The Novelty Bottleneck: A Framework for Understanding Human Effort Scaling in AI-Assisted Work
We propose a stylized model of human-AI collaboration that isolates a mechanism we call the novelty bottleneck: the fraction of a task requiring human judgment creates an irreducible serial component analogous to Amdahl's Law in parallel computing. The model assumes that tasks decompose into atomic decisions, a fraction $ν$ of which are "novel" (not covered by the agent's prior), and that specification, verification, and error correction each scale with task size. From these assumptions, we derive several non-obvious consequences: (1) there is no smooth sublinear regime for human effort it transitions sharply from $O(E)$ to $O(1)$ with no intermediate scaling class; (2) better agents impr...
| Authors | Jacky Liang |
|---|---|
| Primary category | cs.AI |
| Published | 28 March 2026 |
| Last updated at source | 28 March 2026 |
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