🔍 Read the full analysis: A Guide To US Growth, Work Hours, And The Unanswered AI Question on ThorstenMeyerAI.com
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TL;DR
US output and productivity have grown faster than hours worked, while hiring and wage gains have been uneven. The figures are consistent with an AI-related shift but do not establish that AI caused it; adoption remains limited and other explanations are possible.
US economic output is rising much faster than the hours people work, while hiring and pay gains are uneven, according to official figures examined in a November 1 analysis by Thorsten Meyer. The numbers raise the question of whether artificial intelligence is changing labor demand, but they do not establish AI as the cause: only about one in four US businesses reported using AI in an early-October Census Bureau survey.
The Bureau of Economic Analysis’ 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. Investment, consumer spending and government spending all contributed. The figures show a growing economy, not an economy that has stopped expanding.
Labor productivity data tell a different part of the story. In the nonfarm business sector, output grew 2.5% over the year to the second quarter of 2026, while hours worked increased 0.2%; productivity rose 2.2%, according to the Bureau of Labor Statistics. Since the end of 2022, Meyer’s calculations from BLS indexes show output up 10.0% and hours worked up 0.9%. Those are changes over that period, not proof of what caused them.
Employment growth was concentrated in a few areas. In the 12 months to September, payrolls grew by 496,000, while health care and social assistance added 520,000 jobs. The information sector lost 120,000 jobs and remained 12% below its November 2022 peak. These figures point to differing outcomes across industries, but do not by themselves show that AI replaced the lost jobs.
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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Growth Without Broad Job Gains
The gap between output and hours matters because productivity can allow businesses to produce more without increasing labor input at the same pace. That can support economic growth, but the aggregate measure does not say how gains are shared among workers, employers and consumers. In the figures cited by Meyer, the labor share fell to 52.8% of business output in the second quarter, its lowest level since records began in 1947. Real private-sector wages fell 0.6% over the year to September.
Job-market conditions also differ by age and race. Overall unemployment was 4.2%, compared with 8.0% for workers aged 20 to 24 and 7.0% for Black workers. Those rates were higher than the respective 5.5% and 4.8% readings in April 2023. Slow hiring can make it harder for new entrants to get work even when layoffs remain low. The data describe the pressure; they do not identify one single cause.
For employers and workers, the practical issue is whether productivity gains reflect new tools, investment, changes in business processes, or some combination—and whether workers share in the benefits. The reported pattern has consequences for hiring and pay, but the available figures do not yet answer those questions.
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How the Productivity Gap Emerged
Productivity measures output per hour worked. The BLS nonfarm business sector measure covers private businesses but excludes farms; it also excludes government, nonprofits and households. That scope matters: the statistic is not a direct measure of what every worker or industry experiences.
According to BLS figures cited in the analysis, productivity grew at an average annual rate of 2.1% from the end of 2019, compared with 1.5% a year from 2007 to 2019. Meyer uses the launch of ChatGPT on November 30, 2022, as a reference point for comparing the period before and after generative AI became widely discussed. From the fourth quarter of 2022 to the second quarter of 2026, his calculations show output up 10.0% and hours up 0.9%, or productivity growth of about 2.5% a year.
The timing makes AI one possible explanation, not a demonstrated cause. The analysis also cites a sharp rise in investment in computers and software: those categories accounted for about a third of third-quarter growth, according to Meyer’s reading of BEA data. Information-sector activity contributed 0.52 percentage points to second-quarter growth of 2.2%, the second-largest sector contribution. Investment in technology can be connected to AI, but those totals do not isolate AI from other computer and software spending.
“In the same year, output grew 2.5% and hours worked only 0.2%.”
— Thorsten Meyer, summarizing Bureau of Labor Statistics figures
AI’s Role Is Not Established
The data show that output and productivity have risen while hours and employment have followed different paths. They do not show that AI caused those changes. The Census Bureau survey cited by Meyer found that about one in four US businesses used AI in early October, but the material provided does not specify how extensively those businesses used it or how it affected staffing and production.
Other explanations remain possible, including investment unrelated to AI, shifts in demand, changing industry composition and longer-running productivity trends. The analysis notes that technology-sector job declines began before generative AI was in widespread business use. It also describes Europe as showing a different pattern—more jobs alongside flat productivity—without enough detail here to establish why the economies differ. The available figures do not resolve whether AI is responsible for the US pattern, how much of the productivity gain it accounts for, or which workers and companies benefit.
Watch Adoption and Labor Data
The next evidence will come from later releases of productivity, payroll, wage and business-adoption data. Those reports can show whether the current gap between output and hours continues, whether job growth broadens beyond health care and social assistance, and whether wages begin to track productivity gains more closely.
To assess AI’s role, researchers and policymakers will need more than a coincidence in timing. They will need evidence linking adoption at particular businesses or industries to changes in output, hours, hiring and pay, while accounting for other economic shifts. Until such evidence is available, the figures support a description of strong growth and uneven labor outcomes—not a definitive finding that AI is driving them.
Key Questions
What is the main development in the US economy?
Official figures show strong economic growth alongside much slower growth in hours worked. The analysis also finds that job and wage outcomes vary across industries and groups of workers.
Do the figures prove AI is reducing jobs?
No. The figures show a timing and pattern that could fit an AI-related explanation, but they do not establish cause and effect. The analysis cites limited reported business adoption and other possible explanations.
How fast did the economy grow in the third quarter of 2026?
The Bureau of Economic Analysis’ advance estimate put real GDP growth at a 3.4% annual rate in the third quarter, up from 2.2% in the second quarter.
Which industries added or lost jobs?
In the 12 months to September, health care and social assistance added 520,000 jobs, while the information sector lost 120,000. Total payrolls grew by 496,000 over the same 12-month period.
What evidence would help answer the AI question?
More detailed data connecting business AI adoption with changes in output, work hours, hiring and wages could help distinguish AI’s effects from other economic forces.
Source: ThorstenMeyerAI.com
