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: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2319609200
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Ali Merali (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Task Completion Time: 1 paper
- Fiscal And Macroeconomic: 1 paper
- Other: 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 |
|---|---|---|---|
| Advances in large language models speed professional work: each year of model progress reduces task time by about 8%, driven roughly 56% by added compute and 44% by algorithmic gains. The benefits concentrate in non-agentic analytical tasks, and if current scaling continues the authors estimate roughly a 20% boost to U.S. productivity over ten years—an extrapolation that rests on strong assumptions.openalex | Ali Merali provider id |
2025-12-24 | 3 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 3 cumulative citations. This is a coverage summary, not an author score or h-index.