🔍 Read the full analysis: US Growth, Productivity, And AI: What The Hours Data Can Show on ThorstenMeyerAI.com
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TL;DR
US business-sector output has risen 10.0% since the end of 2022, while hours worked increased 0.9%, according to calculations from BLS indexes cited by ThorstenMeyerAI.com. The figures show faster productivity growth, but do not establish that AI caused it; hiring, wages and the distribution of gains remain uneven.
US economic output is rising much faster than the number of hours people work, a pattern that has renewed questions about whether artificial intelligence is changing jobs. An analysis published November 1 by ThorstenMeyerAI.com, drawing on BLS, BEA and Census Bureau statistics, reports that nonfarm business output increased 10.0% since the end of 2022, while hours worked rose 0.9%. The figures show stronger productivity growth, but do not establish that AI caused it.
The latest figures point to an economy that is expanding even as employers add workers slowly. The Bureau of Economic Analysis’ October 29 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. Consumer spending, investment and government spending all contributed, according to the BEA.
For the nonfarm business sector, the Bureau of Labor Statistics reported that output grew 2.5% over the year to the second quarter, while hours worked increased 0.2%. Productivity—the amount of output produced per hour—rose 2.2% over that period. The analysis calculates that from the fourth quarter of 2022 to the second quarter of 2026, output rose 10.0% and hours worked 0.9%, using BLS indexes. That period began around the public launch of ChatGPT, but timing alone does not show a cause.
Employment and pay figures complicate the 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’s account of official data. Information industries lost 120,000 jobs and remained 12% below their November 2022 peak. The analysis also reports a 52.8% labor share in the second quarter—the share of business output paid as wages and benefits—and a 0.6% year-over-year decline in real private-sector wages through 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 and Uneven Pay
Productivity growth can support higher living standards when it leads to greater output without requiring proportionally more work. But the figures do not show that every worker benefits equally, or that job losses in one industry are being offset by new opportunities elsewhere. The analysis reports overall unemployment of 4.2%, compared with 8.0% for people aged 20 to 24 and 7.0% for Black workers. Those rates are higher than the respective 5.5% and 4.8% readings cited for April 2023.
The contrast matters for businesses and workers weighing hiring, investment and training decisions. If output can grow with few additional work hours, employers may need fewer workers for some tasks—or may be using existing staff and capital more effectively. But productivity statistics do not tell readers which explanation applies to a particular company, whether the gains will persist, or how much of the additional output will flow to employees.
The source also says hiring has been slow while layoffs remain low. That combination can make labor-market pressure less visible in headline unemployment: fewer people may be losing existing jobs, while new entrants and people seeking a change can find it harder to get hired. The reported wage and labor-share figures raise distribution questions, but by themselves do not identify what is driving those trends.
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How the Hours Comparison Works
The BLS productivity measure cited in the analysis covers the nonfarm business sector: private businesses excluding farms. It does not represent every part of the economy, including government, nonprofits and households. Productivity is calculated as output divided by hours worked, so it can rise when output increases faster than hours, or when output is maintained with fewer hours.
The analysis compares recent productivity with earlier periods. It reports annual productivity growth of 2.1% since the end of 2019, against 1.5% a year from 2007 to 2019. These are period averages, not proof that a single technology explains the difference. The end-of-2022 comparison is useful for tracking what happened after generative AI tools became widely visible, but other economic changes occurred over the same years.
The source references recent opinion coverage in the Financial Times and The Wall Street Journal, but says the full articles were behind paywalls and that it relied on their available headline or summary wording. Its numerical claims are instead attributed to primary statistical sources: the BLS, BEA, Census Bureau, Federal Reserve and Eurostat. It says only about one in four US businesses reported using AI in an early-October Census Bureau measure; that figure indicates reported adoption, not AI’s contribution to national productivity.
“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 Is Not Established
The central uncertainty is whether AI has materially lifted productivity across the economy. The data cited here show that output grew much faster than hours worked, and that the gap widened during a period when generative AI tools became more available. They do not isolate AI from other explanations, such as investment in computers and software, changes in business demand, workforce composition or other efficiency improvements.
The source says computers and software accounted for about a third of third-quarter growth, with much of that investment tied to AI development. That is an interpretation of the investment figures, not a direct measure of AI-generated productivity. It also notes that technology-sector job declines began before generative AI was widely used by businesses. The reported Census Bureau adoption figure—about one in four businesses using AI in early October—does not reveal how intensively those firms use it or whether it changed staffing or output.
Some figures in the analysis cover different periods and populations: GDP growth is a third-quarter annualized estimate, productivity data run through the second quarter, and payroll changes cover the 12 months to September. Those measures can be read together for context, but they are not interchangeable. Future revisions may also change preliminary economic estimates.
Upcoming Data Could Clarify Trends
The next updates to GDP, productivity, payroll and business AI-use statistics will show whether the current pattern persists. Readers should compare each release’s period and definition rather than treat a new quarterly figure as a direct answer about AI. The BEA’s GDP estimates and the BLS’s productivity releases are revised as more complete data become available.
Evidence about AI’s economic effect would require more than a national output-hours comparison. Useful indicators would include changes in output and hours at firms adopting AI, the timing and scale of adoption, and whether workers’ pay or job prospects change alongside productivity. Until such evidence is available, the confirmed development is faster output growth relative to hours—not a demonstrated AI-driven reduction in employment.
Key Questions
What is the main development in the US data?
The analysis reports that nonfarm business output rose 10.0% from the end of 2022 to the second quarter of 2026, while hours worked rose 0.9%, based on its calculations from BLS indexes. Separately, the BEA estimated third-quarter real GDP growth at a 3.4% annual rate.
Do the figures prove AI is reducing jobs?
No. They show a gap between output growth and hours worked, alongside employment changes in particular sectors. The cited statistics do not establish that AI caused the gap or that it directly reduced overall employment.
What does productivity mean in this report?
Productivity is output divided by hours worked. The BLS figure cited here covers the nonfarm business sector, not the whole economy. It rose 2.2% over the year to the second quarter of 2026, as output grew 2.5% and hours worked 0.2%.
Are workers sharing in the reported growth?
The figures cited by the analysis raise concerns about how gains are distributed: it reports a 52.8% labor share in the second quarter and a 0.6% year-over-year fall in real private-sector wages through September. Those measures do not, on their own, explain why the changes occurred or describe every worker’s experience.
Source: ThorstenMeyerAI.com
