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507 points martinald | 1 comments | | HN request time: 0s | source
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_sword ◴[] No.45055003[source]
I've done the modeling on this a few times and I always get to a place where inference can run at 50%+ gross margins, depending mostly on GPU depreciation and how good the host is at optimizing utilization. The challenge for the margins is whether or not you consider model training costs as part of the calculation. If model training isn't capitalized + amortized, margins are great. If they are amortized and need to be considered... yikes
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lumost ◴[] No.45056523[source]
I wonder how much capex risk there is in this model, depreciating the GPUs over 5 years is fine if you can guarantee utilization. Losing market share might be a death sentence for some of these firms as utilization falls.
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utyop22 ◴[] No.45058970[source]
What I hear nobody talking about is the price elasticity of demand and how this plays into the economics of the model business.
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Jaxkr ◴[] No.45059836[source]
I think some of the power user demand is fairly inelastic. I’ve seen developers who are allergic to spending money happily drop $200/mo on those new Claude subscriptions.
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utyop22 ◴[] No.45063135{3}[source]
Yeah but if you push the price up, given that many users will cancel their subscriptions you will end up with still a tiny market segment relative to what is necessary, in revenues, to justify the valuations purported.
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1. lumost ◴[] No.45064695{4}[source]
It's a tricky one, there is also a lot of push right now to use AI so developers are incentivized to drop money on subscriptions. I'd have difficulty justifying 1k/month for smaller shops - but corporations will be different. If the average engineer is just 20% more productive, then that is a 30-60k value to the company.

I don't have difficulty getting to a 20% productivity gain with AI just from automating the tasks I procrastinate on or can't focus on. Likewise the ability to code a prototype overnight/over the weekend is a reasonable extension of practical working hours.

The challenge I do see is that fully AI generated code bases devolve into slop pretty fast. The productivity cutoffs are much lower compared to human engineers.