🔍 Read the full analysis: What US Productivity Data Can—and Can’t—Tell Us About AI on ThorstenMeyerAI.com
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TL;DR
US business output has risen much faster than hours worked since late 2022, while productivity growth has run above its 2007–2019 pace. The figures do not prove AI caused the increase: adoption remains limited, and other economic forces could produce a similar pattern.
US output is growing much faster than hours worked, but the latest productivity and employment figures do not show whether artificial intelligence is responsible. An analysis published November 1 by ThorstenMeyerAI.com points to stronger productivity since 2022 alongside weak hiring in some industries, while noting that only about one in four US businesses reported using AI in early October.
The Bureau of Economic Analysis estimated that real US GDP grew at a 3.4% annual rate in the third quarter of 2026, up from 2.2% in the second quarter. That measure covers the overall economy. For the narrower nonfarm business sector, the Bureau of Labor Statistics reported that output rose 2.5% over the year to the second quarter, while hours worked increased 0.2%. Productivity, calculated as output per hour, grew 2.2% over that period.
The analysis calculates from BLS indexes that, from the fourth quarter of 2022 through the second quarter of 2026, business-sector output rose 10.0% and hours worked rose 0.9%. It puts annual productivity growth since the end of 2019 at 2.1%, compared with 1.5% a year from 2007 to 2019. These figures establish a productivity pattern; they do not identify what caused it.
Employment and pay indicators offer a more uneven picture. Payrolls grew by 496,000 in the 12 months to September, according to the source’s summary of government figures, with health care and social assistance adding 520,000 jobs. Information industries lost 120,000 jobs and remained 12% below their November 2022 peak. The analysis also reports that the labor share fell to 52.8% in the second quarter, its lowest level in records beginning in 1947, while real private-sector wages fell 0.6% over the year to September.
Infographic 01 · More output, same hours
Ten percent more output, almost the same hours
The US business sector produces much more than three years ago with almost no extra work. The new jobs are mostly in health care.
The last 12 months
Nonfarm business sector, Q2 2025 to Q2 2026
- Outputreal, value added+2.5%
- Productivityoutput per hour+2.2%
- Hours workedall workers+0.2%
Where the jobs went
Payroll jobs, change over 12 months to Sep 2026 (preliminary)
Since Q4 2022: output +10.0%, hours +0.9%, productivity +9.1% (my arithmetic from the BLS indexes). Hiring rate in Aug 2026: 3.3% of jobs (2019 average 3.9%); layoff rate 1.0% (2019: 1.2%). Correlation, not proof of cause.
Sources: BLS, Productivity and Costs Q2 2026 (3 Sep 2026) · BLS, Employment Situation Sep 2026 · BLS, JOLTS Aug 2026.
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Productivity Gains Meet Uneven Hiring
The numbers matter because productivity growth can expand the economy’s capacity to produce goods and services without a matching rise in hours worked. If that improvement continues, it could support stronger output. But productivity figures do not say who benefits: the source reports a falling labor share and lower real private-sector wages, alongside strong output growth.
The job figures also point to differing experiences across groups and industries. The analysis reports unemployment of 4.2% overall, compared with 8.0% for people aged 20 to 24 and 7.0% for Black workers. Those figures describe labor-market conditions, not evidence that AI caused the gaps. For workers, employers and policymakers, that distinction matters: a broad productivity increase is not the same as proof of widespread AI-driven job displacement or shared wage gains.
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The Post-2022 Productivity Comparison
The analysis uses the launch of ChatGPT on November 30, 2022, as a convenient marker for the period when generative AI entered wider public discussion. It compares productivity and employment data before and after that date, drawing on the BLS, BEA, Census Bureau, Federal Reserve and Eurostat. The timing is relevant to the AI question, but a before-and-after comparison alone cannot establish cause and effect.
The piece also describes a contrast with Europe: more jobs but flat productivity, according to its summary of Eurostat data. It cites an October 31 Financial Times opinion column by John Burn-Murdoch, whose summary said productivity was up and tech employment down while the picture elsewhere was less clear. The author says the full FT and Wall Street Journal articles were not available to him, and bases the analysis on official statistics rather than their full arguments.
“Numbers can show that two things happen at the same time. They rarely show that one causes the other.”
— Thorsten Meyer, ThorstenMeyerAI.com
AI’s Role Remains Unproven
The figures show productivity growth and uneven employment trends, but they do not isolate AI’s contribution. The source says only about one in four US businesses reported using AI in early October; it does not specify in the supplied material how much those businesses use it or whether adoption has raised their output per hour. Tech employment also began declining before generative AI was in wide business use, according to the analysis.
Other explanations may fit the same pattern, including investment in equipment and software, shifts in demand, changes in workforce composition, or post-pandemic adjustments. The supplied figures also cover different periods and populations: GDP is an economy-wide measure, while the BLS productivity series cited covers nonfarm businesses and ends in the second quarter. The available data do not establish whether recent gains will persist, how they are distributed across workers, or how much AI contributed.
Watch Adoption and Future Releases
The next evidence will come from later releases of productivity, payroll, wage and business-adoption data. Readers should compare like with like: the same time periods, sectors and measures, while noting that revisions can change initial estimates. Further Census Bureau reporting on business AI use could help show whether adoption is spreading, but adoption rates alone would not prove productivity effects.
A clearer causal assessment would require evidence connecting AI use to measured changes in output, hours, hiring and pay within businesses over time, while accounting for other changes. Until such evidence is available, the confirmed development is a strong productivity pattern alongside uneven hiring—not a government finding that AI has caused it.
Key Questions
What do the latest US productivity figures show?
The BLS reported that nonfarm business productivity rose 2.2% over the year to the second quarter of 2026. Output increased 2.5% and hours worked rose 0.2% over the same period.
Do the figures prove AI is raising productivity?
No. They show productivity growth but do not identify its cause. The source says about one in four US businesses reported using AI in early October, but the supplied data do not measure how much AI contributed to output.
Are US businesses hiring more slowly?
The analysis reports payroll growth of 496,000 over the 12 months to September, with gains concentrated in health care and social assistance. Information industries lost jobs over that period, but these figures alone do not establish that AI caused the pattern.
What is the difference between output and productivity?
Output measures goods and services produced. Productivity measures output relative to hours worked. Output can rise faster than hours, producing higher measured productivity, without revealing which technology or other factor caused the change.
Source: ThorstenMeyerAI.com
