Blog · Research · 8 September 2026 · 9 min read

They Predicted 32%. They Reported 14%. Now They Are Predicting 39%.

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This site spends most of its time on a particular failure: a claim about AI and jobs whose own source document counts something narrower than the claim. Here is a different one, and it is rarer. A large, named, repeated survey asked executives what AI would do to their head count, then asked them a year later what it actually did, and published both numbers next to each other.

The prediction was 32 percent. The reported outcome was 14 percent.

What the report says

McKinsey publishedThe state of AI in 2026: On the road to ROI on 25 August 2026. The relevant sentence is short: "Just 14 percent of respondents from organizations using AI report that AI contributed to an overall decline in workforce size in the past year." The report describes that as "less than half the 32 percent who, in last year's survey, expected workforce reductions over the same period."

Two things are worth fixing in place before anything else. Both figures are self-reported by respondents about their own organisations, not measured from payroll records. And "contributed to an overall decline in workforce size" is a loose standard: it is satisfied by AI being one factor among several, which if anything should bias the 14 percent upward rather than down.

The comparison is legitimate, and the report shows its working

The obvious objection to any year-over-year survey comparison is that the two samples are different people. Ask a different 1,700 executives and you get a different number, and the gap tells you about sampling rather than about the world.

McKinsey anticipates this in footnote 3, which is where the most useful sentence in the report lives:

That is the check that matters. Five hundred and fifty-two people answered both waves, and restricting the comparison to them reproduces the result. This is not a story about two different crowds giving two different answers. Within the group that answered both times, the forecast overshot.

It is worth saying plainly that this is better practice than most of what gets quoted in this field, and that it is the part of the report the coverage tends to leave out.

The number nobody is quoting is 39

The same report also asked what respondents expect for the year ahead:

Be careful with what this is. The 39 percent and the 14 percent answer different questions. One is a forward expectation about the coming year, the other is a backward report about the year that finished. They are not a contradiction and nothing here treats them as one.

The comparable pair is 32 and 39, both forecasts, one year apart. Last year's cohort predicted 32 percent and reported 14. This year's cohort, holding the same survey in the same hands, predicts 39.

McKinsey's own caption on the relevant exhibit puts it in one line: "Last year's respondents overestimated AI's impact on head count, and expected reductions in the next year are larger still."

What the survey actually is

Anyone planning to use these numbers should know what produced them. The report states its own method: "The online survey was in the field from May 4 to June 8, 2026, and garnered responses from 1,719 participants in 97 nations representing the full range of regions, industries, company sizes, functional specialties, and tenures. Thirty-six percent of respondents say they work for organizations with more than $1 billion in annual revenue. To adjust for differences in response rates, the data are weighted by the contribution of each respondent's nation to global GDP."

A survey of people at large organisations, weighted by national GDP share, with a third of respondents at billion-dollar companies, is a specific instrument. It is a good instrument for the question "what do the people running AI programmes at large firms believe and report", which is exactly the question the 32-versus-14 gap answers. It is not a measurement of the labour market.

Two independent readings, pointing the same way

Two things landed this week around the same finding, and they are worth separating from it.

InThe New Yorker on 7 September, John Cassidy reports the same pair independently: "In its 2025 survey, thirty-two per cent of respondents from organizations using A.I. said that they expected to see A.I.-related job cuts this year. In the latest survey, just fourteen per cent of respondents reported seeing actual A.I.-related job reductions." The figures match the report exactly, which is a small thing worth noting on a site that spends most of its time finding cases where they do not.

The same piece carries figures whose underlying government series it does not name, including a monthly layoffs-and-discharges average of about 1.7 million since August 2023, described as little changed from the 2010 to 2019 decade, and a labour share of income at "a historic low of 52.8 per cent." Those are reported by The New Yorker and are not traced here to an origin series, so they are attributed to the magazine and nothing is built on them.

Separately, theBudget Lab at Yale's AI labour market tracker, updated to incorporate July 2026 CPS microdata, states that its analysis "does not provide clear evidence of labor market disruption associated with AI," and that "The occupational mix is not yet changing in ways that clearly align with the introduction of AI into the workforce." That is a different method on different data reaching a compatible conclusion, which is the only kind of corroboration worth much.

What this does not mean

It does not mean nobody has lost a job to AI. A survey question answered at the level of an organisation's total head count cannot see a team that was not backfilled, a contract that was not renewed, or a role that was quietly redefined. Fourteen percent of organisations reporting an AI-attributed decline in total head count is compatible with a great deal of individual disruption underneath it.

It does not mean the forecasts were foolish. Forecasting a year of your own company's head count in the middle of a technology shift is genuinely hard, and being wrong by a factor of two on a one-year horizon is an ordinary result, not an embarrassing one.

And it does not mean AI adoption stalled. The same report finds that "Forty percent of respondents from large organizations (those with annual revenues of more than $1 billion) report scaling AI agents, up from 27 percent last year." Deployment went up. The head count effect respondents reported did not follow it at the rate they had predicted. Those two facts sit together in one document, and the interesting question is why, not which one to believe.

One reading of the 32-to-14 gap worth holding lightly: the forecasts may have been describing an intention rather than an outcome. An executive who expects AI to reduce head count and then does not reduce it has not necessarily been proven wrong about the technology. They may have been wrong about how fast their own organisation moves, which is a different and much more common error.

What would change this reading

The 2027 wave, more than anything. This year's respondents predicted 39 percent. If the 2027 report comes back with another number less than half of it, the pattern stops being an anecdote about one forecast and starts being a measured, repeated bias in how executives predict their own AI-driven head count. If it comes back near 39, the argument that the effect is arriving late rather than not at all gets considerably stronger.

Payroll-record evidence would change more than either. Every number in this post is somebody telling a surveyor what they believe happened at their own company. The Budget Lab's CPS-based tracker is the closest thing here to an external check, and it is currently reporting no clear signal, which is itself a finding with a limited shelf life.

Until then, the defensible summary is narrow. In one large, repeated, methodologically explicit survey, executives predicted AI-driven head count reductions at more than twice the rate they later reported experiencing them. The same executives are now predicting a higher rate again. That is a fact about forecasts, not a fact about the labour market, and it is worth keeping the two apart.

The McKinsey report, The New Yorker piece and the Budget Lab tracker were each fetched and read in full on 8 September 2026, and every quotation above was checked against the page it came from.