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TL;DR
A November 10, 2026, article from ThorstenMeyerAI.com examines the growth of the information or “fourth” sector and its relevance to generative AI. Historical estimates put knowledge-related activity at almost 29% of adjusted U.S. GNP in 1958; newer figures show knowledge occupations make up large shares of employment in several rich economies. AI exposure does not establish that jobs have been lost, and the article says broad U.S. employment data has not yet shown a clear AI effect.
ThorstenMeyerAI.com published a report on November 10 tracing the rise of knowledge and information work and linking that history to generative AI’s exposure of office jobs. The article revisits economists’ estimates that information-related activity had become a major part of the U.S. economy decades ago, while stressing that exposure to AI is not the same as job loss.
The report centers on two historical measurements. Economist Fritz Machlup estimated that knowledge production and distribution accounted for $136.4 billion, or “almost 29 per cent of adjusted GNP,” in the United States in 1958. His 1962 book, The Production and Distribution of Knowledge in the United States, grouped activity into education, research and development, communications media, information machines and information services. The estimate counted costs and spending, not the economic value of knowledge itself.
In a later study for the U.S. Department of Commerce, Marc Porat classified workers by the tasks they performed, rather than by the industry employing them. His 1977 report, The Information Economy, 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. The article says information work had been the largest group since the mid-1950s, according to Porat’s analysis.
The report also cites 2025 International Labour Organization figures for managers, professionals and technicians: these groups held 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. It presents those figures as a current indicator of the scale of knowledge-oriented employment, while noting that occupational groupings do not mean every job in them is equally exposed to AI.
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 Exposure Reaches Office Work
The history matters because generative AI is aimed at many tasks associated with the information economy: reading, writing, calculating, checking and handling information. Earlier waves of automation often brought machinery into factories and other physical workplaces. The report argues that the current technology puts more attention on office tasks and on higher-income and clerical occupations, which are often assumed to be less vulnerable than manual work.
That shift could affect a large share of workers and employers, but the report distinguishes task exposure from employment outcomes. A tool may change how a job is done without eliminating the job; workers may also take on different tasks or use AI to increase output. The article says overall U.S. employment data has not shown a clear AI footprint so far, while citing a possible concern: young workers in the most exposed jobs are falling behind their peers. It does not provide the underlying measure or establish that AI caused that pattern.
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How Economists Counted Information Work
Peter Drucker described the rise of “knowledge work” in The Landmarks of Tomorrow in 1959. He used the term for work in which people earn a living through what they know rather than primarily through manual labor. His argument, as summarized in the report, was that managing workers who create value with knowledge would require different approaches from factory-era systems such as the assembly line.
Machlup then tried to measure the scale of knowledge-related industries by adding up their costs. His 29% figure is an estimate for 1958, not a measure of current AI-related activity or a direct measure of knowledge’s contribution to output. Porat’s later approach classified occupations by function, including information workers employed in sectors such as manufacturing and agriculture. These measures describe different things and should not be treated as interchangeable.
The article calls the information economy the quaternary, or fourth, sector, after agriculture, industry and services. Its account uses the term to describe work centered on producing and distributing information. The historical figures show how such work expanded; they do not by themselves predict how AI will change employment.
What AI Employment Data Shows
The scale and timing of AI-driven job changes remain unclear. The report says there is no clear AI footprint in overall U.S. job data so far, but it does not specify which employment series or period supports that assessment. It also does not give details behind its statement that young workers in the most exposed jobs are falling behind peers, or establish a causal link to AI.
The article describes higher-income and clerical work as being near the front of the line for generative AI exposure, but exposure estimates are not forecasts of layoffs. The source material does not identify a particular study, method or timeframe for that claim. It is also unclear how quickly employers will adopt these tools, which tasks they will automate or augment, and how workers’ job duties and wages may change.
Tracking Adoption and Job Effects
The report’s immediate next step is to continue its series: it says the following episode will examine what economic numbers measure and what they leave out. That subject is relevant to interpreting claims about AI, since headcounts, output, wages and task changes can tell different parts of the story.
For the employment question, readers will need more detailed evidence over time: clear definitions of which jobs and tasks are exposed, measured employer adoption, and workforce data that can distinguish AI effects from other economic changes. No specific new study, policy announcement or employment milestone is identified in the source material. Until such evidence is available, the report’s central point is a historical comparison, not a finding that generative AI has already caused widespread job losses.
Key Questions
What does “fourth sector” mean in the report?
It refers to the information or knowledge economy, described as a sector alongside agriculture, industry and services. It includes work focused on producing, processing or distributing information.
What does Machlup’s 29% estimate measure?
Machlup estimated that knowledge production and distribution represented $136.4 billion, or almost 29% of adjusted U.S. GNP, in 1958. The method counted costs and spending, not the value of knowledge itself.
Does AI exposure mean a job will disappear?
No. Exposure means that tasks may be affected; it does not establish that an employer will eliminate a role. The source article says overall U.S. employment data has not shown a clear AI footprint so far.
What does the report say about young workers?
It says young workers in the most AI-exposed jobs are falling behind their peers, but the supplied material does not identify the underlying data, timeframe or cause. The claim should not be read as proof that AI caused the gap.
Source: ThorstenMeyerAI.com
