I think many of the same assumptions also lead to overestimates of the success odds of an SIAI team in creating safe AI. In general, some features that I would think conduce to safety and could differ across scenarios include:

• Internal institutions and social epistemology of a project that makes it possible to slow down, or even double back, upon discovering a powerful but overly risky design, rather than automatically barreling ahead because of social inertia or releasing the data so that others do the same
• The relative role of different inputs, like researchers of different ability levels, abundant computing hardware, neuroscience data, etc, in designing AI; with some patterns of input favoring higher understanding by designers of the likely behavior of their systems
• Dispersion of project success, i.e. the longer a period after finding the basis of a design in which one can expect other projects not to reach the same point; the history of nuclear weapons suggests that this can be modestly large (nukes were developed by the first five powers in 1945, 1949, 1952, 1960, 1964) under some development scenarios, although near-simultaneous development is also common in science and technology
• The type of AI technology: whole brain emulation looks like it could be relatively less difficult to control initially by solving social coordination problems, without developing new technology, while de novo AGI architectures may vary hugely in the difficulty of specifying decision algorithms with needed precision

Some shifts along these dimensions do seem plausible given sufficient resources and priority for safety (and suggest, to me, that there is a large spectrum of safety investments to be made beyond simply caring about).