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TL;DR
A November 10, 2026, article from ThorstenMeyerAI.com connects the growth of knowledge and information work to current generative AI exposure. It cites historical estimates that information work accounted for 46.4% of the U.S. workforce in 1970, while stressing that exposure does not establish job loss and that broad U.S. employment data shows no clear AI effect so far.
ThorstenMeyerAI.com published an article on November 10 examining how knowledge and information work became a major part of the economy—and why those jobs are now among those most exposed to generative AI. The piece traces that shift through economic studies by Fritz Machlup and Marc Porat, while emphasizing that exposure to AI is not the same as job loss.
The article recounts 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-related activity into education, research and development, communications media, information machines and information services. The figure measured costs and spending, the article cautions, not the economic value of knowledge itself.
It also draws on Marc Porat’s 1977 study for the U.S. Department of Commerce, which categorized workers by the tasks they performed rather than the industry where they worked. Porat estimated that information workers made up 5.8% of the U.S. workforce in 1860 and 46.4% in 1970. The article says information work had become the largest worker group by the mid-1950s, according to Porat’s analysis.
For a present-day comparison, the source cites 2025 International Labour Organization figures showing managers, professionals and technicians accounted for 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, but reports that overall U.S. employment data has not yet shown a clear AI footprint. It also points to research indicating young workers in the most exposed jobs are falling behind peers, without establishing that AI caused the gap.
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 Knowledge Work
The issue matters because generative AI is aimed at tasks associated with the expanding information and knowledge economy: writing, reading, calculating, checking and handling communications. These activities are not confined to technology companies or office-based industries; Porat’s method counted information workers wherever they worked, including clerks in factories and bookkeepers on farms.
That makes exposure a question for a large share of the workforce, not just a narrow technology sector. Still, an occupation’s exposure to AI does not show whether workers will be replaced, whether jobs will change, or whether productivity gains will create other roles. The source distinguishes that uncertainty from the historical evidence that information work grew into a large part of the U.S. economy.
The reported slowdown among younger workers in highly exposed jobs may be an early signal worth tracking, but the source does not establish a causal link to AI. Readers should treat it as an observation, not proof that the technology has already reduced employment opportunities.
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From Knowledge to Information Work
The article places the current AI debate in a longer history. In 1959, Peter Drucker described the rise of knowledge work: employment in which people earn a living through what they know rather than primarily through manual labor. His argument, as presented in the source, was that managing knowledge workers would require different practices from those developed for factory production.
Machlup’s 1962 study attempted to quantify knowledge activity by counting its costs. Porat later classified workers according to what they did during the workday and divided the economy into agriculture, industry, services and information. The source refers to information as a fourth, or quaternary, sector. Those historical categories help explain why AI’s focus on office tasks is part of a wider economic shift, rather than an entirely new development.
The measures are not interchangeable: Machlup estimated spending across knowledge industries, while Porat classified occupations and estimated workforce shares and economic output. Their numbers describe different things and historical periods. The article’s central connection is that work involving information became widespread well before today’s generative AI systems.
““Exposure is not job loss, yet.””
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AI’s Employment Effect Is Unsettled
The source says U.S. employment data shows no clear AI footprint so far, but it does not identify the data series, measurement period or method behind that assessment. It also does not provide the specific estimates or methodology used to identify higher-income and clerical jobs as more exposed to generative AI.
The reported gap for young workers in highly exposed jobs is not evidence on its own that AI caused their relative performance. The source does not specify the size of the gap, the period measured or other possible explanations. Nor does it establish how many jobs might be eliminated, altered or created as companies adopt AI.
Track Evidence Beyond Exposure
The source presents this as the final episode in its first arc on how the economy is built, and says its next episode will examine what economic statistics measure and what they leave out. For the AI question, the next useful evidence will be employment and wage data tied to defined time periods, alongside research that separates AI adoption from other changes in hiring and work.
Until those measures show a clearer pattern, the distinction in the article remains central: knowledge work is extensive, and some of it is exposed to generative AI, but the scale and direction of AI’s effect on jobs are still unsettled.
Key Questions
What does the article mean by the fourth sector?
It refers to the information or quaternary sector, a category for work centered on producing, processing or distributing information and knowledge.
What was Machlup’s 29% estimate?
Machlup estimated that U.S. spending on producing and distributing knowledge in 1958 was $136.4 billion, or almost 29% of adjusted gross national product. The source says this measured costs, not the value of knowledge itself.
How large did Porat estimate the information workforce had become?
Porat’s study estimated that information workers represented 46.4% of the U.S. workforce in 1970, compared with 5.8% in 1860. Those are historical estimates from his classification of workers by their tasks.
Does AI exposure mean a job will be lost?
No. The source explicitly distinguishes exposure to AI from job loss. It says current U.S. employment data shows no clear AI footprint, while noting a reported lag among young workers in some highly exposed jobs.
Which workers are described as more exposed to generative AI?
The article says estimates place higher-income and clerical jobs near the front of the line for generative AI exposure. It does not give a specific estimate of job losses or the methods behind those exposure rankings.
Source: ThorstenMeyerAI.com
