R0041/2026-03-28/Q001/H2¶
Statement¶
No AI vendors are specifically targeting sycophancy in enterprise products. Sycophancy is not a design priority, and no vendor has made meaningful investments in reducing it.
Status¶
Current: Eliminated
Multiple vendors have made significant, documented investments in sycophancy reduction. Anthropic has developed dedicated evaluation tools (Petri), achieved 70-85% sycophancy reduction metrics, and embedded anti-sycophancy principles in their constitutional AI framework. OpenAI rolled back a GPT-4o update specifically because of sycophancy and developed post-training techniques for GPT-5 targeting sycophancy reduction. The Stanford/CMU ELEPHANT study tested 11 models, demonstrating this is an industry-wide concern receiving active attention.
Supporting Evidence¶
No evidence supports this hypothesis.
Contradicting Evidence¶
| Evidence | Summary |
|---|---|
| SRC01-E01 | Anthropic has been evaluating and reducing sycophancy since 2022 with measurable results |
| SRC02-E01 | OpenAI rolled back an update and published detailed analysis specifically about sycophancy |
| SRC04-E01 | Anthropic built a dedicated open-source evaluation framework that includes sycophancy measurement |
| SRC06-E01 | Anti-sycophancy is a core principle in Anthropic's constitutional AI document |
Reasoning¶
This hypothesis is conclusively eliminated. The evidence demonstrates that sycophancy reduction is an active priority for at least two major vendors (Anthropic and OpenAI), with dedicated research, evaluation tools, training techniques, and public commitments. The question is not whether vendors are addressing sycophancy, but how they are addressing it (model-level vs. enterprise-configurable).
Relationship to Other Hypotheses¶
H2 represents the null hypothesis. Its elimination confirms that vendors are indeed working on sycophancy, directing the analysis toward distinguishing between H1 (enterprise product features) and H3 (general alignment improvements).