Scheming seems like an unnatural concept to me. I think we can do better. (note: many/most of these thoughts un-original to me)

• Scheming is typically used as a binary, i.e. "is the AI scheming", whereas the typical human usage of the word scheming is much more continuous. It's not very useful to group humans into "schemers" vs "non-schemers"; most people attempt to achieve goals to some extent, and sometimes this involves deceiving other people.
• Joe Carlsmith uses the following taxonomy to define scheming, i.e., a schemer is an AI which "plays the training game, without intrinsically caring about reward-on episode, in order to get power for itself or other AIs". This definition entirely refers to what the training does in training, not evaluation or deployment, and seems very similar to "deceptive alignment"

• I think it's plausible that AIs start misbehaving in the scary way during deployment, without "scheming" according to the Carlsmith definition. A central reason this might happen is because the AI was given longer to think during deployment during training, and put the pieces together about wanting to gain power, and hence wanting to explicitly subvert human oversight. Carlsmith's definition of scheming seems to come pretty far away from the normal english meaning of the word schemer, which would prototypically include the above example.

I do think there is something important to talk about here, which scheming is trying to get at, which I'll try to lay out here.

• It seems very important to distinguish the type of misalignment where the AI is deliberately thinking about and trying to undermine human oversight and safeguards, from all other types of misalignment, where the AI might happen to take misaligned actions without deliberately trying to obfuscate human understanding that they made a mistake.
• Unlike "schemers", I think "insider threats" is a more crisp dividing line with humans. Most employees of most companies are kinda normal people doing their jobs without trying to subvert the overall goals of the company. Of course, there are constantly myopic power struggles between people in many companies that don't serve the best interests of the company, and many employees do something "schemey" during corporate politics in order to get what they want to happen. But they don't have a hidden long term objective that comes strongly apart from the interest of their employer, like literal spies would.
• • I think it's unclear whether the standard use of scheming within the alignment community applies to myopic powerseeking of the type that managers in giant corporations do, whereas these people are not "insider threats" wrt the company, they just have some motivations which run at cross purposes, but aren't systematically attempting to undermine it.
• I think this suggests a more crisp concept here to describe AIs, which I like to call "adversarial misalignment", which is basically "are the AIs acting like insider threats or not?". This is different from "misalignment" more broadly, because I would say that the current batch of models (e.g. Opus 4.6) are misaligned in a weaker sense, i.e. they aren't consistently honest with me and they often don't try as hard as I would like them to on many tasks, despite them being capable of succeeding on those tasks. I think in practice people use "scheming" along these lines in many cases.
• • Adversarial misalignment wouldn't include myopic powerseeking during deployment that wasn't in service of a long run goal, didn't entail also sabotaging oversight, etc, and so in the human case, wouldn't include normal employees doing some amount of corporate politics.
  • This concept is different from Carlsmith's notion of scheming because it's not primarily about training, it's primarily about deployment, though it also seems quite correlated with AIs intentionally acting nice during training/eval in order to get power later. I think we should call this (intentionally acting nice during training/eval in order to get power later) "deceptive alignment".