> it is shocking how often claude suggests just disabling or removing tests.

Arguably, Claude is simply successfully channeling what the developers who wrote the bulk of its training data would do. We've already seen how bad behavior injected into LLMs in one domain causes bad behavior in other domains, so I don't find this particularly shocking.

The next frontier in LLMs has to be distinguishing good training data from bad training data. The companies have to do this, even if only in self defense against the new onslaught of AI-generated slop, and against deliberate LLM poisoning.

If the models become better at critically distinguishing good from bad inputs, particularly if they can learn to treat bad inputs as examples of what not to do, I would expect one benefit of this is that the increased ability of the models to write working code will then greatly increase the willingness of the models to do so, rather than to simply disable failing tests.