> Right, limitations like: width of human mothers' hips limiting head size, inability to continue growing the brain after adulthood, the decades-long education process, the impossibility of doing repeated experiments of different teaching techniques on the same people and hence iterating quickly and accurately, the impossibility of giving one person's intelligence and knowledge directly to anyone else, old age and death limiting how much a single person can learn or accomplish...

Those aren't actually limitations preventing us from becoming godlike. Those are limitations on us becoming more populous and collectively more experienced, which I argue isn't the same thing at all.

Apotheosis in the context of AI safety as well as for humankind is really about the accumulation of power, rather than the accumulation of knowledge. The extent of our apotheosis is limited only by what we can control, not by what we know.

Knowledge and intelligence only allow you to reason more effectively about paths to power given enough information about the environment, but it doesn't give you a strategic guarantee that you will acquire power - we know there are games that cannot be solved, and games that take too long to solve efficiently, and games that, even with sufficient computing power, rely on factors that cannot be controlled (such as luck), and games for which there are no Nash equilibria or stable winning strategies.

The real world is full of such games. Computers have only managed to beat humans at a small fraction of them, and even then only ones where brute-force and smart pruning (optimal game space search) are winning strategies - bots for games that require probabilistic reasoning, such as poker, typically don't fare any better against expert humans, largely because efficiently solving probabilistic games in general is (per a hazy seminar of AI many years ago, so I could be misremembering) an NP-hard problem.

There are plenty of both natural and artificial limitations around the accumulation of power besides game solvability and probabilistic reasoning's difficulty:

1) System designs that require a minimum set of resources that intelligences can't acquire. For example, individual humans usually can't build their own thermonuclear devices because they don't have access to weapons-grade uranium. Much of our information security relies on making it computationally expensive and infeasible for an attacker without state-level resources to break in, and getting state-level resources isn't a straightforward task at all. We humans haven't even gotten around to building our Dyson spheres yet, because the resources needed to do it far outstrip our ability to acquire them. Intelligence does not improve resource acquisition by itself.

2) Impossibility theorems and optimization difficulty. These place fundamental limits on power - as an example, we know Moore's law has to stop at some point because increasing chips increases power generation, and the amount of effort needed to generate such chips increases with every nanometer shaved off. Most of humanity's most difficult and important problems are optimization problems, and these optimization problems can be tricky to solve in polynomial time. In many cases, we have to rely on fast crude heuristics that give us less than optimal results.

The above limitations partly become easier if you try to parallelize your efforts by duplicating AIs (you can collude to acquire resources and solve problems), but this means you get all the fun parts of distributed systems theory for free: coordination, replication consistency, etc. If only all problems were embarassingly parallel! So it's not necessarily easier to add nodes to a knowledge graph just by adding more processors.