🔍 Read the full analysis: Knowledge Work And AI Collaboration on ThorstenMeyerAI.com
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TL;DR
A November 10, 2026, ThorstenMeyerAI.com report connects the growth of knowledge and information work to today’s generative AI debate. It cites historical estimates that knowledge-related activity accounted for almost 29% of adjusted U.S. GNP in 1958 and information workers made up 46.4% of the workforce in 1970; it also says AI exposure is not the same as job loss and that a clear overall U.S. employment effect has not emerged.
ThorstenMeyerAI.com published a report on November 10, 2026, tracing how knowledge and information work grew into a major part of the economy and why that history matters as generative AI targets office tasks. The report cites historical U.S. estimates that knowledge-related industries accounted for almost 29% of adjusted gross national product in 1958, while stressing that exposure to AI does not by itself establish that jobs have been lost.
The report draws on economist Fritz Machlup’s 1962 study, which estimated that the production and distribution of knowledge in the United States amounted to $136.4 billion in 1958, or “almost 29 per cent of adjusted GNP.” Machlup grouped education, research and development, media, information machines and information services as knowledge industries. The figure measures spending, the report explains, rather than the economic value of knowledge itself.
It also recounts Marc Porat’s classification of workers by the tasks they performed, rather than by the industry where they worked, a distinction explored in AI’s effect on factory work. In his 1977 report for the U.S. Department of Commerce, information workers accounted for 46.4% of the U.S. workforce in 1970, compared with 5.8% in 1860. Porat’s framework divided employment into agriculture, industry, services and information.
For a present-day comparison, the article cites International Labour Organization figures for 2025: managers, professionals and technicians made up 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. It says higher-income and clerical work is especially exposed to generative AI, but reports no clear overall AI footprint in U.S. job data so far, amid broader questions about AI’s impact on work inside banks. It also points to signs that younger workers in the most exposed occupations are falling behind peers, without establishing that AI caused the difference.
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 Faces AI Exposure
The report’s central point is that AI’s labor-market relevance reaches beyond factories or manual occupations. Many knowledge jobs involve reading, writing, calculating, checking and communicating—activities that generative AI systems can assist with or perform parts of. That places a large and economically important category of work in the debate over how automation may change tasks and employment.
But exposure is not a forecast of job destruction. AI may change the composition of a job, support workers, or reduce demand for some tasks; the supplied material does not establish which outcomes will prevail. The distinction matters to workers and employers deciding how to interpret AI tools, and to policymakers monitoring employment. The report’s warning about younger workers is an early signal, not proof of a broad causal effect.
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From Knowledge Work to AI
The historical thread begins with management writer Peter Drucker, who used the term “knowledge worker” in his 1959 book The Landmarks of Tomorrow. Drucker argued that an economy increasingly reliant on people’s learning and judgment could not be managed solely by applying factory-era methods such as assembly-line organization.
Machlup attempted to quantify knowledge-related activity in 1962 by counting its costs. Porat later classified workers according to what they did, making information work visible across industries: a clerk at a steel company counted as an information worker even though the employer belonged to manufacturing. The report calls this a fourth, or quaternary, sector alongside agriculture, industry and services. These historical measures use different methods and years, so they describe related changes rather than a single directly comparable series.
““Exposure is not job loss, yet.””
— ThorstenMeyerAI.com report
What Employment Data Cannot Yet Show
The source report says available overall U.S. job data shows no clear AI footprint so far, but it does not specify the datasets, measurement period or method behind that assessment. It also does not provide detailed figures for the claim that younger workers in the most AI-exposed jobs are falling behind their peers, or establish whether AI explains the gap.
It remains uncertain which tasks will be automated, which will be augmented, and whether changes will lead to fewer jobs, different job requirements or new roles. The historical estimates also rely on differing definitions: Machlup counted expenditure on knowledge industries, while Porat classified workers by their activities. Neither figure directly measures current generative AI use or its effect on productivity and employment.
Tracking AI’s Effects on Workers
The next step is to compare claims about AI exposure with workplace adoption and employment data over time, including evidence about hiring, wages, task changes and outcomes for younger workers. The report does not identify a forthcoming study, policy decision or release date, so no specific next milestone is confirmed.
ThorstenMeyerAI.com frames this article as the close of its first “Work After Work” arc and says the next episode will examine what economic measures capture and what they miss. That distinction will matter in future assessments: counts of spending or occupational exposure do not, on their own, show whether AI is raising output, changing working conditions or reducing employment.
Key Questions
What is the report’s main news development?
ThorstenMeyerAI.com published a report on November 10, 2026, linking the historical growth of knowledge and information work to current generative AI exposure of office occupations.
What did Machlup estimate about knowledge work?
Machlup estimated that producing and distributing knowledge in the United States cost $136.4 billion in 1958, or almost 29% of adjusted GNP. The figure measures spending, not the value of knowledge itself.
Does AI exposure mean a job will be eliminated?
No. Exposure indicates that tasks in an occupation may be affected by AI; it does not establish that a job has been or will be eliminated. The report says overall U.S. job data does not yet show a clear AI footprint.
What remains uncertain about AI and employment?
The scale and timing of AI’s effects on hiring, wages and job tasks remain unclear. The report also does not establish whether AI caused the reported gap between younger workers in highly exposed occupations and their peers.
Source: ThorstenMeyerAI.com
