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:
A5102129005
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Tung-Yu Wu (openalex, provider refresh)
- Wu, Tung-Yu (openalex, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Labor Markets: 1 paper
Claim outcomes
- Regulatory Compliance: 1 paper
- Fiscal And Macroeconomic: 1 paper
- Governance And Regulation: 1 paper
- Other: 1 paper
- Job Displacement: 1 paper
- Market Structure: 1 paper
- Research 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 |
|---|---|---|---|
| Charging levies on AI 'tokens'—a usage tax applied at the point of inference—could give governments a practical way to tax AI-generated value and blunt automation-driven revenue and wage shocks; the approach hinges on robust cryptographic receipts, norm-based rates, white-box audits, and difficult international cooperation.openalex | Wu, Tung-Yu provider id |
2026-03-04 | 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.