What If AI Simply Ruins Your Job Instead of Taking It? (with Sarah O'Connor)

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2:00I'm Stephanie Flanders, Head of Government and Economics at Bloomberg, and this is Trumponomics, the podcast that looks broadly at the economic world of Donald Trump, how he's shaking up the global economy, and what on earth is going to happen next. And we do talk a lot about AI on this podcast. In fact, on or off this show, I'm finding most conversations end up there sooner or later. More often than not, the focus is on how many jobs AI will replace and how quickly. Also, who the winners and losers will be. But there's another worry about AI that I'm hearing more and more and is vividly described in a new book by the always brilliant FT columnist Sarah O 'Connor.

2:38It's the fear that not so much that AI will put humans out of work, but it will make people's jobs worse than they were before, even when they were pretty bad jobs to begin with. What if by making our jobs much easier, they actually take away everything that made them interesting or rewarding? The book I'm talking about is We Are Not Machines, The Fight for the Future of Work. And Sarah is with me. Sarah, welcome to Trumponomics. Thank you for having me.

3:09And thank you for your columns, which I'm often sending to the team. And thank you for your book, which I think does introduce, as often you do with your columns, a whole different. element. Your title isn't exactly employment correspondent, but I wonder whether it should be more kind of humanity correspondent these days. That was kind of the sense I got from the book. You were focused on the human part of jobs and what might happen, and in some cases, as you described, has happened when robots take over. Yeah, that's right. I think the reason I wrote the book was that I was becoming a bit frustrated with the sort of the lack of humanity somehow in this discussion, that even though it is all about us, and indeed, we humans are the ones who've invented AI.

3:48It seemed as if the technology was being portrayed as the protagonist here, and that much of the debate was being driven either by the chief executives of those big technology companies and their big predictions, the utopian ones and the dystopian ones, or by economists who have been doing lots of very careful studies, as you say, trying to map, you know, what are the particular abilities of these new AI systems? And if we break apart every single job in the economy into its constituent tasks, then can we work out which jobs are most exposed. I think that's fine as far as it goes, but it doesn't really get to the heart of things, which is how might it actually feel to be in the position of someone who is suddenly presented with this new technology?

4:29How does it actually change work on the ground? And I'm the sort of journalist who likes to get out of the office, get my notebook, get my comfy boots on and go and meet people. So that's what I decided to do. So rather than the spreadsheet approach, I wanted to take the shoe leather approach here. So I went to meet software developers, translators I went down a deep mine in Sweden which is now crawling with kind of autonomous vehicles a brand new Amazon warehouse which is also full of robots and I just wanted to find out how is this actually going and to talk to the people that it's actually happening to.

5:00And the big picture was you say that you have ended up more optimistic than at the start I don't know whether everyone reading the book will feel more optimistic but what were you gloomy about at the beginning and how did you change your view? I feel like I've been on a real journey with this one. I mean, I used to be a real techno optimist before I even started the book. I basically, because I've worked at the FT for a long time, I've written a lot about the world of work over the last decade, decade and a half. And I felt like over that time, I talked to a lot of people who were in jobs, which weren't really that good, you know, quite boring, quite repetitive, quite physically arduous.

5:34Sometimes I wrote about the first generation of Amazon warehouses where people were walking around for 10 or 15 miles a day, paced by algorithms, tracked by algorithms, They were getting blisters on their feet. And I used to think like, you know, this is exactly why we need more automation. You know, we should be automating away bad jobs. We should be automating away the kind of the dull, the dirty, the dangerous stuff. And so, you know, bring them on. And then, of course, the robots did arrive and so did generative AI. And that started to impact all kinds of white collar jobs as well. And what I started to realize from talking to people was that it wasn't necessarily happening the way I thought it was.

6:14was, as you said in the introduction, rather than being a kind of force for liberation, in some workplaces and in some professions, it was sort of crunching people into systems that were now kind of paced by machines, and also where people were kind of having to plug the inadequacies of the machines, because they're not necessarily capable of doing everything that a person can do, but they can do certain things. And then you're starting to see jobs being redesigned around those sorts of strengths and weaknesses, in which the humans are now sort of in the loop, but they're not necessarily in the loop in a way that's particularly enjoyable, and sometimes was sort of more intense.

6:50So that made me quite depressed about the future. In the end, the reason that I felt more optimistic was because I realised through the course of talking to all these people, that the idea that this is an inevitability, that this is just sort of coming at us, and that some people will sink and some people will swim, or as you say, you know, there'll be winners and losers. I realised that that's not actually the case. None of the people I met could be sort of slotted into a winner or a loser category in an economist's spreadsheet. They were all responding to this new technology in various ways.

7:20Some were trying to take advantage of what was useful about it. Others were sort of pivoting away from risks. Some people had just decided to leave their profession altogether and to create something new for themselves. And it made me think that actually we all have more agency in this than we're sometimes giving ourselves credit for and that fundamentally people are unbelievably adaptable and they will find their ways through but that we need to all kind of be aware that this is a story in which we're the protagonists rather than the technologies. There's so much there and I want to get quite a lot of it.

7:52There was a piece of it that I thought that you start talking about because you focus on what happened to translation and sort of the creativity involved in translating subtitles which I think if anyone's actually been a translator or knows a translator, they know there is a real art to it. There's a sort of classic model, which is the sort of white collar version of what you were describing. You know, you have the sort of drudgery and the drudgery is taken away and you're just making someone's job easier. The version of that in white collar work, which also applies to kind of editors and journalists, is the AI does 80 % of the job and then the human gets to spend less time just sort of tweaking it, making it a bit better.

8:34And I suspect a lot of newsrooms and anywhere that is involved in translation and other things, that's been the way they've introduced AI and they've said, isn't it nice for these people? They don't have to do like the first nine yards, they just perfect it. And you kind of capture that that actually takes away most of what was rewarding about that job. It really made me think about it. Yeah, I mean, one of the things that was so lovely about doing the reporting for the book is talking to people in depth about what they actually do. And it's only when you do that, that you realise how much kind of care and craft and complicated sort of knowledge and skill goes into all kinds of things that we often take for granted.

9:15So I quite often watch TV with the subtitles on, even in English, just because I don't know why my attention span is obviously collapsing. But when I talked to these translators of subtitles, they talked about what a remarkably creative and enjoyable job it is, particularly if you're watching a TV show in English and you have to translate it into say Czech which is what one of my interviewees have to do you have to make so many decisions you know like in English there's just one way of saying you but actually in lots of other languages particularly European languages there's the formal you and there's the informal you and so you as the translator have to decide in any given scene would these people be talking in the informal or the formal and sometimes that might change as their relationship sort of shifts and those are the sorts of decisions that you have to make.

10:00And then there's like, how do you translate a joke that might play really well on the West Coast of America so that it still is funny for people in the Czech Republic? And all of those things are actually a really great creative challenge. You have that great example from Asterix of Obelisk's dog is called Dogmatics, but in French it's Idae Fiks, which is a fantastic example. Exactly. Yeah. So Idae Fiks was the original name of the dog. And then the English translators came up with dogmatics, which is even a better pun, right? It was so fun to talk to those translators about what they love about their job.

10:34But yeah, what's changing for them isn't necessarily that they're being completely cut out of the equation, because these subtitles are still not kind of perfect if they're done by machines. But as you say, they're being left to, they call it now machine translation post-editing. So you're given the machine translation, you're expected to tidy it up to check that it's accurate. And what they said was that actually, this is no longer creative. And also it's cognitively quite challenging actually, because you're sort of checking one thing against the other, but it's lost the sort of the joy and the meaning and the pleasure.

11:05And also they're being paid half the price. And so they have to do it twice the pace. And so there's been a sort of intensification of their work as well. And so some of them said that it felt a bit more like being on a production line compared to what it used to be. I mean, I would say to people who are starting to feel quite depressed, I did also go and visit some much more interesting, optimistic workplaces where I think AI and other kinds of automation technologies are sort of living up to that promise that we all had, which was that it would free us up to be more human. And then the key question, I think, is like, what do you do with that?

11:39I didn't really want to write a book that concludes, well, there's opportunities and dangers, you know, there's winners and losers. I've read a lot of books like that, as I'm sure you have as well. And it's not that that's not true, but I think that... And you also talk about uncertainty as well. It's like there's the winners and losers and it's all very uncertain. And it's all terribly uncertain, exactly. But that's not very helpful, is it, to say there'll be winners and losers. The kind of more difficult question is the next one, which is, well, what makes the difference? Why are some people winning and why are some people losing?

12:07Why is this exact same set of technologies playing out so differently in different people's work? And I think unless you try and challenge yourself to ask that question, we're not really going to get much further in terms of shaping this the way we would quite like it to go. And I think also So you get a little bit into, even when someone might be a winner in economic terms, because they're in a well-paid profession where they have quite a lot of autonomy over the use, you mentioned that, you know, that's part of the thing is, do you have control over how it's used? You're voluntarily adopting these tools and they are making you much more productive, often in sort of coding or in any of those kind of areas.

12:46But as you highlight and people have written about in real time, that intensification thing, people end up feeling they're working much harder. And of course, we've seen that in a kind of macro sense for the last hundred years. Famously, Keynes had said productivity is going to increase quintuple in the next hundred years. And so we'll all have lots of leisure time. Of course, it turns out, yes, the people who've become much more productive, many of them are also quite well off, but they feel like they're working harder than ever and they're worried about how many hours sleep. So I think even that definition of winners and losers, once you start thinking about the quality of life and the quality of your job, it becomes a bit harder to tell which is which.

13:28Definitely. It's very much the case that you can win something and lose something at the same time. And I think that we sometimes don't notice necessarily what we're losing until it's too late or until we sort of look back and think, huh, something about the quality of my work doesn't feel quite the same anymore, but I can't put my finger on exactly what it is. Software developers are really an interesting example because in many ways it's sort of the opposite of those translators. If anything, I think those jobs are becoming more human in the sense that there's less sitting there typing out code by hand.

14:01I mean, no software developers that I know are writing code by hand anymore. And so what is now demanded of them is actually more kind of managerial skills, those sort of more people skills. They've got to coordinate between different AI agents and different humans. They've got to think about the big picture a bit more. And so it's starting to put a premium on those more thoughtful judgment, taste, all of those sorts of things, which does sound more enjoyable. And I think a lot of software developers are enjoying it and are kind of certainly a bit intoxicated by it. But as you say, they're not sort of banking that productivity and going home at lunchtime.

14:34You know, it's interesting that both the sort of the utopian and the dystopian narratives about AI envisage us doing less work, right? In the utopian story, we're all sitting in our hammocks and working two hours a day and writing poetry to each other. And in the dystopian story, there's like 10 people who've become multi-billionaires and everyone else is mass unemployed. But actually, as you say, what seems to be happening is that we're just finding more and more stuff to do with our time, which is often the way it goes. Well, another thing I felt was sort of in the back of my mind as I was reading the book, and it's one of those tweets that went viral a couple of years ago along the lines of, I wanted AI to do my laundry and dishes so I could write a novel, but it seems to be working the other way around.

15:14There is that sort of concern. I think your point is the novels are not very good. You get a lot of management consultants now saying, because it's so easy to do AI written reports and research, The premium will be on interaction with humans and being able to prove that you've had humans involved using their judgment and other things. I mean, that would be great if it's true. But at least some of the examples in your book, it suggests that what's produced by AI may be objectively less good than what humans do, but it'll still end up replacing the humans. I think it's definitely the case that you can't just take what the AI can do or what any kind of automation technology can do, compare it to what a human does, and then say, well, this is how the job will change.

16:04Because so much of it depends, as you say, on what will managers be satisfied with, what will consumers be satisfied with. So it's very possible that we might accept subtitles that are objectively worse, but far, far cheaper. Now, translation is actually a really good example because it's really hard to know if you're on the receiving end of a translation, whether the quality has got worse or not. The whole point is that you don't know what the original language was. And there have been some studies of the quality of subtitles, for example, over time. And since this new kind of machine translation post-editing system has come in, the quality has dropped quite substantially.

16:40The language is less rich, less varied. Everything is becoming sort of a bit thinner. There's sort of odd punctuation. It's all a bit less human. But as you say, from the studio's point of view, if that's much, much cheaper and no one is really going to be in a position to know, then that might happen anyway, even if the humans are still superior. I'm hopeful that ultimately there will be a sort of pushback. And I think we're already starting to see that. There are a lot of people who are saying, I don't want music that is made by AI. I don't want to read a book that is written with the help of AI.

17:14And there are lots of people trying to come up with like labeling schemes, you know, made by humans, all of that sort of thing. I think the tricky thing is that the way AI integrates into people's creative processes is quite subtle and quite messy. And there's a sort of huge gradient between how much did you use AI, whether you're a writer or a researcher, particularly, you know, if you're working in something like TV, if it's used in post-production, does that count? Does that not count? And so I'm not sure that this labeling idea is going to be the thing. But it's definitely true that I think there is a demand from humans for stuff made by other humans.

17:47And so I'm hopeful that in some way the market and capitalism will figure out a way to say, shoot, that demand.

18:04I should say we're recording this on the 16th of June. I'm not entirely sure when you'll be hearing it. But one of the columns you wrote was asking, why is it so controversial what the impact of the Industrial Revolution is? Why, after all these years, are we suddenly fighting with renewed vigour about how the Industrial Revolution affected the economy and society? And the point you made there, I guess, was people were looking in the wrong place when they were just looking at the numbers. And I think we've also had that actually on the show from people like Simon Johnson and Darren Assamoglu. If you read the historians or the novelists of that 19th century, it was about how it changed people's lives as well as the jobs and people out of work.

18:45Exactly. And also, if you look at the kind of fights that broke out politically and even on the streets and certainly inside workplaces during the Industrial Revolution, often they were not about real wages and whether real wages have ticked up 0.5 % or 0.7%. They were about things like craftsmanship, dignity, health and safety, the role of children in the workplace. You know, all of these things are actually much more fundamental to kind of who we are and how we relate to our work. And I think we're beginning to see all of those same debates now. And none of them are really going to be captured in these questions about whether productivity has gone up by 0.7 or 0.9 in the US in the last quarter.

19:23So you talking about the 19th century, it's funny because I did a story a long time ago at the BBC, about how housekeepers in Las Vegas, all the hotels in Las Vegas, the people cleaning the rooms and the hotels are all unionized, which is kind of surprising. But that was from a very conscious effort by the service sector union. And the leader had said to me, look, these great jobs, the manufacturing jobs that people are now trying to fighting to keep in cars industry, for example, that have great benefits and people can have pensions and they can send their kids to college. They didn't just become out of thin air.

19:59Those jobs were terrible 120 years ago, and it was unions that made them better. And of course, in these kind of very human-centric jobs, we've tended to think that they were very hard to unionise. And this is one of the not very many examples, the housekeepers in Las Vegas. Is that part of the answer that these jobs that definitely won't be automated need to become sort of good blue collar jobs in the way that, say, being a plumber or even working in a car factory are good jobs? Yeah, I think that would be a good outcome. I mean, it's tricky, I think, because if you look at something like care work, for example, and I spend a whole chapter with these amazing community nurses in the Netherlands.

20:41The reason that it's difficult to sort of raise wages in the care sector is partly because they're not unionized, but there's some other things going on there as well. One of them is that often the people who are paying for that are taxpayers in one form or another. And we have struggled to put aside enough money, really, to pay for good quality jobs in those sectors, partly because the economies in many places are aging. And so the costs are just going up. There are lots of other kind of competing demands on taxpayers' money. And then the other thing, of course, is that with manufacturing, it's definitely true that unions helped workers get a share of the pie.

21:20but also automation did enable productivity to go up. So there was a growing pie to be divided. And these very labor intensive sectors, it's not very easy to use technology to boost productivity. And some people are looking at things like the shortage of care workers and saying, well, we're going to have to put robots in here. Like, what else can we do? Like, you know, there's just not enough people willing to do these jobs. What was great about the nurses that I met in the Netherlands is actually they'd come to another solution that wasn't really about technology at all, which was more about just redesigning the job and the system entirely.

21:52So I went to an organization called Burtzorg, which some of your listeners might have heard of, where basically they run themselves, these nurses, as sort of semi-autonomous teams with no layers of management above. What that does is that it means that they can form kind of quite close relationships with a small-ish number of clients in their local neighborhood. They actually give them like less care in terms with the hours of care, but they're all highly trained and they bring in sort of local support networks to help so that it turns out to be a cheaper way of providing care, but it's sort of better quality.

22:25And that's what the, all of the sort of metrics suggest. And also it's cheaper, you know, for the insurance companies and the government, because there aren't all these massive sort of overheads. And for the nurses themselves, they really like this way of working because they're not being treated like machines that have to rattle through all of these clients and go from place to place and not form any human relationships. They're making all of the decisions themselves. They've got a lot more autonomy. And so a job that in many countries, including the UK, is a really hard job and quite thankless, becomes quite a meaningful and enjoyable job.

22:58And actually they're paid better, those nurses, because of all of these things about the business model. So I think there are ways to make these jobs better quality and therefore to enable more people to do them and to want to do them. but it won't necessarily be by applying technology. You mentioned some of the examples in Manchester and you're from near there and of course there's lots of focus on Manchester at the moment at least in the UK because you have a potential future prime minister having been mayor there but one of the examples that I had one of the times I was there was around these visits but also because they gave the care worker an extra 15 minutes to talk to I think it was even half an hour they said you have half an hour to really get to know and interview this person about what their situation is, what they need.

23:46And in one case, it turned out there was a kind of older woman who was in an area which she was surrounded by people who weren't from her own age. They were sort of, you know, young professionals who were never there during the day. So she felt kind of lonely. But when they sort of talked to people in the community, they realized they were all people who struggled to have their packages delivered because they were always out during the day. And so she became this person who was delivering people's packages, But then when people came around, they would have a chat and have a cup of tea and whatever else.