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
7Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2314700894
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Eleanor Dillon (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Adoption Rate: 1 paper
- Firm Productivity: 1 paper
- Innovation Output: 1 paper
- Market Structure: 1 paper
- Other: 1 paper
- Task Allocation: 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's biggest economic payoff lies not in automating old processes but in rebuilding them: true productivity gains come when firms redesign workflows, data interfaces, and accountability systems to let machines act and interact. That system-level reconstruction is slow, requires trust and interoperable infrastructure, and depends on incentives that favor broad redesign over piecemeal optimization.arxiv | Eleanor Dillon provider id |
2026-05-28 | 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.