most of these require

1. more preparation & coordination
2. more physical energy from everyone

which can be in short supply

I guess this is sorta about your 3, which I disbelieve (though algorithms for tasks other than learning are also important). Currently, Bayesian inference vs SGD is a question of how much data you have (where SGD wins except for very little data). For small to medium amounts of data, even without AGI, I expect SGD to lose eventually due to better inference algorithms. For many problems I have the intuition that it's ~always possible to improve performance with more complicated algorithms (eg sat solvers). All that together makes me expect there to be inference algorithms that scale to very large amounts of data (that aren't going to be doing full Bayesian inference but rather some complicated approximation).

Relevant: https://andymatuschak.org/hmwl/

o1 CoT: The user is asking for more references about brownies. <Reasoning about what the references should look like> So, the assistant should list these references clearly, with proper formatting and descriptions, and provide actual or plausible links. Remember, the model cannot retrieve actual URLs, so should format plausible ones.

this might encourage it to make up links

description of (network, dataset) for LLMs ?= model that takes as input index of prompt in dataset, then is equivalent to original model conditioned on that prompt

There exist inexpensive real co2 sensors, e.g. https://www.sparkfun.com/products/22396 . Datasheet says only updates every 5 seconds & 60s response time "for achieving 63% of a respective step function", which I guess is what parent comment means by "They’ll likely be extremely slow".

Probably worth searching e.g. digikey for sensors with faster response time.

What about specialized algorithms for problems (e.g. planning algorithms)?

IANAL, but I think that this is currently impossible due to anti-trust regulations.

I don't know anything about anti-trust enforcement, but it seems to me that this might be a case where labs should do it anyways & delay hypothetical anti-trust enforcement by fighting in court.

blueiris's posts read to me as a combination of good concepts & poor quality attacks/attempts to defend leverage (or something?). Personally I'd mind the attacks more if they were more successful and/or less obvious I think? As-is they're annoying but don't seem very dangerous epistemically.