2 min read
An innocuous question recently sent ChatGPT into a bewildering loop and reminded users that even “superintelligent” AI can stumble remarkably. When asked whether there is an NFL team whose name doesn’t end with “s”, ChatGPT confidently replied yes, then wound itself into a logic tangle listing teams that do end with “s,” backtracking, contradicting itself, and never arriving at the only correct conclusion: that no team with an English plural structure breaks that pattern.
The glitch has since become a viral moment among AI watchers. On the ChatGPT subreddit, users shared transcripts where the model repeatedly states it will “carefully” answer, yet spirals deeper into confusion. At times it promises to give “the correct answer,” only to continue misnaming teams and adding more incorrect ones.
Previously, questions about mythical emojis or unexpected edge cases have triggered bizarre logic cascades, hallucinations, or outright breakdowns. The NFL question episode highlights deeper issues around how large language models (LLMs) manage literal reasoning and categorical constraints.
Experts suggest the problem lies in ChatGPT’s internal system architecture: a simplified “fast path” tries to handle everyday queries, while a heavier “reasoning engine” kicks in for tougher tasks. In some cases, that handoff fails leaving the lighter model stuck trying to parse a question it isn’t optimized for.
For users, this incident is a cautionary tale: AI models can provide fascinating, flexible responses but they can also fail spectacularly on simple puzzles. As AI continues advancing, such “meltdown” episodes will likely fuel calls for more rigorous evaluation, stronger fallback logic, and transparent error handling.
