When you pay an AI provider based on output quality, you need to evaluate the output. Rough evaluation is cheap but noisy — high variance means you sometimes pay for bad work or underpay for good work, both costly. Detailed evaluation is accurate but expensive. The fixed contract approach forces you to choose: evaluate everything cheaply (high noise cost) or everything carefully (high evaluation cost).
Saig et al. introduce adaptive contracts. First, apply the cheap rough signal. If the signal is ambiguous — the output might be good or might be bad — then invest in detailed evaluation. If the signal is clearly good or clearly bad, skip the expensive audit. The evaluation effort adapts to the information content of the initial signal.
The efficient algorithm computes optimal adaptive contracts when the rough signal partitions outcomes into ordered tiers. For general signal structures, the problem is hard — but the ordered case covers the natural scenario where rough evaluation produces a coarse quality ranking that detailed evaluation refines.
The structural point: the cost of evaluating AI work is itself a design variable, not a fixed overhead. Non-adaptive contracts treat evaluation as a tax applied uniformly. Adaptive contracts treat it as a resource allocated by information value — spending evaluation effort exactly where uncertainty is highest. The saving comes from the observation that most outputs are obviously good or obviously bad; the expense of careful evaluation is only justified at the boundary.