🔍 Read the full analysis: The Fourth Sector In The Age Of AI on ThorstenMeyerAI.com
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TL;DR
Thorsten Meyer’s November 10, 2026, report revisits the growth of knowledge and information work, the economic sector now facing generative AI exposure. Historical estimates show information work became a major part of the U.S. economy, but exposure to AI does not by itself prove job losses; the report says overall U.S. employment data has not yet shown a clear AI effect.
Generative AI is aimed at the knowledge and information work that now accounts for a large share of employment, according to a November 10 report by Thorsten Meyer. The report traces the rise of this “fourth sector” through research by economists Fritz Machlup and Marc Porat, while cautioning that exposure to AI is not the same as confirmed job loss.
Meyer’s account begins with economist Fritz Machlup’s 1962 study, which estimated that producing and distributing knowledge represented $136.4 billion, or almost 29% of adjusted U.S. gross national product, in 1958. Machlup measured spending associated with knowledge, not the economic value of knowledge itself. His categories included education, research and development, media, information machines and information services.
In a later U.S. Department of Commerce study, Marc Porat classified workers by what they did, rather than the industry where they worked. His 1977 report estimated that information workers made up 46.4% of the U.S. workforce in 1970, compared with 28.6% in industry, 21.8% in services and 3.1% in agriculture. A clerk employed by a factory could count as an information worker under this approach.
The report also cites 2025 International Labour Organization figures showing managers, professionals and technicians represented 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. These occupational categories are not identical to Porat’s measure, but they illustrate the scale of work involving specialized knowledge in wealthy economies. Meyer says higher-income and clerical roles appear particularly exposed to generative AI, while noting that current U.S. employment data shows no clear overall AI footprint.
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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AI Reaches the Knowledge Workforce
The shift matters because AI’s potential reach is not limited to factories or manual labor. Generative systems are designed to handle or assist with tasks such as writing, reading, calculation, checking and information processing—activities that have become routine in many professional and administrative jobs. That places the technology in the part of the economy that earlier researchers identified as a major source of employment and output.
But the scale of exposure should not be mistaken for a forecast of how many people will lose work. A tool may change how a task is done, reduce the time required, or alter demand for certain skills without eliminating a whole job. Meyer reports that young workers in the most AI-exposed jobs are falling behind their peers, but does not provide enough detail in the source material to establish a causal link or quantify the effect. The key question is how employers use the tools and whether productivity gains lead to different work, fewer positions, or both.
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How Information Work Became a Sector
Knowledge work became a distinct subject of discussion before computers became a fixture of offices. In 1959, management writer Peter Drucker used the term “knowledge worker” for people who earn a living through what they know. He argued that the growth of such work would require management practices different from those developed for factory production.
Machlup then tried to quantify knowledge-related activity by adding up its costs. Porat took a different route, classifying workers by their daily tasks and dividing the economy into agriculture, industry, services and information. These approaches are useful historical lenses, but they measure different things: spending on knowledge industries, occupational activity and economic output are not interchangeable. The “fourth sector” is a way to describe information work, not a single universally standardized category.
““Almost 29 per cent of adjusted GNP.””
— Fritz Machlup, as quoted in the 1962 study cited by Thorsten Meyer
Employment Effects Remain Unsettled
The report does not identify a clear, economy-wide effect of AI in U.S. employment data. It also does not specify the dataset, period or method behind its statement that young workers in the most exposed jobs are falling behind. That observation should not be read as proof that AI caused the difference.
Other uncertainties include how quickly companies will adopt generative AI, which tasks they will automate or redesign, and whether any productivity gains will increase demand for workers in other roles. The historical figures also rely on definitions that differ across studies and eras. Exposure estimates indicate potential contact with the technology, not a settled count of jobs at risk.
Tracking Work as AI Spreads
The next development to watch is whether stronger evidence links AI adoption to changes in hiring, wages, hours or job duties across exposed occupations. Employers’ implementation decisions and workers’ experience will help distinguish task assistance from job substitution. Meyer’s report points to a forthcoming discussion of what economic statistics measure and what they leave out, a relevant issue when assessing changes in knowledge work. For now, the available account supports a historical comparison and a warning about exposure, not a definitive verdict on AI’s net effect on employment.
Key Questions
What does “the fourth sector” mean?
It refers to information and knowledge work, considered alongside agriculture, industry and services. Marc Porat used workers’ activities, rather than their employers’ industries, to identify this group.
What did Machlup’s 29% estimate measure?
Machlup estimated that producing and distributing knowledge accounted for almost 29% of adjusted U.S. GNP in 1958. The calculation measured spending on knowledge-related activity, not the direct value of knowledge.
Does AI exposure mean a job will disappear?
No. Exposure means tasks may be affected; it does not establish that an entire job will be eliminated. The report says overall U.S. job data has not yet shown a clear AI footprint.
What employment effect does the report identify?
It says young workers in the most exposed jobs are falling behind their peers, but the source material does not give the underlying dataset or show that AI caused the gap. The claim remains an observation, not proof of causation.
Source: ThorstenMeyerAI.com
