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A November 10, 2026 essay from Thorsten Meyer AI traces how economists and management thinkers identified knowledge work as a major part of the economy. It connects that history to generative AI’s exposure of office work, while noting that exposure does not establish job losses and that broad U.S. employment data shows no clear AI effect so far.
Thorsten Meyer AI published an essay on November 10, 2026, examining how knowledge work became a major part of the economy and why office jobs are now among those exposed to generative AI. The account draws on historical estimates and current employment figures, while stressing that exposure to AI does not by itself show that workers have lost jobs.
Economist Fritz Machlup estimated that producing and distributing knowledge accounted for $136.4 billion, or almost 29% of adjusted U.S. GNP, in 1958. In his 1962 book, he grouped knowledge activity into education, research and development, communications media, information machines and information services. The essay cites a breakdown tabulated by historian Benoît Godin: education made up 44% of the total, media 28%, information services 13%, research and development 8%, and information machines 7%.
Machlup also estimated that the share of U.S. workers in knowledge occupations rose from about 11% in 1900 to 32% in 1959. A later measure by Marc Porat, based on the tasks workers performed, found information work increased from under 6% of the U.S. workforce in 1860 to 46% in 1970, becoming the largest group from the mid-1950s, according to the essay.
The article uses recent occupational figures to show the scale of knowledge work today. Citing the International Labour Organization in 2025, it says managers, professionals and technicians accounted for 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. It says higher-income and clerical work is near the front of the line for generative AI exposure, but reports no clear AI footprint in overall U.S. job data so far.
The Fourth Sector’s Knowledge Work In An AI Era
Knowledge work grew into a major share of economic activity. Generative AI reaches many office tasks, but exposure alone does not show that jobs are being lost.
How knowledge work expanded
Historical estimates tracked both knowledge producing industries and workers whose jobs centered on information.
Where knowledge work appears today
The ILO’s 2025 occupational figures show substantial shares across three countries.
AI reaches office tasks
Generative AI may affect activities found across clerical, professional and management roles.
Routine knowledge work
Reading, writing, calculating, planning, checking and communicating are exposed to AI.
Work may be reorganized
Businesses may automate or change tasks, but the material does not establish how quickly they will adopt AI.
Exposure is not replacement
Task overlap does not establish job losses, wage changes or new jobs.
What the evidence says so far
The essay describes possible pressure on some workers while emphasizing that the cause and scale remain unsettled.
- Young workers in the most exposed U.S. jobs are falling behind their peers.
- Overall U.S. job data shows no clear AI footprint so far.
- The material does not establish that AI caused the young worker gap.
- Exposure does not reveal whether employers will cut or reshape jobs.
AI Reaches the Office Economy
The essay’s central point is that AI is aimed at tasks that have long made up a large share of economic activity: reading, writing, calculating, planning, checking and communicating. These tasks appear across clerical, professional and management jobs, not only in technology companies. That breadth makes changes to knowledge work relevant to workers and employers across many industries.
But task exposure is not the same as replacement. The source says young workers in the most exposed U.S. jobs are falling behind their peers, while overall employment data does not yet show a clear AI effect. That combination points to a possible early pressure on some groups without establishing that AI caused it or that broad job losses have begun.
Machlup’s historical estimate also comes with a measurement limit: it counted the cost of producing knowledge, not the value or quality of what was produced. The comparison offers a way to understand the sector’s economic scale, but it does not measure how much AI might add, save or displace.
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From Knowledge Workers to AI
Management writer Peter Drucker described the rise of knowledge work in 1959, naming workers who made their living through what they had learned rather than primarily through manual labor. As summarized in the essay, Drucker argued that production systems modeled on factory assembly lines would not be enough to manage people whose work depended on knowledge and judgment.
Machlup followed in 1962 with an attempt to count knowledge production, including both industries and white-collar occupations inside companies. His categories captured schools and media as well as office machines and information services. The essay frames that work as an early account of an economy already moving beyond the familiar image of factories and assembly lines.
Its present-day comparison uses occupational shares from the ILO’s 2025 figures and the essay’s summary of AI exposure research. Those measures describe different things: the historical estimates count knowledge activity or workers, while exposure estimates concern how generative AI may affect tasks.
““almost 29 per cent of adjusted GNP””
— Fritz Machlup, as quoted in the essay
Employment Effects Remain Unsettled
The essay does not establish that AI has caused the reported employment gap among young workers in highly exposed jobs. It also says overall U.S. job data shows no clear AI footprint so far. The source provides no specific study details, time window or comparison baseline for the young-worker observation, so its size and causal meaning cannot be assessed from the material presented.
It remains unclear how quickly businesses will adopt generative AI, which tasks they will automate or reorganize, and whether productivity gains will change staffing levels. Exposure measures indicate possible overlap between tools and tasks; they do not by themselves predict job losses, new jobs or wage changes.
Machlup’s 29% figure is an estimate of spending relative to adjusted GNP in 1958, not a measure of knowledge’s economic value. Historical and current figures therefore provide context for the scale of knowledge work, but do not form a direct measure of AI’s future impact.
Tracking AI and Office Jobs
The next developments to watch are changes in employment, wages and job entry among workers in occupations with high generative AI exposure. The essay does not identify a specific forthcoming report or milestone. It says its next episode will examine what economic measures capture and what they leave out, a relevant question when comparing historical estimates with today’s AI claims.
Key Questions
What did Machlup estimate about knowledge work?
He estimated that producing and distributing knowledge accounted for $136.4 billion, or almost 29% of adjusted U.S. GNP, in 1958. The figure measures spending, not the value of knowledge produced.
Does AI exposure mean a job will disappear?
No. Exposure means a job includes tasks that generative AI may affect. It does not establish that an employer will automate those tasks or eliminate the job.
What does the essay say about U.S. employment so far?
It says overall U.S. job data shows no clear AI footprint so far, while young workers in the most exposed jobs are falling behind their peers. The material does not establish that AI caused that gap.
How large is knowledge work today?
Citing the ILO’s 2025 figures, the essay says managers, professionals and technicians account for 52.5% of U.S. jobs, 47.9% in Germany and 62% in Singapore.
Source: Thorsten Meyer AI
