It could be the case that these two goals are separable and independent:

“AI safety”: avoiding existential risk, s-risk, actively negative outcomes
“AI getting-everything-we-want” (CEV)

Getting our actively positive desires fulfilled ≠? Getting safety

This is what Davidad calls this the Deontic Sufficiency Hypothesis.

If the hypothesis is true, it should be possible to de-pessimize and mitigate the urgent risk from AI without necessarily ensuring that AI creates actively positive outcomes. Because, for safety, it is only necessary to ensure that actively harmful outcomes do not occur. And hopefully this is easier than achieving “full alignment”.

Safety first! We can figure out the rest later.

Quotes from Davidad's The Open Agency Architecture plans

This is Davidad’s plan with the Open Agency Architecture (OAA).

A list of core AI safety problems and how I hope to solve them (2023 August)

1.1. First, instead of trying to specify "value", instead "de-pessimize" and specify the absence of a catastrophe, and maybe a handful of bounded constructive tasks like supplying clean water. A de-pessimizing OAA would effectively buy humanity some time, and freedom to experiment with less risk, for tackling the CEV-style alignment problem—which is harder than merely mitigating extinction risk.

Davidad's Bold Plan for Alignment: An In-Depth Explanation — LessWrong (2023 April)

Deontic Sufficiency Hypothesis: This hypothesis posits that it is possible to identify desiderata that are adequate to ensure the model doesn't engage in undesirable behavior. Davidad is optimistic that it's feasible to find desiderata ensuring safety for a few weeks before a better solution is discovered, making this a weaker approach than solving outer alignment. For instance, Davidad suggests that even without a deep understanding of music, you can be confident your hearing is safe by ensuring the sound pressure level remains below 80 decibels. However, since the model would still be executing a pivotal process with significant influence, relying on a partial solution for decades could be risky.

Getting traction on the deontic feasibility [sic] hypothesis
Davidad believes that using formalisms such as Markov Blankets would be crucial in encoding the desiderata that the AI should not cross boundary lines at various levels of the world-model. We only need to “imply high probability of existential safety”, so according to davida

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