🔍 Read the full analysis: The Intersection Of AI And Knowledge Work on ThorstenMeyerAI.com
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TL;DR
A November 10, 2026, report from ThorstenMeyerAI.com examines how knowledge and information work became a major part of the U.S. economy—and why generative AI is aimed at many of those tasks. The source says current evidence does not show a clear overall AI footprint in U.S. employment, while young workers in the most exposed jobs may be falling behind peers.
ThorstenMeyerAI.com published a report on November 10 examining the rise of knowledge work and the growing relevance of generative AI to jobs built around information. Drawing on historical estimates and recent labor figures, the report describes knowledge work as a major part of modern economies, while cautioning that exposure to AI does not establish that jobs have been lost.
The report looks back to economist Fritz Machlup’s estimate for 1958: spending on producing and distributing knowledge in the United States totaled $136.4 billion, or “almost 29 per cent of adjusted GNP.” Machlup’s 1962 study grouped education, research and development, media, information machines and information services as knowledge industries. The figure measured spending, not the value of knowledge itself.
It also cites Marc Porat’s 1977 analysis, which classified workers by the tasks they performed rather than the industry where they worked. Porat estimated that information workers made up 5.8% of the U.S. workforce in 1860 and 46.4% in 1970, the largest of his four sectors that year. The report says information work had been the biggest group since the mid-1950s.
For a present-day comparison, the article cites International Labour Organization figures for 2025: managers, professionals and technicians accounted for 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. These categories indicate the scale of skilled work but are not necessarily identical to every definition of knowledge work.
Infographic 01 · The number to remember
Fritz Machlup's first count of the knowledge industries, by branch.
US spending on knowledge by branch, 1958 · share of $136.4 bn
Machlup measured what knowledge cost (spending, the way national accounts count), not what it was worth. “Almost 29 per cent of adjusted GNP” is his figure for 1958. The branch values are as tabulated by Godin (2008); the labour shares are Machlup’s estimate as summarised by his publisher.
Sources: F. Machlup, The Production and Distribution of Knowledge in the United States (Princeton University Press, 1962), as tabulated by B. Godin, “The Knowledge Economy: Fritz Machlup's Construction of a Synthetic Concept” (2008); Princeton University Press, book description.
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Why Office Tasks Face AI Exposure
Generative AI is aimed at tasks common in clerical and higher-income occupations, including reading, writing, calculating, checking and handling information. Those activities resemble the work that economists and management writers used to describe the expansion of the knowledge economy. That overlap helps explain why AI’s effects may reach offices and professional workplaces, not only factories or manual occupations.
But task exposure is not the same as job elimination. A tool may change how work is done, assist workers with particular assignments, or affect hiring without removing an entire occupation. The distinction matters to employees, employers and policymakers weighing how quickly to adapt training and workplace practices.
The report says available overall U.S. employment data shows no clear AI footprint so far. It also points to signs that young workers in the most exposed jobs are falling behind their peers, but the supplied material does not establish that AI caused that pattern. The evidence therefore supports attention to potential changes, not a conclusion that broad job losses have already occurred.
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From Factories to Information Work
The report frames knowledge work as a fourth broad economic sector alongside agriculture, industry and services. Peter Drucker, the Vienna-born management writer, used the term “knowledge worker” in his 1959 book The Landmarks of Tomorrow. His idea was that more people would earn a living through what they knew rather than primarily through physical labor, requiring different approaches to management than factory production.
Machlup tried to quantify that shift by counting spending across industries and occupations associated with producing or distributing knowledge. His study estimated that knowledge industries grew by 8.8% annually from 1947 to 1958. The report notes a limitation: spending is a measure of cost, not a direct measure of the quality or worth of the knowledge produced.
Porat later classified jobs by what workers did during the day. Under that method, an office worker employed by a factory could count as an information worker. The distinction helps explain why the report treats information work as a sector cutting across industries, rather than as a synonym for services or for technology companies alone.
AI’s Employment Effects Remain Unsettled
The source does not provide a named study, date range or detailed method for its statement that overall U.S. job data shows no clear AI footprint. It also does not identify the specific data behind the reported lag among young workers in highly exposed jobs. The size of that pattern, the occupations included and whether AI is responsible remain unclear from the material provided.
Nor does the report quantify how many jobs will be changed, reduced or created by generative AI. Its historical figures describe the growth of information work; they do not predict the timing or scale of AI’s effects. Exposure measures indicate that tasks may be affected, but they do not settle what employers will do or how workers’ roles will change.
Tracking Work as AI Spreads
The report presents this episode as the final installment of its first series arc and points toward a following episode on what economic measures count and what they miss. For readers tracking AI’s effects, the next useful evidence will be employment and hiring data with clear time periods, occupation definitions and comparison groups, alongside direct information about how workplaces are adopting the technology.
Until those details are available, the source’s central distinction remains the most defensible guide: knowledge work is a large and historically established part of the economy, and many of its tasks are exposed to generative AI, but the extent of resulting job changes is not yet established.
Key Questions
What is the news in this report?
ThorstenMeyerAI.com published an episode on November 10, 2026, about the historical rise of knowledge work and how generative AI may affect jobs centered on information tasks.
What did Machlup estimate?
Fritz Machlup estimated that U.S. spending on producing and distributing knowledge was $136.4 billion in 1958, or almost 29% of adjusted gross national product. The estimate measured spending, not knowledge’s direct value.
Does AI exposure mean a job will disappear?
No. Exposure means tasks in a job may be affected by AI; it does not by itself show that the job will be eliminated. The report says the overall U.S. employment data it references shows no clear AI footprint so far.
What remains uncertain about AI and employment?
The source does not give enough detail to assess the reported lag among young workers in highly exposed jobs or determine whether AI caused it. It also does not quantify future job losses, job changes or job creation.
Source: ThorstenMeyerAI.com
