Thread

1. theropost · 1786045967 · focus · HN ↗

   Anthropic is a bit nuts, I had $260 of credits on my max account for the extra usage the other night. It was expiring, so I figured I'll fire up an agentic swarm to deep dive and make some deep changes to some old cold bases.. literally 25 minutes or less, $260 burnt, it didn't get get into the implementation, just wrote a ton of useless plans for the most part. It really opened my eyes to what they expect to charge people.. wayyyy overpriced.

   1. mikae1 · 1786047217 · focus · HN ↗

      And at that cost they're still not profitable. It's going to be a bumpy road ahead...

      1. arrowleaf · 1786048024 · focus · HN ↗

         I thought they are making a profit on API pricing? A quick Google shows somewhere between 50-70% margins on API inference.

         1. bakugo · 1786048508 · focus · HN ↗

            API pricing is almost definitely profitable, but at this point I assume it's a small minority of their inference traffic compared to subscription usage, and unlikely to make up for the rest of their expenses on its own.

            1. enedil · 1786058686 · focus · HN ↗

               [delayed]
      2. tyre · 1786048090 · focus · HN ↗

         People keep saying this but from what we’ve seen, Anthropic models are marginally profitable and earn back their costs over their lifetime. The company is burning money building the next versions and other ventures (e.g. verticals), but the models themselves have been profitable.

         1. gamblor956 · 1786048505 · focus · HN ↗

            They're EBITDA profitable, not GAAP profitable.
      3. an0malous · 1786048304 · focus · HN ↗

         What’s the blast radius of this bubble popping? It’s all private investment still right?

         1. bhewes · 1786048425 · focus · HN ↗

            Two thirds of most of the DC builds are not compute. So it's a CRE play the last leg holding up that mess.
      4. arikrahman · 1786048374 · focus · HN ↗

         Meanwhile I can do all that and more with reasonix harness for Deepseek with a cache hit rate of 99%. And that's with unsubsidized American providers like cloudflare or Digital Ocean
      5. swalsh · 1786049836 · focus · HN ↗

         I think profitability is a matter of accounting. Inference is where money is made, but training is where money is spent. We keep getting new models every few months, but frankly the old models are still quite usable. I suspect labs will soon start specializing in expert models per use case so they can increase the lifespan of individual models, and change the profitability per model.

         1. CuriouslyC · 1786050604 · focus · HN ↗

            That's not the only reason to go to expert models. The more different domains you try to stuff in there, the more parameters the model needs to keep things coherent and not overload tokens in a way that induces errors. For example, if a model trained only on biology text sees "sonic hedgehog" there's no ambiguity, and this compounds for all the things that are "overloaded," in the training corpus, which turns out to be quite a bit.

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