“Exactly one robot rarely replaces exactly three jobs”

Professor Pfeiffer, are robots and algorithms taking away our jobs?

Professor Pfeiffer, are robots and algorithms taking away our jobs?

Sabine Pfeiffer: We always act as if it’s all about technology versus humanity. But it’s really a matter of human choice whether we want to use technology primarily to reduce human labour. Whether algorithms support us or turn us into appendages, or whether robots become our colleagues or competitors, is not something that’s built into the technology itself. Those are simply consequences that flow from operational decisions. The very technology we’re talking about right now is highly shapeable. It can be used in very different ways.

Can you give an example?

Pfeiffer: Take augmented reality glasses, for example: one can use the same technology either to provide meaningful information to support a highly skilled worker assembling complex products in a large number of variants – or to dictate every single hand movement of a low-skilled worker. In the first case, we keep both the organisation and its employees capable of innovation, while in the other we don’t.

So is the impression correct that it’s mainly the well-trained, highly skilled workers who benefit from digitalisation?

Pfeiffer: As a matter of fact, the use of industrial robotics has made the tasks for skilled workers in maintenance or plant management more demanding. In the actual production process, the role of low-skilled workers has been diminished. Skilled labour is the key resource for implementing Industry 4.0. If you want to assess who is affected, and how, you need to take a look at the entire process and not just the individual job.

Is traditional training still sufficient to implement Industry 4.0? After all, artificial intelligence is a highly complex subject.

Pfeiffer: Not every employee needs to be able to write an AI algorithm. But in the future, technical professions will involve more AI and machine learning than they have in the past. A mechanical engineer already has a lot to do with software. So learning another programming language like Python is not a huge step. The question is rather: how can people who don’t have a technical background, but who work in positions in which they make a lot of decisions, learn about the potential – but also the limits – of the new algorithms?

One proposal for lifelong learning is to give everyone an individual lifetime account. The costs of further training, longer periods of unemployment or professional reorientation could be paid from this account. What’s your assessment of this idea?

Pfeiffer: In itself, it’s good to create more flexibility for education. The key question is who pays into the model and who benefits from it. Of course, employers must play a role when it comes to financing these models. It makes little sense for those who are in the system to pay more and more social welfare benefits for those who aren’t working at the time. So we need to think more about how things are distributed between those at the top and those at the bottom. Regardless of funding, there’s also the question of whether everyone can participate in the first place. Well-qualified people find it much easier to accept models that provide for more free time, but which possibly lead to income reductions over a longer period. These models must be designed in such a way that, above all, they reach those who are most in need of further training.

The most prominent proposal in the context of digitalisation is an unconditional basic income. Is that the solution?

Pfeiffer: First of all, I find it amazing that it’s such a hot topic these days. Our labour market is humming; we’ve extended people’s working lives by raising the retirement age; we’ve got more people in work than ever before. Much more important than the question of what a society without gainful employment would look like in the world of tomorrow are the challenges we’re facing in the here and now, namely, how can we shape the digital transformation in such a way that we create a working world for tomorrow that is good and capable of innovation?

Another idea for achieving redistribution of wealth is a robot tax. Is that even possible?

Pfeiffer: Companies that make significantly higher profits than others must be taxed more heavily. I don’t think it’s practical to pin that on robots, though. It’s rarely the case that exactly one robot replaces three jobs. Digitalisation often changes entire work processes. In the end, no one can say at what point exactly which activity has been eliminated. In addition, this would have to be ascertained on a permanent basis, since the change is ongoing. The important thing we’ve learned is that we have a strong polarisation of profits and wealth in fewer and fewer hands. This imbalance is neither good for our society nor for our small and medium-sized businesses. So rethinking the distribution of wealth is what should really be on the agenda right now. The robot tax, however, is not a viable response.

We’re seeing record employment numbers and have had productivity gains for decades. Why are we actually still working so much? Can’t we just work less?

Pfeiffer: Great question; I’ve been asking myself the same. It’s especially in those highly qualified fields that people are under a lot of psychological strain. Carers are also under enormous stress. We’re productive and we’re generating an ever-increasing national income. The logical answer would be to reduce working hours. The last time we talked about a reduction in working hours at full wage compensation was in the 1980s. Since then, the issue has never really cropped up again. We’d have many good reasons for talking about this. I’d definitely like to see a broad debate on this issue.