CONCEPT
Machine Usefulness
Acemoglu and Johnson's alternative to the machine-autonomy paradigm — AI systems designed to expand what human workers can do, rather than to replace what human workers now do.
Machine usefulness is the design philosophy Acemoglu and Johnson propose as the inclusive alternative to the prevailing AI research agenda. Where current frontier AI investment concentrates on systems that perform tasks without human involvement — autonomous agents, end-to-end replacement — machine usefulness targets systems that extend, augment, and support human capability without displacing the human from the loop. The distinction is not about technological capability but about the target of optimization. A useful machine makes a mediocre radiologist into a good one; an autonomous machine makes the radiologist redundant. Both are technically possible. The second is currently overinvested relative to the first.
In The You On AI Field Guide
The concept draws empirical support from studies showing that augmentation-oriented AI deployments produce larger productivity gains for less-skilled workers than for highly-skilled ones — compressing wage distributions rather than widening them. Erik Brynjolfsson and colleagues' 2023 study of customer service AI found fourteen percent productivity gains concentrated among the lowest-performing workers, with negligible effects on top performers. This