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
6Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2311508388
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Atin Aboutorabi (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
Claim outcomes
- Task Allocation: 1 paper
- Automation Exposure: 1 paper
- Labor Share: 1 paper
- Adoption Rate: 1 paper
- Firm Productivity: 1 paper
- Other: 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 |
|---|---|---|---|
| Because AI performance improves with diminishing returns to data, compute, and model size, near-perfect accuracy is disproportionately costly, so firms often choose partial human-AI collaboration rather than full automation; calibrated to computer vision, cost-effective automation covers roughly 11% of exposed wages at the firm level but can scale far higher when AI services spread fixed costs across users.arxiv | Atin Aboutorabi provider id |
2026-03-31 | 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.