🔍 Read the full analysis: How AI Is Shaping New Ways To Work on ThorstenMeyerAI.com
Get smart everyday buys 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
AI is producing measurable gains in some workplace tasks, including a reported 15% productivity increase for customer-support agents in one study. But executives estimate that AI has added about 0.2 percentage points a year to productivity growth so far, leaving open whether those localized gains will spread across the economy.
A report published November 12 says AI is improving productivity in some workplace tasks, but the gains have not yet translated into a large economy-wide increase. One study cited in the report found customer-support agents handled work about 15% more productively with AI, while executives put AI’s contribution to annual productivity growth so far at roughly 0.2 percentage points.
The report, part of Thorsten Meyer’s “Work After Work” series, defines labor productivity as output per hour worked. Its central point is that AI’s effect should be judged not just by whether a tool helps an individual worker complete a task faster, but by whether those improvements raise output across businesses and the wider economy. The report does not identify the customer-support study by name or provide its methodology, so the 15% figure should be read as a result from one study, not a general estimate for all workers or industries.
The economy-wide estimate is also distinct from firm-level results. According to the report, executives estimate AI has contributed about 0.2 percentage points a year to productivity growth so far. It does not specify the survey, the executives’ industries, or the period covered by that estimate. The figure is presented as an assessment of AI’s current effect, not a forecast or a definitive measure of its future impact.
That gap between visible workplace gains and modest aggregate results echoes a pattern from earlier technologies: new tools can spread before productivity statistics show a sustained rise. The report points to the spread of computers in the 1970s and 1980s, followed by a stronger period of U.S. productivity growth in the late 1990s and early 2000s. Whether AI follows a similar timeline is not yet known.
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).
Powered by Thorsten Meyer AI
Why AI Gains May Take Time
The distinction matters because productivity growth compounds. The report’s figures show that at 2% annual growth, output per hour doubles in about 35 years; at 1%, doubling takes about 70 years. Those are illustrations of the long-run effect of different growth rates, not predictions of what AI will deliver. They help explain why a small sustained change in productivity can matter to living standards over time.
For workers and employers, the practical question is whether AI changes the organization of work, not only the speed of a single task. A tool may help an employee draft, search, or respond more quickly, but broader productivity depends on whether businesses can incorporate those gains into their processes and produce more value with the time and resources available. The source report does not quantify how much of the reported task-level improvement comes from that wider organizational change.
The history also cautions against treating early adoption as proof of an economy-wide transformation. The report says U.S. nonfarm business productivity growth averaged 2.8% a year from 1948 to 1973, then 1.4% from 1973 to 1995, based on its calculations from a Bureau of Labor Statistics index. Growth later rose to about 3.1% a year from 1995 to 2004. These historical rates show how productivity has varied; they do not establish that computers, or AI, alone caused every change.
Products Worth Considering
Samsung 32 Odyssey G5 (G51F) QHD 180Hz Gaming Monitor
Dell UltraSharp U3225QE 31.5-Inch 4K UHD 120Hz IPS Black Thunderbolt Hub Monitor
Dell UltraSharp U2424H 24-Inch FHD Monitor
As an affiliate, we earn on qualifying purchases.
From Computers to AI
The report connects today’s AI debate with economist Robert Solow’s work. In a 1957 study of U.S. output per worker from 1909 to 1949, Solow attributed 87.5% of the increase to technical change and 12.5% to greater use of capital, according to the figures quoted in the report. “Technical change” covers more than machines: it can include improved methods, organization, skills, and technology. Economists use the term total factor productivity for output growth that cannot be explained by more labor or capital alone.
In 1987, as computers spread through workplaces, Solow wrote that “you can see the computer age everywhere but in the productivity statistics.” The line became associated with the Solow paradox: the apparent mismatch between a widely visible technological shift and weak productivity growth. The report says U.S. productivity growth later accelerated, reaching about 3.1% annually from 1995 to 2004. It cites a 2000 estimate by Federal Reserve economists Stephen Oliner and Daniel Sichel that information technology use and computer production accounted for about two-thirds of the one-percentage-point increase in productivity growth they examined.
The source also uses agriculture to illustrate long-run change: U.S. farm output in 2023 was nearly three times its 1948 level, while total inputs had risen by about 9%, according to the report’s summary of Department of Agriculture data. That example shows how output can rise without a comparable increase in inputs. It does not, by itself, establish what AI will do in other sectors.
How Broad Are the AI Gains?
The report does not name or detail the study behind the 15% customer-support result, including its sample, setting, or duration. It also does not provide the source or survey design behind executives’ estimate of AI’s roughly 0.2-percentage-point annual contribution. The two figures measure different things and should not be treated as directly comparable.
It remains unclear whether early gains will persist, spread to other types of work, or appear in economy-wide productivity data. The report also does not establish how much reported improvement reflects AI itself, changes to work processes, or other factors. Its historical comparisons offer context, but they cannot determine the timing or scale of AI’s future economic impact.
Watch Economy-Wide Productivity Data
The next test is whether productivity statistics show sustained improvement as businesses adopt AI and change how work is organized. The report offers no specific date for that milestone and makes no forecast about when a broad effect may appear. Readers should distinguish task-level study results, company-level experience, and national productivity measures, which capture different scales of change.
Future assessments will be clearer if studies disclose their methods, comparison groups, time periods, and the kinds of work measured. For now, the confirmed picture in the source is mixed: AI has produced reported gains in at least one workplace study, while its estimated contribution to overall productivity growth remains comparatively small.
Key Questions
What does productivity mean in this report?
It means output per hour worked: how much value workers produce for each hour of labor.
Has AI increased productivity across the whole economy?
The source report does not establish a large economy-wide increase. It says executives estimate AI has contributed about 0.2 percentage points a year to productivity growth so far, but does not give details about the estimate’s survey or time period.
What does the 15% customer-support figure measure?
The report describes it as a productivity gain for support agents in one study. It does not identify the study or provide its methods, so the figure should not be generalized to all support teams or workers.
What was the Solow paradox?
It refers to the mismatch economist Robert Solow described in 1987: computers were visible across workplaces, but productivity statistics had not yet shown a corresponding increase.
Source: ThorstenMeyerAI.com
