🔍 Read the full analysis: Fourth Sector Perspectives On AI on ThorstenMeyerAI.com
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TL;DR
ThorstenMeyerAI.com’s Nov. 10 report examines the growth of a fourth economic sector—knowledge and information work—and its exposure to generative AI. Historical estimates show this work became a major part of the U.S. economy, but exposure to AI does not establish that jobs will be eliminated; broad employment effects remain uncertain.
ThorstenMeyerAI.com published the final episode of its first “Work After Work” arc on November 10, examining how knowledge and information work grew into a major part of the economy and why those jobs are now directly exposed to generative AI. The report draws on historical estimates by Fritz Machlup and Marc Porat, while stressing that AI exposure is not the same as job loss.
The episode centers on economist Fritz Machlup’s estimate that knowledge-related industries accounted for almost 29% of adjusted U.S. gross national product in 1958. In his 1962 book, The Production and Distribution of Knowledge in the United States, Machlup estimated spending of $136.4 billion on producing and distributing knowledge. The report says education made up the largest share of that total, followed by media, information services, research and development, and information machines.
The episode also recounts how Marc Porat classified workers by the tasks they performed, rather than by the industry or workplace employing them. In his 1977 report for the U.S. Department of Commerce, Porat estimated that information workers made up 5.8% of the U.S. workforce in 1860 and 46.4% in 1970. The report says information work had become the largest occupational group by the mid-1950s, according to Porat’s analysis.
For a contemporary comparison, ThorstenMeyerAI.com cites 2025 International Labour Organization figures showing managers, professionals and technicians held 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. These occupational categories are not identical to every historical definition of knowledge work, so the figures offer a broad indication of its scale rather than a directly comparable time series.
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 Office-Based Work
The report’s central point is that generative AI is directed at tasks common in knowledge jobs: writing, reading, calculating, checking and handling information. This shifts attention from the familiar image of machines replacing physical labor to the possible effects of automation and assistance in offices, professional services and other information-heavy settings.
That exposure could matter to workers, employers and policymakers because knowledge occupations represent a large share of employment in wealthy economies. But the report does not establish how many jobs AI will remove, change or create. It distinguishes a task’s potential exposure from a verified employment outcome, a distinction that matters when interpreting predictions about the labor market.
ThorstenMeyerAI.com also says overall U.S. employment data has not yet shown a clear AI effect, while pointing to signs that younger workers in the most exposed occupations are falling behind their peers. The episode does not provide enough detail in the supplied material to establish the size, cause or duration of that gap. Those observations are best treated as indicators for further study, not proof that AI has caused a broad decline in employment.
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From Factories to Information Work
The episode builds on the prior installment in the series, which described how roughly 80% of workers came to be employed in services. It argues that this broad category conceals a distinct group: people whose work primarily involves producing, processing or distributing knowledge and information. The report calls this a fourth sector, alongside agriculture, industry and services; economists also refer to it as the quaternary sector.
Management writer Peter Drucker used the term “knowledge worker” in his 1959 book The Landmarks of Tomorrow. His argument, as presented in the episode, was that workers creating value through what they know would require approaches different from factory management built around assembly lines and repeated physical tasks.
Machlup and Porat measured different things. Machlup counted the cost of producing and distributing knowledge, while Porat grouped workers according to their information-related activities and also estimated the sector’s share of employment and economic output. Their results helped make less visible work measurable, but the methods and categories do not make every estimate interchangeable. The episode notes that Machlup measured spending, not the value of knowledge itself.
“Almost 29 per cent of adjusted GNP”
— Fritz Machlup, as quoted in ThorstenMeyerAI.com’s summary of his 1962 estimate
The Employment Effects Remain Unsettled
The scale and timing of AI’s effects on jobs remain unclear. The episode cites estimates that higher-income and clerical occupations are among those most exposed to generative AI, but it does not provide a specific exposure model, date range or estimate of jobs likely to be lost. Exposure measures generally concern tasks that technology may affect; they do not, by themselves, show whether employers will cut jobs or how workers’ roles will change.
The report says U.S. employment data shows no clear overall AI footprint so far and that younger workers in the most exposed jobs are falling behind peers. The supplied material does not specify the dataset, comparison period or causal evidence behind those observations. It is therefore not possible from this episode alone to determine whether AI explains the difference or whether it signals a lasting trend.
Historical figures also depend on definitions and accounting choices. Machlup’s 29% estimate refers to spending on knowledge industries in 1958; Porat’s workforce percentages classify occupations by information tasks. Neither figure measures the present-day contribution of AI, and neither directly predicts future employment.
The Series Turns to Measurement
The November 10 episode closes the first arc of “Work After Work.” ThorstenMeyerAI.com says the next installment will examine what economic numbers measure and what they leave out, extending the discussion of Machlup’s cost-based estimate. That topic may help readers distinguish spending, economic output, employment and the value of knowledge—measures that are often discussed as though they were the same.
For the AI question, the next evidence to watch is not simply the share of work that could be affected. More informative signals would include clearly defined, dated employment and wage data, changes in hiring across exposed occupations, and evidence separating AI’s contribution from other economic factors. The episode gives no date for a future labor-market assessment or a specific forecast, so the scale of any changes remains open.
Key Questions
What is the fourth sector discussed in the report?
It refers to knowledge and information work, such as producing, handling or distributing information. The episode places it alongside agriculture, industry and services.
What does Machlup’s 29% figure refer to?
It is Machlup’s estimate that spending on producing and distributing knowledge amounted to almost 29% of adjusted U.S. GNP in 1958. It measures spending, not the intrinsic value of knowledge.
Does AI exposure mean a job will disappear?
No. Exposure means tasks in a job may be affected by AI; it does not establish that the job will be eliminated. The episode says the overall U.S. employment data has not shown a clear AI footprint so far.
What did Porat’s study find about information workers?
Porat’s 1977 report estimated that information workers represented 46.4% of the U.S. workforce in 1970, up from 5.8% in 1860. His classification was based on workers’ tasks, not simply the industry where they worked.
Source: ThorstenMeyerAI.com
