The Effort Cliff

Human effort in AI-assisted work is assumed to decrease gradually as AI capability increases — better tools, smoother workflow, proportionally less human input needed. The actual scaling has a phase transition.

Below a task-specific novelty threshold, AI handles the work and human effort scales as O(1) — constant regardless of task size. Above the threshold, AI cannot handle the novel components and human effort scales as O(E) — linearly with task size. There is no intermediate regime. The transition between constant and linear effort is sharp.

Better AI agents improve the coefficient within each regime but never change the scaling exponent. A more capable AI reduces the constant in O(1) tasks and reduces the slope in O(E) tasks, but the transition between regimes remains discontinuous. The qualitative character of the work — either the human monitors or the human does — is invariant to capability improvement.

The consequence for team design: optimal team sizes decrease as agent capability increases. More powerful AI means fewer humans, not the same number of humans working faster. The human role shifts from distributed execution to concentrated evaluation at the novelty boundary. The bottleneck is not effort quantity but effort type — novel judgment that cannot be parallelized across more people.

The structural observation: AI capability improvement does not smoothly reduce human effort but instead moves the location of a cliff. Everything below the cliff becomes trivially automated; everything above it remains fully human. The cliff moves, but its shape does not soften.