So much of what's happening today in the AI industry is extremely inhumane. But this is the thing that was out of the code. And logically, it could be the case that the civilization, that it's out of the research with AI, is going to be the superior civilization. No, it's not.
This is a prediction that you're making, right? All speaking, Zuckerberg's making. Yes. And do you know what the common feature of all of this?
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 are we doing about it? We need to break up the empires of AI.
You know, I've been covering the tech industry for over eight years, interviewed over 250 people, including former or current opening employees and executives. And I can tell you that there are many parallels between the empires of AI and the empires of old, right? Like, lay claim to 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 develops that skill.
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 a couple of that.
Guys, I've got a favor to ask before this episode begins. The algorithm, if you follow a show, will deliver you the best episodes from that show very prominently in your feed. So when we have our best episodes on this show, the most shared episodes, the most great episodes, I would love you to know. And the simple way for you to know that is to hit that follow button.
But also, it's the simple, easy, free thing that you can do to help us make the show better. I would be hugely grateful if you could take a minute on the app you're listening to this on right now and hit that follow button. Thank you so, so, so much. You've written this book in front of me here called Empire of AI, Dreams and Nightmares in Sam Altman'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 in 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 mission driven startup that was focused on building technologies that would help facilitate the fight against climate change, the board fire, the CEO, because the company was not profitable. And this was in hindsight, a very pivotal moment for me because I thought if this hub is ultimately geared towards building profitable technologies and many of the problems in the world that I think need solved are not profitable problems like climate change, then what are we actually doing here? Like, how did we get to a point where innovation is not actually necessarily working in the public benefit and sometimes even undermining the public benefit in pursuit of profit?
In that moment, I had a bit of a crisis where I thought, well, I just spent four years trying to set myself up for this career that I now don't think I am cut out for. And I thought, well, I might as well just try something totally different. I've always liked writing. And that's how after two years, I landed at a role at MIT technology review covering AI full time.
And that gave me a space to then explore all of these questions of who gets to decide what technologies we build, how does money and ideology also drive the production of those technologies, and how do we ultimately make sure that we actually reimagine the innovation ecosystem to work for a broad base of people all around the world? And so that is kind of how I then set off on this journey of ultimately writing a book. 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 documented in it. Very timely time to start working in artificial intelligence.
For anyone that doesn't know, this is pre-open AI 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 taking around the world, et cetera? I interviewed over 250 people, over 300 interviews, over 90 of those people were former or current opening employees and executives.
So the book covers the inside story of opening eyes 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 state 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?
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 computer, 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 hedged 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 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 opening AI's history, it has defined and redefined it many times.
When Sam Altman is talking with Congress, AGI is a system that's going to cure cancer, solve climate change, cure poverty. When he's talking with consumers that he's trying to sell his products to, it's the most amazing digital system that you're ever going to have. When he was talking with Microsoft, you know, in the deal that opening AI Microsoft struck, where Microsoft invested in the company, it was defined as a system that will generate $100 billion of revenue. And on opening AI's own website, they define it as highly autonomous systems that outperform humans in most economically valuable work.
This is like not a coherent vision of one technology. 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 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 Altman wrote before OpenEye 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 Altman is writing for the public or speaking for the public, he does not just have the public as the audience in mind. There are other people that he is trying to motivate or mobilize when he says these things. And in that particular moment, Altman was trying to convince Elon Musk to join him on co-founding OpenEye, 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 language that Altman uses side by side with the language that Musk was using at the time, it mirrors all the things that Musk was saying.
Ten 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 might actually be more likely to happen, like engineered viruses. It's because up until then, Altman had been talking just about engineered viruses.
And so now that he needs a 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. And are you saying that Sam Oppmann manipulated Musk? Because Elon did end up donating a huge amount of money to opening up co-founding, I believe, with Sam Oppmann.
Elon Musk did end up co-founding up with Oppmann. And certainly from Musk's perspective, he does feel manipulated because he feels like Altman was engineering his language in a way that would make Musk trust him as a partner in this endeavor. And of course, then Musk is, it leaves, and through some of the documents that came out during the lawsuit that Musk and Altman are engaged in now, it has become clear that there was a degree to which Musk was actually muscled out a little bit. And so that's why he's left with this very intense personal vendetta against Altman, saying that somehow Altman tricked him into being part of this.
So in 2015, Sam Oppmann is writing these blog posts saying this is one of the greatest existential threats at the same time. In 2015, Musk is doing some very famous speeches at the time at MIT. He said that AI was the biggest existential threat and compared developing AI to summoning the demon. And what you're saying here is you're saying that Sam Oppmann was just mirroring the language that Elon was using to get Elon involved in open AI, 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 Ilya Satsgever, who was the chief scientist of opening AI at the time and Greg Brockmann, chief technology officer at the time, when they were deciding whether or not to maintain open AI as a nonprofit, because it was originally found as a nonprofit, they decided, okay, we need to create a four-profit entity. But the question was, who should be the CEO of this four-profit entity? Should it be Musk or should it be Altman?
Because they were the two co-chairmen of the nonprofit. And in the emails, it became clear that Ilya and Greg first chose Musk to be the CEO. But through my reporting, I discovered that Altman then appealed personally to Greg Brockmann, who was a friend of his that they'd known each other for many years through this look now I seen, 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 four-profit entity, because, you know, he's a famous guy. He has a lot of pressures in the world.
He could be threatened, he could act erratically, he could be unpredictable, and do we really want a technology that could be super powerful in the future to end up in the hands of this man? And that convinced Greg, and Greg then convinced Ilya, you know, I think there's a point here, do we really want to give this much power to Musk? And that is why Musk then leaves, because then the two switch their allegiance is they actually 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.
I guess this begs the question, what do you think of Sam Altman? 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 what's so interesting about those interviews is people are extremely polarized on Altman. No one has in between feelings about him. Either they think he's the greatest tech leader of this generation akin to the safe 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 of the inputs that you need to then make that future happen. But if you don't agree with his vision of the future, then you begin to feel like you're being manipulated by him to support his vision, even if you fundamentally don't agree with it.
And this is the story, especially of Dario Amade, CEO of Anthropic, who was originally an executive at OpenAI. So people that don't know, Dario now runs Amethropic, which is the maker of Claude, a lot of people probably are familiar with Claude. Yeah. And it's one of the biggest competitors to OpenAI.
And Amade, at the time, when he was an executive at OpenAI, he thought that Altman was on the same page with him. And then over time began to feel that Altman was actually on exactly the opposite page of him and felt that Altman had used 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 OpenAI. 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. You mentioned Dario there. I find it really, what I find really interesting is to look at how people's quotes evolve over time with their incentives.
So I was looking at all of the things they've said on the record on podcasts in their blog post to see how it's evolved over time. Dario, who is the former VP of Research at AI, and has now moved on to Anthropic, who are taking a slightly different approach to developing AI, said back in 2017, while he was still at OpenAI, that this is a quote, I think the extreme end is the Nick Bostrom 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 Ilya, who was a co-founder of OpenAI, and then left. I guess the first question I'll ask is, why did Ilya leave? That's a great question. So he was instrumental in trying to get some of them fired.
He's another one of the people who, over time, began to feel like he was being manipulated by Altman towards contributing something that he didn't believe in. And I interviewed a lot of people. Ilya, 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 Altman 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? So I interviewed him in 2019 for a profile that I did of OpenAI for MIT Technology Review.
And back in 2019, he has a quote where he says, the future's going to be good for AI's 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. 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 feel like we should take a step back to examine is going back to this idea of what even is our official intelligence and what do we mean by intelligence.
And a huge part of the views of the different people in the quotes that you're reading derives from a specific belief that they each have in this question of what is intelligence, what constitutes intelligence. For Ilya, he has throughout his research career felt that ultimately our brains are giant statistical models. This is not something that 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 intelligence 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 the 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 Ilya gave a talk at one point at this really prominent AI research conference that happens every year called Neural Information Processing Systems, it's mouthful. But he gave this keynote where he shows this chart of the size of brains and the intelligence of the species. And it's roughly linear, the bigger the size of the brain, the more intelligent the species. And so for him, he thinks he's building a digital brain because he thinks brains are just statistical engines.
So from that logic, it's like, okay, if we then build a bigger statistical engine than the human brain, then based on this chart, it will be more intelligent and then we will be subjected to the same treatment that we've subjected animals. But it's really important to understand that these are scientific hypotheses of specific individuals within the AI research community. And there's a lot, a lot of debate about whether this is in fact the case. And some of the biggest critics say it's very reductive to think of our brains as simply just a school engine.
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 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, they're, you know, exploiting more and more labor in order to continue on this path. Here's a question that I think is important to ask is, why are we trying to build AI systems that are duplicative of humans? We're kind of having this conversation right now where we've just taken the premise of this industry as a good thing.
Like they said that we should be building AGI, so we say that we should be building AGI. But I would like to ask, like why are we doing that? Why is it that we are building a technology that is ultimately designed to replace and automate people away? That is not the enterprise of technology.
Like we should be building technology and the purpose of technology throughout history has been to improve human flourishing, not to replace people. And so this is like a critical part of my critique of these companies and the scientists that have just adopted this goal and have relentlessly pursued it and have had enormous capital and enormous resources to pursue it, is this the right goal? Like why are we doing this? Why can't we just build AI systems that do things like accelerate drug discovery and improve people's 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 300 people in total, 80 or 90 of them from open AI, the maker of chat GBC. 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 and powers of AI. What do you mean by 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.
They're land grabbing in order to build these computer 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 designed their tools to be labor automating so that when the technologies are deployed, it also affects labor rights because it erodes away labor rights. And this is a political choice that they have. Third, they monopolize knowledge production, so they project this idea that they're the only ones that really understand how the technology works. And so if the public doesn't like it, it's because they don't actually know enough about this technology.
They do this to the public. They do this to policymakers. And they've also captured the majority of the scientists that are working on understanding the limitations and capabilities of AI. 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 Gebru in my book, who was the ethical AI team co-lead at Google. When she was literally hired to critique the types of AI systems that Google was building, she then co-wrote a critical research paper that was showing how large language models specifically were leading to certain types of harmful outcomes. And in an attempt to try and stop this research from being published, Google ended up firing Gebru and then fired her other co-lead, Margaret Mitchell. 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, tech messages, and this person was from one of the big AI companies. This was, OK, I started some of its critics, yeah, as part of what appears to be a campaign of intimidation, but also what appeared to be a campaign of phishing for more information to figure out, to map out the network of critics further. But this was a man who runs a small watchdog nonprofit and they had been doing a lot of work during that time to try and ask questions about opening eyes, attempts to convert from a nonprofit to a for-profit.
Ultimately, opening eye was successful in that conversion, but during the period where it was sort of existential for opening eye to complete this conversion, there were a lot of civil society groups in watchdog groups like Midas who were trying to prevent the process from happening in the dead of night. They were trying to get more transparency. They were trying to have more public debate about this because it's unprecedented. And it was then that there was an knock on his door and he was served papers.
What did the paper say? The paper's asking to reproduce every single piece of communication that he had had that might have involved Musk. So this was like the strange paranoia that opening eye 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, the request he simply was just answered, you know, 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 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 say to the public, like, we're the good empire and we need to be an empire in the first place because they're also bad empires in the world. And if you allow us to take all the resources and use all the labor, then we promise we will bring you progress and modernity for everyone.
We will bring you to this utopic state akin to an AI heaven. But if the evil empire does it first, we will descend into a hell. Evil empire being in this case. In this case, most often it's China.
But actually in the early days, OpenAI evoked Google as the evil empire. So all of their decisions are about, we need to do it first because otherwise Google, this evil corporation that's driven by profit as a benevolent nonprofit, like 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 serve everyone?
It's going to be the age of abundance? Everything's going to go well. We think they believe. So this is so funny is such a core part of the mythology that they create around the industry includes the belief that it could go very badly.
It goes hand in hand. They need that part of the myth in order to then say, and that's why we need to be in control of the technology because that's the only way that it's going to go really, really well. And Altman has said publicly, you know, the worst case lights out for everyone. But best case, we hear cancer, we solve climate change and there's abundance.
And Dario Amade, same kind of rhetoric, it's like worst case catastrophic or existential harm for humanity. Best case, mass human flourishing. So this is like two sides of the same coin, 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 every step of the way.
Some of them did a tweet saying, there are some books coming out about OpenAI and me. We only participated in two of them, one by Keshe Hege. Hege Hege. Hege Hege focused on me and one by Ashley Vance on OpenAI.
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. The quote retweeted them, tweet from Sam Altman and you said, the unnamed book, Empire of AI is mine. Do you believe that tweet from Sam Altman was in reference to your book?
100% because there's only three books coming out about him. And he 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 three days in 2019, my profile comes out in 2020. The leadership are very unhappy. And in my book, I actually quote an email that I received that Sam Altman sent to the company about my profile saying, yeah, this is not great.
And from then on, the company's stance to me was, we are not going to participate in anything that you do. We are not going to respond to anything, any questions that you receive. And this was, you know, this was things that they explicitly articulated. It wasn't like me inferring.
So I had a colleague at MIT technology review that also covered AI. And at one point, OpenAI sent him this press release being like, we love for you to cover the story. And I was like, oh, I'm busy. Will you send it to Karen?
And they're like, oh, no, we have a history you understand. And so for three years, they refused to talk to me. But then I ended up at the Wall Street Journal where if they felt a bit compelled because it was the journal to reopen the lines of communication. And so I started having a 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, we'll use it for interviews. And we did get to a more productive relationship. And then I embarked on the book.
So I left the journal to focus on the book full time. And I told them right away, I'm working on this book. I want to continue this productive conversation where I make sure I reflect OpenAI's perspective in the book. And so they were like, we can arrange interviews for you.
You can come back to the office. We'll set up some conversations. And then as we were going back and forth on this, the board fires them all men. And that's when things started going kind of south because the company started becoming very sensitive to scrutiny.
And so then they started pushing kicking the can down the road down the road down the road. And I kept saying, hey, when are we rescheduling this? What's going on? And then I get an email saying, we are not going to participate at all.
You are not coming to the office. You're not doing interviews. And I had actually already booked my tickets. So I was already going to fly to San Francisco to have the interviews.
And so then I told them, I was like, that's fine. I will still engage in the process well, 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 request or 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, because that's when Altman tweeted this. And they never responded to a single one of the four pages. Someone does a lot of interviews.
Yeah. You know, single 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 Von Joe Rogan podcast all over the world.
I wonder why he won't do mine. Well, maybe. I don't know why. I think I'm fine with everyone.
I just asked questions I genuinely care about. I don't come in with huge preconceptions. I used to meet people for the first time, but I've heard through the grapevine that he doesn't want to do mine. So I mentioned what you were saying earlier that with this, the way that opening on these companies control research, you asked, do they also do this with journalists?
I mean, yes, the answer is yes. And apparently they also do it with anyone who has, you know, broad mouse 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. 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. 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's a particular person in AI whose team have basically dangled the carrot of them coming here for like 18 months. I'm like, you don't have to thank 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 give them a fair shot. I'll ask them all genuinely curious questions about what they're doing their incentives. I won't got to them.
I don't have a history of ever got to anybody, even if I have a different opinion. I'll ask the question. But they dangled carrots. And they say, well, if he's thinking about it, let's think about a day and what the strategy isn't.
I don't think they think those people don't understand is if we just dangle it for long enough, then they will perform in the way that we want them to do. And they'll be pleasant about us. They won't be critical. They won't give a good one.
They won't clap for our critics. And I think all of their game is just dangle the carrot forever. Yes. That's like the optimal outcome.
You just dangle it. If we just tell them, yeah, no, we're just trying to connect the schedule. It just doesn't work. You just have to go there and give your opinion and allow the clash of ideas in the public for it.
Let the viewers decide for themselves what they think. Yeah. But this is such a huge part of their machinery is the way that they use these tactics to massage the public image of these companies and make sure that information that they don't want out and even opinions that they don't want out there go out there. And so this is, you know, I feel very lucky now that opening eyes shut the door early on me at the time I didn't feel like I had screwed myself over.
I was like, should I have been nicer to them in the profile so that I could maintain access? Which is a horrible question to ask as a journalist, right? Like you're supposed to report the truth and you're always supposed to report in the interest of the public. Like that is the point of journalism.
And in that moment, I was like relatively junior in my career. I was like, did I misunderstand what journalism about is about? Like, should I have actually been playing the access game? Like, what's too late?
I have 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, yeah, and just report what I see are the facts being presented to me irrespective of whether the company likes it or not.
And most often the company really does not like it, but I can continue to do the work. They don't need to open the front door for me. I was still able to do more than three interviews. So Sam Altman gets kicked off the open AI 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 Ilios Zetsgever, I see these serious concerns about the way that Altman's behavior is leading to bad research outcomes and poor decision-making at the company. He then approaches a board member, Helen Toner.
Ilios, anyone that doesn't know is the co-founder we mentioned earlier, the co-founder of Open AI we mentioned earlier. Yes. And he kind of does a bit of a sounding board thing to Helen, just because Ilios is freaking out. So I think on this, these concerns for Helen, he's like, if I tell this to someone, this could also be really bad for me if Altman finds out.
And so he asks for a meeting with Toner, and in that first meeting, he's like, he barely says a thing. He's just like dancing around trying to figure out, hey, is this someone that I can maybe trust to divulge more information? And Toner's role in responsibility is opening AI where- She was a board member. Yeah.
And specifically an independent board member. So opening AI, when it was a non-profit, the board was split between people who had a stake financial stake in the company, and then people who were fully independent. And this was meant to be a structure that would balance the decision-making to be in the benefit of the public interest, rather than to be in the benefit of the for profit entity that opening I then created. And Iliya, as a non-independent board member, was approaching Toner as an independent board member to try and see whether or not she was potentially seeing or hearing the same things that he was about the fact that Altman was having on the company.
This then sets off a series of conversations, first between Iliya and Helen, and then between Mira Morati and some of the board members. So Mira Morati was at that point, the chief technology officer of opening AI, 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. He is creating too much instability at the company, and it is the root of the problem. They were trying to say to these independent board members, the problem will not be fixed unless Altman is removed, because of the way that he's pitting teams against each other and creating this environment where people are unable to trust each other anymore, and they're competing rather than collaborating on what's supposed to be this really, really important technology.
When you say instability, that's quite a vague term, that can mean lots of things, like instability can mean pushing people hard to work harder. What do you mean by instability? It's a specific term that you can possibly say them. When chat GPT came out in the world, opening AI was wholly unprepared.
They didn't think that they were launching a gangbusters product. They thought they were releasing a research preview that would help them get the data fly will go in, collect a bunch of data from users that would then inform what they thought would be the gangbusters product, which was a chatbot using GPT-4, and chat GPT was using GPT-3.5. Because of that, there were servers crashing all the time, because they had to scale their infrastructure faster than any company in history, and there were all of these outages, they were trying to also hire faster than any company in history to try and have more personnel there, and they were then sometimes hiring people that they were like, actually, we made a mistake, we shouldn't have hired you. So they were firing people left and right, and people were just disappearing off of Slack, and that's how their colleagues would learn that they were no longer at the company.
And so it was, yes, like many fast-growing companies, a very chaotic environment, and particularly a chaotic environment, because it was extra fast, like they had to accelerate more than any other startup. And on top of that, Miramarati, at least that's ever felt, that Altman 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 AGI could either be devastating or utopic to humanity. And so it's not, yes, it's like any other company, and no, it's not like any other company. You cannot have, like in their view, you cannot have this degree of chaos as the pressure cooker for creating a technology that they, in their conception, could make or break the world. And so that is basically what the independent board members also begin to reflect on.
They have these conversations amongst themselves where they're like, well, based on what we're hearing about Altman's behavior, like if this was an Instacart without warrant firing him, and they concluded maybe not, but this is not Instacart. And that's why they were like, well, crap, maybe this is actually, this does rise to the bar where we should consider replacing him because we are ultimately building a technology that we think could have transformative impacts either in 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 with 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 Cora, which is a tech startup in the Valley, he is out of 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 the company had created to start investing in other startups. And he realizes they'd never really seen documentation about how the startup fund had been set up from Open. They get the documents and it turns out that OpenAI startup fund is not open AI's sort of fund.
It's Altman's startup fund. And this was something like one of several experiences the independent board members were also having where they're like, there's something not right about the fact that they're continuously are 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, OK, 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 Altman 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 really doesn't know our lead investor in opening out the time. Yes. One of the only investors in opening out 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 Altman is reinstalled as CEO days later. This company that I've just invested in, it's growing like crazy. I would be the one to tell you about it because I think it's going to create such a huge productivity advantage for you. It's the 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.
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I work with lots of people here. We have 150 people that work in this business. And those people know me best. 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 driver's CEO, for example, it would take a lot for them to say that. They must have seen some shit off camera. We don't think he's the right person to be on camera for whatever reason. And in the case of AI, which is much more consequential than podcast, that is filmed in my own kitchen.
It almost sends a chill down one's body to think that the co-founder of the business has gone to the board and said, this isn't the guy to leave this conversation. It wasn't just Iliya Muradi then also said, I don't think Altman is the right guy. And then they both left later. So then Altman comes back and lo and behold, Iliya never comes back.
So his concerns about the fact that Altman found out would be bad for him manifested. He ended up not coming back and Muradi then left shortly thereafter. Quite a lot of these people leave, don't they, open AI? They do.
So if you consider one of the origin stories of opening AI, is this dinner that happened at the Rosewood Hotel, which is a very swanky hotel right in the heart of Silicon Valley, that was one of Elon Musk's favorites whenever he was coming up from LA to the area. And there was a dinner that was there where Altman was intending to recruit the OG team that would start opening AI. So he's kind of telling everyone, you might have a chance to meet Musk because Musk is going to come to this dinner and he cold emails Iliya and gets Iliya to then come because Iliya specifically wants to come because I want to meet Musk. And he also emails all these other people, including Greg Rockman, Dario Amadeh.
And they all almost all of them, 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 Altman. And Iliya, 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. Do you know what I mean? If someone like, okay, if I did this podcast with me, and then they left and started a podcast called Safe Podcasting.
I'll take that as a slight. I'll take that as a slight. I'll have people knocking on their door. I'll have people knocking on their door.
One of the things that is happening here is, it is not a coincidence that every single tech billionaire has their own AI company. They want to create AI in their own image. And that's why they keep not getting along. And in fact, it's not just don't get along.
They end up hating each other after working together and then splinter off into their own organizations. So after Musk leaves, he starts XAI, after Dario leaves, he starts enthropic, after Ili leaves, he starts Safe Super Intelligence, after Mira 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 all the other companies out there.
Do you think some of these AICs 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's summoned to 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 wine for chance. If you put bullets in a four chamber revolver and said Steven, the upside is you could become a multigazillionaire and be remembered forever. The downside is that the bullet you had. There is no chance that I would take that there 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've got to say, if we don't try to will, I'm not terrible. Yeah. But if we let anyone else do it other than me, then we're fucked as well. Exactly.
So that means I have to do it and you have to give me money. 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 see more power, more resources to these individuals. And so do they know that they're summoning the demon? I mean, they're purposely trying to create this feeling within the public that they are because it is a crucial part of their power. But if we were to define, just do they realize that the things that they're doing are having already really harmful impacts all around the world on vulnerable people, vulnerable communities, vulnerable countries.
That's where I'm like, maybe us, maybe no, and they don't really care because in the frame of mind, like sometimes you see an analogy that the AI world is like Dune. Dune, frame on that doesn't look Dune. Science fiction epic written by Frank Herbert, and it's set in this intergalactic era where they're 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 religious myth, basically, about the coming of the Messiah that are used as ways to control the people. And Paul Atreides, when he arrives at the planet Arrakis with the intention of trying to then fight against the empire and avenge his father's death, he steps into a myth that has been 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, but because he lives and breathes and embodies it, it kind of starts to blur in his mind whether this is really a myth or whether he's really the Messiah. And this is what I think happens in the AI world. On one hand, there are all these executives that actively engage in myth-making because I have all these internal documents that I write about in the book where they are very keenly aware of how to bring the public along with them by showing them dazzling demonstrations of the technology by using crafting a mission that will sound really good and make people give more leniency to their companies. So they know they're doing the myth-making.
And also, I think many of them lose themselves in the myth because they have to live and breathe and embody it day in and day out. And so when, you know, Dario says he thinks that 10 to 25% of the future could be catastrophic or whatever the probability is 10 to 25%. He is actively engaging in the myth-making, 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. And this is the whole psychology of cognitive dissonance, right? The brain struggles to hold two conflicting worldviews at the same time, so it's incentivized or it endeavors to dismiss one. So if you wanted to be a healthy person but also a smoker, and I pointed out something's bad for you.
The first word is out of your mouth. It could be yes, but smoking helps me with stress. 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 these data centers. So when they're out in the public, they're always fundraising.
All of these major companies are fundraising at the moment. So you can't be fundraising and saying, I'm going to destroy your children's future, potentially this 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 does that. The others sound not doing that as much anymore. And it's because it goes back to each of them kind of distinguish themselves a little bit as the brand that they need to project. Do you think any of them are more, have a stronger moral compass than others?
Because I think Dario often gets the credit for having 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 is 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 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 is 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? And to me, the bigger question is, is the governance structure that we've created a sound one that allows broad participation or an anti-democratic one that has consolidated this decision-making power in the hands of the few? Because no person is perfect.
I don't care who is on the top of these companies. They're not going to have the ability to make decisions on behalf of so many people around the world who live and talk and have 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 on sound.
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 that point of view. So this is me playing Devil's Advocate. OK.
But Karen, if the US don't continue to accelerate the 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 they'll get the scientific discoveries. They'll discover the new era of autonomous weapons. And we will be their backyard.
And 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 all of the United States' electricity, their weapons, systems. It would know exactly how to disable the United States from a side perspective because it would be that smart.
All you've got to imagine is any rate of improvement of any sort of long period of time. So this is a theory that might be true. And if it's true... I mean, yeah, any theory might be true.
But, you know, going to the point of, like, even if it's a small percentage, it's one thing, attention to it 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 something to say, no?
So there's a lot of fundamentals in this argument that would need to be true in order for this to be a viable argument. And let's knock them down one by one. So the first one is that these systems are intelligent and that just scaling them is going to bring us more intelligence. 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 the right analogy, almost. It's sort of like... It's like a calculator can do math problems faster than a human.
Does that make it intelligent? It has a narrow intelligence because it's solving a narrow problem, which is like 1 plus 1 equals 2. 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 something 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? Because the company can only focus on advancing certain types of capabilities.
They 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's needed for that capability, by 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 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 back to his hypothesis about how human intelligence works and what the appropriate model of the brain is. His hypothesis throughout his career has been the brain is a statistical engine.
But that's his hypothesis, and that is not universally agreed upon, especially among people that are not in the AI world. When you talk with neuroscientists and psychologists, people who actually study human intelligence 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 things. It's like AI is already being used in the military and has been used in the military for a long time.
But it's specifically accelerating large language models isn't just the only path for getting military capabilities. Like the companies would have to choose to specifically pick military capabilities to accelerate, not just like general intelligence. It's like, you know what I'm saying? Like they create this myth that they are actually pushing the frontier of all of the capabilities of the model.
But that's not what's actually happening internally. And I have had hundreds of pages of documents on how they were specifically training models. They pick what capabilities they want to advance. And you know how they pick them?
It's based on which industries would be able to pay them the most money for their services. So they pick finance, law, medicine, health care, commerce. It's not actually intelligent like a baby where the more that the baby grows up, they start having these general abilities. I think I have dragon intelligence on this.
I wasn't going to say it. I think I know a little bit about a little bit. No, I know a lot about a little bit. Yeah.
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. 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 a completely retrained 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 one of them lines, they all land. Well, it's just because it's an operating system that has an AI model as part of your training, the AI model.
And then you deploy that AI model across all the self-driving cars. Which is 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 learn what all the other humans learned, that would be, that would give us such an unbelievable advantage. I mean, one of the ways we did that is through communication. Or it 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 that learn the wrong thing?
And they all have the same failure mode. And the part of the human society is that we do have different experiences and we all have different failure modes. I think sometimes we hold AI models to a higher standard than we hold humans to. Because I hear on stage when we're not at the moment, and I'd hear people go, ah, but you know, AI models, they hallucinate sometimes.
I'm like, have you met a human? Like, I hallucinate all the time. I can barely spell, all do math. Yes, but 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 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 matters the most consequential, which is like, okay, it might have a different brain and a different system, but it doesn't 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 Los Angeles. I don't touch a steering wheel and I can drive for many, many hours. And here in Austin, I just saw the ones the other day where they've removed the steering wheel and the pedals, the new cybercabs. 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. Then as far as I'm concerned, intelligence or not, it's like, you know. Yeah, 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 this is a prediction that all the AI... Elia's making, Darius making, Elon's making, Zuckerberg's making, Albin'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 100,000 GPUs, specifically to scale up their grow API models for other 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 who are automating certain tasks that are financially lucrative.
And how do Elon say that? If you're a surgeon after this, there's no point. He was like, don't train to be a surgeon. He says, in a couple of years' time, optimists and AI generally are going to be better than any surgeon that's ever lived.
Yeah. You think he's into true? Well, you know, 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.
And he said a deadline that we've already passed. I don't remember how many years radiology is doing great as a profession. You think it will be in five years? Okay, so this once again goes back to this question of like, why do we build technology and why should we specifically be building AI?
Okay. And for me, like the whole project of technology development advancement is not to advance technology for technology sake. It's to help people. And there's been lots of research that is 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 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 in the rig will be driving themselves? No. You don't think so?
How come? Because of the way the technology works. 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... My parameters. That's what I call parameters. It's pumping a bunch of data into it and then it's analyzing the data and creating 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 the way that they're turning themselves in cars, 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 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 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 for a human to drive. It depends on whether the Tesla was trained to specifically navigate the place that you're driving.
It's in Mumbai, in some places in Vietnam. No, it would not be safer. I would much rather be driven by someone that has been driving in that place their whole life. I'm not arguing against 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 in the world? In the US? In the United States because we're here. I don't actually think that it's like imminently on the horizon.
Ten years? No, I don't think so. I said we darra from Uber. He's pretty convinced that his 9 million curators will be replaced by autonomous vehicles.
I mean, how long has self driving cars been invested in so 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 actually not a technical problem, right?
Like part of it is also a social problem. Like do people even trust getting into these eagles. Part of it is also a legal problem. Which is if 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, 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. In terms of Tesla, pretty much everyone that gets the car, it comes with autonomy now for pretty much most people I believe. Part of what I mean. Yeah, it's called full self driving at the moment.
I mean, yes, it is called full self driving. Full self driving supervised. Well, you kind of have to be looking in the direction. You have to be looking in the right direction.
Yeah, so it's partial to me. And here in Austin, it's full of me because there's no steering wheel on the new car. So you can't drive it anyway. But it is, you know, the Model Y is the undisputed, high selling car, best selling car in the world, across all brands.