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How the AI Bubble Bursts

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Re: How the AI Bubble Bursts

#361

Post by azinman2 »

kmeisthax wrote: post #304
pydry wrote: post #153

Earlier quoted context omitted.

Jevons paradox only applies if demand hasnt already been saturated. The fact that public LLM usage is leveling off at a price of $0 and Jensen "we make the shovels in this gold rush" Huang is rather desperately claiming that you need to spend $250k/year in tokens to be taken seriously suggests that demand saturation may not be that far off. Whether Jevons' Paradox applies to software engineers I think is another open…

I thought we were going to hit token saturation years ago, but they keep inventing new ways to use tokens. Like, instead of asking a chat model to write something and getting ~1000 tokens out of it, you now have an agent producing ~10,000 tokens - or, worse, spawning 10 subagents that collectively burn ~100,000 tokens. All for marginally better answers with significantly higher compute usage. Personally, I would have…

Everyone is interested in using less tokens to accomplish the same task.

Re: How the AI Bubble Bursts

#362

Post by am17an »

joshstrange wrote: post #53

> RAM prices are crashing because new models won’t need as much Reality begs to differ [0] and following the link for that text goes to an article [1] where they talk about Google's TurboQuant which supposedly will lower the RAM requirements. Now if that means RAM prices come down (as speculated, not reported on, in the link) or the AI companies just do more things with their extra ram is yet to be determined. The fa…

Thank you, there are two things I would like to point out:

1) Google releasing something probably means they don't see it as important. 4-bit KV-cache quantization has been known for a long time. The fact there is almost a mass hysteria about this paper makes me think there is a lack of skepticism in this AI mania, even in relatively tech-savvy crowd.

2) But prices for memory companies are crashing! look around, the whole market is crashing.

Re: How the AI Bubble Bursts

#363

Post by sergiotapia »

drakythe wrote: post #160
adjejmxbdjdn wrote: post #141

Earlier quoted context omitted.

I’m not disagreeing with you, but consumer RAM prices are lagging indicators. If commercial RAM prices are dropping then consumers will see those price drops last, especially given the fact that several consumer manufacturers turned to commercial only.

Is there a source that says commercial RAM prices are dropping? I was recently told (without a source, so I am not sure if it is true or not) that OpenAI never even bought any of the RAM they signed deals on last year, and that those deals were just letters of intent. So if prices are coming down I wouldn't be shocked but the economy is pretty well vibe coded these days so who even knows.

here you go: https://x.com/wccftech/status/2037921057097892018

Ram prices are dropping

Re: How the AI Bubble Bursts

#364

Post by martinvol »

HardCodedBias wrote: post #342

In general: Cynicism makes you sound smart. Optimism makes you successful. The cynicism around this technology is everywhere, even though it clearly has real power to solve problems. It is a technology which enables so many use cases that were impossible before, that makes it very highly hyped/expected. And that is causing an immune (over) reaction by natural skeptics, that's an error. People need to take a measured,…

bubbles are created by people investing more than reasonable in something, independent of the actual value it will generate for society.

Re: How the AI Bubble Bursts

#365

Post by atomicnumber3 »

logravia wrote: post #350

The thing I am struggling with is where is the impact of LLM tools, especially given the massive increase in token consumption from 2025 to now and the saturated presence of LLMs everywhere. Naively speaking, I have so many expectations for the impact of this tech. I'd expect a noticeable uptick in applications published on Google, Apple and Microsoft app stores. I'd also expect an uptick of games published to Steam.…

Programming is a necessary but not sufficient condition for software products to exist. So while the programming has to be good, so too do many other things, like product vision, product management, project management, and of course there still needs to be feedback between all of the above so that engineering isn't implementing a misunderstood version of the product and that product isn't asking for 5 years and a PhD research team. And on and on and on. Typing the code is like 2-10% of actually ending up with a software project and it's more toward the 2% for a software business.

So while AI made coding maybe 110% faster, it has also made literally every other person in the process lose their gd minds and they're wanting to break or skip everything else in the process to just shit out code faster.

Re: How the AI Bubble Bursts

#366

Post by vonneumannstan »

pydry wrote: post #153
amelius wrote: post #97

Earlier quoted context omitted.

> Now if that means RAM prices come down (as speculated, not reported on, in the link) or the AI companies just do more things with their extra ram is yet to be determined. I think it is determined: https://en.wikipedia.org/wiki/Jevons_paradox

Jevons paradox only applies if demand hasnt already been saturated. The fact that public LLM usage is leveling off at a price of $0 and Jensen "we make the shovels in this gold rush" Huang is rather desperately claiming that you need to spend $250k/year in tokens to be taken seriously suggests that demand saturation may not be that far off. Whether Jevons' Paradox applies to software engineers I think is another open…

Pretty sure the entire markets for Storage, HBM, DDR5, etc are completely sold out for next several years. How is that saturated?

Re: How the AI Bubble Bursts

#368

Post by dylan604 »

ffsm8 wrote: post #317
drakythe wrote: post #160

Earlier quoted context omitted.

Is there a source that says commercial RAM prices are dropping? I was recently told (without a source, so I am not sure if it is true or not) that OpenAI never even bought any of the RAM they signed deals on last year, and that those deals were just letters of intent. So if prices are coming down I wouldn't be shocked but the economy is pretty well vibe coded these days so who even knows.

Well, all manufacturers of ram have publicly stated that they're sold out for 2026 RAM prices falling during 2026 is insanely unlikely unless AI crashes so hard it starts to actually kill companies. And not just any but big tech I'm not seeing that in 2026. Maybe 2027 (I'd sincerely doubt that too, honestly), but definitely not within the next 9 months. Their runway is _way_ too large for things to spiral out of cont…

If the claims the GP made about letters of intent to buy vs actual purchases are true, that brings additional questions. Like, if you send a letter of intent but do not follow through, are there financial penalties? How hard is it for the chip maker to sell the chips allotted based on that letter of intent? Would someone like Apple buy up the extra, or would they not need it as they've already bought enough for the units they expect to sell? If someone like Apple suddenly had an influx of RAM, that does not mean they would have extra CPU capacity to match. If the supply chain is this closely apportioned, what is the most likely result of a sudden surplus?

Re: How the AI Bubble Bursts

#369

Post by user34283 »

kmeisthax wrote: post #304
pydry wrote: post #153

Earlier quoted context omitted.

Jevons paradox only applies if demand hasnt already been saturated. The fact that public LLM usage is leveling off at a price of $0 and Jensen "we make the shovels in this gold rush" Huang is rather desperately claiming that you need to spend $250k/year in tokens to be taken seriously suggests that demand saturation may not be that far off. Whether Jevons' Paradox applies to software engineers I think is another open…

I thought we were going to hit token saturation years ago, but they keep inventing new ways to use tokens. Like, instead of asking a chat model to write something and getting ~1000 tokens out of it, you now have an agent producing ~10,000 tokens - or, worse, spawning 10 subagents that collectively burn ~100,000 tokens. All for marginally better answers with significantly higher compute usage. Personally, I would have…

Marginally better answers?

Claude Code and co. can now analyze an enterprise codebase to debug issues in a system with multiple services involved.

I don't see how that would have been possible at all in the past.

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