Why does ChatGPT describe my company negatively even though Google results look fine?

This is one of the most common questions we get from CCOs, and the answer is almost always source-mix differences. ChatGPT and Google are not reading the same web at the same time.

Why the two engines diverge

• ChatGPT’s training baseline. In many configurations, ChatGPT weights a fixed training-data snapshot that may be anchored to coverage from a year or more ago. Whatever sources were prominent in that corpus, including older press coverage and forum content, shape the model’s default answer, even after the real-world situation has changed.
• Reddit and forum content in the mix. Reddit is among the most-cited domains in AI-generated answers. If a controversy or negative thread was prominent in the training window, it can anchor the model’s framing of a brand long after the conversation has moved on.
• Google’s current index. Google search returns what Google’s live index considers authoritative today, with a much shorter lag. A brand that has had a quiet, successful year will usually see that reflected in Google results faster than in a model like ChatGPT.
• The same query, different answers. Because the source pools differ, the same brand query can return materially different results across engines, positive in Google, negative or outdated in ChatGPT, without either engine being straightforwardly wrong about what its sources say.

What to do about it

Understanding which specific source each engine is drawing on turns a vague complaint, “ChatGPT is wrong about us”, into an actionable brief: identify the source, address it at the source layer, and let the model’s sourcing catch up. AIQ™ isolates which sources each engine is citing for each prompt, making that diagnosis fast and precise.