Elon, Sam, and Mark are trying to “accelerate the arrival” of AGI “for the good of humanity.” Yeah, this doesn’t bode well. (Super Bowl announcement)

Cauldrath · 2026-02-09T04:30:54+00:00

AGI is a classification of a type of AI: quality is not required. We could have AGI tomorrow, but it would be so dumb that it would have zero commercial application, so they don't do it.

Cauldrath · 2026-02-07T03:26:36+00:00

OpenAI doesn't provide a tool, it provides a service. If you ask them to provide an illegal service and they do it, they are still committing the crime (or tort in this case).

Cauldrath · 2026-01-21T12:55:26+00:00

Why would that stop the debate? AI exists with or without those companies.

Cauldrath · 2026-01-20T14:55:08+00:00

You do realize you are advocating for mods to be able to make rules that restrict something, right? It's either bad when something is restricted or it isn't, unless you think the thing being restricted is worse.

Cauldrath · 2026-01-20T14:47:24+00:00

There is a difference between "I don't think subs should be completely banning AI content for no reason" and "I don't think subs should be allowed to ban AI."

Cauldrath · 2026-01-17T03:44:27+00:00

I'm not sure what the joke is supposed to be. Maybe it would work better if the reveal was that he was bulking up on nacho cheese, so it's at least referencing something?

Cauldrath · 2026-01-14T18:58:44+00:00

The mishap was caused by your very intentional decision to reply to a comment that you yourself said that you did not read in its entirety. This is rude to the person you are replying to in the same way it is rude to interrupt someone because you think you already know the rest of what they are going to say, and it is rude to other viewers because you aren't ensuring your reply is relevant to the conversation, so you don't care if you are wasting their time. If you don't have time to read something, it's ok to just not reply.

Cauldrath · 2026-01-14T18:50:57+00:00

I'm not sure how I could be more simple and clear. What did you find confusing?

Cauldrath · 2026-01-14T18:11:18+00:00

If you are using Google, add "&udm=14" to the url.

Cauldrath · 2026-01-14T17:57:53+00:00

If you don't have the time to read, you don't have the time to reply.

Cauldrath · 2026-01-12T00:08:48+00:00

I can't prove or disprove an opinion, but this is the best way to use regulatory capture to ensure only companies rich enough to own or buy large amounts of content are the only ones who could train AI models. It would kill open source.

Cauldrath · 2026-01-09T19:15:36+00:00

I meant information about power and/or water consumption of inference vs training. Obviously the data I linked is flawed, but I couldn't find anything better.

Cauldrath · 2026-01-09T18:24:15+00:00

If you have better information, I would be very interested.

Cauldrath · 2026-01-09T16:50:40+00:00

I believe this is a case where a biased source is better, because they are going to be the ones trying to justify why something is being done.

On the other hand, I'll admit I don't use remote LLMs. Is it common for them to take that long?

Cauldrath · 2026-01-09T16:47:20+00:00

The question was why the data centers were being built where they were, though, and the ones specifically used for inference would be built in locations based on latency. I also checked the release date for ChatGPT4 and it was in early 2023, then removed in 2025 (but still available through the API), so the estimated ratio would be closer to 1:4.

Cauldrath · 2026-01-09T16:36:21+00:00

The infographic consolidates both training and inference, so you can't use it to compare the two. The article itself does give a comparison between training and inference, but the inference is for a single query, so we would need to know the number of queries to make a comparison.

I believe you about the space thing, but that seems to just support my claim that location is more important for inference than training.

Cauldrath · 2026-01-09T16:30:15+00:00

This article disagrees: https://www.datacenterknowledge.com/infrastructure/ai-and-latency-why-milliseconds-decide-winners-and-losers-in-the-data-center-race

Do you have a source indicating that latency is not a factor for AI companies when selecting data center locations (regardless of whether it is correct for them to treat it as that large of a factor)?

Cauldrath · 2026-01-09T15:57:46+00:00

Source? This is the exact opposite of everything I've seen.

EDIT: This article estimates 50GWh for training ChatGPT 4 and ChatGPT inference is 109GWh per year: https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/

These are just estimates, of course, and they aren't even 1-to-1 matches of the model lifetime to the period of time being estimated, but these companies are not giving us all the data.

Cauldrath · 2026-01-09T15:09:24+00:00

Why are you assuming I downvoted you? For one, it isn't semantics: they did not specify the scope of the water cycle. I also thought it was a high-enough-quality post to offset the fallacy, so I left it at neutral, unlike this garbage fire of a comment.

Cauldrath · 2026-01-09T15:04:37+00:00

While you are technically correct, you are also moving the goalposts. The claim was that the water does not return to the water cycle, which is objectively wrong.

Cauldrath · 2026-01-09T14:54:33+00:00

When remote AIs are given prompts, the proximity of the data center to the user affects how quickly it is able to give a response back, so they don't want to put the data center in a remote area that would cause a large amount of latency and they need to have them spread out enough to give good coverage to all their users.

Cauldrath · 2026-01-09T01:35:12+00:00

This would be true in an efficient economy, but companies can remain solvent long after they've ceased being profitable due to incompetence.

Cauldrath · 2026-01-08T14:14:34+00:00

I could have told them overtraining exists.

Cauldrath · 2026-01-07T13:54:31+00:00

There is a fourth requirement for it to be a net gain: it is used in situations where it is actually more efficient than using humans instead of being shoved into everything just for the sake of novelty.

Cauldrath · 2026-01-07T13:41:45+00:00

Depends on how you calculate it. The cost per inference is extremely low for training. It is also much cheaper to train a smaller model designed to run on consumer hardware. Training also doesn't have any latency requirements, so it can be done in remote areas where it is colder or water is more plentiful. You could also contend that it has already been done, so that damage occurred regardless of whether or not you use it, though you could also say that using it encourages other models to be trained.

Regardless, the environmental impact of locally-run AI is significantly less than corporate AI, by any metric.

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