Compute is not the limiting factor for mammalian intelligence. Mammalian brains are organized to maximize communication. The gray matter, where most compute is done, is mostly on the surface and the white matter which dominate long range communication, fills the interior, communicating in the third dimension.
If you plot volume of white matter vs. gray matter across the various mammal brains, you find that the volume of white matter grows super linearly with volume of gray matter. https://www.pnas.org/doi/10.1073/pnas.1716956116
As brains get larger, you need a higher ratio of communication/compute.
Your calculations, and Cotras as well, focus on FLOPs but the intelligence is created by communication.
dy/dt = f(y) = m*y whose solution is the compound interest exponential, y = e^(m*t).
Why not estimate m?
An exactly right law of diminishing returns that lets the system fly through the soft takeoff keyhole is unlikely.
This blog post contains a false dichotomy. In the equation, m can take any value and there is no special keyhole value, and there is no line between fast and slow.
The description in the subsequent discussion is a distraction. The posted equation is meaningful only if we have an estimate of the growth rate.
What if we don't make the super powerful AI be the thing that parks cars. Then we can turn it off and still have our cars parked!
Sure. For the supercomputer, the measure you are looking for is TEPS as the counterpart to FLOPS. You can get started with this brief explanation. https://en.wikipedia.org/wiki/Traversed_edges_per_second
There are a number of articles on mammalian brain white matter vs. gray matter, including some that attempt to derive geometric constraints. From the geometric constraints papers, it appears that evolution is maximizing the interconnect and there are arguments that say the folds in the gray matter facilitate communication as well.
Your background is interesting. I've been interested the physics of computation since Feynman taught a course in it when I was an undergrad. I have focused on optical computing and optical communications for silicon compute. The field is very rich.