R0042/2026-03-28/Q003/H1¶
Statement¶
At least one enterprise or research institution has published a case study or white paper describing a private AI system built with explicit anti-sycophancy objectives as a primary or named design goal.
Status¶
Current: Eliminated
No enterprise has published a case study documenting a private AI system where sycophancy reduction was an explicit design goal. Anti-sycophancy work is performed exclusively by model providers (Anthropic, Google DeepMind, OpenAI) and academic researchers, not by enterprise customers building private systems.
Supporting Evidence¶
No evidence directly supports this hypothesis.
Contradicting Evidence¶
| Evidence | Summary |
|---|---|
| SRC01-E01 | Anthropic built anti-sycophancy into Constitutional AI — but Anthropic is a model provider, not an enterprise customer |
| SRC02-E01 | Google DeepMind developed consistency training — but this is model provider research, not enterprise deployment |
| SRC03-E01 | Comprehensive survey contains zero enterprise deployment examples |
| SRC04-E01 | OpenAI addressed sycophancy as a provider responsibility |
Reasoning¶
Across 50 search results from 5 targeted searches, plus 4 deeply analyzed sources, no enterprise private AI deployment with explicit anti-sycophancy objectives was found. This is a robust absence — the searches were specifically designed to find this evidence, and the comprehensive academic survey (SRC03) would have referenced such work if it existed.
Relationship to Other Hypotheses¶
H1 is clearly eliminated. The distinction between model provider anti-sycophancy work (which exists) and enterprise customer anti-sycophancy deployment (which does not) is the key finding that supports H3.