🔍 Read the full analysis: 10% More Output, Similar Hours: What The US Data Leaves Unanswered on ThorstenMeyerAI.com
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TL;DR
US nonfarm 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 from BLS indexes cited by Thorsten Meyer. The figures show rising productivity, but do not establish that AI caused it or explain how gains are distributed among workers.
US nonfarm 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 from Bureau of Labor Statistics indexes cited in an analysis published November 1 by Thorsten Meyer. The gap points to faster productivity growth, but the figures alone do not show that artificial intelligence caused the change or establish how its benefits are reaching workers.
The most recent productivity data cited in the analysis cover the year to the second quarter of 2026. In the nonfarm business sector, output increased 2.5% over that year, hours worked rose 0.2%, and productivity rose 2.2%, according to the BLS. The sector covers private businesses excluding farms, and does not include government, nonprofits or households. The 10.0% output and 0.9% hours figures are Meyer’s calculations from BLS indexes between the fourth quarter of 2022 and the second quarter of 2026.
Separate GDP data point to continued overall growth. The Bureau of Economic Analysis’s advance estimate, released October 29, put third-quarter real GDP growth at a 3.4% annual rate, up from 2.2% in the second quarter. Consumer spending, investment and government spending contributed to the increase. GDP covers a broader part of the economy than the BLS productivity series, so the two measures should not be treated as identical.
The labor-market figures cited are less uniform. Payrolls grew by 496,000 in the 12 months to September, while health care and social assistance added 520,000 jobs. Information industries lost 120,000 jobs and remained 12% below their November 2022 peak. Those figures describe different industries’ employment changes; they do not, by themselves, show that jobs lost in one sector moved to another or that AI drove the pattern.
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, Uneven Job Growth
The gap between output and hours matters because productivity measures output per hour worked, a key way economists track how efficiently businesses produce goods and services. Sustained productivity growth can support higher incomes and living standards, but the figures do not guarantee that workers will receive a larger share of the gains. The analysis reports that the labor share fell to 52.8% in the second quarter, its lowest level in the record dating to 1947, and that real private-sector wages fell 0.6% over the year to September. These are measures of the overall distribution and movement of pay, not proof of what caused either change.
Employment conditions also vary across groups. The analysis cites overall unemployment of 4.2%, compared with 8.0% for people aged 20 to 24 and 7.0% for Black workers. It says those subgroup rates were up from 5.5% and 4.8%, respectively, in April 2023. If hiring remains slow while output grows, new entrants and workers in industries losing jobs may find it harder to secure work, even when the wider economy is expanding. The data cited do not establish that every worker, company or sector is experiencing the same conditions.
For employers, the productivity numbers are relevant to questions about investment, staffing and the way work is organized. For workers and policymakers, the distribution of gains matters alongside headline growth. But the analysis’s figures are economic indicators, not a direct measure of AI’s effect: they cannot identify which tools businesses used or what would have happened without them.
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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 comparing economic performance before and after generative AI became widely discussed. From the end of 2019 through the second quarter of 2026, the cited BLS productivity series grew at an average annual rate of 2.1%, compared with 1.5% a year from 2007 to 2019. That comparison suggests a faster recent pace, but it spans periods with different economic conditions and does not isolate the effect of any one technology.
Other indicators complicate a simple AI explanation. According to the analysis, about one in four US businesses said it used AI in early October, based on Census Bureau data. The analysis also notes that tech-sector job declines began before generative AI was in broad business use. Meanwhile, its cited figures show that health care and social assistance accounted for more job additions than the overall net payroll increase in the year to September, while information industries shed jobs.
Meyer points to opinion coverage published immediately before his analysis: a Financial Times column on October 31 asked whether labor-market trends were the first clear evidence of AI’s impact, and The Wall Street Journal’s November 1 headline described growth that does not reach everyone. Meyer says he could access the FT column’s summary and the WSJ headline, but not either article in full. His analysis says its detailed figures come from primary data published by the BLS, BEA, Census Bureau, Federal Reserve and Eurostat.
What the Data Cannot Attribute
The central uncertainty is what caused the productivity acceleration. The figures show output rising faster than hours worked, but they do not separate AI’s contribution from other technology, investment, business changes, shifts in the mix of industries, or broader economic conditions. The Census estimate that about one in four businesses used AI does not reveal how intensively firms used it, what tasks it affected, or whether adoption raised measured output.
The comparisons also cover different time periods and measures: the headline output-hours calculation runs through the second quarter, while GDP growth is reported for the third quarter. The job and wage figures extend through September, and the source does not provide enough detail here to reconcile every series or assess revisions. It is also unclear from the cited figures how productivity gains are distributed among different workers, firms and industries, or whether the recent pace will persist.
The analysis mentions Europe as a contrast—more jobs alongside flat productivity—but gives no detailed comparison figures in the supplied material. That claim cannot be independently assessed from the numbers presented here. A short-term difference between regions would not, on its own, identify why their productivity and employment patterns diverged.
Upcoming Data Could Refine the Picture
The next evidence will come from updated releases of productivity, GDP, payroll and wage data, which can show whether the recent pattern continues and whether earlier estimates are revised. Those releases may clarify the timing of output and employment changes, but aggregate statistics alone are unlikely to identify AI’s causal role.
A clearer assessment would require more detail on when businesses adopt AI, which tasks and workers are affected, and how output, hours, hiring and pay change in firms that use it compared with relevant firms that do not. The October Census estimate provides a broad adoption measure, not that kind of causal evidence. Until more specific evidence is available, the supported conclusion is narrower: US business productivity has grown faster than hours worked in the period cited, while the causes and distribution of the gains remain open questions.
Key Questions
What does the 10% output figure measure?
It is Thorsten Meyer’s calculation from BLS indexes for the nonfarm business sector, comparing the fourth quarter of 2022 with the second quarter of 2026. It is a change in business output over that period, not an annual growth rate.
Does the data prove that AI increased productivity?
No. The figures show that output rose much faster than hours worked, but they do not isolate AI from other possible causes. The cited Census data say about one in four US businesses used AI in early October; they do not measure its effect on productivity.
How did US GDP perform in the third quarter?
The BEA’s advance estimate, released October 29, put real GDP growth at a 3.4% annual rate in the third quarter of 2026, up from 2.2% in the second quarter. That is a separate measure from the BLS nonfarm business productivity series.
Were jobs growing while output rose?
Yes, but job changes differed by sector. Payrolls increased by 496,000 in the 12 months to September; health care and social assistance added 520,000, while information industries lost 120,000. Those figures do not establish that AI caused the sector-level changes.
What remains unknown about workers’ share of growth?
The analysis reports a falling labor share and lower real private-sector wages over the year to September, but the cited figures do not explain the causes or show how gains were distributed across individual workers, industries and companies. Whether the productivity trend continues is also unknown.
Source: ThorstenMeyerAI.com
