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TL;DR
A Nov. 10, 2026, article from ThorstenMeyerAI.com traces the growth of information and knowledge work, drawing on estimates that it accounted for nearly 29% of adjusted U.S. GNP in 1958 and 46.4% of the workforce in 1970. It argues that generative AI is directed at many office tasks, while stressing that exposure is not the same as job loss and that broad U.S. employment data has not yet shown a clear AI effect.
ThorstenMeyerAI.com published a report on November 10 tracing the rise of knowledge work and explaining why generative AI could affect office occupations. The article draws on historical estimates that knowledge-related activity made up almost 29% of adjusted U.S. GNP in 1958 and that information workers accounted for 46.4% of the U.S. workforce in 1970. It cautions that exposure to AI does not itself establish that jobs will disappear.
The report, the eighth episode of the series “Work After Work,” revisits economist Fritz Machlup’s 1962 study, which estimated that the production and distribution of knowledge cost $136.4 billion in the United States in 1958. That was “almost 29 per cent of adjusted GNP,” according to the study’s estimate. The figure measures spending on knowledge-related activity, not the total economic value that knowledge created.
Machlup grouped knowledge industries into education, research and development, communications media, information machines and information services. Education accounted for 44% of the estimated total, while communications media made up 28%, information services 13%, research and development 8%, and information machines 7%. The breakdown is attributed in the source article to historian of statistics Benoît Godin’s tabulation.
The report also describes Marc Porat’s later classification of workers by the tasks they performed, rather than by the industry where they worked. In Porat’s 1977 account, information workers rose from 5.8% of the U.S. workforce in 1860 to 46.4% in 1970, becoming the largest of four groups—agriculture, industry, services and information. The article connects that history to current AI concerns, citing 2025 International Labour Organization figures showing managers, professionals and technicians represented 52.5% of U.S. jobs and 47.9% of German jobs.
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 Work Is in Focus
The development matters because generative AI systems are designed to perform or assist with many tasks common in knowledge jobs, including writing, reading, summarizing, calculating and checking. These activities are widespread across sectors, not limited to technology companies: a clerk in a factory or a bookkeeper on a farm may do information work even though their employer belongs to another industry.
The report says higher-income and clerical roles are among those most exposed to generative AI. That describes potential contact between technology and job tasks; it does not establish how many positions will be automated, whether workers will be replaced, or whether productivity gains will create other work. Those distinctions matter to workers, employers and policymakers weighing training, hiring and workplace changes.
The article also reports that overall U.S. employment data has not yet shown a clear AI footprint, while young workers in the most exposed jobs are falling behind their peers. The source provides no underlying measure or detailed comparison for that latter observation. It should be read as a reported concern, not proof that AI caused the difference.
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From Knowledge to Information Work
The historical account begins with Peter Drucker’s 1959 book, “The Landmarks of Tomorrow,” in which he described the growth of knowledge work and the knowledge worker. Drucker’s broader argument, as recounted by the source, was that organizations built around factory-era production methods would need different approaches to managing people whose work depends on learning and expertise.
Machlup then tried to quantify knowledge-related activity by measuring its costs, because knowledge itself could not be counted like physical goods. His estimate covered 1958 and used the categories of industries and occupations available to him. Porat later used a task-based approach to identify information workers across the economy. These are historical frameworks and estimates, not directly comparable measurements of today’s workforce or a forecast of AI’s employment effects.
The source also cites 2025 ILO figures for the share of jobs held by managers, professionals and technicians: 62% in Singapore, 52.5% in the United States and 47.9% in Germany. Those occupational categories illustrate the scale of knowledge-oriented work in several economies, but they are not presented as a complete count of every worker whose tasks involve information.
“Exposure is not job loss, yet.”
— ThorstenMeyerAI.com
What AI Exposure Can Show
The source does not provide a specific forecast for how many jobs or tasks generative AI will replace, nor does it identify a time frame for large-scale employment changes. Exposure estimates are not job-loss counts: work may be automated, assisted, reorganized or left largely unchanged, depending on the occupation and how employers use the technology.
The article says U.S. employment data shows no clear AI footprint so far but does not specify the data series, period or method behind that assessment. It also reports that young workers in highly exposed jobs are falling behind peers without detailing the comparison or establishing AI as the cause. The scale and persistence of any effect remain unclear.
Historical estimates have limits as well. Machlup measured costs rather than the value produced by knowledge, and Porat’s categories reflect a specific study of the U.S. economy. The source does not offer a directly comparable current measure of the quaternary or information sector.
Evidence to Watch in Employment
The immediate question is whether future labor-market data can distinguish AI-related changes from other influences on hiring, wages and workplace organization. Useful evidence would identify the period, occupations and comparison groups being measured, and would separate changes in specific tasks from changes in total employment.
The article is the final installment of the series’ first arc and points toward a subsequent discussion of what economic statistics measure and what they leave out. For readers tracking AI’s effects, the next developments to watch include employer adoption, changes to job duties and training, and labor-market results over time. No specific job-loss total or new employment forecast is provided in the source.
Key Questions
What is the news development?
ThorstenMeyerAI.com published a report on November 10, 2026, connecting the historical growth of knowledge work with current concerns about generative AI and office occupations.
What did Machlup estimate?
Machlup estimated that knowledge production and distribution cost $136.4 billion in the United States in 1958, or almost 29% of adjusted GNP. The estimate measured spending, not the full value created by knowledge.
Does AI exposure mean a job will be eliminated?
No. Exposure means a job includes tasks AI may affect; it does not show that the job will disappear. The source provides no estimate of how many positions will be lost.
What does the report say about current U.S. employment?
The article says broad U.S. job data has shown no clear AI footprint so far. It does not identify the data series or method behind that statement, and it says the effects remain uncertain.
Source: ThorstenMeyerAI.com
