| ▲ | oersted 34 minutes ago | |||||||
And then everyone would stop using their inference as soon as a better model for a reasonable price came out. The R&D expenditure is a critical requirement for the inference profits, to the point where we should probably lump their financials together, at which point is definitely not profitable. What will it look like when R&D plateaus (and yes it definitely will, but it could take a while), investment falls, and a few main competitors remain in the music chairs? It's very difficult to predict. The inference profits we are seeing the profits of a company that is temporarily ahead, but the revenue will level-out in a more stable market, depending on how many survived. It's also hard to tell where the costs will be at the end of the game, with constant efficiency optimisation mixed with cost increases for higher intelligence. I think it will be quite similar to the semiconductor industry, where, yes there are some key monopolies, but they are not the initial big players, and none of it is actually very profitable; while the real profits are reaped by those that make popular consumer products based on the foundational tech. I guess the main difference is that OpenAI and specially Anthropic have been quite effective at directly tapping into the consumer market rather than remaining technology providers. | ||||||||
| ▲ | aurareturn 18 minutes ago | parent [-] | |||||||
Exactly. It's competition now that is driving high training costs - not a business model problem. There will be winners and losers. The losers won't be able to keep up with the training costs forever. See my post here: https://news.ycombinator.com/item?id=49119265 | ||||||||
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