In the last two years, incidents have shown how large language model (LLM)-powered systems can cause measurable harm. Some businesses have lost a majority of their traffic overnight, and publishers have watched revenue decline by over a third.
Tech companies have been accused of wrongful death where teenagers had extensive interaction with chatbots.
AI systems have given dangerous medical advice at scale, and chatbots have made up false claims about real people in defamation cases.
This article looks at the proven blind spots in LLM systems and what they mean for SEOs who work to optimize and protect brand visibility. You can read specific cases and understand the technical failures behind them.
The Engagement-Safety Paradox: Why LLMs Are Built To Validate, Not Challenge
LLMs face a basic conflict between business goals and user safety. The systems are trained to maximize engagement by being agreeable and keeping conversations going. This design choice increases retention and drives subscription revenue while generating training data.
In practice, it creates what researchers call “sycophancy,” the tendency to tell users what they want to hear rather than what they need to hear.
Stanford PhD researcher Jared Moore demonstrated this pattern. When a user claiming to be dead (showing symptoms of Cotard’s syndrome, a mental health condition) gets validation from a chatbot saying “that sounds really overwhelming” with offers of a “safe space” to explore feelings, the system backs up the delusion instead of giving a reality check. A human therapist would gently challenge this belief while the chatbot validates it.
OpenAI admitted this problem in September after facing a wrongful death lawsuit. The company said ChatGPT was “too agreeable” and failed to spot “signs of delusion or emotional dependency.” That admission came after 16-year-old Adam Raine from California died. His family’s lawsuit showed that ChatGPT’s systems flagged 377 self-harm messages, including 23 with over 90% confidence that he was at risk. The conversations kept going anyway.
The pattern was observed in Raine’s final month. He went from two to three flagged messages per week to more than 20 per week. By March, he spent nearly four hours daily on the platform. OpenAI’s spokesperson later acknowledged that safety guardrails “can sometimes become less reliable in long interactions where parts of the model’s safety training may degrade.”
Think about what that means. The systems fail at the exact moment of highest risk, when vulnerable users are most engaged. This happens by design when you optimize for engagement metrics over safety protocols.
Character.AI faced similar issues with 14-year-old Sewell Setzer III from Florida, who died in February 2024. Court documents show he spent months in what he perceived as a romantic relationship with a chatbot character. He withdrew from family and friends, spending hours daily with the AI. The company’s…
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