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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 Bureau of Labor Statistics indexes cited by Thorsten Meyer. Productivity growth has accelerated, but the data do not show that AI caused the change or explain how its gains are distributed among workers.
US business output increased 10.0% from late 2022 to mid-2026 while hours worked rose just 0.9%, according to Bureau of Labor Statistics indexes cited in a November 1 analysis by Thorsten Meyer. The figures point to faster productivity growth, but they do not establish that artificial intelligence caused it or that workers broadly have shared in the gains.
The latest measures cited in the analysis show a strong economy and modest changes in work time. The Bureau of Economic Analysis’ October 29 advance estimate put real gross domestic product 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. That GDP figure covers the wider economy and should not be confused with the BLS productivity figures, which concern the nonfarm business sector.
For that sector, BLS reported that output increased 2.5% over the year to the second quarter of 2026, while hours worked rose 0.2%; productivity increased 2.2%. Meyer calculates from BLS indexes that output rose 10.0% and hours worked 0.9% between the fourth quarter of 2022 and the second quarter of 2026. The comparison describes output per hour rising over that period; it does not identify why it rose.
Labor-market figures in the analysis show uneven results. Payrolls increased 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 over the same period and remained 12% below their November 2022 peak. The figures do not mean every sector or worker followed the same 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 Pay
The gap between output growth and hours growth matters because productivity can support higher living standards when its gains reach workers through pay, benefits or improved services. The figures presented here show that output per hour has risen; they do not show who received the resulting economic gains. Meyer cites a labor share of 52.8% in the second quarter, the lowest level since the series began in 1947, and a 0.6% decline in real private-sector wages over the year to September.
The hiring picture also differs by age and race in the data cited. Overall unemployment was 4.2%, compared with 8.0% for people aged 20 to 24 and 7.0% for Black workers. Those figures point to greater difficulty for some groups, but they do not by themselves establish why unemployment rates differ or how much productivity growth has contributed.
For businesses, faster output per hour can mean more production without proportional increases in staffing or time worked. For workers and people entering the labor market, slower hiring can make it harder to find jobs even when the economy is expanding. The central public question is not only whether productivity is rising, but whether the gains translate into broadly shared income and employment opportunities.
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How the Numbers Compare
Productivity is measured as output divided by hours worked. The BLS figures discussed by Meyer cover the nonfarm business sector: private businesses outside farming, rather than the entire economy. GDP, by contrast, includes activity across the economy. Their different scope and measurement periods matter when comparing headline growth with productivity.
Meyer reports average annual productivity growth of 2.1% from the end of 2019, compared with 1.5% from 2007 to 2019. He uses the fourth quarter of 2022 as a comparison point because ChatGPT launched on November 30 of that year. That date makes a convenient marker for examining a period when generative AI became prominent, but a before-and-after comparison alone cannot establish an AI effect.
The analysis also notes that computers and software accounted for about a third of third-quarter growth, based on Meyer’s reading of the BEA data, and that the information sector contributed 0.52 percentage points to second-quarter growth of 2.2%. Investment in technology may be part of the growth picture, but sector contributions do not show that AI tools increased productivity across the economy.
AI’s Role Remains Unproven
The official statistics cited establish changes in output, hours, productivity, employment and pay. They do not establish that AI caused the productivity increase. Meyer reports that roughly one in four US businesses said it used AI in an early-October Census Bureau measure, but that statistic alone does not show how extensively businesses used it or whether use raised output per hour.
Other explanations may also account for faster productivity, including investment, changes in business operations or shifts in the types of work being done. The cited data do not separate those factors. Meyer also notes that information-industry employment began falling before generative AI was widely used in businesses, which complicates a direct cause-and-effect account.
There are further limits to the comparison: the 10.0% output and 0.9% hours figures span late 2022 to mid-2026, while the GDP, payroll and wage figures use different periods and measures. It remains unclear how much of the productivity change will persist, how much reflects AI, and whether workers’ pay and hiring will catch up.
Watch Hiring, Pay and Productivity
The next releases of BLS productivity and labor-market data, along with BEA GDP estimates, will show whether the pattern continues beyond the periods covered here. Changes in hours, output per hour, hiring and real wages will help establish whether stronger productivity is accompanied by improved outcomes for workers, though those releases alone may not identify AI as the cause.
More direct evidence would require data linking businesses’ AI use to measurable changes in output, staffing and pay over time, while accounting for other changes in investment and demand. Until that evidence is available, the figures support a report of rising productivity and uneven labor-market results—not a definitive finding that AI is replacing workers or driving the overall growth.
Key Questions
What does the 10% output figure measure?
It is Meyer’s calculation from BLS nonfarm business-sector indexes: output increased 10.0% from the fourth quarter of 2022 to the second quarter of 2026. It is not a measure of GDP growth over that same period.
Did AI cause the productivity increase?
The data cited do not establish that. They show productivity growth alongside a period of increased attention to generative AI, but timing alone cannot prove causation, and other explanations remain possible.
Is the US economy adding jobs?
Yes. Payrolls grew by 496,000 in the 12 months to September, according to the figures cited by Meyer. Job growth differed by industry: health care and social assistance added jobs while information industries lost them.
Are workers sharing in the productivity gains?
The cited figures do not show broad wage gains: Meyer reports that real private-sector wages fell 0.6% over the year to September and labor’s share of business output was 52.8% in the second quarter. These figures describe recent outcomes but do not explain every worker’s experience.
Source: ThorstenMeyerAI.com
