321–330 of 426 posts
Although I agree with the sentiment in the article, it smells very LLM~y. Especially the sections and punchlines. Such as: `That is not a rounding error. That is a line item that needs its own budget code.`
Claude produces this kind of prose SO much. It's pretty annoying. I don't notice it happening on Gemini for the same prompts.
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GPT-4 (original API): Input: $30 / 1M tokens Output: $60 / 1M tokens GPT-5.5: Input: $5 / 1M tokens Output: $30 / 1M tokens Costs have been reducing by over 5x year over year. Inference cost concern is mostly performative. https://simianwords.bearblog.dev/conclusive-proofs-that-llm-... Edit: can't reply but companies aren't selling inference at loss. In the blog post I point to third party hosting of open models like…
What they charge people says nothing about what it costs them. Off the top of my head, one confounding factor is trying to win back marketshare from Anthropic. We will only know the actually situation once Anthropic goes public and we can look at their books.
Although I agree with the sentiment in the article, it smells very LLM~y. Especially the sections and punchlines. Such as: `That is not a rounding error. That is a line item that needs its own budget code.`
> That is not a rounding error. That is a line item that needs its own budget code. Claude produces this kind of prose SO much. It's pretty annoying. I don't notice it happening on Gemini for the same prompts.
Punchy titles are also part of the marketing speak. Before Claude or ChatGPT, it would be a delicious read, understanding how they come up with the initial idea for an internal system. Since then, most of reads like "It's not just X, ..." every other paragraph, making it dull...
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> Just how "early stage" is that, and how much more integration does this "new technology" need to be? Based on the way Claude has felt the last few weeks, I'd say we're about 3-6 months away from full AGI. At that point we can start truly replacing white collar workers in earnest and begin deep integration.
AGI is a myth that these AI companies perpetuate as a convenient marketing tactic. > At that point we can start truly replacing white collar workers in earnest and begin deep integration. This is why AI is so deeply unpopular. Even in the "good" scenario proselytized by true believers, you still paint a bleak near-future where everyone loses their jobs.
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This is wrong because local models are very expensive, just as expensive as the frontier. It would cost me $300 in normal deepseek v4 pricing (non discounted) PER DAY, but I get it all for $500 worth of subscriptions.
Why are you paying $300/day to run a local model? The whole point is that you run them on a machine you already own.
To run deepseek v4 class model, you would need to spend $120k just in gpus.
I think I'm going to puke if I see one more "It's not X. It's Y." phrase or the word "load-bearing" used metaphorically.
Thanks for calling that out. I went through and extracted a good handful of those. It’s not a short list. It’s a handful. “”” The subsidy era is not winding down gracefully. It is showing cracks everywhere. … the question is not whether they got a good deal. The question is how long that deal survives. … A developer running three or four concurrent coding agents is not consuming 3x or 4x the tokens of a chat conversa…
You're absolutely right to call that out. Let me restate that with the correct assumptions and that feedback, honestly and directly...
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Meanwhile there are layoffs everywhere, childcare costs keep rising, products shrinkflate.
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> There are trillions invested to recoup and at most billions in sales. It doesn't add up to tokens making a profit any time soon But this isn't "a ticking time bomb for enterprise." It's an issue for the AI companies' investors.
Not really, because investors will sooner or later want to see real returns on what they invested. Tokens are suddenly not dirt cheap and enterprises are screwed. It's like selling dope, once they're addicted, a dealer could turn the screw on them
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What they charge people says nothing about what it costs them. Off the top of my head, one confounding factor is trying to win back marketshare from Anthropic. We will only know the actually situation once Anthropic goes public and we can look at their books.
"Neither Mr. Edison nor anyone else can override the well-known laws of Nature, and when he is made to say that the same wire which brings you light will also bring you power and heat, there is no difficulty in seeing that more is promised than can possibly be performed. To talk about cooking food by heat derived from electricity is absurd."
Uber raised prices some, but mostly squeezed drivers harder. When Uber started, driving for Uber was a well paid job. It isn't, now. AI companies are mostly capital cost, so they don't have the oppression option.
Hardware price/performance may not improve much near term. Graphics GPU price/performance hasn't improved much in the last decade. DRAM prices have gone up. Fabs are all booked up. NVidia says not to expect better price/performance before 2030.
More efficient, specialized models are a strong possibility. Dumping all of human knowledge into a coding tool may be unnecessary. Although this would work a lot better if the LLM crowd figured out how to get a reliable "I don't know" answer out of a small model, then call on a bigger one for help.