It is just there, in that last step, that Kurzweil invokes a fallacy. In general, if you have two different kinds of machines, and you want to program each to compute the same result -- upper bounds on the program size on machine tell you nothing about the the upper bounds on the other machine.
Right on, upper bounds on program size do not directly translate from one machine to another. So we can't set a hard bound, and we always need to speak about information content relative to a given machine - too few people have noticed this.
However, science doesn't give up when hard bounds prove impossible. There's always statistics to fall back on.
And statistically speaking, we can absolutely translate information content claims across machine boundaries if we're careful, as long as we have no reason to believe that the machine in question is somehow "special".
A priori, knowing nothing else about machines A or B, we would expect that if we observe an upper complexity bound on an algorithm relative to machine A, there's better than a 50/50 shot that the same upper complexity bound will also hold relative to machine B (it's greater than 50% because it's almost certain that our observation did not pick out the best theoretical upper complexity bound).
As good Bayesians, exactly how this all translates into a best estimate for the bound relative to machine B depends on a lot of stuff (all our prior distributions - in fact, in cases like this talking about upper bounds is silly, it's far better to talk about the distribution of upper bounds, but I digress).
But it's simply wrong not to adjust your complexity estimates relative to machine B in some manner based on an observation of machine A, unless you know something special about machine A that differentiates it from all machines in such a way that it's well-suited to express the algorithm under consideration.
So I ask you this: do you know a fact about "zygote machines" that makes them much better suited to constructing intelligence algorithms than most other machines? Do you have some reason to suspect that their dynamics are more likely to lead to intelligent computations than, for instance, normal computers?
Because otherwise, Kurzweil is a hell of a lot closer to the "right" estimate than you are.
(Edit) FWIW, I posted a more detailed argument elsewhere in this thread (http://news.ycombinator.com/item?id=1621053) that more carefully boils Myers' claim down to just about the same question I asked you.