AI washing is masking an insidious labor crisis
By Catherine Thorbecke
SINGAPORE-BASED CRYPTO.COM said last week that it was cutting 12% of its workforce, the latest company among a growing cohort from Atlassian Corp. to Block, Inc. to cite artificial intelligence (AI) adoption for job losses.
But what’s lacking in these pronouncements is the evidence of how, exactly, AI is replacing workers. Comprehensive data on whether the technology is destroying jobs, lifting productivity, or reshuffling routine tasks remain patchy at best. And that vacuum is being filled with fearmongering and market-friendly spin. It is undoubtedly reshaping how people work, but for governments and business leaders to effectively react, far more data and transparency is needed.
In a world where engagement algorithms shape public speech, the loudest voices are rarely the most nuanced. Stories of white-collar bloodbaths have gone mega-viral in recent weeks — and even moved markets — despite offering little hard evidence. The narrative is potent. But the bigger danger may not be that AI is already causing a “jobpocalypse,” but that these headlines obscure how the technology is being used to quietly erode the entry level roles that train tomorrow’s workforce. Combined, so-called AI washing becomes a distraction from the harder policy work required during periods of rapid technological change.
Investors are rewarding AI washing, but they shouldn’t be fooled so easily. Recasting pandemic over-hiring and cyclical belt-tightening as innovation and efficiency may help send share prices higher in the near term, but they don’t vouch for sound fundamentals or wise management.
The layoffs narrative does not neatly fit in Asia. In Japan, one survey found that nearly 30% of 246 listed companies were increasing their workforce after adopting AI. An OECD report published last October argued AI-induced job losses may be less common in Japan than elsewhere due to chronic labor shortages driven by demographic decline and people’s tendency to stay at one company for extended periods. Japanese workers, it found, are more likely to see AI as a source of new jobs than a destroyer of them. A similar tension is emerging in South Korea. IMF researchers say that while about half of jobs are “exposed to AI,” the negative effects of an aging population could be mitigated through the technology’s adoption.
That is not an argument for complacency. Older workers, non-regular employees, and entry-level staff are less likely to benefit from the shift, which makes targeted training and workplace adoption programs all the more important. The real balancing act is how to use AI to ease labor shortages without allowing it to widen inequality or disrupt livelihoods. This is where Asia can show leadership.
The gap between what is known and what is being claimed is already too wide. Breathless predictions from industry leaders about how all white-collar work will be automated within 18 months aren’t analysis, they’re marketing. And they’re also bad for the technology itself. Public trust in AI is already fragile. Tech leaders who want it widely adopted should stop selling every restructuring as proof that humans are becoming obsolete and machines more powerful.
Even if the overall scale of layoffs is being overstated, early signals suggest the pain may fall hardest on entry-level workers. It may make short-term business sense to use a model for tasks once handed to an intern or junior employee. But it’s a shortsighted bargain.
One of AI’s biggest limitations is still hallucinations. Human oversight remains essential for its use in businesses, hospitals, and broader society. But people cannot check a machine’s output if they’ve never developed the expertise themselves. Companies risk hollowing out the apprenticeship layer where knowledge workers learn by doing, badly and repeatedly at first, and under supervision.
In my line of work, for example, an experienced editor can immediately spot the cliches, repetitive phrases, or dramatic but inconsistent metaphors that AI tools love to sprinkle into prose. An engineer who has reviewed hundreds of design drawings can spot when an overconfident computer system’s elegant solution will fail in the real world.
That should worry governments as much as employers — especially in China and across Southeast Asia where a Gen Z job crisis was already brewing. Failing to invest in the next generation of talent will backfire. Large numbers of unemployed, educated young people do not make for a stable society. Companies, universities, and policymakers need to do more to protect these training paths and junior roles.
Lawmakers trying to tackle AI’s impact on jobs should start by requiring companies that publicly cite AI as a reason for layoffs to disclose what that actually means: where the technology was utilized, what work changed, what productivity gains were measured, and how many jobs were truly eliminated as a result. Only then can governments build sensible responses, from stronger social safety nets to targeted training and reskilling programs.
AI is already reshaping the labor market. But the bigger danger may not be today’s headline-hogging layoffs, it’s the slow hollowing out of the career ladder itself.
BLOOMBERG OPINION
