In practice, much is down to how fast scientific and technological progress can accelerate. If seems fairly clear that progress is autocatalytic - and that the rate of progress ramps up with the number of scientists, which does not have hard limits.

It might ramp up with increasing the number of scientists, but there are clear diminishing marginal returns. There are more scientists today at some major research universities than there were at any point in the 19th century. Yet we don't have people constantly coming up with ideas as big as say evolution or Maxwell's equations. The low hanging fruit gets picked quickly.

Algorithm limits seem to apply more to the question of how smart a computer program can become in an isolated virtual world.

Matt Mahoney has looked at that area - though his results so far do not seem terribly interesting to me.

I agree that Mahoney's work isn't so far very impressive. The models used are simplistic and weak.

I think one math problem is much more important to progress than all the other ones: inductive inference.

Many forms of induction are NP-hard and some versions are NP-complete so these sorts of limits are clearly relevant. Some other forms are closely related where one models things in terms of recognizing pseudorandom number generators. But it seems to me to be incorrect to identify this as the only issue or even that it is necessarily more important.. If for example one could factor large numbers more efficiently, an AI could do a lot with that if it got minimal internet access.