AI Whistleblower: We Are Being Gaslit By AI Companies, They’re Hiding The Truth! - Karen Hao

So much of what's happening today in the. AI industry is extremely inhumane. >> But this is me playing devil's advocate. And logically, it could be the case that. the civilization that accelerate their. research with AI is going to be the. superior civilization. >> No, it's not. This is a prediction that. you're making, right? >> Making Zuckerberg's making. >> And do you know what the common feature. of all of them is? They profit. enormously off of this myth. You know, I. have all these internal documents. showing that they're purposely trying to. create that feeling within the public so. that they can extract and exploit and.

extract and exploit. So, what do we do. about it? >> We need to break up the empires of AI. >> You know, I've been covering the tech. industry for over 8 years, interviewed. over 250 people, including former or. current OpenAI employees [music] and. executives. And I can tell you that. there are many parallels between the. empires of AI and the empires of old, right? like Lelay claimed the. intellectual property of artists, writers, and creators in the pursuit of. training these models. Second, they. exploit an extraordinary amount of. labor, which breaks the career ladder. because someone gets laid off and then. they work 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. [music] And when they talk about. that there's going to be some new jobs. created that we can't even imagine, a. lot of the jobs that are created are way. worse than the jobs that were there. And. then there's the environmental and. public health crisis that these. companies have created and how they're. able to also spend hundreds of millions. to try and kill every possible piece of. legislation that gets in their way and. will censor researchers that are. inconvenient to the empire's agenda. But.

what I'm saying is not that these. technologies don't have utility. It's. that the production of these. technologies right now is exacting a lot. of harm on people. But we have research. that shows that the very same. capabilities could be developed in a. different way that doesn't have all of. these unintended consequences. So let's. talk about all of that. This is super interesting to me. My team. given me this report to show me how many. of you that watch this show subscribe. And some of you have told us according. to this that you are unsubscribed from.

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single month, we fight harder and harder. and harder and harder to bring you the. guests and conversations that you want. to hear. I've stayed true to that. promise since the very beginning of the. D of Sio and I will not let you down. Please help us. Really appreciate it. Let's get on with the show. [music]. Karen, how you've written this book in. front of me here called Empire of AI: Dreams and Nightmares in Sam Alman's. Open AI. I guess my first question is. what is the research and the journey you.

went on in order to write this book. we're going to talk about and the. subjects within it today. >> I 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 missiondriven. 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. [snorts] 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 4 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 2. 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. I. didn't realize that I was working. towards writing a book, but starting in.

2018 when I took that job was. essentially the moment in which I began. researching the story that I I document. in it. >> A very timely time to start working in. artificial intelligence. For anyone that. doesn't know, this is pre OpenAI chat. GPT launch moment that shook the world. But in writing this book, you. interviewed a lot of people and went to. a lot of places. Can you give me a. flavor of how many people you've. interviewed, where it's taken you around. the world, etc. >> I interviewed over 250 people. So over. 300 interviews, over 90 of those people.

were former or current OpenAI employees. and executives. So the book covers the. inside story of opening eyes's first. decade and how it ultimately got to. where it is today. But I didn't want to. write a corporate book. I felt very. strongly that in order to help people. understand the impact of the AI. industry, we would also have to travel. well beyond Silicon Valley. These. companies tell us that AI is going to. benefit everyone and that's their. mission. But you really start to see.

that rhetoric break down when you go to. the places that look nothing like. Silicon Valley, that speak nothing like. Silicon Valley, and that have a history. and culture that are fundamentally. different as well. And that's where you. start to really understand the true. reality of how this industry is. unfolding around us. >> Karen, I often try and steer. conversations, but in this situation, I. feel like it's probably my. responsibility to follow. So with that.

in mind, I'm going to ask you where does. this journey begin and where should we. be starting if we're talking about the. subjects of empire of AI, AI generally. artificial intelligence and also I'd say. one thing I'm really keen to do in this. conversation which is I often see in. conversations is left out is let's. assume that our viewers know nothing. about AI. >> Yeah. So they don't know what scaling. laws are or GPUs or comput or whatever. and let's try and keep this as simple as. we possibly can in terms of language or. explain all the complicated language so. that we can bring as much people with us.

as we possibly can. >> Yes. >> Where should we start? >> 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 it. 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. There's no definition. from psychology, 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. um 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. So in. OpenAI's history, it has defined and. redefined it many times. When Sam Alman. 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 hundred 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 [laughter]. vision of one technology. These are very. different definitions that are spoken. out loud to the audience that needs to. be mobilized to ward off regulation or. get more consumer buy in into the the. industry's quest or to get more capital. more resources for continuing on this. journey with ambiguous definitions. I.

mean, speaking about different. definitions through time, in 2015, in a. blog post that Sam Waltman wrote before. open air was officially announced, he. explicitly outlined the existential risk. by saying, "Development of superhuman. machine intelligence is probably the. greatest threat to the continued. existence of humanity. There are other. threats that I think are more certain to. happen, for example, an engineered. virus, but AI is probably the most. likely way to destroy everything. >> in general." When Alman 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, Alman was. trying to convince Elon Musk to join him. on co-founding OpenAI. And Musk in. particular 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 the language that Alman uses. side by side with the language that Musk. was using at the time, it mirrors all. the things that Musk was saying. >> identical. I mean, 10 years ago, Musk. was going on podcast saying, tweeting, whatever, that the greatest existential. risk to humanity was AI. >> 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. Alman had been talking just about.

engineered viruses. And 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 [laughter]. >> and are you saying that Sam Alman. manipulated Musk because Elon did end up.

donating a huge amount of money to um. open AAI and co-founding it I believe. with Sam Alman. Elon Musk did end up. co-ounding it with Altman. And certainly. from Musk's perspective, he does feel. manipulated because he feels like Alman. was engineering his language in a way. that would make Musk trust him as a a. partner in this endeavor. And of course. then Musk is leaves. Um and through some.

of the documents that came out during. the 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 Alman. tricked him into being part of this. So. in in 2015, Sam Alman 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. [clears throat] And what you're saying. here is you're saying that Samman was. just mirroring the language that Elon. was using to get Elon involved in open. open AAI. And later it appears and again. there's a legal case taking place now. that Sam might have muscled Elon out in. some capacity. >> Yeah. So we know from the lawsuit and. the documents that have come out in the. lawsuit that Ilia Sgver who is 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 founded as a. nonprofit. They decided okay 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 Alman? because it's. they were the two co-chairmen of the. nonprofit. And in the emails, it became. clear that Ilia 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 had known, they had 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 Ilia, 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 they 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.". >> So, it sounds like Sam again managed to. persuade someone to do something. >> Mhm. >> I guess this begs the question, what do. you think of Sam Orman? >> I think he's a very controversial. figure. >> You did an interesting pause. It's a. pause where someone tries to select. their words. Well, this is this is this. is what's so interesting.

about those interviews is people are. extremely polarized on Alman there. 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 Daria Amade, CEO of. Enthropic, who was originally an. executive at OpenAI. 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. And it's one of the biggest. competitors to OpenAI. And Amade at the time when he was an ex. executive at OpenAI, he thought that Alman 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 Amade's. intelligence, capabilities, skills to. build things and bring about a vision of. the future that he actually. fundamentally didn't agree with. And so. that's why people end up with this bad. taste in their mouths. And so, you know,

I've been covering the tech industry for. over eight years and covered many. companies. I've covered Meta, Google, Microsoft in addition to Open AI. and. OpenAI and Altman is it's the only. figure that I've seen this degree of. polarization with where people cannot. decide. whether he's the greatest or the worst. [laughter]. >> You mentioned Dario there and I found it. really what I found really interesting. is to look at how people's quotes evolve. over time with their incentives. So I.

was looking at all of the all of the. things they've said on the record on. podcasts in their blog post to see how. it's evolved over time and Dario who was. the former VP of research open AAI and. has now moved on to enthropic who are. taking a slightly different approach to. developing AI said back in 2017 while he. was still at open AI that this is a. quote I think at the extreme end is the. Nick Bostonramm style of fear that an. AGI could destroy humanity. I can't see. any reason in principle why that. couldn't happen. My chance that.

something goes really quite. catastrophically wrong on the scale of. human civilization. might be somewhere between 10% and 25%. And also you mentioned Ilia who was a. co-founder of OpenAI and then left. I. guess the first question I'd ask is why. did I leave? >> It's a great question. [gasps]. So he was instrumental in trying to get. Sam Alman fired and he's another one of. the people who over time began to feel.

like he was being manipulated by Alman. towards contributing something that he. didn't believe in. And for. >> you know. >> because I interviewed a lot of people. Ilia in particular had. two pillars that he cared about deeply. One is making sure we get to so-called. AGI and the other is making sure that we. get to it safely. And he felt that. Altman was actively undermining both. things. He felt that Alman was creating.

a very chaotic environment within the. company where he was pitting teams. against each other where he was telling. different things to different people. >> Have you ever spoken to him? >> I have. So, so I interviewed him in 2019. for a profile that I did of OpenAI um. for MIT Technology Review. >> and back in 2019, he has a quote where. he says, "The future's going to be good. for AIs regardless. It would be nice if. it was also good for humans as well. It's not that it's going to actively. hate humans or want to harm them, but. it's just going to be so powerful. And I.

think a good analogy would be the way. that humans treat animals. It's not that. we hate animals. I think humans love. animals, and I have a lot of affection. for them. But when the time comes to. build a highway between two cities, we. are not asking the animals for. permission. We just do it because it's. important to us. And I think by default, that's the kind of relationship that's. going to be between us and AI, which are. truly autonomous and operating on their. own behalf. And that was in 2019, the. year that you interviewed him. >> One of the things that I 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 Ilia, 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 um 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 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. >> Why why does it matter to know the. mechanism? Is it not just important to know the. outcome which is that it's going to be. able to do make a video for me or agents.

are going to be able to do the work that. I do. Does it does it really really. matter for us to know the mechanism. behind it? >> Yes 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 uh 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. 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.

a critical part of my critique of these. companies and 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 is this the right goal? What 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 health care. outcomes, which are systems that have. nothing to do with the statistical.

engines that they're trying to build to. duplicate the human brain? >> So why are they doing it? I mean, you've. interviewed all these people. I think. it's what, 300 people in total, 80 or 90. of them from OpenAI, the maker of. CHACHBC. Why do you think they're doing. it? I think it's because they're driven by. an imperial agenda. And that is why I. call these companies empires of AI. >> What do you mean by an imperial agenda? What does that term mean? >> 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. Their 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 US 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. erodess away labor rights. And this is a. political choice that they have. Third, they monopolize knowledge production. And 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 policy. makers. And they've also captured the. majority of the scientists that are. working on understanding the limitations. and capabilities of AI. >> You think they're gaslighting the public. in a way? >> 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?

>> No. >> 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. Timmy Gabru 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 Gabru and then. fired her other co-lead Margaret.

Mitchell. And so they control and quash the. research that is inconvenient to the. empire's agenda. >> Did you have an example where this is. happening to journalists as well that. are asking questions of their team. members? I think I was watching a video. of yours where there was a young man. that was saying he had someone show up. at his door, knocked on his door and. asked for information, emails, text. messages, and this person was from one. of the big AI companies.

>> This was opening. I started subpoenaing. some of its critics. Yeah. Um as a as. part of a. what's what appears to be a campaign of. intimidation, but also what appeared to. be a campaign of fishing 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. But. during the period where it was sort of. existential for open AI to complete this. conversion, there were [clears throat] 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 um there was a knock on his. door and he was served papers. >> What did the papers say? >> The papers asked him to reproduce every. single piece of communication that he. had had that might have involved Musk. So this was like this strange paranoia. that OpenAI had that Musk was somehow. funding these people to block the. conversion. None of them were actually. funded by Musk. So in this particular. case their request he simply was just. answered you know I I don't have any.

documents because this doesn't exist. >> So going back to this point of empires. you were saying that one of the factors. of an empire is a land grab and then the. next one was. >> was labor exploitation. >> labor exploitation. The third one, controlling knowledge production. >> 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 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. [clears throat]. But if the evil empire does it first, we. will descend into a hell. >> And the [clears throat] evil empire. being in this case,

>> in this case, most often it's China. But. actually in the early days, Open AI. 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 this is a. critical contest of who wins. >> 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 up well. What do you think. they believe? What do you think Sam. believes? >> So, [laughter]. 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 Alman 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 Amade same kind of rhetoric was. like worst case catastrophic or. existential harm for humanity best case. mass human flourishing. So this is like. two sides of the same coin. Like 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. They must be the ones. controlling it at every step of the way. >> Sam Orman did a tweet saying, "There are. some books coming out about open AI and. me. We only participated in two of them. one by Kesh Hegy. >> Keegy. >> Khaggy focused on me and one by Ashley. Vance on OpenAI.

Um he went on to say no book will get. everything right especially when some. people are so intent on twisting things. but these two authors are trying to. you quote retweeted that tweet from Sam. Alman and you said the unnamed book. empire of AI is mine. Do you believe that tweet from Sam Alman. was in reference to your book? >> 100%. Because there's only three books. coming out about him. >> and he had caught wind that your book. was coming out and.

>> he 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 3 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 Alman 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 of the. questions that you receive. And this. was, you know, this was things that they. explicitly articulated. It wasn't like. me inferring. Um, so I I had a 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, so for. three years they they refused to talk to. me, but then I ended up at the Wall. Street Journal where if they felt a a. bit compelled because it was the journal. to reopen the lines of communication.

And so I I 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 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 fired Sam Alman. And that's when things started going. kind of south because the company. started becoming very sensitive to. scrutiny. [clears throat]. 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 the. interviews. And so, then I told them I. was like, "That's fine. I will still. engage in the process where I'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 Alman tweeted this. >> H. >> and they never responded to a single one. of the one of the 40 pages. >> Sam Alman does a lot of interviews. >> Yeah. >> You know, he's doing a lot of interviews. all the time. He's done every podcast.

I've seen him on everything from Tucker. Carlson to I think he's done Theo, Joe. Rogan, um podcasts all over the world. >> I wonder why he won't do mine. [laughter]. >> Well, maybe. >> I don't know why. I I I don't know. I. think I'm fair with everyone. I just ask. I just ask questions I genuinely care. about. I don't come in with huge. preconceptions or at least meet people. for the first time. But I've heard. through the grape vine. um that he doesn't want to do mine. I.

mean, going back to what you were saying. earlier that. with this 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 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. >> 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 who um 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 going to speak. to whoever I want to regardless of the. carrot or not. And when this person. comes, if they want to come, I'll 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. [clears throat] history of ever. gotchering anybody. Even if I dis 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 he's thinking. about it, let's think about a date." And. what what the strategy is, and I don't. think they they think those 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. 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, look, we're just trying 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 un decide for themselves. >> Yeah. >> What they think. >> Yeah. >> Um, but this is a Yeah. 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. >> Mhm. >> And so this is this is you know I feel.

very lucky now that opening I shut the. door early on me. >> at the time I didn't feel lucky. I felt. like I had screwed myself over. I was. nicer. access. to 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. it I I was like relatively junior in my.

career. I was like, did I misunderstand. what journalism about is is about? Like. >> should I have actually been playing the. access game? >> Mhm. >> But [clears throat] it was too late. I. had the door shut to me and so I had to. build my career understanding that the. door 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. like 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. [laughter] 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. I was still able to do more than 300. interviews. >> So Sam Alman gets. kicked off the OpenAI executive team. Did you find out why that happened? >> Yeah, there's a.

scene by scene recounting. >> from who? 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, Ilia Satskever. is seeing these serious concerns about. the way that Altman's behavior is. leading to. bad research outcomes and poor.

decision-m at the company. He then approaches a board member, Helen. Toner. Ilia, for anyone that doesn't. know, is the the co-founder we mentioned. earlier. The co-founder of OpenAI we. mentioned earlier. >> Yes. And he kind of does a bit of a. sounding board thing to Helen just. because Ilia is freaking out. He's like. he's been like sitting on this these. these concerns for a while and he's like. if I tell this to someone, this could.

also be really bad for me if Alman finds. out. And so he asks for a meeting with Toner. and in that first meeting he's like. re 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. >> And Toner's role and responsibilities at. OpenAI were. >> she was a board member. >> Just a board member.

>> Yeah. And and specifically an. independent board member. So opening eye. 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-m to be in. the benefit of the public interest. rather than to be in the benefit of the. for-profit entity that opening I then. created [clears throat]. >> and. Ilia as a. non-independent board member was. approaching toner as an independent.

board member her to try and see whether. or not she was potentially seeing or. hearing the same things that he was. about the effect that Alman was having. on the company. This then sets off a. series of conversations first between. Ilia and Helen and then between Amir. Moratti and some of the board members. Samir Moratti 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 they they were. trying to say to these independent board. members like the problem will not be.

fixed unless Alman 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. When you say. instability, that's a 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 spec. as specific terms as you can possibly.

say them? >> When chat GBT came out in the world, OpenAI was wholly unprepared. >> They didn't think that they were. launching a gangbusters product. >> Yeah. 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. gang busters product, which was a. chatbot using GPT4 and chat GBT was. using GPT 3.5.

And because of that, there were servers. crashing all the time because they they. weren't they had to scale their their. infrastructure, you know, faster than. any company in history. And there were. um 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 mirror Morati and. Ilasgiver felt that Alman was making it. worse like he was not actually.

effectively ameliorating the. circumstances of the chaos. He was. actually sewing 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 [clears throat]. AGI could either be devastating or. utopic to humanity. [clears throat]. 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 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 Kora, uh, which is, you know, start 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 way. that OpenAI has structured its OpenAI.

startup fund, which was this fund that. they the company had created to start. investing in other startups. >> Mhm. [clears throat]. and he realizes they'd never really seen. documentation about how the startup fund. had been set up from Alman. And finally. [clears throat] they get the documents. and it turns out that OpenAI startup. fund is not OpenAI's startup fund. It's. Altman's startup fund. And this was. something like one of several. experiences that the independent board.

members were also having where they're. like there's something not right about. the fact that there continuously are. inconsistencies inconsistencies between. the way that Altman is portraying. what is being done versus what is. actually being done. And so when these. two executives approach the board or the. independent board members, then they're. like, "Okay, this lines up with also the. experiences that we've been having.". And at that point, they then have this.

series of very intense discussions where. they're meeting almost every day talking. about should we actually really consider. removing Altman? And in the end they conclude, yes, we. should. And if we're going to do it, we. need to do it quickly. Because they were. very concerned that the moment that. Alman found out, his persuasive. abilities would make it impossible to. do. And so they end up firing Altman.

without telling anyone. You know, they. don't talk to any stakeholders to get. them on the same page. Microsoft gets a. call right before they execute the. action saying, "We're going to fire. Altman.". >> And Microsoft, for anyone that doesn't. know, are a lead investor in OpenAI at. the time. >> Yes. One of the only investors in OpenAI. at the time. And that is what then. devolves the whole thing because every. single person that is affected by this. decision is now extremely angry that.

they were not involved. And that is what. then creates this campaign to bring. Altman back. And then Alman is. reinstalled as CEO days later. >> This company that I've just invested in, it's grown like crazy. I want to be the. one to tell you about it because I think. it's going to create such a huge. productivity advantage for you. Whisper. Flow is an app that you can get on your. computer and on your phone on all your. devices and it allows you to speak to. your technology. So, instead of me. writing out an email, I click one button. on my phone and I can just speak the.

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page 357 where you say about Ilia. saying, "I don't think Sam is the guy. who should have the finger on the button. for AGI." Now, I I asked myself this. question. You know, I work with lots of. people here. We have 150 people that. work in this business and. those people know me best. >> Yeah. >> They see me on camera. They see me off. camera. So if they said that we don't. think Steven is the right person to host. the direc. >> Yeah. >> It would take a lot for them to say. that. >> Yeah. >> They must have seen some off camera.

for them to go we don't think he's the. right person to be on camera. Yeah. >> Or for whatever reason. And in the case. of AI, which is much more consequential. than a podcast that is, you know, filmed. in my old kitchen. Um it almost sends a. chill down one's body to think that the. co-founder of a business has gone to the. board and said this isn't the guy to. lead this consequ I mirror Marotti then. also said I don't think Alman is the. right guy. >> and then they both left later. >> So then Altman comes back and lo and. behold Ilia never comes back. So his.

concerns about the fact that Alman. founding out would be bad for him. manifested. He ended up not coming back. and Miriam Marotti then left shortly. thereafter. >> Quite a lot of these people leave, don't. they? Open AAI. >> they do. So if you consider. one of the. origin stories of open AI is this dinner. that happened at the Rosewood Hotel, which is a very swanky hotel um right. right in the heart of Silicon Valley.

that uh was one of Elon Musk's favorites. whenever he was coming up from LA to the. Bay Area. And there was this dinner that. was there where Altman was intending to. recruit the OG team that would start. OpenAI. So he's kind of telling everyone. you might have a chance to meet Musk. because Musk is going to come to this. dinner dinner. And he cold emails Ilia. and gets Ilia to then come because and. Ilia specifically wants to come because. he wants to meet Musk. And he also.

emails all these other people including. Greg Brockman, Dario Amade. These are. all people [clears throat] that ended up. working at Open. >> and they all almost all of them not not. every one of them but almost all of them. end up working at OpenAI. >> and leaving. >> almost all of them end up leaving. specifically after they clash with Alman. >> and Ilia he left and launched a company. called Safe Super Intelligence. >> Yeah.

>> Which is I mean that's an indirect if. I've ever heard one. [laughter] Do you. know what I mean? Do you know what I. mean? If someone like co-ounded this. podcast with me and then they left and. started a podcast called Safe. Podcasting, I [laughter]. I'd take that as a slight. I' I'd have people knocking on their. door [laughter]. and asking for their texts. One of the. things that is happening here is.

>> it is not a coincidence that every. single tech billionaire has their own AI. company. >> Mhm. >> 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. >> Mhm. and [clears throat] then splinter. off into their own organizations. So. after Musk leaves, he starts XAI. After. Dario leaves, he starts Anthropic. After.

Ilia leaves, he starts Safe Super. Intelligence. After Meera leaves, she. starts thinking machines lab. They want. to have control over their own vision of. this technology. And the best way that. they have. derived from their experiences of trying. to put their vision into the arena is by. creating a competitor and then competing.

with OpenAI and with all the other. companies out there. Do you think some. of these AICOs realize that they are. quite literally summoning the demon as. Elon said 10 years ago, but they don't. really care because being the person. that summoned the demon is makes you. consequential and powerful and. historical even if the outcome is. potentially horrific. Even if there's. like a 20% outcome of it being horrific. I remember I think it was Dario, he's. the one that said there's somewhere. between a 10% and 25% chance of things.

going catastrophically wrong on the. scale of human civilization. 25% is a. one in4 chance. If you put bullets in a fourchamber. revolver and said Steven, the upside is. you could become a multi-gazillionaire. and be remembered forever. The downside. is that there [laughter] would be a. bullet in your head. There is no chance. that I would take take that bet with a. 25% potential chance of things going. catastrophically wrong.

>> So, 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's 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. >> I got it. So on one end, you got to say. if we don't, China will and that's. terrible. >> Yeah. But if we let anyone else do it. other than me, then we're as. well. >> Exactly. >> So that means that I have to do it and. you have to give me money and support. >> 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 seed 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 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 are 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. >> Dune for anyone that doesn't know Dune. >> science fiction epic written by Frank. Herbert and it's set in this. intergalactic era where there are all.

these houses and they're fighting each. other for spice. So it's a call back 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 seated on the. different planets about a a religious. myth basically about the coming of the. Messiah that are used as ways to control. the people. And Paul at Trades when he arrives at. the planet Iraqis uh with with the. intention of um trying to then fight.

against the empire and um avenge his. father's death. He steps into a myth. that has been seated 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, [clears throat] 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 mythmaking 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 uh. and and and make people give more. leniency to their companies. So they. know they're doing the mythmaking 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. And so when you know Daario.

says he thinks that 10 to 25% of the. future could be catastrophic or or. whatever the probability is 10 to 25%. He is actively engaging in the. mythmaking but also he's losing himself. in the myth. Like I think if you were to. ask him, "Do you genuinely believe. that?" He would be like, "Yes, I. genuinely believe that." Because there's. been a blurring of when he's saying. something just to say something versus. when he actually believes what is he's.

required to believe in order to then. continue. doing the things that he's doing. [clears throat]. >> And this is the whole psychology of. cognitive dissonance, right? where you. the brain struggles to hold two. conflicting worldviews at the same time. So it's it's incentivized or it. endeavors to dismiss one. So if you you. know if you wanted to be a healthy. person but also a smoker. Um and I. pointed out that smoking is bad for you. The first words out of your mouth are. going to be yes but. >> smoking helps me with stress. Yeah, but.

I only do it when I think I don't know I. kind of see that at the moment because. these companies have to raise. extortionate like huge amounts of money. to fund their AI research and they're. building out all of these data centers. >> So when they're out in the public, they're always fundraising. All of these. major companies are fundraising all the. time at the moment. >> So you can't be fundraising and saying, "I'm going to destroy your children's. future potentially. There's 25% chance. that your children aren't going to have. a great life.". Which might be the truth. I mean that is. actually what they say Dario. This is.

what famously Dario Amade does. He's. like. >> he does that but the others Sam's not. doing that as much anymore. >> Yes. And it's because you know. it goes back to like each of them kind. of distinguish themselves a little bit. as as the brand that they need to. project. >> Do you think any of them are more have a. stronger moral compass than others? cuz. I think Dario often gets the credit for. having more of a, you know, more of a. backbone and being more conscious of. implications.

>> He does get a lot of credit for that. >> He's from Claude and Anthropic. For. anyone that doesn't know, >> I 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, which 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. lives around the. world and those billions of people do. not get any say in how it goes. >> 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. >> 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? >> [clears throat]. >> 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. It. does I don't I don't care who is on 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. um and 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 unound. it does not actually. maximize the chances of most people in. the world being able to live dignified. lives. >> I'm going to try and take on their point. of view. So, this is me playing devil's. advocate. Okay. But Karen, if the US. don't continue to accelerate their. research with AI, at some point, China's.

model is going to become so smart and. intelligent that we're basically going. to have to rent it off them and we're. going to be, you know, they'll get the. scientific discoveries. They'll discover. the new era of autonomous weapons and we. will be their backyard. And like. logically. that argument does appear to be pretty. true. >> No, it's not. >> If we scale up, if we just imagine any. rate of change with this intelligence, at some point we're going to come to a. weapon that could theoretically disable.

um all of the United States electricity, their weapons systems. It would know. exactly how to disable the United States. from a cyber perspective because it. would be that smart. All you've got to. imagine is any rate of improvement of. any period any sort of long period of. time. So this is a theory that might be. true and if it's true [laughter]. >> I mean yeah any theory might be true. >> but but if but but you know again going. to this point of like even if it's a. small percentage it's worth paying. attention to on the other side of the.

foot. This is a theory that people talk. about. It could be the case that the. most intelligent civilization is going. to be the superior civilization. Logically, that's a pretty sound thing. to say. No. >> So, there's a lot of 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. So far so true. >> 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 is a calculator a calculator. can do math problems faster than a. human. Does that make it intelligent? >> It has a narrow intelligence because. they're solving a narrow problem which. is like 1 plus 1 equals 2. But.

>> 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? is 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 uh 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. >> I would argue that most of the 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 Jeffrey Hinton does. >> And again, it's 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 [clears throat] 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 in 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 one of the. things is like AI. is already being used in the military. and has been used in the military for a. long time. But ex specifically. accelerating large language models. isn't just the only path for getting.

military cap. like the companies would. have to choose to specifically pick. military capabilities to accelerate not. just like general intell 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.

countries would be able to pay them the. most money for their services. So they. pick finance, law, medicine, healthcare, commerce. It's not actually intelligent. like a like a a baby where you the the. more that you that the baby grows up, they start having this like general. these general abilities. >> I think I have jagged intelligence. I'll. be honest. I wasn't going to say it, but. I think I know a little I know a little. bit about uh No, I know a lot about a.

little bit. >> Yeah, but if but you also have the. capability to learn and acquire. knowledge by yourself. And you also have. the ability to choose what you're going. to learn and acquire by yourself. >> It's not easy and it takes a lot more. time than these models. It seems less. compute, but. >> and you can learn how to drive in one. place and then immediately know how to. drive in another place. These models. cannot do that. Every time a. self-driving car is shifted to another. location, it has to completely retrain. on that location. It's like all the. self-driving cars. I mean, we're sitting. in Austin right now and there's all.

these self-driving cars that are driving. through Austin. But when one of them learns, they all. learn. >> which is which. >> well it's just because it's a it's an. operating system that is has an AI model. as part of it and you're training the AI. model and then you deploy that AI model. across all the self-driving. >> a big advantage because if one optimist. robot learns one thing in one factory. they all learn it and imagine that. imagine if humans if we all learned what. all the other humans learned that would.

be that would give us such an. unbelievable competitive advantage. I. mean one of the ways we did that is. through communication. >> They could not because they could be. learning the wrong thing which has also. happened again and again with these. technologies is that all of them then. learn the wrong thing and they all have. the same failure mode. I mean part of. the resilience of human society is that. we do have different expertises and we. also have different failure modes. >> I think sometimes we hold AI models to a. higher standard than we hold humans to. And in a weird because I I' I'd hear on. stage we're in we're in Austin at the. moment and I'd hear people go ah but you. know them AI models they hallucinate.

sometimes. I'm like, "Have you met a. human?" Like, [laughter] I I hallucinate. all the time. I can barely spell or do. math. [laughter]. >> So, >> yes, but it's it's once again like using. this analogy that was specifically. picked in the early days of the field as. a way to market these technologies. like. we're repeatedly using the intelligence. analogy and relating these machines to. human intelligence as a a way to try and. gauge whether or not it is good or. worthy or capable in society. I think.

the output is the thing that really m is. the most consequential which is like. okay it might have a different brain and. a different system but does it arrive at. the same capability like does it is it. able to do surgery on someone's brain is. it able to drive a car like my car. drives itself in in Los Angeles I don't. touch the steering wheel and I can drive. for many many hours and in here in. Austin I just saw the ones the other day. where they've removed the steering wheel. and the pedals the new cyber cabs so I. go it doesn't really matter if it's. using a different system if it's. navigating through the world as a car it. has a better safety record than human.

beings. Um then as far as I'm concerned, intelligence or not, it's like. >> yes, you know, >> but that was not the original argument. that you made, which was like these. systems are just generally going to. become more intelligent across different. things based on the prediction. This is. a prediction that you're making, right? Like that and [clears throat] this is a. prediction that all the AI um. >> Ilia's making, Dario's making, Elon's. making, Zuckerberg's making, man's. making, Dennis is making. >> And do you know what the common feature. of all of them is? They profit. enormously off of this myth.

>> Elon has recently spearheaded the. construction of Colossus, a massive. supercomputer in Memphis housing a. 100,000 GPU specifically to scale up. their API models faster than their. competitors. It appears that they've all. converged around this idea that you can. brute force your way to greater, more. generalized intelligence. They've. converged around the idea that you can. brute force your way into models that. they can sell to people for automating. certain tasks that are that are. financially lucrative.

>> And I heard Elon say that if you're a. surgeon, there's just no point. He was. like, don't train to be a surgeon. He. says in a couple of years time, Optimus. and AI generally are going to be better. than any surgeon that's ever lived. >> Yeah. You know, >> do you think these things are true? Well, you know, I I'm pretty sure it was. Hinton that famously slash infamously. said there would be no need for. radiologists anymore. >> There would be no need for radiologists. anymore in he set a deadline that we've. already passed. I don't remember how. many years. Radiology is doing great as a.

profession. >> Do you think it will be in 5 years? >> 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. technologies sake. >> It's to [clears throat] help people. And there have 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. and for the for the human expert to use. the AI model as a tool as an input into. their judgment. And it is that. combination that leads to the most. accurate and early diagnoses of certain. types of cancer that then help improve. the prognosis of the patient. >> Do you believe that in the coming years. all the cars pretty much all the cars on. the road will be driving themselves?

>> No. >> You don't you don't think so? >> Mm-m. >> How come? >> Because of the way the technology works. >> Because because these are statistical I. mean currently the way that AI models. are primarily developed. They're. statistical engines. You have what's. called a neural network, which is a. piece of software that has a bunch of. densely connected nodes and. [clears throat]. >> like parameters. Is this what they call. parameters? >> Yeah, pretty much. And you're just. pumping a bunch of data into it and then. it's analyzing the data and creating.

this all of these finding all these. correlations in the data, finding all. these patterns and then it's through. those patterns that the machine is then. able to act autonomously, right? And so. [clears throat] the way that they're. training a self-driving car is they're. they're recording all this footage and. then they have tens of thousands or. hundreds of thousands of human. contractors that draw literally around. every single vehicle in the footage, every single pedestrian, every single.

traffic light, every single lane marking. and label it exactly as such. So that. then it's fed into an AI model that can. identify all of these different. components and then it's connected to. another piece of software that is not AI. that's saying okay if you if the AI. model recognizes the pedestrian we do. not run over the pedestrian. If the AI model recognizes a red traffic. light we stop. And so the like the thing.

about statistical engines is that it's. based on probabilities. It's not based. on deterministic logic. So. systems make errors all the time and. it's impossible. It is technically. impossible to get them to stop making. errors. >> Humans make errors way more than. >> systems in this case. Like the safety. record is like isn't it like 10 times. more safe to be driven in a Tesla with. autonomous driving than it is to for a. human to drive? >> It depends on the place. It depends on.

whether the Tesla was trained to. specifically navigate the place that. you're driving. >> Get drunk. >> because if it's in Mumbai, >> in some place in Vietnam, no, it would. not be safer. I [laughter] WOULD MUCH. RATHER be driven. >> by someone that has been driving in that. place their whole life. I'm I'm not. arguing against like the fact that in. certain places where the car has been. explicitly trained to drive in this. place that it has a better safety record. than the humans that are driving in that.

place. But you specifically asked if I. think that all of the. >> most cars. >> most cars in the world in the US. >> in the United States cuz we're here. >> I don't actually think that it's like. imminently on the horizon. >> 10 years. >> No, I don't think so. >> I sat with Dra from Uber and he's pretty. convinced that his 9 million couriers. will be replaced by autonomous vehicles. >> I mean, how long have has self-driving. cars been. invested in thus far? It's been more. than 10 years. And what percentage of. cars right now are autonomous.

>> on the US roads? I mean, so part of it. is it's actually not a technical. problem, right? Like part of it is also. social problem like do people even trust. getting into these vehicles? Part of it. is also a legal problem which is if the. car the self-driving car kills someone, which it has happened. >> Yeah, it has happened. >> Who is responsible? So, in the case in. LA, it was both Tesla and the driver. because the driver dropped their phone, they looked down, and this was a couple.

of years ago, I believe. Um, and they. went to grab their phone and they hit. someone, and so it went to court, and. they were held both responsible, both. the driver and Tesla. Um, in terms of. Tesla, pretty much everyone that gets the car, it comes with autonomy now for pretty. much most people, I believe. >> Partial autonomy. Yeah, it's called full. self-driving at the moment where it's. like. >> I mean, yes, it is called full. self-driving. >> Full self-driving supervised where you. kind of have to be looking in the d. You. have to be looking in the right. direction, but. >> Yeah. So, it's partial autonomy.

[laughter]. >> And here in Austin, it's full autonomy. cuz there's no steering wheel. >> Yeah. >> On the new car. Um, so you can't drive. it anyway. But it is, you know, the. Model Y is the undisputed highest. selling car, bestselling car in the. world across all brands. Well, I guess. my point here is like these predictions. where they say AI is going to completely. change transportation and driving. It's. going to completely change lawyers. aren't going to have jobs. Accountants. aren't going to have jobs. Um, do you. believe that they are true? Do you.

believe that there's going to be mass. job displacement? >> Okay, so I do think that there is going. to be huge impacts on employment and we. 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 CLA 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. >> I actually DM'd him about this. If. you're hearing this, this is because. I've DM'd 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. mention Sebastian and Cler as the. example, I wanted to clarify with him.

what the truth was. >> He said, "It's great to hear from you. Um, 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. Cler. 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, will 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. China 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. I'm really glad you read. this because I think it touches on some. really important nuances to. the AI. Yeah. Like the impact that AI is. going to have on employment. So I think. the there's often these binary.

narratives. It's like AI is going to. come for every job. >> Mhm. >> Or people say AI is not actually working. and it's not actually coming for jobs. And like the reality is 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. >> or they made a mistake with hiring. They.

blowed their team and it's a great. convenient thing to say. >> Exactly. Like there's there's there's. many reason but like clearly we're. already seeing impacts on the job. market. Like the um US jobs report that. came out earlier this year showed that. there has been a decline in hiring is a. slowdown in hiring across especially. white collar professional industries. And you saw Anthropic's report the new. this week. The TLDDR is it matches kind. of what you were saying where they. Anthropic 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. prediction. >> extrapolated 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 would 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 that they purposely. >> finance, math, law, >> media and arts. That's me cooked. >> Yeah. office and admin. I mean they do focus a. lot on like assistant type and. managerial work. >> So but but the the other thing that the. CLO 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 humanto human interaction. >> and so what we're seeing right now is. yeah the the thing that happens with.

every wave of automation which is that. [clears throat] there is a bunch of. entry-level work that gets automated. away and there There are also new jobs. created, but the jobs that are created. are one 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. catchall for a lot of people that are. struggling with finding job. opportunities right now, including like. awardwinning 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. Um, 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 higher. order jobs and then way more lower order. jobs that get created. And so, how do. people continue to progress in their. careers? There's no more rungs on the. ladder. >> I actually don't know the answer to this. question. And I've been furiously trying. to find a good answer to this question. because I can, you know, everything is. theory. And for my audience, I would say. most of my audience don't run. businesses. A lot of them do, a lot of. them aspire to, but they don't run. businesses. So, they're kind of, they're.

also in the land of theory. They're. hearing lots of different things. Jack. Dorsey does his tweet saying he's. halfing his headcount because of AI. They don't know what's true. They don't. know the sort of internal economics at. Jack's company and did he bloat the. company during the pandemic and he's. just using this as an excuse to make. this share price spike seven points. because his investors now think they're. an AI company or whatever. >> Mh. >> It's hard to pass through. So eventually. I go, okay, what am I doing? >> I have hundred hundreds of team members, probably 70 companies I invest in, maybe. five or six that I'm like the lead. shareholder in. What am I actually doing. on a day-to-day basis right now? I am.

I'm also I also consider myself to be. head of recruitment. >> but in the last month in particular I. have met extremely capable candidates in. terms of cultural alignment hard work. those kinds of things but I've had to. take a great deal of pause because when. I run the experiment of can I get an AI. agent to do that exact same thing the. answer is increasingly yes. >> especially in a world of open clause. >> and so what I'm curious like. >> now you confront this decision where. you're seeing in this short-term period.

you could just choose the AI agent. and in the long-term period. there is no career ladder. So, so who. are you promoting into these senior. roles? Like what how do you resolve it. for your own company? >> Yeah, it's a good question. So, there's. kind of two ways I'm thinking about it. I think really deep expertise is very. very valuable because if you're now the. orchestrator of potentially AI agents, it's really about um having a deep. understanding of the right question to. ask and and that's someone who has deep.

expertise on something. So I need my CFO. >> because if she's going to be. orchestrating our team of agents that. might be doing financial analysis or. whatever else, she needs to understand. what to tell them to do in our company. >> Mhm. >> And in turn financial analysts can't do. that. They need this the 50 odd years of. experience that you know CLA has. On the. other end, I need Cass. Cass is 25. Cass. knows everything about AI agents. He's a. young Japanese kid who's highly highly. curious. You know, on the weekend, he's. building AI agents to solve problems in.

my life. I need those two kinds of. thinking, which is highly proficient. agent maxing young kids or they don't. necessarily need to be young, but like. really lean in high curiosity. That's. creating a force multiplier in my. business. And then I need deep. expertise. Now the everything else. outside of there is another one I've. thought of another group is like people. with extremely great IRL people skills. >> because we do meet people in real life. We greet you when you arrive here. We. greet we when we go for lunch with big. clients that we have whether it's Apple.

or LinkedIn or whoever it might be. We, you know, we need to smoosh. >> Mhm. >> And we have teams who, you know, are in. person in the office. So, we we do a lot. of stuff IRL and increasingly we're. building communities even for this show. We're doing community events all around. the world. So, we need people that are. good at that as well. IRL, bringing. people together in real life and. organizing stuff. Those are the three. groups of people that I'm like, you. know, irreplaceable right now. And if. you were to to all of the all the roles. that could be done by AI agents, if we.

were to replace them with AI agents, do. you think you would still have these. three roles pools of people to hire and. promote into the three critical things. that you need in the long term? >> If things carry on at the the current. rate of trajectory, >> yeah, >> one could assert that even those roles. would experience pressure. If you just. imagine like people think of things. either statically or linearly or. exponentially. Yeah, >> you imagine an exponential rate of. improvement, which is kind of what I've. seen. Even like a 10% compounding rate. of improvement at some point,

[laughter]. >> at some point, at some point, I think. what remains is actually the IRL. irreplaceably human stuff, human to. human, our Maslovian needs of being in. person like we are now aren't going to. change. We need connection. Humans get. very sick when they don't have other. human beings in their life and strong, deep relationships. 100% agree. So that. stuff is going to matter a whole lot. I. have this contrarian weird take that. actually maybe this is the first. technology that's going to deliver on. the promise of making us human and. connected because we're going to be.

rendered useless of everything else. other than what humans are good at. Cuz. all the other technology said, "Oh, we're going to make you more connected, connecting the world." And they. disconnected the world and isolated the. world. But maybe this is the one. It's. so intelligent now that it doesn't need. us to around in spreadsheets. anymore. >> Do you see. that actually happening in real time. right now that it's making us more. able to be in person, connected with one. another, having deeper social community. engagements. >> Yes. >> Yes. >> And I'll give you some data points.

>> Okay. >> Data point number one, the Financial. Times released a report on social media. usage. And what they saw is 2022 was the. peak and it's plateaued ever since. The. generation that's plateaued the fastest. and heading down is the younger. generations. The boomers are still off. to the races, right? So on Facebook and. stuff. And then you look at the way Gen. Alfa are using social media. They're not. posting as much. They call it uh posting. zero. They're scrolling sometimes, but. they're in dark social environments like. WhatsApp and Snapchat and iMessage. They're not like performing to the.

world. They also value IRL experiences. much more than any other generation. They're like not getting smashed. We're. seeing every brand has a run club. um I mean runs exploding around the. world and we're seeing this real sort of. sort of almost like innate realization. that like technology let us down at some. fundamental level like dating apps let. us down social networking kind of has. let us down and we're seeing I think. maybe a bifocation of society where a. lot of people are going this like I. want to go back to what it is to be a. human. >> and I I would imagine that in such a.

world where intelligence is so. sophisticated that we no longer needed. to sit at laptops and like I think. screen time is going to continue to. fall. I think you go into an office, you're not going to see people sat at. laptops. You're gonna see something. completely different. And I think maybe, you know, and then we talk about robots. and Optimus robots. Elon says there'll. be 10 billion Optimus robots. Elon has. been wrong with timing before. He's. almost never been wrong on the big. things completely. He's just his timing. is got a bad track record. Um, so I.

think he's he's probably right. You. know, I think I've I've got some people. on the way from Boston Dynamics and. these other big companies like Scale AI, and they're actually bringing the robots. here to show it, like folding laundry, doing the dishes. I'm not saying that's. what I would want in my home, but I. think factory work is going to. completely change. I think a lot of. manual labor is going to completely. change, and I think we're going to be. forced to do what only we can do. Um, [clears throat]. Sebastian, who's the CEO of Cler, has. actually just called me. [laughter]. >> Hello, Sebastian. You're right.

>> Hey, how are you? >> I'm good. How are you? [laughter] It's been a while. >> It has been a while since you're on the. show. I was just saying we do need to. get you back on. >> I I just I just had a couple of simple. questions cuz you know I do a lot of. interviews and um Clan has always. mentioned because I think the media has. said that you like double down on AI. then you reversed because it didn't work. out. So I know I spoke to you a while. ago and we exchanged a couple of DMs. about it but that was more than a it was. almost a year ago now. >> So I just wanted to get an update on. Cler's business AI agents and all of. that if possible. First and foremost, we.

were early on uh released um AI uh to. support our customer service which had. that uh initial uh benefit of uh 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. Um 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 um we. have then hence doubled down on. providing more of that but at the same. time the efficiency gains within the. company has continued. I mean we used to. be about 6,000 people and and now we are. less than 3,000 which is 2 3 years since. we stopped recruiting and at same point. in time our revenue has doubled right so. you can clearly see that AI has allowed. us to be 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% per year to continue. and to become fewer. I think the big. breakthrough was really in November. December last year where even the kind.

of more most skeptical. uh engineers who were like very. well-renowned and 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 uh. you don't need to code anymore and that. was kind of a common sentiment. So I. think in in coding that's definitely an. engineering work that has been a. tremendous shift in the last six months. >> What do all these people go do. Sebastian? >> 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 and so forth. But I think in the. longer term, I I am optimistic what it. means for society and humanity. >> Thank you so much, Seb. I'll chat to you. soon. Thank you for taking the time. I. appreciate you, mate. Thanks. >> All right. All right. Byebye. Byebye. >> You know the little traditional SIM card.

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made for you. I've realized that the Dio. audience are strivals. that we want to accomplish. And one of. the things I've learned is that when you. aim at the big big big goal, it can feel. incredibly psychologically uncomfortable. because it's kind of like being stood at. the foot of Mount Everest and looking. upwards. The way to accomplish your. goals is by breaking them down into tiny. small steps. And we call this in our. team the 1%. And actually this. philosophy is highly responsible for.

much of our success here. So, what we've. done so that you at home can accomplish. any big goal that you have is we've made. these 1% diaries and we released these. last year and they all sold out. So, I. asked my team over and over again to. bring the diaries back, but also to. introduce some new colors and to make. some minor tweaks to the diary. So, now. we have a better range for you. So, if. you have a big goal in mind and you need. a framework and a process and some. motivation, then I highly recommend you. get one of these diaries before they all.

sell out once again. And you can get. yours at the diary.com. And if you want the link, the link is in. the description below. >> Any thoughts? Well, I actually had. thoughts on something that you said. before he called, >> which is you were saying that the. Jenzers 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 inerson interactions rather. than staring at a spreadsheet. And so. they're no longer doing the typing, 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. But what.

the piece talks about is the working. class like people 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. Um the 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.

>> And for anyone that doesn't know what. data annotation is. >> Yeah. So data annotation is the process. of teaching these chat bots or or any AI. system to do what they ultimately are. able to do. So the fact that chat GBT. 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,

chatgbt 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 [clears throat] a. >> 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 know 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 or or are struggling to find. work. And these are highly educated. people. They're college graduates, PhD. graduates, law degree graduates, doctors, um and again like award-winning. directors that are 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 being serving. this industry and the industry is. designed in a way that is extremely. inhumane because what the companies the. companies that use these data annotation. services like there's these third party. providers that are data annotation firms. an open AI a gro um a Google they will. hire these firms to then find the. workers to perform the data annotation.

tasks that they need for these 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 the the bid the. the contract from the 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.

because 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 off of school." And I just.

started tasking furiously because I. don't know what's going to go and I need. to earn as much money as possible in. this window of opportunity. So then my. when my kid came home and tried to talk. to me, I screamed at my child for 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 [clears throat]. 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 these this. class of workers, the business owners that get to become. more human because there are all of. these AI models now [clears throat].

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. >> and instead of becoming more human. [clears throat] they feel like their. humanity has been squeezed and. diminished and they have no ability to. have control, agency and dignity in.

their lives anymore. I think this is a. big I think this is a big question that. kind of pertains to this graph here. which is you know all of these people if. we believe anthropics prediction of who. will be disrupted these people in these. industries like arts and media legal um. life and social sciences architecture. and engineering computer and maths. business and finance and management and. also office and admin. These people if. we believe this would have to retrain at. something else and unlike the industrial. revolution where you might get 10 20.

years to retrain because factories take. a long time to build. The distribution. layer that AI sits on top of is the open. internet. So this is why chat can go and. get hundreds of millions of users in no. time at all and become the fastest. growing company of all time. Um one of. my fears is that this disruption takes. place at a speed where we can't. transition. And that was you know that I think you. you you said that sentence in the. passive voice the transition would. happen at a speed but who is driving.

that speed? >> Um. >> it's the companies. >> and their race with one another. >> Yeah. And so they are driving the. transition to happen at a speed at which. it would be really hard to take care of. all of the people that would be. bulldozed over by. >> this is one of the crazy questions that. no one can answer for me when I sit with. these people that are AI CEOs. So I go, "So what happens to the people if this. is if you agree that this is going to. happen at super speed?" You know, I. spoke to that CEO of Uber, Dar, who said.

very similar things to what you're. saying is, you know, there'll be data. labeling jobs, for example, for the. drivers. But um they can't all become. data labelers. And there's a question. around meaning and purpose and. fulfillment. And that comes from losing. your meaning in life. I s also sit here. with so many people who talk about how. their father lost their job in Iran or. some some other country and came to the. United States and had to be a a toilet. cleaner on particular case was a doctor. in Iran but came to the US and was a. toilet cleaner and had to deal with the.

sense of shame that that particular. person felt and the lack of dignity that. that caused and how that made that. person's self-esteem feel and the. depression alcoholism that transpired. from that. um if this happens at a large. scale across society, there's going to. be a ton of consequences like that. >> I mean, this is 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 halves and have nots in. an extreme form that we have. It's 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 [clears throat]. are even 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.

supercomput facilities. there and and in. in comm community like communities all. around the world and they specifically. pick some of the most vulnerable. communities. We're sitting in Texas. right now. Open AAI's largest one of its. largest data center projects is being. built in Abalene, 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 will when it's finished will. consume more than a gigawatt of power. which is over 20%. over 20%. So this is actually a little. bit inaccurate now. Um this was. something that circulated online for a. while but there's updated numbers. >> just for someone that can't see cuz. they're listening on Spotify or. something. It's a picture of the size of. this facility. >> So this is not the Abene Texas one. This. is a meta facility. Yeah. So, let's. first talk about opening eyes 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. >> 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. 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. >> But objectively, do you think that the. job disruption that they talk about. where. >> Yeah. Yeah. I mean I I mentioned. >> real. >> well I. >> I don't want to comment specifically on. like this chart but it's like we've. already seen in job reports that there. is a restructuring of the economy. happening right now. Yeah. >> But but going back to like the data.

center. 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 Abene Texas one and use half of the. average power demand of New York City. So it's one the size of Manhattan. This. makes it seem like almost all of. Manhattan, but it's it would be 1/5 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 freshwater resource. they're under. a drought when a facility comes in and. then there are people the community is. actually like competing with this. facility for fresh water. I talk about. one of those communities in my book and. also sometimes these facilities instead. of connecting to the grid they instead a. a power plant pops up next to it. So in.

Memphis Tennessee where Musk built. Colossus the supercomputer for training. Grock 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 supercomput 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. that it's it's one of the communities. that has the highest rates of um lung. cancer. and so. >> and that supercomputers taking their. jobs. >> and then they also have supercomputers. taking their jobs. So, so this is what I. mean is like the halves 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? >> What do we do about it? >> Yes. >> 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 uh. sustainable options. We need a. transition towards you know public. transport, electric vehicles. And we. don't we don't ever say everyone should. get a rocket to do every to serve 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 not no sense. It's just. a disproportionate use of resources to. get the benefit [laughter] of getting. from point A to point B. This 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 like the costs of developing this. technology. Why don't we build more bicycles of AI?

This is things like deep minds alpha. fold 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're just 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 emissions, so on and so. forth. And you're providing enormous. benefit to people. >> It feels like the. horse has left the stable in this regard. because they've already taken people's. IP, they've taken media, they they train. on this podcast. We know they do because. it it shows that they do. Um I think. there's a button actually in the back. end of YouTube now that allows you just. to click it and it says we will train on.

your YouTube channel. Um so the horses. kind of left. >> Here's the thing. If the horse truly had. left the stables, they wouldn't have to. train on anything anymore. Why is it. that their appetite for data has. actually expanded? It's because in order. to build the next generations of their. technologies, in order to have the. technologies continue to be relevant and. continue to update with the pace of new. knowledge creation and society's. evolvement, they need to train again and.

again and again and again. And why are. they employing actually more and more. and more data annotation workers over. time? It's because they need more and. more of that work over time. I mean, I've been reporting on data annotation. work for over 7 years now, and it's not. gone down. It's gone it's increased. >> Do you think there's any chance of it. going down? Do you think there's any. chance of this sort of brute force. scaling approach where you take data, you take computational power, energy,

and you, you know, you have um the data. labelers and, you know, building out. more and more parameters for the models. Do you think there's any chance it's. going to stop or go in a different. direction other than the one it's going. in now? >> 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. >> Yeah. because this sounds like stop.

>> and I just feel like stop is like a HUD. 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. >> 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. [clears throat]. 80% of Americans in the most recent poll.

think that the AI industry need to be. regulated. >> Yeah. >> When was the last time that 80% of. Americans were on the same side of an. issue? >> No. Yeah. When I have these. conversations on the podcast, the. comment section are clear. >> Yeah. >> There's no there's no disagreement. There's no one in there going, "Oh, no. I think they should crack on.". >> Yeah. Dozens dozens of protests against. data centers have broken out all around. this country and the US, all around the. world. >> So, what do we do about it? >> So, these are thing 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. [laughter]. >> 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. >> Yeah. Well, well, 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 like 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's a fair. exchange. you 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 [clears throat] 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. We're seeing that with artisan 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? It's like I three. weeks ago I met Megan Garcia who is the. mother of Sul Settzer III who is the. 14-year-old who died by suicide after. being sexually groomed by a. characterized chatbot. 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? So, what.

do you think my audience should do if. they if they agree with everything. written in your book, Age Empire of AI, Dreams and Nightmares, and Sam Mortman'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 they don't want. everyone to become data labelers, they. don't think that's a, you know, particularly great solution, what what. can they actually go and do? >> When I was writing the book, the only. discourse that was happening was this is. the best thing since sliced bread. >> Mhm. because of all of the actions of.

these people like saying when they're. comp they're 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 of. the ways that your life intersects with. the resources and the 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 [clears throat]. 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 are doing. like they're. suing these companies to withhold 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 open air 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 everyone of your.

viewers is let's not make it go. flawlessly if we don't agree with what. they are doing. >> Ah, okay. I got you. >> And then let's build alternatives. 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. But. we have research that shows that the. very same capabilities 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. So let's break up the empire and. let's forge new paths of AI development. that are broadly beneficial to everyone. >> It's strange. I'm quite I think I'm I'm. I've trained myself to deal with. dichotoies in my head. And this for me.

is such is a dichotomy where I as a CEO. and as a founder, as an entrepreneur and. someone that loves technology, I think. it's incredible. It's absolutely. incredible AI. It's just so amazing and. incredible the things it's enabled me to. do and create. >> Yeah. Because it's designed to enable. people like you. >> And my car driving in the morning and. being safer. Incredible. Um I think you. know the billion odd people that use AI. tools or chat or whatever it might be, they'd probably say that it's added. value to their life. But and this is the.

part that people find confusing that you. can and I like I invest in companies. that are you know heavily using AI but. and the big butt is is it possible to. think that is true and also think that. there are significant unintended. consequences which technology in the. history of technology should have taught. us to take a moment to pause to talk. about because. >> I think this is absolutely like you can. have both of these things in your head. and what I'm saying is that this tension. doesn't have to be a tension because we. could actually preserve the utility and.

benefits of these technologies but. actually develop and design them in a. different way that doesn't have all of. these unintended consequences. >> Yes. And I think there needs to be a big. social conversation which is why I have. so many conversations about AI in the. show like there needs to be a big social. conse uh conversation about being. intentional about the social impact um. the social and environmental impact and. that conversation is not being had in. the in government. From what I can see, the conversation takes place in the. industry and actually trying to pull it.

out of the industry and and open. people's minds to it is hopefully what. we've been doing over the last couple of. months with this subject because. >> I think it's actually been it it has. been been happening everywhere outside. of the industry and for local. governments and state level governments. there have been huge conversations about. this everywhere. Like I've been on book. tour, I've been to dozens of cities. around the world. People are having. these crucial conversations everywhere. I have not gone to a single city. >> Yes. Everywhere. Even here in South by. >> Yeah. I haven't gone to a single city.

where the room is not packed and people. are not wrestling with the same exact. questions as every other person in every. other room that I've been in. >> Speaking of packed rooms, I know you've. got to go cuz [laughter] you've got. you've got to talk today. So, I'm going. to we've got a last question which is. the closing tradition on this podcast. How would your advice to a friend with a. terminal diagnosis differ from what you. would do yourself? >> That's a great question. >> Differ from what you would do yourself? >> Oh my god. I have. I I would tell them like enjoy.

like live life for yourself. Um you. wouldn't do it. >> and take it easy. And yeah, I I I. am not taking it easy. >> Well, I think it's a good thing you're. not taking it easy because you're. leading a conversation which is. incredibly important. And I think that's. the thing. I think the conversation is. the important thing. And so, you know, because of algorithms and echo chambers, it's so rare to have a conversation. >> these days, especially a long form one. I agree. >> Like this. So, I think they're so. important. And your book is for anyone. that's curious about. >> I think a lot of people would have.

learned a lot of stuff today cuz I sit. here with and interview AI people all. the time and I've learned so much today. From reading your book and the extensive. objective perspective that your book. takes, you you're able to unravel all of. these stories that we sometimes see in. tweets and we don't know if they're true. or not because you've gone and met the. people and you've done your research and. you're incredibly intelligent person, extremely intelligent person who clearly. has humanity's interests as your north. star and that shows up in everything you. do and everything you say. So please. continue to fight in the way that you.

are um because it's an incredibly. important one. people like you that are, I think, galvanizing the world to take the. collective action that we're starting to. see everywhere. >> Yeah. >> Empire of AI: Dreams and Nightmares in. Sam Alman's Open AI by Karen How. I'll. link it below for anyone that wants to. read this book. I highly recommend you. do. It's a New York Times bestseller for. good reason. Karen, thank you. >> Thank you so much, Stephen. >> YouTube have this new crazy algorithm. where they know exactly what video you. would like to watch next based on AI and.

all of your viewing behavior. And the. algorithm says that this video is the. perfect video for you. It's different. for everybody looking right now.