In sum, I see that claim as I remembered it, but it's probably not applicable to this particular discussion, since it addresses an entirely distinct route to AGI alignment. So I stand corrected, but in a subtle way that bears explication.
So I apologize for wasting your time. Debating who said what when is probably not the best use of our limited time to work on alignment. But because I made the claim, I went back and thought about and wrote about it some more, again.
I was thinking of point 21.1:
The first thing generally, or CEV specifically, is unworkable because the complexity of what needs to be aligned or meta-aligned for our Real Actual Values is far out of reach for our FIRST TRY at AGI. Yes I mean specifically that the dataset, meta-learning algorithm, and what needs to be learned, is far out of reach for our first try. It's not just non-hand-codable, it is unteachable on-the-first-try because the thing you are trying to teach is too weird and complicated.
BUT, point 24 in whole is saying that there are two approaches, 1) above, and a quite separate route 2), build a corrigible AI that doesn't fully understand our values. That is probably the route that Matthew is thinking of in claiming that LLMs are good news. Yudkowsky is explicit that the difficulty of getting AGI to understand values doesn't apply to that route, so that difficulty isn't relevant here. That's an important but subtle distinction.
Therefore, I'm far from the only one getting confused about that issue, as Yudkowsky states in that section 24. Disentangling those claims and how they're changed by slow takeoff is the topic of my post cited above.
I personally think that sovereign AGI that gets our values right is out of reach exactly as Yudkowsky describes in the quotation above. But his arguments against corrigible AGI are much weaker, and I think that route is very much achievable, since it demands that the AGI have only approximate understanding of intent, rather than precise and stable understanding of our values. The above post and my recent one on instruction-following AGI make those arguments in detail. Max Harms' recent series on corrigible AGI makes a similar point in a different way. He argues that Yudkowsky's objections to corrigibility as unnatural do not apply if that's the only or most important goal; and that it's simple and coherent enough to be teachable.
That's me switching back to the object level issues, and again, apologies for wasting your time making poorly-remembered claims about subtle historical statements.