Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
3Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2347486347
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Yan Dai (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
Claim outcomes
- Creativity: 1 paper
- Output Quality: 1 paper
- Market Structure: 1 paper
- Wages: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| Neither laissez-faire nor blanket IP protects the future of creative content for AI: permissive access starves creators of compensation, while strong rights blunt originality — and even a high‑quality model can erode itself by causing creators to homogenize their output. A data intermediary that internalizes cross-creator externalities and subsidizes novel contributions can both preserve incentives and improve long-run model quality.arxiv | Yan Dai provider id |
2026-06-10 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.