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
6Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2280253437
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Ziqiang Zhong (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Org Design: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Labor Share: 1 paper
- Other: 1 paper
- Skill Acquisition: 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-native engineering can slash development resource use by an order of magnitude: replacing horizontal specialization with vertically integrated, AI-augmented engineers yielded 8–33× lower resource consumption in two case studies, driven by reduced coordination and new 'super employee' roles; firms should reorganize around Human–AI Collaboration Efficacy rather than individual productivity metrics.arxiv | Ziqiang Zhong provider id |
2026-01-30 | 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.