🔍 Read the full analysis: AI Transforming Knowledge Work on ThorstenMeyerAI.com
Get the latest gadgets delivered free with Prime
- Fast, free delivery on millions of items
- Prime Video, Amazon Music and more included
- Member-only deals all year
TL;DR
A November 10, 2026, article from ThorstenMeyerAI.com traces the growth of knowledge and information work, drawing on estimates that it accounted for 29% of adjusted U.S. GNP in 1958 and 46.4% of the workforce in 1970. It argues that generative AI is aimed at many of these office tasks, while stressing that exposure does not establish job loss and that clear economy-wide effects have not yet emerged.
ThorstenMeyerAI.com published a report on November 10 tracing the rise of knowledge work and arguing that generative AI is aimed at tasks central to that sector, including writing, planning, reading and calculation. The article pairs historical estimates of knowledge work’s economic scale with a caution: exposure to AI is not the same as job loss, and the source says U.S. employment data has not yet shown a clear overall AI effect.
The installment revisits economist Fritz Machlup’s 1962 estimate that producing and distributing knowledge accounted for $136.4 billion, or “almost 29 per cent of adjusted GNP,” in the United States in 1958. Machlup measured spending on knowledge-related activities rather than the economic value of knowledge itself. The source attributes the category breakdown to historian of statistics Benoît Godin: education made up 44% of the total, media 28%, information services 13%, research and development 8%, and information machines 7%.
It also draws on Marc Porat’s 1977 analysis, which classified workers by what they did rather than the industry where they worked. Porat estimated that information workers rose from 5.8% of the U.S. workforce in 1860 to 46.4% in 1970, making information the largest of his four categories by that year. The report says that today managers, professionals and technicians account for 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany, citing the International Labour Organization in 2025.
The article presents these figures as context for AI’s reach into office work, not as evidence that automation has already eliminated jobs at scale. It says higher-income and clerical roles are among those most exposed to generative AI and reports that young workers in the most exposed jobs are falling behind peers. The source does not provide the underlying studies or enough detail to establish the size or cause of that gap.
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.
Powered by Thorsten Meyer AI
AI’s Reach Into Office Work
The report’s central point is that AI is directed at work that has become a major part of modern economies: processing and communicating information. Tools that can assist with writing, calculation, checking or planning may affect tasks across professions and workplaces, not only jobs in technology companies. That makes the discussion relevant to employers, workers and policymakers weighing how generative AI could change job duties.
But the distinction between task exposure and employment outcomes matters. A system may assist with or automate parts of a role without removing the role altogether; the source offers no confirmed count of jobs lost to AI. Its reference to younger workers in exposed jobs falling behind is a signal that warrants attention, not proof that AI caused the difference. The historical figures help explain the scale of the work at issue, but they do not predict the pace or distribution of future changes.
Products Worth Considering
Logitech MX Keys S Combo Wireless Keyboard and Mouse, Graphite
Samsung Galaxy Tab A11+ (6GB RAM, 128GB Storage) – Gray
Samsung Galaxy Tab A9+ 11-Inch Android Tablet, 64GB, Graphite
As an affiliate, we earn on qualifying purchases.
From Knowledge Work to AI
Management writer Peter Drucker described knowledge work in 1959, as more people earned a living through what they knew rather than manual labor. Machlup then attempted to quantify knowledge-related industries in a 1962 book, using their costs because knowledge itself could not be counted like a physical commodity. His estimate included education, research, media, information machines and information services.
Porat extended the approach in a 1977 report for the U.S. Department of Commerce, sorting workers by their activities. Under that method, a clerk in a factory counted as an information worker even though the employer belonged to manufacturing. The source calls this expanded category a fourth, or quaternary, sector alongside agriculture, industry and services. These historical measures use different definitions and years, so they should not be treated as a single continuous time series.
““Exposure is not job loss, yet.””
— ThorstenMeyerAI.com report
What the Employment Data Shows
The source says overall U.S. job data shows no clear AI footprint so far, but it does not identify the data series, time window or method behind that assessment. It also does not provide the study supporting its statement that young workers in highly exposed jobs are falling behind, or establish that AI caused the reported pattern.
“Exposure” is not defined as a uniform measure across the jobs discussed, and the historical statistics cover different categories and periods. The article does not quantify how many tasks AI systems can perform reliably, how often workplaces deploy them, or whether their use is changing hiring, wages or staffing. The scale and distribution of any employment effects remain unclear.
Tracking AI’s Labor Effects
The report identifies no specific follow-up study, policy decision or employment milestone. Its immediate next step is a further installment in the series, which the source says will examine what economic measures capture and what they miss. For readers assessing AI’s labor effects, the unresolved questions are whether exposure is translating into workplace adoption, how job duties and hiring change over time, and whether reliable employment data can separate AI’s influence from other economic forces.
Key Questions
What is the report’s main development?
A November 10, 2026, installment from ThorstenMeyerAI.com connects the historical growth of knowledge work with generative AI’s focus on office tasks. It does not announce a new AI system or claim that a particular number of jobs has been eliminated.
What did Machlup estimate about knowledge work?
Machlup estimated that knowledge-related production and distribution cost $136.4 billion in the United States in 1958, equal to almost 29% of adjusted GNP. The figure measures spending under his categories, not the total value of knowledge.
Does AI exposure mean a job will disappear?
No. The source explicitly distinguishes exposure from job loss. AI may affect tasks within a job, but the article gives no confirmed estimate of jobs eliminated because of AI.
What evidence does the report give about current job effects?
It says overall U.S. employment data shows no clear AI footprint so far and reports that young workers in highly exposed jobs are falling behind peers. It does not provide the underlying data or establish that AI caused that reported difference.
Source: ThorstenMeyerAI.com
