R0042/2026-03-28/Q003/H2¶
Statement¶
No enterprise has publicly documented building a private AI system specifically for sycophancy reduction. The concern is confined to model providers and academic research.
Status¶
Current: Supported
This is the most accurate characterization of the evidence. Anti-sycophancy work exists at three levels — model providers (Anthropic, OpenAI, Google), academic researchers (MIT, various arXiv papers), and AI safety organizations — but not at the enterprise customer level.
Supporting Evidence¶
| Evidence | Summary |
|---|---|
| SRC01-E01 | Anti-sycophancy is a model provider design goal (Anthropic Constitutional AI), not enterprise customer goal |
| SRC02-E01 | Anti-sycophancy is a research institution goal (Google DeepMind), not enterprise deployment goal |
| SRC03-E01 | Academic survey of sycophancy field contains no enterprise deployment examples |
| SRC04-E01 | When sycophancy occurs, the model provider (OpenAI) fixes it, not the enterprise customer |
Contradicting Evidence¶
No evidence contradicts this hypothesis.
Reasoning¶
The evidence clearly shows that anti-sycophancy is treated as a supply-side concern — model providers and researchers work on it because they believe it is important for AI quality and safety. Enterprise customers benefit from this work but do not independently pursue anti-sycophancy as a deployment objective.
Relationship to Other Hypotheses¶
H2 is the strongest characterization but does not capture the nuance that H3 adds — that anti-sycophancy exists as a component in model provider design even if it is not an enterprise primary goal.