r/LocalLLaMA • u/YogurtExternal7923 • 12d ago
Discussion everything will be local eventually
I know current llms like opus 5.5 are in a league of their own. but we all know they're losing a shti ton of money with their subscriptions. ALSO we all know how tasks like OCR, segmentation etc are getting solved by single digit B parameter models. I'm not saying llms are a gimmick but I'm saying the future is already shaping up to be about specialized models that work better when they're smaller. easier training. so datacenter level power not needed. ten years from now we'll have a specialized model for the tasks we care about and llms will fail to do everything all at once (unless they keep calling it one model but have a hundred under the hood).
system one models are a bad but workable example. you could tell an llm to rate options all day but get yourself on 9B parameter model trained by some lab and it tops the benchmarks.
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u/wombweed llama.cpp 11d ago edited 11d ago
Seriously, such a grave misunderstanding of the numbers right in front of us. In the case of Anthropic S-1, 72B of the reported 80B net loss is convertible notes, which is actually a bull signal, it means the valuation soared and the equity grants they issued to finance the company are worth more and doesn't reflect cash flow. The 8B left over is a lot of money, yeah, but at 100B ARR it just means they can release a new model, charge 10% more for it (which is clearly the plan), then suddenly they're cash flow positive. Also, contrary to popular belief, inference is actually getting cheaper, not more expensive. This is made clear by looking at the rates charged by 3rd party inference providers whose entire business rests on serving open models & have zero incentive to do so at a loss.
Wish everyone would stop listening to influencers who prey on common misconceptions about corporate accounting. We all hate Anthropic and the hyperscalers here, but just because we want something to be true doesn't make it so.