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That’s not how it works. There are already cases where the fix of one problem made a previous existing capability worse.

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That's exactly how it works. Every input of AI performance improves over time, and so do the outcomes.

Can you damage existing capabilities by overly specializing an AI in something? Yes. Would you expect that damage to stick around forever? No.

OpenAI damaged o3's truthfulness by frying it with too much careless RL. But Anthropic's Opus 4 proves that you can get similar task performance gains without sacrificing truthfulness. And then OpenAI comes back swinging with an algorithmic approach to train their AIs for better truthfulness specifically.

Depends on the input. More BS training data leads to worse answers and the good sources are nearly all already used.

The next round of data is partially AI generated what leads to further deterioration

that must be why gpt5 can’t count the number of B’s in “blueberry”

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