Speaker 1: Pushkin.
Speaker 2: Hello and welcome back to Drilled. I'm Amy Westerbelt. Today
Speaker 2: we're bringing you another installment of our ongoing series Drilling Deep,
Speaker 2: where we speak to authors of recent books that are
Speaker 2: either about climate or about things that intersect with climate
Speaker 2: in a big way. Today is a super timely one.
Speaker 2: We're going to talk about AI. Specifically, we're going to
Speaker 2: talk about Karen Howe's new book Empire of AI, Dreams
Speaker 2: and Nightmares in Sam Altman's Open AI. In this episode,
Speaker 2: Adam Lowenstein interviews journalist Howe, who argues that AI and
Speaker 2: the profit driven infrastructure that surrounds it is a colonial project.
Speaker 2: What openI boss Altman and his fellow ideologues in Silicon
Speaker 2: Valley are pursuing, how says, is not just corporate power,
Speaker 2: but imperial power. It makes sense when you think about
Speaker 2: the fact that AI, at its core, its most basic
Speaker 2: and ongoing purpose is about twenty four hour surveillance to
Speaker 2: keep all us plates in line. These guys are building empires,
Speaker 2: and as history shows, empires are built on resource extraction,
Speaker 2: particularly the old fashioned kind labor, energy, minerals, land, and water.
Speaker 2: It seems like almost overnight, big techts feel good climate
Speaker 2: promises have evaporated. They've been swapped seamlessly for slippery promises
Speaker 2: that so called artificial general intelligence will solve climate, never
Speaker 2: mind that it's a fantastic concept that has no agreed
Speaker 2: upon definition, or more fundamentally, that appears nowhere close to
Speaker 2: actually existing. In big tech's frenzied pursuit the quote unquote
Speaker 2: hyper scale AI dominance that evangelists claim will unlock AGI,
Speaker 2: as well as its expanding alliances with fossil fuel backed
Speaker 2: petro states, fossil fuel companies, and authoritarian political movements, it's
Speaker 2: become an increasingly central contributor to the climate crisis. In
Speaker 2: an October conversation with Drilled, How discussed how Silicon Valley
Speaker 2: giants appear to be following the oil and gas industry's
Speaker 2: playbook of disinformation and deceit, how Altman and Open AI's
Speaker 2: secrecy and disingenuous rhetoric transformed the field of AI research
Speaker 2: into corporate pr and why the destructive trajectory of AI
Speaker 2: scale and commercialization is not inevitable, no matter what its
Speaker 2: power hungry proponents would have You. Believe that conversation is
Speaker 2: coming up after this quick break and find a condensed
Speaker 2: written version of this interview on our website at drill
Speaker 2: dot Media.
Speaker 1: How does it feel having the book out? Because it's
Speaker 1: it's a monumental achievement. I know you've gotten lots of
Speaker 1: praise for them already, but it's just it's just epic
Speaker 1: and I can't imagine the amount of work that went
Speaker 1: into producing it.
Speaker 3: Yeah, I was the most intense and painful thing that
Speaker 3: I've ever worked on me, like emotionally and physically painful
Speaker 3: because I was typing so much that I developed tendonitis
Speaker 3: on my hands. But yeah, I know, it's I mean,
Speaker 3: it's I think it's been interesting. Have you ever written
Speaker 3: a book before?
Speaker 1: It was not as long as Empire of Ai, but
Speaker 1: I have some understanding of the like the long term
Speaker 1: nature of a writing project like this, but not to
Speaker 1: the same extent with the amount of reporting in the
Speaker 1: yashion that you were wrestling with here.
Speaker 3: The thing that I didn't really prepare for was like
Speaker 3: the one to eighty flip from being completely alone and
Speaker 3: isolated working on the book to like suddenly talking with
Speaker 3: like thousands of people about the same thing once the
Speaker 3: book was launched, And that has been both like a
Speaker 3: very fun and new experience, and one that comes with
Speaker 3: like a lot of relief that like people are resonating
Speaker 3: with the thing, and also like a very uh like
Speaker 3: slightly not slightly extremely chaotic transition, because like you go
Speaker 3: from like mode of like total silence and not having
Speaker 3: really feedback from anyone about anything that you're doing, to
Speaker 3: suddenly getting bombarded with everyone's thoughts about what you've been
Speaker 3: working on. So yeah, so it's like it's like a
Speaker 3: it's like a strange mix of relief and and like
Speaker 3: new sources of stress since the price.
Speaker 1: Have you heard from either sources or industry players with
Speaker 1: feedback about the book.
Speaker 3: I've heard from a few sources who really loved the book,
Speaker 3: which is always a relief. M M.
Speaker 1: Yeah, you want to do them justice for sharing their stories.
Speaker 3: Yeah, And I've I've heard from some like people within
Speaker 3: industry that are not like exacts, but you know, employees
Speaker 3: at these various companies that I've mentioned that were not sources,
Speaker 3: but that have mentioned that they really appreciated the book.
Speaker 3: But in terms of like formal responses from Open AI
Speaker 3: for other companies, there's been nothing, which I much prefer
Speaker 3: to like a lawsuit, So I'll take silence any day. Yeah,
Speaker 3: for sure.
Speaker 1: One of the things I found most troubling eye opening
Speaker 1: the way that you convey how artificial intelligence or the
Speaker 1: concept of it is an ideology in Silicon Valley and
Speaker 1: in these companies. And one of the things that the
Speaker 1: book does such a brilliant job communicating, and that I
Speaker 1: wanted to convey in this conversation is the fact that
Speaker 1: these are not like normal companies, and this is not
Speaker 1: a normal technology, whatever normal means. Yeah, can you talk
Speaker 1: a bit about how AI is an ideology and then
Speaker 1: how these companies are already or are pursuing this status
Speaker 1: as what you describe as empires.
Speaker 3: Yeah, So if you think about just the fact that,
Speaker 3: like if we look at the history of AI and
Speaker 3: the founding of the discipline, the scientific discipline in nineteen
Speaker 3: fifty six, even back then, it was already an ideological
Speaker 3: project because it was a group of scientists deciding they
Speaker 3: wanted to try and replicate human intelligence and computers. And
Speaker 3: that's a very political choice, Like why do that? You know,
Speaker 3: from a it's not really clear what the scientific reason is.
Speaker 3: It is more of a of just like a there
Speaker 3: was an intention behind that choosing that path and setting
Speaker 3: up a field that defined its goal as attempting to
Speaker 3: mimic and therefore ultimately potentially replace humans. And Joseph Weisenbauman,
Speaker 3: MIT professor, who was kind of part of the initial
Speaker 3: co word of scientists who did start researching AI, then
Speaker 3: later quickly became a critic of the entire endeavor, still
Speaker 3: back in the nineteen sixties, and he said at the time,
Speaker 3: in a very prophetic way, there is something inherently disturbing
Speaker 3: about a goal that intends to replace a thing that
Speaker 3: we already have, because ultimately, all this is going to
Speaker 3: do is allow people empower politicians and corporate CEOs to
Speaker 3: impose their will through algorithms and use the AI system
Speaker 3: as the load bearer of responsibility, essentially, like it allows
Speaker 3: them to do whatever they want, but to say that
Speaker 3: something else did it. And so he was already recognizing
Speaker 3: back then that there's an ideological project behind the creation
Speaker 3: of AI, and so fast forward all the way to today,
Speaker 3: that ideology has not only continued to permeate and drive
Speaker 3: the industry and its goals, it has now become much
Speaker 3: more extreme because they're layered on top of this fundamental
Speaker 3: assumption that it is somehow inherently good to recreate human
Speaker 3: intelligence and computers. We now have a particular approach to
Speaker 3: doing that that these companies index on, which is scaling
Speaker 3: at these models at all costs, which is also an ideology.
Speaker 3: There is no scientific basis for choosing that approach, and
Speaker 3: there's no scientific reason for why these companies are trying
Speaker 3: to dominate as monopolies. This is also based on these
Speaker 3: worldviews and values that a certain small group of people
Speaker 3: have chosen, and that group of people have an extraordinary
Speaker 3: amount of power to make that choice affect everyone. I'm
Speaker 3: really glad that you're asking about the ideological aspect of it,
Speaker 3: because I think this is a really huge part of
Speaker 3: the story of AI that has often missed. People keep
Speaker 3: thinking that this is just a business story. It's you know,
Speaker 3: Silicon value doing commercial things and ultimately trying to drive
Speaker 3: for profit maximization. But that is only half the story
Speaker 3: when it comes to AI. There is a deep ideological
Speaker 3: drive to recreate human intelligence too, and a belief in
Speaker 3: the idea that this is somehow going to create to
Speaker 3: solve all of our problems or potentially destroy us, and
Speaker 3: that in order to do all these things that we
Speaker 3: have to consume the entire planet's resources and all of
Speaker 3: those that need to be scrutinized for what they are,
Speaker 3: which is actually just an extremely narrow view about how
Speaker 3: the world is and how it should be.
Speaker 1: The idea of artificial general intelligence or AGI, I think,
Speaker 1: is something people hear about a lot these days because
Speaker 1: obviously it's great for headlines, and it's also great for
Speaker 1: CEOs of these companies to throw out as an idea,
Speaker 1: you know, implying that it's already here or it's just
Speaker 1: around the corner is a great way to boost a
Speaker 1: stock price. But they also, as you show in the book,
Speaker 1: they also use the idea of AGI is basically a
Speaker 1: pretext for whatever they want to do. Can you talk
Speaker 1: a little bit about what AGE is or as close
Speaker 1: as we can come to a definition, and then how
Speaker 1: they use this hypothetical concept to basically justify whatever it
Speaker 1: is they want to do.
Speaker 3: Yeah. So, the loosest definition of AGI is the point
Speaker 3: at which an AI system fully encapsulates all the dimensions
Speaker 3: of human intelligence, which was the original definition of AI.
Speaker 3: And the reason why we now use the term AGI
Speaker 3: instead of AI is because over the decades, the term
Speaker 3: AI has sort of been cheapened by the fact that
Speaker 3: companies keep using it to market existing products and services,
Speaker 3: and so now AI has come to mean what we
Speaker 3: have currently and AGI is supposed to indicate a return
Speaker 3: to that original premise of this scientific discipline. But the
Speaker 3: problem why AGI is so ill defined is because we
Speaker 3: don't have scientific cosensus around what human intelligence is. There's
Speaker 3: no signs, there's no neurological, biological, psychological definition for this
Speaker 3: special characteristic that humans seem to have more of than
Speaker 3: other animal species. And in fact, like when you think
Speaker 3: about the history of trying to measure, quantify rank human intelligence,
Speaker 3: it has always been driven by extremely dark, dark motives eugenics,
Speaker 3: the justification of oppressing different groups of people. And so
Speaker 3: this is why AGI remains an extremely nebulous milestone because
Speaker 3: different people have totally different ideas of what does it
Speaker 3: mean that we've finally achieved human level intelligence and open
Speaker 3: eye jokes about like this has been a long standing
Speaker 3: joke within the company. If you ask thirteen AI researchers
Speaker 3: what AGI is, you'll get fifteen definitions, and so you know,
Speaker 3: they're kind of self aware about the fact that this
Speaker 3: lacks consensus, but it becomes a tool that the executives
Speaker 3: use to their advantage, and Altman in particular in that
Speaker 3: because there is no neither a definition within the company
Speaker 3: that has full consensus nor externals of the company that
Speaker 3: has full consensus, they can just paint these visions of
Speaker 3: AGI based on whatever is necessary to overcome a particular
Speaker 3: hurdle that they're facing in that moment. So when Altman
Speaker 3: is speaking in front of Congress, you'll paint AGI as
Speaker 3: this magical system that is like a magical want that
Speaker 3: you can waive that's going to solve climate change, cure cancer,
Speaker 3: do all these things that people have wanted for a
Speaker 3: very very long time. And then when you're talking to
Speaker 3: like CEOs, AGI suddenly an employee like something that they
Speaker 3: can buy as a service so that they don't have
Speaker 3: to hire real humans. And then when you're talking to
Speaker 3: you know, an average consumer, AGI suddenly a friend or
Speaker 3: it's an assistant or whatever it is that you know
Speaker 3: compels that person to put down money or compels the
Speaker 3: regulators to not put in regulation. And so AGI just
Speaker 3: shape shifts and morphs in these ways that when you
Speaker 3: actually list out all of the possible things that supposedly
Speaker 3: AGI is that Altman or any other company executive has
Speaker 3: ever said it's a completely incoherent vision of a technology,
Speaker 3: like it just doesn't make sense, and like opening eye uses.
Speaker 3: They've also written down in documents completely different definitions of
Speaker 3: this as well, Like they just they officially say on
Speaker 3: their website that AGI is highly autonomous systems that outperform
Speaker 3: humans and most economically valuable work, So they are defining
Speaker 3: it as a labor automating technology. But then in their
Speaker 3: contracts with Microsoft, as reported by the Information, AGI is
Speaker 3: apparently an AI system that can generate one hundred billion
Speaker 3: dollars in revenue, which is like a completely different definition.
Speaker 3: So yeah, it's just it's just a rhetorical tool forgetting
Speaker 3: what they want.
Speaker 1: On a side note, and I think this is probably
Speaker 1: obvious to a lot of people, but I think still
Speaker 1: bears emphasizing is the hunger that corporate CEOs have to
Speaker 1: get rid of their needy employees. And by needy, I
Speaker 1: mean like healthcare benefits, vacation, parental leave, potential union organizing.
Speaker 1: They salivate at the idea of replacing workers with machines,
Speaker 1: and so you can see how the concept of AGI
Speaker 1: would be so appealing to a CEO.
Speaker 3: Absolutely yeah, and this is why this has continued, in
Speaker 3: part to be a self perpetuating myth. I guess you
Speaker 3: could say that it is possible to build AGI is
Speaker 3: because there is such a high demand for that magical
Speaker 3: thing that a rep sense that it is just all
Speaker 3: of the market incentives and all of the political incentives
Speaker 3: push Silicon Valley to continue propping that myth up and
Speaker 3: using it to dominate the narrative.
Speaker 1: Something you mentioned earlier and that you trace throughout the
Speaker 1: book is the way that the promise of AGI solving
Speaker 1: the climate crisis has been used to rationalize really unfathomable
Speaker 1: environmental costs. And you describe it at one point in
Speaker 1: the book as a gamble that are basically saying, like,
Speaker 1: it doesn't really matter resource consumption, the land grabbing, not
Speaker 1: to mention the human exploitation that it requires right now,
Speaker 1: because this magical mystery technology will solve this crisis that
Speaker 1: we're contributing to in a massive way at some point
Speaker 1: in the future. Can you talk a bit about that
Speaker 1: gamble that they are making the planet's chips, even though
Speaker 1: no one actually gave them those chips to play with,
Speaker 1: but they're basically the extent to which they do not
Speaker 1: seem to care about any of the impacts of the
Speaker 1: technologies they're building because of this future promise to solve
Speaker 1: this existential crisis. It's really pretty astonishing.
Speaker 3: It is incredibly astonishing. Yeah, I think the first to
Speaker 3: just quantify what level of environmental impact we're talking about,
Speaker 3: there was this McKenzie report that came out earlier this
Speaker 3: year that did an estimation on how much energy would
Speaker 3: actually be required to sustain the current projections of AI
Speaker 3: industry growth, and we would need to add two to
Speaker 3: six times the amount of energy consumed by California onto
Speaker 3: the global grid in five years just to sustain the
Speaker 3: AA industry and its state of center of demands. That
Speaker 3: is insane. That is six possibly six times the fifth
Speaker 3: largest economy in the world, and there have been, you know,
Speaker 3: other projections that have said that's more energy than all
Speaker 3: of India, the largest energy consumer at this point globally,
Speaker 3: and most of that will come from fossil fuels. So
Speaker 3: we are not only talking about an acceleration of climate change,
Speaker 3: we are also talking about an acceleration of air pollution.
Speaker 3: We are already seeing reports of methane gas turbines, for example,
Speaker 3: being used to power Colossus, the supercomputer that Elon Musk
Speaker 3: built for XAI and for training Rock in Memphis, Tennessee.
Speaker 3: That's pumping thousands of tons of pollutants into working class
Speaker 3: communities that have already had a long history of being
Speaker 3: denied the fundamental right to clean air for decades. And
Speaker 3: we also see the exacerbation of the water the clean
Speaker 3: water crisis. There is a huge crisis of people getting
Speaker 3: access to clean drinking water around the world, in part
Speaker 3: due to the acceleration of climate change, and AI also
Speaker 3: exacerbates that. There was this investigation from Bloomberg that found
Speaker 3: that two thirds of the data center is being built
Speaker 3: for the AI industry are going into places that are
Speaker 3: already scarce on freshwater resources, and data centers require freshwater
Speaker 3: resources to cool in order to make sure that these
Speaker 3: extremely expensive computer chips don't overheat and bust. And so
Speaker 3: you have this multi layered environmental public health crisis kind
Speaker 3: of being pushed to the brank and undermining people's fundamental
Speaker 3: ability to live a decent quality life. And that's happening now.
Speaker 3: This is not speculative, like this is literally playing out currently,
Speaker 3: and the AA industry justifies all of this base on
Speaker 3: a speculative assumption that maybe we will get to a
Speaker 3: point where these AI systems can then fix all of
Speaker 3: those problems. And that is the thing that is so
Speaker 3: mind blowing to me, is that in this AI era,
Speaker 3: somehow future speculation can be used to justify present day,
Speaker 3: real world substantial harm. And the question that I always
Speaker 3: ask people is like, how long do we wait for
Speaker 3: this potential speculative future. How much harm do we suffer
Speaker 3: before we realize that future might not arrive and we
Speaker 3: might not survive these harms. And of course, like the
Speaker 3: people who are ultimately making the decisions, they're not the
Speaker 3: ones that they're the brunt of these harms. They're not
Speaker 3: the ones that are bearing the brunt of climate having
Speaker 3: changed already, and they're not the ones breathing in this
Speaker 3: toxic air from fossil fuels burning in their communities. And
Speaker 3: so for THEMB the question of how long can we
Speaker 3: kind of bear to do all these things before we
Speaker 3: potentially reach euphoria is really really long compared to most
Speaker 3: of the rest of the world.
Speaker 1: There's a lot of ways in which the tech industry
Speaker 1: broadly but especially AI, if there is still any separation
Speaker 1: between the two seems to be following the fossil fuel
Speaker 1: industry playbook, and one of those ways seems to be
Speaker 1: this narrative of you know, A, this is inevitable, It's
Speaker 1: happening whether we like it or not, and so we
Speaker 1: might as well might as well be the ones doing it.
Speaker 1: And b this is the you know, the price of progress,
Speaker 1: and if you are against this, then you're against human progress.
Speaker 1: These really powerful narratives of inevitability and progress really struck
Speaker 1: me because it's the exact same rhetoric that the fossil
Speaker 1: fuel industry has been deploying in some form or another
Speaker 1: for decades.
Speaker 3: Yeah. That's such a great point, I mean, and it
Speaker 3: is the trump card for any industry that knows that
Speaker 3: they're engaging in something that's deeply incorrect. And yeah, I mean,
Speaker 3: these technologies are not inevitable. Like the thing that I
Speaker 3: spend a lot of time trying to do with my
Speaker 3: book is document the moments in which open AI employees
Speaker 3: made decisions that fundamentally shape the trajectory of how the
Speaker 3: technology was introduced into society based purely on you know,
Speaker 3: whims of judgment, Like technology is always a product of
Speaker 3: human choices and sometimes those choices are actually seem really
Speaker 3: minor in the moment. Yeah, So, like, how can it
Speaker 3: be inevitable when the shape of a technology can be
Speaker 3: swayed one way or the other based on just like
Speaker 3: a person sitting in a room deciding on the fly
Speaker 3: to do one thing or another and the other thing
Speaker 3: with progress. So yeah, this is like the thing that
Speaker 3: I hope we can have a more nuanced conversation about
Speaker 3: moving forward. Is there's always this idea that the tech
Speaker 3: industry perpetuates that AI is there's only one form of AI,
Speaker 3: and so if we want progress from AI, we just
Speaker 3: have to accept the costs of this progress, meaning the
Speaker 3: colossal environmental impact, the colossal public health impacts, and so
Speaker 3: on and so forth. AI is actually a collection of
Speaker 3: many different technologies that work in very different ways, that
Speaker 3: have very different costs benefit trade offs. And I often
Speaker 3: make the analogy that it's like transportation transportation. There's many
Speaker 3: different types of transportation, and when we talk about how
Speaker 3: we need to improve our transportation options as part of
Speaker 3: client resiliency and fighting climate change, we're not saying like
Speaker 3: we just need more transportation. We're saying we need more bikes,
Speaker 3: we need more public transit. We need less gas guzzling trucks.
Speaker 3: We need to electrify cars. There's like a much more
Speaker 3: nuanced conversation about like which mode of transportation are we
Speaker 3: talking about, and how to tweak and tune and adjust
Speaker 3: the supply and demand of each of these different types
Speaker 3: of transportation in order to get that progress. And we
Speaker 3: need to have a similar nuanced conversation about AI. The
Speaker 3: large scale AI models that Silicon Valley has imposed on
Speaker 3: everyone that I describe as an imperial consolidation project. That
Speaker 3: is the form of AI that is the most costly
Speaker 3: and has very unclear benefits that just do not justify
Speaker 3: those costs. But when talking about the kinds of progress
Speaker 3: that we would actually want in general, putting technology aside,
Speaker 3: like things like wanting client change to be resolved, wanting
Speaker 3: better drugs, better healthcare for treating different types of diseases
Speaker 3: like these are there are actually types of progress that
Speaker 3: we already know how to build AI systems for that
Speaker 3: look fundamentally different from these large SCALEI systems. Those types
Speaker 3: of AI systems they're task specific, they're often very very
Speaker 3: small and cost extremely small amounts of energy to develop.
Speaker 3: They're trained on highly curated data sets that do not
Speaker 3: require the type of labor exploitation and content moderation that
Speaker 3: goes into something like Tatgybut and they have already documented.
Speaker 3: We know that when we build these systems, we get
Speaker 3: benefits on the end, rather than this speculative maybe in
Speaker 3: the future will reach AGI one day and it will
Speaker 3: solve everything. And so once you kind of realize that,
Speaker 3: once you realize there's actually a portfolio of different options
Speaker 3: for the shape of AI and what it can do,
Speaker 3: it suddenly becomes like blatantly obvious that we're actually in
Speaker 3: this colossal capital misallocation problem where we're putting all of
Speaker 3: our capital in the one type of AI that has
Speaker 3: the absolute worst trade offs, and we should be rapidly
Speaker 3: urgently shifting that capital to all of the other types
Speaker 3: of AI that have the correct trade offs very little
Speaker 3: cost for maximal benefit.
Speaker 1: The question of scale, or the idea or the obsession
Speaker 1: with scale, I guess you describe as a doctrine in
Speaker 1: Silicon Valley and in these companies that the only way
Speaker 1: to do this, as you're just referencing, is to scale
Speaker 1: as big and as fast as possible, And something I
Speaker 1: see a lot in headlines these days is in press
Speaker 1: releases is talk of hyper scaling and mega campuses and
Speaker 1: all of this stuff, which is obviously touted is a
Speaker 1: good thing. When you see hyper scale and mega campus
Speaker 1: and where bigger is better, so we must be doing
Speaker 1: something revolutionary and profitable. But it seems like the biggest
Speaker 1: certainly from an environmental perspective, the biggest costs come from
Speaker 1: the scale.
Speaker 3: Right.
Speaker 1: It's not necessarily the technology itself, but the size at
Speaker 1: which that they are obsessed with doing this stuff.
Speaker 2: Is that?
Speaker 1: Is that right?
Speaker 3: Yeah? Exactly. That is my fundamental critique about silicon valleys
Speaker 3: a Perrhai. They've created this idea that you can no
Speaker 3: longer challenge within the valley, that in order to reach
Speaker 3: some kind of utopic state through the creation of AGI,
Speaker 3: you just have to pour ever more data into these
Speaker 3: models and train them on ever larger supercomputers. And we
Speaker 3: are now talking about supercomputers the size of Manhaeian. Meta
Speaker 3: and Opening Eye have both drafted plans to build supercommuters
Speaker 3: the size of and that consume the same power demands
Speaker 3: as Manhaian. This is not actually scientifically sound, you know,
Speaker 3: like when I had the privileges of starting to cover
Speaker 3: AI research. Before chat GBT came out, and well before
Speaker 3: large language models became the obsession, AI research was actually
Speaker 3: heading in the exact opposite direction. At the time. People
Speaker 3: were looking to build smaller and smaller models, and it
Speaker 3: was all about tiny AI because they were all of
Speaker 3: these different techniques that researchers were experimenting with that found
Speaker 3: that you can build really powerful AI systems with minimal
Speaker 3: amounts of data, and also on pocket like a phone,
Speaker 3: like your pocket computer, and that kind of understanding that
Speaker 3: we can actually create new algorithms, create new training techniques,
Speaker 3: just redesign AI systems from the ground up to use
Speaker 3: lesson less resources, something that's been totally thrown out the
Speaker 3: window by Silicon Valley. And it does not make sense
Speaker 3: to me why we have got and stuck in Well,
Speaker 3: I mean it does because some of the people, the
Speaker 3: people that are putting us on this map have an
Speaker 3: extraordinary out of capital, extraordinary out of narrative power and
Speaker 3: so on. But yeah, it's not scientifically technically justified, and
Speaker 3: it has gotten us into this absolutely twisted state where
Speaker 3: we are burning down the earth in order to do
Speaker 3: something that's wholly unnecessary.
Speaker 1: And I keep coming back to the psychology and the
Speaker 1: worldview of the people making these decisions, because talking about social, environmental, political,
Speaker 1: cultural trends, you know, we often talk for good reason
Speaker 1: about problems that are systemic and structural rather than individual.
Speaker 1: But I feel like this is one of those cases
Speaker 1: where it is all of those things, but it's also
Speaker 1: very individual in the sense that so few people are
Speaker 1: making these decisions that are impacting so many of us.
Speaker 1: And I'm just wondering, because you've spent so much time
Speaker 1: with folks at you know, current former employees at open
Speaker 1: Ai as well as the other companies, and there's obviously
Speaker 1: a lot of churn between them, Like, how much do
Speaker 1: you think this trajectory that we're on from you know,
Speaker 1: pocket sized ai have a decade ago to Manhattan sized
Speaker 1: supercomputers potentially in the near future. How much of that
Speaker 1: do you think is driven by just individual desire for
Speaker 1: wealth and more specifically power and domination, versus actually wanting
Speaker 1: to believing in their hearts that these technologies will solve
Speaker 1: the problems that they claim to be able to solve.
Speaker 1: I'm just wondering how you think about the psychology at
Speaker 1: play there and how much of our future is being
Speaker 1: dictated by a really small number of people's desire for
Speaker 1: domination over the rest of us.
Speaker 3: Yeah, I think the best way to answer this question is,
Speaker 3: I think in order to be one of these people,
Speaker 3: you have to be self delusional, and so I'm sure
Speaker 3: that they believe that they believe that they're doing this
Speaker 3: because of out of goodness. I don't think you can
Speaker 3: sustain waking up every morning and orchestrating these deals and
Speaker 3: decisions that are terraforming her than reshaping geopolitics and rewiring
Speaker 3: everything in these ways without deluding yourself into believing that
Speaker 3: you are on a mission for the benefit of all
Speaker 3: of humanity. And I think that is that was not
Speaker 3: something I fully understood until I reported my book and
Speaker 3: spoke with so many people in this world. Was I
Speaker 3: did think that there was more distance between the rhetoric
Speaker 3: that they used publicly to leverage whatever public opinion or
Speaker 3: their political capital or narratives to get what they wanted
Speaker 3: and their actual true beliefs. And what I realized the
Speaker 3: more that I reported is actually the rhetoric and what
Speaker 3: they believe. The distance collapses over time because you just
Speaker 3: cannot continue to say all of these things and do
Speaker 3: all of these things without giving telling yourself that that
Speaker 3: is true, that is your truth. People's beliefs become molded
Speaker 3: by what they need to accumulate more power and you
Speaker 3: accumulate more wealth. You know, I had the because I
Speaker 3: started reporting on Opening Eye several years before chat Gibt
Speaker 3: came out. Like, I interviewed people back then that I've
Speaker 3: been and reinterviewed for the book, So I sort of
Speaker 3: have seen people transition in their beliefs over time as
Speaker 3: they've become more and more part of the company and
Speaker 3: this world, and it really does completely transform them and
Speaker 3: who they think they are and what their worldviews are.
Speaker 3: Like I was talking to people back then who were like,
Speaker 3: I don't really believe in the AGI think, but you know,
Speaker 3: I just get I get a good salary, I get
Speaker 3: a lot of resources to play around with cutting edge research.
Speaker 3: It's basically like working in academia. But just like way
Speaker 3: better funded through have then now become complete AGI believers,
Speaker 3: like do not even question for a second that they
Speaker 3: are building something that could be akin to a god
Speaker 3: and will have utopic qualities, and that this is the
Speaker 3: reason that they're brought to this earth.
Speaker 1: I'm sure it's intoxicating to feel that. I can see
Speaker 1: how people might get addicted to that feeling of being
Speaker 1: on some sort of divine mission.
Speaker 3: Yeah, absolutely, and not just a divine mission, Like people
Speaker 3: have to get addicted to just the sheer. They get
Speaker 3: addicted to a lot of things. They get addicted to
Speaker 3: the sheer size of the resources that they can have
Speaker 3: access to. They get addicted to the amount of influence
Speaker 3: that they can have. You know, like I've had people
Speaker 3: say to me, well, there's really very few other companies
Speaker 3: that you can work to have ripple effects on billions
Speaker 3: of people around the world. And for them, that is
Speaker 3: They're not saying that in a machi Abelian way. They're
Speaker 3: saying that in a I want to change the world
Speaker 3: to be a better place, So what better leverage can
Speaker 3: I get than being at a company that touches billions
Speaker 3: of people's lives, Like they're saying it from a place
Speaker 3: of this is, how could you not let take that opportunity?
Speaker 3: If you, like, if you wanted to change the world
Speaker 3: and you stepped into my shoes, wouldn't you do the
Speaker 3: same thing.
Speaker 1: It's really unsettling to think about.
Speaker 3: Yeah, it's it totally the realization that the self delusion
Speaker 3: is that strong. Breading the book really made me feel
Speaker 3: a lot more freaked out about Yeah, like where we
Speaker 3: where we could be heading if we don't as a
Speaker 3: society rise up to contain the empire of AI.
Speaker 1: Yeah, I mean, it just shows why empire is really
Speaker 1: the perfect description for what is happening. Was there a
Speaker 1: point when you were reporting the book or when you
Speaker 1: were writing it, when that became clear to you that
Speaker 1: this was the project they were undertaking, was the construction
Speaker 1: of not just a company or a conglomerate, but an empire.
Speaker 3: So interestingly, I actually when I first started working on
Speaker 3: this book, it had very little to do with OPENINGI
Speaker 3: and only to do with the AI industry as an empire.
Speaker 3: And the reason is because I had been working on
Speaker 3: a project called AI Colonialism for MIT Technology Review just
Speaker 3: a year before Chatbut came out, and I had already
Speaker 3: been thinking about the parallels between the colonialism and the
Speaker 3: way that the AI industry was operating around the world.
Speaker 3: And this was based in part on me reading a
Speaker 3: bunch of scholarship that I identified these parallels and then
Speaker 3: growing really fascinated by it and looking to see whether
Speaker 3: or not that was actually happening the real world and
Speaker 3: traveling to different countries to document those parallels and finding
Speaker 3: extremely striking parallels in the process. And so I'd originally
Speaker 3: cold see that the book is just let me just
Speaker 3: write more about that, about the argument that there is
Speaker 3: something very neo colonial happening here with silicon values push
Speaker 3: to build and deploy this technology. And then when chat
Speaker 3: GBT came out, my agent was actually the one that said, well,
Speaker 3: how does this change things? Do you think that's fixed
Speaker 3: the problems? And I was like, no, it's made it
Speaker 3: way worse, like orders of magnitude worse, because now they've
Speaker 3: fired the first shot where every single company is now
Speaker 3: going to go after scale the way that Opening Eye
Speaker 3: has religiously done, which was not at all the norm
Speaker 3: before then, and that's going to accelerate the resource extraction,
Speaker 3: accelerate the labor exploitation, accelerate the environmental degradation, and in
Speaker 3: all of the ways that I was already seeing those
Speaker 3: parallels to the Empire, and so yeah, so the revelation
Speaker 3: went the other way around. It was starting with Empire
Speaker 3: and then realizing that Open AI was the center of
Speaker 3: so much of this story. Rather than starting with Open
Speaker 3: AI and then realizing they were an empire.
Speaker 1: Something you point out throughout the book is the way
Speaker 1: that open AI kind of sets the precedent for the
Speaker 1: way the rest of the industry goes, certainly like the
Speaker 1: obsessional scale as you mentioned. Another one is the kind
Speaker 1: of rapid evolution or devolution from these being essentially research
Speaker 1: hubs doing peer reviewed research. And yes they're funded by
Speaker 1: private industry, but everything's very open, very academic, lots of
Speaker 1: collaboration with universities and scholars, to in another parallel to
Speaker 1: the fossil fuel industry, very secretive. They have the information
Speaker 1: about what these things do as best as anyone does,
Speaker 1: but they're not publishing it, they're not sharing it. Critical
Speaker 1: research gets suffocated, people get fired for criticizing the regime.
Speaker 1: Do you talk about the kind of the suppression of
Speaker 1: inconvenient information and how fast that that shift occurred from
Speaker 1: we're essentially you know, private research hubs to this is
Speaker 1: proprietary information and you know, do not deviate from the narrative.
Speaker 3: Yeah. So I think people who start becoming aware of
Speaker 3: the air industry and as practice post Chatjubut would think
Speaker 3: it's perfectly normal that these companies are very secretive about
Speaker 3: their quote unquote intellectual property, and any kind of AI
Speaker 3: research would be considered intellectual property, and therefore it's not
Speaker 3: weird to them that these companies are are typefisted about
Speaker 3: that kind of information and not transparent For people who
Speaker 3: knew what the norms were in AI research before chat GBT,
Speaker 3: this is a dramatic one eighty shift. Before even though
Speaker 3: much of the tech innutry was bankrolling AI research, the
Speaker 3: understanding was that in order to be competitive in attracting
Speaker 3: talent you had to entice AI researchers by promising them
Speaker 3: that they would ultimately get to publish all of their
Speaker 3: work in public. Because AI research was still very academically driven,
Speaker 3: and so even within it, even if you were a
Speaker 3: Google researcher or a Meta researcher or whatever, your street
Speaker 3: cred was still based on how many papers are you
Speaker 3: publishing in top publications, how many citations are they getting.
Speaker 3: It was just like the credentialing was just very very
Speaker 3: academically coded. I guess you could say Opening Eye. Not
Speaker 3: only did they close themselves off in a great irony
Speaker 3: to their name, they closed the entire industry off by
Speaker 3: doing a lot of work early on to see this
Speaker 3: idea that maybe opening up a lot of this AI
Speaker 3: research could be dangerous, and therefore it was actually the
Speaker 3: responsible thing to shutter this research so that quote unquote
Speaker 3: bad actors could not get access to it. And that
Speaker 3: created a cascading effect across the industry where suddenly when
Speaker 3: it became clear to companies that they could still retain
Speaker 3: the top researchers and be competitive in the talent race
Speaker 3: while being closed off, then it became sort of like
Speaker 3: dead obvious to all of them that was the best
Speaker 3: business decision is you want to keep your competitive advantage
Speaker 3: close to your chest and not share that information. And
Speaker 3: so what we've seen happen is very quickly, because structurally
Speaker 3: most AIR researchers were ready employed by big tech, Suddenly
Speaker 3: the majority of the research discipline now becomes distorted based
Speaker 3: on what companies think is appropriate or not appropriate for
Speaker 3: the public to know. And so it's not actually a
Speaker 3: science anymore. I mean, it's just pr And we have
Speaker 3: an absolute, completely inaccurate understanding now of what the true
Speaker 3: capabilities and limitations of these AI systems really are because
Speaker 3: you cannot audit what these companies claim the ANA systems
Speaker 3: can do without any of the transparency on how they
Speaker 3: were built, what data were they trained on, what kind
Speaker 3: of tests are being run on them, what contexts have
Speaker 3: they been stress tested in, and so on.
Speaker 1: And that's true for some of the environmental impacts and
Speaker 1: carbon emissions too, right, it's hard to understand the true
Speaker 1: impact without them revealing some of this information.
Speaker 3: Yeah, exactly. So we don't know how much energy it
Speaker 3: takes to train a model, I mean, And of course
Speaker 3: there are open source models, and there's been plenty of well,
Speaker 3: I shouldn't say plenty, there has been some really fantastic
Speaker 3: research on using open source models as a proxy for
Speaker 3: understanding the energy and environmental impacts of training these large
Speaker 3: SCALEI systems. But we only know what the real impacts
Speaker 3: of the commercial closed source systems are when companies deem
Speaker 3: it okay to release that information, which has been very
Speaker 3: rare but has happened when they've come under extreme public
Speaker 3: pressure to do so.
Speaker 2: So.
Speaker 3: Google has over time slowly dribbled out some information about
Speaker 3: the environmental impacts of its models, and Meta has tried
Speaker 3: to also do a little bit of that to try
Speaker 3: and generate some like good pr around their transparency and
Speaker 3: trying to position themselves as sort of like an open
Speaker 3: source leader, but it is so limited and the transparency
Speaker 3: is completely disproportional to what is actually needed based on
Speaker 3: the size of the environmental impact and energy consumption.
Speaker 1: There's something that you noted at some point in the
Speaker 1: book you made the what When I read it, it
Speaker 1: seemed very obvious, but I hadn't thought about it at all,
Speaker 1: The point that to solve a global challenge like the
Speaker 1: climate crisis whatever, again, whatever solve means well, we'll also
Speaker 1: need more social cohesion and global cooperation, and of course
Speaker 1: that is precisely what these empires are undermining. I guess
Speaker 1: I had been thinking about it, you know, as the
Speaker 1: way they wanted me to think about it in some ways,
Speaker 1: as a technology. Could you just unpack that a little bit,
Speaker 1: the way that these empires are undermining the very cohesion
Speaker 1: in cooperation that you would need to actually make progress.
Speaker 3: Yeah, this is something that I have has been sort
Speaker 3: of a revolution that I've had again and again and
Speaker 3: again and reporting on technology, is that so often as
Speaker 3: a society we are vulnerable to believing that technology will
Speaker 3: solve social problems, and actually only social solutions will solve
Speaker 3: social problems. And ultimately, climate change at this point is
Speaker 3: not a technology problem. In fact, we have an abundance
Speaker 3: of solutions that we could be deploying, and it's just
Speaker 3: a lack of political will. It's a lack of global cooperation,
Speaker 3: a lack of all of these soft skills that have
Speaker 3: caused us to fail at actually getting there. There's this
Speaker 3: organization called Climate Change AI that I really love. It's
Speaker 3: a nonprofit that was set up by a bunch of
Speaker 3: AI researchers around the world to think deeply about what
Speaker 3: is how do we actually what can AI actually do
Speaker 3: to try and mitigate climate change? And they document this
Speaker 3: long list in a white paper of all of these
Speaker 3: different computational challenges because ultimately, like AI is a computational
Speaker 3: technology that lends itself to computational challenges. So it documents
Speaker 3: all these computational challenges that could be helpful for solving
Speaker 3: when it comes to climate change medication, like integrating more
Speaker 3: renewable energy into the grid, more accurate weather prediction, more
Speaker 3: accurate climate disaster prediction, reducing the energy demands of buildings
Speaker 3: and cities. And then in the white paper it says, ultimately,
Speaker 3: it's not just these things, these technologies that are going
Speaker 3: to get us there because these technolog like they list
Speaker 3: all the AI tech techniques that they list have absolutely
Speaker 3: nothing to do with large scale AI generative AI systems.
Speaker 3: They're all techniques that have been around for basically a decade.
Speaker 3: And they were like, that is evidence enough to show
Speaker 3: you that it's not actually a technical problem at the
Speaker 3: end of the day. And so that was kind of
Speaker 3: what I was getting at in that line, is like,
Speaker 3: these companies are creating AI systems currently that are straining resources,
Speaker 3: which heats up geopolitical competition over those resources. They are
Speaker 3: perpetuating an arms race narrative that also erodes the willingness
Speaker 3: of different geopolitical powers to cooperate with one another. They
Speaker 3: are creating machines of misinformation and disinformation that's eroding the
Speaker 3: fabric of trust in various societies that is also the
Speaker 3: building block for cohesion and cooperation. And so they're kind
Speaker 3: of basically like, when you start to realize it that
Speaker 3: client change isn't really a technology problem and it is
Speaker 3: a social problem, all of the things that we need
Speaker 3: to fortify are actually being chipped away at maybe not
Speaker 3: chipped away, like halfed away by the industry with the
Speaker 3: current systems that they're building.
Speaker 1: There's a very hopeful point that you make in a
Speaker 1: couple of places throughout the book, which is that despite
Speaker 1: the power that these empires have, they're not entirely invulnerable.
Speaker 1: Could you share one of the stories of some of
Speaker 1: the communities that have successfully fought back. I was particularly
Speaker 1: moved by the way, you know, in response to some
Speaker 1: of these companies trying to come into communities in Chile
Speaker 1: or elsewhere. Yeah, under the secrecy of shell companies trying
Speaker 1: to sneak in a massive data center that's going to
Speaker 1: use up all the water in an already drought stricken
Speaker 1: part of the world. People see what's going on, and
Speaker 1: they mobilize, and they have had some really inspiring successes.
Speaker 3: Yeah. So one reason I also think the empire analogy
Speaker 3: is extremely pertinent is because empires are made to feel inevitable,
Speaker 3: and yet they've always fallen in history, and it's because
Speaker 3: their foundations are extremely weak. You cannot actually ultimately sustain
Speaker 3: this degree of extractivism and exploitation has such a large
Speaker 3: scale without people revolting, And that is what I document
Speaker 3: happening in Chile, where Chile has a very long history
Speaker 3: of extractivism and being a concept that came out of
Speaker 3: decolonial scholars from Latin America talking about the harvesting of
Speaker 3: their resources for the benefit of people far away rather
Speaker 3: than the local community. And they essentially see the AI
Speaker 3: industry and all the data centers and the mining that
Speaker 3: the industry is doing to try and build these colossal
Speaker 3: silicon monstrosities as just an extension of this history, like
Speaker 3: yet again, we are dealing with a greedy industry trying
Speaker 3: to extract at scale our precious earth. And so they
Speaker 3: immediately became activated when they discovered that this was happening,
Speaker 3: and I spoke with these activists in this community right
Speaker 3: on the outskirts of Santiago called Serrijos. That was they
Speaker 3: learned that Google was trying to build a data center
Speaker 3: in the one town that actually has access to a
Speaker 3: public drinking water source. Because in Chile, due to its
Speaker 3: history of dictatorship, most things were privatized, including water, but
Speaker 3: the one municipality that had a free public drinking water
Speaker 3: resource was the municipality that Google then was like, yep,
Speaker 3: We're going to put our data center in there and
Speaker 3: then tap into this freshwater resource and use tool our facilities,
Speaker 3: and this community lit up and was like absolutely not.
Speaker 3: These activists started knocking on all of the neighbors doors,
Speaker 3: posting flyers, creating you like memes and art, political art
Speaker 3: and all this stuff to educate their neighbors that this
Speaker 3: project is not at all going to benefit our community
Speaker 3: and is in fact taking the one extremely precious resource
Speaker 3: that we have to your point in a drought stricken time.
Speaker 3: And they made so much noise that escalated all the
Speaker 3: way to both Google's had quarters in Mountain View and
Speaker 3: to the national Chilean government. And basically after this extraordinary
Speaker 3: pressure where they've stalled this project for five years, the
Speaker 3: Chilean government has now created a roundtable for consulting residents
Speaker 3: and environmental activists on their data center plans and putting
Speaker 3: them in conversation with companies like Google and Microsoft. It's
Speaker 3: not a perfect solution, and that the activists have said,
Speaker 3: the moment that they blank, everything can fall apart, and
Speaker 3: so they have to continue being vigilant, they have to
Speaker 3: continue protesting, resisting being on the streets. But it is
Speaker 3: a remarkable step that they got the government to make
Speaker 3: to even bring them to the table, and is just
Speaker 3: a lesson to be learned by everyone around the world
Speaker 3: that if you remember that you actually still have agency,
Speaker 3: you can absolutely shape the trajectory of AI development by loudly,
Speaker 3: forcefully making that kind of trouble when these companies are
Speaker 3: engaging in this imperial activity.
Speaker 1: So such a good note to end on. Thanks so
Speaker 1: much for this conversation, Karen, and for what is truly
Speaker 1: such an important piece of work in scholarship. So I
Speaker 1: am grateful for the enormous amount of time that went
Speaker 1: into it.
Speaker 3: Thank you so much for having me Adam.
Speaker 2: Drilled is an original Critical Frequency production. This episode was
Speaker 2: reported and written by Adam Lowenstein and produced by Peter duff.
Speaker 2: Artwork for Drill is by Matt Fleming, fact checking by
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Speaker 2: Huge thanks to the team there, including Greta Cohen, Eric Sandler, Grease, Ross,
Speaker 2: Morgan Rattner, Owen Miller, Kira Posey, Jordan McMillan, Brian Schreberneck,
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