Empire of Ai. You're using the word empire here. Yeah, Can you tell me a little bit more about that decision?
Yeah, So the term empire in the title is specifically an argument that I make in the book that we really need to start thinking of companies like open Ai as new forms of empire.
Karen Howe is a journalist and the author of a new book called Empire of Ai Dreams and Nightmares and Sam Altman's Open Ai, which just came out last month. In case you're not familiar with her work, she herself is a data engineer, and she's also the first person to ever really cover open Ai, the company that makes chat gpt. On the surface, the book is a story of the rise of open Ai and the story of its founder, Sam Altman, and if you like drama, let me tell you there's a lot of it. Someone could
definitely make a Netflix series about this. And they are also comparisons to be made between Sam Altman and Steve Jobs, and we will get to that. But before we do anything, we should talk about a word that a lot of the reviews I've seen are kind of glossing over, which is weird because it's the first word in the title empire, and.
The reason is four different features that empires of AI share with empires of old. The first one is that they lay claim to resources that are not their own, but they reinterpret the rules to suggest that those resources were always their own. That refers to how these companies just scrape the data on the Internet, same with the intellectual property that these companies just take, like the work of artists, the work of writers, the work of creators.
The second feature of empires is that they engage in a lot of labor exploitation, and that refers not just to the fact that these companies they contract a lot of workers, often in the Global South or other economically vulnerable communities in the Global North, and pay them extraordinarily small amounts of money to do extremely exploitative work data annotation, data cleaning, data preparation, content moderation, where workers are left with the same level of trauma that social media content
moderators were left with. It also refers to the fact that these companies are inherently building labor automating technologies. So opening eyes definition of artificial general intelligence is highly autonomous systems that outperform humans at most economically valuable work and so they're explicitly saying we're going to automate away the things that people usually get paid for, and that in
and of itself is a labor exploitation. So there's labor exploitation happening on the way in and happening on the way out.
Karen is not kidding. In the book, she travels all over the world and speaks to people who work in all levels of the AI industry. And what you've found was that countries that were undergoing some kind of economic crisis were being targeted by the industry as places to hire workers who train AI models to understand what kind of stuff to allow and what to block. This is work that only a human can do. So who's this empire for? And where do you and I fit in here?
I'm afraid Kaleidoscope and iHeart podcasts. This is kill switch. I'm doctor Thomas.
I'm sorry, I'm goodbye.
Getting back to Karen's definitions of colonial empires, the first is that they change the rules so that when they find something like oil in the ground or art and literature on the internet, they can say that it belongs to them. The second is that they exploit people for their labor. The third is that they control what people
can know about them. In the case of the AI industry, this can look like companies just finding the top researchers who are working at universities, paying them lots of money to drop their university job and come to the company, and then censoring anything that those researchers write that is critical of their technology or of how they treat the environment. Again, controlling what the public knows about them. But I think it's the fourth element where things start to really get interesting.
And then the last future is that empires always have this narrative that there are good empires and their evil empires. So they the good empire need to be empire in the first place and engage in all this resource extraction and labor exploitation because that is what will make them strong enough to beat back the evil empire. So back in the day and during European colonialism, the British Empire would say that they were better than the Dutch Empire.
The French Empire would say they were better than the British Empire. And part of being the good empire is that what they were ultimately doing was civilizing the world. They were bringing progress and modernity to everyone, and they were giving humanity the chance to enter heaven instead of hell. And this is literally the language that AI people use these days. They talk about heaven and hell. They talk about recreating God and about bringing the next era of civilization into being.
Yeah, and I mean Sam Altman twenty twenty three going before Congress and saying, look, if we don't put a whole bunch of resources in AA, guess who will China? Would you like China to win? I don't think you do. I know that you're scared of China. Let's play nice here so that you know, play nice specifically with our industry.
Also play nice with my company so that we can come out on top, which again is very similar to how an empire justifies itself, which is, if we don't do this, the bad guys will, such as you lay on the book open AI really starts out by saying, listen, AI is going to happen. AJI is going to happen. It needs to be the good guys. We're the good guys.
And I think that's something that you do in the book is I think we've gotten really used to this idea that everything was inevitable this all was going to happen, and the stuff that happens tomorrow it was always going to happen, And the stuff that happens three years from now,
it was always going to happen. But you put it together that none of this is actually inevitable, and not only that, it's decisions are being made, and actually a lot of these decisions are being made by a pretty small handful of people in Silicon Valley.
Absolutely, this is definitely one of the core messages that I hope people can take away from the book is technology is very much a product of human choices, and it just so happens that AI has been the product of a very small handful of people's choices who have a very what I would argue, narrow world view, a narrow view of how the world works now and how
it should continue to work. And if I had to point to one decision that was not inevitable, but that Opening Eye sort of ushered in with the shape of AI today, it is the fact that they decided to scale this technology aggressively, and that happened because open Ai early on identified that they had to be number one based on this. There's the evil Empire out there, and
we the Good Empire, have to race aggressively. And they realized that the fact fastest and easiest way to get to number one was by taking existing techniques within the AI research field and blowing it up with an unprecedented amount of data and an unprecedented amount of computational resources. So they realized, if we can build the largest supercomputers in the world, we will have the best chance at
dominating this technology. All of a sudden, all of the tech companies saw what opening I did and when we want to do that too, And suddenly you see a step change in the sheer amount of resources that are now going into developing this technology, and it very much becomes a scale at all costs paradigm for AI development.
This very specific scaling decision is what leads to significant amounts of labor exploitation and is what leads to significant amounts of environmental harm, because that is when you start talking about covering the earth with data centers and supercomputers, leading to climate harm, healthcarms, the exacerbation of the fresh water crisis because these data centers need to be cooled with fresh water, and also in order to meet the data imperative for the size of these models. That is,
when they start training on polluted data sets. That was not a norm at all in AI research. In fact, before Opening I started training on polluted data sets, the norm was actually shifting towards really small, extremely curated, and clean data sets. A lot of research was coming out at the time pre the CHATGBT moment where people realize you could actually get away with very tiny data sets
if you prepared it correctly. But then Opening I shifted to let's use huge data sets, poor quality data sets, and that's why you end up having the need for content moderation, because when you're pumping a bunch of gunk into the models, then a bunch of gun comes out, and then you have to create content moderation filters to strip that gunk out before it reaches the user. And that involves humans, and that involves humans who then are left with a significant amount of trauma.
There is a reason that we're talking about Sam Altman and open ai so much. Straight up. It's the same reason that people say chat GBT as a generic term for AI in general. Open Ai was the first company to break through and everyone copy them, and so the company culture at open Ai has influenced how other companies act, even Google and Microsoft, and then in turn, that's influenced how the public thinks about AI. And really a lot
of our conversation about AI isn't about computer science. It's about culture, or maybe subculture, and that subculture is very interested in a fight between the good and evil use of AI. And it has this question that keeps coming up in the book what's your P? Doom? So I And before somebody listens to this and thinks, dexter, what did you just ask this person? P parentheses in the word doom.
So I'm not a great fan of this phrase. It refers to probability of doom, and that is a shorthand within a particular community, ideological community that believes that AI has the potential to destroy humanity, and so probability of doom means the probability that humanity will be destroyed by artificial intelligence. The reason why I'm not a fan of it is because this ideology is predicated on the idea that we can fundamentally recreate human intelligence in computers, which
is something that is still heavily scientifically debated. And this community also believes that once that happens, AI systems will develop their own consciousness or self motivation or self preservation, whatever it is that then makes them quote unquote go rogue and be unable to be controlled by the humans that originally program them. And that's what will lead to potential disaster of them just killing everyone or consuming all the resources such that most people have horrible lives, or
keeping us as pets. You know, these are all scenarios, but the community talks about.
When you say this is what the community talks about. There are people who like ask other people, like at a party or something, Yeah, so what's your P doom in somebody else? Yes, Oh mine's thirty five, And oh yeah, you know what I'm at about a seventy five. Recently, my P doom is seventy five.
This happens, Yes, this happens. In addition to me fundamentally disagreeing with the ideological foundations of probability of doom, I also disagree with the veneer of rigor that a phrase like p doom ascribes to something that is inherently unrigorous. So they're putting mathematical values to something that is inherently illogical. There is no scientific evidence that we can point to that AI will go rogue, that it will do any
of these things. It really is based on a belief, and over the last few years, especially within Silicon Valley, there's been the cultivation of what I call quasi religious movements around what AI is, what it will be, and
ultimately how it will impact people. And we just talked about what most people call the Doomer ideology, and then there's the boomer ideology, which also believes that it's fundamentally possible to recreate human intelligence in computers, but for them this will be a positive civilizational transformation rather than a negative one. And both of them do not actually have
scientific evidence one where or the other. They are talking about theoretical scenarios in projecting into the future based on their own conceptions of what human intelligence is and the idea that human intelligence is fundamentally computable, and they do not actually observe the real world harms or benefits of this technology as part of these philosophies, these ideologies.
Okay, clarify here when people talk about boomers and doomers in the world of AI. Again, a boomer is someone who is really optimistic about AI. For example, believing that once we get artificial general intelligence, or basically an AI that's overall smarter than humans, that that will lead us to a utopia, prosperity, solving disease, fixing the climate, having a colony on the moon, food for everybody, all that
sort of thing. A doomer is pessimistic about it. They think that AI will get smart and then suddenly go full sky net and kill everybody. So usually when we hear that there are two sides to an argument, we figure, Okay, the truth is probably somewhere in the middle there. But what if both sides are wrong? What if we're arguing about a question that doesn't even make sense? And this is one of the really interesting things I think about the process of reading the book. Right, it starts off
with us as an inner office drama. There's some backstabbing here. There's a group of employees that really believes in this leader that goes over there and they're arguing and who's going to be the next leader, and do we need to get rid of this guy? Do we not? You need to get rid of this guy? But you've talked to so many people as you start to see how people are interacting with each other. You realize, Wait, this is an inner office drama or personalities coming up against
each other. This is people who fundamentally believe something fairly deeply, and those things are really clashing against each other.
Yeah. The shocking thing was as I was interviewing people who I identified as part of the Boomer group or part of the Doomer group. I mean the boomers, like their eyes would light up when they were talking about this potential future where prosperity was abundant and everything would be perfect, and the doomers, like I spoke to people whose voices were quivering with anxiety. This was a genuine emotional,
visceral reaction to the ideas. Wow, we only had a few years left on this earth potentially if we did not figure out how to get a handle on this technology and make it go well instead of badly. And that's when I began to understand more deeply. Oh, there are so many more layers to all of the headlines that we see about this company, about this technology, and
about the dramatic firing and rehiring of Sam Woman. There's so many deeper spiritual layers behind the clashing that's happening to shape this technology.
But really, what is this spiritual fight that we're trying to have here? More on that after the break. So it's interesting that people sometimes compare Sam Altman, the head of open Ai, to Steve Jobs. You say in the book that in some ways he's this generational talent, but you also explain that there's a lot of people who really just don't trust him, that he's polarizing, because obviously there's a way in which I mean Steve Jobs is also a polarizing figure too. There's people who think he's
an absolute genius. There's people who think he's a jerk who just stole ideas, and there's people who think he's a genius and he's a jerk. Yeah, yeah, right, But then if you think here about what are the stakes of what Steve Jobs was doing? If you want to give him all the credit, yeah, is what was he doing? What's the mission? Make beautiful products, make very easy to use computers great? What are the stakes for open ai? Actually, hold on, let me not even try to answer this.
What is open AI's mission because that's something that seems to change through the book. So what is open ai trying to do here?
Yeah? So their mission, which on paper has never changed, is to ensure artificial general intelligence benefits all of humanity. That's the direct quote. The challenge is that each of these components of its mission are extremely ill defined. I mean, there used to be a joke with an open eye. If you ask thirteen researchers at the company what artificial general intelligence is, you'll get fifteen answers. And it's pretty
true for all the components of the mission. So what's happened over the course of the organization's history it originally started as a nonprofit, now it's one of the most capitalistic companies in Silicon Valley history, is that different people interpret the mission in a fundamentally different way. The Boomers interpret benefiting all of humanity as build this technology as fast as possible and unleash it onto the world as
quickly as possible. The Doomers interpret as build this technology as fast as possible and hold onto the technology so that we have a lead time to do more research on it before bad actors have a chance to do
research on it as well. And then there are plenty of other people who are not necessarily in the boom or a dumer category that are just regular tech company, people who came from Facebook, who it came from Google, who came from Microsoft, who are just like the mission for benefiting all of humanity is building products that people
want to pay for. There's such a vast range of interpretations that essentially all the mission does is it just allows people to put a mirror up to themselves and say what is it that I want and to make themselves the protagonist of their own story and say what
I want is the most beneficial for humanity. So that's my interpretation of what Opening I should be doing, and that is part of the reason why Opening I has had so much drama through its history, because no one can ever agree on the most fundamental building block of the company. No one can agree on what direction they should actually be going.
Speaking of benefiting all of humanity, a couple episodes ago, we did an episode about the impact of AI on the environment, climate change, things like that. But then there's this kind of background for anybody who's really interested in AI, really kind of AI promoter. There's this counterclaim that, Okay, any bad stuff we do to the environment, Ai'll fix it. Yeah, AI can fix climate change. Yeah, you've run up against this claim in person more than I have. Have you
been able to make any sense of that? What's the argument here that AI is going to fix climate change or AI can help there?
The most charitable relaying of the argument is for people who believe that human intelligence is fundamentally computable, and therefore if you have enough data and you have enough compute, you will inevitably be able to recreate it and create so called artificial general intelligence. Then you should be able to solve any problem at that point, because the challenge with our as humans, our inability to deal with the climate crisis is a lack of cooperation and digital intelligence.
They won't have egos, they won't be like clashing against each other, so the saying goes, and so they won't have any issue cooperating, and they also won't have any lack of ideas and ability to experiment, develop new energy storage solutions, develop new forms of renewable energy, and so on and so forth. So that is the kind of most charitable version of why artificial general intelligence could fundamentally
solve climate change. My critique is, again, we don't have scientific evidence that AGI will ever come to pass, and so we are basically trying to justify current day vast environmental harms and the acceleration of the climate crisis with a speculative possibility that it might one day be able to go away. And so we're essentially trying to cover up real scientific evidence of present day reality with a spiritual belief that it'll all be okay in the end.
Listen. I would go further. And I mean my issue with this is even if an AI agent, your chatbot, can spit out the answer, Hey, here's what to do, do this, do this, and do this, and climate change will grind to a halt. What if we're not interested in listening, you know what I mean?
Yeah? Right?
You could get on chat YOUBT, you can get on claud you can get on Gemini or whatever and say, hey, hey, AI friend, I'm I'm feeling really sick. I'm eating all of this cake and I really don't feel good. What should I do? And I'll say, hey, buddy, stop eating cake. I'm gonna keep eating cake.
Yo.
You don't actually have to listen. The computer can't make you listen, even if it has the answer. And here in the States particularly, we've got the data. There are some ideas on things we could do to reduce the impact on the environment.
We're just not doing them exactly. I think this is one of the things that has always broken down in these theoretical future AGI arguments of whether it's going to be fundamentally positively or negatively transformative. There's never a clear
articulation of how it's operationalized in the physical world. Are they going to have robot bodies, are they going to be mining the earth and developing and cultivating these new energy storage solutions, or are they directing humans to do that, at which point we still run into all of the same problems that we've always right pad, which is humans
don't listen. So I think you're hitting upon one of the core weaknesses of just many of the arguments in this community is they do not actually acknowledge the social, political, and economic aspects of the way that technology ultimately impacts society. Like it's not just you have a technical capability and everything is suddenly solved. There's so many more layers to how the capability then translates into real world impact.
Yeah, I'm sympathetic to some of the ideas that are put forth, like AI could cure diseases. Hey, look, you can mix these three chemicals and it gives you a pill and it's two bucks. Give it to everybody. Okay, I'll buy that. And also healthcare obviously has a huge social component to it. But yeah, something that truly is linked to our behavior. I struggle to see how exactly that's going to work.
What's interesting about the drug discovery or curing diseases thing, too, is there's a word game that people within the AI world play where they talk about when they talk about AGA is going to cure cancer. What's confusing is that there are plenty of AI technologies that can help us advance and tackle that challenge, but it has nothing to do with the type of AI that Open AI or the rest of these Silicon Valley companies are building. So they play this game where they just say AI and
AI is a huge umbrella term. It's like the word transportation, like you could be talking about a bicycle or a bus, or a gaskillsing truck or a rocket. These are all different forms of transportation, and they're building rockets, but they're pointing to the benefits of bicycles and public transport. And so when these companies say AI is going to help us cure diseases. There are plenty of AI technologies that literally have no relation to what they do that are
making positive impacts on healthcare. There are machine learning models that can be trained on MIRI scans to identify cancer, and there have been studies that show that if you give these tools to trained radiologists, they will be able to identify cancer far earlier with a much higher accuracy, such that patients can actually intervene early on and have
a much higher likelihood of kicking that disease. There is also Last year, the twenty twenty four Nobel Prize in Chemistry was awarded to a team at DeepMind that pre them joining this large language model race. They developed this tool called alpha fold and it was able to predict with extremely high accuracy all of the protein structures of different amino acids, and that in and of itself is going to be one extremely critical building block for understanding
disease and for discovering new drugs. That was trained on extremely clean, highly curated data sets. It was just amino acids and protein folding structures. That was a very task specific AI model that then was able to do incredible things because fundamentally it was a well scoped highly computational problem, and that's what AI is good at. You throw AI
at a highly computational problem and it will compute. But what these companies are doing, and the critique that I have is they pretend that they're trying to build everything machines, and they're trying to do that by then scraping the entirety of the English language Internet with just a boatload of polluted data that has nothing to do with healthcare. But it's just like people throwing curse words at each other online and then they're like, this is going to
cure cancer, and it's like what are you smoking? Like, like, how is that gonna get us to? Like what? Like, we already have these other AI technologies that are making these advancements, that are being trained on actual, high quality data sets. And then you're gonna pump a bunch of gunk into this large, nebulous, large language model that really has very little articulated purpose and say it's going.
To do the same thing and pump gunk can do the environment.
And pump gunk into the environment and exploit a lot of labor and get like potentially automate away a ton of jobs in the process.
So, yeah, there are people in Silicon Valley who love to pitch AI as a silver bullet for everything from cancer to climate change. But of course the reality is not that simple. What this company or that company you're building could really solve some of these problems. But as they're doing it, they're also doing things that you might have read about before, not in Forbes or in Wired magazine,
but in your middle school history textbook. Companies in the industry are starting to act a lot more like empires, Empires that extract resources, expand rapidly, and then leave communities to deal with the consequences. We'll get into some of those consequences after the break. Some of the consequences of AI's colonialism you might already be able to imagine, like when Google went to a part of Chile that was in the middle of a huge water crisis and tried
to build a massive data center. If you think back to our episode on AI and the Environment, you know that data centers use up a ton of water that didn't seem to matter to Google. But it's not just environmental. In the book, Karen also talks about how the industry always seems to go to countries undergoing some kind of economic crisis and then find workers there to exploit. Karen found that open ai used an intermediary company to contract workers in Kenya for less than two dollars an hour
to build automated content moderation filters. For these filters to work, you need humans to catalog, sometimes hundreds of thousands of examples of things like the graphic content that OpenAI wanted to prevent its model from generating. One worker on the quote sexual content team had to review fifteen thousand pieces of sexual content per month. And we're not talking just
regular porn, I also mean child sexual abuse material. Some of this material was even generated by open AI's own software, so that again they could check if the filters were catching the bad stuff. But the workers who were manually sifting through this stuff day after today, it caused some serious mental consequences environmental exploitation, worker exploitation. This sounds like
straight up empire behavior, and it feels familiar. You could take a map of the colonial powers and the colonies like India, Latin America, parts of West Africa from a couple centuries ago and lay it over a map of where these AI companies are exploiting now, and it would line up almost exactly. So we've read about this stuff in the past in school. So now what this brings
me to a question? Though I don't know if this is pushing back or maybe I'm just a little bit pessimistic here, But if you talk about colonialism, you know, empire's imperialism, those words mean different things in different places. Absolutely, I think if you go to a place that was colonized, it's going to bring back memories of slavery, members of exploitation, memories of poverty, starvation, generations of wrecked governments, things like that.
For an American, we think colonial is a furniture style, you know what I mean. We think it's a we think it's a cool way to build your house. Oh God, let's be real. That is so real. We'd be real.
And so I think about this, and I think about your explaining imperialism of AI companies, and I think one of the features of an empire, one of the features of a colonialist empire is not only the leaders, but the people who live there also start to believe that that's just the natural way of things, that oh, there are people in Kenya who are being forced to see just absolutely horrific carnage imagery and getting paid cents and oh, well,
that's just the way things go. That hey, third world country, Global South, that's what happens there and it doesn't happen here. Let's just be real. How do you make a reader who is in the empire understand that?
You know, I went on book tour. I stopped in Seattle, and I had this wonderful opportunity. You talked to Ted Chang, one of the most decorated science fiction writers ever, and we were talking about this exact question, and he told me, and I think he's exactly right. He was like, your job is not to convince the people that are already
convinced of something completely ideologically opposite to you. If someone already is convinced that there should be a hierarchy in the world and that certain people don't deserve fundamental, basic human rights like you do, not waste your time convincing them. Who you're trying to convince is the broader public and people who just don't really know how to think about
this technology and don't really know how to interface with it. Ultimately, all empires are made to feel inevitable, but all empires fall because the majority of people under subjugation end up rising up and protesting the empire, and those are the people that you should be speaking to. Another feature of empire building is there are people who are richly rewarded by empire, usually the people that are most powerful politically
and economically. Those are the people that are rewarded, and that's why empires are able to perpetuate so long in the first place, because the people who are totally coddled into believing that this is a great state of affairs are the people that then have the most access to all of the levers to maintain the status quo. And
so he was like, don't talk to those people. Those are not the people that you should focus on, because they're never going to change their minds, like both philosophically because they don't see a problem with an exploitaive, extractive worldview, but also because all of the evidence that they are exposed to reinforces the idea that things are just fine, and so focus on everyone else, focus on rallying the
majority of the world around this idea. When I tell people this empire metaphor, outside the US, no one has ever questioned me. When I was talking with Chilean water activists for my book about the expansion of data centers in their community, they were first to bring up the relation to their history, their colonial history. Really yeah, I
didn't actually even say it. They were like, what's happening now is what's happened to us for centuries, first at the hands of Spanish colonizers, then at the hands of American multinationals, and now at the hands of new American multinationals. And to your point that, like every different country experiences colonialism differently, I mean, it was remarkable how they still experience it differently, but in exactly the same way as before.
So in Kenya they were like, there is a connection between slavery and labor exploitation happening now with the AI industry. And in Chile they are a country that has been extracted for their natural resources again and again. And the term extractivism, which is an anti colonial term that refers to the idea that massive amounts of resources are extracted from one place and used to benefit a far away place,
no benefit goes to the local community. That was originally about the Spanish and Portuguese term coined by scholars in Latin America extractavismo or estrachivismo in Spanish and Portuguese, and they were like, this is extractivism, right, We've seen this. There's a connection between the Spanish colonial extractivism and the
air industry's extractivism. So it literally is a replaying. Yeah, and people who live that history and understand that history, there's no leap that they have to make to connect the two.
That makes a lot of sense. And to be clear, this isn't just happening in what we consider quote the global South. It happens in the US too. That example of Google trying to build a data center in Chile despite the residents not wanting it might sound familiar. In our episode on Ai in the Environment, we talked a little bit about Memphis, where Elon Musk's company Xai has been running gas turbines without permits for months now. It's
making people there sick. Just a couple of weeks ago, More Perfect Union published a deeper dive on what's happening in Memphis that gives some more details, and Xai has already started building a second data center. So in Chile, the local people organized and successfully stopped Google from building that data center in Memphis. That fight is still ongoing. But even if they're able to shut those turbines down tomorrow, what happens to the people who were already hurt by
what XAI has done to the environment there. It's just another example of what Karen Howe describes as the rise of AI empires, taking resources, dodging accountability, and leaving communities to deal with the consequences. You talk about some potential alternative ways that this is going to be used or this could be used, and so you're not necessarily talking about everybody turn off the computer type thing. But yeah, just tying in with the title, where's the kill switch
on this thing? And what hitting the kill switch?
So to me, hitting the kill switch in this context is killing the imperial conception of AI development. Not killing all AI development, but the imperial one where people at the top can just say this is how it's going to go and then consume the entire world of resources in a pursuit of a Morphis vision of progress. The thing that I want to see and I think how we can get there. I want to see broadly beneficial, task specific AI models that are developed and deployed through
the participation of communities. And when you think about the AI supply chain, which I try to make visible in my book, there's data, there's land, there's energy, there's water, there's labor. There are spaces that companies need access to deploy their technologies, like schools and hospitals and government agencies. Silicon Valley has done a really great job over the last ten years of making people feel like these resources in these spaces are actually owned by Silicon Valley, but no,
they're owned by us. That day is your data, it's my data. That in intellectual property is the intellectual property of artists, writers, creators. The schools that's collectively owned by teachers and students. Those hospitals are collectively owned by doctors, nurses, and patients. And we're already seeing movements around the world of people actually fighting back and reclaiming ownership of those
resources in those spaces. So artists and writers that are suing these companies saying no, you can't just take our
intellectual property. The Chland water activists that are write about in my book who said no, you can't just take our fresh water and successfully stalled Google from building a data center within their community for now five years, many many movements around the world that are replicating that to push back against data centers, teachers and students that are having public debates now saying do we actually want AI
in our schools and if so, under what terms? Because we wanted to foster curiosity and create a critical thinking, not just totally eroded away. And if we can have those conversations one hundred thousand times full and start moving more towards task specific AI technologies, we will get to a place where we do have AI that is broadly beneficial and actually works for the people rather than us working for AI.
Thank you, Thank you so much. Hope to be able to chat with you again.
Thank you so much. Dexter.
All right, so this is the part of the episode where I usually have some kind of closing thoughts, you know, at my little two cents, three cents go on a little bit, But this time I'm going to keep it to four words. Go read Karen's book. Seriously, Just go
read Karen's book. One thing we didn't talk about, and one thing I really do like about the book that I should say is that you could pick it up with absolutely no idea how AI works and you'll not only understand the societal implications that you know we kind of talked about in this episode, but you'll come up with a better understanding of the technology of AI than most people, even if you're one of those people who says you don't like computers, you hate computers, you don't
understand them for real. It breaks it down in a way that I've never seen done before. So highly recommend it. And if you've already read the book or you just want to talk about something else, let us know what you think. We're on Instagram at kill switch pod, or you can hit me at dex Digi that's d e x DGI again on Instagram or am alsa Wan Blue Sky. And if you haven't done it yet, leave us a
review on your favorite podcast platform. People actually read those things, and your review could be the thing that convinces someone or someone's to check us out. And this show is hosted by Me Dexter Thomas. It's produced by Shena Ozaki, Darluk Potts, and Kate Osbourne. Our theme song is by me and Kyle Murdoch, and Kyle also mixed the show from Kaleidoscope. Our executive producers are Ozma Lashin, Mangesha Gadur
and Kate Osborne from iHeart Our. Executive producers are Katrina Norville and Nikki Etor catch On the Next One, Goodbye,