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.