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TL;DR
A November 10, 2026, article revisits the development of the information or “fourth” sector, drawing on economists Fritz Machlup and Marc Porat. It connects that history to generative AI’s exposure of office and knowledge work while stressing that exposure is not the same as job loss, and that broad US job 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 potential effects on office jobs. Drawing on economist Fritz Machlup’s 1962 estimate that knowledge production and distribution represented almost 29% of adjusted US gross national product in 1958, the installment argues that AI is now aimed at work that has become central to advanced economies. It also cautions that AI exposure does not establish job loss.
Machlup’s estimate covered spending on producing and distributing knowledge, rather than a direct measure of its economic value. His categories included education, research and development, communications media, information machines and information services. The article reports his estimate of $136.4 billion in 1958, or “almost 29 per cent of adjusted GNP.” It cites a breakdown tabulated by historian of statistics Benoît Godin: education made up 44% of that total, media 28%, information services 13%, research and development 8%, and information machines 7%.
The report also draws on Marc Porat’s 1977 study for the US Department of Commerce, which classified workers by the tasks they performed rather than the industry employing them, a distinction explored in another look at the information economy. Porat divided the workforce into agriculture, industry, services and information. In his estimates, information workers rose from 5.8% of the US workforce in 1860 to 46.4% in 1970, becoming the largest group by that year. The source says Porat found information work had become the largest group by the mid-1950s.
To connect those historical figures with the present, the article 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 work is among the job categories most exposed to generative AI estimates, while noting that exposure measures do not prove roles will disappear. The source reports no clear AI footprint in overall US job data so far, but says young workers in the most exposed jobs are falling behind their peers; it provides no underlying dataset or further detail for that comparison, amid broader predictions about AI’s effects on work.
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 article’s central point is that AI’s potential reach differs from earlier automation narratives: generative systems can perform or assist with tasks associated with reading, writing, calculation and information handling. Those activities now account for a large share of employment in many wealthy economies, according to the figures cited in the report. Changes to these roles could affect workers across industries, not only people employed by technology companies.
But the distinction between task exposure and employment outcomes matters for workers, employers and policymakers. A job may include tasks that AI can assist with without being eliminated. Outcomes could depend on how employers use the tools, whether they replace or complement staff, and how work is reorganized. The source’s account of current US data does not establish a broad employment effect, and its warning about younger workers is a specific concern rather than proof of economy-wide job losses.
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From Knowledge Work to AI
The historical thread begins with Peter Drucker’s 1959 description of knowledge work. Drucker, a Vienna-born management writer, used the term for work in which people earn a living through what they have learned. The report says his argument was that managing employees who create value through knowledge would require rules different from those developed for factory production.
Machlup attempted to put an economic measure on knowledge three years later by counting expenditure across industries and occupations. Porat later classified workers according to whether their tasks involved information, even when they worked in sectors such as farming or steelmaking. Together, their work supplies the report’s historical frame for a “fourth sector” alongside agriculture, industry and services. These estimates use different methods and measures, so they should not be treated as one continuous statistical series.
“Exposure is not job loss, yet.”
— ThorstenMeyerAI.com article
AI Exposure Is Not Job Loss
The source does not specify which studies or models underpin its statements about generative AI exposure, or how exposure is defined. It also does not provide the data, time period or comparison group behind its observation that younger workers in the most exposed jobs are falling behind their peers. That claim should not be read as proof that AI caused the difference.
It remains unclear how quickly employers will adopt generative AI, which tasks they will automate or augment, and whether any resulting changes will reduce headcount, alter job duties or create new work. The report says overall US job data show no clear AI footprint so far, but gives no measure or analysis to quantify that statement. The historical estimates describe earlier economic structures; they do not, by themselves, predict the scale or timing of AI’s labor-market effects.
Tracking Work and Adoption
The source frames this installment as the final episode of its first arc, with a following episode planned on what economic statistics measure and what they leave out. That topic is relevant to the AI discussion: claims about changes in productivity, employment or output depend on the definitions and data used.
For the labor-market picture to become clearer, readers will need evidence that tracks workplace AI adoption alongside job duties, hiring and employment over time. The article does not announce a specific next data release or forecast when such evidence will settle the question. For now, its conclusion is limited: information work is a major part of modern economies, generative AI is directed at some of its tasks, and the scale of any resulting job losses remains unconfirmed.
Key Questions
What is the “fourth sector” in the article?
It refers to the information or knowledge sector, alongside agriculture, industry and services. The article uses Marc Porat’s classification of workers by the information-related tasks they perform.
What did Machlup’s 29% estimate measure?
It estimated spending on producing and distributing knowledge in the United States in 1958: $136.4 billion, or almost 29% of adjusted GNP. The article stresses that this measured costs, not the direct value of knowledge.
Does AI exposure mean a job will be eliminated?
No. The report says exposure is not the same as job loss. AI may affect some tasks within a role, but the source does not establish how employers will use it or what the net effect on employment will be.
Has AI already changed US employment overall?
The source says overall US job data show no clear AI footprint so far. It does not provide the underlying data or method, so that statement does not settle whether effects are occurring in particular occupations or groups.
Source: ThorstenMeyerAI.com
