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
2Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2263539371
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Zheng Liu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Market Structure: 1 paper
- Adoption Rate: 1 paper
- Consumer Welfare: 1 paper
- Firm Revenue: 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 |
|---|---|---|---|
| AI could first concentrate markets then democratize them: the authors’ calibrated general-equilibrium model shows industry concentration rises at low AI adoption as large incumbents capture early gains, but falls once adoption becomes widespread (peak near ~15% adoption). Average markups increase when AI supply expands, while demand-driven adoption generates a non-monotonic markup response; a small (~3%) revenue subsidy to AI adopters is welfare-optimal in the U.S. calibration.openalex | Zheng Liu provider id |
2026-08-10 | 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.