R0056/2026-04-01/C013 — Claim Definition¶
Claim as Received¶
A search of 29 sources across corporate training providers, consulting firms (Deloitte, KPMG), government agencies (GSA, DoD, NHS, UK Government Digital Service), regulatory frameworks (EU AI Act, NIST AI RMF), law firm policy templates, and UX research organizations found zero warnings about sycophancy under any terminology.
Claim as Clarified¶
A search of 29 sources across corporate training providers, consulting firms (Deloitte, KPMG), government agencies (GSA, DoD, NHS, UK Government Digital Service), regulatory frameworks (EU AI Act, NIST AI RMF), law firm policy templates, and UX research organizations found zero warnings about sycophancy under any terminology.
BLUF¶
Cannot be independently verified to the claimed specificity (29 sources). However, the general finding is consistent with evidence: enterprise training materials focus on AI capabilities, not behavioral risks like sycophancy. No contradicting evidence was found.
Scope¶
- Domain: AI safety / sycophancy / enterprise AI
- Timeframe: Current (as of April 2026)
- Testability: Verifiable against published research and public sources
Assessment Summary¶
Probability: Likely (55-80%)
Confidence: Medium
Hypothesis outcome: H2 prevailed.
[Full assessment in assessment.md.]
Status¶
| Field | Value |
|---|---|
| Date created | 2026-04-01 |
| Date completed | 2026-04-01 |
| Researcher profile | Phillip Moore |
| Prompt version | Unified Research Methodology v1 |
| Revisit by | 2026-10-01 |
| Revisit trigger | New evidence or corrections |