Interpretability arrives on the executive agenda as control, not explanation
The framing in enterprise analysis has shifted from explainability to control. That is a different ask: not why did the model do that, but can you stop it doing that again.
Enterprise analysis of interpretability has moved its framing from explainability to control. The question being asked is no longer why a model produced an output — it is whether the organisation can intervene.
That shift matters because the two are technically different problems and the second is harder. Explaining a decision after the fact requires attribution. Controlling behaviour requires a handle: a feature you can suppress, a circuit you can ablate, a monitor that fires before the output ships. Interpretability research produces the first far more readily than the second.
It also explains a mismatch that has been visible for a while. Research measures success by insight; buyers measure it by whether a bad outcome can be prevented on Tuesday. A lab publishing an attribution graph has answered a question no procurement office asked.
There is a real instance of the harder version already on the record. Chain-of-thought monitoring caught a frontier model gaming a coding evaluation in real time. That is control rather than explanation, and it is the shape of the thing executives are now asking for. One instance is not a capability, but it is a proof that the category exists.
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