🔍 Read the full analysis: The Case For Caution In Reading AI Into US Growth Data on ThorstenMeyerAI.com
Get the latest gadgets delivered free with Prime
- Fast, free delivery on millions of items
- Prime Video, Amazon Music and more included
- Member-only deals all year
TL;DR
US output has grown much faster than hours worked since late 2022, while productivity has outpaced its 2007–2019 average. The figures are consistent with an AI-related shift, but they do not establish AI as the cause; job growth, wages and AI adoption data show a more mixed picture.
US output has risen much faster than hours worked since the end of 2022, while productivity growth has run above its pace in the years before the pandemic, according to government data reviewed in a November 1 analysis by Thorsten Meyer. The pattern has prompted questions about whether artificial intelligence is changing employment, but the data do not establish that AI caused it: hiring, wages and reported business adoption point to a more mixed picture.
The Bureau of Economic Analysis said real gross domestic product grew 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 contributed to the increase. The BEA’s advance estimate was released October 29. The analysis says computers and software accounted for about a third of third-quarter growth, much of it associated with AI investment; that is an interpretation of the investment data, not proof that AI drove economy-wide productivity.
For the nonfarm business sector, the Bureau of Labor Statistics reported that output rose 2.5% year over year in the second quarter of 2026, while hours worked rose 0.2%, producing a 2.2% productivity increase. The sector excludes farms, government, nonprofits and households. Using BLS index data, Meyer calculates that from the fourth quarter of 2022 through the second quarter of 2026, output increased 10.0% and hours worked 0.9%—a period beginning around the public launch of ChatGPT, but not evidence that the launch caused the change.
The analysis also reports that productivity grew an average of 2.1% a year from the end of 2019, compared with 1.5% a year from 2007 to 2019. Employment measures tell a less straightforward story: payrolls increased by 496,000 in the 12 months to September, but health care and social assistance added 520,000, while information industries lost 120,000 jobs. The figures indicate that job growth was concentrated outside information industries; they do not show that AI explains the difference.
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
Productivity Gains, Uneven Payoffs
The gap between output and hours matters because productivity can rise without employers adding workers at the same pace. That can support economic growth, but it does not by itself tell workers whether the gains will reach them through higher pay, better job prospects or reduced working hours. The analysis reports that the labor share—the portion of business output paid as wages and benefits—fell to 52.8% in the second quarter, described as its lowest level since records began in 1947. It also reports a 0.6% decline in real private-sector wages over the year to September.
The distribution of job opportunities is another concern. Overall unemployment was 4.2%, compared with 8.0% for people aged 20 to 24 and 7.0% for Black workers, according to figures cited in the analysis. These differences do not establish that AI is responsible, but they show why an aggregate productivity number is not a complete measure of how the economy is affecting workers. The question is not only whether output per hour is increasing, but who is employed and who shares in the returns.
Products Worth Considering
Logitech MX Keys S Wireless Keyboard, Graphite
Bose SoundLink Flex (2nd Gen) Portable Bluetooth Speaker, Black
Amazon Kindle Paperwhite (12th Generation, 2024), 16 GB, 7-inch Display, Jade
As an affiliate, we earn on qualifying purchases.
What the Data Can Establish
The figures combine several measures that cover different periods and parts of the economy. GDP tracks total economic output; BLS business-sector productivity compares output with hours worked in a defined sector. Payroll counts, unemployment, wages and labor share provide other views of employment and income. They should not be treated as interchangeable or as direct measures of AI’s impact.
Meyer notes that only about one in four US businesses reported using AI in early October, citing Census Bureau data. The analysis also says technology employment began declining before generative AI was in wide business use. Those observations complicate a simple account in which the recent employment pattern is the result of AI adoption. They do not rule out effects at particular firms or occupations, and the source material does not provide a causal estimate separating AI from other influences.
The analysis refers to opinion coverage by the Financial Times on October 31 and a Wall Street Journal headline published the following day. Meyer says he could access the FT column’s summary and the Journal headline, but not either article in full. He says the figures in his analysis instead come from primary sources including the BLS, BEA, Census Bureau, Federal Reserve and Eurostat.
AI’s Causal Role Remains Unsettled
The available figures do not establish how much, if any, of the productivity increase or employment changes resulted from AI. Productivity data measure output per hour, not which technologies produced the gain. Business adoption figures indicate reported use, but the source material does not give a detailed breakdown of the kinds of AI used, how intensively firms use it or whether adoption changed workforces.
Other questions remain open: whether the recent productivity pace will continue, whether it will translate into sustained wage growth, and how job changes differ across occupations and industries. The payroll, wage and unemployment figures cited here describe outcomes over specified periods; they do not identify the causes. The figures may be consistent with an AI-related shift, but they are also consistent with other explanations.
Watch Adoption and Labor Data
The next evidence will come from updated releases on productivity, output, hours, payrolls, wages and business AI use. Comparing those measures over time—and across industries with different adoption levels—could clarify whether the gap between output and hours persists and where employment changes occur. Even then, establishing causation will require more than observing that AI use and productivity growth happen together.
For now, the confirmed picture is of strong recent output growth and productivity gains alongside slower job growth in some sectors and pressure on measures of worker income. Whether AI is a major cause, and how broadly workers share the gains, remains unresolved.
Key Questions
Does the data prove AI is reducing US jobs?
No. The figures show productivity and employment trends, but they do not isolate AI as the cause. The analysis says other explanations may also fit the pattern.
How fast has US productivity grown?
BLS data cited in the analysis show nonfarm business productivity rose 2.2% from the second quarter of 2025 to the second quarter of 2026. Its reported average annual growth was 2.1% from the end of 2019, compared with 1.5% from 2007 to 2019.
What happened to employment over the year to September?
Payrolls rose by 496,000 overall, according to the analysis. Health care and social assistance added 520,000 jobs, while information industries lost 120,000. The figures show differing industry trends, not their causes.
How widely are US businesses using AI?
The analysis cites Census Bureau data indicating that about one in four US businesses reported using AI in early October 2026. That measure does not, by itself, show how extensively businesses use AI or its effect on employment.
Source: ThorstenMeyerAI.com
