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:
2352333093
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- C.P. Gujar (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Firm Productivity: 1 paper
- Fiscal And Macroeconomic: 1 paper
- Adoption Rate: 1 paper
- Automation Exposure: 1 paper
- Consumer Welfare: 1 paper
- Employment: 1 paper
- Inequality: 1 paper
- Job Displacement: 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 add hundreds of billions to India's GDP by 2030, but the gains look uneven: big firms and digitally ready workers will likely capture most benefits while MSMEs and informal workers lag without targeted skills, finance and governance interventions.openalex | C.P. Gujar provider id |
2026-08-11 | 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.