Here's a pure quantum, information theoretic, no computability assumptions version that might or might not be illustrative.  I don't actually know if the quantum computer I'm talking about could be built -- I'm going off intuition.  EDIT I think this is 2 party quantum computation and none of the methods I've found are quite as strong as what I list here (real methods require e.g. a number of entangled qbits on order of the size of the computation).

You have two quantum computers, Alice and Bob, preforming the same computation steps.  Alice and Bob have entangled qbits.  If you observe the qbits of either Alice or Bob in isolation, you'll forever get provably random noise from both of them.  But if you bring Alice and Bob together and line up their qbits and something something mumble, you get a pure state and can read off their joint computation.

Now we have all sorts of fun thought experiments.  You run Alice and Bob, separating them very far from one another.  Is Alice currently running a mind computation?  Provably not, if someone looked at Bob last year.  But Bob is many many light years away -- how can we know if someone looked at Bob?  What if we separate Alice and Bob past each other's cosmic horizons, such that the acceleration of the expanding universe makes it impossible for them to ever reach each other again even if they run towards each other at the speed of light?  Or send Bob to Alpha Centauri and back at close to the speed of light so he's aged only 1 year where Alice has aged 8.  Has Alice been doing the mind thing for the past 7 years?  Depends on whether you look at Bob or not.

(but I'll note that for me, this version, like the homomorphic version, is mostly saying that your description of a quantum physics state shouldn't be purely local.  A purely local description must discard information, something something mixed state Von Neumann entropy)

Forgive me, I'm probably being stupid again 😬.

On efficient computability being necessary for reality: I'm not sure I understand the logic behind this.  Would you not always get diagonalization problems if you want supervening "real" things to be blessed with R-efficiently computability?  For example, take R to be something like a Solomonoff induction.  R-efficiently computable there means Turing computable.  For our M which supervenes on R, instead of Minds, let's let M be the probability p of a given state.  The mapping function g: R->M, mapping states to the probability of states, cannot be R-efficiently computed (no matter what sort of Turing machine or speed prior you use for R) for diagonalization reasons.  So the probabilities of states aren't a "real" thing?  It seems like a lot of natural emergent things wouldn't be R-efficiently computable.

On homomorphic encryption being un-reversible: quantum computers are reversible, right?  So if you say physics is as powerful as a quantum computer, and you want homomorphic encryption to be uncomputable in polynomial time, you have to make P's physics "state" throw quantum information away over time (which it could, in e.g. Copenhagen or objective collapse interpretations, but does not in e.g. many worlds) or maybe restrict the size of the physical universe you're giving as state to not include information we radiated away many years ago (less than 62.9 billion light years).

(Don't feel obligated to reply)

I suspect this about many things, e.g. the advice in the US to never talk to the police.
With the Streisand effect I'm less sure.  Conflict sells.  The areas in e.g. popular science I know the most about tend not to be the ones that are most established or important -- they tend to be the ones that are controversial (group selection, deworming wars, arsenic biology).

Not if the ELO algorithm isn't run to completion.  It takes a long time to make large gaps in ELO, like between stockfish and Random, if you don't have a lot of intermediate players.  It's hard for ELO to different between +1000 ELO and +2000 ELO -- both mean "wins virtually all the time".

Aren't ELO scores conserved?  The sum of the ELO scores for a fixed population will be unchanged?
The video puts stockfish's ELO at 2708.4, worse than some human grandmasters, which also suggests to me that he didn't run the ELO algorithm to convergence and stockfish should be stealing more score from other weaker players.
EDIT ChatGPT 5 thinks the ELOs you suggested for random are reasonable for other reasons.  I'm still skeptical but want to point that out.

I do not believe random's Elo is as high as 477.  That Elo was calculated from a population of chess engines where about a third of them were worse than random.

I'm not at all convinced this isn't a base rate thing.  Every year about 1 in 200-400 people have psychotic episodes for the first time.  In AI-lab weighted demographics (more males in their 20's) it's even higher.  And even more people get weird beliefs that don't track with reality, like find religion or Q-Anon or other conspiracies, but generally continue to function normally in society.
Anecdotally (with tiny sample size), all the people I know who became unexpectedly psychotic in the last 10 years did so before chatbots.  If they went unexpectedly psychotic a few years later, you can bet they would have had very weird AI chat logs.

Light disagree.  Prefix modifiers are cognitively burdensome compared to postfix modifiers.  Imagine reading:

"What I'm about to say is a bit of a rant.  I'm about 30% confident it's true.  Disclosure, I have a personal stake in the second organization involved.  I'm looking for good counter arguments.  Based on a conversation with Paul.  I have a formal writeup at this blog post.  Part of the argument is unfair, I apologize.  I..."

Gaaa, just give me something concrete already!  It's going to be hard enough understanding your argument as it is; it's even harder for me to understand your argument while having to keep unresolved modifiers loaded in my mental stack.

Ha, and I have been writing up a long-form for when AI-coded-GOFAI might become effective, one might even say unreasonably effective.
LLMs aren't very good at learning in environments with very few data samples, such as "learning on the job" or interacting with the slow real world.  But there often exist heuristics, ones that are difficult to run on a neural net, with excellent specificity that are capable of proving their predictive power with a small number of examples.  You can try to learn the position of the planets by feeding 10,000 examples into a neural network, but you're much better off with Newton's laws coded into your ensemble.  Data constrained environments (like, again, robots and learning on the job) are domains where the bitter lesson might not have bite.