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How the AI Bubble Bursts

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Re: How the AI Bubble Bursts

#521

Post by Tade0 »

dist-epoch wrote: post #224
Tade0 wrote: post #161

Earlier quoted context omitted.

My main worry is - once this is all over, the market consolidates and using LLMs will become a requirement in job listings, what's the highest price per million tokens companies will be able to charge us? Currently on a given day I'm chewing through approximately the equivalent of my lunch money, but where there's opportunity to extract wealth, someone will find a way to do it.

Jensen is already talking about $1000/mil tokens soon. But there is no real higher limit. Imagine a LLM which could answer the question "what does my company need to do to beat the competition?". And then realize that the competition asks their LLM the same question. So now everybody is bidding the price up or using more tokens to get a better answer

This is the kind of bullshit I'm worried about the most.

Nvidia has a de facto monopoly in the datacenter-tier GPU market. He says this sort of stuff because he knows he can keep jacking up prices, because the cost will be transferred onto consumers - mainly software engineers.

Re: How the AI Bubble Bursts

#522

Post by ffsm8 »

eru wrote: post #518
ffsm8 wrote: post #317

Earlier quoted context omitted.

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 spot market for RAM is reasonably efficient, prices should be about as likely to fall as to increase further. Otherwise you could make a surefire profit by just buying some RAM and waiting a few months to re-sell. (All of this is modulo interest rates etc to finance this.)

> Otherwise you could make a surefire profit by just buying some RAM and waiting a few months to re-sell.

Yes, that's why scalping is so widespread right now, because that's essentially what it is

Re: How the AI Bubble Bursts

#523

Post by eru »

ffsm8 wrote: post #522
eru wrote: post #518

Earlier quoted context omitted.

If the spot market for RAM is reasonably efficient, prices should be about as likely to fall as to increase further. Otherwise you could make a surefire profit by just buying some RAM and waiting a few months to re-sell. (All of this is modulo interest rates etc to finance this.)

> Otherwise you could make a surefire profit by just buying some RAM and waiting a few months to re-sell. Yes, that's why scalping is so widespread right now, because that's essentially what it is

And all morality aside: people will scalp ever harder, until prices are as likely to go down as up.

That's not just RAM, but pretty much any commodity or financial instrument.

Re: How the AI Bubble Bursts

#524

Post by dist-epoch »

Tade0 wrote: post #521
dist-epoch wrote: post #224

Earlier quoted context omitted.

Jensen is already talking about $1000/mil tokens soon. But there is no real higher limit. Imagine a LLM which could answer the question "what does my company need to do to beat the competition?". And then realize that the competition asks their LLM the same question. So now everybody is bidding the price up or using more tokens to get a better answer

This is the kind of bullshit I'm worried about the most. Nvidia has a de facto monopoly in the datacenter-tier GPU market. He says this sort of stuff because he knows he can keep jacking up prices, because the cost will be transferred onto consumers - mainly software engineers.

If those software engineers keep buying it must mean they get more value out of it than they pay, right?

Re: How the AI Bubble Bursts

#525

Post by danaris »

smt88 wrote: post #341
Zardoz84 wrote: post #146

Earlier quoted context omitted.

I don't paid anything to YouTube and I don't see any ads. Because I block ads.

Do you feel good about YouTube spending money on hosting and video producers spending time/money on content that you're paying nothing for? How is that sustainable?

Frankly? That's Google's (well, Alphabet's, I guess) problem.

They're a multibillion-dollar international monopoly with absolutely staggering amounts of money and power, actively engaging in a wide variety of activities directly aimed at making the lives of every normal person on the planet worse so that they can have more power, more control, and more money. Me blocking ads on YouTube not only costs them effectively nothing, it's also the act of a flea against a polar bear.

If Alphabet showed any signs of actually wanting to create a sustainable alternative to the surveillance economy, I might have some sympathy for them. But not only do they not do this, they are the ones who created it in the first place.

Re: How the AI Bubble Bursts

#526

Post by danaris »

mcv wrote: post #170
Aurornis wrote: post #60

This article tries to build upon a lot of half-truths or incorrect facts, like this: > OpenAI is struggling to monetize. They turned to showing ads in ChatGPT, The ads aren’t going into your paid plans (except maybe a highly discounted tier, depending on the market). The ads are a play to offer a free version. Having an ad-supported free tier isn’t new. The discussion about being unprofitable also repeats the reducti…

I've heard "They're losing money" since the 1990s. About Amazon and nearly every other tech company. The strategy is always: * Build something useful * Give it away for free to get people exited * Convince investors that this is going to rule the world * Grow to dominate the world * Enshittify

I don't know about others, but with Amazon specifically, it's always been very clear that their "losing money" in aggregate was purely on paper, for tax purposes: their ability to undercut everyone else was initially based on being online without the brick-and-mortar costs that other stores did, then on economies of scale, and now on being the 900kg juggernaut that just has more money than God and can blow it on running you out of business if they feel like it.

Re: How the AI Bubble Bursts

#527

Post by danaris »

pier25 wrote: post #98
nopinsight wrote: post #11

> nobody is sure if even their metered pricing is profitable This is most likely wrong. Lab executives insist that serving tokens is profitable. It's the cost of training next-gen models that requires them to keep raising ever larger rounds. More importantly, many independent providers price tokens of open-weight models at a fraction of Anthropic's prices.

So these companies will be profitable if training stops? Is that even a real possibility?

Any given company could stop training tomorrow, and, as some others have said here, they'd be generating quite a bit of profit until their models visibly fell behind, however long that ended up taking, at which point they'd probably just fall over completely.

Over the whole industry? No; they can never, ever stop training, or they'll cease to be useful at all very soon.

Training is what keeps the models up-to-date on current events, which includes new programming languages, frameworks, and techniques. It's already been observed that using LLM assistance on some types of programming is much more effective than others, based on how well-represented they are in the training data: if everyone stopped training tomorrow, and next month a new programming language came out, none of them would ever be able to help you program in that new language.

This can be extended to other aspects of programming, too. If training stopped, coding assistants would gradually start giving you wrong answers on how to implement code for APIs, frameworks, and languages that continued to evolve, as they will always do, in much subtler (and likely harder-to-debug) ways than how they'd deal with a new language whose existence they don't even know about.

Re: How the AI Bubble Bursts

#528

Post by veunes »

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…

Bingo. Even if some magic drops tomorrow that compresses the KV cache down to literally zero bits, that saved VRAM will instantly get swallowed up by bumping the batch size or pushing the context window to 10 million tokens. There is no such thing as "excess memory" in ML, only under-trained models

Re: How the AI Bubble Bursts

#529

Post by veunes »

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…

Demand is stagnating only applies to the B2C segment, where people are already bored of generating poems and funny pictures. In B2B, the demand hasn't even started yet because corporations are still terrified of shoving their NDA data into public APIs. The second local models and secure private clouds get cheaper, the enterprise is going to devour literally any amount of available compute just to automate internal document workflows

Re: How the AI Bubble Bursts

#530

Post by veunes »

aurareturn wrote: post #92

This is an awful article. I don't know how it reached #1 on HN. Bottom line is that H100 prices are near 3 year highs, A100s are still profitable to run, B200 prices are increasing, no one has enough compute. Google, OpenAI, Anthropic, Meta, AWS, Azure are all compute constrained. Every single one of them said so publicly. Neo clouds are telling customers they're all sold out now and you even have to book compute in…

OpenAI overtaking Microsoft? Seriously? Microsoft has a massively diversified business spanning from gaming and cloud infra to B2B software that the entire world runs on. OpenAI has exactly one product (matrix weights), which is getting heavily commoditized by open-source models every single day. Once a theoretical Llama 4 catches up to GPT-5, an API price war is going to completely nuke their hyper-margins
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