{
  "id": 14949,
  "url": "https://arxiv.org/abs/2607.28041v1",
  "title": "When AI Does the Work, What Is Learning For? Post-Instrumental Learning and the Risk of Capacity Dissolution",
  "summary": "As AI systems become capable of producing the essays, code, reports, summaries, plans, and decisions through which institutions usually recognize competence, a familiar question becomes harder to answer: what is learning for? Existing AI ethics rightly emphasizes present failures--bias, opacity, hallucination, labor extraction, privacy risk, and weak accountability. But if the case for learning rests only on those failures, then each technical improvement appears to weaken it. This article devel",
  "authors": "Kai Yao",
  "category": "research",
  "topics": "bias-fairness,privacy-surveillance,jobs-economy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-30T11:24:56.000Z",
  "fetched_at": "2026-07-31T05:10:57.675Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/14949",
  "original_url": "https://arxiv.org/abs/2607.28041v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}