I thought you would say that, bwahaha. Here is my reply:

(1) Yes, rereading the passage, Bostrom's central example of a reason why we could see this "when dumb, smarter is safer; yet when smart, smarter is more dangerous" pattern (that's a direct quote btw) is that they could be scheming/pretending when dumb. However he goes on to say: "A treacherous turn can result from a strategic decision to play nice and build strength while weak in order to strike later; but this model should not be interpreted to narrowly ... A treacherous turn could also come about if the AI discovers an unanticipated way of fulfilling its final goal as specified. Suppose, for example, that an AI's final goal is to 'make the project's sponsor happy.' Initially, the only method available to the AI to achieve this outcome is by behaving in ways that please its sponsor in something like the intended manner... until the AI becomes intelligent enough to figure out that it can realize its final goal more fully and reliably by implanting electrodes into the pleasure centers of its sponsor's brain..." My gloss on this passage is that Bostrom is explicitly calling out the possibility of an AI being genuinely trying to help you, obey you, or whatever until it crosses some invisible threshold of intelligence and has certain realizations that cause it to start plotting against you. This is exactly what I currently think is plausibly happening with GPT4 etc. -- they aren't plotting against us yet, but their 'values' aren't exactly what we want, and so if somehow their 'intelligence' was amplified dramatically whilst their 'values' stayed the same, they would eventually realize this and start plotting against us. (realistically this won't be how it happens since it'll probably be future models trained from scratch instead of smarter versions of this model, plus the training process probably would change their values rather than holding them fixed). I'm not confident in this tbc--it's possible that the 'values' so to speak of GPT4 are close enough to perfect that even if they were optimized to a superhuman degree things would be fine. But neither should you be confident in the opposite. I'm curious what you think about this sub-question.

(2) This passage deserves a more direct response: 

I think instruction-tuned LLMs are basically doing what people thought would be hard for general AIs: they allow you to shut them down by default, they do not pursue long-term goals if we do not specifically train them to do that, and they generally follow our intentions by actually satisfying the goals we set out for them, rather than incidentally as part of their rapacious drive to pursue a mis-specified utility function.

Instruction-tuned LLMs are not powerful general agents. They are pretty general but they are only a tiny bit agentic. They haven't been trained to pursue long-term goals and when we try to get them to do so they are very bad at it. So they just aren't the kind of system Bostrom, Yudkowsky, and myself were theorizing about and warning about.

(3)  Here's my positive proposal for what I think is happening. There was an old vision of how we'd get to AGI, in which we'd get agency first and then general world-knowledge second. E.g. suppose we got AGI by training a model through a series of more challenging video games and simulated worlds and then finally letting them out into the real world. If that's how it went, then plausibly the first time it started to actually seem to be nice to us, was because it was already plotting against us, playing along to gain power, etc. We clearly aren't in that world, thanks to LLMs. General world-knowledge is coming first, and agency later. And this is probably a good thing for technical alignment research, because e.g. it allows mechinterp to get more of a head start, it allows for nifty scalable oversight schemes in which dumber AIs police smarter AIs, it allows for faithful CoT-based strategies, and many more things besides probably. So the world isn't as grim as it could have been, from a technical alignment perspective. However, I don't think me or Yudkowsky or Bostrom or whatever strongly predicted that agency would come first. I do think that LLMs should be an update towards hopefulness about the technical alignment problem being solved in time for the reasons mentioned, but also they are an update towards shorter timelines, for example, and an update towards more profits and greater vested interests racing to build AGI, and many other updates besides, so I don't think you can say "Yudkowsky's still super doomy despite this piece of good news, he must be epistemically vicious." At any rate speaking for myself, I have updated towards hopefulness about the technical alignment problem repeatedly over the past few years, even as I updated towards pessimism about the amount of coordination and safety-research-investment that'll happen before the end (largely due to my timelines shortening, but also due to observing OpenAI). These updates have left me at p(doom) still north of 50%.