Blog · Research · 7 August 2026 · 9 min read

One Fed Survey Says 1%. Challenger Says 33%. Both Are Right.

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Within roughly twenty-four hours last week, two credible institutions published numbers about AI and layoffs that point in opposite directions.

The Federal Reserve Bank of New York said firms report very few AI-driven layoffs. Challenger, Gray & Christmas said AI was cited in a third of all US job cuts announced in July, the fifth straight month it topped the list.

Both are accurate. Neither is misleading. They are measuring different quantities, and the distance between them is the most useful thing published on this subject all week — because once you can see why they diverge, you can read every AI layoff headline for the rest of the year without being pushed around by it.

The 1%

On 5 August 2026 the New York Fed publishedAI's Impact on Labor and Hiring, the first post in a new commentary series by Kartik B. Athreya, the director of research and head of the Research and Statistics Group at the Bank. His summary of the evidence is that "the evidence on adoption suggests more muted effects on labor and hiring so far, but anxiety remains just the same."

The underlying numbers come from the Bank'sAugust 2025 regional business surveys, published 4 September 2025 by Jaison R. Abel, Richard Deitz, Natalia Emanuel, Ben Hyman and Nick Montalbano. On layoffs the finding is blunt: "Only 1 percent of service firms reported letting go of workers in response to AI over the past six months, a decrease from 10 percent who said they had laid off workers due to AI in last year's survey." No manufacturers reported layoffs in either year, or expected any.

Adoption over the same period went the other way. Service firms using AI rose from 25% to 40%, manufacturers from 16% to 26%.

The survey found more going on underneath than the layoff line suggests. About 12% of service firms using AI had hired fewer workers because of it. Meanwhile 11% of service firms and 7% of manufacturers had hired more workers because of it. Just over a third of service firms reported retraining workers in response to AI, and nearly half expected to over the following six months. And 13% of service firms said they anticipated AI-related layoffs in the next six months, against the 1% who had actually made them.

The authors attach their own limit, and it is a real one: these results "apply only to the 25 to 40 percent of firms that are using it. Thus, any implied economywide labor market impacts are likely to be relatively modest."

The 33%

On 6 August 2026 Challenger, Gray & Christmas publishedits July 2026 job cut report. US employers announced 33,429 cuts in July, down 27% from June's 45,849 and down 46% from July 2025's 62,075. AI led all stated reasons with 10,970 cuts, or 33% of the month.

Year to date, AI has been cited in 112,713 announcements out of 477,033 total cuts, about 24%. Since 2023, when Challenger began tracking AI as a distinct reason, the cumulative figure is 184,538.

Sitting immediately beside those numbers, and almost entirely absent from the coverage: announced hiring plans for July came to 16,095, up 47% from June, with 107,500 year to date. As Challenger put it, AI is shifting the labour market rather than dismantling it.

Four reasons the two numbers cannot be compared

They ask different questions. The Fed asks employers whether AI drove a decision they made. Challenger records the reason an employer gave when announcing cuts publicly. One is a private answer on a survey form, the other is a public statement with an audience.

They count different units. The Fed's 1% is a share of firms. Challenger's 33% is a share of announced job cuts. A single large employer citing AI can move the second figure substantially while barely touching the first.

They cover different periods. This one gets missed constantly, including in coverage of the Fed post itself. The 1% comes from the August 2025 survey wave. The 33% is July 2026. They are roughly a year apart, and the Fed's own series shows this measure moving fast — it was 10% the year before it was 1%.

They cover different geographies. The Fed surveys the New York-Northern New Jersey region. Challenger counts announcements across the United States.

The sentence that explains the gap

The most valuable line in either document is not a statistic. It comes from Andy Challenger, workplace expert and chief revenue officer at the firm that produces the count:

That is the firm publishing the most detailed AI layoff count in the US economy telling you, in its own report, that the variable it measures is partly a communications decision. It is unusual and it is creditable, and it should change how the number is read — not to zero, but away from "this is how many jobs AI took."

Challenger adds a second caveat pointing the other way: "As regulations start to take shape, companies will be even more careful in their announcements, which would make tracking the impact of AI on jobs more opaque." If the incentive to name AI reverses, the series bends again — and that movement would tell you about disclosure conditions, not about automation.

How a cut becomes an AI cut

Challenger operates two buckets, and the distinction is genuinely useful to borrow.

The first is confirmed AI, where a company's own announcement names it. Visa's announced 7% workforce reduction, which the company attributed to an efficiency push involving AI, is that kind of case.

The second is "Technological Update (possibly AI)", used when a company cites new technology without tying it to AI. Challenger tracked 20,219 cuts under that heading in 2025. The existence of that bucket means the confirmed count is a floor with a judgement call sitting under it, not a clean measurement.

The report's own worked example shows how contested that boundary is. At a Bronx hospital system, 12 utilization review nursing positions were eliminated after the adoption of Datavant software. The New York State Nurses Association filed a class-action grievance and issued a press release saying the hospital was replacing human workers with AI software. Hospital leaders called that characterisation misleading. Challenger declines to resolve it, filing the cuts under "Technological Update (possibly AI)".

That is one case, unresolved, with both parties on the record. We are not going to tell you who is right, because the report that raised it does not, and because the point stands either way: the label on a layoff is frequently contested by the people closest to it.

What the legal filings say

There is a third source people reach for, and it is worth knowing what it does not contain.

When a large US employer lays off staff, it files a WARN notice, and states publish those in databases such asCalifornia's WARN report. We pulled California's live file during this run. It records the employer, county, address, industry code, whether the event is a layoff or a closure, the number of employees, and the notice, processed and effective dates.

There is no field for a reason. Not a blank one — the schema does not have the column. Every causal attribution attached to a layoff in the news comes from a company statement, a reporter's sourcing, or an inference. None of it comes from the filing. That is worth holding on to the next time a headline treats WARN data as evidence about AI in either direction.

What these two sources do not establish

Neither measures automation. The Fed measures self-reported attribution. Challenger measures stated reasons in announcements. Nobody in this story audited a workflow to establish what a machine now does that a person used to do.

Announcements are not completed layoffs. Challenger's figures are announced intentions, compiled from company statements, WARN notices and media reports. Announcements get revised, phased and sometimes reversed.

The Fed's sample size is not published on the page. We looked. The regional surveys' respondent count for the August 2025 AI questions is not stated in the post, so we cannot give you one, and neither should anyone else without citing a methodology page that does.

A falling layoff share is not an all-clear. The same survey shows 12% of AI-using service firms hiring fewer people because of AI, and nearly a quarter of prospective adopters expecting to. Reduced hiring does not appear in a layoff statistic at all, and it is the most plausible place for a real effect to be sitting.

What to do with this

If you are an employee: the more informative number in the Fed data is not the 1%. It is the 12% hiring fewer people and the just-over-a-third retraining. Those describe a labour market that is changing what it asks of people faster than it is removing them, which is both less frightening and more demanding than the headline version.

If you manage people: notice that 13% anticipated layoffs while 1% had made them, and that the anticipation figure has been the volatile one. Planning off anticipation produces cuts that the operational reality did not require. Athreya's framing is the useful one here: the exposure is specialisation, not AI in the abstract, because "our specialization leaves us vulnerable to a sudden collapse in the value of the only skills we may have."

If you are citing either number: say which one you mean and what it counts. If you write that AI caused 10,970 job losses in July, you have made a claim Challenger explicitly does not make. If you write that only 1% of firms cut jobs for AI, you are quoting a year-old regional survey as though it were current national data. Both errors are common and both are avoidable in one extra sentence.

The honest summary is short. Employers reporting AI-driven layoffs remain a small minority. Employers publicly citing AI when they announce cuts have become a large and growing share. The first is a fact about operations. The second is a fact about disclosure. Watching them diverge is considerably more informative than picking whichever supports the point you already wanted to make.