E01¶


Research	R0057 — RLHF Yes-Men Claims v3
Run	2026-04-01
Claim	C029
Source	SRC01
Evidence	SRC01-E01
Type	Analytical

Engagement optimization and sycophancy reduction are opposed — users prefer sycophantic AI, creating market incentives against safety

URL: https://www.law.georgetown.edu/tech-institute/insights/reduce-ai-sycophancy-risks/

Extract¶

Georgetown Law notes firms may resist safeguards contrary to monetization. Brookings identifies sycophancy as creating positive feedback loops undermining accuracy. Stanford shows users rate sycophantic responses 9-15% higher quality and show 13% greater return likelihood, creating perverse developer incentives.

Relevance to Hypotheses¶

Hypothesis	Relationship	Strength
H1	Supports	Directly addresses claim accuracy
H2	Supports	Allows for partial correctness
H3	Contradicts	Evidence contradicts material inaccuracy

Context¶

Three independent authoritative sources converge on the same finding.