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
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5120546142
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Andrew Koh (openalex, provider refresh)
- Koh, Andrew (openalex, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
Claim outcomes
- Firm Productivity: 1 paper
- Other: 1 paper
- Automation Exposure: 1 paper
- Fiscal And Macroeconomic: 1 paper
- Governance And Regulation: 1 paper
- Job Displacement: 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 |
|---|---|---|---|
| Endogenous data makes automation self-reinforcing but slow: data both improves existing automation and widens the automation frontier, yet the share of tasks performed by labor falls only asymptotically as a power law. The model predicts explosive aggregate growth when capital is endogenous but persistent wage stagnation and a generic role for policy to reorient data accumulation toward socially valuable directions.openalex | Koh, Andrew provider id |
2026-06-08 | 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.