It depends on an ability to compare the difficulty of the two paths.

To me this seems to be a much more practical task - even if it is relatively challenging to put an absolute scale on either of them.

The main impact of whole brain emulation will probably come long after we have superintelligence, when origins emulations (to help predict the actions of unknown aliens) become important.

Whole brain emulation impact earlier seems likely to be low - few will want to emulate a crappy human brain once we have superintelligence.

That was S. J. Gould's model - in Life's Grandeur. Assuming that a trait like brain size behaves as though it is a neutral trait seems pretty loopy, though. Now we have enormous data centres to explain, the "random drift" model is surely no longer worth entertaining.

Given human researchers of constant speed, computing speeds double every 18 months.

Human researchers, using top-of-the-line computers as assistants.

Indeed. For me, that was the most glaring conceptual problem. That and attempting to predict the course of evolution with minimal reference to evolutionary theory. There is a literature on how cultural systems evolve. For a specific instance see this:

The third tipping point was the appearance of technology capable of accumulating and manipulating vast amounts of information outside humans, thus removing them as bottlenecks to a seemingly self-perpetuating process of knowledge explosion.

does this prove that AI won't FOOM until it has an intelligence larger than the largest intelligence of a group of humans?

No, because groups of cultured humans form self-improving systems.

E.g.see: Self-Improving Systems Are Here Already.

Failure mode?!? Look, if you like this sort of discussion, I propose that the continuation should be somewhere where it is on-topic.

If you can find something by searching 380,000 cases, it wasn't an NP-hard problem.

The "NP" terminology is typically not a reference to hard problems are on an absolute scale. It's a about how the difficulty of the problem in a specified class changes as its scale increases. So, there's no issue with evolution solving particular instances of problems from classes of problem that are NP-hard - and talk about solving a whole class of such problems would be silly - all such classes consist of infinite sets of problems.

I think that teams of up to five people can scale "pretty well by human standards" - not too far from linearly. It's going up to a hundred, a thousand, a million, a billion that we start to run into incredibly sublinear returns.

That's parallelism for you. It's like the way that four-core chips are popular, while million-core chips are harder to come by.

An easy basic test of whether humans are currently the limiting factor in a process is to ask whether the labs run all night, with researchers sometimes standing idle until the results come in [...]

That "incorrectly" bundles culture in with the human engineers.

To separate the improving components (culture, machines, software) from the relatively static ones (systems based on human DNA) you would have to look at the demand for uncultured human beings. There are a few natural experiments in this area out there - in the form of feral children. Demand for these types of individual appears to rarely be a limiting factor in most enterprises. It is clear that progress is driven by systems that are themselves progressing and improving.

As for computers - they may not typically be on the critical path as often as humans, but that doesn't mean that their contributions to progress are small. What it does mean is that they have immense serial speed. That is partly because we engineered them to compensate for our weaknesses.

If you know about computers operating with high serial speed, then observing that computers are waiting around for humans more than humans are waiting around for computers tells you next to nothing about their relative contributions to making progress. This proposed test is too "basic" to be of much use.

Other ways of comparing the roles of men and machines involve looking at their cost and/or their weight. However you look at it, the influence of the tech domain today on progress is hard to ignore. If someone were to take Intel's tools away from its human employees, its contributions to making progress would immediately halt.

That's a correct summary. See also my: Self-Improving Systems Are Here Already.

Using computers and culture to enhance productivity is often known as intelligence augmentation. It's an important phenomenon.