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A November 9, 2026, article by Thorsten Meyer argues that the long-term growth of service work in the United States predates the decline in manufacturing and reflects differences in how easily jobs can be made more productive. It connects that pattern to Baumol’s cost disease and cites a study in which an AI assistant increased customer-support agents’ issues resolved per hour by 15% on average; whether AI will reduce service employment remains uncertain.
About four in five U.S. workers were employed in services in 2025, according to International Labour Organization estimates cited by Thorsten Meyer in an article published November 9. The piece argues that the shift began well before manufacturing employment peaked and examines whether artificial intelligence can raise productivity in service work, where tasks often depend on human time and attention.
Meyer’s figures show services made up 62% of U.S. payroll jobs outside farming in 1939, rising to 67% in 1967, 72% in 1979 and 86% in 2025. Those figures are based on the Bureau of Labor Statistics’ Current Employment Statistics survey, annual averages, which excludes farm workers and self-employed people. The separate ILO estimate counts all workers and puts the 2025 services share at 79.6%. The measures have different coverage and should not be treated as identical.
The article uses Baumol’s cost disease to explain why prices and employment can grow in sectors where productivity gains are harder to achieve. In a 1967 paper, economist William Baumol distinguished work where labor is an input that machinery can reduce from work where human labor is central to the service. A quartet still requires musicians to play together; a factory can produce more goods per worker through changes to machinery and production methods.
To show how prices have diverged, Meyer reports that the official, quality-adjusted U.S. price of televisions fell 98% between 2000 and 2025, while hospital services rose 274%, college tuition 188% and car repair 143%. The figures are presented as price changes over that period, not as proof that every increase has the same cause. The article also cites a study of 5,172 customer-support agents in which an AI assistant increased issues resolved per hour by 15% on average, a finding related to growing AI subscriptions.
80% Of AI In Services: What’s Behind The Trend?
Services have long accounted for most U.S. work. AI may help raise productivity, but its wider effects on jobs, costs and service quality remain uncertain.
A long shift into services
Services were already the larger share of non-farm payroll jobs before U.S. manufacturing employment peaked.
Why service productivity is different
Baumol’s cost disease describes how work requiring human time can be harder to make more productive than work transformed by machinery.
- A quartet still requires musicians to play together.
- Some service tasks depend on human attention and contact.
- Factories can produce more per worker through machinery and production changes.
- AI may help service workers handle more tasks in the same time.
Prices moved in different directions
Reported U.S. price changes from 2000 to 2025 show a sharp contrast, but do not establish a single cause.
| Category | Price change |
|---|---|
| Televisions | Down 98% |
| Hospital services | Up 274% |
| College tuition | Up 188% |
| Car repair | Up 143% |
What the AI evidence shows—and doesn’t
One customer-support study offers a measured example, not a forecast for every service job.
An AI assistant helped 5,172 customer-support agents resolve 15% more issues per hour on average.
The article does not establish effects on staffing, wages, customer satisfaction or total jobs.
Other service settings need evidence on productivity, quality, costs and workers’ conditions.
AI Meets Labor-Intensive Services
The argument matters because services account for most U.S. employment, while their costs affect household budgets and public spending. If AI helps workers handle more tasks in the same amount of time, it could change the productivity limits that Baumol’s theory describes. The cited support-agent study offers one measured example: workers using an assistant resolved 15% more issues per hour on average. That result is evidence about a particular study, not proof that AI will produce the same gains across health care, education, repair work or other services.
The effects could extend beyond prices. Employers might use productivity gains to serve more customers, change staffing levels, or shift workers toward tasks that still require human judgment and contact. Which outcome follows depends on how organizations adopt the tools, the nature of the work and whether customers accept AI-assisted service. The source article frames this as an open question rather than a settled forecast.
Meyer also cautions against explaining rising service prices only through inefficiency or institutional costs. Haircuts and car repairs, which do not rely on health insurance or student loans, also rose in the cited comparison. At the same time, the article notes that the United States spends 18.0% of its economy on health, compared with 12.4% in Germany. Those figures suggest that Baumol’s explanation is not the only relevant factor in the size of a bill; the article does not provide a full breakdown of the causes of the difference.
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The Long Shift Into Services
The source article places the trend against the history of U.S. manufacturing employment. Manufacturing reached a peak of almost 20 million jobs in June 1979, according to the figures cited in the piece, but services were already the larger part of payroll employment outside farming decades earlier. That chronology challenges a simple account in which services expanded only after factories began to shrink. The service share increased before and after the 1979 peak.
Baumol and economist William Bowen examined performing-arts finances in their 1966 book, Performing Arts: The Economic Dilemma. Their example was the time required to perform a Schubert string quartet: four musicians playing for 45 minutes use three musician-hours, and the performance cannot be shortened substantially without changing the work. Baumol developed the broader theory in his 1967 paper, “Macroeconomics of Unbalanced Growth: The Anatomy of Urban Crisis.”
The theory is not a strict division between jobs that can and cannot improve. Baumol described it as a matter of degree. Retail, for example, has adopted supermarkets and self-service, while technology can change how services are delivered without removing the human element entirely. Meyer’s article uses that qualification to connect the historical theory with AI: software may assist workers, but the scale and consequences of those gains are not established by one example.
““Human ingenuity has devised ways to reduce the labor necessary to produce an automobile, but no one has yet succeeded in decreasing the human effort expended at a live performance of a 45-minute Schubert quartet much below a total of three man-hours.””
— William Baumol, quoted from his 1967 paper
How Far AI Gains Will Reach
The source does not identify the customer-support study’s publication, date, research methods or comparison conditions, so readers cannot assess those details from the article alone. Its reported 15% average productivity gain should not be generalized to other service occupations without further evidence. The piece also does not establish whether the improvement changed wages, staffing, customer satisfaction or the total number of jobs.
It remains unclear how widely AI tools will be adopted, which tasks they can handle reliably, and how employers will use any time saved. The source’s price comparisons likewise do not isolate the reasons costs changed in each category. Its figures show different price movements over the period, but do not establish that Baumol’s cost disease alone caused them.
Evidence From Wider Adoption
The next test is whether productivity gains reported in customer support appear in other service settings and persist outside a study. Useful evidence would compare output and service quality before and after adoption, while also tracking staffing, costs and workers’ conditions. The source article provides no specific rollout schedule or policy milestone.
For now, the reported employment shares establish the scale and long history of the shift toward services, while the support-agent result points to a possible way AI could affect that work. Further studies and employer data will be needed to show whether these tools lower costs, change service quality, reduce demand for some tasks or create different work for employees.
Key Questions
What does the article mean by “about four in five” workers?
The article cites an ILO estimate of 79.6% of U.S. workers in services in 2025. It separately reports that services were 86% of payroll jobs outside farming in the BLS survey; the measures cover different worker groups.
What is Baumol’s cost disease?
It describes how costs can rise in work that is difficult to make more productive when wages rise across the economy. A live performance, for example, still requires musicians to spend time playing, while factories may increase output per worker through machinery and process changes.
Does the cited study show AI will eliminate service jobs?
No. The article reports that an AI assistant helped 5,172 customer-support agents resolve 15% more issues per hour on average. It does not say that the study measured job losses or predict what employers will do with the productivity gain.
Why does the article compare U.S. and German health spending?
Meyer cites health spending equal to 18.0% of the U.S. economy and 12.4% of Germany’s to show that labor-productivity pressures do not explain every difference in service costs. The article does not provide a detailed analysis of the reasons for the gap.
Source: Thorsten Meyer AI
