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

(upbeat music) Pushkin. Tim here with an exciting announcement about the Corsionary Club, our Patreon community for Corsionary Tales listeners. So many of you joined our live table read earlier this year that we're doing it again. Join me and our production team for a live reading of an unreleased episode about the people who almost invented the iPhone 15 years early and the surprising reasons they failed. This will be a chance for you to see how the stories we tell are developed in real time and ask your burning questions about Corsionary Tales. It's on the 8th of July at 5 p.m. U.K. or Noon Eastern. If you join the Patreon before then or if you already remember, you'll get your exclusive invitation. Head to patreon.com/CorsionaryClub. That's patreon p-a-t-r-e-o-n. dot com/CorsionaryClub. - This is an iHeart podcast, guaranteed human. - Here's a Corsionary tale plenty of parents have lived through teaching a kid to ride a bike. For generations it meant tears, skidneys and a parent hunched over the seat until their back gave out. We've named ourselves, blame the kid, nobody blamed the bike, but it was the bike's fault all along. Too heavy, too tall, cheap components and too hard for small hands to stop. Guardian bikes fix the design. Their bikes are lightweight and low to the ground and their patented, sure stock brakes stock both wheels with a single lever for an easier, faster, more controlled stop and their design and assembled in the USA. The result, many kids learn to ride a just one day no training wheels needed. It's why Guardian is America's favorite kids bike with ever one million bikes sold and a 4.9 out of five star customer rating. And it's been the New York Times wire cutters top pick four years in a row. This holiday season, Guardian is offering its biggest deal of the year, save over 40% on kids bikes plus get over $120 in free accessories, a lock, pump, bell, grips, clamp and a coloring book and sticker kit. They have helmets and adult bikes too for the whole family. Visit guardianbikes.com, that's guardianbikes.com. This is Jacob Goldstein. When you're a business owner interested in expanding to new markets, there's a lot to consider and countless questions to answer before making the call. The most important one being, are you ready? That's where chat GPT comes in. Give it a goal, add your files and ask chat to help you map out different scenarios for the future of your business. So when growth is on the table, you've got an expansion plan ready to implement. Get started today at chatgpt.com. Small businesses are the pulse of every community. They bring people together, create opportunities and drive growth. Chase for business helps business owners like you with personalized guidance and convenient digital tools all in one place. With that guidance and your determination, you can take your business father and help build a brighter future for your community. Learn more at chase.com/business. Chase for business. Make more of what's yours. The Chase mobile app is available for select mobile devices. Message and data rates may apply. JP Morgan Chase Bank NA, member FDIC, copyright 2026, JP Morgan Chase and company. Mrs. Goodair is enjoying a leisurely breakfast while she reads the morning paper. It's April 1812 and the headlines are dominated by Wellington's military successes in Spain. A strange noise jolt her from her reading. It sounds like the iron gates to her home rattling. She rushes upstairs to get a better view and sees the mob for it is a mob more clearly. They're throwing stones, making lured gestures and shouting for her to open the gates. Two men appear to be wearing dresses proclaiming loudly to be general lads' wives. She knows exactly what this is about. It's about what's in her husband's mill next door. So why are they at her house? Her husband, John Goodair, is out of town. She hovers at the window, unsure of what to do. The mob leaders grow impatient. They shout to their comrades that it's time to move on and begin marching through the streets of Stockford. As they weave through the town, they smash windows and break down factory doors. By noon, the mob has swelled over 2,000 men. Soon, they run out of targets. So they turn back to where they began. John Goodair's enormous mill. The place many of them have spent their working lives. Once a symbol of pride in their craft, now a symbol of everything they feel is wrong with the industrialized world. They break down the doors, smash through the windows and destroy all they can inside. Every last frame, every last spindle. The mob has grown so large that even the iron gates to the good air home aren't hold them back. Mrs. Goodair has already gone. A wise move. The luddites burn the house to the ground. I'm Tim Hartford, and you're listening to cautionary tales live at the Bristol Festival of Economics. (audience applauds) (upbeat music) Loyal listeners will remember this is not the first time the luddites have featured on this podcast. Two years ago, I told you about the failure of the luddite revolution. The weavers who were incensed that automation was devaluing their craft in general luds rage against the machines. Today, the luddites seem more relevant than ever as the so-called AI revolution threatens to overturn the world of work. So should we be smashing up large language models or is resisting as futiles it was for the luddites? Who's going to be most affected? And have economists been asking the wrong questions all along? I'm joined by a very special guest, Sarah O'Connor. She's a financial times journalist with a specialism in technology and work, and she's written a book called We Are Not Machines, the fight for the future of work. She is the perfect person to guide us through. What's coming next? And she's got plenty of cautionary tales to tell us. Welcome, Sarah. - Hello, Tim. Thank you for having me. - Oh, it's a pleasure. We've got to get something out of the way first. So, Sarah O'Connor, have you seen the Terminator movies yet? (audience laughs) - No. - You're gonna have to watch them. It's a point of principle. - Yeah, it now feels like stubbornness. - Yes, so you might have noticed that my name bears a faint resemblance to the ruin of the Terminator films. Growing up occasionally, people made Terminator jokes to me, and as a result, I never watched them on principle because it annoyed me. And so about a decade ago, I went accidentally, sort of globally viral. When I very simply tweeted a new story that I'd seen come out, which was that a robot had killed a worker in a Volkswagen factory. And didn't realize that this would cause the internet to break. So, yeah, so people just found it very hilarious that someone called Sarah O'Connor had written about robots. And for about a decade, people have occasionally sent me, you know, Terminator gifts, posters of the Terminator films with my face photoshopped in. (audience laughs) And as a result, no, I have not watched those films. - Okay, so the book is fantastic. We are not machines. What's the headline? What are you basically driving out with this book? - I think what I'm trying to say in the book is that, you know, we're all sort of drowning in information, predictions about AI and what it might do to the world of work. And a lot of those are coming from either economists or the big tech executives who have created large language models and are marketing them. - And both these sources are unreliable for different reasons. - Yeah, the tech guys have something to sell, right? And so they have their own motivations. And then, you know, economists, I think, are trying their best to figure out what's going on. But the way in which they're doing that, I think, is very, feels very abstract to me. As someone, you know, I'm a reporter at heart. So, my favorite thing to do is to, like, get my notebook, put my boots on and, like, go and meet people and stand outside the factory gates and talk to people about what's actually happening. And so, over the past few years, I just felt very frustrated that everything that I'm hearing about this is coming from people who are either looking at spreadsheets and models or who have a product to sell. And I just wanted to hear less about what these men say is going to happen. And more about what's already actually happening from the people that it's actually happening to. So, in the book, I basically went to find people and places and workplaces that are on the front lines of what's already happening. So people who are, yng Nghymru'r anoddwch yng Nghymru'r gwaith o'r ffwrt o ffwrt o'r yng Nghymru'r gwaith yng Nghymru'r gwaith yng Nghymru'r gwaith yng Nghymru'r gwaith yng Nghy Mae'n yw'n gwneud i'n gwneud i'n gwneud i'n gwneud i'n gwneud i'n gwne yn gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio gweithio a dystoriaeth ydylwyd. Mae'n yw'r unrhyw i'n gwybod, sy'n gwybod, yn yw'r unrhyw sy'n gwybod yw'r unrhyw i'n gwybod yw'r unrhyw i'n gwybod yw'r unrhyw i'n gwybod yw'r unrhyw i'n yw'n mynd yw'n mynd i'n mynd mewn gwneud i'n mynd mewn gwneud i'n mynd mewn gwneud. Mynd gwneud i'n mynd gwneud i'n mynd gwneud i'n mynd gwneud i'n mynd gwneud i'n mynd gwneud i'n mynd gwneud i'n mynd gweith. i ddyn nhw'n ddyn nhw'n ddyn nhw'n ddyn nhw'n ddyn nhw'n ddyn nhw'n d a yw i'n gweithio gwneud â'n gweithio a'n gweithio a'n gweithio a'r gweithio a'r gweithio a'r gweithio a'r gweithio a'r gweithio a'r gweithio a'r i'n ames yn ymwyr i'n ames yn ymwyr i'n ames yn ymwyr i'n ames yn yr hynny. i'n ames yn ymwyr i'n ames yn ymwyr i'n ames yn ymwyr i'n ames yn yr hynny. Ymwyr i'n ames yn ymwyr i'n ames yn ymwyr i'n ames yn yr hynny. Ymwyr i'n ames yn yr hynny. Yn ames yn yr hynny. oeddwn i'n gweithio. skid knees and apparent hunched over the seat until their back gave out. We blamed ourselves, blamed the kid, nobody blamed the bike, but it was the bike's fault all along. Their bikes are lightweight and low to the ground and their patented, sure-stop brakes stop both wheels with a single lever for an easier, faster, more controlled stop. And their design and assembled in the USA. The result, many kids learn to ride in just one day, no training wheels needed. It's why Guardian is America's favourite kid's bike, with ever one million bikes sold and a 4.9 out of 5 star customer rating. And it's been the New York Times' wire cutters top pick four years in a row. This holiday season, Guardian is offering its biggest deal of the year, save over 40% on kid's bikes plus get over $120 in free accessories, a lock, pump, bell, grips, clamp and a coloring book and stick a kit. Visit guardianbikes.com. That's guardianbikes.com. With ChatGBT, you can do more than get answers. You can get things done. When you're running a business and looking to expand, you need a clear view of the landscape before making any decisions. ChatGBT can help you prepare. Connect your Google Drive, point to relevant files and reports, and ask it to research competitors, industry forecasts and market signals. It can turn the findings into a strategic deck in your team's template that you can review before presenting to your leadership team. Growing a business is ambitious, and ChatGBT can help you do it with confidence. One assumption can change an entire story. The same is true for the software you use every day. With Base44, you can build the software you've always wanted, somebody by describing it in your own words. Tell it who uses it, what they need to see and how it should work. Base44 handles the database, login, back end and hosting for you. So when you see a better way to get something done, you can actually build it. Start free today and upgrade when you're ready. Visit base44.com. We're back. This is the Bristol Festival of Economics. I am Tim Halford, and my special guest is Financial Times Journalist and author Sarah O'Connor. So, Sarah, perhaps the canaries in the coal mine are the translators. When I thought about translators, I just thought, oh, you guys, you've lost all your jobs because of Google Translate. It's all gone, but actually the story you tell in the book is a lot more interesting and more complex than that. Yeah, so I interviewed some translators for my book. One of them is a guy called Peter. He lives in the Czech Republic, and he translates subtitles for TV shows, which is a great job. I mean, he told me lots of things about why it's more difficult than you might imagine to do that. So, for example, you know, in English, we just have one way of saying you, but in lots of languages, including Czech, there's like a formal tense of you and an informal tense of you. And so, when you're writing the translated subtitles, you have to think like, how well do these characters know each other? Like, what tense would they be speaking in? And that might change, you know, through the course of an episode or through the course of a series. And so, there were lots of kind of interesting judgment calls that you have to make. And then, you know, translating jokes is another thing that's like incredibly difficult. And because so many jokes are like culture-specific, or they're like plays on language. We're in a bookshop. We are the guests of waterstones in Bristol. Must be some asterix and obelix somewhere in the bookshop. Yeah, they're the perfect example, right? So, obelix is dog. In the original French is called edafix, which means obsession. And the brilliant English translation is dogmatics. You know, so like, there's so much sort of creativity and humour involved in being a really good translator. And so, like, this is a great job for people that enjoy doing that sort of thing. But what's happened to him and to people like him is that he has not lost his job entirely. But it's actually quite hard to get a machine to translate things with that level of cultural knowledge and understanding. So, just to explain slightly the way a lot of translation works is a lot of translators of freelancers. And there are agencies that will take work from, you know, a TV studio and then parcel it out to freelance translators. And what those agencies have started to do is to take something, get it translated by a large language model or a different kind of machine translation service. And then give it to the translator and say, hey, can you just check that this is right and maybe like just finesse it a little bit, polish it, make it sound a bit more human. And this has become the kind of bet noire of lots of translators because what they say is that it's a, you know, you're expected to do this much faster and be you expect to do it for much less money. But in fact, if you care about quality, it's very difficult to actually check a translation is accurate. And then even things like trying to make it sound a bit better, like that's actually what the translators told me was it's actually quite hard to do when there's already like an answer in front of you. And it feels much less creative and more kind of cumbersome and less enjoyable. And so a job that was sort of creative and challenging and interesting has become faster, harder, more monotonous, feels more mechanical and fundamentally kind of less satisfying. And I think that is a real cautionary tale. And when I was speaking to them, it made me think a lot of the Luddites because what the translators will tell you is that the end product is less good quality than there are even some studies that have checked this. So there's a big study that looked at a bunch of subtitles from TV shows before and after the introduction of machine translation post editing and the quality has kind of deteriorated. It's the stockings again. It's the stockings again, exactly. And I guess partly obviously we care about the quality of jobs. That's important. But I guess one of the interesting questions from the point of view of the consumer because the whole idea here is even if individuals get worse jobs, the consumer gets more choice, more quality, lower prices, I always do sort of wonder is the product actually better and will the market deliver kind of the right trade-offs or are we actually just going to make all kinds of mistakes and we end up with crappy products that we don't want. And I'm just wondering whether I know I don't sound like an economist here, but I'm wondering whether we're all just going to get stockings that fall apart and translations that are joyless and but some manager somewhere was convinced by some AI salesman somewhere that it would be, that it would be fine. Yeah, I mean I think this is one of the things that I realise working on the book is that you know economists when they're sort of doing their modelling about which jobs might be displaced or degraded or changed by technology, they look at like how good is the technology and compare that to how good is the human, but actually like can a robot do my job as well as me is not always a particularly useful question like can someone persuade my boss that a robot can do my job as well as me is a more relevant question, or would my customers or consumers be content to have someone do my job less well than me, but for a much cheaper price? With the case of the translators is this fundamentally about the technology, this just happens to be a thing that computers can do at a certain speed with a certain quality that kind of is is maximally disruptive to the job of being a translator or is it actually more to do with the fact for example that all these people are freelancers? Is it actually to do with economic power and not to do with technology at all? I think it's to do with both, but I think you make a good point which is that often this this question about like what will AI do to the world of work and seen as a technical question, but it's not just a question of the technical side, it's also a question of like consumer choice and institutions and culture and bargaining power and economic power. So yeah, I mean partly the reason it's been very disruptive to translators is that the translating is like them basically all they do like that warm task of like translating something if you're a freelance translator that is pretty much your whole job whereas for a journalist writing an article is like actually quite a small part of my job. My job also involves doing a lot of reading, going out to interview people, doing stuff like this and so it's more it's more disruptive in some particular professions depending on like how much of your tasks are automatable and then also yeah like your your your market power right if you're if you're a freelancer then that's a very different thing to being a well protected minor in a in a unionized mine company in Sweden. And can we talk about coders because superficially programmers software coders seems quite like translators it turns out that for some reason large language models seem to be pretty good at coding at least that's what I'm told I wouldn't know but people say it's pretty good so the code our code is in the same position as as translators suddenly I've got all this terrible code and kind of they want me to do twice as much work for half the salary do they feel the same way as the translator. Most of the ones I interviewed do not feel like that I think coding is an example of a job that in many ways actually has probably been enriched and made more fun and more productive by this new technology and I think the reason that it is playing out different is partly you know this thing about bargaining power and where you sit in this sort of economic chain. but also if you're a computer programmer like you're what you really care about and love is not literally sitting there and writing out lines of code what you love doing is like solving problems and so what the tools do they might automate some of the kind of the actual literal coding but they're not automating that kind of really fun creative problem solving bit it just allows you to like try things out or do stuff faster. This is the promise, this is what they keep telling us it's going to do for all the jobs, it's going to do the boring stuff so you can do the fun stuff. And this is why they're so enthusiastic and think it's brilliant because for their jobs it is pretty brilliant. What is it then that determines whether your job is like translating or whether your job is like coding and is it under our control or not? Yeah so I think one of the things that I've realised and I now really flinch when I hear it and I flinch when I think that I've probably said it sometimes as well in the past, is that I think we're really using some of the wrong metaphors when we talk about this so I don't know if you've noticed this but I find that like technology executives in particular but also sometimes economists will talk about like there's a tsunami of change coming or there is a new wave of a technological change. I think it's misleading because it makes it sound like it's akin to a natural phenomenon like it's something that's just happening that just came out of nowhere and it's going to happen to us and it invites us to think that at best all we can do is A prepare for it and B get ready to mop up after it and you know these metaphors I think are really useful to technology executives in their conversations with policymakers because you know nobody wants to look like the fool who thinks you can hold back the tide right and so it invites you to think of this as something that is happening anyway and you know that there's very little that you can fundamentally do about it but that's not true at all. That's never been true. Technology is stuff that is made by people and implemented by people and there is a huge variety as we've just discussed in the ways in which it can play out in different occupations and in different countries and in different parts of the labour market and a lot of that is dictated by the choices that people make and the things they choose to value. So you can do something about this because you can write columns in the financial time as telling Sam Altman that he's being a naughty boy. What can ordinary people who do not have a newspaper columns and who are not running AI companies just regular people? What can they do to help direct this change in a more productive empowering way? I mean I think people do and are and will direct and determine this next progression of technological change because that's what's always happened. I mean some people obviously have more power than others particularly in terms of like what kind of working conditions they may or may not have to accept but you know everyone that I interviewed in my book is responding to what's happening and you know whether that is that they are going on strike or forming a union or whether it's that they are changing their profession or figuring out some new way of doing what they do in a different way. Also as consumers you know a lot of the questions we've been talking about is like well what will the market accept? We are the market you know so it's kind of up to us to decide whether we like AI music or real music. I feel so flat that everyone's ever said I'm the market before. You are the market. It's the nicest thing anyone's ever said to me that's great. How to compliment an economist? Absolutely. So you heard it fight back, stand up, show show the man why you're better than the machine. Sarah and I are going to talk about large language models after the break. Here's a cautionary tale plenty of parents have lived through teaching a kid to ride a bike. We blamed ourselves, blamed the kid, nobody blamed the bike but it was the bike's fault all along. Guardian bikes fixed the design. Their bikes are lightweight and low to the ground and their patented sure stock brakes stop both wheels with a single lever for an easier, faster, more controlled stop and their design and assembled in the USA. The result many kids learned to ride in just one day no training wheels needed. It's why Guardian is America's favourite kids bike with ever one million bikes sold and a 4.9 out of five star customer rating and it's been the New York Times wire cutters top pick four years in a row. This holiday season Guardian is offering its biggest deal of the year, save over 40% on kids bikes plus get over $120 in free accessories, a lock, pump, bell, grips, clamp and a coloring book and stick a kit. Visit GuardianBikes.com That's GuardianBikes.com. With Chat GBT, you can do more than get answers. Chat GBT can help you prepare. Connect your Google Drive, point to relevant files and reports and ask it to research competitors, industry forecasts and market signals. Growing a business is ambitious and Chat GBT can help you do it with confidence. With Base 44, you can build the software you've always wanted, simply by describing it in your own words. Base 44 handles the database, log in, back end and hosting for you. Visit base44.com. We're back. I'm Tim Harford. We are live at the Bristol Festival of Economics and this is a cautionary conversation with experts in artificial intelligence, expert in workplaces, author of, we are not machines, Sarah O'Connor, who is also my colleague at the Financial Times. The title of the book is, we are not machines. You've given us some really striking examples of demands of made of workers to be more machine-like, being under constant surveillance, having to move in a very weird and unnatural way. I wanted to bring it back to the experience a lot of white collar workers have of sort of sitting at a computer every day and suddenly discovering there's this thing called chatgpt. Do you think that chatgpt is this making us more machine-like and we don't know it? So this is something that a lot of the translators that I interviewed brought up. I think because they're linguists, they have like an ear for language and how it's changing, but a number of them said to me independently that they had noticed that the sorts of language that we are using is becoming narrower and flatter and more homogenous. And I wonder if that is partly because we are using chatgpt more to write our LinkedIn posts and our emails and maybe that even seeps into the way that we communicate with one another. So I think there are certain risks there. But also, you know, I think large shopping models do clearly have the capacity to do that thing that we talked about at the start, which is like take away some of the boring stuff. And I think there were plenty of examples of people doing that, whether it's like, oh no, I don't have to take any minutes for this meeting. Or now I don't have to spend two hours googling three relevant research articles. But I do think it's also, it's very easy to start also using it for things that maybe in the past, we would have put a bit of human care and attention into, like, you know, I think a lot of people are using chatgpt now to write wedding speeches or speeches at funerals and that sort of thing, which, you know, you might once have thought we're among the more human tasks that we did. Yeah, although I have heard a few wedding speeches, chatgpt would have done better than some of them. So, you know, yeah, rising tide lift sunboats. Are you frightened or are you hopeful for the future? I'm a lot more hopeful now than I was when I started writing the book actually. And I think that's probably because of the people that I met along the way. So like another of these sort of metaphors or like cliches that we often hear is, you know, people would say, well, you know, inevitably there's going to be winners and losers, you know, there's there's dangers and opportunities. And that sort of invites us to think like, oh, go on, but I just wait and find out if I'm going to be a winner or a loser and like, you know, fingers crossed, I'll be a winner. But you just kind of got your lottery ticket and then you wait and see. But like, that's not actually what people are doing. Like, people actually are maneuvering themselves and trying to figure out what they can do to make sure that things work out well for them. Like, everyone is shaping and determining this. So in that sense, I ended up more optimistic because I think that we have a lot more agency to figure out how this goes and to make sure it goes in a way that we want than we're sometimes led to believe. Thank you so much Sarah. Let's take some questions. So we've got a question from a cautionary club member. Emily, by the way, anyone who wants to join the Corsionary Club, it's very wonderful, you get the podcast, add free, you get bonus episodes, all very good. And you get your question read out live at the Bristol Festival of Economics. So Emily says there's a research institute called METR, which found that both the expected and the perceived efficiency improvements from using AI in software R&D were substantially overstated. She asks, "Do you think businesses are likely to scale back AI investment until the efficiency gains promised are demonstrated?" Or have they sunk so much money in at this point that they feel they have to justify the expense? I think it remains to be seen. I think that METR study was really interesting because what it sort of highlights to me is that we were talking about this distinction between tasks and jobs, and that actually your whole job might disappear but some tasks within it might change. But when you think about something at the level of an entire organisation, you've also got to think about jobs and systems. And so a computer programmer on his or her own might have become much more productive if they can use a coding AI assistant. But actually if there are still bottle necks and other parts of that system, you might not really see the benefit of it. You might just create lots and lots and lots of code, but then you still don't have enough people to review it, for example, or whatever it might be. And so I think what a lot of companies are finding is that suddenly being able to do certain tasks within jobs much more and much faster doesn't necessarily mean that you're going to see the same kind of productivity outcome filter through to the whole organisation. The other thing I would say is that we're still like in the very early stages of companies trying to figure out how to use these new tools effectively. And what I think we might start to see is companies kind of redesigning workflows around what the machines can and can't do. I mean in a way that's exactly what Amazon has done, right? I mean it's completely changed its workflow in order to make the most of the machines that it has available to it at the moment and plugged humans in to do the bits that the machines can't do. But I think it's still a really open question, you know, particularly with large language models. I mean the kind of the hallucination problem I think is quite could be quite existential for a lot of sort of high-risk professions, you know. Certainly we're not going to be using large language models at the FT to write our articles because we just can't possibly trust them not to hallucinate. Yeah, I mean I always find when I'm using large language models, the first question is will anybody else ever see any of this? And if the answer is yes, then it's unusable. Like if it's just for me, then sometimes it comes up with useful ideas, but like I absolutely cannot trust it at all to produce anything that anybody else will ever see. So anyway, more questions. There were loads of questions. Can we get the microphone? Somebody looking quite young and fresh face there. I want a question from. Hi, I have a question. Which industry do you think is most at risk of AI replacing the job or having the biggest amount of the job being taken away? So Sarah, which industry is most at risk of just having done these jobs completely removed? I mean, I hate to say it, but I'm. Is the translators? I know I was going to say it's I think sort of creative industries, more generally. I mean, not that I think that, you know, people will stop wanting to pay for human created stuff, but I think that there are lots of people who work in, you know, they're not necessarily originating the ideas, but they work in the kind of in that long process between someone coming up with an idea and the thing being made, whether that's like working in special effects in Hollywood or music production or storyboarding in an advertising agency or whatever it might be or copyrighting. I think there are lots and lots of jobs there which, you know, are good jobs and people like them. And so I think it's a real shame if they become disrupted, but I can imagine that those are the sorts of roles that could be quite vulnerable, but I might be wrong and I hope I am. So you heard it here first. Human creativity is dead. It's definitely what I said, thanks Tim. This from the woman who calls herself an optimist. So thank you for that. We've got time for one more question. You said that AI is not advanced enough to translate the text accurately enough, but it's really been around for two years, like woven by 2030s is going to be enough advanced to replace some of the jobs. Surely it will lead to like structural unemployment and the regular government will layer happen. Thank you very much. So basically I think two ideas nested in that question. So one is this point that we can point to what the AI's can't do yet, but they change fast. So maybe a lot of the things that we say AI vision doesn't work that well, AI translation doesn't work that well, well, maybe the answer is just wait. And then the second question is if this really is incredibly disruptive to labour markets, our government is actually going to just do something drastic to outlaw it. So the first part of your question, I mean we've already seen how much better these things have gotten the last few years. I mean when things like mid-journey first came out, the image generation like every person's hand had like six or seven fingers, right? And we thought it was hilarious. And now they're not making those mistakes anymore. So yes, definitely things are getting better. And in a way that's what's a bit difficult about writing about this topic, is it's such a kind of moving target. So you're like you're trying to write about something that just keeps changing. But as I was saying, like I don't actually think the capability of what they can do is the only thing that matters because it's also about like what kind of trade-offs are people willing to accept. And then in terms of if we started to see really big labour market dislocations, would government step in and do something drastic? Maybe. I mean I don't think there's any precedent for that that I can think of. I mean in the example of the ludicists, the government stepped in on behalf of the owners of the machines and sent the ludicists to Australia, so that was like the opposite. Well, the precedent surely is tariffs. I mean effectively, this is the point that economists like to make, but trade with China for example, it's effectively a technology. I mean, yeah, it's China over there, but it could be just some robots in a factory in Los Angeles next to the docks. And you know whether you're shipping grain into the robot factory and they're turning it into television sets or whether you're shipping the grain across the Pacific and the Chinese by the grain and then they send you television sets. I mean it kind of doesn't matter, so you could imagine taxing robot produced goods. I just don't know if people hate robots as much as they hate foreigners. That is sadly all we have time for. Thank you so much for coming. It's been absolute pleasure to be here in Bristol and thank you so much to my wonderful guest, Sarah O'Connor, her book. We are not machines. The fight for the future of work is out now. Do please pick up a copy, but before then, please join me in thanking my guest, Sarah O'Connor. Porsche Retails is written by me Tim Harford with Andrew Wright, Ryan Dilly and Alice Fines. This live episode was produced by Georgia Mills. It helped from Marilyn Russ. The sound design and original music was the work of Pascal Wise. 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