Full Transcript for Cory Doctorow Says Be Skeptical of the AI Sales Pitch

their bosses. And broadly, we find that when workers are in charge of automation adoption, they tend to use new tools in order to improve the quality of their outputs. Whereas capital, because they want to maximize a resource that's depreciating off their balance sheet or a subscription they're paying as a fixed cost monthly, wants to improve throughput, realize a good profit from that at the expense of your suppliers and your customers. And so this brings us to AI and Centaurs and reverse Centaurs. In automation theory, a centaur is someone who's assisted by a machine. So whether using a spell checker or riding a bicycle, you are the human head. You have the discernment. You have the judgment. You exercise will. And then there's a machine below you, you know, the human head on the horse's body. And it's stronger than you and it's faster than you. It's able to go longer than you can. But it's not bossing you around. And the corollary, of course, is that a reverse centaur is the reverse. It's the machine head on the human body, the person who's being directed by the machine. And, you know, where you have a system where you're trying to increase throughput because you've automated, you are always going to be running the machine at the limit of the weakest, slowest part of the system, which is the person. We see this with Lucy and Ethel at the end of the conveyor belt, trying to get as many bonbons as possible into the chocolate box or Charlie Chaplin being sucked into the gears of the machine in modern times. The reason that the motif of automation is the speed up is that when you're a peripheral for a machine doing the part of the job the machine can't do when you're only there because your boss put seven figures into the Amazon warehouse and wants to realize the maximum dividend from it, that machine's going to run as fast as it can, which is really as fast as you can. And as long as you can go, and we all know if you work as fast and as long as you can, you will make a mistake. 00:04:49 Speaker 1: We actually did have a discussion around the latest round of Amazon automation in the UK a few weeks ago. And I think some of these absolutely sort of what they would call the cutting edge ones have less injury, but the jobs themselves have become less. if anything, even more soulless than they were and even more kind of demotivating than in the past. People aren't running around these warehouses anymore. All these robots are coming to them, but they are definitely still probably suffering more from the psychological problems of having a completely soulless job, which, as you say, is defined by what the machines can't do. The way you started reminded me that you have what some people would say was a kind of Marxist tinge to the way you think about the world. And particularly when you focus on the sort of power relations in AI, you say we should look at not what the AI tool does, but who to and who for. 00:05:47 Speaker 2: Yeah, I don't know if that's quite Marxist, but certainly I do think that doing a neoclassical econ degree inflicts a kind of very specific neurological injury that makes you incapable of perceiving or reasoning about power and leads you to engage in fantasies about things like reveal preferences, where you're You know, you observe someone who's flogged their kidney to make the rent and go, look at that person with the revealed preference for one kidney rather than thinking at all about power. 00:06:11 Speaker 1: Well, as someone who studied Marxism alongside neoclassical economics, I don't know whether that's just kind. 00:06:16 Speaker 2: Of termly confused. 00:06:19 Speaker 1: You're speaking to something that a lot of people say in terms of having this desire to have a more human-centered approach to AI, to have AI be supporting people. humans in their work as opposed to humans kind of running around trying to fill the holes left over by AI. But that doesn't feel like the path we're on when you look at the way these tech companies are structured and how much money they have invested on mass automation. 00:06:46 Speaker 2: Well, I would say that that human centricity is not just about being squishy and big hearted and wanting people to have a good working life. It's also about the kinds of outputs that AIs can produce and And the limitations on producing high quality output with AI. So one of the things about the centaur versus centaur framing is that it resolves a seeming paradox, which is that you encounter, especially like skilled white collar workers who are quite senior and are historically good, reliable narrators of their own experience who say, well, I've just started using AI to write software. I write software. I'm good at it. And I can't tell you how great the software I'm writing is today. It's so wonderful. I've become just like an incredible, like Louis Pasteur of app making. And then on the other hand, you have equally reliable narrators of their experience who are equally senior who say like, I work in aviation systems. No one should get on an airplane ever again because we're shoveling tech debt into the sky with the help of AI at a rate never seen. And that will never be mitigated, right? And so the question is how do we resolve the seeming paradox of two groups of people, very similar, seem to be doing the same thing, but having a different experience. Well, once you realize that one group are the centaurs and the other group are the reverse centaurs, you realize they're not doing the same thing at all, right? There's just some superficial commonalities that when a skilled worker can direct their adoption of a tool broadly, I think that that is good for the quality of their outputs because that's what it means to be skilled and have discernment. But specifically in the case of AI, these defects that are intrinsic to AI systems that they whimsically call hallucinations, which I think sounds better than we have a defective product, These defects can only

be mitigated by people who can distinguish a valid output from an invalid output. When we talk about using chatbots to teach kids, by definition, kids don't understand the subject the chatbot is trying to teach them. They are incapable of knowing when the chatbot is quote-unquote hallucinating, experiencing a defect, which means that you really can only get reliable outputs out of an AI if if you operate it at the speed that corresponds to the ability of a skilled practitioner to carefully examine its outputs, and when you increase that speed, even if you have a very skilled worker in the human in the loop, they develop what is called automation blindness. And anyone who's ever gone through an airport checkpoint understands this. You cannot remain vigilant for things that occur very infrequently, which is how we've produced in the form of the TSA the world's water bottle spotting as mother * * * * * * history has ever seen, who 95% of the time fail to spot the guns that red teams smuggle through the checkpoints, right? And so we see this with white shoe law firms that have these skilled lawyers who get the chatbots to help them with their briefs and they run down the slippery slope. And the next thing you know, there's a federal judge tearing three strips out of them and saying, I catch you with one more hallucinated citation, I'm referring you to the bar, and I'm gonna make your client pay your opposing counsel's legal bills for reviewing your defective draft. 00:09:53 Speaker 1: The classic things in medicine, people say, where you'll be able to use the AI for scanning all these radiology and other things, And you point out, again, this kind of automation blindness actually makes the doctors less good at spotting things with that. 00:10:06 Speaker 2: Well, I guess it really depends on the configuration. I actually do think AI can produce higher quality outputs. But the difference is, I think that those higher quality outputs don't realize a cost savings. I think that if you want to fire 99 of your programmers and have the remaining one mark the AI's homework, you're going to get a lot of bad software. If you keep all the programmers on the strength and add an AI that they use or don't use as they see fit with the token budget to go with it, some of them will use it to produce better software, right? But that's not the same as a wage savings. And while I think there is a market for tools that improve the quality of outputs, no one who's investing in Sam Altman's money furnace is saying Let's see how many sales calls you can have where your salespeople go out to a manager and say, how can I increase your cost today? Right. That's just not the return that they're anticipating. So you mentioned radiology. I've got a personal experience with this. About seven weeks ago now, a radiologist at the Kaiser Hospital in Los Angeles told me that I'm cancer free. It's very nice. It was always considered a very treatable form of cancer. But you worry and I've

spent a lot of time. talking to radiologists during my course of treatment and also reading the literature on radiology and AI. And it seems very clear that there are some solid mass tumors that some chatbots can spot that some radiologists miss. And that Kaiser Hospital, it's right next to the big church of Scientology because that's how we roll in Los Angeles. And if there was like an AI salesman like, deking out a Scientologist who wanted to give him a personality test to meet with the CEO of the Kaiser Hospital and say, look, here's the plan. You've got the 10 radiologists you employ at $ 300, 000 a year each. That's a $ 3 million wage bill. Each of them runs 100 x-rays a day. Give me another million dollars. And we're going to stick a chat bot behind them that a couple of times a day taps them on the shoulder and says, take another look at that one. So in addition to the extra million, you're probably going to have to hire another part-time radiologist to take up the slack. but fewer people are going to die of cancer. As a cancer survivor, I'm totally all over that. That's not the sales call, right? The sales call is you go past the Scientologist, avoid this personality test, get up to the CEO's office, say fire nine of your 10 radiologists, that's 2.7 million a year in wage savings, split half of it between you and Sam Altman, have the remaining radiologists mark the AI's homework, turn them into what Dan Davies calls the accountability sink. They're the one who takes the blame when someone dies of cancer. And I think that's like the sales pitch everyone's counting on. That's why we see this, I think, frankly, very irrational exuberance to invest in AI companies. And it's why I think this sector does not have a future as a sector dominated by extremely money-hungry firms that replace every trivial task with heavily subsidized AI. It's just not going to happen. I guess. 00:12:56 Speaker 1: They may not be very good at saving costs for companies, but they could be very good at selling to your employer that they can save costs. So the risk is, and you identify this in the book, you could still get all the disruption that people are worried about, even if ultimately the savings aren't there or certainly the quality of the work is not there when you replace most people with AI. There's one piece of this future that's been very prominent. There was the Citrini kind of analysis, probably on the far end of, this is going to happen much faster than people think. And you're probably on the other end of the spectrum. There was a recent Economist magazine cover that was basically suggesting that agents were going to cause total chaos for government departments all over the world because everyone was suddenly going to be able to put in every claim and resist every parking ticket, whatever it is, use all their rights to the absolute fullest without needing to pay for lawyers. But that world where also we're all sort of able to do enormous amounts of comparison shopping for our car hire or any of these other things, you claim relies on companies and websites all over the world agreeing to consistently label things and organize themselves in a way that agents can easily navigate and as you point out they don't necessarily have that much interest in doing that so you basically say that's not going to happen or at least not to the degree that people are saying and that just seemed to me is that if true that just seemed to be a massive flaw in this whole view of the world Yeah, so. 00:14:34 Speaker 2: The first generation of agentic AI really failed badly. So this was where you tried to create a single agent and say to it, figure out how to use a search engine, then find the travel websites, then figure out how to book a flight, then go all the way through the workflow, then tell me how much the cheapest flight is that satisfies the following requirements, right? And that was just catastrophic. Those tasks are so specialized that no one could get an agent to really do them. So now what the agentic workflow looks like is decomposing the task to a bunch of specialist bots. So you have the web searching bot, and you have the thing that knows what airport codes are, and you have the thing that can parse out the junk fees and whatever. And so you're breaking this down to a series of specialist agents, each of which hands off to the next one. And the claim of the agentic firms, so let's just sort of steel man their claims here, right? The claim of the agentic firms is that they can get something like 95% accuracy out of these highly specialized bots. Well, the thing is that if you decompose a project into like 20, 95% accurate steps, you get error rates, right? They're multiplicative, multiplicative error rates. A 95% accurate task performance multiplied by another 95% task performance. Once you get about 14 steps in, you're under 4% reliability. So that's looking like it's not going to be hugely reliable either, except for some pretty trivial instances. And not only that, but it's very expensive. because each of these chatbots is burning its own set of tokens, right? So decomposing the task at the expense of doing more compute. And so then you have the big idea that everyone's got now, which is agentic workflow, which is that you get everyone in the world who has a website that might be addressed by an agent to redesign that website for machine-to-machine communication, where the agent says, what is the true price of a flight from New York to London next Wednesday? And it gives a faithful response. Now, websites go to enormous lengths to hide this ball, right? To stop you from actually knowing what these prices are. This is how we get surveillance pricing, right? The entire direction travel runs against this right now. And I'm not saying that's good. 00:16:44 Speaker 1: You should explain what it is. I mean, although it's sort of an element of like basic economics segmenting your market, but it's sort

of on steroids. 00:16:51 Speaker 2: Yeah. So the idea here is that whatever AI is bad or good at, one thing it's really good at is multivariate statistical analysis and automatic experimentation. So you have these massive surveillance dossiers on everyone who's buying and selling in the world, whether that's workers selling their labor or consumers buying the products of labor. Largely, that's because the U.S. hasn't updated its privacy law since 1988 when it became illegal for video store clerks to disclose your VHS rental history. And every other form of consumer privacy invasion remains legal in the United States because they just can't pass a privacy law. And in Europe, they do have a privacy law, but all the tech companies pretend they're Irish so it doesn't get enforced. So you have this kind of privacy vacuum in global law. So you've got these very detailed dossiers on everyone. And so you ask the AI not to do what they were doing in the Don Draper era, which is like dreaming up demographic categories and going, oh, you're a depression-haunted can stalker. You're a high school dropout from the Midwest. You're, you know, whatever. And then like running focus groups to find out about you. Instead, you say naively, take all these people... Based on their behavioral data and just naively put them in different buckets and now start experimenting on them. So when a consumer comes to you before eight in the morning, that's a variable you can control. You might discover without ever planning to that parents before eight in the morning, when they're trying to get the kids at the door, we'll take a 15% charge increase without ever even having to plan it. Same thing happens with wages. AI is actually remarkably good at this is just basic multivariate analysis. with a lot of automated A-B testing. And it's the kind of thing AI is great at. And so what we see is more and more labor and consumer surplus being harvested by capital. This is a thing that cuts against the Hayekian idea of markets as accumulators of information, right? Because you're not getting these demand and supply signals. It's effectively a command economy being run by people at the center of the economy who are acting as economic planners on the basis of giant amounts of surveillance data. Thank you. 00:19:05 Speaker 1: You mentioned the cost of the credits, the tokens that are being used by the AI. I just wonder whether that will end up putting a sum constraint on this because it goes to the sort of economics, what you would say was the bad economics of AI, that every model has cost more to produce, which is exactly opposite of what we've seen with many other technologies. And we see that also in terms of the users as well. You know, famously, people having been madly encouraged by their employers to use AI and now having this desperate reverse ferret, as we would say in the UK, where it's like, no, no, you have to be limiting your tokens. You have to show the value. Are costs going

to be a constraint sooner than some might think? Or does it just produce more stratification between the sort of have AIs and the have-nots or the have-shoddy AIs? 00:19:50 Speaker 2: Well, I think the costs are so high that the haves and the have-nots don't really apply here. I think that there is a kind of trap where you're amortizing your fixed costs across a lot of customers. At the same time, you're subsidizing them a great deal. Effectively, the AI companies are selling $ 100 bills for $ 1 apiece. And when they started cleaning out their balance sheets preparatory to their flotations, they were like, maybe we can charge $ 5 for these $ 100 bills. And as you say, all the CEOs who've been saying, if I catch you not using AI, you're fired. Switch from saying, if I catch you using AI, you're fired. Because those $ 100 bills are not worth $ 5 each. And the thing is that if you switch everyone in a $ 200 plan to a $ 2, 000 plan and 90% of them nope out, then like everyone else has to pay more. Right. And so more of them are going to know about, too. So you're spreading the same fixed costs among a diminishing pool. So I don't know that there is a future at all for like this as presently constituted form of business. Statistical extrapolation has got a future, obviously. 00:20:53 Speaker 1: Right. 00:20:53 Speaker 2: But but like the kind of business that we have now doesn't. And as you say, their unit economics suck. Every user they add costs the money. Every use costs the money. Every generation is more expensive than the last. This isn't like the web where the business press was full of audit editorials about how bosses are going to cope with the incoming cohort of young workers who demand the web at the office. Now it's full of like, how will bosses cope with the incoming group of Gen Zs who think AI is garbage and don't want to use it at all. 00:21:21 Speaker 1: Right. 00:21:21 Speaker 2: So like, it's just not the same story. And then you have the amortization schedules where these data centers, they're depreciating them on a five-year schedule with the GPUs in them. But in actual fact, they're actually like turning them over every two to three years and And then a lot of people, I think, assume that when you throw away the GPUs, you can keep the data center. And one of the ways that NVIDIA is getting so much new performance out of each generation of GPU in order to convince people that they need this year's chips in preference to last year's chips is by jettisoning the normal bedrock of product design, which is to maintain as much backwards compatibility as possible. So these new generations of chips... often have radically different power consumption, heat dissipation, networking requirements. And the retrofit, it's like you could do it, but it might be cheaper to scrape the data center to the foundations lab and start over. So, you know, these are not durable assets where you do it once and then you get to run it forever. And then even if

they solve all of that, this claim that, okay, we'll make the model work and then we'll amortize it over the life of the model by getting people to use it with low cost operating expenditures. What we saw with the new generation of Claude is that it was because it was visibly better than ChatGPT and because the switching costs are so low, everyone decamped from OpenAI to Anthropic. Well, the implication here is that OpenAI now has to train a new foundation model. And when they ship it, if it's better than Claude, everyone's going to leave Anthropic and go to OpenAI. And then Anthropic is going to have to do the capital expenditure again. So there's no sign that this business ever stops being a Red Queens race where they lose ground every day. 00:22:57 Speaker 1: And I guess some sharp listeners will note that your references to the things that AI can do, you point out many times in the book, the things that you can use AI in a lucrative way for are generally things now that people don't like. 00:23:09 Speaker 2: Or are cheap, right? That's the other thing. It's like, I can't figure out the mania for robo-taxis. What language contains the phrase, as rich as a taxi driver? We are talking about displacing some of the lowest wage, most precaritized workers, workers who are providing their own capital. What an amazing deal Uber has. You have a worker who you can treat as a contractor who shows up with their own capital, who doesn't even know how to do the amortization schedule. So they're not even depreciating their car when they factor in whether it's worth doing a job. At best, they're thinking about petrol and insurance. Why would you replace that with a robot that you own and internalize your capital costs? 00:23:47 Speaker 1: To your point, I guess it takes us right back to the beginning about power relations. They can focus on those because they are precisely the weaker end. I think one could argue that the sort of less developed bit of the book is the sort of guide to life after bit. But one big argument that you point to that's kind of a source of a positive future is that AI works can't be copyrighted and that this could give a financial reason for companies to not want to just get rid of humans altogether because You need the human component in order to have any kind of ability to copyright and presumably to make money out of any of these things. 00:24:23 Speaker 2: Yeah. So I've spent the last 25 years working for the Electronic Frontier Foundation, which is, I think, the most important and most effective digital rights group in the world. I've been an activist with them. And one of the great frustrations of having done this for 25 years, although it's very rewarding work, is that you see people making the same mistakes over and over again. And so I have watched my colleagues in the media industry brief for more copyright as kind of human shields for their bosses for like a quarter of a century. And indeed, since the 70s, since the 1976 Copyright Act in the U.S., copyright has monotonically expanded, not just in America, everywhere, because the U.S. trade rep made this a condition of their deals with their trading partners. So it lasts longer, covers more kinds of works. The statutory damages are amazing, right? $ 150, 000 per infringement in the US for copyright infringement. It restricts more activities. The media companies are more valuable than they've ever been, and they have higher profits than they've ever had. And the share going to the labor force is lower than it's ever been. We make more, both in real terms and as a proportion of those increased profits. And the paradox, again, reveals itself to be completely self-explanatory once you understand that we are bargaining with five publishers and four studios and three labels and two companies that control the ad tech and one company that controls audio books and eBooks and giving us more to bargain with is giving us more to bargain away. It's like, while it's true, they are suing the AI companies for taking the training data without asking. It's not because they want to stop models from being trained and used to erode the wages of creative workers. They're very clear that they view having to pay creative workers as a bug, not a feature. The US Copyright Office and I are, for a rare moment, entirely in accord because they hold correctly that copyright accrues only to works of human creativity. So I used to be a delegate to the United Nations World Intellectual Property Organization. This is built into global copyright treaties. This is how it works. And so what they've said is if an algorithm makes it, you can't copyright it. 00:26:21 Speaker 1: I think a lot of people would listen to this. And in fact, some of the people that I've described your book to have sort of said, oh, great. So we don't have to worry so much about AI. 00:26:31 Speaker 2: Taking over the world. 00:26:33 Speaker 1: Then they will laugh at your examples, this sort of ludicrous number of times that tech people, Elon Musk and others, have sort of made great claims for AI, even using, you know, the dancing robot who dances better than any other robots ever danced as a sign of the future of the humanoids. And that turns out to be a little person in a robot. So you've got various examples like that and all the jokes about ChatGPT, you know, Gujarati people typing. So you might feel relieved. But then you look at the paper and you see the latest round of stories about AI's breaking out of their sandboxes and hacking places they shouldn't hack. It sort of feels like it may be that AI can't do all the things that are supposed to save companies money, but it feels like it could still pose a pretty existential threat if we don't keep control of some of these bots. 00:27:22 Speaker 2: Well, this is one of my wheelhouses. I speak most years at DEF CON, the big hacker conference in Las Vegas, and I've been actually gutted because it was last week and I didn't

go, and there were lots of talks about this stuff. And here's a thing, because security practitioners are like, their arts are not widely understood outside the field. It's a little insular. But it is not uncommon for automated hacking tools to escape containment. And when they do, we turn to the security researchers who built that tool and we say, why are you so bad at making sandboxes? Why are you so bad at containing your tool? Not, my God, you are a wizard. You've made the tool that can defeat a sandbox. And I think that the credulity of the reports about these security tools that, quote, escape containment, not one of which I've seen is questioned whether or not, except outside the specialist security press, has said, like, why are these companies with all this money playing with these very advanced tools so crummy and honestly lax and irresponsible in building their security sandboxes for testing this stuff? And they should be better. I think that's the more parsimonious explanation. Now, that's not to say that finding defects in software is an unimportant thing. And I think the security tools that we've seen for automatically finding defects have been very good. It's exciting to see because our software sucks, right? One of the things that at the Electronic Frontier Foundation, we spent 25 years sounding the alarm about is the extremely low quality of software being produced by commercial entities. which especially when coupled with highly surveillance practices where you have these large corpuses of data being amassed by firms that then fail to secure them well, you have this pipeline of like things that Facebook knows about you that become things that are for sale on the dark net that become things that are used to steal your identity or do a business account takeover and empty out your corporate treasury. This is a very dangerous thing. Any tools we have that improve that are great. And I think the AI tools are improving it just like many generations of tools before have improved it. It's wonderful to see. We should be happy about it. I think sometimes what we're seeing is that there's a combination of hype and misdirection, as with this sandbox thing, or, you know, Anthropic turning around one day and saying, we have an IPO coming and there's two things you need to know this week in our news cycle. The first one is that we're profitable, but not according to generally accepted accounting practices. We're actually using a different math because we're too cool for regular accounting. And we can't explain how it works to you because it would break your brain. And then also, we've made this tool called Mythos that's a really good hacking tool. But if we show you how it works, the world will end. And so you just have to take our word for it. Did we mention we have an IPO coming? Now, maybe that tool is very good. I spoke to one security researcher who said, yeah, based on what I know about them, what I think they've done. is they've built a

tool that'll do $ 20, 000 worth of security consulting for $ 50, 000 worth of tokens, which would be a really good reason not to let other people play with it. So again, let's just have enough object permanence to win a game of peekaboo and not assume that when these people who are barreling towards a flotation, who are losing money at a rate never seen before, who are reliant on normal investors bailing out their insiders... As a way of walking away with their pocketbooks intact, let alone having the technology continue. Their statements just shouldn't be taken at face value. There should be a certain degree of healthy skepticism when they talk about this stuff. 00:30:50 Speaker 1: Skepticism is, I'm sure, warranted and welcome. And people listening to this will enjoy the tone of your approach to this. Just to push back on some of these examples, I'm sure they are reported with breathless credulity. I'm sure that we should be turning our gaze to the people who are creating these sandboxes. But there is a sort of quite lazy thing, which actually neoclassical economists tend to do, which is, you know, they might want to talk up the risks, but if they are known for creating chaos and bringing down financial systems, or at least allowing their bots to steal people's money. That's not going to be in their commercial interest. So we should just assume that they will sort this out. I mean, we've made similar arguments before the financial crisis. You know, it's in the bank's interest to understand their investments and whether they can blow themselves up. And that hasn't always worked out so well. So is there a little bit of a complacency running through some of your view on that? Can we count on them really to be able to build these better sandboxes, for example? 00:31:52 Speaker 2: I do think we'll have to make sandboxes better as well as tools better and so on. I'm not saying there's never going to be another instance in which a security tool malfunctions in a way that's dramatic and bad. And obviously that's going to happen. When I think about whether the practices will go on or won't go on and so on, and this mantra, incentives matter, I think about the possibility that you can get out, especially by doing flotations directly into the indexes, without having to pay any consequences if the actual thing doesn't work, right? If you say to an investor, it doesn't matter whether it works, you make money either way. Why does the investor care so much if it works? They're not there to make a better widget. They're there to make a better alpha. 00:32:36 Speaker 1: Last question, taking it back to the kind of worldview that we often are adopting, or at least thinking about when we do this show. We have voters in America who UK, other places, feeling pretty angry or at least concerned about AI. We see on the ground all the people complaining about the processing centers, but also people worried about their kids' jobs, all of those things. At the same time,

to your point, a huge amount of money in their 401ks or their ISAs or whatever, their investments, now tied up with these companies who have invested enormous amount of physical resource of the world in building these products. If you're right... in your basic assessment of how AI's ability to really save companies money or do some of the things that it says it can do, is that good news or bad news for those voters? 00:33:28 Speaker 2: Well, I think it's bad news. I mean, let's not overestimate the likelihood that the median voter has got money in the stock market. The median American worker has $ 955 saved for retirement. 00:33:37 Speaker 1: But they are invested in the economy, which could be seriously affected. 00:33:40 Speaker 2: That's what you have to worry about. And so seven companies, the Magnificent Seven, are 35% of the S & P 500. NVIDIA is loaning them money to buy its products. This is never a good sign. They're all passing around the same $ 100 billion IOU and pretending it's in all their bank accounts at once. You know, I think there's a chance we vaporize a third of the American stock market. And if we respond to that the way that we've responded to every crisis this century, except to a limited extent COVID, with austerity, We are going to drive more people into the arms of fascists. I think that is our big worry. It's not that we're going to teach too many words to the word-guessing machine that's going to wake up, become God, and turn us into paperclips. It's like worrying that if you breed horses to run faster and faster, eventually they're going to give birth to a world-destroying fleet of locomotives. We're not word-guessing machines that know more words than the computer. We're different. It's not to say that someday someone might not make an artificial consciousness, just not by guessing words. But I do worry... that history will repeat itself. And the thing that it's going to repeat is that financial engineers are going to suck up a ton of the real economy into a totally unproductive piece of financial gamesmanship that is going to destroy the economy. We're going to have to hold the bag for it. We're going to have to clean up for it. And that it's going to further erode our politics and our civilization. And I'm scared as hell about that. Corey, Dr. Ong. 00:35:01 Speaker 1: So that is The Reverse Centaur's Guide to Life After AI, a bracing 222 pages for your summer reading list. It's definitely a different take than we've heard, but it also, I think, will resonate with a lot of people listening. So thanks again. 00:35:17 Speaker 2: Oh, it's absolutely my pleasure. Thank you. 00:35:23 Speaker 1: Thanks for listening to Trumponomics from Bloomberg. It was hosted by me, Stephanie Flanders. And I was joined by the blogger, journalist, science fiction writer and author, Corrie Doctorow. Trumponomics was produced by Moses Andam and Sam Asadi with help from Amy Key. And sound design, as ever, is by Blake Maples. Cheryl Bromley is now Bloomberg's head of podcasts. To help others find us, please rate and review us highly wherever you listen.