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Data Centres: Boom and Backlash
22OCT

The AI layoffs nobody is counting

4 min read
16:40UTC

A March ResumeBuilder survey found 59% of managers overstate AI in layoffs; Stanford puts the real loss at a million hires never made. Two numbers, opposite errors.

IndustryDeveloping
Key takeaway

AI's labour effect is over-attributed in press releases and under-measured in official data; the true channel is hires never made.

59% of hiring managers say they deliberately overstated artificial intelligence as the reason for their layoffs, according to a ResumeBuilder survey of 1,000 managers published in March⁠1. Only 9% said AI had actually replaced roles at their organisations. ResumeBuilder is a US job-search and CV platform; the survey asked managers directly why they had cut staff and whether AI was the genuine cause. The question turned live again this week, as the Office for National Statistics reported UK unemployment at 4.9% and the European Parliament stripped a worker AI-literacy right.

The 59% reads, on its own, as proof the job losses are exaggerated. Oxford Economics, a UK forecasting firm, puts genuine AI-driven cuts at around 4.5% of US layoffs in the first 11 months of 2025, roughly 55,000 positions against about 245,000 lost to ordinary cost pressure. Firms credit the machine for decisions the budget had already made. Sam Altman of OpenAI has acknowledged the practice, the same week his company moved toward a September listing above one trillion dollars.

The bigger number runs the other way. The Stanford Digital Economy Lab, Stanford University's economics research unit under Erik Brynjolfsson, calculates that AI is preventing roughly 950,000 to one million US hires a year. Set that against about 145,000 declared AI layoffs since 2023, per the outplacement firm Challenger, Gray & Christmas through May. Most of the loss never reaches a redundancy notice; it shows up as the requisition never opened and the graduate never called back, so a graduate entering the workforce this year faces a far tighter market than one who arrived in 2023. MIT Sloan economist Paul Osterman told Fortune in May that AI attribution often dresses up cuts that were planned anyway, and the New York Federal Reserve has found displacement signals that predate ChatGPT entirely.

Deep Analysis

In plain English

Companies have been announcing layoffs and blaming artificial intelligence, but a new survey shows most of them are exaggerating. ResumeBuilder asked 1,000 hiring managers: 59% admitted they deliberately overstated AI's role in cutting jobs, and only 9% said AI had actually replaced anyone at their company. Oxford Economics researchers found that genuine AI-driven cuts made up about 4.5% of US layoffs in 2025; roughly 55,000 jobs out of more than a million. Stanford researchers found a separate but bigger effect: AI is causing companies to post fewer new jobs than they otherwise would; perhaps 950,000 to 1 million fewer vacancies per year in the US. That second effect does not show up in layoff counts at all, which is why the official numbers feel much smaller than the anxious headlines suggest.

Deep Analysis
Root Causes

The measurement gap has three structural causes.

First, no US or UK government agency yet publishes an AI-attribution layer in official labour statistics. The ONS, BLS, and Eurostat all collect layoff data without requiring employers to identify the automation driver. This is not an oversight; there is no agreed methodology for distinguishing AI-driven from ordinary workforce reduction.

Second, Securities and Exchange Commission disclosure rules create an incentive misalignment. Firms that attribute layoffs to AI signal innovation to equity markets; firms that attribute them to cost pressure signal management failure. The ResumeBuilder finding (59% deliberate exaggeration) is the logical consequence of this incentive structure.

Third, the Stanford JOLTS analysis measures hire suppression; roles that were never posted; which is by definition invisible to layoff trackers. The 34:1 ratio between suppressed hires and declared AI layoffs is not a paradox; it reflects two separate mechanisms operating simultaneously.

What could happen next?
  • Consequence

    Without an official AI-attribution layer in BLS or ONS data, policymakers cannot distinguish cyclical from structural unemployment, risking under-investment in targeted retraining programmes.

    Medium term · Assessed
  • Risk

    SEC disclosure practice that rewards AI attribution creates a self-reinforcing narrative: even companies not using AI to cut jobs gain investor approval by saying they are, making the measurement problem worse each quarter.

    Short term · Assessed
  • Opportunity

    The ResumeBuilder survey data, if replicated at scale by an academic institution, would give plaintiffs' employment lawyers evidence to challenge AI-attributed layoffs as pretextual, potentially triggering WARN Act liability for firms that used the excuse to avoid notice obligations.

    Medium term · Reported
First Reported In

Update #14 · The AI layoffs nobody is counting

Metaintro· 20 Jun 2026
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Written with AI from Lowdown's published reporting. Not individually reviewed by an editor. Editorial standards.