this post was submitted on 18 May 2026
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Fuck AI

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A place for all those who loathe AI to discuss things, post articles, and ridicule the AI hype. Proud supporter of working people. And proud booer of SXSW 2024.

AI, in this case, refers to LLMs, GPT technology, and anything listed as "AI" meant to increase market valuations.

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Excerpt:

The IPO Math Forces the Issue

Both OpenAI and Anthropic are on IPO timelines for the second half of 2026. OpenAI completed the largest private funding round in history in April, $122 billion at an $852 billion post-money valuation. Anthropic has reportedly surpassed $30 billion in annualized revenue. Massive numbers, both of them. Also both attached to companies that are still burning cash at extraordinary rates.

Public markets will not tolerate the gap between subscription revenue and compute cost that has defined the past three years. The moment either company files, analysts will demand unit economics that show a path to margin. Usage-based billing is the fastest way to demonstrate that path.

None of this contradicts the repricing thesis. The pricing war is the last land grab before the gate closes. Both companies are spending aggressively now to lock in users whose switching costs will make them sticky when prices rise. OpenAI offers two months free. Anthropic offers 50% more capacity. Both expire in July. What comes after July is the real pricing.

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[–] brucethemoose@lemmy.world 1 points 1 day ago* (last edited 1 day ago)

Actually, what Cerebra’s does is no different than any generic host. They provide API access to LLM weights, though most providers will do it with some standard open source serving software like VLLM or SGLang.

And they all use the same open weights LLMs. They arent the software developer.

Cerebras doesn’t train their own model. And I think this is fine for service guarantees as long as the weights do not change, hence will provide the exact same deterministic results at zero temperature (and generally perform the same when used as a service).

My experience is that a lot of “enterprise” LLM stuff is used in bulk, for results that can be “good enough” with a reasonable error rate. Like (for example) extracting info from literally millions of documents. Or as RAG/querying their own internal documentation.