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TL;DR
US business output rose 10.0% from the fourth quarter of 2022 to the second quarter of 2026, while hours worked increased 0.9%, according to calculations using Bureau of Labor Statistics indexes. Productivity is running faster than in the previous decade, but the figures do not show that AI caused the change, and gains in output have not translated evenly into hiring or workers’ pay.
US business output has grown far faster than hours worked since late 2022, a pattern consistent with rising productivity but not proof that artificial intelligence is causing it. Bureau of Labor Statistics data show output up 10.0% and hours worked up 0.9% from the fourth quarter of 2022 to the second quarter of 2026, according to calculations in an analysis published November 1 by ThorstenMeyerAI.com.
The latest figures show a growing economy alongside modest employment gains. The Bureau of Economic Analysis’s advance estimate put real GDP growth at a 3.4% annual rate in the third quarter of 2026, up from 2.2% in the second quarter. Separately, BLS data for the nonfarm business sector show that in the year to the second quarter, output rose 2.5%, hours worked rose 0.2%, and productivity increased 2.2%.
The analysis says productivity has grown at an annual average rate of 2.1% since the end of 2019, compared with 1.5% from 2007 to 2019. Those periods and measures describe output per hour in the nonfarm business sector; they do not identify what drove the change. The post-2022 comparison is also an arithmetic calculation from BLS indexes, not a separate causal finding by the agency.
Employment and pay figures present a less uniform picture. Payrolls increased by 496,000 in the 12 months to September, while health care and social assistance added 520,000 jobs, according to the source analysis. It reports losses of 120,000 jobs in the information industries, which were 12% below their November 2022 peak. The analysis also reports that the labor share of business output fell to 52.8% in the second quarter, and real private-sector wages declined 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.
Powered by Thorsten Meyer AI
Output Growth Has Uneven Payoffs
The gap between output and hours matters because productivity can support economic growth without requiring the same increase in labor input. But aggregate productivity figures do not show whether the benefits reach workers through higher wages, more jobs, or other gains. The figures cited in the analysis point to a mismatch: output is increasing quickly, while hiring is modest and the measured labor share and real wages have fallen.
That distribution is relevant to workers and employers beyond the technology sector. The analysis reports unemployment of 4.2% overall, compared with 8.0% among people aged 20 to 24 and 7.0% among Black workers, using the figures available in the source. Those rates do not by themselves show why unemployment differs, but they illustrate how a healthy headline growth rate can coexist with more difficult conditions for particular groups.
For businesses, the data raise a practical question: whether output gains reflect tools and processes that let existing staff do more, or factors such as investment, demand, and changes in the mix of industries. For workers, the key issue is whether productivity growth will eventually be reflected in pay and employment. The current numbers describe the gap; they do not settle either question.
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What the Productivity Measures Cover
Productivity is output divided by hours worked. The BLS measure cited here covers the nonfarm business sector, which excludes farms and does not represent all of the economy, including government, nonprofits, and households. A rise means more measured output per hour; it does not, on its own, explain why output per hour changed or how the resulting income is distributed.
The end of 2022 has become a common reference point in discussions of generative AI because ChatGPT launched on November 30, 2022. Comparing data before and after that date can show whether measures moved during the same period. It cannot establish that the launch, or AI adoption more broadly, caused the movement. The source notes that about one in four US businesses said it used AI in an early-October Census Bureau survey, a measure of reported use rather than an estimate of AI’s effect on productivity.
The analysis also points to two opinion pieces: a Financial Times column dated October 31 and a Wall Street Journal headline published a day later. Its author says the full articles were behind paywalls and describes relying instead on primary figures from the BLS, BEA, Census Bureau, Federal Reserve, and Eurostat. The data are the basis for the analysis, while its interpretation of what they may mean remains the author’s.
“That is the bakery: ten percent more bread with almost the same hours.”
— Thorsten Meyer, describing the output-hours comparison
AI’s Role Is Not Established
The central unknown is whether AI is responsible for a meaningful share of the productivity increase. The figures show that output per hour rose during a period of expanding AI investment and use; they do not isolate AI from other explanations, such as capital spending, demand, industry shifts, or changes in work organization. The analysis itself says the pattern fits an AI explanation but also fits other possibilities.
Reported business adoption is limited: the source cites an early-October Census Bureau survey in which about one in four US businesses said it used AI. That statistic does not reveal how intensively firms use AI, which tasks it affects, or whether adoption has changed measured output. Nor do job losses in information industries establish that AI replaced those workers. The source notes that technology employment began to shrink before generative AI was in wide business use.
Other limits concern the pace and distribution of gains. Hiring, wages, and the labor share can be affected by multiple forces, and the available figures do not establish why each changed. The cited output-hours comparison ends in the second quarter, while the GDP estimate is for the third quarter; they cover different periods and measures. Further data may revise the picture.
Watch Adoption, Jobs and Pay
The next evidence will come from updated labor, productivity, output, and business-adoption releases. Repeated BLS reports can show whether output continues to outpace hours and whether productivity growth persists. Future Census Bureau survey results may clarify how widely businesses report using AI, though adoption figures alone will not measure the technology’s contribution to output.
Employment and compensation data will help show whether growth reaches workers more broadly. Analysts will be watching payroll growth by industry, unemployment among younger workers, real wages, and the labor share. Revisions to national accounts and productivity estimates could also alter current readings. Until those measures can be linked more directly to business practices and outcomes, AI’s contribution remains an open question, rather than a confirmed explanation for the US productivity trend.
Key Questions
What does the US hours data show?
Using BLS indexes, ThorstenMeyerAI.com calculates that from the fourth quarter of 2022 to the second quarter of 2026, nonfarm business output rose 10.0% while hours worked rose 0.9%. The comparison indicates that output grew much faster than labor hours over that period.
Does this prove AI is raising productivity?
No. The data show productivity and output increased during a period when AI investment and adoption were growing, but they do not establish cause and effect. Other factors, including investment and changes in demand or industry composition, may also contribute.
How fast is US productivity growing?
The analysis reports that nonfarm business productivity rose 2.2% in the year to the second quarter of 2026. It also calculates average annual growth of 2.1% since the end of 2019, compared with 1.5% from 2007 to 2019.
Are workers sharing in the growth?
The cited indicators do not show a clear, even distribution. The source reports a 0.6% decline in real private-sector wages over the year to September and a 52.8% labor share in the second quarter. Those figures describe outcomes but do not establish what caused them.
What data could clarify AI’s economic impact?
Updated productivity and hours data, business surveys on AI adoption, and employment and wage figures can show whether current patterns persist. To attribute changes to AI, researchers would also need evidence that separates its effects from other economic and industry changes.
Source: ThorstenMeyerAI.com
