[cbennett]

> If it scales faster than linear, then your recursive bootstrapping operation takes longer and longer each time,

Wait, does that really follow? What if you have a better than linear bootstrapping compiler. To unpack that a bit, imagine we not only have $n$ such units, but we have them wired to together in a creative way-- i dont know whether it is hierarchy, or some clever topology, but lets say that the bootstrapper now gets sub-linear scaling properties as it grows $n$.

If we look at the brain, there is a lot to be understood from the dynamics of recurrent neural fields. They are wired in a very complex way which seems to allow for some kind of very special booting (re-booting) operations. And thats just at one level of abstraction, then we re-wire them into meta-fields (like the columnar abstractions that Hawkin's builds his HTM theories around). If we have a sort of fractal information encoding, we ultimately approach shannon efficient coding. Is that what evolution has selected brains to do? And do you think it is possible the first seed AI may realize this and exploit the same strategy, just 1000x (10kx?) faster?