Learning from Ricardo and Thompson
The 2024 paper by Nobel laureate Daron Acemoglu and MIT economist Simon Johnson does something the AI discourse had largely avoided: it takes Thompson's historical framework seriously as an analytical tool for the present. The paper argues that the same dynamics Thompson identified in the early industrial period — concentration of productivity gains in capital, degradation of working conditions through surveillance and loss of autonomy, dependence of outcomes on the balance of power between workers and employers — are reproducing themselves in the AI transition. Its central claim, stated with characteristic economist's directness: wages are unlikely to rise when workers cannot push for their share of productivity growth. The paper is significant not because it tells Thompson scholars anything new but because it demonstrates that the framework has been independently rediscovered, by researchers at the pinnacle of mainstream economics, as essential for understanding AI.
In the AI Story
The paper emerges from Acemoglu and Johnson's larger 2023 book Power and Progress, which surveyed a thousand years of technological transitions to argue that shared prosperity from technology has always been a political achievement rather than an automatic outcome. The Ricardo-Thompson paper distills that argument for an economics audience, explicitly crediting Thompson's historical analysis as central to the framework.
The paper's specific observations about AI parallel Thompson's observations about early industrialization with uncomfortable precision. Handloom weavers who had possessed considerable control over when and how hard they worked lost this control as tasks were taken over by weaving machines. Contemporary knowledge workers are losing analogous control as AI systems restructure the rhythms and conditions of their labor.
The paper's political intervention is its insistence that outcomes are choices rather than destinies. Critically, this is a choice — the phrase appears with significance in a field that typically treats market outcomes as natural. The paper argues that the current trajectory of AI deployment (heavy on automation and surveillance, light on augmentation of worker capability) reflects a power imbalance that will not self-correct through market mechanisms, and that deliberate institutional action is required to produce different outcomes.
For the Thompson volume, the Acemoglu-Johnson paper serves as the scholarly bridge between nineteenth-century labor history and twenty-first-century AI economics. It demonstrates that applying Thompson's framework to AI is not an interpretive stretch but an analytical necessity, now performed by economists whose credentials render dismissal difficult.
Origin
Published in the Annual Review of Economics vol. 16 (2024), titled "Learning from Ricardo and Thompson: Machinery and Labor in the Early Industrial Revolution and in the Age of Artificial Intelligence."
Key Ideas
Thompson in mainstream economics. The paper brings Thompson's framework into the center of contemporary economics rather than leaving it at the margins of labor history.
Worker power determines wages. The central empirical claim is that productivity gains translate to wages only when workers possess collective power — the Thompson insight translated into economist's vocabulary.
Surveillance and control. The paper emphasizes AI's use for worker monitoring and control, not merely for automation — extending Thompson's concerns beyond pure displacement.
Choice, not destiny. Outcomes depend on political decisions about institutional structure, not on inherent properties of the technology.
Appears in the Orange Pill Cycle
Further reading
- Daron Acemoglu and Simon Johnson, "Learning from Ricardo and Thompson" (Annual Review of Economics, 2024)
- Daron Acemoglu and Simon Johnson, Power and Progress (PublicAffairs, 2023)
- Daron Acemoglu and Pascual Restrepo, "Automation and New Tasks" (Journal of Economic Perspectives, 2019)