Do anthropic considerations undercut the evolution anchor from the Bio Anchors report?

I think that doing N independent parallel computation and selecting one of them is way less useful than doing an N times longer serial computation. This kind of selection only helps you guess something that is impossible to deduce in any other way. So if anthropics is tacitly selecting the earth out of N other worlds, that doesn't contribute a factor of N to the total computation, it's a much smaller factor.

EDIT: intended to write a comment rather than an answer.

This estimate is based on various biological comparisons, and for the purposes of this question I want to focus on the most conservative estimate from the report, that coming from how much computation was done by evolution in the history of life.

There is being reasonably conservative, and then there is that. There is a singularly correct way to generate predictive theories (bayesianism/solomonoff/etc), and it always involves the product (or sum in logprob bits) of theory prior complexity and theory posterior postdiction power. The theory based on anchoring to the sum "computation done by evolution in the history of life" completely fails to postdict the success of deep learning, and thus is so wildly improbable as to not be worth any further consideration, and seriously undermines the entire report.

There is a much simpler model which tightly postdicts the evidence to date: AI progress is straightforwardly predictable by availability of compute equivalent to that required to train some relevant set of functionally similar minimal-sized brain modules.

brains operate far from the Landauer limit at the bit level (though maybe not if you condition on information transmission via electrons, see this post [LW · GW]),

The point of that post was to disprove/prevent that common claim about brain energy efficiency, as it's based on naive oversimplified layman models (which any good semiconductor engineer would already scoff at, but the wisdom from that field is not widely enough distributed). Perhaps I didn't communicate the simplest form of the argument, but it's something like this:

First you can't compare complex high level analog synaptic ops or floating-point ops (which are used to simulate synaptic ops) to low level binary switch ops: the former complex elements are built out of multiple OOM quantity of the simpler elements.

Secondly any real computer/brain is ultimately built out of minimal atomic elements which themselves are also computers to which the Landauer limit applies. So a computer could only operate directly at the (error rate corrected) thermodynamic limit implied by counting only it's switching elements (transistors) if it didn't require wires/interconnect. But in actuality any real computer/brain built out of such minimal elements ends up being mostly wires to be useful. Worse yet, the thermal output of a maximally miniaturized machine (to minimize interconnect cost) would be far beyond practical cooling capacity, and even worse worse yet the required cooling pipes (for the otherwise optimal 3d layout, as used in brains) would then inflate the volume and thus interconnect. Or you go 2d (as in silicon chips) and you can cool using a heatsink in the vertical dimension but then you have much worse interconnect scaling.

Or put it another way: latest '4 nm' foundry tech is now within about 10x or so of minimal feature scaling, but energy efficiency scaling is already slowing down, and we still haven't yet matched the brain there. The probability that biological evolution (which quickly evolved optimal nanotech bots operating right at the landauer limit), and the vast intelligence of our semiconductor research industry (humanity's greatest technological achievement by landslides) just happened to reach about the same physical limits, without those limits being fundamental, is very small.

Case in point, the new RTX 4090 built on latest TSMC 4N process has 7.6e10 transistors switching at 2.2e9 hz while using 450 watts, or roughly 1.6e20 transistor bit erasures in 450J, or 3.7e-17 J/erasure, which is only about 200x larger than the Landauer bound of 1eV [LW(p) · GW(p)] (1.6e-19 J) for reliable digital switching - if we wrongly assumed non-dissipative wires. If we assume more realistically that wire/interconnect dissipation uses 90% of the energy, then we are already only around 20x from hard limits.

This is why the future planned process nodes continue to offer die shrinkage, but disappointing shrinking energy efficiency improvements. It's also why GPUs increasingly have more flops than can even be usefully shuffled to registers, and why GPU energy use is growing exponentially with shrinkage.

If this is true, producing general intelligence can be a much harder problem than this calculation gives it credit for, because anthropic considerations mean we would only be asking the question of how difficult general intelligence is to produce in worlds where general intelligence was actually produced.

If one starts doing anthropic adjustments, you don't just stop there. The vast majority of our measure will be in various simulations, which dramatically shifts everything around to favor histories with life leading to singularities.