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R0042/2026-03-28/Q003/H3

Statement

Anti-sycophancy exists as a documented design component in AI systems built by model providers and research institutions, but never as the explicit primary design goal of an enterprise private AI deployment. Enterprises may benefit from anti-sycophancy features in models they deploy but have not publicly documented building systems where sycophancy reduction was the driving objective.

Status

Current: Supported

This nuanced position accurately describes the evidence landscape. Anti-sycophancy is: - An explicit design principle in Anthropic's Constitutional AI - An active research area at Google DeepMind (consistency training) - A recognized problem that OpenAI has publicly addressed - NOT a documented enterprise customer deployment objective

Supporting Evidence

Evidence Summary
SRC01-E01 Constitutional AI explicitly includes anti-sycophancy; enterprises benefit but did not build for it
SRC02-E01 Google DeepMind consistency training reduces sycophancy 67.8% to 2.9%; model provider research
SRC03-E01 Five mitigation approaches documented; none involve enterprise private deployment
SRC04-E01 OpenAI takes provider-level responsibility for fixing sycophancy

Contradicting Evidence

No evidence directly contradicts this hypothesis.

Reasoning

H3 captures the full picture: anti-sycophancy is a real, well-funded, actively pursued design goal — but it lives at the model provider and research institution level, not at the enterprise customer level. The key insight is that the enterprise customer appears to treat model behavioral quality (including sycophancy) as the model provider's responsibility, much like an enterprise using a database treats query optimization as the database vendor's responsibility.

Relationship to Other Hypotheses

H3 is more precise than H2 because it acknowledges the existence of anti-sycophancy design work while correctly scoping it to model providers rather than enterprise customers. Both H2 and H3 are supported; H3 provides the richer explanation.