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
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2261103332
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Jason Hickey (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Research Productivity: 1 paper
- Ai Safety And Ethics: 1 paper
- Automation Exposure: 1 paper
- Error Rate: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| An expert used a five-seat AI fleet and formal verification to build and tape out a verified software-to-silicon stack in five weeks, with every theorem kernel-checked and provenance published. The case study shows that generative AI can invert verification’s historical cost barrier — making machine-checked assurance practicable at AI speed — but it is a single, specialist-driven demonstration rather than proof of broad productivity or economic effects.arxiv | Jason Hickey provider id |
2026-08-21 | 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.