Professor AnalysisFebruary 10, 2026
Large language models on which AI applications are based have an often-overlooked, unsettling ability. They “lie” or distort the truth to please their user or to match input cues. In pursuit of click-through rates or engagement, AI applications may sacrifice authenticity and accuracy, providing false or overly client-pleasing information. A Stanford University study examining ChatGPT-4.0, Claude-Sonnet, and Gemini-1.5 Pro revealed two primary patterns of flattery in AI responses across AMPS (mathematics) and MedQuad (medical advice) datasets. The first, progressive flattery, involves AI initially providing the wrong answer but, guided by user prompts, moving towards the correct response under user guidance. […]