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
1Unique collaborators
2/2Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
101277768
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- S. Dzreke (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Org Design: 2 papers
- Human Ai Collab: 1 paper
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Market Structure: 2 papers
- Governance And Regulation: 2 papers
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Decision Quality: 1 paper
- Firm Productivity: 1 paper
- Firm Revenue: 1 paper
- Skill Acquisition: 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 |
|---|---|---|---|
| AI is reshaping American industrial geography: capital‑intensive production in EV batteries, chips and advanced manufacturing is moving inland as AI reduces the need for coastal agglomerations, land constraints and energy access now trump proximity to traditional hubs.openalex | S. Dzreke provider id |
2026-06-01 | 0 |
| Human–AI co‑creation that treats segmentation, targeting and positioning as a self‑organizing system markedly improves firms' adaptability: it was 44% more resilient to shocks, cut planning cycle times by about 90% and raised detection of major market shifts nearly sixfold. These results come from a Fortune 500 lab ethnography, 150 million customer interactions and calibrated agent‑based simulations, but hinge on single‑firm data and simulation assumptions.openalex | S. Dzreke provider id |
2026-03-15 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 2 of 2 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.