AI can run interpretability experiments, but still misses what is actually a breakthrough
dejavucoder · x · 2026-07-27
The post echoes a view that AI systems are already pretty good at doing AI interpretability research, but still bad at judging which results are actually important.
The key point is not execution quality:
- models can run experiments well
- they can collect and organize results
- they can summarize findings effectively
The weakness is scientific judgment:
- they may misread whether a result is a real breakthrough or just meh
- they can produce competent research artifacts without understanding significance
So the takeaway is that AI can already assist research workflows, but the human role in deciding what matters remains critical.
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