Yeah, this is the classic silicon valley strategy of selling at a loss and then once they have captured the market inflate prices.

See Uber, Netflix, etc.

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CraigRood 3 hours ago | parent | next [-]

I don't see them capturing anything at this point. If inference was profitable then they could compete on price/model and capture the market. Then increase price and pay back the model training.

Feels like they are just pulling in as much as they can whilst competing on capabilities instead. At which point its a case of who can last the longest.

Doesn't feel like Uber/Netflix.

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simianwords 4 hours ago | parent | prev [-]

This is a constantly repeated conspiracy theory and is not true at all. The api costs do increase but aggregate costs per task decrease. The question is: do people need lower intelligence models at all? The answer is a resounding NO!

How many people do you see using haiku or sonnet? I see very few and most people default to the latest model and just play with thinking effort. I think three layers are good enough and supporting more is not a good UX.

	▲	phainopepla2 3 hours ago \| parent \| next [-]
		Are you only considering coding use cases? Many enterprise use cases, such as simple data extraction, are well served by cheaper models.
	▲	gonzalohm 4 hours ago \| parent \| prev \| next [-]
		Do I need the most intelligent model to generate boilerplate code, which is my main usage for AI? Resounding No. For my use case a model from a year ago is good enough
	▲	unknownfuture 3 hours ago \| parent \| prev [-]
		I... use them all the time: plan with a more advanced model, build with a cheaper one. Anthropic literally packages a metamodel (opusplan) for that pattern. Also: calling the SV blitzscaling strategy of using VC money to fund loss leader products with the goal of building a monopoly via dumping a conspiracy is quite the position given there's entire books written in the topic...