{
  "id": 6631,
  "url": "https://arxiv.org/abs/2603.27438v1",
  "title": "The Novelty Bottleneck: A Framework for Understanding Human Effort Scaling in AI-Assisted Work",
  "summary": "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 deri",
  "authors": "Jacky Liang",
  "category": "research",
  "topics": "regulation,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-28T22:50:13.000Z",
  "fetched_at": "2026-07-14T16:32:37.310Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6631",
  "original_url": "https://arxiv.org/abs/2603.27438v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}