll be they’ll be pleasant about us they won’t be critical They won’t give a give a won’t platform our critics our critics and i think a lot of their game is just dangle the carrot forever yes yeah that’s like the optimal outcome is if we just dangle It if we just tell them yeah no we’re just trying to looking at the schedule it just doesn’t work i think in the modern world you just have to go there and give your opinion and allow the clash Of ideas in the public forum let the viewers decide for themselves what they think. Karen Hao Yeah, but this is such a huge part of their machinery, is the way that they use these tactics to massage the public image of these companies and make sure that information that they don’t Want out, and even opinions that they don’t want out there, go out there. And so this is, you know, I feel very lucky now that opening eyes shut the door early on me. At the time, I didn’t feel lucky. I felt like I had screwed myself over. I was like, should I have been nicer to them in the profile so that I could maintain access? Which is a horrible question to ask as a journalist, right? Like you’re supposed to report the truth and you’re always supposed to report in the interest of the public. Like that is the point of journalism. And in that moment, I was like relatively junior in my career. I was like, did I misunderstand what journalism is about? Like, should I have actually been playing the access game? But it was too late. I had the door shut to me. And so I had to build my career understanding that the front door was never going to be open. Yeah. And that actually really strengthened my own ability to just tell it like it is. Be objective. Yeah. And just report what I see are the facts being presented to me, irrespective of whether the company likes it or not. And most often the company really does not like it, but I can continue to do the work. They don’t need to open the front door for me. (Time 0:38:42)
What Triggered Sam Altman’s Brief Firing
- Karen Hao reports Ilya Sutskever and Mira Murati pushed OpenAI’s board to remove Sam Altman over chaos, mistrust, and inconsistencies.
- Board members also discovered the OpenAI Startup Fund was effectively Altman’s, reinforcing fears he was not candid. Transcript: Karen Hao Yeah. There’s a scene by scene recounting. Steven Bartlett From who? Karen Hao I can’t remember the exact number of sources, so I don’t want to misquote myself. But it was around six or seven people that were directly involved or had spoken to people directly involved in the decision-making process. So Ilya Satskever is seeing these serious concerns about the way that Altman’s behavior is leading to bad research outcomes and poor decision making at the company. He then approaches a board member, Helen Toner. Steven Bartlett Ilya, for anyone that doesn’t know, is the co-founder we mentioned earlier, the co-founder of OpenAI we mentioned earlier. Karen Hao Yes. And he kind of does a bit of a sounding board thing to Helen just because Ilya’s freaking out. He’s been like sitting on these concerns for a while. And he’s like, if I tell this to someone, this could also be really bad for me if Altman finds out. And so he asks for a meeting with Toner. And in that first meeting, he’s like, he barely says a thing. He’s just like dancing around trying to figure out, hey, is this someone that I can maybe trust to divulge more information? Steven Bartlett And Toner’s role and responsibilities at OpenAI were? Karen Hao She was a board member. Just a board member. Yeah. And specifically an independent board member. So OpenAI, when it was a nonprofit, the board was split between people who had a stake, financial stake in the company, and then people who were fully independent. And this was meant to be a structure that would balance the decision making to be in the benefit of the public interest rather than to be in the benefit of the for-profit entity that opening Eye then created and ilia as a non-independent board member was approaching toner as an independent board member to try and see whether or not she was potentially seeing or hearing The same things that he was about the effect that Altman was having on the company. This then sets off a series of conversations, first between Ilya and Helen, and then between Mira Moradi and some of the board members. So Mira Moradi was at that point the chief technology officer of OpenAI, where these two senior leaders essentially through these conversations and through documentation that they’re Pulling together, like email, Slack messages, and so forth, they convey to the independent board members, three independent board members, we are very concerned about Altman’s Leadership. Like he is creating too much instability at the company. And it is like he is the root of the problem. It’s not, they were trying to say to these independent board members, like, the problem will not be fixed unless Altman is removed. Because of the way that he’s pitting teams against each other and creating this environment where people are unable to trust each other anymore. And they’re competing rather than collaborating on what’s supposed to be this really, really important technology. Steven Bartlett When you say instability, that’s quite a vague term. That could mean lots of things. Like instability could mean pushing people hard to work harder. Right. What do you mean by instability in as specific terms as you can possibly say them? Karen Hao When ChatGPT came out in the world, OpenAI was wholly unprepared. They didn’t think that they were launching a gangbusters product. They thought they were releasing a research preview that would help them get the data flywheel going, collect a bunch of data from users that would then inform what they thought would Be the gangbusters product, which was a chatbot using GPT-4 and chat GPT was using GPT-3 And because of that, there were servers crashing all the time because they had to scale their Infrastructure faster than any company in history. And there were all of these outages. They were trying to also hire faster than any company in history to try and have more personnel there. And they were then sometimes hiring people that they were like, actually, we made a mistake. We shouldn’t have hired you. So they were firing people left and right. And people were just disappearing off of Slack. And that’s how their colleagues would learn that they were no longer at the company. And so it was, yes, like many fast growing companies, a very chaotic environment, and a particularly chaotic environment because it was extra fast, like they had to accelerate more Than any other startup. And on top of that, Miramirati and Eliasetsk ever felt that Altman was making it worse. Like, he was not actually effectively ameliorating the circumstances of the chaos. He was actually sowing more chaos, getting these teams to be more divided. And this is where it’s important to understand that the executives and the independent board members, they’re all operating under this idea that they’re building AGI and that AGI Could either be devastating or utopic to humanity. And so it’s not, yes, it’s like any other company, and no, it’s not like any other company. You cannot have, like in their view, you cannot have this degree of chaos as the pressure cooker for creating a technology that they, in their conception, could make or break the world. And so that is basically what the independent board members also begin to reflect on. They have these conversations amongst themselves where they’re like, well, based on what we’re hearing about Altman’s behavior, like if this was an Instacart, would that warrant Firing him? And they concluded, maybe not. But this is not Instacart. And that’s why they were like, well, crap. Maybe this is actually, this does rise to the bar where we should consider replacing him. Because we are ultimately building a technology that we think could have transformative impacts, either in the positive or negative direction. And so that is what happens. It’s like these two executives and then the independent board members also, they were hearing other feedback as well from their connections within the company, with other people In the industry. At one point, Adam D’Angelo, who is one of the independent board members and the CEO of Quora, which is, you know, a tech startup in the Valley, He is at a party in San Francisco, and he starts To hear some of these rumors that there’s something weird about the (Time 0:42:39)
Why Every Tech Billionaire Wants Their Own AI Lab
- Karen Hao says it is no accident that AI founders keep splintering into rival labs after clashing with each other.
- Musk, Dario Amodei, Ilya Sutskever, and Mira Murati each left to build AI in their own image. Transcript: Karen Hao It is not a coincidence that every single tech billionaire has their own AI company. They want to create AI in their own image. And that’s why they keep not getting along. And in fact, it’s not just don’t get along. They end up hating each other after working together and then splinter off into their own organizations. So after Musk leaves, he starts XAI. After Dargo leaves, he starts Anthropic. After Ilya leaves, he starts Safe Superintelligence. After Mira leaves, she starts Thinking Machines Lab. They want to have control over their own vision of this technology. (Time 0:57:05)
Why AI Doom Talk Also Builds Power
- Karen Hao argues AI doom talk functions as myth-making that helps executives justify grabbing more power and resources.
- She compares the pattern to Dune: leaders seed belief, embody the myth, then blur strategy and sincere conviction. Transcript: Karen Hao I have a very long answer to this because do they know if they’re summoning the demon? It really depends on what we define as summoning the demon. And in this particular case, to go back to what we were saying before, there is a mythology that the AI industry uses where summoning the demon is an integral part of convincing everyone That therefore they can be the only ones that are developing this technology. Steven Bartlett I got it. So on one end, you’ve got to say, if we don’t, China will. Karen Hao And that’s terrible. Yeah. Steven Bartlett But if we let anyone else do it other than me, then we’re fucked as well. Exactly. So that means that I have to do it and you have to give me money and support. Karen Hao Exactly. So when they’re saying these things, we should understand it as not as like a genuine prediction based on what they’re seeing. Because first of all, we don’t predict the future. We make it. We should understand this as an act of speech to persuade other people into believing that they should seek more power, more resources to these individuals. And so do they know that they’re summoning the demon? I mean, they are purposely trying to create this feeling within the public that they are, because it is a crucial part of their power. But do they, if we were to define just, do they realize that the things that they’re doing are having already really harmful impacts all around the world on vulnerable people, vulnerable Communities, vulnerable countries? That’s where I’m like, maybe yes, maybe no. And they don’t really care because in the frame of mind, like I sometimes use the analogy that the AI world is like Dune. Steven Bartlett Dune, for anyone that doesn’t know Dune. Karen Hao Science fiction epic written by Frank Herbert. And it’s set in this intergalactic era where there are all of these houses and they’re fighting each other for spice. So it’s a callback to colonialism and empire. And they all are trying to control the spice. But one of the features of this story is that there are these myths that are seeded on the different planets about a religious myth, basically, about the coming of the Messiah that are Used as ways to control the people. Paul Atreides, when he arrives at the planet Arrakis with the intention of trying to then fight against the empire and avenge his father’s death, he steps into a myth that has been seeded On this planet that says that one day there will be a messiah that comes and saves the planet. So he steps into the role of the messiah and leans into this idea in order to better control the people and rally them behind him as a leader to help with this quest. He knows that it’s a myth in the beginning, but because he lives and breathes and embodies it, it kind of starts to blur in his mind whether this is really a myth or whether he’s really the Messiah. And this is what I think happens in the AI world. On one hand, there are all these executives that actively engage in myth-making because, you know, I have all these internal documents that I write about in the book where they are very Keenly aware of how to bring the public along with them by showing them dazzling demonstrations of the technology, by using, crafting a mission that will sound really good and make People give more leniency to their companies. So they know they’re doing the myth-making. And also, I think many of them lose themselves in the myth because they have to live and breathe and embody it day in and day out. (Time 0:59:05)
Why Better AI CEOs Would Not Fix The Problem
- Karen Hao says swapping AI CEOs would not solve the core problem because the system itself centralizes decisions affecting billions.
- Her real critique is anti-democratic governance, not whether one leader seems morally better than another. Transcript: Karen Hao Don’t think it truly matters, that question, the answer to that question. Because to me, even if you were to swap all the CEOs for someone that people would say is better at running these companies, it doesn’t fix the problem that I identify in the book. Is that there is a system of power that has been constructed where these companies and the people running these companies get to make decisions that affect billions of people’s lives Around the world. And those billions of people do not get any say in how it goes. Steven Bartlett Those people, they can go to the polls, right? So if the public are sufficiently educated, they can go to the polls and pick a leader that says they’re going to legislate or pass laws or try and pass laws. Karen Hao Yes. But at the speed and pace at which these companies operate and at the sheer scale and size, they’re able to also spend extraordinary amounts of money, hundreds of millions in this upcoming Midterms, to try and kill every possible piece of legislation that gets in their way and craft legislation that would codify their advantage. And so to me, I think sometimes as a society, we obsess a little bit with, are these leaders good or bad people? And to me, the bigger question is, is the governance structure that we’ve created a sound one or that allows broad participation or an anti-democratic one that has consolidated this Decision-making power in the hands of the few? Because no person is perfect. I don’t care who is at the top of these companies. They’re not going to have the ability to make decisions on behalf of so many people around the world who live and talk and have a culture and history that are fundamentally different From them without things going wrong. And so that is why throughout history we’ve moved from empires to democracy. It’s because empire as a structure is inherently unsound. (Time 1:05:11)
Why Scaling AI Does Not Automatically Win Geopolitics
- Karen Hao rejects the simple US-versus-China intelligence race framing and says model capabilities are chosen, not automatically generalized.
- She argues labs optimize for lucrative sectors like finance, law, medicine, and commerce because buyers there pay most. Transcript: Karen Hao So there’s a lot of fundamentals in this argument that would need to be true in order for this to be a viable argument. And let’s knock them down one by one. So the first one is that these systems are intelligent and that just scaling them is going to bring us more intelligence. Steven Bartlett So far, so true? Karen Hao No, it’s actually not. Because, first of all, again, we don’t actually know if these systems are, like, intelligence is not, it’s not like the right analogy, almost. It’s sort of like, it’s like, as a calculator, a calculator can do math problems faster than a human. Does that make it intelligent? Steven Bartlett It has a narrow intelligence because it’s solving a narrow problem, which is like one plus one equals two. Karen Hao And these systems, they actually also are quite narrowly intelligent in the sense that even though these companies say that they’re everything machines that can do anything for anyone, They actually can only do some things for some people. This is like the jagged frontier of these AI models. Like some of the capabilities are quite good. Other capabilities are not that good. You know why that happens? It’s because the company can only focus on advancing certain types of capabilities. It can’t literally focus on advancing all types of capabilities. They have to actually set their mind to advancing a certain, by gathering the data that is needed for that capability, by taking, you know, getting a bunch of human contractors to annotate And train the model to do that exact thing. And so scaling these models is actually a perpendicular question to, are we actually getting more cyber capabilities specifically and more military capabilities specifically? Steven Bartlett I would argue that most of the top people in AI believe that the intelligence is going to continue to scale for some time. A lot of them do, like Geoffrey Hinton does. Karen Hao And again, it’s back to his hypothesis about how human intelligence works and what the appropriate model of the brain is. His hypothesis throughout his career has been the brain is a statistical engine. But that’s his hypothesis, and that is not universally agreed upon, especially among people that are not in the AI world. When you talk with neuroscientists and psychologists, people who actually study human intelligence and the human brain, that is where you start to get a lot of debate and disagreement About this particular view that Hinton has. And so this is kind of like one of the things. It’s like AI is already being used in the military and has been used in the military for a long time. But specifically accelerating large language models isn’t just the only path for getting military capability. Like the companies would have to choose to specifically pick military capabilities to accelerate, not just like general intelligence. It’s like, you know what I’m saying? Like they create this myth that they are actually pushing the frontier of all of the capabilities of the model, but that’s not what’s actually happening internally. And I have, I had hundreds of pages of documents on like how they were specifically training models. They pick what capabilities they want to advance and you know how they pick them. It’s based on which industries would be able to pay them the most money for their services. So they pick finance, law, medicine, healthcare, commerce. (Time 1:08:48)
Why AI Works Best As A Clinical Copilot
- Karen Hao says the best healthcare outcomes often come from pairing experts with AI, not replacing experts outright.
- She points to radiology research showing human judgment plus model assistance improves earlier and more accurate cancer diagnoses. Transcript: Karen Hao Okay. So this, this once again, goes back to this question of like, why do we build technology and why should we specifically be building AI? Okay. And for me, like the whole project of technology development advancement is not to advance technology for technology’s sake. It’s to help people. And there’s been lots of research that has shown that actually the best outcomes for people in a healthcare setting is for the radiologist to have the AI model in their hands. For the human expert to use the AI model as a tool, as an input into their judgment. (Time 1:16:37)
How AI Automation Breaks The Career Ladder
- Karen Hao says AI job loss comes from both model capabilities and executive choices to cut staff when AI looks cheap or convenient.
- Laid-off professionals increasingly end up in data annotation, training systems to perform the same work that displaced them. Transcript: Karen Hao So I do think that there is going to be huge impacts on employment, and we are already seeing those impacts. It is not simply because the AI models are just automating those jobs away. It is specifically because the models are improving in certain capabilities based on what the companies that are developing them choose to improve them on. And executives at other companies are then deciding to fire or lay off their workers because they think that AI can replace the worker, irrespective of whether that might be true. And there, you know, there have been cases of like the Klarna CEO who laid off a bunch of people thinking that he would replace everyone with AI, and then it didn’t actually work. And he had to ask some people to come back. Steven Bartlett I actually DMed him about this. If you’re hearing this, this is because I’ve DMed Sebastian and he’s fine with me sharing this. He said, because I’ve heard his name mentioned a lot. And so when I, when we talked about AI in the past and people mentioned Sebastian and Klarna as the example, I wanted to clarify with him what the truth was. He said, it’s great to hear from you. I think sometimes people struggle with two things can be true at the same time. I think it might be time to come back on your podcast. To your point, this is the media misinterpreting my tweet. We are doubling down on AI more than ever. Klarna is shrinking with almost 100 employees per month due to AI. We used to be 7,400 at the peak. A year ago, 5,500. Now we’re 3,300. And by the end of summer, so this was last year, we’ll be 3,000 people. AI handles 70% of our customer service conversations at this moment. This is because we have realized that with AI, the production cost of software comes down to almost zero. Just like manufacturing used to be all handcrafted and then the machines came, code used to be all handcrafted up until a few years ago. And now it is machine produced. And ultimately we pay people more than ever for the unique handcrafted man-made stuff. Klarna is a bank. People will want to connect to humans, not only machines. They want us to be personable, relatable, even flawed. So we need to make sure while we are automating, replacing with AI in parallel, we make sure we offer a super available human experience. Karen Hao I’m really glad you read this because I think it touches on some really important nuances to the AI, like the impact that AI is going to have on employment. So I think there’s often these binary narratives. It’s like AI is going to come for every job, or people say AI is not actually working and it’s not actually coming for jobs. And like, the reality is that it’s coming for jobs. There are definitely jobs that are being automated away because of the capabilities of their models. And there’s also jobs that are being lost because executives are deciding to lay off the workers, even if the models don’t match the capabilities because it’s good enough. Like they would rather have the good enough model for way cheaper. Steven Bartlett Or they made a mistake with hiring. They blotted their team and it’s a great convenient thing to say. Exactly. Karen Hao Like there’s many reasons, but like clearly we’re already seeing impacts on the job market. Like the US jobs report that came out earlier this year showed that there has been a decline in hiring, a slowdown in hiring across especially white collar professional industries. Steven Bartlett And you saw Anthropics report, didn’t you, this week? The TLDRs, it matches kind of what you were saying, where they, Anthropics looked at exactly how people were using their models. And they looked at like what people are saying. Yeah. And they said that there’s been a 40% reduction in entry-level jobs in particular. And then they made this graph, which has gone viral over the internet. The red shows where we are now in terms of capability. And based on how people are currently using the models they that’s their prediction extrapolate it out that the blue part will be the disrupted parts this is the things that they say Ai can do right now but people don’t realize it yet so if you look at it it’s like it’s kind of all the stuff you’d expect yeah it’s the physical real world human stuff which robots maybe Can do someday like construction or agriculture that are untouched but like office and admin um like saying finance stuff math and notice that these are all the things that i just named Karen Hao That they purposely finance math law media and arts that’s me cooked yeah so office and admin i mean they do focus a lot on like assistant type and managerial work. So, but the other thing that the Clarno CEO said was, but people also want human experiences. So it’s not actually just about the capabilities of the models. It’s also about what people want, like some things they would turn to AI for and some things they wouldn’t, irrespective of whether or not AI is capable of doing it. But because of a preference that they want human to human interaction. And so what we’re seeing right now is, yeah, the thing that happens with every wave of automation, which is that there is a bunch of entry-level work that gets automated away. And there are also new jobs created, but the jobs that are created are in one of two categories. There are people that get even higher skilled jobs. And what he was saying, like, we pay people more for like the handcrafted code now. And there’s also the people who get way worse jobs. And so there was this amazing article in New York Magazine that was talking about how a lot of people are getting laid off. And then they end up working in data annotation, which is the labor that I’ve been referring to throughout this conversation that companies need in order to teach their models the next Thing that the companies are trying to automate. And so like a marketer gets laid off and then they go and work for a data annotation firm to train the models on the very job that they were just laid off in, which will then perpetuate more Layoffs if that model then develops that skill. And the article was talking about how this has become a huge catch-all for a lot of people that are struggling with finding job opportunities right now, including award-winning directors In Hollywood that are actually secretly doing this data annotation work to put food on the table. And so when they talk about there’s going to be mass unemployment and then there’s going to be some new jobs created that we can’t even imagine, I think a lot of these narratives rarely Talk about, like, first of all, why are some jobs going away? It’s not just because of the model capabilities, it’s also because of executive choices and because of the rhetoric that they use if they want to just downsize. But the other thing that is rarely talked about is the jobs, a lot of the jobs that are created are way worse than the jobs that were there. And it breaks the career ladder. So it’s the entry level and the mid tier jobs that get gouged out. It’s (Time 1:22:07)
Klarna Halved Headcount While Revenue Doubled
- Klarna CEO Sebastian Siemiatkowski told Steven Bartlett AI shrank Klarna from roughly 6,000 employees to under 3,000 while revenue doubled.
- He said coding has effectively been solved and the company now relies mostly on natural attrition instead of heavy hiring. Transcript: Speaker 3 And foremost, we were early on released AI to support our customer service, which had that initial benefit of more calls being dealt with by AI, which customers liked because those Calls or chat messages were much, much faster and more qualitative. Then since then, that has actually expanded slightly. What we did, however, try to communicate as well is that we believed in a world of where AI is cheap and available, the value of human interaction will be regarded as higher. So, the future of customer service VIP is a human. We have then, hence, doubled down on providing more of that. But at the same point of time, the efficiency gains within the company has continued. I mean, we used to be about 6,000 people, and now we are less than 3,000, which is two, three years since we stopped recruiting. And at the same point in time, our revenue has doubled, right? So you can clearly see that AI has allowed us to do more with less people, but we have avoided layoffs and instead relied on natural attrition when people kind of move on to other jobs. I mean, from my perspective, we will continue to be very, you know, not really recruit much. I mean, we recruit a little bit here and there, but we expect that kind of natural attrition of 10, 15 percent per year to continue on to become fewer. Even the kind of more most skeptical engineers who are like very well renowned and appreciated like the founder of Linux and stuff like that basically said that coding has now been resolved And hence is not you know you don’t need to code anymore and that was kind of a common sentiment so I think in coding that’s definitely an engineering work that has been a tremendous shift In the last six months. Steven Bartlett What do all these people go do, Sebastian? Speaker 3 I am optimistic. I mean, I think obviously people will have a lot of opinions about this topic, but I still believe that we are going to move towards a richer society. Now, in the short term, there could be more worry about what happens if people don’t get a job and so forth. (Time 1:36:34)
Why AI Convenience For Some Creates Misery For Others
- Karen Hao says AI can make owners feel more human while pushing displaced workers into dehumanizing data-annotation labor.
- She cites workers glued to Slack for tasks, unable to step away, with one mother screaming at her child when work finally appeared. Transcript: Karen Hao Well, I actually had thoughts on something that you said before he called, which is you were saying that the Gen Zers, like there’s this trend that they’re actually disconnecting from Technology. So they’re becoming more in person. And then there’s this other class of workers that are actually leaning into the technology, but then becoming more human because they’re leaning into the technology because they’re Realizing that they should actually just be spending more time doing in-person to person interactions rather than staring at a spreadsheet. And so they’re no longer doing the typing and whatever. I really want to go back to this New York magazine piece that just came out because what you’re describing is true for a very specific category of people, which is often like the business Owners and leadership within companies that actually can make these decisions on how they spend their time and what they ultimately do with their time. What the piece talks about is the working class, like people who are not business owners that are then having to experience being laid off and then working for the data annotation industry, Which is now one of the top jobs on LinkedIn, by the way. Really? Yeah, so LinkedIn had a report that showed the top 10 jobs with the highest growth in the last year. And data annotation is on that list. Steven Bartlett And for anyone that doesn’t know what data annotation is. Karen Hao Yeah, so data annotation is the process of teaching these chatbots or any AI system to do what they ultimately are able to do. So the fact that ChatGPT can chat is because there were tens of thousands or hundreds of thousands of people that were literally typing into a large language model and showing it, this Is how you’re supposed to then respond when a user types in a prompt like this. Before they did that work, ChatGPT didn’t exist. Like it just, it would just, you would prompt the model and the model would generate some text that was not in dialogue with the person it would kind of generate something that was adjacently Related is this what they call reinforcement learning where you kind of you give it like it’s a part of the process of reinforcement learning so you do data annotation which is literally Um showing lots of different um you, examples of things that you want the model to know. And then reinforcement learning is getting the model to then train on those examples iteratively in a way that then gives the model some of those capabilities. And what the New York Magazine piece highlighted is many, many of the people that are getting laid off now or are struggling to find work. And these are highly educated people. They’re college graduates, PhD graduates, law degree graduates, doctors, and again, like award-winning directors that are then struggling to find employment in the economy because The economy has been very much restructured by AI. They are then finding themselves serving this industry. And the industry is designed in a way that is extremely inhumane. Because what the companies that use these data annotation services, like there’s these third-party providers that are data annotation firms. An OpenAI, a Grok, a Google, they will hire these firms to then find the workers to perform the data annotation tasks that they need. For these firms, these third-party firms, they are incentivized to pit workers against each other because they want this data annotation to happen at speed and as cheaply as possible So that they can also compete with one another in this middle layer to get the contract from the client. And so all of these workers that were interviewed for this New York Magazine story talk about how they actually no longer have an ability to be human. They are waiting at their laptop to be pinged on Slack for when a project is going to open up for data annotation because they’ve tried job hunting. They literally can’t find anything else. This is the thing that’s going to help them put food on the table for their kids. And there was this one woman who said, like, I have so much anxiety about when the project is going to come, when it’s going to leave, that when the project came, it was right when my kid Was coming off of school. And I just started tasking furiously because I don’t know when it’s going to go. And I need to earn as much money as possible in this window of opportunity. So then when my kid came home and tried to talk to me, I screamed at my child for distracting me. And then she was like, I’ve become a monster and I’m not even allowed to go to the bathroom or take care of my kids, let alone myself, because this industry that is absorbing more and more Of the workers that are being laid off is mechanizing my life, atomizing my work, devaluing my expertise, and then harvesting it for the perpetuation of this machine that all of these AI executives are saying is then going to come for everyone else’s jobs. And so what you were saying about this class of workers, the business owners that get to become more human because there are all of these AI models now doing the tasks that they don’t have To do anymore. It is at the cost of the vast majority of people who are not business owners that are struggling to find work, getting absorbed into the work of then providing these technologies that The business owners can use. (Time 1:41:05)
How AI Data Centers Shift Costs Onto Poor Communities
- Karen Hao says AI deepens inequality beyond work by concentrating pollution, water strain, and power costs in vulnerable communities.
- She cites xAI’s Colossus in Memphis, where residents reported a gas-leak smell from 35 methane turbines powering the supercomputer. Transcript: Karen Hao Mean, this is like the core themes of my work. And the reason why I’m critical of these companies is that they are creating technologies in a way that creates the haves and have-nots in an extreme form that we have. It’s exacerbating the inequality that we already see in the world. Like the people who have things will have way more riches. They’ll have way more free time. They’ll be allowed to be more human. But the people who don’t have things are being squeezed even more. And it’s not just from a work perspective. I mean, I talk in my book also about the environmental and public health crisis that these companies have created, where they are building these colossal supercomputer facilities And in communities all around the world. And they specifically pick some of the most vulnerable communities. We’re sitting in Texas right now. OpenAI’s largest, one of its largest data center projects is being built in Abilene, Texas as part of the Stargate initiative, which was an effort announced at the beginning of Trump’s Second administration to spend $500 billion on AI computing infrastructure. This facility consumes, when it’s finished, will consume more than a gigawatt of power, which is over 20%. So this is actually a little bit inaccurate now. This was something that circulated online for a while, but there’s updated numbers. Steven Bartlett Just for someone that can’t see because they’re listening on Spotify or something, it’s a picture of the size of this facility. Karen Hao So this is not the Abilene, Texas one. This is a meta facility. So let’s just talk about opening a facility in Texas. That one would be the size of Central Park and it would run a million computer chips and it would require the power of more than 20% of New York City. Steven Bartlett Do you know one of the things which I found confusing, so I’d like to like alleviate the dissonance, is I thought you were saying earlier that you didn’t think the job disruption promises Were real. Karen Hao No, what I was saying is that when we talk about what these executives predict about the future, we need to understand that they are ultimately trying to influence the public in a way That allows them to continue maintaining control over the technology. Steven Bartlett But objectively, do you think that the job disruption that they talk about, where you think this is real? Karen Hao Well, I don’t want to comment specifically on this chart, but it’s like we’ve already seen in job reports that there is a restructuring of the economy happening right now. But going back to the data science, so this supercomputer facility, it’s a meta supercomputer facility, is being built in Louisiana. And it would be four times the size of the Abilene, Texas one and use half of the average power demand of New York City. So it’s one fifth the size of Manhattan. This makes it seem like almost all of Manhattan, but it would be one fifth the size of Manhattan. When these facilities go into these communities, what happens? Power utility increases, grid reliability decreases. The facilities also need fresh water to generate the power for powering them as well as fresh water to cool. And there have been lots of documented stories of communities that are already really constrained in their fresh water resource. They’re under a drought when a facility comes in. And then there are people, the community is actually competing with this facility for freshwater. I talk about one of those communities in my book. And also, sometimes these facilities, instead of connecting to the grid, they instead, a power plant pops up next to it. So in Memphis, Tennessee, where Musk built Colossus, the supercomputer for training Grok, he used 35 methane gas turbines to power the facility. This is a working class community, a black and brown community, a rural community that was not even told that they would be the hosts of this facility. And they discovered it because they literally smelled what seemed like a gas leak in all of their living rooms. And that’s when they discovered that these methane gas turbines were taking away their right to clean air. And this is a community that’s already been facing a history of environmental racism. They had already had lots of struggles to access their right to clean air. And now there’s this huge supercomputer that’s landed in their midst that is pumping thousands of tons of toxins into their air, exacerbating the asthmatic symptoms of the children, Exacerbating the respiratory illnesses of other people. It’s one of the communities that has the highest rates of lung cancer. And so… Steven Bartlett And that supercomputer is taking their jobs. Karen Hao And then they also have supercomputers taking their jobs. So this is what I mean. It’s like the haves and have-nots are fundamentally being pulled apart even further. Like if you in this version of Silicon Valley’s future are in the misfortunate category of being a have-not, we are talking about you now getting a job that is way worse than what you had Because you might be doing data annotation and you might be treated as a machine rather than as a human to extract value, the value of your labor for perpetuating this labor automating Machine that these people are building. You might be competing with these facilities for freshwater resources. They’re also polluting your air. Your bills have increased. So the affordability crisis is getting worse. Like, how is that making people able to be more human? (Time 1:49:11)
Why We Need Bicycles Of AI Not Just Rockets
- Karen Hao says people should think about AI like transportation: not every problem needs a rocket.
- She contrasts giant language models with DeepMind’s AlphaFold, which used curated biology data to win a Nobel and aid drug discovery efficiently. Transcript: Karen Hao Okay. So one of the analogies that I always use is AI is like the word transportation. Transportation can literally refer to everything from a bicycle to a rocket. And we have nuanced conversations about transportation, where we always say we need to transition our transportation towards more sustainable options. We need to transition towards, you know, public transport, electric vehicles. And we don’t ever say everyone should get a rocket to do every, to all of their transportation needs, right? Like we’re in Austin. If you use a rocket to fly from Dallas to Austin, like that would just make no sense. It’s just a disproportionate use of resources to get the benefit of getting from point A to point B. That’s how we should think about AI. So all of the models that we’ve been talking about, I like to think of them as the rockets of AI. They use an extraordinary amount of resources and they provide benefit, some dramatic benefit to some people, but they’re also exacting an extraordinary cost on a large swath of people Because of the costs of developing this technology. Why don’t we build more bicycles of AI? This is things like DeepMind’s AlphaFold, which is a system that predicts how proteins will fold based on amino acid sequences. It’s really important for accelerating drug discovery, for understanding human disease. And it won the Nobel Prize in Chemistry in 2024. And the reason why it’s a bicycle of AI is because you’re using small curated data sets. You just have data that has amino acid sequences and protein folding. So that means you need significantly less computational resources to develop the system, which means significantly less energy, which means less emission, so on and so forth. And you’re providing enormous benefit to people. (Time 1:55:11)
How Karen Hao Says Citizens Can Push Back On AI
- Karen Hao urges people to resist empire-style AI by contesting data grabs, local data centers, and blanket adoption policies.
- She says these companies need flawless rollout to hit extreme revenue targets, so citizens should withhold cooperation where they disagree. Transcript: Karen Hao I would love to reframe the question and say, what should we be doing in this moment where it’s not going down? Where we do recognize that actually these companies in this moment need continued resources, inputs and labor to perpetuate what they are doing. Steven Bartlett Yeah, because this sounds like stop. And I just feel like stop is like a hard, it feels like, I just think, you know, with the government in place, they’re supporting these companies like crazy. Globally, this is happening. So I’m like, stop doesn’t feel. Karen Hao I always say we need to break up the empire and we need to develop alternatives. And we are already seeing a flourishing of incredible grassroots movements that are applying an enormous amount of pressure to the way that the empire is trying to unfold its agenda. 80% of Americans in the most recent poll think that the AI industry need to be regulated. When was the last time that 80% of Americans were on the same side of an issue? Steven Bartlett No, yeah. When I have these conversations on the podcast, the comment section are clear. Yeah. There’s no disagreement. There’s no one in there going, oh no, I think they should crack on. Karen Hao Yeah. Dozens, dozens of protests against data centers have broken out all around this country in the US, all around the world. Steven Bartlett So what do we do about it? Karen Hao So these are people that are doing something about it. They are actually reasserting their agency and exercising democratic contestation against the ways that the empires are going about their business. Steven Bartlett What goal should we be aiming at? So if I said to my audience, Janet at home, because this is kind of what I see in the comments, it’s hopelessness. It’s like, what can I do? I’m just a… Karen Hao Yeah, well, the goal is not that we completely get rid of this technology. The goal is that these companies need to stop being empires. And the way I define like a typical business versus an empire is that the empires are predicated on this idea that they do not have to provide a fair exchange of value with the workers who Work for them or the people who use them or all of the other people that are involved in the supply chain of producing and deploying these technologies. They can extract and exploit and extract and exploit and get more value than what they offer. Whereas typical businesses, there is a fair exchange. You buy a service, you feel like you got the same amount of value as the service that you provided. But like for these data annotation workers, for example, they do not feel in any way that they’re being paid the same value that they provide to these companies. So that’s like, for me, the North Star is like, we should be pushing back and holding accountable these companies when they operate in an imperial way. And that’s what we’ve seen with all of these people that are now literally protesting in the streets against data centers and having an enormous effect, by the way, actually stalling Data center projects and also completely banning data centers from being developed in their localities. Seeing that with artists and writers that are suing these companies for intellectual property infringement and creating a huge public conversation about what is it that we actually, How do we actually want to protect our intellectual property? I met Megan Garcia, who is the mother of Sewell Setzer III, who is the 14-year who died by suicide after being sexually groomed by a character I as Trapod. And she, when that happened, I mean, obviously was incredibly devastated by what had happened to her son. She also decided to do something about it. She sued the companies and that lawsuit then sparked many other parents and families who were actually experiencing similar things to sue these companies as well. That has created an enormous public conversation about what these companies are actually doing when they exploit and they extract. What is the cost to the lives of people around the world, including children? Steven Bartlett So what do you think my audience should do? If they agree with everything written in your book, Age, Empire of AI, Dreams and Nightmares, and Sam Altman’s Open AI, if they agree with everything said here, if they agree with everything We’ve discussed today, they’re concerned about their kids, they don’t want everyone to become data labelers, they don’t think that’s a particularly great solution. What can they actually go and do? Karen Hao When I was writing the book, the only discourse that was happening was this is the best thing since sliced bread. Because of all the actions of these people, like saying when they’re not happy with the things that these companies are doing, we now have 80% of Americans that want to regulate this Industry. And so I would say to people, think about all the ways that your life intersects with the resources that the AI industry needs to perpetuate what they do, and also the spaces that they Would need to deploy these technologies to continue having broad-based adoption in their work. So you’re a data donor to these companies. You could withhold that data. And that’s what those artists and writers are doing. Like they’re suing these companies to try and create mechanisms by which that data would then be withheld. You probably have a data center popping up around you. If you’re at a school environment or a company environment, you’re probably having a discussion in those environments right now about what should the AI adoption policy be. And these companies, they, like, I was talking with some OpenAI employees just the other day, and they were telling me that it’s understood internally that the revenue targets for The company are extraordinary. And they need things to go flawlessly for it to all work out. And so they would need every single person to adopt this, every single space to adopt this. They would need to be able to build their data centers at the speed that they’re trying to build them. And so what I would say to every one of your viewers is let’s not make it go flawlessly if we don’t agree with what they are doing. Because the thing is, what I’m saying is not that these technologies don’t have utility. It’s that specifically the political economy that has emerged to support the production of these technologies right now is exacting a lot of harm on people. Could be developed with much more efficient methods, with much less resource consumption. And we have a lot of different other AI systems at our disposal that are like the bicycles of AI that we also know provide extraordinary benefit at very little cost. (Time 1:58:38)
Concrete Actions People Can Take Against Big AI
- Steven Bartlett asks what listeners can do if they agree with the concerns raised about AI companies.
- Karen Hao says public pressure has already shifted opinion: ~80% of Americans now support regulating the industry.
- Identify how your life supplies resources AI needs (data, deployment locations) and act on those touchpoints.
- Practical steps: withhold your data (as artists/writers are pursuing via lawsuits) and push back on local data center projects.
- Influence institutional adoption by raising AI adoption policy discussions at schools and workplaces. Transcript: Steven Bartlett What do you think my audience should do? If they agree with everything written in your book, Age, Empire of AI, Dreams and Nightmares, and Sam Altman’s Open AI, if they agree with everything said here, if they agree with everything We’ve discussed today, they’re concerned about their kids, they don’t want everyone to become data labelers, they don’t think that’s a particularly great solution. What can they actually go and do? Karen Hao When I was writing the book, the only discourse that was happening was this is the best thing since sliced bread. Because of all the actions of these people, like saying when they’re not happy with the things that these companies are doing, we now have 80% of Americans that want to regulate this Industry. And so I would say to people, think about all the ways that your life intersects with the resources that the AI industry needs to perpetuate what they do, and also the spaces that they Would need to deploy these technologies to continue having broad-based adoption in their work. So you’re a data donor to these companies. You could withhold that data. And that’s what those artists and writers are doing. Like they’re suing these companies to try and create mechanisms by which that data would then be withheld. You probably have a data center popping up around you. If you’re at a school environment or a company environment, you’re probably having a discussion in those environments right now about what should the AI adoption policy be. (Time 2:02:27)