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A November 10, 2026, article in Thorsten Meyer AI’s “Work After Work” series examines the rise of knowledge work and its exposure to generative AI. Historical estimates put knowledge production at nearly 29% of adjusted U.S. GNP in 1958; current figures show managers, professionals and technicians make up large shares of employment in several rich economies. AI exposure does not establish job loss, and the article says U.S. employment data has yet to show a clear overall AI effect.
Thorsten Meyer AI published a new installment of its “Work After Work” series on November 10, examining how knowledge work became a major part of the economy and why generative AI is directed at office tasks. The article draws on historical estimates and recent employment figures, while noting that exposure to AI does not by itself show that jobs have been lost.
Economist Fritz Machlup estimated that the production and distribution of knowledge accounted for $136.4 billion, or almost 29% of adjusted U.S. GNP in 1958. His estimate counted spending on activities such as education, research and development, media, information machines and information services. It measured the cost of producing and spreading knowledge, rather than the value or quality of the knowledge itself.
The article also cites labor figures attributed to the International Labour Organization in 2025: managers, professionals and technicians accounted for 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. Those categories are used as an indicator of knowledge work. The article says estimates place higher-income and clerical jobs near the front of the line for generative AI exposure, but it does not equate exposure with replacement.
AI and the changing nature of knowledge work
Knowledge work became a major part of the economy. Generative AI targets office tasks, but exposure alone does not show that jobs have been lost.
Knowledge work grew into a major sector
Historical estimates track the rising share of work centered on producing, sharing and using information.
Machlup estimated annual growth of 8.8% from 1947 to 1958.
His estimate rose from about 11% of workers in 1900 to 32% in 1959.
Porat found their share grew from under 6% in 1860 to 46% in 1970.
Office tasks face exposure
Generative AI is directed at tasks used across many industries.
Information work
Reading, writing, planning, calculating and handling information may change.
Potentially exposed
Higher-income and clerical jobs are described as near the front of the line for exposure.
No clear overall effect
U.S. employment data has yet to show a clear overall AI footprint.
Exposure is not a job-loss count
The article distinguishes potential contact with AI from evidence of what employers and workers experience.
- Some tasks may be affected by AI.
- Employers may automate tasks or redesign roles.
- That a role will be eliminated.
- How many jobs will change or disappear.
What to track next
Future evidence can show whether AI exposure is translating into labor-market change.
- Hiring and job numbers
- Wages and tasks performed
- Changes in clerical and other highly exposed roles
Office Work Faces AI Exposure
The development matters because AI systems are being applied to tasks associated with a large share of modern employment: reading, writing, planning, calculating and handling information. If these tools alter how such work is done, the effects could reach beyond technology firms to offices across many industries.
The source distinguishes that possibility from evidence of realized job losses. It reports no clear overall AI footprint in U.S. job data so far, while saying young workers in the most exposed jobs are falling behind their peers. The latter is a reported pattern, not proof that AI caused the difference. The available material does not specify the dataset or measure behind that comparison.
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From Knowledge to a Sector
In 1959, management writer Peter Drucker described the rise of knowledge work: employment in which people earn a living through what they have learned. Three years later, Machlup attempted to quantify the activity. He grouped knowledge industries into education; research and development; media; information machines; and information services, and also considered white-collar occupations within companies.
Machlup’s estimate put knowledge industries’ annual growth at 8.8% from 1947 to 1958. His estimate of workers in knowledge occupations rose from about 11% in 1900 to 32% in 1959. Separately, the article cites Marc Porat’s finding that information workers grew from under 6% of the U.S. workforce in 1860 to 46% in 1970, becoming the largest worker group from the mid-1950s. These figures use different definitions and periods, so they describe related trends rather than a single directly comparable series.
““Exposure is not job loss, yet.””
— Thorsten Meyer AI article
AI’s Employment Effects Remain Unclear
The source does not establish how many jobs have been eliminated or changed by AI, nor does it provide a timeframe or method for its statement that young workers in highly exposed jobs are falling behind peers. Exposure measures potential contact with AI; they do not show whether employers will automate tasks, redesign roles or increase demand for other work.
The historical estimates also depend on definitions. Machlup counted spending and specified occupations, while newer categories such as managers, professionals and technicians are not identical measures. The figures show the scale of knowledge-related activity, but they do not predict the size or direction of AI’s labor-market effects.
Tracking Work as AI Spreads
The next question is whether future employment data shows changes in hiring, job numbers, wages or the tasks people perform, particularly in clerical and other highly exposed roles. The article does not name a specific forthcoming study or release date. Its series also signals a follow-up discussion of what economic statistics measure and what they leave out.
Key Questions
What is the news development?
Thorsten Meyer AI published a report on November 10, 2026, linking the historical growth of knowledge work to generative AI’s focus on office tasks.
What did Machlup estimate?
Machlup estimated that producing and distributing knowledge cost $136.4 billion, nearly 29% of adjusted U.S. GNP, in 1958. The figure measures spending, not the economic value or quality of knowledge.
Does AI exposure mean workers will lose their jobs?
No. Exposure indicates that job tasks may be affected; it does not establish that a role will be eliminated. The source says U.S. job data has not shown a clear overall AI footprint so far.
Which workers may be most exposed to generative AI?
The article says estimates place higher-income and clerical jobs near the front of the line for exposure. It does not provide a specific estimate of how many jobs will change or disappear.
Source: Thorsten Meyer AI
