Jobs survive, pay and purpose don’t: The quiet risk of workplace AI

Interesting and a cause for further consideration of implications:

…As sociologists of work, we see several reasons for concern, even if fears of immediate and widespread AI displacement are potentially overblown. Claims of a “white-collar bloodbath” and “job apocalypse” – that is, “something alarming happening to the job market” – certainly make for attention-grabbing headlines (and, at this stage of the purported advancements, they probably should).

Erosion before displacement

If predictions about future AI capabilities are even partly correct, we may be seeing only the early contours of what lies ahead. Already, signs are emerging that the conditions and perceived value of some white-collar work is shifting. At Amazon, software engineers report that AI tools are accelerating production timelines while reducing time for thoughtful coding and increasing output expectations. According to New York Times reporting, many now spend more time reviewing and debugging machine-generated code than writing their own. The work remains, but its character is changing – less autonomous, more pressure, and arguably less fulfilling.

This shift in work quality may be creating broader economic ripples. Barclays economists have found that workers in AI-exposed roles are experiencing measurably slower wage growth – nearly three-quarters of a percentage point less per year for every 10-point increase in AI exposure. Employers may already be recalibrating the value of these positions, even as hiring continues.

Uneven impacts

Different forms of white-collar work may face vastly different futures under AI, depending on professional autonomy and control over the technology. Consider radiologists, initially seen as vulnerable given AI’s strength in image analysis. Yet, the profession has grown, with AI enabling faster analysis rather than replacement. Crucially, radiologists retain control. They make final diagnoses, communicate with patients and carry legal responsibility. Here, AI complements human expertise in what economists refer to as Jevons Paradox – where technological efficiency increases demand by making services cheaper and more accessible.

Medical transcription offers a more cautionary tale. As AI speech-to-text tools improve, transcriptionists have shifted from producing reports to editing and error detection. In theory, this sounds like higher-skilled oversight work. In practice, it often means scanning AI output under time pressure and reduced job discretion. While jobs such as this one still exist, their perceived value is diminishing. The U.S. Bureau of Labor Statistics projects a 5 per cent employment decline between 2023 and 2033 – and given the rapid improvement in transcription models, that estimate may prove conservative.

Adaptation isn’t necessarily promotion

AI will undoubtedly create new roles and opportunities, particularly where human judgment remains essential. But we shouldn’t assume this future will preserve job quality. The story of retail banking offers a sobering lesson: automation first increased the number of teller jobs – but didn’t raise pay. Ultimately, tellers weren’t replaced by machines but by digital banking, shifting many to call centre jobs with less autonomy and lower wages. Even in the absence of widespread job displacement, AI may follow a similar pattern –reshaping many jobs in ways that reduce discretion, increase surveillance and erode its overall value.

There remains considerable debate about how disruptive AI will be. But amid that uncertainty lies a risk of public complacency – or even disengagement from the issue. As Canadians, we need a sustained and open conversation about how these workplace changes are unfolding and where they might lead.

Paul Glavin is an associate professor of sociology at McMaster University. Scott Schieman is professor of sociology and Canada Research Chair at the University of Toronto. Alexander Wilson is a graduate student in sociology at the University of Toronto.

Source: Jobs survive, pay and purpose don’t: The quiet risk of workplace AI