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:
2343048822
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Gleb A. Arhangelsky (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Task Allocation: 1 paper
- Firm Productivity: 1 paper
- Market Structure: 1 paper
- Skill Obsolescence: 1 paper
- Task Completion Time: 1 paper
- Worker Satisfaction: 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 |
|---|---|---|---|
| Firms that pair speed with anticipatory, attention-managing AI gain outsized, durable advantages—simply making tasks faster is no longer enough; proactive information infrastructures that forecast needs and allocate attention determine winners. Policymakers and managers should therefore treat attention as an economic input and weigh the ethical risks of attention capture and surveillance.openalex | Gleb A. Arhangelsky provider id |
2026-08-20 | 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.