Author here. I spent 40 years building engineering teams where the culture demanded disagreement. Now the most powerful tool I've ever worked with has sycophancy baked into its core.
This article digs into why: RLHF amplifies human preference for agreement, no vendor offers enterprise anti-sycophancy products, the vocabulary gap between AI safety researchers ("sycophancy") and regulated industries ("automation bias") means nobody's connecting the dots, and the one technical fix that eliminates the preference signal only works where the problem matters least.
Every claim is independently verified — 33 claims, three fact-check iterations, 100% pass rate. The full evidence archive (sources, scorecards, search logs) is published alongside the article. The methodology is open source: https://github.com/wphillipmoore/ai-research-methodology
Happy to discuss any of the findings or the methodology.
This article digs into why: RLHF amplifies human preference for agreement, no vendor offers enterprise anti-sycophancy products, the vocabulary gap between AI safety researchers ("sycophancy") and regulated industries ("automation bias") means nobody's connecting the dots, and the one technical fix that eliminates the preference signal only works where the problem matters least.
Every claim is independently verified — 33 claims, three fact-check iterations, 100% pass rate. The full evidence archive (sources, scorecards, search logs) is published alongside the article. The methodology is open source: https://github.com/wphillipmoore/ai-research-methodology
Happy to discuss any of the findings or the methodology.