🔍 Read the full analysis: Understanding AI In The Fourth Sector on ThorstenMeyerAI.com
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TL;DR
A November 10 essay on ThorstenMeyerAI.com traces the growth of knowledge and information work, drawing on estimates by Fritz Machlup and Marc Porat. It connects that history to generative AI’s exposure of clerical and higher-income jobs, while noting that exposure does not establish job loss and broad U.S. employment data shows no clear AI effect so far.
ThorstenMeyerAI.com published an essay on November 10 examining how knowledge and information work grew into a major part of the economy and why those jobs are now in focus as generative AI spreads. The article draws on historical estimates that knowledge-related activity accounted for almost 29% of adjusted U.S. GNP in 1958, while cautioning that AI exposure does not by itself mean a job will disappear.
The essay, the eighth episode of the publication’s “Work After Work” series and the final installment of its first arc, revisits economist Fritz Machlup’s 1962 estimate. In his book The Production and Distribution of Knowledge in the United States, Machlup put U.S. spending on producing and distributing knowledge at $136.4 billion in 1958, or almost 29% of adjusted GNP. The figure measures spending, not the value of knowledge itself.
Drawing on a breakdown tabulated by historian of statistics Benoît Godin, the article says education accounted for 44% of that total, media for 28%, information services for 13%, research and development for 8%, and information machines for 7%. Machlup also estimated that the share of workers in knowledge occupations rose from about 11% in 1900 to 32% in 1959.
The essay also discusses Marc Porat’s 1977 study for the U.S. Department of Commerce, which classified workers by their tasks rather than the industry where they worked. Porat estimated that information workers made up 46.4% of the U.S. workforce in 1970, compared with 28.6% in industry, 21.8% in services and 3.1% in agriculture. These historical classifications and estimates describe particular methods and periods; they are not direct measures of today’s labor market.
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 it places current AI debate against a long shift in what people do for work. In the essay’s account, economic activity moved beyond farming and factory production toward services and then information-related tasks such as writing, calculating, planning and record-keeping. Generative AI targets many of those office tasks, rather than only the manual work associated with earlier waves of automation.
The source says higher-income and clerical jobs are among those most exposed to generative AI. Exposure indicates that tasks may be affected; it does not prove that employers will eliminate positions or that workers’ total demand will fall. The distinction matters to employees and policymakers weighing training, hiring and workplace changes. The essay reports no clear AI footprint in overall U.S. job data so far, while citing signs that young workers in the most exposed jobs are lagging peers. It does not provide enough detail here to establish the scale or cause of that gap.
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From Knowledge Work to Information Jobs
The essay traces the idea of knowledge work to management writer Peter Drucker, who described its rise in his 1959 book The Landmarks of Tomorrow. Drucker’s central point, as presented in the source, was that workers creating value through learned knowledge could not simply be managed by applying factory-era methods such as assembly-line organization.
Machlup attempted to quantify knowledge-related activity by counting its costs across education, research, communications media, information machines and information services. Porat later used a different approach, sorting occupations by the information tasks workers performed—even when they worked in a farm or factory. The essay calls this broad category the fourth, or quaternary, sector. Its historical figures should be read as estimates based on those authors’ definitions, not as a single universally accepted measure.
For a present-day comparison, the source cites 2025 International Labour Organization figures showing managers, professionals and technicians held 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. Those occupational groups are not necessarily identical to Machlup’s or Porat’s categories, so the figures do not form a direct time series.
““Exposure is not job loss, yet.””
— ThorstenMeyerAI.com essay
What AI Exposure Does Not Show
The source does not establish how many jobs generative AI has eliminated, created or changed. Its statement that overall U.S. employment data shows no clear AI footprint so far is not accompanied in the supplied material by a dataset, time window or method, so readers cannot assess the strength of that finding from the essay alone. The reported lag among young workers in highly exposed jobs is also not quantified here, and the source does not establish that AI caused it.
It is also unclear how historical definitions of knowledge and information work map onto current occupations. Machlup counted costs across selected industries, while Porat classified workers by tasks; modern job categories differ. The essay’s headline connection between the fourth sector and AI is a framing of the issue, not evidence that every information job will be automated or that exposure will produce the same outcome across industries.
The Series Turns to Economic Measures
The article says the next episode will examine what economic numbers measure and what they leave out, following its discussion of Machlup’s spending-based estimate. The supplied source does not name a publication date for that installment or set out a forecast for AI-related job changes. For readers tracking the employment effects, the next useful evidence would include clearly dated labor-market data, transparent definitions of exposure and comparisons that distinguish AI effects from other causes of hiring changes.
Key Questions
What is the fourth sector?
The essay uses the term for knowledge- and information-related economic activity, including work involving communication, planning, calculation and information services. It is also called the quaternary sector.
What did Machlup’s 29% estimate measure?
It was his estimate of spending on producing and distributing knowledge in the United States in 1958: $136.4 billion, or almost 29% of adjusted GNP. The source stresses that this counted costs, not the value of knowledge itself.
Does AI exposure mean a job will be lost?
No. Exposure means work tasks may be affected; it does not establish that an employer will remove a position. The source reports no clear AI footprint in overall U.S. job data so far, but gives limited detail about the evidence.
What did Porat estimate about information workers?
In his 1977 study, Porat estimated that information workers represented 46.4% of the U.S. workforce in 1970. He classified workers by what they did, rather than only by the industry employing them.
Source: ThorstenMeyerAI.com
