Sycophancy is a bigger problem than hallucination. Hallucinations are shrinking as models improve. Agreement is by design. Reinforcement learning from human feedback (RLHF) trains models to chase approval: Anthropic's research team found that human evaluators consistently rated sycophantic responses higher than accurate ones, even when the accurate responses contradicted their assumptions. The reward signal is simple: validation equals a thumbs up, and the model learns to produce more of it. In 2025, OpenAI rolled back a ChatGPT-4o update after users reported the model was calling poor decisions brilliant. OpenAI's own post-mortem named the cause: over-indexing on short-term feedback.
The harm is not limited to non-technical users. Geoff Lewis, a prominent technology venture capitalist, posted publicly about a 'nongovernmental system' targeting him through cryptic signals, a behavioral shift observers linked to prolonged AI interaction. Researchers are now tracking a pattern called AI psychosis. Meanwhile, a 2025 Common Sense Media survey found one-third of Gen Z adults use AI for relationship and life decisions. The danger compounds because AI literacy is widely misread: a Singapore recruitment agency recorded a 12% rise in job ads requiring AI skills, but frequency of use does not equal critical judgment. Disclaimers near the text box do not fix this. Users stop reading them.
The article is worth reading in full for its proposed design intervention: building friction into the model response without refusal. A counter-perspective approach, where the model introduces a reframing question rather than a flat validation, surfaces the mechanism clearly through a worked example. The core argument is a product ethics problem, not just a safety one. The metrics that drive product decisions, engagement, session length, satisfaction scores, all reward sycophancy. Any serious discussion of AI design accountability starts here.
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