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TL;DR
A November 10, 2026, report revisits how knowledge and information work grew into a major part of the economy, citing historical estimates and current workforce data. It says generative AI is more likely to affect many clerical and higher-income roles, but exposure does not establish job loss; overall U.S. employment data has not yet shown a clear AI effect.
ThorstenMeyerAI.com published a report on November 10 examining how knowledge and information work became a large part of the economy and why generative AI is now aimed at many office tasks. The report points to historical estimates and 2025 workforce figures, while cautioning that exposure to AI is not the same as job loss and that U.S. employment data shows no clear overall AI footprint so far.
The report draws on economist Fritz Machlup’s 1962 estimate that producing and distributing knowledge accounted for $136.4 billion, or almost 29% of adjusted U.S. gross national product, in 1958. Machlup measured spending across areas such as education, research and development, media, information machines and information services. The figure describes the cost of knowledge-related activity under his method, not a direct measure of its value.
It also describes Marc Porat’s 1977 analysis, which classified workers by the tasks they performed rather than by the industry employing them. In Porat’s estimates, information workers made up 5.8% of the U.S. workforce in 1860 and 46.4% in 1970, when they formed the largest of his four categories. These figures are historical estimates using Porat’s definitions, not a direct count of today’s jobs.
For a present-day comparison, the report cites 2025 International Labour Organization figures showing managers, professionals and technicians account 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 employment most exposed to generative AI. The figures and exposure assessments do not show how many jobs will be eliminated, changed or created.
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 Office Workforce
The issue matters because AI tools can perform or assist with tasks common in knowledge work, including writing, reading, calculating, checking and organizing information. That puts the technology’s potential effects in offices and professional workplaces, not only in manufacturing or other settings where automation has historically been discussed.
But exposure measures indicate that a task could be affected; they do not predict a particular employment outcome. Some work may be automated, some may be reorganized around AI tools, and some may remain largely unchanged. The report says overall U.S. job data has not yet shown a clear AI effect, while citing a more limited concern: young workers in the most exposed jobs are falling behind their peers. It does not provide enough detail here to establish the cause or scale of that difference.
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From Knowledge Work to Information Jobs
Management writer Peter Drucker used the term “knowledge worker” in his 1959 book, The Landmarks of Tomorrow. The report describes his argument that workers who create value through learning and expertise would require different management approaches from factory workers organized around assembly lines.
Machlup’s 1962 work sought to quantify knowledge-related activity by counting its costs, while Porat’s later study classified jobs according to whether workers handled information. Together, the approaches helped frame the idea of a fourth, information-focused sector alongside agriculture, industry and services. Their definitions and methods differ, so their historical percentages should not be treated as interchangeable measures.
The report’s present-day figures use a narrower occupational grouping—managers, professionals and technicians—to illustrate the scale of knowledge-heavy work in three countries. That grouping is not identical to Porat’s information-worker category, and it should not be read as a direct continuation of his time series.
““Exposure is not job loss, yet.””
— ThorstenMeyerAI.com report
Employment Effects Still Unsettled
The source does not specify which AI exposure estimates it relies on, their methodology, or the time period they cover. It also does not quantify how many roles are likely to be automated, augmented or newly created. Exposure is a measure of potential contact with the technology, not evidence that a worker or job will disappear.
The report says U.S. employment data has shown no clear overall AI footprint so far, but gives limited detail about the datasets or measures behind that assessment. Its statement about younger workers in highly exposed jobs also does not establish that AI caused their relative employment outcomes. The scale, timing and distribution of any future effects remain uncertain.
Watch Workplace Data Develop
The next developments to watch are whether employment and wage data begin to show sustained changes in occupations with high AI exposure, and whether those changes differ by age, industry and income. Employers’ use of AI and workers’ reports of how duties are changing may help clarify whether the technology is replacing tasks, assisting workers or shifting the mix of skills required.
The report’s series also points to a forthcoming discussion of how economic statistics measure activity and what those measures leave out. For now, its central finding is historical and its AI implications provisional: knowledge work is a major feature of modern economies, while the employment consequences of generative AI are not yet settled.
Key Questions
What does the report say happened on November 10, 2026?
ThorstenMeyerAI.com published a report tracing the growth of knowledge and information work and discussing how generative AI may affect office jobs.
What did Machlup’s 29% estimate measure?
Machlup estimated that knowledge production and distribution cost $136.4 billion, or almost 29% of adjusted U.S. GNP, in 1958. It was a spending-based estimate, not a direct measure of knowledge’s economic value.
Does AI exposure mean a job will be lost?
No. Exposure means work may include tasks that generative AI could affect; it does not establish that a job will be eliminated. Work could also change or be supported by AI.
Has AI already caused a clear change in overall U.S. employment?
The source report says overall U.S. job data shows no clear AI footprint so far. It does not identify the specific datasets or measures behind that assessment, and future effects remain uncertain.
Source: ThorstenMeyerAI.com
