I think there are risks:

- AI pricing is variable, probably the cheapest it will ever be right now

- AI produces a lot more shit for humans to review, and you will always need humans. If you don’t focus on keeping things simple you will probably play yourself unless you’re good at separating out blast radiuses.

- I see a lot of super low quality work that doesn’t solve the problem but it’s like look that guy solved the problem in one day! Promote him! Everyone is happy except for the end users who for whatever reason are being totally ignored (whose problem it fails to appropriately solve) and I saw this in accounting software so…hello eventual lawsuits?

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CuriouslyC an hour ago | parent | next [-]

AI is definitely not the cheapest it will ever be right now. The frontier is getting more expensive, but the same capability will get cheaper over time.

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valvar 2 hours ago | parent | prev [-]

Why wouldn’t inference just keep getting better and cheaper as hardware and algorithms improve?

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MobiusHorizons 2 hours ago | parent [-]

The typical playbook for a VC funded startup is to race to a monopoly where the company can have higher margins. Prices continuing to go down for the consumer over time would require competition to stay high in the long term, and even then it’s not clear if even current prices are profitable.

	▲	zozbot234 an hour ago \| parent [-]
		The current level of AI has plenty of inherent competition from local models. In the long term, most of the profit will probably be from very smart models that run at something closer to datacenter scale over long inference loops - where local inference can't do much and even third-party inference/small neoclouds will be severely challenged. That is a very natural "moat" and has natural cross-efficiencies with AI model training, which requires a similar scale.