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TL;DR
A November 10, 2026, article from ThorstenMeyerAI.com examines the rise of the information economy and its exposure to generative AI. It cites historical estimates that information work became the largest US employment group by the mid-1950s, while stressing that exposure to AI does not establish job loss and that broad labor-market effects remain unclear.
ThorstenMeyerAI.com published an article on November 10 examining how knowledge and information work grew into a major part of the US economy—and why that history matters as generative AI reaches office tasks. The account draws on studies by Fritz Machlup and Marc Porat, while cautioning that AI exposure is not the same as job loss.
The article revisits economist Fritz Machlup’s 1962 study, which estimated that producing and distributing knowledge accounted for $136.4 billion in 1958, or “almost 29 per cent of adjusted GNP.” Machlup measured spending associated with knowledge industries, rather than the value of knowledge itself. His categories included education, research and development, communications media, information machines and information services.
It also draws on Marc Porat’s 1977 report, The Information Economy, which classified workers by the tasks they performed rather than the industry or workplace employing them. Porat estimated that information workers made up 5.8% of the US workforce in 1860, rising to 30.8% in 1950 and 46.4% in 1970. The report said information work had become the largest employment group by the mid-1950s.
The article links those historical shifts to current debate about generative AI. It says higher-income and clerical jobs are among the roles most exposed to the technology, but reports that US employment data has not yet shown a clear overall AI effect. It also cites signs that younger workers in more exposed occupations are falling behind their 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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AI Reaches the Information Economy
The argument matters because generative AI is directed at tasks common in knowledge jobs: writing, reading, calculating, checking and planning. Those activities were part of the work Machlup described decades before current AI systems existed. The historical perspective shifts attention from factory automation alone to the large share of modern work built around handling information.
But the scale of information work does not reveal how many jobs AI will replace, or how quickly. A job can include tasks that AI may assist with alongside duties that still require human judgment, responsibility or communication. Exposure measures potential contact with AI; it does not, by itself, predict layoffs, wage changes or productivity gains.
The distinction is relevant to workers, employers and policymakers weighing training, hiring and workplace rules. If AI changes how office tasks are done, the effects may show up first in particular occupations or among new entrants, rather than as an immediate fall in overall employment. The article’s account offers a way to frame that debate, not a forecast of its outcome.
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From Knowledge Work to a Fourth Sector
Management writer Peter Drucker described the rise of “knowledge work” in The Landmarks of Tomorrow, published in 1959. He argued that people increasingly earned a living through what they knew rather than through manual labor, and that organizations would need different approaches to managing this work than the factory methods associated with assembly lines.
Machlup attempted to quantify the shift by tracking the costs of industries that produced or circulated knowledge. In his estimate for 1958, education accounted for 44% of the total, media for 28%, information services for 13%, research and development for 8%, and information machines for 7%. The figures describe the composition of his spending estimate, not a direct measure of each field’s economic value.
Porat later classified employment into four broad groups: agriculture, industry, services and information. This framework is sometimes described as a fourth, or quaternary, sector. The source article also cites 2025 International Labour Organization figures for managers, professionals and technicians: 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. Those occupational categories are not identical to Porat’s information-worker measure, so they should not be treated as a direct continuation of his series.
“Almost 29 per cent of adjusted GNP.”
— Fritz Machlup, as quoted in his 1962 study
AI’s Employment Effects Remain Unclear
The source article does not provide a named study, dataset or measurement method for its statement that US job data shows no clear overall AI footprint. It also does not identify the evidence behind its observation that young workers in highly exposed jobs are falling behind peers, or establish whether AI is responsible.
It remains unclear which tasks will be automated, which will be reshaped or supported, and how those changes may affect hiring, pay and productivity. The historical estimates also use different definitions: Machlup measured spending, Porat classified work activities, and the cited 2025 occupational figures cover specified job groups. They offer useful context but are not directly comparable measures of one continuous trend.
The source provides no quantified estimate of jobs lost or created because of generative AI, and no timetable for expected changes. Its claims about exposure should be read as a description of potential vulnerability, not a confirmed account of labor-market outcomes.
Watch for Task-Level Labor Data
The next evidence to watch is labor-market data that distinguishes changes in particular tasks and occupations from broader shifts in employment. Hiring patterns for younger workers, job openings, wages and reported workplace use of AI may help clarify whether early effects are concentrated in specific groups.
The article presents this installment as the final episode in its first arc on how the economy is built, with a follow-up planned on what economic measures capture and what they miss. For the AI question, the central test remains whether reported exposure turns into measurable changes in work—and whether those changes reflect substitution, assistance or new demand.
Key Questions
What is the “fourth sector” in this article?
It refers to the information or knowledge economy: work centered on producing, processing and sharing information. Porat’s framework placed it alongside agriculture, industry and services.
What did Machlup estimate about knowledge work?
Machlup estimated that US spending on producing and distributing knowledge reached $136.4 billion in 1958, or almost 29% of adjusted gross national product. He measured spending, not the intrinsic value of knowledge.
Does AI exposure mean a job will disappear?
No. Exposure indicates that some tasks in a job may be affected by AI; it does not establish that the whole job will be eliminated. The article provides no quantified estimate of AI-driven job losses.
Has AI already changed US employment overall?
The source article says US employment data has not shown a clear overall AI footprint so far. It also points to possible difficulties for younger workers in more exposed jobs, but does not establish that AI caused them.
Source: ThorstenMeyerAI.com
