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How the AI Bubble Bursts

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Re: How the AI Bubble Bursts

#121

Post by hnthrow0287345 »

I don't see this bubble really popping as-in sinking the economy. Some circular investing and enough write offs will happen to avoid the largest recession indicators from informing the general population that there's actually a recession. You also have a government willing to do shady shit for their own benefit at the expense of responsible governing and ethics, and we have already seen the business leaders of the biggest tech companies cozy up to the administration.

My guess is that cloud companies will scoop up the data centers for pennies on the dollar and the GPUs get written off or fire-sold to enthusiasts still wanting to run local models. Then they can offer exceptionally low initial prices to new customers and get more people to be locked in. Or maybe we see a couple of new cloud companies start up but that would likely need lower interest rates.

Re: How the AI Bubble Bursts

#122

Post by tapoxi »

piker wrote: post #31

> They lose a big customer for their cloud services. Even worse considering that now, using the AI they helped fund, everyone can compete with their sub-par products. GitHub is a good candidate for disruption, and that’d be just the start. Look, I'm a Microsoft hater like the rest of us, but calling Microsoft's products sub-par discredits the author a good bit. I invite anyone who thinks this to try and compete with…

The state of GitHub and Windows 11 certainly qualify as sub-par.

Re: How the AI Bubble Bursts

#123

Post by ajay-b »

I would be very sad to lose services like ChatGPT. It has significantly improved my workflow by digesting and analyzing huge documents, and helping me to synthesize and respond better. May be I am part of a minority.

Re: How the AI Bubble Bursts

#124

Post by BoredPositron »

faangguyindia wrote: post #78
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…

If the gains are real why the limits are so bad? Google can barely serve Anti-gravity.

You get more Claude tokens from a Google subscriptions via antigravity than from anthropic. Especially if you use the 5 other "family" accounts you can share the subscription with...

Re: How the AI Bubble Bursts

#125

Post by woadwarrior01 »

amelius wrote: post #97
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…

> 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

Yeah, even if one efficiency trick lands, people will end up spending the saved budget right back on bigger models, and/or more "thinking" tokens.

Re: How the AI Bubble Bursts

#126

Post by schmidtleonard »

fotcorn wrote: post #84
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…

Also, there is zero reason to think that the big labs did not have anything similar to TurboQuant for a long time already. The recent blog post from Google announcing TurboQuant does not change anything regarding RAM planning for the big labs. TurboQuant itself is already a year old! So even smaller labs have probably seen and implemented it.

The open source tooling got quantization support 3 years ago! It was a lesser type of quantization, but more than enough to prove that the savings just go to bigger models.

Re: How the AI Bubble Bursts

#127

Post by chasd00 »

dash2 wrote: post #26
sunaurus wrote: post #17

Earlier quoted context omitted.

The point is that you can’t just serve tokens without also training the next models. It’s an inseparable part of your costs, so naturally you can’t be profitable unless the price you are charging ALSO covers training.

Is that right? I think that you can serve tokens without training the next models. It would be bad strategy, but it would work. So it's an important question, are they covering their operating expenditure? If they are the business has legs (and it will be worth spending a lot to train the next models). If not, maybe not.

i don't think it will work, it's too easy to switch models. When google comes out with a new model people will just switch. I think Google wins in the long run, they have the money to just wait until everyone else goes bankrupt and they also have the Apple contract and therefore the mobile market.

Re: How the AI Bubble Bursts

#128

Post by ainch »

aurareturn wrote: post #76
throwaway27448 wrote: post #72

Earlier quoted context omitted.

> We have very strong indicators that inference is not a money loser for these companies and is likely very profitable. Why is OpenAI specifically losing money hand over fist then?

Training. But training costs are a smaller and smaller percentage of revenue as inference revenue grows faster than training costs.

Do you have any evidence that inference revenue is growing faster than training costs? RLVR is significantly less compute-efficient than token-prediction pretraining - especially as labs are trying to train models to achieve agentic tasks which take tens of minutes per rollout.

Re: How the AI Bubble Bursts

#129

Post by iterateoften »

thereitgoes456 wrote: post #50
infecto wrote: post #30

It’s incredible how polarizing the AI rush is. I keep the perspective that the technology is an absolute step change but I have no idea where the cards will fall. I take a lot of issue with these style of articles. I get a sense that the authors are being overly defensive. The cost to serve tokens is absolutely profitable today and that’s been true for at least a year. What’s unclear is how R&D and capex fit into the…

> The cost to serve tokens is absolutely profitable Can you explain why you know better than the analyst at Cursor cited in this article?

Open router is an upper bound of compute cost for the open source models. So people assume that opus and sonnet really isn’t sucking up 10x the resources because open source models aren’t 10x worse. Idk if it’s true or not, but haiku is $5/m tokens and it is much worse than the $2-3/mt models imo

Re: How the AI Bubble Bursts

#130

Post by user34283 »

myspy wrote: post #36
general_reveal wrote: post #15

Earlier quoted context omitted.

You have to be uneducated to even read an “AI is bubble article”. Anyone working this stuff knows how much more compute we need.

Thanks for clearing this up, as I don't work in that area. Personally I'd say that it's a problem that prices of consumer goods go up that far to satisfy this part of the market. We could need a more sensible way to advance the technology.

That problem seems to mostly impact teenage gamers who need more than 16 GB of memory and can't afford the extra $300.

In my opinion this is incomparable to what we are seeing with agentic AI that is rapidly replacing handwriting code.

I figure chances are AI is not going to stop here.

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