The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading

Aral (MIT, one of the most cited scholars in digital economics) and Caosun construct a formal dynamic model that makes precise what practitioners sense but cannot yet argue: AI adoption can be individually rational at every step while producing an outcome worse than non-adoption, a structural trap rather than a mistake. The decomposition into five adoption regimes is the kind of analytical architecture that travels — product directors can use it to classify their own deployments rather than simply assert that 'it depends.' The skill-divergence result — that small differences in initial experience, compounded by managerial incentive horizons, can send workers to permanently opposite equilibria — connects directly to the library's themes on bounded rationality, principal-agent problems, and how institutional incentives shape organisational capability. Read alongside Acemoglu and Restrepo on automation and task displacement, and against Brynjolfsson's more optimistic productivity framing, this paper provides the missing dynamic: the cost that arrives after the measurement window closes.