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This is like looking at mainframe pricing in 1990 and concluding that PCs will only be for the rich. The price of each new level of capability is going to drop like crazy very quickly. It won't be that long before practically any consumer use case will be possible on models that are dirt cheap.

by weakfish5 days ago|

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This premise is based around the assumption that Moore's law is still working, which it very much isn't [0]

[0] https://cap.csail.mit.edu/death-moores-law-what-it-means-and...

by andrewmunsell5 days ago|

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Improvements in model performance aren't always strictly compute-constrained in a way that makes them reliant on Moore's Law. Open weight models-- in particular, from Chinese labs-- are optimizing model intelligence with less compute. They're "behind" frontier models by months, but as others have noted, it's possible to get Sonnet 4.5+ level performance at reduced cost, today, from open weight labs.

by modeless5 days ago|

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No, I'm not assuming Moore's law. The efficiency of AI datacenters will continue to improve even without Moore's law, but more importantly the efficiency of packing intelligence into gigabytes and FLOPS will improve by leaps and bounds over the coming years, just as it has for the past few years if not faster.

by calf5 days ago|

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Then you're assuming an efficiency that is analogous to how Moore's law made it efficient for chips. Same difference. The problem is that AI scaling in the longest term is a completely unknown problem.

by modeless4 days ago|

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Training improvements and Moore's Law are "analogous" but not "same difference." They are far from the same thing, governed by completely different factors, and one can happen and has been happening independently from the other.

by calf4 days ago|

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Well I never said nor meant that, rather, my third (3) sentence should've hinted that I already believe what you are saying in your second sentence (2). Whereas my second (2) sentence was handwaving at the notion that if the parent commenter's remark (about improvment trends) were to be assumed then the rational argument must be subject to the same standards, ergo same difference (in argument standards). (Also I use a phone, please excuse any confusion due to not spelling out my online opinions in full)

To clarify another way, it seems the parent commenter and obviously many, many lay people seem to think ALL sorts of technology improves eventually and are always very assured of that. That's a common mistaken premise or axiom used in their arguments. (Arguably Moore's law (up until now) has been a factor in confounding this observation because so much other tech has historically benefited from it directly or indirectly)

by modeless2 days ago|

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Sorry, but a plain reading of your comment does not imply at all that you agree with me, rather the opposite. I'm not basing my opinion on any mistaken axiom of inevitable technology improvement, of course. I'm projecting obvious trends of the past few years which are overwhelmingly likely to continue in the medium term.

"Same difference" could only mean that you believe my argument should fail in the same way as an argument based on Moore's law. If that's not what you meant then you should have used different words. If that is what you meant, with the justification that "AI scaling in the longest term is a completely unknown problem", I disagree with that too.

In the "longest term" the ultimate scaling of AI doesn't matter for the original question of whether "AGI will most likely only be for the rich". Nobody looks at the TOP500 list today and says "computing is only for the rich". This is because we have an abundance of iPhones and gaming PCs in the consumer market, providing practically any application of computing that a consumer could want at very attainable prices. Similarly, practically any application of AGI will be accessible to consumers at attainable prices. Continued AI scaling after a certain point will be relevant mostly to industry (whose products will still be priced attainably, analogously to the way weather forecasts produced on TOP500 supercomputers are readily accessible to the public today).

by ishurand44 days ago|

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Its a quadratic graph. It starts low but not that capable, gets better and more expensive, and then the time comes in which the capability needed is not the ones of the frontier models and then the price goes down on the companies who host the models that the capability is "good enough"

by hootz5 days ago|

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You are only priced out if you only care for SOTA right now and can't wait for the inevitable cheap model coming in 6 months. DeepSeek, Xiaomi and Moonshot are already really cheap and match frontier performance from 6 months ago.

by dyauspitr5 days ago|

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But they’re artificially cheap. When will they be cheap while the company makes a profit.

by hootz5 days ago|

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They are not artificially cheap, they are still cheap even when hosted by independent inference providers. Are all providers subsidizing their open-weight models?

by modeless5 days ago|

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Nobody's making profits right now, not because they're selling tokens for less than their cost but because they're always investing in the next bigger model.

by dyauspitr5 days ago|

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Hardware manufacturing hasn’t caught up yet. Once it does, especially in China these token prices are going to drop hard.