Speaker 1: Welcome to tech Stuff. I'm Os Voloshen. If you've been
Speaker 1: listening to this podcast, or if you spend any time
Speaker 1: on the Internet, you won't be able to miss the
Speaker 1: word in shitification. And our guest today is the man
Speaker 1: who coined it, Corey doctor Ol. He's a science fiction author,
Speaker 1: a technology activist, and a journalist. His new book is
Speaker 1: called The Reverse Centaur's Guide to Life After AI, How
Speaker 1: to Think about Artificial Intelligence before It's too late. Corey,
Speaker 1: Welcome to text Stuff.
Speaker 2: Thanks as it's a pleasure to be on.
Speaker 1: For anyone who did miss it. What is in ghentification?
Speaker 2: Well, I've worked for the Electronic Frontier Foundation, which is
Speaker 2: an NGO, for twenty five years on the question digital rights,
Speaker 2: and getting people to engage with that question is very
Speaker 2: hard because digital rights questions are abstract and technical and
Speaker 2: relate to things that are going to happen in the future,
Speaker 2: and for very good reasons, people mostly care about concrete
Speaker 2: things happening right now. And what I have done over
Speaker 2: the years has come up with different framing devices and
Speaker 2: similes and metaphors and narratives too. I'm a science fiction novelist,
Speaker 2: but it turned out that a dirty word initification was
Speaker 2: the way to get people to engage in it, and
Speaker 2: so more specifically in sitification is this theory and description.
Speaker 2: It describes how the platforms that we rely on have
Speaker 2: turned into piles of shit, where first they are good
Speaker 2: to their end users but lock them in, and then
Speaker 2: having locked in the end users and made it difficult
Speaker 2: for them to leave, they make it worse for them
Speaker 2: to make it tempting for businesses. And then when the
Speaker 2: businesses are also locked in, they also extract everything they
Speaker 2: can from those businesses and line their own pockets and
Speaker 2: turn into a pile of shit. But the more interesting
Speaker 2: part of it is why they're doing it, And this
Speaker 2: maybe relates to the conversation we're having today, which is
Speaker 2: about the economics and political economy of monopoly, that when
Speaker 2: you let firms get too big to fail, they become
Speaker 2: too big to jail, and then that makes them too
Speaker 2: big to care, and they destroy our lives for the
Speaker 2: same reason your dog looks its balls because they can
Speaker 2: and no one makes them start.
Speaker 1: I want to get into all of that with you,
Speaker 1: but I've read the acknowledgments of your book, not just
Speaker 1: the acknowledgements. If I'm always interesting acknowledgments, and you had
Speaker 1: the same editor for this book, this new book, the
Speaker 1: Reverse Centaur's Guide, as you did for last year's in
Speaker 1: Certification book, and you kind of credit him with helping you,
Speaker 1: not just with in Certification argument, but with this argument.
Speaker 1: And I'm curious, like, where does the reverse central sit
Speaker 1: in the lineage of inertifications? What is it a development
Speaker 1: of the same argument or is it totally new argument?
Speaker 1: Because in Certification was essentially about the tech platforms of
Speaker 1: the Internet, and this is about the experience of AI.
Speaker 2: The common lineage between in Certification and my critique of
Speaker 2: AI is grounded in the dysfunctions and pathologies of firms
Speaker 2: that conquer their markets of saturate their markets. Monopolies, cartels doopolies.
Speaker 2: And one of the consequences of having monopoly is that
Speaker 2: you become ungovernable in the worst sense, not in the
Speaker 2: cool sense of like a raccoon, but in the bad
Speaker 2: sense of like Donald Trump. And you are so liberated
Speaker 2: from consequence that you can do unlimited bad things to
Speaker 2: lots of people, including people you would think would have
Speaker 2: some power in the economy, and just get away with it.
Speaker 2: And really the worst ideas of the worst people become
Speaker 2: the most profitable way of doing business. So that's like
Speaker 2: in shitification at its theoretical core. But reverse centaur is
Speaker 2: about another epiphenomenon, another thing that a consequence of monopoly,
Speaker 2: which is that if your firm that has saturated its
Speaker 2: market right Google has a ninety percent market share, then
Speaker 2: you can't grow anymore. And you know, there's this saying
Speaker 2: among leftists and crunchy granola types that endless growth is
Speaker 2: the ideology of a tumor, and that grounds it in
Speaker 2: this idea that the reason firms want to continue growing
Speaker 2: as ideological and not practical or concrete. And while there
Speaker 2: is some ideology at work there in the way that
Speaker 2: we think about our economy and in the environmental consequences
Speaker 2: of that, there are very practical, material reasons that companies
Speaker 2: want to be perceived as growing and not as mature
Speaker 2: and at the end of their growth cycle. And that
Speaker 2: is because the share price of a company that is
Speaker 2: growing is much higher based on the total economic turnover
Speaker 2: of that company than the share price of affirm with
Speaker 2: the same turnover. But that is static, and that's not irrational.
Speaker 2: A share and affirm is a claim on its future earnings.
Speaker 2: Firms that are growing have more future earnings to return
Speaker 2: to you than firms that are not growing. And the
Speaker 2: corollary of this is that when your firm stops growing,
Speaker 2: it becomes overvalued because the future earnings have contracted when
Speaker 2: you reach the top of your game and you stop growing,
Speaker 2: and that provokes these mass sell offs by investors.
Speaker 1: You mentioned, I think you mentioned that you know the
Speaker 1: Invidia cell of when the deep Seak moment happened. Obviously, Yeah,
Speaker 1: in video rebounded from that, but it raised the it
Speaker 1: raised a specter that growth may not be unlimited.
Speaker 2: Yeah, the video sell off was the largest decapitalization in
Speaker 2: twenty four hours of any firm. And when your firm
Speaker 2: becomes illiquid, right when it's not, when it's not traded
Speaker 2: at this high multiple and so liquid, you can't grow
Speaker 2: by buying other firms by offering them stock. You have
Speaker 2: to give them money. And money is an exogenous substance
Speaker 2: that's produced outside the firm, and shares are things that
Speaker 2: are produced inside the firm by typing zeros and to
Speaker 2: a spreadsheet. And so it's always preferable to be able
Speaker 2: to acquire firms and talent using shares. This endogenous substance
Speaker 2: you can produce on demand, and you can only do
Speaker 2: that while you're growing. So this is another kind of
Speaker 2: growth paradox, right, which is that it's easy to keep
Speaker 2: growing when you're growing because you can buy other companies,
Speaker 2: but it's hard when you stop growing to start growing again,
Speaker 2: not least because all those key employees that have been
Speaker 2: paid in shares suddenly see their net worthfall off a
Speaker 2: cliff when you when your share price declines, and they
Speaker 2: might go look for work elsewhere, which means they're not
Speaker 2: going to help you start the firm up again. They're
Speaker 2: not going to help you get a back on a
Speaker 2: growth trajectory.
Speaker 1: So that the central argument of the book is that
Speaker 1: a lot of technology companies order the practice order. The
Speaker 1: technology companies have an extraordinary incentive to persuade the white
Speaker 1: world that in a AI is inevitable, and that that
Speaker 1: kind of mythology is of the essence to protecting their
Speaker 1: value and therefore keeping their employees and their ability to
Speaker 1: keep growing by buying other companies with their stock.
Speaker 2: Yes, So what I would say is that the book
Speaker 2: is exploring why tech bubbles exist at all. And this
Speaker 2: is not the first one we've had, you know, metaverse
Speaker 2: and cryptocurrency and web three, all those other things.
Speaker 1: But they were all palpably confections, right, those those were
Speaker 1: not things that people use, whereas, like all you do
Speaker 1: is any conversation you over hear in the subway, in
Speaker 1: a restaurant, whatever, is people telling each other how they
Speaker 1: use AI. So that arguably is a big difference.
Speaker 2: So it's just so those bubbles were smaller. Why is
Speaker 2: the a bubble larger? And some of it is because
Speaker 2: AI is realer than those things. So I'm a fake
Speaker 2: computer scientist. I have an honorary doctor in computer science
Speaker 2: from the Open University, and in my capacity as a
Speaker 2: fake computer scientist, I can tell you that AI is
Speaker 2: pretty interesting.
Speaker 1: And from a user point of view, I would argue
Speaker 1: a feeling of technology magic for the first time in
Speaker 1: a long time.
Speaker 2: Sure, yeah, no, very impressive, although I would also say
Speaker 2: that that feeling regresses to the mean pretty quickly. Right
Speaker 2: that if there was a place where there was like
Speaker 2: some kind of weird localized gravity storm, and you took
Speaker 2: a big pile of leaves and you threw it in
Speaker 2: the air and it fell down, in a sentence, we
Speaker 2: would all go throw leaves in the air and look
Speaker 2: at the sentences they made for a while, and then
Speaker 2: we'd be like, they're just not good sentences.
Speaker 1: Going back to the Internet bubble, obviously Internet bubble crash,
Speaker 1: but then very shortly afterwards, Facebook and Google and Amazon emerged.
Speaker 1: So even if there is like a big froth cycle
Speaker 1: around some of the valuations and companies and new billionaires
Speaker 1: who are emerging, I mean, if it's a bubble like
Speaker 1: the web, it maybe a short term financial bubble that
Speaker 1: will burst, but a fundamental platform shift in terms of
Speaker 1: how we interact with technology in the world, Like do
Speaker 1: you believe that will be true of AI or didn't
Speaker 1: the AI in more similar to web three or to
Speaker 1: the metaverse?
Speaker 2: Yeah? Sure, I think AI has some similarities to the
Speaker 2: Web bubble as compared to other bubbles of the day.
Speaker 2: So you know, a bubble that was roughly concurrent with
Speaker 2: the Web bubble was the Enron bubble, right, the energy
Speaker 2: trading bubble, and that was just accounting fraud, and when
Speaker 2: it was over there was nothing but like indictments and
Speaker 2: a language corpus because they couldn't be arked to pay
Speaker 2: a lawyer to redact their emails before they submitted them
Speaker 2: for discovery, whereas the Web left behind like a couple
Speaker 2: million humanities undergraduates who'd been inveigled to drop out and
Speaker 2: become Pearl and python in HTML jocks, and so you know,
Speaker 2: you say that what succeeded the dot com bubble was
Speaker 2: Amazon and Google and Facebook and so on, but there
Speaker 2: was an intermediate step there, and the intermediate step was
Speaker 2: Web too. It was a ton of little amazing startups
Speaker 2: that were what you got when skill practitioners were liberated
Speaker 2: from the need to indulge the foolish fantasies of their
Speaker 2: bosses who were very good at getting capital and very
Speaker 2: bad at coming up with products. So that was pretty amazing.
Speaker 2: Although we shoul it's say that the Enron bubble, the
Speaker 2: Web bubble, and the AI bubble foundationally are not about
Speaker 2: what they produce. They're about separating suckers from their money,
Speaker 2: right that like the point of a bubble is to
Speaker 2: get insiders to cash out and leave ordinary investors holding
Speaker 2: the bag. So no bubble is good, but some bubbles
Speaker 2: have productive residues. So in that sense, AI, I think
Speaker 2: is like the Web in that we're going to have
Speaker 2: a lot of GPUs at ten cents on the dollar,
Speaker 2: a lot of skilled practitioners looking for work and maybe
Speaker 2: hiring each other. And also these open source models that
Speaker 2: will continue to function whether or not there's a company
Speaker 2: that produce them, because open source software continues to exist
Speaker 2: for as long as it exists. But there's a big difference,
Speaker 2: actually two. So the first is the vibe. So if
Speaker 2: you go back and you read like Harvard Business Review
Speaker 2: articles or NBER reports on the workforce in the era
Speaker 2: of the web, what you see are all these articles
Speaker 2: about like bosses who have to be dragkicking and screaming
Speaker 2: into allowing their workers to use a technology that they
Speaker 2: see as transformative. And when you look at comparable reports today,
Speaker 2: it's full of bosses threatening to fire workers if they
Speaker 2: don't use AI. Right. This is now getting to the
Speaker 2: material way in which the web is different from AI.
Speaker 2: The unit economics of the web were very good. Every
Speaker 2: new user of the web made the web more profitable.
Speaker 2: Every new use of the web made the web more profitable,
Speaker 2: and every generation of the web was more profitable than
Speaker 2: the previous generation. That's the opposite of AI. Right, So
Speaker 2: AI companies are selling hundred dollars bills for a dollar apiece.
Speaker 2: So if you become an AI customer, they start to
Speaker 2: lose money. If you continue to be an AI customer,
Speaker 2: they lose more money. And the next generation of AI
Speaker 2: is going to be selling two one hundred dollars bills
Speaker 2: for a dollar apiece, and so it's going to be worse.
Speaker 2: And so that makes it a qualitatively and quantitatively different
Speaker 2: kind of bubble to the web bubble.
Speaker 1: So what's your role in all of this? And I
Speaker 1: think you mentioned the book or in your speech that
Speaker 1: you gave Washington that you know, as a science fiction
Speaker 1: right to people always want to ask you if your
Speaker 1: opinion on AI because I guess so many of the
Speaker 1: AI overlords talk about how science fiction governs their thoughts
Speaker 1: about what they're building. You know, you've said as well,
Speaker 1: science fiction is anti inevitableist literature, as it was a
Speaker 1: great phrase. I mean, what is what is your role
Speaker 1: in this? What do you what do you want people
Speaker 1: to understand to do differently?
Speaker 2: Yeah, I think that the people who run a bubble,
Speaker 2: whether or not it has a productive residue, you always
Speaker 2: want to style themselves as avatars of the great forces
Speaker 2: of history and the iron laws of economics. Right, they're
Speaker 2: not imposing a technology on you. Rather, they are just
Speaker 2: doing what the circumstances demand. And if it wasn't them,
Speaker 2: it'd be someone else, although it has to be them
Speaker 2: because they're extraordinary. But but you know, this is the
Speaker 2: moment at which we are going to get AI and
Speaker 2: we're all going to use AI, and there is no
Speaker 2: future without AI, and the only II that we're going
Speaker 2: to have in the future is the AI they're making,
Speaker 2: and everybody else just shut up. And I call this
Speaker 2: a kind of species of vulgar Thatcherism, because you know
Speaker 2: Margaret Thatcher, she had this aphorism, there is no alternative,
Speaker 2: and this is the mother of all thoughts stopping cliches,
Speaker 2: because what she meant by there is no alternative is
Speaker 2: don't you dare try and think of an alternative? Right.
Speaker 2: Another way of saying there is no alternative is resistance
Speaker 2: is futile, but there is no alternative implies Also, if
Speaker 2: you're unhappy with this, don't get mad at me. Get
Speaker 2: mad at the iron laws of economics and the great
Speaker 2: forces of history, because it could be no other way,
Speaker 2: and science fiction is entirely concerned with all the other
Speaker 2: ways things could be right and in challenging the idea
Speaker 2: that everything is inevitable and nothing is contingent.
Speaker 1: So what's the alternative vision you're laying out for what
Speaker 1: the future may be? I mean, right now it looks like,
Speaker 1: you know, three trillion dollar IPOs, this year's basic exentropic
Speaker 1: open AI. You know, new trillionaires and billionaires and multimillionaires,
Speaker 1: all of whom will then have the capital. Well, the
Speaker 1: share evaluation is to quiem more companies the cash money
Speaker 1: to support politicians and packs like. What might disrupt the
Speaker 1: futures is currently emerging.
Speaker 2: So I can't predict when the bubble will pop. But
Speaker 2: firms that are making tens of billions of dollars a
Speaker 2: year and spending hundreds of billions of dollars a year
Speaker 2: and that are telling us they need to spend more
Speaker 2: hundreds of billions of dollars a year, but whenever they
Speaker 2: try to raise prices. I mean, Anthropic just tried to
Speaker 2: clean up its balance sheet a little by saying we're
Speaker 2: not selling hundred dollar bills for a dollar apiece anymore.
Speaker 2: They're going to be five dollars apiece, and all their
Speaker 2: best customers are like, I guess we can't use AI
Speaker 2: anymore because at five dollars, these hundred dollar bills are
Speaker 2: not worth it. So when they run out of peace,
Speaker 2: people who will give the money. Because listed are privately held,
Speaker 2: you can't spend more than your taking in unless you
Speaker 2: have a creditor. Right, it doesn't matter how big your
Speaker 2: IPO was, Right, you have operating expenses. I mean, I
Speaker 2: guess you could just like loan the company money from
Speaker 2: your own shares, but you know this kicks off the
Speaker 2: crunch where you flog your shares to get operating capital
Speaker 2: for the company. The supply of shares goes up, the
Speaker 2: value of the shares goes down, and you're in a
Speaker 2: death spiral. So you know, I don't know what's going
Speaker 2: to shatter this fragile system we're in. Maybe it's like
Speaker 2: Golf States say, sorry, Dario, I know you told us
Speaker 2: that we were going to make God here, but we've
Speaker 2: decided that we need to have LNG terminals because Donald
Speaker 2: Trump keeps getting the Iranians to blow ours up, and
Speaker 2: we don't think we have a future without LNG terminals,
Speaker 2: even if we are going to own eight percent of God,
Speaker 2: and then, like without the golf, you lose the the
Speaker 2: rest of the financing collapses. Who knows, right, it could
Speaker 2: be any or all of the above, but anything that
Speaker 2: can't go on forever eventually stops.
Speaker 1: I think, I think, I guess that the pushback would
Speaker 1: be to say, well, maybe right, but you know, if
Speaker 1: there's there are other companies like Google, for example, who
Speaker 1: can continue to fund their plans which are very similar
Speaker 1: to Anthropic and opening eyes into SpaceX is because of
Speaker 1: their you know, they're they're they're profitably if they're operating
Speaker 1: operating business, and that like somebody may they all Elon
Speaker 1: may be able to convince the public and public markets
Speaker 1: believing for long enough that he can bring together space
Speaker 1: and robotics and data centers and all these types of things.
Speaker 1: I mean, I guess like the proof will being the
Speaker 1: pulling is it's just unknowable, but certainly that is what
Speaker 1: is clear is that the it's a historical disconnect between
Speaker 1: the financial performance of these companies and the evaluation and
Speaker 1: and and I mean, that's that's unarguable. After the break,
Speaker 1: what if a computer program decided whether or not you
Speaker 1: deserve a job stay with us. We sort of spent
Speaker 1: quite a lot of time I think setting the stakes,
Speaker 1: the backdrop. There's two more things I want to talk
Speaker 1: about while we're together. One is people and the other
Speaker 1: is politics. But we've had too much fun to define
Speaker 1: what a reverse centaur is.
Speaker 2: Yeah, well, I think that in a long history of automation,
Speaker 2: where automation is driven by workers, it tends to be
Speaker 2: about improving quality, whereas capital driven automation is tilted towards
Speaker 2: improving throughput. Not because bosses are mustache twirling villains, but
Speaker 2: because when you have an asset that's depreciating, you want
Speaker 2: to get as much use out of it as you
Speaker 2: can before it falls off balance sheet. Another aspect that's
Speaker 2: closely related in automation theory is the idea of a centaur,
Speaker 2: which is a person assisted by a machine. So that's
Speaker 2: you on a bicycle, you with a spell checker, you
Speaker 2: with an ide right, Like when I started programming, we
Speaker 2: didn't have debuggers, right, So like that makes you a
Speaker 2: centaur too, whereas a reverse centaur is a person conscripted
Speaker 2: to serve as a peripheral for a machine, and because
Speaker 2: of those dynamics of how capital views its investments, that
Speaker 2: person is usually the bottleneck in the machine's performance. Right.
Speaker 2: People generally can't work as long or as fast as
Speaker 2: a machine, and that person is now worked at the
Speaker 2: edge of their capability and to the edge of their endurance,
Speaker 2: because that's how you maximize the return on the hard
Speaker 2: asset that they are assisting. And again this is not
Speaker 2: a new idea, Like there's a reason that you know,
Speaker 2: whether you're looking at Charlie Chaplin in Modern Times or
Speaker 2: Lucille Ball in that episode where she and Ethel are
Speaker 2: putting chocolates in the chocolate box on the conveyor belt,
Speaker 2: that the motif of automation is the speed up, right,
Speaker 2: and it's it's the worker being used up by the machine.
Speaker 2: And you know, the warehouses that are most automated in
Speaker 2: America are Amazon's warehouses, and they're also the warehouses with
Speaker 2: the highest rate of significant injury. Those aren't coincidences. Those
Speaker 2: are like those are co determined, right, because if you're
Speaker 2: going to put seven figures into warehouse automation, you want
Speaker 2: to work the warehouse automation as quickly as you can.
Speaker 2: So an Amazon driver is there because even if you
Speaker 2: could get the van to drive itself in urban traffic, well,
Speaker 2: it couldn't get the parcel onto your porch or onto
Speaker 2: your mailbox or you know, into your office building, and
Speaker 2: so it needs to have a driver there, and so
Speaker 2: you work that driver at the outer limit of their
Speaker 2: ability and their endurance. And the reason people pee in
Speaker 2: the vans is because you cannot make the quota unless
Speaker 2: you operate at a speed that requires that you not
Speaker 2: have kidneys.
Speaker 1: I mean you said in the book that you get
Speaker 1: penalized for swerving even if you're avoiding Yeah, a danger.
Speaker 1: I mean that does that really true?
Speaker 2: Yeah? So Katie Wells just published a good ethnography. She's
Speaker 2: done a lot of work on Amazon drivers and on
Speaker 2: algorithmic wage discrimination and algorithmic management. She just published some
Speaker 2: ethnographies where she worked with another Amazon a former Amazon driver,
Speaker 2: to interview a bunch of current and past Amazon drivers,
Speaker 2: including one who was fired for swerving when a big
Speaker 2: rig crossed the medium because the driver had fallen asleep
Speaker 2: and was headed for a head on collision. And they
Speaker 2: were fired for making a dangerous maneuver, which is to say,
Speaker 2: swerving out of the way of a truck that would
Speaker 2: have killed them. And there's lots of petty examples too, though, right,
Speaker 2: you can get dinged on your performance if the camera
Speaker 2: thinks that your mouth has opened too much, because singing
Speaker 2: is considered distracted driving. You know, it's something of a
Speaker 2: paradox that these heavily surveilled workers who you would think
Speaker 2: those cameras might be used to vindicate them, find that
Speaker 2: those cameras are only an ever used to penalize them.
Speaker 2: Right that if you've got a million cameras on your truck,
Speaker 2: that's when you show your boss, hey, look I got
Speaker 2: I swerve because the truck was coming straight at me.
Speaker 2: But that's never in the field. In fact, you never
Speaker 2: get to talk to the person who fires you, because
Speaker 2: the way that it works with the DSPs is that
Speaker 2: they don't they're not Amazon, they're they are themselves so
Speaker 2: called independent businesses that hire you. And then the app
Speaker 2: tells your boss to fire you. And then you can
Speaker 2: tell your boss all day long, I swerve to avoid
Speaker 2: a truck, But your boss doesn't get to keep you
Speaker 2: on the payroll if Amazon says that they're not allowed
Speaker 2: to and Amazon makes that determination based on their own
Speaker 2: internal logic.
Speaker 1: Unpack this idea of Amazon telling the DSP to fire
Speaker 1: the driver, and the driver is say, why don't you
Speaker 1: review the well, you know, the video footage and the
Speaker 1: DSP saying, well, even if we could do that, it
Speaker 1: wouldn't change our decision. I mean, how is this being exactive?
Speaker 1: Because the Amazon worker ethnography stuff is haunting, but has
Speaker 1: been preceded for sure the AI error? Right, Like, what's
Speaker 1: what's being accelerated by AI? In what you're describing here?
Speaker 2: You know you've hit on one of the points that
Speaker 2: I wanted to make, which is that we can understand
Speaker 2: the problems of AI and also the potential of AI,
Speaker 2: both as being in a lineage and not being with
Speaker 2: earlier phenomena. I'm firmly the belief that AI is a
Speaker 2: normal technology and not extraordinary, neither extraordinarily evil nor extraordinarily amazing.
Speaker 2: It's just a new technology, and it has all the
Speaker 2: potential and all the problems that technologies have. The bubble
Speaker 2: is extraordinary only because of its scale. And so there
Speaker 2: has been so much well publicized conduct by firms that
Speaker 2: have made workers redundant or limited how workers worked and
Speaker 2: insisted that they work alongside of output from bots with
Speaker 2: predictably disastrous results, right like, so not like it. It's
Speaker 2: the only way you could be surprised is if you
Speaker 2: were deliberately not listening to people who are warning you
Speaker 2: why you shouldn't do it. And that is a feature
Speaker 2: of the bubble, right, That's a feature first of all
Speaker 2: of leaders themselves just being gripped by FOMO and being insecure,
Speaker 2: but also the fact that investors are quite excited by
Speaker 2: news that a firm has added AI to its operations,
Speaker 2: and so there's maybe even a rational basis for harming
Speaker 2: the firm's outputs in order to produce a narrative that's
Speaker 2: friendly to investors. And it's one of those things where
Speaker 2: we were told that if only we made top executives
Speaker 2: shareholders of the firm, they would be incentivized to do
Speaker 2: what's best for the firm. It actually turns out that
Speaker 2: what's best for the firm might involve some short term
Speaker 2: pain to the share price. But if your net worth
Speaker 2: is tied up and shares in the firm you work for,
Speaker 2: then you become as short term as the most nimble,
Speaker 2: footloose hedge fund guy who has taken a position for
Speaker 2: three weeks and is going to be out again and
Speaker 2: on the road and never look back.
Speaker 1: You also talk about sort of how the harm of
Speaker 1: technology percolates. You said you have to find people without
Speaker 1: social power and you grind down the rough edges on
Speaker 1: their bodies, which is haunting phrase, But then it goes
Speaker 1: to you in the end that metastasizes. And you've also
Speaker 1: said for AI to be valuable, it has to replace
Speaker 1: high wage workers. Talk about both of those ideas.
Speaker 2: Yeah, so you're describing something I call the shitty technology
Speaker 2: adoption curve, which is basically, when you've got it, when
Speaker 2: you've got something you want to do that's terrible and
Speaker 2: hurts the people who use it. If you make me
Speaker 2: the first person who uses it, unlike a mouthy, affluent
Speaker 2: white guy who speaks English as a native language and
Speaker 2: has a giant platform, and it will be hard for
Speaker 2: you to deploy it. But if you start with people
Speaker 2: who don't have social capital, who no one listens to,
Speaker 2: and who can't say no, you can both normalize the
Speaker 2: technology but also sand down the things that people find
Speaker 2: most odious about it. And so you know, if you
Speaker 2: think about an example, here would be the way that
Speaker 2: algorithmic wage discrimination works. So the first really widespread use
Speaker 2: of algorithm wage discrimination was at uber, where they were
Speaker 2: making inferences about the economic desperation of drivers based on
Speaker 2: whether or not they signed on to take rides that
Speaker 2: were bad deals, right, whether they poorly compensated, and then
Speaker 2: the drivers who took poorly compensated rides were offered progressively
Speaker 2: lower compensation. Right, the algorithm is seeking the floor at
Speaker 2: which they'll pay. And so you know uber drivers, we
Speaker 2: call them unskilled. I don't think that's fair, but they're
Speaker 2: certainly not professionally certified workers. But Katie Wells, again the
Speaker 2: woman who's done all this work on algorithmic wage discrimination
Speaker 2: and algorithm management, has written about how nurses are now
Speaker 2: experiencing this. So you know, hospitals in the US preferentially
Speaker 2: higher nurses as contractors because that allows them to do
Speaker 2: union avoidance. And it used to be that you would
Speaker 2: hire a contract nurse through a body shop, through a
Speaker 2: staffing agency in town, and there'd be three or four
Speaker 2: or maybe one or two. And now there's just four apps,
Speaker 2: and they operate nationally, and they all advertise themselves as
Speaker 2: an uber for nursing, and at least two of them,
Speaker 2: if not all of them, before they offer a shift
Speaker 2: to a nurse, look up the nurse's credit history and
Speaker 2: they make inferences about the economic desperation about the nurse
Speaker 2: based on how much credit card debt they're carrying and
Speaker 2: whether it's delinquent, and they offer a lower wage based
Speaker 2: on that. So they extract a desperation premium from nurses
Speaker 2: before they're offered a shift. And so you can see
Speaker 2: how this starts with Uber drivers, who, if they're not
Speaker 2: unskilled at least don't require anything more than a car
Speaker 2: and a driving license to get started with Uber, and
Speaker 2: goes to nurses who are trained for several years and
Speaker 2: have to maintain professional licensure in order to do their job.
Speaker 2: And you could imagine that that's going to come for
Speaker 2: other kinds of workers too. And so, you know, if
Speaker 2: you want to know what the future looks like, I'm
Speaker 2: a science fiction writer. I don't know that I can
Speaker 2: predict it. But if you want to know what it's
Speaker 2: leading indicators are, look to the terrible things that are
Speaker 2: being done to people with less social capital as you.
Speaker 2: Because if they're profitable to do to people with less
Speaker 2: social capitalis you, They'll be even more profitable if they're
Speaker 2: done to you. And someone is going to get the
Speaker 2: idea of trying to do it now, whether or not
Speaker 2: they're successful is something that's socially determined, not inevitable, but
Speaker 2: you should imagine that it's going to happen, you know.
Speaker 2: William Gibson said, the future is here is just not
Speaker 2: evenly distributed. If you want to find out where the
Speaker 2: worst aspects of the future are pulling up very thick
Speaker 2: and on the ground, look at Amazon warehouse workers and
Speaker 2: Uber drivers and immigrants and people in ice detention and
Speaker 2: kids and so on. That's where it's all headed.
Speaker 1: And Gibson also said, as I learned from reading the
Speaker 1: introduction to a book, the street finds its own use
Speaker 1: for things. Yeah, I talk about the anti inevitable. Is
Speaker 1: this where politics comes in. I might listen to you
Speaker 1: and as recline with Tim Woo and getting into you know,
Speaker 1: some some fairly detailed policy discussions or policy solutions. But
Speaker 1: is policy and regulation the antidote to all of this?
Speaker 2: Well, the regulation sets the contours on which markets operate.
Speaker 2: I will confess that I don't think markets are the
Speaker 2: only or necessarily the best way to solve our allocation problems.
Speaker 2: But you know, the term free market, as first coined
Speaker 2: or popularized at least by David Ricardo, did not mean
Speaker 2: a market free from regulation. I meant a market free
Speaker 2: from rents, right, A meant a market where factors of
Speaker 2: production were freely traded and not owned by financiers. And
Speaker 2: so if you wanted to make something, you didn't have
Speaker 2: to go rent it. You could just acquire it on
Speaker 2: the market or buy it, or another firm would sell
Speaker 2: it to you that was doing something productive with it.
Speaker 2: So it's basically the end of landlords. That's that's what
Speaker 2: a free market was, whether that was landlords for ideas
Speaker 2: in the form of patents or landlords for property or
Speaker 2: what have you. So that's the origin of the idea
Speaker 2: of a free market. And so every market, whether it's
Speaker 2: the markets that we call heavily regulator to the markets
Speaker 2: that we call regulated, is determined by regulation, by what
Speaker 2: we allow firms to do, by what we prohibit from firms,
Speaker 2: and by the extent to which firm's conduct can be
Speaker 2: practically speaking policed. You know, it's very hard for the
Speaker 2: referee to ensure a fair game if the players are
Speaker 2: more powerful than the ref What do you do with that?
Speaker 1: I guess because like it's tempting to ground these conversations
Speaker 1: in like AI and technology and big technology, but they
Speaker 1: sort of the set of problems you describe and we're
Speaker 1: talking about are problems of how society is organized by
Speaker 1: government and what role government plays as power broken between
Speaker 1: citizens and organizations.
Speaker 2: Right, it really depends on which aspect of AI we're
Speaker 2: talking about, Like if we're talking about the question of
Speaker 2: AI and creative labor, I don't think copyright solves this.
Speaker 2: We keep expanding copyright and then we discover that in
Speaker 2: a market where all creative workers have to sell their
Speaker 2: work through a handful of firms, you know, five publishers
Speaker 2: and four studios and three labels, two companies that do
Speaker 2: the apps, and one company does the ebooks and audiobooks,
Speaker 2: it doesn't matter how many rights we have, we just
Speaker 2: bargain them away. Right. It's like giving bulliked school kids
Speaker 2: extra lunch money. It just doesn't matter how much you
Speaker 2: give them. They don't get lunch. But we do have
Speaker 2: answers to this right that, we've seen successful gambits by
Speaker 2: creative workers to resist using AI to erode their wages
Speaker 2: and working conditions. The one group of workers who did
Speaker 2: it most successfully was the screenwriters. And the big difference
Speaker 2: between the screenwriters and every other kind of worker except
Speaker 2: for other Hollywood workers is that they are allowed to
Speaker 2: do sectoral bargaining, which was outlawed in the Taft Hartley
Speaker 2: Act of nineteen forty seven. Which sectoral bargaining is when
Speaker 2: all the workers in a sector, like everyone who works
Speaker 2: at a fast food restaurant, is organized under a union,
Speaker 2: and every fast food owner is bound by the contract
Speaker 2: with those workers. So, you know, if you see creative
Speaker 2: workers kind of at a crossroads, and on one fork
Speaker 2: they can argue for more copyright, which is the thing
Speaker 2: their bosses want and no other work in America gives
Speaker 2: a shit about. And on the other fork is sectoral bargaining.
Speaker 2: I think their bosses would hate and every other worker
Speaker 2: in America would benefit from. It seems to me like
Speaker 2: that it's just you could just do basy and reasoning
Speaker 2: without knowing a single thing about the specifics of these policies.
Speaker 2: You know, broadly speaking, you're on the right side of
Speaker 2: history when you're on the same side as all the
Speaker 2: workers and the opposite side is your boss if you
Speaker 2: care about labor rights. When we're talking about data centers,
Speaker 2: I mean, I think it's not hard to imagine a
Speaker 2: regulatory regime that would make data centers better, like, we
Speaker 2: could have rules about the carbon emissions of them. We
Speaker 2: could have democratic determination about their water usage and their
Speaker 2: energy usage and the noise that they create. We could
Speaker 2: have very very strict limits or possibly a total prohibition
Speaker 2: on the use of eminent domain to expropriate farmers and
Speaker 2: other people who have large tracks of land to build
Speaker 2: data centers. I mean, all of those things are like,
Speaker 2: none of them are new, they're just like again back
Speaker 2: to AI as a normal technology. This does not require
Speaker 2: extraordinary new regulations to stop people from you know, polluting
Speaker 2: and just uptive job sites near your home in ways
Speaker 2: that make your quality of life worse, and that also
Speaker 2: sometimes results in your home being stolen from you through
Speaker 2: an eminent domain. And if we're worried about the way
Speaker 2: that local councils are being uveiled into doing this, we
Speaker 2: could also have rules about like taking bribes, which is
Speaker 2: basically how they're getting these regulatory things through. It doesn't
Speaker 2: require anything extraordinary to make that happen. In terms of
Speaker 2: the decision outcomes right where we're using AI to decide
Speaker 2: who gets parole or who gets bond in Gaza, or
Speaker 2: which people DOGE is going to fire or which people
Speaker 2: ice are going to round up or shoot in the
Speaker 2: face or send to a concentration camp in El Salvador.
Speaker 2: I mean, we can just say those things should be illegal,
Speaker 2: and we can say that if someone needs to be fired,
Speaker 2: that there should be a normal procedure for firing them,
Speaker 2: and that you should have to show evidence and so on,
Speaker 2: you have to fire them for cause, and we could
Speaker 2: honor the existing union contracts that federal workers have. Again, like,
Speaker 2: it doesn't require anything new. It just requires that we
Speaker 2: not treat AI as an extraordinary technology that when it
Speaker 2: says fire someone, we get to ignore all the rules
Speaker 2: about when you can fire someone.
Speaker 1: I mean, I guess the one politician who has put
Speaker 1: forth the kind of national platform of you know, the
Speaker 1: relationship between government technology is Bernie Sanders with his AI
Speaker 1: sovereign Wealth fund, which interesting he Trump is also a
Speaker 1: fan of of, as is Simultman. So there's something something
Speaker 1: there which maybe should tasty.
Speaker 2: But I mean, look, if AI were profitable, it would
Speaker 2: be a great target for a sovereign wealth fund. I
Speaker 2: don't know why we would want the money losing this
Speaker 2: thing in the world to be in public ownership unless
Speaker 2: we thought it was a public good, in which case
Speaker 2: it might make sense. I mean, there's a sense in
Speaker 2: which schools don't make money. It would be weird if
Speaker 2: schools made money, you know, elementary schools, public schools. But
Speaker 2: I don't think that's what Bernie means. I think he means,
Speaker 2: I think these are going to throw off extraordinary amounts
Speaker 2: of profit. I just think he's wrong. I think he's mistaken.
Speaker 2: And that's getting back to being a better AI critic.
Speaker 2: I think being a better AI critic means not accepting
Speaker 2: the facially the claims that AI is very valuable and
Speaker 2: profitable or will be shortly and requiring at least ordinary
Speaker 2: evidence for such an extraordinary claim, for a way that
Speaker 2: a company that can't raise the price of its one
Speaker 2: hundred dollar bills from one dollars to five dollars without
Speaker 2: losing most of its customers will someday be profitable.
Speaker 1: You close the book with this line. The future can
Speaker 1: be ours if we never stop remembering that the most
Speaker 1: important fact about technology isn't what it does, it's who
Speaker 1: it does it for and who it does it to.
Speaker 1: What do we do with that? What should our listeners?
Speaker 1: What should they do this afternoon tomorrow? Having heard this conversation,
Speaker 1: you have to join a polity.
Speaker 2: I wish, because hell truly is other people. I wish
Speaker 2: we could all be as solive cystic as the AI
Speaker 2: bros want us to be, and we could just solve
Speaker 2: things with our individual action. We could just go like
Speaker 2: shop our way out of a monopoly and recycle our
Speaker 2: way out of the wildfires and so on. Alas that
Speaker 2: is not possible. Systemic problems require system solutions, and the
Speaker 2: way that you make systemic solutions is being part of
Speaker 2: a polity. And that would mean joining the Electronic Frontier Foundation,
Speaker 2: getting involved in local politics, getting involved with unions and
Speaker 2: your job site. And if there isn't one and you
Speaker 2: work in tech, look up Tech Solidarity and the Tech
Speaker 2: Workers Coalition. You know, that's that's how you make a difference,
Speaker 2: right as being part of a group. And I'm not
Speaker 2: saying that because I think it's easier fun to work
Speaker 2: in groups. It can be, but like it is just
Speaker 2: outrageous how hard it is to convince other people that
Speaker 2: you are right. You know, I mean, as much as
Speaker 2: I insist on it it as an Agelino, people just
Speaker 2: will not listen to me when I say when I
Speaker 2: need to get on the five. That's when everyone else
Speaker 2: should get the hell off the five. But you know
Speaker 2: the fact is that anytime you want to do something superhuman,
Speaker 2: something that exceeds the capability of one person, you need
Speaker 2: to find a way to work with someone else to
Speaker 2: do something that two or more people can do. And
Speaker 2: the only way to do that is to be part
Speaker 2: of a polity ry. Doctor.
Speaker 1: Oh, thank you, thank you for tech Stuff. I'm oz Veloscian.
Speaker 1: This episode was produced by Eliza Dennis and Melissa Slaughter.
Speaker 1: It was executive produced by me Julian Nutter and Kate
Speaker 1: Osborne for Kaleidoscope and Katrin Norvel for iHeart Podcasts. Jack
Speaker 1: Insley mixed this episode and Kyle Murdoch wrote out theme song.
Speaker 1: Please do rate and review the show where ever you listen,
Speaker 1: and reach out to us at tech Stuff podcast at
Speaker 1: gmail dot com.