Corporate-speak is masking the impact of AI on job losses-here's why that's a problem

Corporate managements are using the term and other euphemisms for job cuts or hiring freezes far more in the AI age. As tech-enabled headcount reductions increasingly draw public ire and political pushback, company leaders are leaning on vague talking points about “efficiency,” “automation” and, yes, “delayering.”

My concern is that these terms and phrases confirm our poor visibility into the biggest economic sea change to hit the labour market in generations. We have only a hazy notion of how AI is affecting employment, and we can’t say to what degree, much less pinpoint the regions, industries and job types that are being hardest hit. AI job cuts aren’t even uniformly labelled, so how can we possibly monitor them?

The takeaway is that lawmakers and policymakers must build better AI job-loss monitoring tools before the avalanche arrives, so they can respond appropriately. That would entail deploying appropriate assistance to affected workers and their families, including retraining programmes and support to relocate for jobs.

If their response is slow or insufficient, the US is at risk of suffering a repeat of the China shock of the 2000s, when increased global trade displaced workers in America’s manufacturing cities and towns, fuelling a backlash that’s still reverberating.

To be sure, the labour market is in fine shape overall, with a government report earlier this month showing that unemployment dipped to 4.1% in October, from 4.2% in September.

But subtle signs of AI’s emerging impact are too important to ignore. Companies have cited AI in about 121,400 job cut announcements thus far in 2026, up from some 54,800 in all of 2025, according to outplacement firm Challenger, Gray & Christmas, which has acknowledged the uncertainty around the AI-tracking exercise.

And those are the layoffs Challenger is aware of; many others fall under the radar because they don’t meet statutory triggers for mass layoff disclosures and voluntary reporting remains spotty.

Even a firm’s own employees may at times publicly disagree with employers about AI’s role in layoffs. Andy Challenger, chief revenue officer for the outplacement firm, predicted earlier this year that companies will become “even more careful in their announcements, which would make tracking the impact of AI on jobs more opaque.”

Another issue is that headcount reductions often happen through attrition now. The latest Bureau of Labor Statistics (BLS) data put the layoff rate at 1% of total employment, which is near the lower end of history and compares with a higher quits rate of 2.1%.

Wells Fargo CEO Charles Scharf said in 2025 that the firm would “use attrition as our friend.” Its third-quarter report showed the bank’s headcount was down about 8% from a year earlier.

Similarly, Bank of America CEO Brian Moynihan said in September that while headcount was falling, he’s “not laying off anybody.” “All we do is just manage the hiring carefully,” he added.

The Trump administration and the newly elected Democrats should address these blind spots by improving government data on AI. The starting point is increasing the BLS’s budget. More funding won’t automatically solve problems around low survey response rates, but it will produce far better outcomes than the government choosing to keep its head in the sand.

With adequate resources, the BLS and the Census Bureau could potentially, for example, expand the biennial Displaced Worker Supplement (DWS) report and increase its frequency. The DWS is a detailed snapshot of jobs lost that economists use to analyse AI’s impact on the labour market.

According to Goldman Sachs economist Pierfrancesco Mei, the latest supplement released this year showed that workers displaced by AI take longer to find new jobs and are more likely to move into new industries upon re-employment. This type of data is essential for designing smart policies to mitigate the disruption.

The process of integrating AI into the economy is still in its early days and may not play out as many expect. But that’s all the more reason to institute tools to monitor developments, so that the labour market isn’t fully “automated” and “delayered” into oblivion before we fully understand what’s happening. ©Bloomberg

The author is a columnist focused on US markets and economics.