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 →
2Distinct papers
2Unique collaborators
2/2Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2064094862
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Christoph Kolb (semantic scholar, provider refresh)
- Christoph Kolb (semantic scholar, source metadata)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 2 papers
- Governance: 1 paper
- Labor Markets: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Organizational Efficiency: 2 papers
- Adoption Rate: 2 papers
- Task Allocation: 1 paper
- Developer Productivity: 1 paper
- Skill Acquisition: 1 paper
- Employment: 1 paper
- Error Rate: 1 paper
- Job Displacement: 1 paper
- Other: 1 paper
- Output Quality: 1 paper
- Research Productivity: 1 paper
- Task Completion Time: 1 paper
- Team Performance: 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 |
|---|---|---|---|
| Generative AI won't mechanically eliminate work — its economic impact depends on institutions: adoption, verification, apprenticeship and bargaining shape who benefits and whether expertise endures. Policymakers and firms must treat AI deployment as governance and workflow design, not plug-and-play automation.semantic_scholar | Christoph Kolb provider id |
2026-08-11 | 0 |
| Orchestrated human–AI workflows sharply cut effort and defects: across three real modernization projects, the Chiron platform reduced modeled person-days from 1080 to 232.5 and senior-equivalent effort from 1080 to 139.5 days, halved validation issues and raised first-release coverage from 77% to 90.5%, with largest gains when AI was embedded in the end-to-end delivery flow rather than used as an isolated coding assistant.arxiv | Christoph Kolb provider id |
2026-03-20 | 4 |
Citation observation summary
Semantic Scholar supplied counts for 2 of 2 papers in this view; 0 are missing. The observed paper counts sum to 4 cumulative citations. This is a coverage summary, not an author score or h-index.