🔍 Read the full analysis: Knowledge Work Trends In AI Era on ThorstenMeyerAI.com
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TL;DR
A November 10, 2026, ThorstenMeyerAI.com report traces how knowledge and information work grew into a major part of the U.S. economy, drawing on research by Fritz Machlup and Marc Porat. It says generative AI is aimed at many office and professional tasks, but exposure does not establish job loss, and broad U.S. employment data has not yet shown a clear AI effect.
ThorstenMeyerAI.com published a report on November 10 tracing the growth of knowledge work and asking how generative AI may affect the office occupations that now make up a substantial share of employment. The installment, the final episode in the site’s first “Work After Work” arc, draws on economic research dating to the 1950s and 1970s; it cautions that AI exposure is not the same as job loss.
The report centers on economist Fritz Machlup’s estimate that U.S. spending on producing and distributing knowledge reached $136.4 billion in 1958, or “almost 29 per cent of adjusted GNP.” In his 1962 book, The Production and Distribution of Knowledge in the United States, Machlup grouped education, research and development, communications media, information machines and information services as knowledge industries. The figure measures spending, the report says, rather than the value of knowledge itself.
It also draws on Marc Porat’s 1977 study for the U.S. Department of Commerce, which classified workers according to the information tasks they performed, not just the industry where they worked. Porat estimated that information workers represented 5.8% of the U.S. workforce in 1860 and 46.4% in 1970. The report says information work had become the largest of four employment groups by the mid-1950s.
For a more recent comparison, the source 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. Those occupational categories are not identical to Machlup’s or Porat’s definitions, so the figures should not be read as a directly comparable time series.
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 historical account matters because it frames AI as a technology aimed at tasks that already occupy a large share of modern work: reading, writing, calculating, checking, planning and communicating. The report’s central point is that office work is a major economic domain, not a small corner of employment. Changes to those tasks could affect many workers and employers, even if they do not eliminate entire occupations.
But potential exposure is not a forecast of layoffs. The source says overall U.S. job data has not shown a clear AI footprint so far, while citing evidence that young workers in the most AI-exposed jobs are lagging behind peers. That observation does not, by itself, establish that AI caused the difference. Readers and policymakers need to distinguish changes in tasks, hiring and wages from demonstrated job displacement.
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From Knowledge Work to AI
Management writer Peter Drucker used the term “knowledge worker” in 1959, describing people who earn their living through what they know rather than primarily through manual labor. His argument, as summarized in the source, was that organizations could not rely only on factory-era management methods when more workers created value through knowledge and judgment.
Machlup’s work followed in 1962, attempting to quantify the production and distribution of knowledge through its costs. Porat later classified information work across the whole economy, including clerks or bookkeepers employed outside traditionally information-focused industries. Together, the studies offer different ways to describe a structural shift: knowledge and information became central to economic activity, although their categories and measurements are not interchangeable.
The installment places generative AI within that longer shift, describing it as a technology directed at tasks commonly performed in offices. Its historical figures provide context for the scale of knowledge work; they do not establish how quickly AI will change employment or how benefits and costs will be distributed.
““Exposure is not job loss, yet.””
— ThorstenMeyerAI.com report
AI’s Employment Effects Remain Unsettled
The source does not provide a specific dataset, study title or measurement method for its statement that U.S. employment data shows no clear AI footprint. It also does not identify the analysis behind its observation about younger workers in highly exposed jobs. The timing, size and causes of that reported gap therefore cannot be assessed from the installment alone.
It remains unclear which tasks employers will automate, which will be changed or supplemented, and whether those shifts will lead to fewer positions, different job requirements or new work. Estimates of exposure vary with how researchers define occupations and tasks. No job-loss total or causal estimate is established in the material provided.
Evidence to Watch in Employment Data
The immediate next step for readers is to watch for more detailed employment research that connects AI adoption with hiring, hours, wages and job changes over time. Evidence would need to separate AI-related effects from other forces affecting the labor market and account for differences among occupations, industries and age groups.
The source presents this installment as the end of its first “Work After Work” arc and previews a subsequent episode on what economic statistics measure and what they miss. That topic is relevant to interpreting the AI debate: the conclusions depend on what gets counted, how knowledge work is defined and whether measures track tasks, spending or employment outcomes.
Key Questions
What is the report about?
It traces the rise of knowledge and information work and discusses how generative AI could affect office tasks. The piece is the final episode in ThorstenMeyerAI.com’s first “Work After Work” arc.
What did Machlup estimate?
Machlup estimated that U.S. spending on producing and distributing knowledge was $136.4 billion in 1958, or almost 29% of adjusted gross national product. The measure was spending, not a direct estimate of knowledge’s value.
Does AI exposure mean a job will disappear?
No. The report says exposure is not job loss. It does not establish how many jobs, if any, will be eliminated as employers adopt generative AI.
What does the report say about current U.S. employment?
It says broad U.S. job data has not yet shown a clear AI footprint, while also reporting that young workers in the most exposed jobs are falling behind peers. The supplied material does not identify the underlying analysis or establish that AI caused the reported difference.
Source: ThorstenMeyerAI.com
