The second statement seems pretty plausible (when we consider human-accessible AGI designs, at least), but I’m not super confident of it, and I’m not resting my argument on it.
The weaker statement you provide doesn’t seem like it’s addressing my concern. I expect there are ways to get highly capable reasoning (sufficient for, e.g., gaining decisive strategic advantage) without understanding low-K “good reasoning”; the concern is that said systems are much more difficult to align.
Small note: my view of MIRI’s nondisclosed-by-default policy is that if all researchers involved with a research program think it should obviously be public then it should obviously be public, and that doesn’t require a bunch of bureaucracy. I think this while simultaneously predicting that when researchers have a part of themselves that feels uncertain or uneasy about whether their research should be public, they will find that there are large benefits to instituting a nondisclosed-by-default policy. But the policy is there to enable researchers, not to annoy them and make them jump through hoops.
(Caveat: within ML, it’s still rare for risk-based nondisclosure to be treated as a real option, and many social incentives favor publishing-by-default. I want to be very clear that within the context of those incentives, I expect many people to jump to “this seems obviously safe to me” when the evidence doesn’t warrant it. I think it’s important to facilitate an environment where it’s not just OK-on-paper but also socially-hedonic to decide against publishing, and I think that these decisions often warrant serious thought. The aim of MIRI’s disclosure policy is to remove undue pressures to make publication decisions prematurely, not to override researchers’ considered conclusions.)