AI and the reshuffling of labor

A summer anecdote: when on holidays in France, on a sunny August day, we visited a castle garden in rural Normandy. When buying the tickets, we got the usual: “Your postcode or country of origin?” We said “Germany,” and suddenly the face of the young woman at the counter lit up. “Would you mind doing us a favor?,” she asked, while fishing a piece of paper from a big pile – it turned out to be a German version of the leaflet about the garden. “We’ve recently made those, but they need to be checked. Would you mind having a look?” “Sure,” we said, and asked for a pen to insert whatever corrections might be needed.

Quite a few, it turned out. We spend far more time in this garden than we had anticipated. But it was lovely: we took turns looking at the flowers and reading through the leaflet, helping each other out with formulations or googling the names of plants to see whether the translations were correct. All along, we were musing whether the translation had been done by a French person who had learned German the way French people learn German – or by AI. The longer we went on, the clearer it became that it was AI (as the woman at the counter later confirmed). As is often the case with AI, it got most factual statements right but could not deal with context. On a little plan that showed a “lookout point,” it had translated it as “point of view.” “We’re probably robbing a local translator of their job,” my husband said afterwards, and suddenly my heart sank. I had thought a lot about how algorithmic management might change the future of work (see some thoughts here), but I had not yet taken into account how customers might be part of the picture, too.

For us, it had been fun: almost a gamified activity that was pleasurable to do on a sunny summer morning, without any pressures. But the dynamics in this story can play out in far less benign ways: tasks that were bundle into jobs get rebundled, not only between different workers, but also by being redistributed to customers or volunteers. They end up being cheaper – but not because less work has been done.

If you’ve ever been on the receiving end of AI system, trying to deal with some problem at a bank, insurance company, or service provider, you probably know that value can not be added, but also be destroyed – at least in the sense of people’s time being massively wasted. A few days after our holidays, I stood in the queue of a postal office, and at the counter next to me a rather desperate elderly man told the – also increasingly desperate – officer about his odyssey of failed phone calls, many of which obviously with automated systems, in his attempts to get a mistake corrected. Nothing had worked, spelling mistakes had gotten in and made things worse, his battery run out when waiting endlessly in the phone line. The redistribution of work to customers can be brutal if the systems don’t work well, if people feel left alone, and if things that matter to them are at stake. Meanwhile, service workers have to deal with more cases of people who are frustrated and angry because the systems failed them.

The longer AI is around, the longer I’m waiting to see the cases where it would really bring progress to humanity, on dimensions that really matter – rather than make work worse and alienate human beings from each other. The greater ease at finding information is undeniable, but it is undermined by the lack of reliability. The reshuffling of tasks can mean that jobs that included some elements of creativity and agency – such as translation – risk becoming a matter of error-correction under increased time pressure. In countries where union exist, they struggle how to protect employees against employers’ expectation that everything can be sped up when AI gets used, failing to consider that somewhat simpler tasks in between things can also be moments that allow people to breathe.*

I’m very much hoping that my pessimism will be proven wrong, but when I look at the power relations – in workplaces, but also in many company-customer relations – under which AI gets introduced these days, I don’t see much hope. Apart from the fact that large language models have been a massive operation of expropriation, the risk is real that they will be used to survey and control ever more areas of human life. Maybe some jobs will become nicer because “boring” elements can be done faster – but overall, the number of “bad” jobs** is likely to increase, and the divide between “good” and “bad” jobs to widen, because AI can be used to instruct, but also control, employees in ever more fields of work.

I’ll therefore end with a shoutout to this excellent essay by Jelena Belic and Kritika Maheshwari, about what universities should do when called up to prepare students for an AI-driven labor market. Maybe not only critical thinking, but also strategies of resistance, need to be included in the curricula!

• An example that was given to me by a union representative was the writing of minutes of conversations between nurses and patients, which can be replaced by AI-produced transcripts.

** Or not even jobs, but gigs algorithmically managed on platforms.