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TL;DR
A November 12 article from ThorstenMeyerAI.com explains why productivity growth matters and compares today’s AI debate with earlier delays between new technology and economy-wide gains. It reports strong results in some workplace studies but says executives estimate AI has so far added about 0.2 percentage points a year to productivity growth.
AI’s productivity gains have not yet translated into a large economy-wide boost, according to a November 12, 2026, article by Thorsten Meyer on ThorstenMeyerAI.com. The article says executives put AI’s current contribution to productivity growth at about 0.2 percentage points a year, even as one study found a 15% productivity increase among customer-support agents using the technology.
The article defines labor productivity as output per hour worked and argues that its growth shapes long-run living standards. At a sustained annual rate of 2%, output per hour doubles in about 35 years; at 1%, the doubling time is about 70 years. These are illustrative compounding estimates, not predictions about future incomes for any particular household.
It draws on economist Robert Solow’s 1957 analysis of U.S. output per worker-hour from 1909 to 1949. Solow attributed 87.5% of the increase to technical change and 12.5% to increased use of capital, such as machinery. The article uses this to explain why productivity gains can reflect technology, skills, work methods and organization, not just more equipment.
The article’s current AI figures offer two different views of progress. A study of support agents reported a 15% productivity increase, while the article says executives estimate AI has contributed about 0.2 percentage points a year to overall productivity growth so far. The measures have different scopes: a result within one occupation does not establish a comparable gain across the full economy.
Infographic 01 · The sector clock
Productivity moves the sector clock
US farms produced nearly three times their 1948 output in 2023, with only about 9% more inputs in total. Over the same decades, farming's share of US jobs fell from about one in eight to 1.5%.
- Field12.1%
- Factory29.9%
- Services58.0%
Lebergott · own calculation
- Field1.5%
- Factory18.9%
- Services79.6%
ILO estimate
Farm output +210% (1948-2023, 1.52% a year); all inputs +0.12% a year (about +9% in total); total factor productivity +1.40% a year (USDA ERS). 1950: services = remainder, incl. unemployed.
Sources: USDA ERS, Agricultural Productivity in the U.S. (updated 25 Aug 2026); US employment shares: S. Lebergott, in US Census Bureau, Historical Statistics of the United States (1975; 1950, own calculation, see episode 2); ILO modelled estimates via World Bank (2025).
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Why Economy-Wide Gains Take Time
Productivity growth matters because it can raise the amount of goods and services produced for each hour of work. Over time, that can support higher incomes and living standards without relying only on longer working hours or a larger workforce. The article’s comparison between 1% and 2% annual growth shows how a small difference in the yearly rate compounds over decades.
For workers and businesses, the gap between a successful AI deployment and a broad productivity shift is important. A tool may help a team complete specific tasks faster, but economy-wide statistics also reflect adoption across industries, changes to work processes and the value of what is produced. The reported 15% result is evidence about one setting, not proof that AI has lifted productivity for all workers.
The discussion also bears on expectations for AI-related investment. If companies spend heavily on new systems, the eventual economic return will depend on whether those tools lead to lasting output gains, rather than simply changing how existing tasks are performed. The cited executive estimate suggests the broad effect remains modest so far, though it does not settle what future gains may be.
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The Computer-Era Productivity Puzzle
The article places AI in a longer pattern of debate over new technology and measured output. U.S. productivity growth in the nonfarm business sector averaged 2.8% a year from 1948 to 1973, then 1.4% from 1973 to 1995, according to the article’s calculations from a Bureau of Labor Statistics index. Computers spread during the period of slower growth, prompting Solow’s 1987 observation that the computer age was visible everywhere except in productivity statistics.
The slowdown was followed by a rebound. The article calculates that productivity grew about 3.1% a year from 1995 to 2004. In a 2000 study, Federal Reserve economists Stephen Oliner and Daniel Sichel estimated that information technology use and computer production accounted for about two-thirds of a one-percentage-point acceleration in productivity growth between the periods they examined. That episode is relevant to AI, but it does not show that the same timing or effect will recur.
The article also cites agriculture as an example of productivity changing the structure of work. U.S. farm output in 2023 was nearly three times its 1948 level, while total inputs had risen by about 9%, according to figures it attributes to the U.S. Department of Agriculture. It says the sector’s workforce share fell to an estimated 1.5% by 2025, citing the International Labour Organization.
“You can see the computer age everywhere but in the productivity statistics.”
— Robert Solow, quoted in Thorsten Meyer’s article from his 1987 review
AI’s Broad Effects Still Unmeasured
The article does not identify the specific study behind the reported 15% support-agent gain, including its sample, methods, duration or working conditions. Without those details, readers cannot judge how widely the result applies or whether it persisted over time.
It also does not name the source, survey period or calculation method behind the executive estimate of about 0.2 percentage points a year. The estimate should be read as an attributed assessment, not a definitive national-accounts measurement. The article does not establish how much of any productivity change can be assigned to AI rather than other factors.
More broadly, the timing and scale of future gains remain unknown. The computer-era rebound shows that productivity effects can appear after a delay, but it does not guarantee that AI will produce a similar increase. The article offers no forecast for when, or whether, a substantial economy-wide effect will emerge.
Tracking Adoption and Output
The next evidence will come from continued measurement of output per hour alongside workplace studies that describe how AI is being used. National productivity data can show whether output is growing faster relative to hours worked, while firm-level research can help identify the tasks, occupations and conditions associated with gains.
For now, the article’s central comparison is between measurable improvements in some work settings and a much smaller estimated contribution to overall productivity growth. Future assessments will need to clarify the methods behind both figures and test whether reported gains endure as businesses reorganize work around AI.
Key Questions
What does productivity mean in this article?
Productivity means output per hour worked: the amount of value produced during an hour of labor. It is not the same as working longer or producing more by adding workers.
What does the article say AI has contributed so far?
It reports that executives estimate AI’s contribution to productivity growth at about 0.2 percentage points a year. The article does not provide the survey details or calculation method behind that estimate.
Does the reported 15% gain apply to all workers?
No. The article describes a 15% increase in a study involving customer-support agents. It does not provide enough study details to establish whether the result applies to other roles or workplaces.
Why does the article compare AI with computers?
Computer adoption initially coincided with slower productivity growth, but U.S. productivity later accelerated from 1995 to 2004. The comparison shows that technology’s broad economic effects can take time to appear; it does not prove AI will follow the same path.
Source: ThorstenMeyerAI.com
