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

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

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Speaker 1: Bloomberg Audio Studios, podcasts, radio news.
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Speaker 2: I'm Stephanie Flanders, head of Government and Economics at Bloomberg,
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Speaker 2: and this is Trumpnomics, the podcast that looks broadly at
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Speaker 2: the economic world of Donald Trump, how he's shaking up
0:26
Speaker 2: the global economy and what on earth is going to
0:28
Speaker 2: happen next. And we do talk a lot about AI
0:31
Speaker 2: on this podcast. In fact, on or off this show,
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Speaker 2: I'm finding most conversations end up there sooner or later.
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Speaker 2: More often than not, the focus is on how many
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Speaker 2: jobs AI will replace and how quickly, also who the
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Speaker 2: winners and losers will be. But there's another worry about
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Speaker 2: AI that I'm hearing more and more and is vividly
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Speaker 2: described in a new book by the always brilliant FT
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Speaker 2: columnist Sarah O'Connor. It's the fear that not so much
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Speaker 2: that AI will put humans out of work, but it
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Speaker 2: will make people's jobs worse than they were before, even
1:03
Speaker 2: when they were pretty bad jobs to begin with. What
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Speaker 2: if by making our jobs much easier they actually take
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Speaker 2: away everything that made them interesting or rewarding. The book
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Speaker 2: I'm talking about is we Are Not Machines. The Fight
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Speaker 2: for the Future of Work, and Sarah is with me, Sarah.
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Speaker 2: Welcome to Trump and Norman. Thank you for having me,
1:26
Speaker 2: and thank you for your columns which I'm often sending
1:29
Speaker 2: to the team, And thank you for your book, which
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Speaker 2: I think does introduce, as often you do with your columns,
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Speaker 2: a whole different element. Your title isn't exactly employment correspondent,
1:38
Speaker 2: but I wonder whether it should be more kind of
1:40
Speaker 2: humanity correspondent these days. That was kind of the sense
1:43
Speaker 2: I got from the book. You were focused on the
1:45
Speaker 2: human part of jobs and what might happen and in
1:49
Speaker 2: some cases, as you describe, has happened when robots take over.
1:53
Speaker 1: Yeah, that's right. I think the reason I wrote the
1:55
Speaker 1: book was that I was becoming a bit frustrated with
1:57
Speaker 1: the sort of the lack of humanity somehow in this
1:59
Speaker 1: discussion that even though it is all about us, and
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Speaker 1: indeed we humans are the ones who've invented AI, it
2:04
Speaker 1: seemed as if the technology was being portrayed as the
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Speaker 1: protagonist here, and that much of the debate was being
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Speaker 1: driven either by the chief executives of those big technology
2:12
Speaker 1: companies and they're big predictions, the utopian ones and the
2:15
Speaker 1: dystopian ones, or by economists who have been doing lots
2:19
Speaker 1: of very careful studies, as you say, trying to map,
2:22
Speaker 1: you know, what are the particular abilities of these new
2:25
Speaker 1: AI systems. And if we break apart every single job
2:29
Speaker 1: in the economy into its constituent tasks, then can we
2:32
Speaker 1: work out which jobs are most exposed. I think that's
2:34
Speaker 1: fine as far as it goes, but it doesn't really
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Speaker 1: get to the heart of things, which is how might
2:39
Speaker 1: it actually feel to be in the position of someone
2:42
Speaker 1: who is suddenly presented with this new technology? How does
2:46
Speaker 1: it actually change work on the ground. And I'm the
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Speaker 1: sort of journalist who likes to get out of the office,
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Speaker 1: get my notebook, get my company boots on, and go
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Speaker 1: and meet people. So that's what I decided to do.
2:55
Speaker 1: So rather than the spreadsheet approach, I wanted to take
2:58
Speaker 1: the shoe leather approach. Here went to meet software developers, translators.
3:03
Speaker 1: I went down a deep mine in Sweden which is
3:06
Speaker 1: now falling with kind of autonomous vehicles, a brand new
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Speaker 1: Amazon warehouse which is also full of robots, and I
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Speaker 1: just wanted to find out how is this actually going
3:13
Speaker 1: and to talk to the people that it's actually happening too.
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Speaker 2: And the big picture was you say that you have
3:19
Speaker 2: ended up more optimistic than at the start. I don't
3:22
Speaker 2: know whether everyone reading the book will feel more optimistic,
3:25
Speaker 2: but what were you gloomy about at the beginning and
3:27
Speaker 2: how did you change your view?
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Speaker 1: I feel like I've been on a real journey with
3:31
Speaker 1: this one. I mean I used to be a real
3:32
Speaker 1: techno optimist before I even started the book. I basically
3:36
Speaker 1: because I've worked at the ETT for a long time.
3:38
Speaker 1: I've written a lot about the world of work over
3:40
Speaker 1: the last decade decade and a half, and I felt
3:43
Speaker 1: like over that time, I'd talked to a lot of
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Speaker 1: people who were in jobs which weren't really that good,
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Speaker 1: you know, quite boring, quite repetitive, quite physically arduous. Sometimes
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Speaker 1: I wrote about the first generation of Amazon warehouses, where
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Speaker 1: people were walking around for ten or fifteen miles a day,
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Speaker 1: paced by algorithms, tracked by algorithms. They're getting blisters on
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Speaker 1: their feet. And I used to think, like, you know,
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Speaker 1: this is exactly why we need more automation. You know,
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Speaker 1: we should be automating away bad jobs. We should be
4:10
Speaker 1: automating away the kind of the dull, the dirty, the
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Speaker 1: dangerous stuff, and so you know, bring them on and then,
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Speaker 1: of course, the robots did arrive, and so did generative AI,
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Speaker 1: and that started to impact all kinds of white collar
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Speaker 1: jobs as well. What I started to realize from talking
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Speaker 1: to people was that it wasn't necessarily happening the way
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Speaker 1: I thought it was, as you said in the introduction,
4:33
Speaker 1: rather than being a kind of force for liberation in
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Speaker 1: some workplaces and in some professions, it was sort of
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Speaker 1: crunching people into systems that were now kind of paced
4:41
Speaker 1: by machines, and also where people were kind of having
4:44
Speaker 1: to plug the inadequacies of the machines because they're not
4:48
Speaker 1: necessarily capable of doing everything that a person can do,
4:51
Speaker 1: but they can do certain things. And then you're starting
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Speaker 1: to see jobs being redesigned around those sorts of strengths
4:57
Speaker 1: and weaknesses in which the humans are now sort of
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Speaker 1: in but they're not necessarily in the loop in a
5:02
Speaker 1: way that's particularly enjoyable and sometimes was sort of more intense.
5:06
Speaker 1: So that made me quite depressed about the future. In
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Speaker 1: the end, the reason that I felt more optimistic was
5:11
Speaker 1: because I realized, through the course of talking to all
5:14
Speaker 1: these people that the idea that this is an inevitability,
5:17
Speaker 1: that this is just sort of coming at us and
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Speaker 1: that some people will sink and some people will swim,
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Speaker 1: or as you say, you know, they'll be winners and losers.
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Speaker 1: I realized that that's not actually the case. None of
5:27
Speaker 1: the people I met could be sort of slotted into
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Speaker 1: a winner or a loser category and an economist's spreadsheet.
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Speaker 1: They were all responding to this new technology in various ways.
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Speaker 1: Some were trying to take advantage of what was useful
5:39
Speaker 1: about it. Others were sort of pivoting away from risks.
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Speaker 1: Some people had just decided to leave their profession altogether
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Speaker 1: and to create something new for themselves. And it made
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Speaker 1: me think that actually we all have more agency in
5:51
Speaker 1: this than we're sometimes giving ourselves credit for, and that
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Speaker 1: fundamentally people are unbelievably adaptable and they will find their
5:59
Speaker 1: ways through, but that we need to all kind of
6:01
Speaker 1: be aware that this is a story in which we're
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Speaker 1: the protagonists rather than the technologies.
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Speaker 2: There's so much there, and I want to get quite
6:08
Speaker 2: a lot of it. There's a piece of it that
6:10
Speaker 2: I thought that you start talking about because you focus
6:13
Speaker 2: on what happened to translation and sort of the creativity
6:17
Speaker 2: involved in translating subtitles, which I think if anyone's actually
6:21
Speaker 2: been a translator or knows a translator, they know there
6:23
Speaker 2: is a real art to it. There's a sort of
6:26
Speaker 2: classic model, which is the sort of white collar version
6:30
Speaker 2: of what you were describing. You know, you have the
6:33
Speaker 2: sort of drudgery, and the drudgery is taken away and
6:36
Speaker 2: you're just making someone's job easier. The version of that
6:39
Speaker 2: in white collar work, which also applies to kind of
6:40
Speaker 2: editors and journalists, is the AI does eighty percent of
6:44
Speaker 2: the job and then the human gets to spend less
6:47
Speaker 2: time just sort of tweaking it, making a bit better.
6:51
Speaker 2: And I suspect a lot of newsrooms and anywhere that
6:55
Speaker 2: is involved in translation and other things. That's been the
6:57
Speaker 2: way they've introduced AI, and they've said, isn't it nice
7:00
Speaker 2: for these people? They don't have to do like the
7:02
Speaker 2: first nine yards, They just perfect it and you kind
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Speaker 2: of capture that that actually takes away most of what
7:10
Speaker 2: was rewarding about that job. It really made me think
7:13
Speaker 2: about it.
7:14
Speaker 1: Yeah, I mean, one of the things that were so
7:16
Speaker 1: lovely about doing the reporting for the book is talking
7:19
Speaker 1: to people in depth about what they actually do. And
7:21
Speaker 1: it's only when you do that that you realize how
7:24
Speaker 1: much kind of care and craft and complicated sort of
7:27
Speaker 1: knowledge and skill goes into all kinds of things that
7:30
Speaker 1: we often take for granted. So I quite often watch
7:32
Speaker 1: TV with the subtitles on it, even in English, just
7:34
Speaker 1: because I don't know why my attention span is obviously collapsing.
7:38
Speaker 1: But when I talk to these translators of subtitles, they
7:40
Speaker 1: talked about what a remarkably creative and enjoyable job it is,
7:45
Speaker 1: particularly if you're watching a TV show in English and
7:48
Speaker 1: you have to translate it into say, check, which is
7:50
Speaker 1: what one of my interviews have to do. You have
7:53
Speaker 1: to make so many decisions, you know, like in English,
7:56
Speaker 1: there's just one way of saying you, but actually in
7:59
Speaker 1: lots of other languages, particularly European languages, there's the formal
8:02
Speaker 1: you and there's the informal you, And so you, as
8:04
Speaker 1: the translator, have to decide in any given scene, would
8:07
Speaker 1: these people be talking in the informal or the formal
8:10
Speaker 1: and sometimes that might change as their relationship sort of shifts.
8:14
Speaker 1: And those are the sorts of decisions that you have
8:16
Speaker 1: to make. And then there's like how do you translate
8:18
Speaker 1: a joke that might play really well on the West
8:21
Speaker 1: coast of America, so that it still is funny for
8:24
Speaker 1: people in the Czech Republic, you know, and all of
8:25
Speaker 1: those things are actually a really great creative challenge.
8:28
Speaker 2: But you have that great example from Asterisks of Obelisks's
8:32
Speaker 2: dog is called Dogmatics, but in French it's Ed Fichs,
8:37
Speaker 2: which I just is a fantastic example exactly.
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Speaker 1: Yeah, so Dave Fichs was the original name of the dog,
8:42
Speaker 1: and then the English translators came up with Dogmatics, which
8:45
Speaker 1: is even a better pun. Right. It was so fun
8:48
Speaker 1: to talk to those translators about what they love about
8:50
Speaker 1: their job. But yeah, what's changing for them isn't necessarily
8:52
Speaker 1: that they're being completely cut out of the equation, because
8:57
Speaker 1: these subtitles are still not kind of perfect if they're
8:59
Speaker 1: done by miss but as you say, they're being left
9:01
Speaker 1: to They call it now machine translation post editing, so
9:05
Speaker 1: you're given the machine translation that you're expected to tidy
9:07
Speaker 1: it up to check that it's accurate. And what they
9:09
Speaker 1: said was that actually this is no longer creative, and
9:13
Speaker 1: also it's cognitively quite challenging actually because you're sort of
9:15
Speaker 1: checking one thing against the other. But it's lost the
9:19
Speaker 1: sort of the joy and the meaning and the pleasure.
9:22
Speaker 1: And also they're being paid half the price, and so
9:24
Speaker 1: they have to do it twice the pace, and so
9:26
Speaker 1: there's been a sort of intensification of their work as well,
9:29
Speaker 1: and so some of them said that it felt a
9:31
Speaker 1: bit more like being on a production line compared to
9:34
Speaker 1: what it used to be. I mean, I would say
9:36
Speaker 1: to people who are starting to feel quite depressed. I
9:38
Speaker 1: did also go and visit some much more interesting, optimistic
9:42
Speaker 1: workplaces where I think AI and other kinds of automation
9:46
Speaker 1: technologies are sort of living up to that promise that
9:49
Speaker 1: we all had, which was that it would free us
9:51
Speaker 1: up to be more human. And then the key question,
9:53
Speaker 1: I think is like, what do you do with that.
9:56
Speaker 1: I didn't really want to write a book that concludes, well,
9:59
Speaker 1: there's a opportunities and dangers. You know, there's winners and losers.
10:03
Speaker 1: I've read a lot of books like that, as I'm
10:04
Speaker 1: sure you have as well. And it's not that that's
10:06
Speaker 1: not true, but I think.
10:07
Speaker 2: You have to talk about uncertainty as well. There's the
10:09
Speaker 2: winners and losers, and it's all very answering.
10:11
Speaker 1: And it's all terribly uncertain exactly. But that's not very helpful,
10:14
Speaker 1: is it to say there'll be winners and losers. The
10:16
Speaker 1: kind of more difficult question is the next one, which is, well,
10:19
Speaker 1: what makes the difference? Why are some people winning and
10:22
Speaker 1: why are some people losing? Why is this exact same
10:25
Speaker 1: set of technologies playing out so differently in different people's work.
10:29
Speaker 1: And I think unless you try and challenge yourself to
10:31
Speaker 1: ask that question, we're not really going to get much
10:33
Speaker 1: further in terms of shaping this the way we would
10:35
Speaker 1: quite like it to go.
10:36
Speaker 2: And I think also you get a little bit into
10:40
Speaker 2: even when someone might be a winner in economic terms
10:44
Speaker 2: because they're in a well prayed profession where they have
10:46
Speaker 2: quite a lot of autonomy over the use you mentioned that,
10:49
Speaker 2: you know, that's part of the thing is do you
10:51
Speaker 2: have control over how it's used. You're voluntarily adopting these
10:56
Speaker 2: tools and they are making you much more productive, often
10:59
Speaker 2: in sort of coding or in any of those kind
11:01
Speaker 2: of areas. But as you highlight, and you know people
11:04
Speaker 2: have written about in real time that intensification thing, people
11:08
Speaker 2: end up feeling they're working much harder. And of course
11:11
Speaker 2: we've seen that in a kind of macro sense for
11:14
Speaker 2: the last hundred years. Famously, Canes had said productivity is
11:19
Speaker 2: going to be increased, you know, quintuple in the next
11:21
Speaker 2: hundred years, and so we'll all have lots of leisure time.
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Speaker 2: Of course, it turns out, yes, the people who've become
11:26
Speaker 2: much more productive, many of them are also quite well off,
11:29
Speaker 2: but they feel like they're working harder than never and
11:32
Speaker 2: they're worried about how many hours sleep. So I think
11:34
Speaker 2: even that definition of winners and losers, once you start
11:38
Speaker 2: thinking about the quality of life and the quality of
11:41
Speaker 2: your job, it becomes a bit harder to tell which
11:44
Speaker 2: is which.
11:45
Speaker 1: Definitely, it's very much the case that you can win
11:47
Speaker 1: something and lose something at the same time, and I
11:50
Speaker 1: think that we sometimes don't notice necessarily what we're losing
11:54
Speaker 1: until it's too late, or until we sort of look
11:57
Speaker 1: back and think, huh, something about the quality of my
12:00
Speaker 1: work doesn't feel quite the same anymore, But I can't
12:02
Speaker 1: put my finger on exactly what it is. Software developers
12:05
Speaker 1: are really an interesting example because in many ways it's
12:08
Speaker 1: sort of the opposite of those translators. If anything, I
12:11
Speaker 1: think those jobs are becoming more human in the sense
12:14
Speaker 1: that there's less sitting there typing out code by hand.
12:17
Speaker 1: I mean no, no software developers that I know are
12:20
Speaker 1: writing code by hand anymore, and so what is now
12:23
Speaker 1: demanded of them is actually more kind of managerial skills,
12:26
Speaker 1: those sort of more people skills. They've got to coordinate
12:28
Speaker 1: between different AI agents and different humans, They've got to
12:31
Speaker 1: think about the big picture a bit more, and so
12:33
Speaker 1: it's starting to put a premium on those more thoughtful
12:36
Speaker 1: judgment taste, all of those sorts of things, which does
12:39
Speaker 1: sound more enjoyable, and I think a lot of software
12:41
Speaker 1: developers are enjoying it and are kind of certainly a
12:44
Speaker 1: bit intoxicated by it, But as you say, they're not
12:47
Speaker 1: sort of banking that productivity and going. You know, it's
12:51
Speaker 1: interesting that both the sort of the utopian and the
12:53
Speaker 1: dystopian narratives about AI envisit just doing less work. Right
12:57
Speaker 1: in the utopian story, we're all sitting in our hammocks
13:00
Speaker 1: and working two hours a day in writing poetry to
13:02
Speaker 1: each other. And in the dystopian story, there's like ten
13:05
Speaker 1: people who've become multi billionaires and everyone else's mass unemployed.
13:10
Speaker 1: But actually, as you say, what is seems to be
13:12
Speaker 1: happening is that we're just finding more and more stuff
13:15
Speaker 1: to do with our time, which is often the way
13:16
Speaker 1: it goes. Well.
13:18
Speaker 2: Another thing I felt was sort of in the back
13:20
Speaker 2: of my mind as I was reading the book, and
13:21
Speaker 2: it's you know, one of those tweets that went viral
13:23
Speaker 2: a couple of years ago along the lines of I
13:25
Speaker 2: wanted AI to do my laundry and dishes so I
13:27
Speaker 2: could write a novel, but it seems to be working
13:29
Speaker 2: the other way around. There is that sort of concern.
13:32
Speaker 2: I think your point is the novels are not very good.
13:36
Speaker 2: You get a lot of management consultants now saying because
13:39
Speaker 2: it's so easy to do AI written reports and research,
13:44
Speaker 2: the premium will be on interaction with humans and being
13:47
Speaker 2: able to prove that you've had humans involved using their
13:50
Speaker 2: judgments and other things. I mean, that would be great
13:53
Speaker 2: if it's true, but at least some of the examples
13:57
Speaker 2: in your book, it suggests that what's produced by AI
14:01
Speaker 2: may be objectively less good than what humans do, but
14:05
Speaker 2: it'll still end up replacing the humans.
14:08
Speaker 1: I think it's definitely the case that you can't just
14:11
Speaker 1: take what the AI can do, or what any kind
14:14
Speaker 1: of automation technology can do, compare it to what a
14:17
Speaker 1: human does, and then say, well, This is how the
14:20
Speaker 1: job will change, because so much of it depends, as
14:23
Speaker 1: you say, on what will managers be satisfied with, what
14:26
Speaker 1: will consumers be satisfied with. So it's very possible that
14:30
Speaker 1: we might accept subtitles that are objectively worse but far
14:34
Speaker 1: far cheaper. Now. Translation is actually a really good example,
14:37
Speaker 1: because it's really hard to know if you're on the
14:40
Speaker 1: receiving end of a translation whether the quality has got
14:43
Speaker 1: worse or not. The whole point is that you don't
14:44
Speaker 1: know what the original language was. And there have been
14:47
Speaker 1: some studies of the quality of subtitles, for example, over time,
14:51
Speaker 1: and since this new kind of machine translation post editing
14:53
Speaker 1: system has come in, the quality has dropped quite substantially.
14:57
Speaker 1: The language is less rich, less varied. Everything is becoming
15:00
Speaker 1: sort of a bit thinner. There's sort of odd punctuation,
15:03
Speaker 1: it's all a bit less human. But as you say,
15:06
Speaker 1: from the studios point of view, if that's much much
15:08
Speaker 1: cheaper and no one is really going to be in
15:10
Speaker 1: a position to know, then that might happen anyway, even
15:13
Speaker 1: if the humans are still superior. I'm hopeful that ultimately
15:18
Speaker 1: there will be a sort of pushback, and I think
15:19
Speaker 1: we're already starting to see that. There are a lot
15:21
Speaker 1: of people who are saying, I don't want music that
15:25
Speaker 1: is made by AI. I don't want to read a
15:28
Speaker 1: book that is written with the help of AI. And
15:30
Speaker 1: there are lots of people trying to come up with
15:32
Speaker 1: like labeling schemes, you know, made by humans, all of
15:35
Speaker 1: that sort of thing. I think the tricky thing is
15:37
Speaker 1: that the way AI integrates into people's creative process, it's
15:41
Speaker 1: quite subtle and quite messy, and there's a sort of
15:43
Speaker 1: huge gradient between how much did you use AI, whether
15:47
Speaker 1: you're a writer or a researcher, particularly, you know, if
15:50
Speaker 1: you're working in something like TV, if it's used in
15:52
Speaker 1: post production, does that count? Does that not count? And
15:55
Speaker 1: so I'm not sure that this labeling idea is going
15:57
Speaker 1: to be the thing, but it's definitely I think there
16:00
Speaker 1: is a demand from humans for stuff made by other humans,
16:04
Speaker 1: and so I'm hopeful that in some way the market
16:06
Speaker 1: and capitalism will figure out a way to associate that demand.
16:21
Speaker 2: I should say we're recording this on the sixteenth of June.
16:24
Speaker 2: I'm not entirely sure when you'll be hearing it, but
16:26
Speaker 2: one of the columns you wrote was asking why is
16:29
Speaker 2: it so controversial what the impact of the Industrial Revolution is,
16:32
Speaker 2: why after all these years, we suddenly fighting with renewed
16:36
Speaker 2: vigor about how the Industrial Revolution affected the economy in society.
16:41
Speaker 2: And the point you made there, I guess was people
16:44
Speaker 2: were looking in the wrong place when they were just
16:46
Speaker 2: looking at the numbers. And I think we've also had
16:48
Speaker 2: that actually on the show from people like Simon Johnson
16:50
Speaker 2: and Darrenis and Moglu. If you read the historians or
16:53
Speaker 2: the novelists of that nineteenth century, it was about how
16:57
Speaker 2: it changed people's lives as well as the jobs and
17:00
Speaker 2: people out of work exactly.
17:02
Speaker 1: And also if you look at the kind of fights
17:04
Speaker 1: that broke out politically and even on the streets and
17:07
Speaker 1: certainly inside workplaces during the Industrial Revolution, often they were
17:11
Speaker 1: not about real wages and whether real wages have ticked
17:14
Speaker 1: up point five percent or point seven percent. They were
17:17
Speaker 1: about things like craftsmanship, dignity, health and safety, the role
17:21
Speaker 1: of children in the workplace. You know, all of these
17:23
Speaker 1: things are actually much more fundamental to kind of who
17:26
Speaker 1: we are and how we relate to our work. And
17:28
Speaker 1: I think we're beginning to see all of those same
17:31
Speaker 1: debates now and none of them are really going to
17:34
Speaker 1: be captured in these questions about where the productivity has
17:36
Speaker 1: gone up by zero point seven or point nine in
17:38
Speaker 1: the US in the last quarter, So.
17:40
Speaker 2: You talking about the nineteenth century. It's funny because I
17:44
Speaker 2: did a story a long time ago at the BBC
17:46
Speaker 2: about how housekeepers in Las Vegas, all the hotels in
17:50
Speaker 2: Las Vegas, the people cleaning the rooms and the hotels
17:53
Speaker 2: are all unionized, which is kind of surprising, but that
17:56
Speaker 2: was from a very conscious effort by the service sector.
17:59
Speaker 2: Union leader had said to me, Look, these great jobs,
18:02
Speaker 2: the manufacturing jobs that people are now trying of fighting
18:06
Speaker 2: to keep in cars industry, for example, that have great
18:10
Speaker 2: benefits and people can have pensions and they can send
18:12
Speaker 2: their kids to college. They didn't just become out of
18:15
Speaker 2: thin air. Those jobs were terrible one hundred and twenty
18:17
Speaker 2: years ago, and it was unions that made them better.
18:20
Speaker 2: And of course, in these kind of very human centric jobs,
18:24
Speaker 2: we've tended to think that they were very hard to unionize.
18:27
Speaker 2: And this is one of the not very many examples
18:29
Speaker 2: the housekeepers in Las Vegas, is that part of the
18:32
Speaker 2: answer that these jobs that definitely won't be automated need
18:36
Speaker 2: to become sort of good blue collar jobs in the
18:39
Speaker 2: way that say, being a plumber or even working in
18:43
Speaker 2: a car factory are good jobs.
18:46
Speaker 1: Yeah, I think that would be a good outcome. I
18:48
Speaker 1: mean it's tricky, I think because if you look at
18:51
Speaker 1: something like care work, for example, and I spend a
18:53
Speaker 1: whole chapter with these amazing community nurses in the Netherlands.
18:58
Speaker 1: The reason that it's difficult to sort of raise wages
19:02
Speaker 1: in the care sector is partly because they're not unionized,
19:06
Speaker 1: but there's some other things going on there as well.
19:08
Speaker 1: One of them is that often the people who are
19:10
Speaker 1: paying for that are taxpayers in one form or another,
19:13
Speaker 1: and we have struggled to put aside enough money really
19:19
Speaker 1: to pay for good quality jobs in those sectors, partly
19:22
Speaker 1: because the economies in many places are aging and so
19:25
Speaker 1: the costs are just going up. There are lots of
19:27
Speaker 1: other kind of competing demands on taxpayers money. And then
19:31
Speaker 1: the other thing, of course, is that with manufacturing, it's
19:33
Speaker 1: definitely true that unions helped workers get a share of
19:36
Speaker 1: the pie, but also automation did enable productivity to go up,
19:39
Speaker 1: so there was a growing pie to be divided, and
19:42
Speaker 1: these very labor intensive sectors, it's not very easy to
19:47
Speaker 1: use technology to boost productivity, and some people are looking
19:50
Speaker 1: at things like the shortage of care workers and saying, well,
19:52
Speaker 1: we're going to have to put robots in here, like
19:54
Speaker 1: what else can we do? Like, you know, there's just
19:56
Speaker 1: not enough people willing to do these jobs. What was
19:59
Speaker 1: great about the is that I met in the Netherlands
20:01
Speaker 1: is actually they'd come to another solution that wasn't really
20:03
Speaker 1: about technology at all, which was more about just redesigning
20:07
Speaker 1: the job and the system entirely. So I went to
20:10
Speaker 1: an organization called Birdsorg, which some of your listeners might
20:12
Speaker 1: have heard of, where basically they run themselves. These nurses
20:16
Speaker 1: are sort of semi autonomous teams with no layers of
20:19
Speaker 1: management above. What that does is that it means that
20:22
Speaker 1: they can form kind of quite close relationships with a
20:25
Speaker 1: small ish number of clients in their local neighborhood. They
20:29
Speaker 1: actually give them less care in terms of the hours
20:32
Speaker 1: of care, but they're all highly trained and they bring
20:35
Speaker 1: in sort of local support networks to help, so that
20:38
Speaker 1: it turns out to be a cheaper way of providing care,
20:40
Speaker 1: but it's sort of better quality and that's what all
20:43
Speaker 1: of the sort of metrics suggests, and also it's cheaper,
20:46
Speaker 1: you know, for the insurance companies and the government because
20:49
Speaker 1: there aren't all these massive sort of overheads, and for
20:52
Speaker 1: the nurses themselves. They really like this way of working
20:55
Speaker 1: because they're not being treated like machines that have to
20:58
Speaker 1: rattle through all of these clients go from place to
21:00
Speaker 1: place and not form any human relationships. They're making all
21:03
Speaker 1: of the decisions themselves. They've got a lot more autonomy,
21:06
Speaker 1: and so a job that in many countries, including the UK,
21:09
Speaker 1: is a really hard job and quite thankless, becomes quite
21:13
Speaker 1: a meaningful and enjoyable job, and actually they're paid better
21:16
Speaker 1: those nurses because of all of these things about the
21:19
Speaker 1: business model. So I think there are ways to make
21:22
Speaker 1: these jobs better quality and therefore to enable more people
21:26
Speaker 1: to do them and to want to do them. But
21:28
Speaker 1: it won't necessarily be by applying technology.
21:31
Speaker 2: You mentioned some of the examples in Manchester, and you're
21:34
Speaker 2: from near there, and of course there's lots of focus
21:36
Speaker 2: on Manchester at the moment, at least in the UK,
21:38
Speaker 2: because you have a potential future Prime minister having been
21:41
Speaker 2: mayor there. But one of the examples that I had
21:44
Speaker 2: one of the times I was there was around this
21:46
Speaker 2: visits but also because they gave the care worker an
21:50
Speaker 2: extra fifteen minutes to talk to I think it was
21:54
Speaker 2: even half an hour. They said, you can you know,
21:56
Speaker 2: you have half an hour to really get to know
21:58
Speaker 2: and interview this person about what their situation is, what
22:02
Speaker 2: they need. And in one case, it turned out there
22:04
Speaker 2: was a kind of older woman who was in an
22:07
Speaker 2: area which she was surrounded by people who weren't from
22:09
Speaker 2: her own age. They were sort of, you know, young
22:11
Speaker 2: professionals who were never there during the day, so she
22:13
Speaker 2: felt kind of lonely. But when they sort of talked
22:17
Speaker 2: to people in the community, they realized they were all
22:19
Speaker 2: people who struggled to have their packages delivered because they
22:22
Speaker 2: were always out during the day. And so she became
22:24
Speaker 2: this person who was delivering people's packages. But then when
22:27
Speaker 2: people came around, they would have a chat and they
22:28
Speaker 2: have a cup of tea and whatever else, and it
22:30
Speaker 2: became and to your point, she needed fewer visits because
22:33
Speaker 2: she developed this then a network of people who for
22:37
Speaker 2: whom she was providing a service. But then they all
22:39
Speaker 2: kind of got to know her and she became part
22:40
Speaker 2: of the community. I worried a little bit when I
22:42
Speaker 2: was reading the book that certainly in most capitalist economies
22:47
Speaker 2: now there isn't the space to have that kind of
22:51
Speaker 2: policy experimentation because to your point, people would be rushing
22:55
Speaker 2: to the cheaper option, even when it's a bit crap.
22:59
Speaker 2: There will be more all pressure to do that than
23:02
Speaker 2: to find the more inventive thing that in the end
23:04
Speaker 2: might have saved you money.
23:05
Speaker 1: Yeah, I mean, in a way, that's kind of one
23:09
Speaker 1: of the hard to remind you.
23:10
Speaker 2: That you ended up more optimistic at the end.
23:11
Speaker 1: Of the One of the themes that I kept thinking
23:16
Speaker 1: about when I was writing the book was about efficiency.
23:19
Speaker 1: There's a sort of short term's way to think about efficiency,
23:22
Speaker 1: and often it involves treating systems and even people like
23:26
Speaker 1: machines and driving more and more out of them. But actually,
23:29
Speaker 1: because people are not machines and we don't work that way,
23:32
Speaker 1: that's often in the long run inefficient and causes lots
23:36
Speaker 1: of other externalities as an economist would call it, or
23:39
Speaker 1: like problems as a normal person would call it. And
23:41
Speaker 1: I think that's definitely the case with things like care. Yes,
23:45
Speaker 1: it might seem more efficient that rather than have one
23:49
Speaker 1: very skilled expert relatively expensive nurse go and visit a
23:53
Speaker 1: patient and do everything for them, whether that's helping them
23:55
Speaker 1: in the shower or whether that's tending to the wound
23:58
Speaker 1: on their knee. It might see more efficient to have
24:01
Speaker 1: a lower paid person go and do the shower and
24:03
Speaker 1: then someone with a middle skill do that middle skilled role,
24:06
Speaker 1: and then someone else do that bit. But then what
24:08
Speaker 1: happens is for the person who's receiving that care, they're
24:11
Speaker 1: seeing five or ten different people in a week, and
24:13
Speaker 1: this happens all the time in the UK, whereas actually
24:17
Speaker 1: it can be more efficient if you have one person
24:19
Speaker 1: who goes and really gets.
24:21
Speaker 2: To know them.
24:21
Speaker 1: This nurse gave me an example of a client that
24:24
Speaker 1: she had where she saw that the sandwich that she'd
24:26
Speaker 1: made for her was in the bin sort of three
24:28
Speaker 1: days in a row, and she said, if there have
24:30
Speaker 1: been different people coming in every day, they wouldn't have
24:33
Speaker 1: noticed that, or they might not have thought anything more
24:36
Speaker 1: about it. But she sort of had noticed and so
24:39
Speaker 1: she could have a conversation about it. But she said, actually,
24:42
Speaker 1: losing your appetite can be a sign that you're dying.
24:44
Speaker 1: You know, that appetite is the first thing to go.
24:46
Speaker 1: Or it could be that she hates that kind of cheese,
24:49
Speaker 1: but actually if you don't have some continuity, then you
24:52
Speaker 1: lose all of that, and fundamentally those things do matter.
24:56
Speaker 1: That's why we often end up sort of running faster
24:59
Speaker 1: and faster in these systems because we're chasing after efficiency
25:02
Speaker 1: in one way, but actually we're just sort of causing
25:05
Speaker 1: many other inefficiencies that we're not quite capable of counting.
25:08
Speaker 2: Pushback that sort of somewhat pessimistic slant on your book
25:12
Speaker 2: is just you know that you and many others, but
25:14
Speaker 2: you in particularly because you really have got your feet
25:17
Speaker 2: and hands dirty in the workplace, have been talking about
25:22
Speaker 2: the dehumanization of work for a long time, and you
25:26
Speaker 2: had gone to that Amazon factory, and you also highlighted
25:29
Speaker 2: other ways in which people's jobs had just got worse,
25:33
Speaker 2: particularly in the lower half of the income distribution. So
25:37
Speaker 2: given that it's been going this way for a long time,
25:41
Speaker 2: it does seem at least possible that AI could make
25:43
Speaker 2: some of these things better. And actually you described someone
25:46
Speaker 2: who was in a very dehumanizing job nearly killed him,
25:50
Speaker 2: and then thanks to the Internet age was able to
25:53
Speaker 2: develop a whole new business that couldn't have been a
25:57
Speaker 2: thriving business. I think ten or twenty years ago.
26:00
Speaker 1: Yeah, I mean, I think this goes back to the
26:01
Speaker 1: point that people fundamentally have more agency in this than
26:05
Speaker 1: we give them credit for, and often people don't really
26:07
Speaker 1: wait for policymakers or trade unions or tech entrepreneurs to
26:13
Speaker 1: solve their problems for them. So this guy was a
26:16
Speaker 1: truck driver and he was working around the clock, particularly
26:20
Speaker 1: during COVID during the lockdowns, obviously truck drivers were designated
26:24
Speaker 1: as key workers. There was a massive shortage of them,
26:26
Speaker 1: and so it became a really intense job. Had a
26:29
Speaker 1: very scary moment where he was sort of suffering from
26:31
Speaker 1: exhaustion and realized that he just wasn't safe to drive anymore,
26:35
Speaker 1: and so he decided he'd sort of developed this quite
26:37
Speaker 1: unusual hobby which he was doing in his spare time
26:40
Speaker 1: when he wasn't driving trucks, which was there's a website
26:42
Speaker 1: called findergrave dot com where you can go and find
26:46
Speaker 1: graves on behalf of people who might be in a
26:48
Speaker 1: different country and are trying to find their ancestors, and
26:51
Speaker 1: they'll say, hey, can anyone find this person? I think
26:53
Speaker 1: they're buried in this graveyard. So it's a bit like
26:55
Speaker 1: a treasure hunt for him. It was just nice to
26:57
Speaker 1: get out into the fresh air do something a bit different.
27:00
Speaker 1: But he decided along his travels, he realized that a
27:03
Speaker 1: lot of these graves were just looking really sad, you know, bedraggled,
27:06
Speaker 1: not cared for. And so he had a week off
27:08
Speaker 1: from work and he's set up a website and became
27:11
Speaker 1: a gravetender, and he has done phenomenally well. You know,
27:14
Speaker 1: he has amazing reviews on his Facebook page. And I
27:18
Speaker 1: went and spent a day with him in a graveyard
27:20
Speaker 1: watching him work. And he has to developed like such
27:24
Speaker 1: a kind of pride and craft in this job. He's
27:27
Speaker 1: figured out all the best tools for it. His favorite
27:29
Speaker 1: thing he takes like a before photo and an after photo,
27:32
Speaker 1: and then at night he just sits and like flicks
27:34
Speaker 1: through his photos and just feels kind of proud of
27:37
Speaker 1: what he's done. And so, yeah, he himself without the
27:41
Speaker 1: help of anyone, but certainly, as you say with I
27:44
Speaker 1: guess the help of the fact that the Internet exists,
27:46
Speaker 1: and it's.
27:47
Speaker 2: Now finegrave dot com exists, knows how you found that originally.
27:51
Speaker 1: And also, let's face it, he set up his business
27:53
Speaker 1: in a week. It's much easier to do that. And
27:55
Speaker 1: even now with these new AI tools, it will become
27:57
Speaker 1: even easier to become a one band or an entrepreneur,
28:01
Speaker 1: being able to code a website, to create marketing materials.
28:06
Speaker 1: All of that is now much much cheaper, and so,
28:08
Speaker 1: you know, I do think one of the kind of
28:10
Speaker 1: hopeful outcomes could be that a lot of people can
28:12
Speaker 1: become more entrepreneurial and just start things for themselves that
28:16
Speaker 1: they want to do.
28:16
Speaker 2: Okay, I'm determined to end an AI focused episode on
28:20
Speaker 2: an upbeat note. So, Sarah O'Connor, thank you so.
28:23
Speaker 1: Much, Thank you for having me, Thanks for listening.
28:32
Speaker 2: To Trumpnomics from Bloomberg. It was hosted by me, Stephanie Flanders,
28:35
Speaker 2: and I was joined by Sarah O'Connor, author, columnist and
28:39
Speaker 2: associate editor at The Financial Times. Trumponomics was produced by
28:42
Speaker 2: Moses Ander and Samma Sadi with help from Amy Kean,
28:46
Speaker 2: and sound design was by Blake Maples and Kelly Garry.
28:49
Speaker 2: And to help others find us, please rate and review
28:52
Speaker 2: it highly wherever you listen.