The Economics of Autonomous Research

Autonomous research agents that propose, implement, and refine ML solutions are priced out by the loop, not the model. Published agents assume a frontier model drives every step, so cost scales with trajectory length on runs that are long by design. Teams either cap the horizon — removing the thing that made it work — or don’t run it at all.

Hypothesis: a small open-weight model runs the loop end to end, with a frontier advisor called in at those points on a metered budget, holding most of the baseline’s result quality at a fraction of its cost. The open question is whether the escalation trigger is reliable enough to justify the calls it buys — our first version often bought advice the loop would have reached on its own.