The only part of the chain of logic that I don't fully grok is the "FOOM" part. Specifically, the recursive self improvement. My intuition tells me that an AGI trying to improve itself by rewriting its own code would encounter diminishing returns after a point - after all, there would seem to be a theoretical minimum number of instructions necessary to implement an ideal Bayesian reasoner. Once the AGI has optimized its code down to that point, what further improvements can it do (in software)? Come up with something better than Bayesianism?

Now in your summary here, you seem to downplay the recursive self-improvement part, implying that it would 'help,' but isn't strictly necessary. But my impression from reading Eliezer was that he considers it an integral part of the thesis - as it would seem to be to me as well. Because if the intelligence explosion isn't coming from software self-improvement, then where is it coming from? Moore's Law? That isn't fast enough for a "FOOM", even if intelligence scaled linearly with the hardware you threw at it, which my intuition tells me it probably wouldn't.

Now of course this is all just intuition - I haven't done the math, or even put a lot of thought into it. It's just something that doesn't seem obvious to me, and I've never heard a compelling explanation to convince me my intuition is wrong.