I found the OP insightful and worth a read. Thank you for sharing it on HN.

The only aspect that is poorly analyzed by the OP is business model viability. All players are investing insane amounts of money in infrastructure with the expectation that their future profits will justify all that investment. The winner or winners in the AGI race, they believe, will find the proverbial "pot of gold at the end of the rainbow."

The OP glosses over questions of business model viability with a brief qualitative discussion and very little hard data. For example, to earn an annual return > 10% on every trillion dollars of capital sunk into infrastructure, the owners of that infrastructure must earn free cash flow (operating profit less investment) in excess of $100 billion per year in perpetuity. Is that feasible? Why? How?

The OP does not really consider such questions.

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otherme123 4 minutes ago | parent | next [-]

The problem source is that the "cost" of tokens are taken at face value from business that are losing money at record speeds. E.g. https://artificialanalysis.ai says "doing task A costed us $10 using OpenAI", and that is the "cost" the OP used as basis for "tokens are cheap". Meanwhile OpenAI is losing $19 for each $1 in revenue... So right now OpenAI should be charging around $200 to do task A just to break even, but that would mean their use base would collapse.

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sanderjd an hour ago | parent | prev | next [-]

I think the article's analysis is basically right in a vacuum. That is, I think it's clear that inference is a viable business model. But what isn't clear is whether it will be such a profitable business model for any given company that it will justify the investment that company has taken. I kind of think the winners might be a follow-on generation of companies that focus on this commodity inference business model instead of the invent-machine-god-first "business model" and thus are wiser about their level of investment and capital costs.

	▲	cs702 an hour ago \| parent \| next [-]
		> I think it's clear that inference is a viable business model. You may be right. I'm not so sure. Inference looks like a viable business model for those operators that have SOTA infrastructure in place, but the investment required to have it is enormous, and appears to be never-ending, because if an operator stops investing aggressively, its infrastructure quickly becomes non-competitive, and customers will quickly leave for alternatives. SOTA infrastructure is a moving target.
	▲	twoodfin an hour ago \| parent \| prev [-]
		A phrase comes to mind: "Your margin is my opportunity."

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BenzeneDream 2 hours ago | parent | prev | next [-]

They are already turning profits and inference has shown to be a cash cow. And they've already secured compute for the next several years.

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cs702 2 hours ago | parent | next [-]

Some frontier labs are reporting positive "adjusted EBITDA" (earnings before interest, taxes, depreciation, and amortization, with extra adjustments to make the figure positive).

Free cash flow (operating profit less investment), actual cash coming in, is deeply in the red.

EBITDA can be a sensible measure of profitability when there isn't much need for additional investment. That doesn't seem to be the case with these operators. They need to invest aggressively to avoid losing customers to competitors. All of these operators have made multi-year commitments to invest more in infrastructure. In addition, they have guaranteed quite a bit of debt to fund it.

Maybe it all will work out fine (and I sure hope it does!), but I didn't see any hard data from the OP, or from you, supporting that view.

	▲	0cf8612b2e1e an hour ago \| parent [-]
		EBITDA might make sense for the resellers who package up open weight models and sell inference. It is not appropriate for the labs who have billions in debt for RAM, new data centers, gobbling up competitors, etc. Those real debt obligations are going to want to be paid back.

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sanderjd an hour ago | parent | prev | next [-]

Definitely. The question is: Is it enough to recoup the enormous capital costs and justify the level of investment they've received. I think there's a decent chance that it will be. But maybe not. And the longer they keep focusing on training new models more so than on inference, the more uncertain I become that it's all going to work out.

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ofjcihen 15 minutes ago | parent | prev | next [-]

Labs are playing money games with EBITDA, which is not uncommon, but also hides the extent to which they are in the red (deeply, deeply, in the red, and projected by them to get worse).

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metalliqaz 2 hours ago | parent | prev [-]

Who is the "they" that are turning profits?

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alpineidyll3 3 hours ago | parent | prev [-]

Exactly. "Cost-to-distill" is a critical parameter. Right now usage of frontier models for all tasks is both subsidized and irrationally popular even at the subsidized price. Deepseek would solve most tasks faster and 10x cheaper. I agree with the author that just as Deloitte exists, frontier labs will exist. But not because their products are proprietary technical marvels or gods, but rather because of branding.

	▲	ido an hour ago \| parent [-]
		DS wouldn't be 10x cheaper than the subsidized subscription plans from openai/anthropic. Although it is of course much cheaper than the enterprier/API pricing- I think if you're on the subscription plans, you can't beat that on performance per price.