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
0Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5141012444
ORCID evidence
Observed aliases (1)
- Mihir Khanna (openalex, provider refresh)
Topics and outcomes in this view
Assessment themes
- Labor Markets: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Automation Exposure: 1 paper
- Hiring: 1 paper
- Employment: 1 paper
- Firm Productivity: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| After the 2022 LLM shock, India's most AI-exposed IT-services firms reduced hiring sharply while raising output per worker, consistent with augmentation rather than mass job loss; the full-sample estimates are noisy but strengthen on a higher-quality subsample and show no analogous response to the 2016 deep-learning/cloud wave.openalex | Mihir Khanna orcid |
2026-07-16 | 0 |
Citation observation summary
OpenAlex 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.