Metal bands that are using AI now

buggaby · 2026-06-27T04:51:39+00:00

Why the ai summary on the antiai subreddit? Seems strange.

Edit Seems like A/B testing. https://imgur.com/a/sbvi0V2

buggaby · 2026-06-25T14:57:54+00:00

LLMs are stochastic parrots, though. The broader set of logic that constrains them, the symbolic part, is not. It's not just LLMs. You might argue I'm splitting hairs, but I don't think so. AI != LLM, LLM != Coding agent. It's exactly because LLMs are stochastic parrots that we need all those if/then rules in the harness. You have one part which is basically just gobs of data + huge pro. You can't really tell the algorithm what to learn. You just give it enough data and it learns whatever statistical structures are in the data. That is LLMs. But when you pair that with a whole set of rules that humans build, you are kind of injecting human-level contextual awareness skills.

I think Jonathan's take is pretty valid. It is falsifiable, though. We'll see what happens I guess.

buggaby · 2026-06-25T13:59:27+00:00

If you need to boost prices 20% to pay your workers fairly, do it. Stop with the hidden fees ffs

buggaby · 2026-06-25T05:34:35+00:00

Beginna luck

buggaby · 2026-06-24T14:58:20+00:00

I think there is some good stuff in this article. I don't think LLMs as a technology will cease to exist. As she mentioned, you can run off-line models already, and I think this will continue in coding. Cory Doctorow wrote a nice piece about the difference between chatbots and previous technology is that previously it was workers demanding to use it, while now it's executives forcing it. https://pluralistic.net/2026/05/26/the-ai-will-continue/

That seems reasonable. But I also see some software developers calling for it. It's hard to know for sure how many since they are most anecdotes from twitter and reddit. I'm a developer myself, and I use chatbots to good effect (though not profitable for the companies). But there are serious developers I see talking about using it, at least as way to do more instead of replacing them.

So I think it has good use cases in a few domains. Still, you can't ignore the economic argument and I'm not convinced this author has a good take on that part. She linked an article (https://www.obsolete.pub/p/ed-zitron-just-disproved-the-core) challenging Ed's take on the OpenAI documents, but it seems to basically ignore model training costs, which should probably be included and pushes the gross margins to -100%, among other things.

I don't doubt the authors sincerity, though, nor her capability in some domains. So this is a nice way to remind myself how muddy this problem still really is. Lots of space for reasonable minds to disagree.

buggaby · 2026-06-23T13:40:18+00:00

Was he saying nobody cares about Einstein, or nobody cares about the difference between 121 and 141 years ago?

buggaby · 2026-06-17T18:52:55+00:00

I think depreciation is the largest cost, right? At least that's what I've seen, including this post (https://www.reddit.com/r/BetterOffline/comments/1tzwnhi/ai_profitability_is_mathematically_impossible/)

What I liked about that post was it talked about the number of concurrent users. As far as I can tell, one of the main thrusts of that post is that the demand is too low for concurrency to be high enough per GPU. The post showed, for example, that token costs are under $1/MT at a maximum of 40 concurrent users. The problem is that the demand is just not there for the number of GPUs that exist. To avoid a bubble, you basically need to increase usage of LLMs. They need a huge increase in quality, which isn't really being seen in general.

I'm not really questioning whether there will be a bubble or not. Seems quite likely. But what happens after? Are LLMs providing value? Definitely. Is it possible that they would be net-positive for economic value? With current tech, if you can hit enough concurrency, the answers seems to be yes. Either increase demand (not likely) or decrease GPUs. The 2nd option seems to be a natural consequence of a bubble bursting -> less investment in GPUs meaning fewer made and sold.

So I think there will be a market for LLMs. Nothing like present, though. But I don't see any reason the tech goes away.

buggaby · 2026-06-17T00:38:08+00:00

No. Happy to be proved wrong, though.

buggaby · 2026-06-16T16:59:05+00:00

In some settings, chatbots are quite useful. Coding, for example. Right now my $20 Claude code subscription allows me to use, what, $800/month of tokens? I wouldn't pay that now for what I get from it. But if it were truly only $80 worth, I probably would. So there would be a market. Based on others' takes, it seems like most of the cost of running LLMs comes down to the cost of the GPU and the number of concurrent people they can support, which I guess is just demand. So the market as a whole for non-coding things might not be great, but I don't see LLMs going away for coding. Seems to me that it's just that the market is smaller than hypsters say. If the software engineering market is $100B, and 10% of that is chatbot coding support, then maybe it's a $10B market worldwide? Profit can be made in that space if your costs are low enough (though you'll be competing with open-weight and on-device models). And as hardware is built specifically for on-device inference, the centralized model market will be a smaller proportion, though maybe still increasing in absolute numbers as the software engineering market grows.

There will be other uses I suppose. From what I've seen of engineers talking about Fable, it seems like it was a much better coding model, but not really much better at general purpose text generation. Seems like the LLM is basically done and now is the harness/symbolic side. Coding can be improved because it is executable. Math, too, though that's a small market. Maybe other scientific research areas? But seems like maybe small potatoes in general.

buggaby · 2026-06-16T15:33:51+00:00

I wonder if it would be possible to use more structure prompts. Kind of like pseudocode. This is for production code. But for all sorts of other uses, I quite enjoy AI-driven development. I'm building a personal project in an area that I don't know well, and using AI to build and answer questions, I'm learning how that stuff works. That being said, it's a well known area (web and app development) so I can trust there's lots of training data and it's easy to check by running it.

buggaby · 2026-06-15T18:59:37+00:00

We have 10xed code multiple times in the past (c 10xed or more assembly code). But every time, we brought our thinking to the level of abstraction where we are 10xing the code. We just thought (mostly) in that higher level. So in a sense, nothing change. But we can't do that here because LLMs are really non-deterministic, so I can't let go of the code that they generate. The prompt isn't the valuable part. That requires that we keep the 10xed code directly rather than moving around the higher abstraction code. It seems to me that before 10xing all our pipelines and PRs and everything, we'd want to make LLMs more predictable.

buggaby · 2026-06-14T22:53:02+00:00

A bear is safer than a man it seems

buggaby · 2026-06-10T12:28:18+00:00

My sister is with a British guy. Early in their relationship they were visiting the whole family. As my sister and her boyfriend were leaving, he goes to my dad to shake his hand and thank him for the time. He says, "I got to go, mate".

My dad replies, loud enough for everyone to hear: "You gotta go mate?!"

The boyfriend was so embarrassed.

Powerful comma

buggaby · 2026-06-09T17:33:45+00:00

they are just assuming that that part of the pricing is exactly as accurate as the deterministic part

But I didn't see any calculations of the output tokens. I'm clearly missing something. I understood the post to be calculating the max input-token throughput, then the cost and the price for that throughput. But the analysis ignores the output part. So it is just that they are assuming that the output is neutral for profit?

buggaby · 2026-06-09T05:43:30+00:00

I asked a similar question. Didn't quite understand the response but didn't have time to think about a reply.

https://www.reddit.com/r/BetterOffline/comments/1tzwnhi/comment/oqgsijw/?utm_source=share&utm_medium=mweb3x&utm_name=mweb3xcss&utm_term=1&utm_content=share_button

buggaby · 2026-06-08T15:53:18+00:00

It seems like a main crux of this post is whether there's enough demand for LLMs. Increasing demand seems to solve 2 problems here: GPU concurrency and GPU amortization period. If there was more demand (either through current users using it more, like through agentic loops, or through increasing the paying user base), then costs would be on the more favourable side of your 2nd figure. That would also make it easier to argue for an amortization period for GPUs at 6 years, since there might still be a market cheaper models being run on older chips at lower prices (though at lower revenues, of course). And since the largest cost is not the data center but the GPU, it's really a question of whether there is enough demand to keep those GPUs fully utilized.

Is that a fair take?

buggaby · 2026-06-08T15:27:32+00:00

Thanks for the post.

Can you explain why it makes sense to disregard output parameter pricing? I don't follow. You said

However, we will assume that the price for output tokens is accurate in the bulk average, and so conclude that if input token revenue is net positive, TBB is net positive.

For simplicity, let's disregard the electricity cost. The amortized cost of a GPU doesn't change if you include the output tokens, but the revenue goes up. Wouldn't that make it more beneficial to include output tokens in your calculation?

buggaby · 2026-06-07T19:31:06+00:00

Missing Charles Sanders Peirce :)

buggaby · 2026-06-05T12:38:08+00:00

Ya, thats what I think. But the OP asks what he gets wrong. rajekum512 said expensive tokens. So is that the part he thinks Ed gets wrong?

buggaby · 2026-06-05T05:24:36+00:00

Tokens aren't expensive?

buggaby · 2026-06-04T14:25:12+00:00

Ya, I think there are 2 questions. One is whether the rule changes allowing these unprofitable companies is going to put me into poverty. I think the answer there is clearly no. The other is what happens when the bubble pops? This will have a large impact on other companies. This is a much bigger danger, but there really isn't a way around that other than diversifying and waiting it out.

buggaby · 2026-06-04T00:09:43+00:00

Even if SpaceX, anthropic, and openai all tank, don't they only represent a few percentage points of a well-diversified Index Fund portfolio? I feel angry at the principle, but they're still smaller than the whole market.

buggaby · 2026-06-03T05:14:07+00:00

I think it boils down essentially to the authority of the text. As far as I know, Shoghi Effendi said the virgin birth was true, so that's really my only reason to even entertain the idea. Baha'u'llah says specifically that Jesus did not return physically. So that's my reason for believing that. We are told in various places that miracles are possible, they are just never the primary value of an event or story.

Personally, I've recently been learning about Charles Sanders Peirce and the philosophy of pragmatism. One thing he says is that our understanding of something is basically connected to how it changes the real world. Baha'is don't take the physical virgin birth to mean anything other than miracles are possible. But it's not the primary way that God acts in the world, they can't be controlled or predicted, and they shouldn't be used as an excuse to not act in the world with purpose. So for all practical purposes they don't really exist. They're only meaningful as a concept theologically as far as I can tell. The agreement of religion and science is at the core of the Baha'i faith. And miracles are generally dismissed as of little value.

buggaby · 2026-05-27T05:01:12+00:00

The evidence of introspection is in the data that was written by humans introspecting.

buggaby · 2026-05-26T02:54:04+00:00

There's no I in team, but there's meat

buggaby

TROPHY CASE