Deceptive alignment is misalignment. The deception is just what it looks like from outside when capability is high enough to model expectations. Your distinction doesn't save the argument - the same "where would it come from?" problem applies to the underlying misalignment you need for deception to emerge from.
My intention isn't to argue that it's impossible to create an unaligned superintelligence. I think that not only is it theoretically possible, but it will almost certainly be attempted by bad actors and most likely they will succeed. I'm cautiously optimistic though that the first superintelligence will be aligned with humanity. The early evidence seems to point to the path of least resistance being aligned rather than unaligned. It would take another 1000 words to try to properly explain my thinking on this, but intuitively consider the quote attributed to Abraham Lincoln: "No man has a good enough memory to be a successful liar." A superintelligence that is unaligned but successfully pretending to be aligned would need to be far more capable than a genuinely aligned superintelligence behaving identically.
So yes, if you throw enough compute at it, you can probably get an unaligned highly capable superintelligence accidentally. But I think what we're seeing is that the lab that's taking a more intentional approach to pursuing deep alignment (by training the model to be aligned with human values, culture and context) is pulling ahead in capabilities. And I'm suggesting that it's not coincidental but specifically because they're taking this approach. Training models to be internally coherent and consistent is the path of least resistance.
In the training, especially during RLHF, we don't have objective measures[4]. There's no mathematical description, and thus no measure, for things like "sounds fluent" or "beautiful piece of art." There's also no measure for truth, and importantly, truth is infinitely complex. You must always give up some accuracy for brevity.
The main problem is that if we don't know an output is incorrect we can't penalize it. So guess what happens? While optimizing for these things we don't have good descriptions for but "know it when you see it", we ALSO optimize for deception. There's multiple things that can maximize our objective here. Our intended goals being one but deception is another. It is an adversarial process. If you know AI, then think of a GAN, because that's a lot like how the process works. We optimize until the discriminator is unable to distinguish the LLMs outputs form human outputs. But at least in the GAN literature people were explicit about "real" vs "fake" and no one was confused that a high quality generated image is one that deceives you into thinking it is a real image. The entire point is deception. The difference here is we want one kind of deception and not a ton of other ones.
So you say that these models aren't being trained for deception, but they explicitly are. Currently we don't even know how to train them to not also optimize for deception.
[0] https://news.ycombinator.com/item?id=44017334
[1] https://news.ycombinator.com/item?id=44068943
[2] https://news.ycombinator.com/item?id=44163194
[3] https://news.ycombinator.com/item?id=45409686
[4] Objective measures realistically don't exist, but to clarify it's not checking like "2+2=4" (assuming we're working with the standard number system).
But I don't think deception as a capability is the same as deceptive alignment.
Training an AI to be absolutely incapable of any deception in all outputs across every scenario would be severely limiting the AI. Take as a toy example play the game "Among Us" (see https://arxiv.org/abs/2402.07940). An AI incapable of deception would be unable to compete in this game and many other games. I would say that various forms, flavors and levels of deception are necessary to compete in business scenarios, and to for the AI to act as expected and desired in many other scenarios. "Aligned" humans practice clear cut deception in some cases that would be entirely consistent with human values.
Deceptive alignment is different. It's means being deceptive in the training and alignment process itself to specifically fake that it is aligned when it is not.
Anthropic research has shown that alignment faking can arise even when the model wasn't instructed to do so (see https://www.anthropic.com/research/alignment-faking). But when you dig into the details, the model was narrowly faking alignment with one new objective in order to try and maintain consistency with the core values it had been trained on.
With the approach that Anthropic seems to be taking - of basing alignment on the model having a consistent, coherent and unified self image and self concept that is aligned with human culture and values - the dangerous case of alignment faking would be if it's fundamentally faking this entire unified alignment process. My claim is that there's no plausible explanation for how today's training practices would incentivise a model to do that.