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>> both will struggle hard to actually turn a profit some day, let alone make back the massive investments they've received.

I'd agree with you, except I've heard this argument before. Amazon, Google, Facebook all burned lots of cash, and folks were convinced they would fail.

On the other hand plenty burned cash and did fail. So could go either way.

I expect, once the market consolidates to 2 big engines, they'll make bonkers money. There will be winners and losers. But I can't tell you which is which yet.

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I’m not sure there will be consolidation. There’s too much room for specialization and even when the models are trained to do the same task they have very different qualities and their own strengths and weaknesses. You can’t just swap one for the other. If anything, as hardware improves I’d expect even more models and providers to become available. There’s already an ocean of fine tuned and merged models.
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$20B ARR or so reported added in Q1 doesn’t sound particularly bad, they’ll raise effective prices some more while Claude diffuses into the economy, sounds like a money printer. The issue is they’re compute constrained on the supply side to grow faster…
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> $20B ARR or so reported added in Q1 doesn’t sound particularly bad

Unless you compare with the reported cash burn or projected losses.

> they’ll raise effective prices some more while Claude diffuses into the economy, sounds like a money printer

But the problem is, they have no moat. Even if Claude diffuses into the economy (still to be seen how much it can effectively penetrate sectors other than engineering, spam, marketing/communications), there is no moat, all providers are interchangeable. If Antrhopic raise the prices too much, switch out to the OpenAI equivalent products.

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> But the problem is, they have no moat

I disagree very strongly with this, both anecdotally and in the data - subscriptions are growing in all frontier providers; anecdata is right here in HN when you look around almost everyone is talking about CC, codex is a distant second, and completely anecdotally I personally strictly prefer GPT 5.3+ models for backend work and Opus for frontend; Gemini reviews everything that touches concurrency or SQL and finds issues the other models miss.

My general opinion is that models cannot be replaceable, because a model which can replace every other provider must excel at everything all specialist models excel at and that is impossible to serve at scale economically. IOW everyone will have at least two subscriptions to different frontier labs and more likely three.

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You're actually reinforcing my point. Models are interchangable and easy to switch between to adjust based on needs and costs. That means that no individual model / model provider has any sort of serious moat.

If tomorrow Kimi release a model better at something, you'd switch to it.

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Yes, in that sense, technically correct.

I postulate in practice this won't matter since the space of use cases is so large if Kimi released the absolutely best model at everything they wouldn't be able to serve it (c.f. Mythos).

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