# LIVE: We Are Not Machines - with Sarah O'Connor

An episode of [Cautionary Tales with Tim Harford](https://invisiblenetwork.ai/podcast/1484511465), 3 Jul 2026, 42 min.

Audio: https://podtrac.com/pts/redirect.mp3/pdrl.fm/18db03/tracking.swap.fm/track/SxlTEPDY7xDg35RXkASs/traffic.omny.fm/d/clips/e73c998e-6e60-432f-8610-ae210140c5b1/c0ae8c6e-22f0-4e9b-ac1c-ae390037ac53/6a67f2bc-896a-4cee-8f76-b47b0149395e/audio.mp3?utm_source=Podcast&in_playlist=7f5a4714-6b10-4ccf-a424-ae390037ac70

When the Luddites smashed factory frames in a bid to defend their craft and livelihoods, the machines came out on top. Today, AI and automation threaten to wipe out skilled jobs and flood us with inferior products at a fraction of the price: is history going to repeat itself? FT journalist and author of We Are Not Machines Sarah O'Connor joins Tim to discuss her findings from the front lines about the future of work. Which professions are most at risk, and is there anything workers can do about it? See the show notes at TimHarford.com Sarah's book, "We Are Not Machines: The Fight for the Future of Work", is available here (UK) and for pre-order here (US). See omnystudio.com/listener for privacy information.

## Transcript

[0:15] Speaker 1: Pushkin. Tim here with an exciting announcement about the Cautionary Club,
[0:22] Speaker 1: our Patreon community for Cautionary Tales listeners. So many of
[0:26] Speaker 1: you joined our live table read earlier this year that
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[0:37] Speaker 1: people who almost invented the iPhone fifteen years early and
[0:43] Speaker 1: the surprising reasons they failed. This will be a chance
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[0:53] Speaker 1: Cautionary Tales. It's on the eighth of July at five
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[1:09] Speaker 1: Club that Patreon p A t R e O N
[1:13] Speaker 1: dot com slash Cautionary Club. Missus Goodair is enjoying a
[1:25] Speaker 1: leisurely breakfast while she reads the morning paper. It's April
[1:29] Speaker 1: eighteen twelve, and the headlines are dominated by Wellington's military
[1:34] Speaker 1: successes in Spain. A strange noise jolts her from her reading.
[1:40] Speaker 1: It sounds like the iron gates to her home rattling.
[1:46] Speaker 1: She rushes upstairs to get a better view and sees
[1:49] Speaker 1: the mob for it is a mob. More clearly, they're
[1:53] Speaker 1: throwing stones, making lude gestures, and shouting for her to
[1:58] Speaker 1: open the gates. Two men appear to be wearing dresses,
[2:02] Speaker 1: proclaiming loudly to be General Lud's wives. She knows exactly
[2:08] Speaker 1: what this is about. It's about what's in her husband's
[2:12] Speaker 1: mill next door. So why are they at her house?
[2:18] Speaker 1: Her husband, John Goodair, is out of town. She hovers
[2:22] Speaker 1: at the window, unsure of what to do. The mob
[2:26] Speaker 1: leaders grow impatient. They shout to their comrades that it's
[2:30] Speaker 1: time to move on and begin marching through the streets
[2:33] Speaker 1: of Stockport. As they weave through the town, they smash
[2:38] Speaker 1: windows and break down factory doors. By noon, the mob
[2:42] Speaker 1: has swelled over two thousand men. Soon they run out
[2:48] Speaker 1: of targets, so they turned back to where they began,
[2:53] Speaker 1: John Goodair's enormous mill, the place many of them have
[2:58] Speaker 1: spent their working lives. Once a symbol of pride in
[3:02] Speaker 1: their craft, now a symbol of everything they feel is
[3:06] Speaker 1: wrong with the industrialized world. They break down the doors,
[3:12] Speaker 1: smash through the windows, and destroy all they can inside
[3:17] Speaker 1: every last frame, every last spindle. The mob has grown
[3:23] Speaker 1: so large that even the iron gates to the Good
[3:26] Speaker 1: Air Home can't hold them back. Missus Goodair has already
[3:31] Speaker 1: gone a wise move. The Luddites burn the house to
[3:38] Speaker 1: the ground. I'm Tim Harford and you're listening to Cautionary
[3:43] Speaker 1: Tales live at the Bristol Festival of Economics. Loyal listeners
[4:08] Speaker 1: will remember this is not the first time the Luddites
[4:11] Speaker 1: have featured on this podcast. Two years ago I told
[4:15] Speaker 1: you about the failure of the Luddite Revolution, the weavers
[4:18] Speaker 1: who were incensed that automation was devaluing their craft in general,
[4:24] Speaker 1: Lud's rage against the machines. Today, the Ludites seem more
[4:29] Speaker 1: relevant than ever as the so called Ai revolution threatens
[4:34] Speaker 1: to overturn the world of work. So should we be
[4:39] Speaker 1: smashing up large language models or is resisting as futile
[4:43] Speaker 1: as it was for the Luddites? Who's going to be
[4:46] Speaker 1: most affected? And have economists been asking the wrong questions
[4:51] Speaker 1: all along? I'm joined by a very special guest, Sarah O'Connor.
[4:57] Speaker 1: She's a Financial Times journalist with a specialism in technology
[5:00] Speaker 1: and work, and she's written a book called we are
[5:04] Speaker 1: not machines the fight for the future of work. She
[5:09] Speaker 1: is the perfect person to guide us through what's coming next,
[5:13] Speaker 1: and she's got plenty of cautionary tales to tell us.
[5:16] Speaker 2: Welcome Sarah, Hello Tim, Thank you for having me.
[5:19] Speaker 1: Oh it's a pleasure. We've got to get something out
[5:22] Speaker 1: of the way first. So, Sarah O'Connor, have you seen
[5:25] Speaker 1: the Terminator movies yet? No, you're gonna have to watch them.
[5:31] Speaker 2: It's a point of principle.
[5:33] Speaker 1: That, yeah, it now feels like stubbornness.
[5:34] Speaker 2: Yes, so you might have noticed that my name bears
[5:37] Speaker 2: a faint resemblance to the heroine of the Terminator films.
[5:40] Speaker 2: Growing up, occasionally people made Terminator jokes to me, and
[5:43] Speaker 2: as a result, I never watched them on principle because
[5:45] Speaker 2: it annoyed me. And so about a decade ago, I
[5:48] Speaker 2: went accidentally sort of globally viral when I very simply
[5:53] Speaker 2: tweeted a news story that i'd seen come out, which
[5:56] Speaker 2: was that a robot had killed a worker in a
[5:58] Speaker 2: Volkswagen factory, and didn't realize that this would cause the
[6:02] Speaker 2: Internet to break. So yeah, so people just found it
[6:06] Speaker 2: very hilarious that someone called Sarah O'Connor had written about robots,
[6:10] Speaker 2: and for about a decade people have occasionally sent me,
[6:14] Speaker 2: you know, terminator gifts posters of the Terminator films with
[6:18] Speaker 2: my face photoshopped in, and as a result, no, I
[6:21] Speaker 2: have not watched those.
[6:22] Speaker 1: Okay, So the book is it's fantastic, we are not machines.
[6:28] Speaker 1: What's the headline? What are you basically driving out with
[6:30] Speaker 1: this book?
[6:31] Speaker 2: I think what I'm trying to say in the book
[6:34] Speaker 2: is that, you know, we're all sort of drowning in
[6:37] Speaker 2: information predictions about AI and what it might do to
[6:43] Speaker 2: the world of work, and a lot of those are
[6:44] Speaker 2: coming from either economists or the big tech executives who
[6:49] Speaker 2: have created large language models and are marketing them.
[6:53] Speaker 1: And both these sources are unreliable for different reasons.
[6:56] Speaker 2: Yeah, the tech guys have something to sell, right, and
[6:59] Speaker 2: so they have their own motivations. And then you know, economists,
[7:05] Speaker 2: I think are trying their best to figure out what's
[7:07] Speaker 2: going on. But the pay which they're doing that I
[7:10] Speaker 2: think is very feels very abstract to me as someone
[7:12] Speaker 2: you know, I'm a reporter at heart, So my favorite
[7:15] Speaker 2: thing to do is to like get my notebook, put
[7:17] Speaker 2: my boots on and like go and meet people and
[7:20] Speaker 2: stand outside the factory gates and talk to people about
[7:22] Speaker 2: what's actually happening. And so over the past few years,
[7:25] Speaker 2: I've just felt very frustrated that everything that I'm hearing
[7:28] Speaker 2: about this is coming from people who are either looking
[7:30] Speaker 2: at spreadsheets and models or who have a product to sell,
[7:35] Speaker 2: and I just wanted to hear less about what these
[7:37] Speaker 2: men say is going to happen, and more about what's
[7:39] Speaker 2: already actually happening from the people that it's actually happening too.
[7:42] Speaker 2: So in the book, I basically went to find people
[7:45] Speaker 2: and places and workplaces that are on the front lines
[7:48] Speaker 2: of what's already happening. So people who are encountering autonomous
[7:52] Speaker 2: robots in their workplaces, or are working with or for
[7:57] Speaker 2: or around large luggage models, and they're starting to change
[8:00] Speaker 2: the way they work. And when I did that, what
[8:02] Speaker 2: I realized was that you, no, I wasn't really hearing
[8:05] Speaker 2: this very utopian story that this is liberating me, this
[8:09] Speaker 2: is freeing me from dull, dirty, dangerous work and making
[8:13] Speaker 2: everything fantastic. But nor was I really hearing a lot
[8:16] Speaker 2: of stories that were like my job has been completely
[8:19] Speaker 2: wiped out. What I was hearing instead were stories about
[8:22] Speaker 2: quite profound changes to the nature of people's jobs and
[8:25] Speaker 2: how they felt about their jobs and whether they enjoyed
[8:28] Speaker 2: their jobs or not. And some of those were quite
[8:31] Speaker 2: cautionary tales.
[8:32] Speaker 3: You know.
[8:32] Speaker 2: There were a lot of people who were saying, actually,
[8:35] Speaker 2: what's happening to me is almost the opposite of this
[8:38] Speaker 2: idea that technology is going to give us space to
[8:41] Speaker 2: be more human. People were finding themselves sort of crunched
[8:44] Speaker 2: into systems that were paced by machines run by machines,
[8:47] Speaker 2: in which judgment was being overtaken by machines, in which
[8:52] Speaker 2: creativity was being sort of compressed or contained by machines.
[8:56] Speaker 2: And I thought, actually, if that's what's beginning to happen
[8:58] Speaker 2: to people, then everyone should know about that, and we
[9:00] Speaker 2: should try and talk about why that's happening and whether
[9:03] Speaker 2: there is a way to avoid that, because fundamentally, I
[9:06] Speaker 2: don't think that's what anybody wants.
[9:07] Speaker 1: Before we sort of take a trip to to the
[9:09] Speaker 1: cutting edge, I just I wanted to go back a
[9:12] Speaker 1: couple of hundred years we began with the ludd heightes.
[9:15] Speaker 1: Are they still relevant? Do people misunderstand the story of
[9:17] Speaker 1: the loudtes?
[9:18] Speaker 3: Yeah?
[9:18] Speaker 2: I think they are still relevant. I mean, Luddite is
[9:20] Speaker 2: just now used as a sort of a flippant term
[9:23] Speaker 2: for somebody who's sort of reflexively anti technology and just
[9:26] Speaker 2: doesn't like any new tech. But actually the ludd Heites,
[9:29] Speaker 2: they weren't really fighting against technology full stop. They were
[9:33] Speaker 2: fighting against the way in which machines were being put
[9:36] Speaker 2: to a new use, which was to cut them out
[9:39] Speaker 2: of the equation. These sort of knitting and wide frame machines.
[9:43] Speaker 2: They weren't displacing work entirely. What they did was they
[9:47] Speaker 2: allowed people who had no skill and no experience to
[9:50] Speaker 2: make a less good quality product. And in that sense,
[9:54] Speaker 2: as we might come onto it, actually has an awful
[9:56] Speaker 2: lot of similarities with some of the technologies that we're
[9:59] Speaker 2: seeing today.
[10:00] Speaker 1: Instead of knitting a stocking, you knit a massive bit
[10:03] Speaker 1: of cloth that you can make up punch stockings out of,
[10:05] Speaker 1: and you just cut them into strips and then you
[10:07] Speaker 1: make the stockings. The stockings the terrible they fall apart.
[10:10] Speaker 1: So you've suddenly got interesting, well paid jobs being replaced
[10:13] Speaker 1: by boring, badly paid jobs. You've got expensive, high quality
[10:19] Speaker 1: work being replaced by cheap, crappy work.
[10:22] Speaker 2: Even if they were lower quality. They were also a
[10:25] Speaker 2: much lower price, and that I think that was a
[10:27] Speaker 2: deal that a lot of consumers were willing to make.
[10:29] Speaker 1: So let's talk about warehouses. The picture I have in
[10:33] Speaker 1: my mind, or the picture I had in my mind
[10:35] Speaker 1: before I read the book, was, in fact, I realized
[10:38] Speaker 1: a picture painted for me by you about ten years ago.
[10:42] Speaker 1: One of the things that some workers in these had
[10:45] Speaker 1: was they had this earpiece, the Jennifer unit, basically just
[10:49] Speaker 1: a voice in their ear telling them where to go
[10:51] Speaker 1: in the warehouse, what to pull off the shelves. You
[10:54] Speaker 1: don't need them to think about the best way to
[10:56] Speaker 1: get around the warehouse or to remember anything, just tobay
[10:59] Speaker 1: the voice in your head. So this sort of creation
[11:02] Speaker 1: of these flesh robots. So I was reading this article
[11:05] Speaker 1: by you about ten years ago and thinking that that
[11:07] Speaker 1: sounds bad. But of course that's ten years ago. So
[11:11] Speaker 1: how accurate is that image of what is now happening?
[11:15] Speaker 1: What's changed?
[11:17] Speaker 2: Yeah, so quite a lot has changed. And actually I
[11:19] Speaker 2: think that article from ten years ago and that sort
[11:22] Speaker 2: of reporting that I've done, And just to clarify that
[11:24] Speaker 2: Jennifer unit isn't something that's used in Amazon warehouses, but
[11:27] Speaker 2: it is used in some other warehouses really informed the
[11:30] Speaker 2: way I used to think about technology. So, yeah, I
[11:32] Speaker 2: had met and interviewed a lot of people over the
[11:34] Speaker 2: years who worked in jobs in which they were basically
[11:36] Speaker 2: expected to work like robots. I used to think, well,
[11:41] Speaker 2: you know, bring on the down robots. Then you know, like,
[11:43] Speaker 2: let's get real robots in to do these jobs, like
[11:45] Speaker 2: this is a waste of kind of human potential, and
[11:48] Speaker 2: this is a great example of where automation would be brilliant.
[11:51] Speaker 2: And so I used to be quite a techno optimist
[11:53] Speaker 2: in the sense that I thought there were huge numbers
[11:55] Speaker 2: of roles that actually could and should be automated. So
[11:58] Speaker 2: what's happened now in Amazon warehouses, at least in the
[12:02] Speaker 2: one that I went to visit, is that the robots
[12:06] Speaker 2: have arrived, but not really in the way that I had.
[12:09] Speaker 2: So I'd basically imagined like a one for one swap, right,
[12:12] Speaker 2: so instead of a person walking around, you'd have like
[12:14] Speaker 2: a kind of human humanoid robot doing it.
[12:17] Speaker 1: But it's never actually like that, is it. It's never
[12:20] Speaker 1: ever like that, Like, a robot accountant is basically Microsoft exell.
[12:24] Speaker 1: A robot accountant is not C three post sitting in
[12:27] Speaker 1: your chair top.
[12:28] Speaker 2: No, exactly, and it's not how it works in Amazon either.
[12:31] Speaker 2: So I went to visit a new Amazon warehouse which
[12:35] Speaker 2: has their sort of latest automation technology. And the way
[12:38] Speaker 2: it works now is that rather than you, as a
[12:41] Speaker 2: worker walking around for like maybe ten miles a day
[12:43] Speaker 2: just weaving between all of these shelves and picking things off,
[12:47] Speaker 2: now on each floor there's a big fence and around
[12:50] Speaker 2: the perimeter of that fence, the workers stand stationary in
[12:54] Speaker 2: one place and inside the fence of the robots. But
[12:56] Speaker 2: the robots are basically like giant rumbers, and what they
[12:58] Speaker 2: do is they drive around inside this perim defence and
[13:01] Speaker 2: they pick up the shelves and they bring the shelves
[13:04] Speaker 2: to the workers. And so you now, as a worker,
[13:06] Speaker 2: you stand in one place and a robot brings you
[13:08] Speaker 2: a shirt, and then a light illuminates which part of
[13:12] Speaker 2: the shelf you need to look at, and a screen
[13:14] Speaker 2: gives you a picture of what the thing is you
[13:15] Speaker 2: need to pick off. And I tried this out. I
[13:17] Speaker 2: think I had to pick off like a mobile phone
[13:19] Speaker 2: case with a dolphin on it, so you know, you
[13:21] Speaker 2: pick it out, and then another light illuminates which box
[13:24] Speaker 2: that you need to put it into and you push
[13:25] Speaker 2: a big button and then that robot takes that shelf away,
[13:28] Speaker 2: and the next robot's already queuing up for you with
[13:30] Speaker 2: the next shelf, and you do the same thing.
[13:32] Speaker 1: How long did you do it? For?
[13:33] Speaker 2: Two minutes?
[13:34] Speaker 1: Okay? How long? How long do they do it for?
[13:36] Speaker 2: Ten hours a day, forty hours a week with two
[13:39] Speaker 2: thirty minute breaks? And so this is not what I
[13:42] Speaker 2: had imagined when I sort of cheered on the robots
[13:45] Speaker 2: and thought this would be great. You know, in some
[13:47] Speaker 2: ways I think that job is better. It is less
[13:50] Speaker 2: physically demanding than walking around for ten hours a day.
[13:53] Speaker 2: Amazon says that it's safer so they have, you know,
[13:56] Speaker 2: fewer injuries and accidents, and I think that's probably true.
[13:59] Speaker 2: But if anything, I think it's also become more boring
[14:02] Speaker 2: and more monotonous. And also it's still not that easy
[14:06] Speaker 2: on the human body to stand up for ten hours
[14:08] Speaker 2: a day, and because they're now much more productive, right
[14:11] Speaker 2: because you're not walking around from one place to the next,
[14:14] Speaker 2: and so the products are coming at you, so you're bending,
[14:17] Speaker 2: you're lifting, you're twisting over and over and over again.
[14:20] Speaker 2: So I think it's still quite quite a physically demanding
[14:22] Speaker 2: job and arguably like a bit more monotonous and a
[14:25] Speaker 2: bit lonelier.
[14:27] Speaker 1: Presumably there are people who did the old job and
[14:28] Speaker 1: now do the new job, So what do they think.
[14:31] Speaker 2: Well, I interviewed one worker who had volunteer to transfer
[14:35] Speaker 2: from a manual, old fashioned warehouse to a new one,
[14:39] Speaker 2: and he wanted to go back. He said, actually, I
[14:41] Speaker 2: don't like working with the robots. It's too intense, it's
[14:44] Speaker 2: too exhausting, it's really lonely. I don't get to talk
[14:46] Speaker 2: to anyone anymore. And he yeah, he's now transferred back
[14:49] Speaker 2: to his old warehouse.
[14:51] Speaker 1: By way of contrast, you also visited a mine in Sweden,
[14:56] Speaker 1: So how is automation taking off there?
[14:59] Speaker 2: Yeah, so this mine in Sweden was right up near
[15:02] Speaker 2: the fringe of the Arctic Circle and their mine copper
[15:06] Speaker 2: there and various other minerals, and it is one of
[15:10] Speaker 2: the most sort of technologically advanced mines in the world,
[15:13] Speaker 2: or so they say. So it used to be that
[15:14] Speaker 2: miners would drive vehicles around below. Grounds are very deep down.
[15:19] Speaker 2: It's horrible down there. It's like really dark, it's really
[15:22] Speaker 2: humid and claustrophobic. And now the vehicles are autonomous, so
[15:26] Speaker 2: they drive themselves, and the miners sit in a control room.
[15:30] Speaker 2: They can take over the vehicles if they need to,
[15:32] Speaker 2: but a lot of the time they're just they're sitting there,
[15:35] Speaker 2: They're watching what's happening on big screens. They're in a
[15:37] Speaker 2: comfy chair, and they're listening to Spotify, they're listening to
[15:41] Speaker 2: the ice hockey. And the ones that I spoke to
[15:44] Speaker 2: were quite satisfied with this change in their working conditions.
[15:47] Speaker 1: Is the fact that the Swedish mineworkers are happy and
[15:50] Speaker 1: the Amazon workers are at best ambivalent about the way
[15:54] Speaker 1: their job works. What explains the difference.
[15:57] Speaker 2: So I think there's a few differences. One difference is that,
[16:00] Speaker 2: you know, the reason that in Amazon they don't have
[16:02] Speaker 2: humanoids walking around doing the whole thing is that that
[16:05] Speaker 2: technology is not ready yet and so in some ways
[16:09] Speaker 2: are still kind of plugged into a system which is
[16:12] Speaker 2: like part robot, part human, but is increasingly being paced
[16:17] Speaker 2: by the capabilities of the robots, Whereas I guess for
[16:20] Speaker 2: the miners, they do not need to be in the
[16:23] Speaker 2: cab anymore, and so that means that there's the potential
[16:26] Speaker 2: for a much kind of greater improvement in their health
[16:30] Speaker 2: and safety. But as well as that, I think there
[16:32] Speaker 2: is a governance thing. So I don't know how much
[16:35] Speaker 2: everyone knows about Sweden, but they have a very distinctive
[16:38] Speaker 2: kind of labor market whereby trade unions are very powerful,
[16:43] Speaker 2: and there is a rule that any new technology has
[16:46] Speaker 2: to be bargained collectively about before it happens. And so
[16:50] Speaker 2: before they introduced this new technology, they had to sit
[16:52] Speaker 2: down with the workers and they had to talk about
[16:54] Speaker 2: it and how it would go and what the workers'
[16:56] Speaker 2: concerns were and what the company wanted to get out
[16:59] Speaker 2: of it. And you know, more broadly in Scandinavian countries,
[17:03] Speaker 2: what you find is that when people answer surveys about
[17:06] Speaker 2: how they feel about technology, people are like much more
[17:09] Speaker 2: positive than they are in the UK or in the US,
[17:11] Speaker 2: and I think that's because they have a sense that
[17:13] Speaker 2: they will have a seat at the table when those
[17:15] Speaker 2: decisions get made.
[17:16] Speaker 1: Thank you, Sarah. You're listening to a special cautionary conversation
[17:20] Speaker 1: recorded live at the Bristol Festival of Economics. And after
[17:24] Speaker 1: the break, my guests Sarah O'Connor will be telling a
[17:28] Speaker 1: cautionary tale about language translators at artificial intelligence. Stay with us,
[17:45] Speaker 1: we're back this is the Bristol Festival of Economics. I
[17:49] Speaker 1: am Tim Harford and my special guest is Financial Times
[17:53] Speaker 1: journalist and author Sarah O'Connor. So, Sarah, perhaps the canaries
[17:59] Speaker 1: in the coal mine are the translators. When I thought
[18:03] Speaker 1: about translators, I just thought, oh, you guys are just
[18:07] Speaker 1: all You've lost all your jobs because of Google Translate.
[18:09] Speaker 1: It's all gone. But actually your the story you tell
[18:13] Speaker 1: in the book is is a lot more interesting and
[18:15] Speaker 1: more complex than that.
[18:16] Speaker 2: Yeah. So I interviewed some translators for my book. One
[18:20] Speaker 2: of them is a guy called Peter. He lives in
[18:22] Speaker 2: the Czech Republic and he translates subtitles for TV shows,
[18:26] Speaker 2: which is a great job. I mean, he told me
[18:28] Speaker 2: lots of things about why it's more difficult than you
[18:31] Speaker 2: might imagine to do that. So, for example, you know,
[18:34] Speaker 2: in English, we just have one way of saying you,
[18:36] Speaker 2: but in lots of languages, including Zech, there's like a
[18:39] Speaker 2: formal tense of you and an informal tense of you.
[18:42] Speaker 2: And so when you're writing the translated subtitles, you have
[18:45] Speaker 2: to think like, how well do these characters know each other?
[18:48] Speaker 2: Like what tense would they be speaking in? And that
[18:51] Speaker 2: might change, you know, through the course of an episode
[18:53] Speaker 2: or through the course of a series, and so there
[18:55] Speaker 2: are lots of kind of interesting judgment calls that you
[18:57] Speaker 2: have to make. And then, you know, translating jokes is
[19:00] Speaker 2: another thing that's incredibly difficult because so many jokes are
[19:04] Speaker 2: like culture specific or they're like plays on language.
[19:07] Speaker 1: We're in a bookshop where the guests of war systems
[19:09] Speaker 1: in Bristol. There must be some asterisks and obeliqs somewhere
[19:12] Speaker 1: in the bookshop.
[19:13] Speaker 2: Yeah, they're the perfect example.
[19:14] Speaker 1: Right.
[19:15] Speaker 2: So Obelix's dog in the original French is called ede feaks,
[19:20] Speaker 2: which means obsession, and the brilliant English translation is dogmatics.
[19:24] Speaker 2: You know. So like, there's so much sort of creativity
[19:27] Speaker 2: and humor involved in being a really good translator, and
[19:30] Speaker 2: so like, this is a great job for people that
[19:32] Speaker 2: enjoy doing that sort of thing. But what's happened to
[19:34] Speaker 2: him and to people like him, is that he has
[19:37] Speaker 2: not lost his job entirely. Like, it's actually quite hard
[19:40] Speaker 2: to get a machine to translate things with that level
[19:43] Speaker 2: of cultural knowledge and understanding. So, just to explain slightly
[19:47] Speaker 2: the way a lot of translation works is a lot
[19:50] Speaker 2: of translators of freelancers, and there are agencies that will
[19:53] Speaker 2: take work from you know, a TV studio and then
[19:57] Speaker 2: parcel it out to freelance translators. And what those agencies
[20:00] Speaker 2: have started to do is to take something, get it
[20:03] Speaker 2: translated by a large language model or a different kind
[20:06] Speaker 2: of machine translation service, and then it to the translator
[20:10] Speaker 2: and say, hey, can you just check that this is
[20:12] Speaker 2: right and maybe like just finesse it a little bit,
[20:14] Speaker 2: polish it, make it sound a bit more human. And
[20:16] Speaker 2: this has become the kind of bete noir of lots
[20:18] Speaker 2: of translators, because what they say is that it's a
[20:23] Speaker 2: you know, you're expected to do this much faster and
[20:26] Speaker 2: be you're expected to do it for much less money.
[20:29] Speaker 2: But in fact, if you care about quality, it's very
[20:31] Speaker 2: difficult to actually check a translation is accurate, and then
[20:35] Speaker 2: even things like trying to make it sound a bit
[20:38] Speaker 2: better like that's actually what the translators told me was,
[20:40] Speaker 2: it's actually quite hard to do when there's already like
[20:42] Speaker 2: an answer in front of you, and it feels much
[20:45] Speaker 2: less creative and more kind of cumbersome and less enjoyable.
[20:49] Speaker 2: And so a job that that was sort of creative
[20:52] Speaker 2: and challenging and interesting, has become faster, harder, more monotonous,
[20:58] Speaker 2: feels more mechanical, and fundamentally kind of less satisfying. And
[21:03] Speaker 2: I think that is a real cautionary tale. And when
[21:05] Speaker 2: I was speaking to them, it made me think a
[21:06] Speaker 2: lot of the luod ites, because what the translators will
[21:09] Speaker 2: tell you is that the end product is less good
[21:12] Speaker 2: quality than There are even some studies that have checked this.
[21:15] Speaker 2: So there's a big study that looked at a bunch
[21:18] Speaker 2: of subtitles from TV shows before and after the introduction
[21:22] Speaker 2: of machine translation post editing, and the quality has kind
[21:26] Speaker 2: of deteriorated.
[21:27] Speaker 1: It's the stockings again.
[21:28] Speaker 2: It's the stockings again, exactly.
[21:30] Speaker 1: And I guess partly obviously we care about the quality
[21:33] Speaker 1: of jobs. That's important. But I guess one of the
[21:36] Speaker 1: interesting questions from the point of view of the consumer,
[21:41] Speaker 1: because the whole idea here is even if individuals get
[21:45] Speaker 1: worse jobs, the consumer gets more choice, more quality, lower prices.
[21:51] Speaker 1: I always do sort of wander is the product actually
[21:56] Speaker 1: better and will the market deliver kind of the right
[22:01] Speaker 1: trade offs or are we actually just going to make
[22:03] Speaker 1: all kinds of mistakes and we end up with crappy
[22:06] Speaker 1: products that we don't want. I'm just wondering whether I
[22:09] Speaker 1: know it don't sound like an economist here, but I'm
[22:12] Speaker 1: wondering whether we're all just going to get stockings that
[22:14] Speaker 1: fall apart and translations that we that are joyless. But
[22:19] Speaker 1: some manager somewhere was convinced by some AI salesman somewhere
[22:24] Speaker 1: that it would be that it would be fine.
[22:26] Speaker 2: Yeah, I mean, I think this is one of the
[22:28] Speaker 2: things that I realized working on the book is that
[22:30] Speaker 2: you know, economists, when they're sort of doing their modeling
[22:33] Speaker 2: about which jobs might be displaced or degraded or changed
[22:38] Speaker 2: by technology, they look at like how good is the
[22:41] Speaker 2: technology and compare that to how good is the human?
[22:44] Speaker 2: But actually, like can a robot do my job as
[22:47] Speaker 2: well as me? It's not always a particularly useful question,
[22:49] Speaker 2: Like can someone persuade my boss that a robot can
[22:53] Speaker 2: do my job as well as me? Is a more
[22:55] Speaker 2: relevant question? Or would my customers or consumers be content
[22:59] Speaker 2: to have someone do my job less well than me
[23:03] Speaker 2: but for a much cheaper price.
[23:04] Speaker 1: With the case of the translators, is this fundamentally about
[23:08] Speaker 1: the technology? This just happens to be a thing that
[23:11] Speaker 1: computers can do. At a certain speed with a certain
[23:13] Speaker 1: quality that kind of is maximally disruptive to the job
[23:19] Speaker 1: of being a translator. Or is it actually more to
[23:22] Speaker 1: do with the fact, for example, that all these people
[23:25] Speaker 1: are freelancers. Is it actually to do with economic power
[23:27] Speaker 1: and not to do with technology at all.
[23:29] Speaker 2: I think it's to do with both. But I think
[23:31] Speaker 2: you make a good point, which is that often this
[23:34] Speaker 2: question about what will AI do to the world of
[23:38] Speaker 2: work as seen as a technical question, but it's not
[23:41] Speaker 2: just a question of the technical side. It's also a
[23:43] Speaker 2: question of consumer choice and institutions and culture and bargaining
[23:49] Speaker 2: power and economic power. So yeah, I mean, partly the
[23:55] Speaker 2: reason it's been very disruptive to translators is that the
[23:58] Speaker 2: translating is like them basically all they do, like that
[24:02] Speaker 2: one task of like translating something. If you're a freelance translator,
[24:05] Speaker 2: that is pretty much your whole job, whereas for a journalist,
[24:10] Speaker 2: writing an article is actually quite a small part of
[24:12] Speaker 2: my job. My job also involves doing a lot of reading,
[24:16] Speaker 2: going out to interview people, doing stuff like this, and
[24:19] Speaker 2: so it's more disruptive in some particular professions, depending on
[24:27] Speaker 2: how much of your tasks are automatical. And then also, yeah,
[24:31] Speaker 2: like your your market power, right, If you're a freelancer,
[24:35] Speaker 2: then that's a very different thing to being a well
[24:38] Speaker 2: protected miner in a unionized mind company in Sweden.
[24:42] Speaker 1: And can we talk about coders, because superficially programmers software
[24:46] Speaker 1: coders seems quite like translators. It turns out that for
[24:52] Speaker 1: some reason, large language models seem to be pretty good
[24:55] Speaker 1: at coding. At least that's what I'm told. I wouldn't know,
[24:57] Speaker 1: but people say it's pretty good. So coders in the
[25:01] Speaker 1: same positions as translators, suddenly I've got all this terrible
[25:04] Speaker 1: code and kind of they want me to do twice
[25:06] Speaker 1: as much work for half the salary. Do they feel
[25:07] Speaker 1: the same way as the translators.
[25:09] Speaker 2: Most of the ones I interviewed do not feel like that.
[25:13] Speaker 2: I think coding is an example of a job that
[25:16] Speaker 2: in many ways actually has probably been enriched and made
[25:19] Speaker 2: more fun and more productive by this new technology. And
[25:23] Speaker 2: I think the reason that it is playing out different
[25:26] Speaker 2: is partly, you know this thing about bargaining power and
[25:29] Speaker 2: where you sit in the sort of economic chain, but also,
[25:33] Speaker 2: if you're a computer programmer, like you're what you really
[25:35] Speaker 2: care about and love is not literally sitting there and
[25:38] Speaker 2: writing out lines of code. What you love doing is
[25:41] Speaker 2: like solving problems. And so what the tools do. They
[25:45] Speaker 2: might automate some of the kind of the actual literal coding,
[25:48] Speaker 2: but they're not automating that kind of really fun creative
[25:52] Speaker 2: problem solving.
[25:54] Speaker 3: Bit.
[25:54] Speaker 2: It just allows you to like try things out or
[25:58] Speaker 2: do stuff faster.
[25:59] Speaker 1: This is the problem. This is what they keep telling us.
[26:00] Speaker 1: It's going to do for all the jobs. It's going
[26:02] Speaker 1: to do the boring stuff so you can do the
[26:04] Speaker 1: fun stuff.
[26:05] Speaker 2: And this is why they're so enthusiastic and think it's brilliant,
[26:07] Speaker 2: because for their jobs it is. That's pretty brilliant.
[26:09] Speaker 1: What is it then, that determines whether your job is
[26:15] Speaker 1: like translating or whether your job is like coding, And
[26:19] Speaker 1: is it under our control or not?
[26:21] Speaker 2: Yeah. So I think one of the things that I've realized,
[26:25] Speaker 2: and I now really flinch when I hear it, and
[26:27] Speaker 2: I flinch when I think that I've probably said it
[26:30] Speaker 2: sometimes as well in the past, is that I think
[26:32] Speaker 2: we're really using some of the wrong metaphors when we
[26:36] Speaker 2: talk about this. So I don't know if you've noticed this,
[26:38] Speaker 2: but I find that like technology executives in particular, but
[26:41] Speaker 2: also sometimes economists. We'll talk about like there's a tsunami
[26:45] Speaker 2: of change coming or there is a new wave of
[26:49] Speaker 2: technological change. I think it's misleading because it makes it
[26:52] Speaker 2: sound like it's akin to a natural phenomenon, like it's
[26:55] Speaker 2: something that's just happening, that just came out of nowhere,
[26:57] Speaker 2: and it's going to happen to us, and it invites
[26:59] Speaker 2: us to think that at best, all we can do
[27:02] Speaker 2: is a prepare for it and be get ready to
[27:05] Speaker 2: mop up after it. And you know, these metaphors I
[27:08] Speaker 2: think are really useful to technology executives in their conversations
[27:12] Speaker 2: with policymakers because you know, nobody wants to look like
[27:15] Speaker 2: the fool who thinks you can hold back the tide, right,
[27:18] Speaker 2: and so it invites you to think of this as
[27:21] Speaker 2: something that is happening anyway, and you know that there's
[27:25] Speaker 2: very little that you can fundamentally do about it. But
[27:27] Speaker 2: that's not true at all. That's never been true. Technology
[27:30] Speaker 2: is stuff that is made by people and implemented by people,
[27:33] Speaker 2: and there is a huge variety, as we've just discussed,
[27:36] Speaker 2: in the ways in which it can play out in
[27:38] Speaker 2: different occupations and in different countries and in different parts
[27:41] Speaker 2: of the labor market, and a lot of that is
[27:43] Speaker 2: dictated by the choices that people make and the things
[27:46] Speaker 2: they choose to value.
[27:47] Speaker 1: So you can do something about this because you can
[27:49] Speaker 1: write columns and the Financial Time as telling Sam Altman
[27:52] Speaker 1: that he's being a naughty boy. What can ordinary people
[27:55] Speaker 1: who do not have newspaper columns and who are not
[27:59] Speaker 1: running AI companies, just regular people, what can they do
[28:04] Speaker 1: to help direct this change in a more productive, empowering way.
[28:10] Speaker 2: I mean, I think people do and are and will
[28:15] Speaker 2: direct and determine this next progression of technological change, because
[28:19] Speaker 2: that's what's that's what's always happened. I mean, some people
[28:22] Speaker 2: obviously have more power than others, particularly in terms of
[28:25] Speaker 2: like what kind of working conditions they may or may
[28:27] Speaker 2: not have to accept. But you know, everyone that I
[28:31] Speaker 2: interviewed in my book is responding to what's happening. And
[28:35] Speaker 2: you know, whether that is that they are going on
[28:38] Speaker 2: strike or forming a union, or whether it's that they
[28:41] Speaker 2: are changing their profession or figuring out some new way
[28:45] Speaker 2: of doing what they do in a different way. Also
[28:49] Speaker 2: as consumers, you know a lot of the questions we've
[28:51] Speaker 2: been talking about is like, well, what will the market accept?
[28:53] Speaker 2: We are the market, you know, so it's kind of
[28:55] Speaker 2: up to us to decide whether we like AI music
[28:59] Speaker 2: or real music.
[29:00] Speaker 1: You know, so flat that the one's ever said on
[29:01] Speaker 1: the market before you are the market. The nicest thing
[29:04] Speaker 1: anyone's ever said.
[29:05] Speaker 2: How to complement an economist?
[29:07] Speaker 1: Absolutely so you heard it. Fight back, stand up show, show,
[29:11] Speaker 1: show the man, why you're better than the machine. Sarah
[29:14] Speaker 1: and I are going to talk about large language models
[29:20] Speaker 1: after the break. We're back. I'm Tim Harford. We are
[29:34] Speaker 1: live at the Bristol Festival of Economics, and this is
[29:38] Speaker 1: a cautionary conversation with expert in artificial intelligence, expert in workplaces,
[29:46] Speaker 1: author of We Are Not Machines, Sarah O'Connor, who is
[29:51] Speaker 1: also my colleague at the Financial Times. The title of
[29:55] Speaker 1: the book is We Are Not Machines. You've given us
[29:58] Speaker 1: some really striking examples of demands of made of workers
[30:05] Speaker 1: to be more machine like, being under constant surveillance, to
[30:09] Speaker 1: move in a very kind of weird and unnatural way.
[30:14] Speaker 1: I wanted to bring it back to the experience a
[30:17] Speaker 1: lot of white collar workers have of sort of sitting
[30:21] Speaker 1: in a computer every day and suddenly discovering there's this
[30:23] Speaker 1: thing called chat gpt. Do you think that chat gpt
[30:28] Speaker 1: is this making us more machine like and we don't
[30:31] Speaker 1: know it.
[30:32] Speaker 2: So this is something that a lot of the translators
[30:34] Speaker 2: that I interviewed brought up. I think because they they're linguist,
[30:38] Speaker 2: so they have like an ear for language and how
[30:41] Speaker 2: it's changing. But a number of them said to me
[30:43] Speaker 2: independently that they had noticed that the sorts of language
[30:47] Speaker 2: that we are using is becoming narrower and flatter and
[30:52] Speaker 2: more homogeneous. And I wonder if that is partly because
[30:56] Speaker 2: we are using chat GPT more to write our LinkedIn
[30:59] Speaker 2: posts and our emails, and maybe that even seeps into
[31:02] Speaker 2: the way that we communicate with one another. So I
[31:05] Speaker 2: think there are certain risks there. But also, you know,
[31:08] Speaker 2: I think large angers models do clearly have the capacity
[31:12] Speaker 2: to do that thing that we talked about at the start,
[31:14] Speaker 2: which is like take away some of the boring stuff.
[31:16] Speaker 2: And I think there are plenty of examples of people
[31:19] Speaker 2: doing that, whether it's like, oh, now I don't have
[31:22] Speaker 2: to take any minutes for this meeting, or now I
[31:24] Speaker 2: don't have to spend two hours googling three relevant research
[31:28] Speaker 2: articles but I do think it's also it's very easy
[31:31] Speaker 2: to start also using it for things that maybe in
[31:34] Speaker 2: the past we would have put a bit of human
[31:36] Speaker 2: care and attention into. Like, you know, I think a
[31:38] Speaker 2: lot of people are using chat gupt now to write
[31:40] Speaker 2: wedding speeches or speeches at funerals and that sort of
[31:44] Speaker 2: thing which you know, you might once have thought were
[31:47] Speaker 2: among the more human tasks that we did.
[31:49] Speaker 1: Yeah, although I have heard a few wedding speeches, chatchupt
[31:53] Speaker 1: would have done better than some of them. So you know, yeah,
[31:58] Speaker 1: rising tide lifts sundboats. Are you are you frightened or
[32:02] Speaker 1: are you hopeful for the future.
[32:04] Speaker 2: I'm a lot more hopeful now than I was when
[32:06] Speaker 2: I started writing the book, actually, and I think that's
[32:10] Speaker 2: probably because of the people that I met along the way.
[32:15] Speaker 2: So like another of these sort of metaphors or like
[32:18] Speaker 2: cliches that we often hear is you know, people will say, well,
[32:21] Speaker 2: you know, inevitably there's going to be winners and losers.
[32:24] Speaker 2: You know, there's there's dangers and opportunities, and that sort
[32:28] Speaker 2: of invites us to think, like, oh good, I'd better
[32:30] Speaker 2: just wait and find out if I'm going to be
[32:31] Speaker 2: a winner. Or a loser, and like you know, fingers crossed,
[32:34] Speaker 2: I'll be a winner. You just kind of got your
[32:36] Speaker 2: lottery ticket and then you wait and see. But like
[32:38] Speaker 2: that's not actually what people are doing. Like people actually
[32:41] Speaker 2: are maneuvering themselves and trying to figure out what they
[32:45] Speaker 2: can do to make sure that things work out well
[32:47] Speaker 2: for them, Like everyone is shaping and determining this. So
[32:51] Speaker 2: in that sense, I ended up more optimistic because I
[32:54] Speaker 2: think that we have a lot more agency to figure
[32:57] Speaker 2: out how this goes and to make sure it goes
[33:00] Speaker 2: in a way that we want. Then we're sometimes led
[33:02] Speaker 2: to believe.
[33:03] Speaker 1: Thank you so much, Sarah. Let's take some questions. So
[33:06] Speaker 1: we've got a question from a Cautionary Club member, Emily.
[33:12] Speaker 1: By the way, anybody wants to join the Cautionary Club,
[33:15] Speaker 1: it's very wonderful. You'd get the podcast ad free, you
[33:17] Speaker 1: get bonus episodes all very good, and you get your
[33:20] Speaker 1: question read out live at the Bristol Festival of Economics.
[33:24] Speaker 1: So Emily says, there's a research instituite called me et
[33:28] Speaker 1: R which found that both the expected and the perceived
[33:32] Speaker 1: efficiency improvements from using AI in software R and D
[33:37] Speaker 1: were substantially overstated. She asks, do you think businesses are
[33:42] Speaker 1: likely to scale back AI investment until the efficiency gains
[33:46] Speaker 1: promised are demonstrated, or have they sunk so much money
[33:51] Speaker 1: in at this point that they feel they have to
[33:54] Speaker 1: justify the expense.
[33:55] Speaker 2: I think it remains to be seen. I think that
[33:57] Speaker 2: matr study was really interesting because what it sort of
[34:01] Speaker 2: highlights to me is that, you know, we were talking
[34:04] Speaker 2: about this distinction between tasks and jobs, and that actually
[34:08] Speaker 2: your whole job might disappear, but some tasks within it
[34:10] Speaker 2: might change. But when you think about something at the
[34:13] Speaker 2: level of an entire organization, you've also got to think
[34:16] Speaker 2: about jobs and systems, and so you know, a computer
[34:21] Speaker 2: programmer on his or her own might have become much
[34:24] Speaker 2: more productive if they can use a coding AI A system,
[34:28] Speaker 2: but actually, if there are still bottlenecks in other parts
[34:30] Speaker 2: of that system, you might not really see the benefit
[34:33] Speaker 2: of it. You know, you might just create lots and
[34:36] Speaker 2: lots and lots of code, but then you still you
[34:38] Speaker 2: still don't have enough people to review it, for example,
[34:41] Speaker 2: or you know, whatever it might be. And so I
[34:43] Speaker 2: think what a lot of companies are finding is that
[34:46] Speaker 2: suddenly being able to do certain tasks within jobs much
[34:51] Speaker 2: more and much faster, doesn't necessarily mean that you're going
[34:53] Speaker 2: to see the same kind of productivity outcome filter through
[34:57] Speaker 2: to the whole organization. The other thing I would say
[35:00] Speaker 2: is that we're still like in the very early stages
[35:02] Speaker 2: of companies trying to figure out how to use these
[35:06] Speaker 2: new tools effectively. And what I think we might start
[35:09] Speaker 2: to see is companies kind of redesigning workflows around what
[35:15] Speaker 2: the machines can and can't do. I mean, in a way,
[35:17] Speaker 2: that's exactly what Amazon has done, right. I mean, it's
[35:20] Speaker 2: completely changed its workflow in order to make the most
[35:23] Speaker 2: of the machines that it has available to it at
[35:25] Speaker 2: the moment, and plugged humans in to do the bits
[35:29] Speaker 2: that the machines can't do. But I think it's still
[35:31] Speaker 2: a really open question, you know, particularly with large language models.
[35:34] Speaker 2: I mean, the kind of the hallucination problem I think
[35:37] Speaker 2: is quite could be quite existential for a lot of
[35:40] Speaker 2: sort of high risk professions. You know, certainly, you know,
[35:44] Speaker 2: we're not going to be using large language models that
[35:46] Speaker 2: the ft to write our articles because we just can't
[35:49] Speaker 2: possibly trust them not to hallucinate.
[35:51] Speaker 1: Yeah, I mean, I always find when I'm using large
[35:52] Speaker 1: language models The first question is will anybody else ever
[35:55] Speaker 1: see any of this? And if the answer is yes,
[35:58] Speaker 1: then it's unusable. Like if it's just for me, then
[36:02] Speaker 1: sometimes it comes up with useful ideas, but I absolutely
[36:05] Speaker 1: cannot trust it at all to produce anything that anybody
[36:08] Speaker 1: else will ever see. So anyway, more questions. There were
[36:13] Speaker 1: loads of questions, can we get the microphone? Somebody looking
[36:16] Speaker 1: quite young and fresh face there. I want a question
[36:19] Speaker 1: from Hi.
[36:22] Speaker 4: I have a question, which industry do you think is
[36:25] Speaker 4: most at risk of AI replacing the job or having
[36:32] Speaker 4: the biggest amount of the job being taken away?
[36:38] Speaker 1: So, Sarah, which industry is most at risk of just
[36:42] Speaker 1: having these jobs completely removed?
[36:44] Speaker 2: I mean, I hate to say it, but.
[36:46] Speaker 1: It's the translators.
[36:48] Speaker 2: I know I was going to say it's I think
[36:50] Speaker 2: sort of creative industries more generally. I mean not that
[36:53] Speaker 2: I think that, you know, people will stop wanting to
[36:56] Speaker 2: pay for human created stuff, but I think that there
[37:02] Speaker 2: are lots of people who work in you know, they're
[37:08] Speaker 2: not necessarily originating the ideas, but they work in the
[37:12] Speaker 2: kind of in that long process between someone coming up
[37:15] Speaker 2: with an idea and the thing being made. Whether that's
[37:18] Speaker 2: like working in special effects in Hollywood, or music production,
[37:25] Speaker 2: or storyboarding in an advertising agency or whatever it might
[37:29] Speaker 2: be your copywriting. I think there are lots and lots
[37:32] Speaker 2: of jobs there which you know, are good jobs and
[37:36] Speaker 2: people like them, and so I think it's a real
[37:38] Speaker 2: shame if they become disrupted. But I can imagine that
[37:42] Speaker 2: those are the sorts of roles that could be quite
[37:45] Speaker 2: quite vulnerable. But I might be wrong, and I hope
[37:48] Speaker 2: I am.
[37:49] Speaker 1: So you heard it here first, human creativity is dead.
[37:52] Speaker 2: It's definitely what I said. Thanks to him.
[37:54] Speaker 1: This from the woman who calls herself an optimist, So
[37:58] Speaker 1: thank you for that. We've got time for one more question.
[38:01] Speaker 3: You said that AI is no advanced enough to translate
[38:05] Speaker 3: the tags accurately enough, but it's really been around or
[38:09] Speaker 3: two years, like even by twenty SAIDs is going to
[38:12] Speaker 3: be enough advance to replace some of the jobs. Surely
[38:16] Speaker 3: it will lead to like structural unemployment and the Regon
[38:20] Speaker 3: government will there happen.
[38:22] Speaker 1: Thank you very much. So basically I think two ideas
[38:25] Speaker 1: nested in that question. So one is this point that
[38:28] Speaker 1: we can point to what the AIS can't do yet,
[38:30] Speaker 1: but they change fast, So maybe a lot of the
[38:35] Speaker 1: things that we're saying AI can't the AI vision doesn't
[38:37] Speaker 1: work that well, AI translation doesn't work that well. Well,
[38:40] Speaker 1: maybe the answer is just wait. And then the second
[38:43] Speaker 1: question is, if this really is incredibly disruptive to labor markets,
[38:50] Speaker 1: are government's actually going to just do something drastic to
[38:53] Speaker 1: outlaw it?
[38:55] Speaker 2: So the first part of your question, I mean, we've
[38:58] Speaker 2: already seen how much better these things have gotten in
[39:01] Speaker 2: the last few years. I mean, when things like mid
[39:04] Speaker 2: Journey first came out, the image generation, like every person's
[39:07] Speaker 2: hand had like six or somenings right, and we thought
[39:10] Speaker 2: it was hilarious. And now they're not making those mistakes anymore.
[39:14] Speaker 2: So yes, definitely things are getting better. And in a way,
[39:17] Speaker 2: that's what's a bit difficult about writing about this topic
[39:20] Speaker 2: is it's such a kind of moving target. So you're like,
[39:22] Speaker 2: you're trying to write about something that just keeps keeps changing.
[39:25] Speaker 2: But as I was saying, like, I don't actually think
[39:28] Speaker 2: the capability of what they can do is the only
[39:32] Speaker 2: thing that matters, because it's also about like what kind
[39:35] Speaker 2: of trade offs are people willing to accept? And then
[39:37] Speaker 2: in terms of if we started to see really big
[39:41] Speaker 2: labor market dislocations, would governments step in and do something
[39:46] Speaker 2: drastic maybe. I mean, I don't think there's any precedent
[39:50] Speaker 2: for that that I can think of. I mean, in
[39:53] Speaker 2: the example of the ludd Ites, the government stepped in
[39:56] Speaker 2: on behalf of the owners of the machines and sent
[39:59] Speaker 2: the luodyes to Australia. So that was like the opposite.
[40:03] Speaker 1: Well, the President surely is is tariffs because I mean, effectively,
[40:09] Speaker 1: this is the point that economists like to make. But
[40:12] Speaker 1: trade with China, for example, it's effectively a technology. I mean, yeah,
[40:16] Speaker 1: it's China over there, but it could be just some
[40:19] Speaker 1: robots in a factory in Los Angeles next to the docks.
[40:22] Speaker 1: And you know, whether you're shipping grain into the robot
[40:27] Speaker 1: factory and they're turning it into television sets, or whether
[40:30] Speaker 1: you're shipping the grain across the Pacific and the Chinese
[40:33] Speaker 1: by the grain and then they say and they send
[40:35] Speaker 1: you television sets. I mean, it kind of doesn't matter.
[40:38] Speaker 1: So you could imagine taxing robot produced you know, robot
[40:43] Speaker 1: produce goods. I just don't know if people hate robots
[40:45] Speaker 1: as much as they hate foreigners.
[40:49] Speaker 2: Let's find out.
[40:50] Speaker 1: That is sadly all we have time for. Thank you
[40:53] Speaker 1: so much for coming. It's been an absolute pleasure to
[40:55] Speaker 1: be here in Bristol. Thank you so much to my
[40:57] Speaker 1: wonderful guest Sarah O'Connor. Her book We Are Not Machines
[41:01] Speaker 1: The Fight for the Future of Work is out now
[41:04] Speaker 1: do please pick up a copy, But before then, please
[41:08] Speaker 1: join me in thanking my guest, Sarah O'Connor. Horse Retales
[41:16] Speaker 1: is written by me Tim Harford, with Andrew Wright, Ryan
[41:19] Speaker 1: Dilly and Alice Fines. This live episode was produced by
[41:23] Speaker 1: Georgia Mills with help from Marilyn Russ. The sound design
[41:27] Speaker 1: and original music was the work of Pascal Wise. The
[41:31] Speaker 1: sound engineer for tonight's show was Tom Dunn with eduardo' galley.
[41:36] Speaker 1: Thank you to xaviie Levantis and the Bristol Festival of
[41:39] Speaker 1: Economics

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