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.

Back in the GOFAI days, when AI meant A* search, I remember thinking: 

1. Computers are wildly superhuman at explicit (System 2 reasoning) like doing arithmetic or searching through chess moves
2. Computers are garbage at (System 1 reasoning), like recognizing a picture of a cat
3. When computers get good at System 1, they will be wildly superhuman at everything

Now transformers appear to be good at System 1 reasoning, but computers aren't better at humans at everything.  Why?
I think it comes down to:

Computers' System 1 is still wildly sub-human at sample efficiency; they're just billions of times faster than humans

LLM's work because they can train on an inhuman amount of reading material.  When trained on only human amounts of material, they suck.

LLM Agents aren't very good because they can't learn on the job.  Even dumb humans learn better instincts after a little on-the-job practice.  We can just barely improve LLM's System 1 from its System 2, but only by brute forcing an inhuman number of roll-outs.

Robots suck, because the real world is slow and we don't have good tricks to train their System 1 by brute force.

We're in a weird paradigm where computers are billions of times faster than humans, but thousands of times worse at learning from a datum.

I think I disagree.  It's more informative to answer in terms of value as it would be measured today, not value after the economy adjusts.

Suppose someone from 1800 wants to figure out how big a deal mechanized farm equipment will be for humanity.  They call up 2025 and ask "How big a portion of your economy is devoted to mechanized farm equipment, or farming enabled by mechanized equipment?"  We give them a tiny number.  They also ask about top-hats, and we also give them a tiny number.  From these tiny numbers they conclude both mechanized farm equipment and top-hats won't be important for humanity.

EDIT The sort of situation I'm worried about your definition missing is if remote-worker AGI becomes too cheap to meter, but human hands are still valuable.

Would you agree your take is rather contrarian?

 * This is not a parliamentary system.  The President doesn't get booted from office when they lose majority support -- they have to be impeached^[1].
 * Successful impeachment takes 67 Senate votes.
 * 25 states (half of Senate seats) voted for Trump 3 elections in a row (2016, 2020, 2024).
 * So to impeach Trump, you'd need the votes of Senators from at least 9 states where Trump won 3 elections in a row.
 * Betting markets expect (70% chance) Republicans to keep their 50 seats majority in the November Election, not a crash in support.

1. ^{^}

   Or removed by the 25th amendment, which is strictly harder if the president protests (requires 2/3 vote to remove in both House and Senate).

...your modal estimate for the timing of Vance ascending to the presidency is more than two years before Trump's term ends?