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TL;DR
ThorstenMeyerAI.com’s Nov. 10, 2026, episode revisits research showing how information and knowledge work became a major part of the U.S. economy. It connects that history to generative AI’s exposure of clerical and higher-income jobs, while stressing that exposure does not establish job loss and that broad employment effects remain uncertain.
ThorstenMeyerAI.com published an episode on November 10 examining how information and knowledge work grew into a major part of the U.S. economy and why those jobs are now exposed to generative AI. The report draws on historical estimates and current labor-market indicators, but says exposure is not the same as job loss; broad U.S. employment data has not yet shown a clear AI effect.
The episode revisits 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 knowledge industries into education, research and development, communications media, information machines, and information services. The figure measures costs and spending, not the total value of knowledge produced.
It also summarizes Marc Porat’s 1977 analysis, which classified workers by their tasks rather than by the industry or workplace employing them. Porat estimated that information workers made up 5.8% of the U.S. workforce in 1860 and 46.4% in 1970. In his account, information work had become the largest employment group by the mid-1950s. These figures are historical estimates, not a direct measurement of today’s workforce.
For more recent context, the episode 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. It says higher-income and clerical roles are among the jobs most exposed to generative AI, while noting that U.S. employment data has not established a broad AI-driven change. The episode also reports that younger workers in the most exposed jobs are falling behind peers, but the material does not establish 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 is aimed at tasks that already occupy a large share of work: reading, writing, calculating, checking and handling information. That makes the technology’s potential effects relevant beyond a narrow group of technology firms. Clerical staff and professionals may encounter AI tools as part of ordinary office workflows, even when their jobs are not eliminated.
However, exposure measures whether tasks could be affected by a technology; it does not show how employers will use that technology or what happens to staffing, wages or productivity. The report distinguishes possible task change from confirmed job loss, a distinction readers need when interpreting forecasts about AI and employment. Its account of younger workers’ relative performance is a signal to monitor, not proof that AI has already changed their job prospects.
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How Information Became a Sector
The episode frames the history as a shift from agriculture and industry toward services and information work. It recalls Peter Drucker’s 1959 description of the “knowledge worker”: a person whose livelihood depends primarily on learned knowledge rather than manual labor. Drucker argued that managing such workers would require rules different from those developed for factory production.
Machlup attempted to quantify the growing knowledge economy by tracking spending across industries. Porat later classified workers according to the information tasks they performed, including employees doing clerical work inside factories or farms. That approach produced a four-part account of the economy—agriculture, industry, services and information—and underlies the episode’s description of information as a fourth sector. The episode presents these studies as historical frameworks; their categories and measures should not be treated as identical to current labor statistics.
“Exposure is not job loss, yet.”
— ThorstenMeyerAI.com episode
What AI Exposure Cannot Yet Show
The source material does not identify the specific studies or methods behind its claims about which jobs are most exposed to generative AI. It also does not provide a time period, comparison group or causal analysis for the reported gap between younger workers in the most exposed jobs and their peers. The cause and scale of that gap remain unclear.
Nor does the episode specify which employers have adopted AI, how many workers’ duties have changed, or whether any changes have affected hiring, pay or hours. The statement that U.S. job data shows no clear AI footprint is not accompanied here by a named dataset or measurement window. Historical estimates from Machlup and Porat also use different definitions from the cited modern occupational shares, so they do not by themselves establish a direct trend into 2025.
Track Jobs, Tasks and Adoption
The next useful evidence will be labor-market reporting that identifies its data sources and time periods, alongside details on how employers are deploying generative AI. Readers should watch for changes in hiring, wages, hours and job duties—not exposure estimates alone—to judge whether AI is reshaping work in practice. No specific next labor-market milestone or forecast is given in the source material.
The episode also points toward a follow-up on what economic measurements count and what they miss. That question matters here because Machlup measured spending on knowledge industries, while Porat classified workers and economic output using a separate framework. Those measures answer different questions, and neither alone establishes the present-day employment impact of AI.
Key Questions
What is the development in this report?
ThorstenMeyerAI.com published an episode on November 10, 2026, linking the historical growth of information work to current concerns about generative AI and office jobs.
What did Machlup’s 29% estimate measure?
It was Machlup’s estimate that U.S. spending on producing and distributing knowledge in 1958 was $136.4 billion, or almost 29% of adjusted GNP. It measured costs, not the total value of knowledge.
Does AI exposure mean a job will be eliminated?
No. Exposure indicates that tasks could be affected; it does not establish that an employer will automate them or eliminate a position.
Has AI already produced a clear effect on U.S. employment?
The episode says available overall U.S. job data shows no clear AI footprint so far. It does not name the dataset or measurement period, and it reports a possible difference for younger workers in highly exposed jobs without establishing AI as the cause.
Source: ThorstenMeyerAI.com
