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How the AI Bubble Bursts

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Re: How the AI Bubble Bursts

#191

Post by guzfip »

keybored wrote: post #185
SirensOfTitan wrote: post #56

Earlier quoted context omitted.

This is a classic HN mistaking the map for the territory. R&D and capex absolutely figure into de-facto profitability and sustainability for AI labs, despite their separate treatment in accounting. > well most of us here on HN have benefited from decades of overinflated engineering salaries being paid by often companies that were not profitable and not only unprofitable This is a really concerning perspective: people…

> This is a really concerning perspective: people were paid what they were worth. Even interpreting what-they-were-worth in the usual sense, I’m not so sure about this. We have seen wage collusion reported by the usual US West Coast-based companies. And some news on here[1] have reported that some engineer with a salary of $100K[2] might be producing $1M of value. And even factoring in the usual “but benefits and ove…

> As long as they are paid well compared to other workers, it’s fine.

Well I’m sure they’ll be thrilled to know they can collect $100 a week more in unemployment benefits than their neighbor.

Re: How the AI Bubble Bursts

#192

Post by michaelcampbell »

gruez wrote: post #151
hirako2000 wrote: post #117

Earlier quoted context omitted.

Tulips sales also skyrocketed. Seriously, what value are tokens providing other than justifying layoffs. Concretely. Today. Not in the speculating scenario that cardiologist could be replaced with models. We see this new trend of agentic coding, again a promise software will be written that way going forward, despite the number of fiasco already experienced when trusting a model turned bad. The use case may provide v…

>Seriously, what value are tokens providing other than justifying layoffs. Concretely. Today. It's adding tests for me and doing medium complexity refactors that I'd otherwise have to spend hours on

Same, and constructing at least drafts of huge documents that I can iteratively fine-tune that have (at least last week) saved me 10's of hours.

And based on reality (code) rather than my feelz of what I vaguely remember the code to have been doing in some long past.

Re: How the AI Bubble Bursts

#193

Post by infecto »

boriskourt wrote: post #48
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 today and that’s been true for at least a year. > For the data center build outs, demand for tokens is still exceeding supply. Can you provide any numbers for this?

Most/all private labs have cited inference is profitable. This was happening before the large push to scrap plans and largely charge folks the underlying api rates. Second take a look at the pricing of open models. Now certainly it’s not direct 1-1 comparison but we can use it as a baseline. Now of course folks might not be telling the truth but one of those situations where I see too many markers on the true side.

For supply look at outages and growth rates at companies like openrouter. The demand is growing every week.

Re: How the AI Bubble Bursts

#194

Post by gruez »

jerojero wrote: post #188
wongarsu wrote: post #166

Earlier quoted context omitted.

I can get Kimi K2.5 inference on openrouter for about $0.5/MTok input + $2.5/MTok output, from six providers that have no moat besides efficiently selling GPU time. We can assume they are doing so at a profit (they have no incentive to do this at a loss), giving us those numbers as the cost to serve a 1T-a32b model at scale. Now we don't know the true size of any of the proprietary models, but my educated guess is th…

Companies doing foundational models need to cover the cost of training which is much more expensive than training something like kimi.

>Companies doing foundational models need to cover the cost of training [...]

But that's moving the goalposts? The original claim was on inference itself, not the whole company.

> The cost to serve tokens is absolutely profitable today and that’s been true for at least a year.

Re: How the AI Bubble Bursts

#195

Post by drakythe »

paulddraper wrote: post #87
boriskourt wrote: post #48

Earlier quoted context omitted.

> The cost to serve tokens is absolutely profitable today and that’s been true for at least a year. > For the data center build outs, demand for tokens is still exceeding supply. Can you provide any numbers for this?

Anthropic has said inference is profitable. That’s a biased source, but the math pencils. This is why switching to local open weight models saves a lot of money. (Even though it’s not apples to apples.)

Anthropic also recently tweaked their usage limits to discourage use during peak hours. Why would they do that if inference was profitable?

Re: How the AI Bubble Bursts

#196

Post by fcarraldo »

SirensOfTitan wrote: post #56
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…

This is a classic HN mistaking the map for the territory. R&D and capex absolutely figure into de-facto profitability and sustainability for AI labs, despite their separate treatment in accounting. > well most of us here on HN have benefited from decades of overinflated engineering salaries being paid by often companies that were not profitable and not only unprofitable This is a really concerning perspective: people…

> Software is or was one of the few remaining arenas wherein a person can find a consistently

Software salary inflation and expansion has made this the case. Tech’s accessibility to the educated has accelerated gentrification massively, rising up prices on rent and food. While the statement is correct, tech’s contribution to income inequality is part of the issue. If you’ve lived in Austin or Chicago (especially Austin) prior to ~2010 you’ll have seen this first hand.

Re: How the AI Bubble Bursts

#197

Post by KaiserPro »

The problem with these kind of posts is that "How" is almost useless, I can tell you how the bubble pops: The value of these AI companies crash and take out a lots of other stuff with it.

The interesting questions are: "What triggers it" and "what also goes tits up"?

The issue with high/international finance is that a good percentage of it (if not more) is fraudulent or semi fraudulent bollocks.

"Here is a startup that is worth x million because y" Both of those statements are bollocks. However its in the interest of most people to agree with that bollocks to get money. If enough money is given there is a chance that the startup will make money.

If we look a few year back, NFTs fulfil that niche quite nicely. It was obviously bollocks, but a very convenient way to launder money, or run a series of rugpull operations.

The problem we have to contend with now is that the sheer amount money that has been invested all disappearing at once would require 2007/8 levels of coordination to unfuck. The US government does not have the requisite number of admins to pull that off again, and no political will to ever have that expertise again. So if AI does go pop, and it takes a lot of money with it, I would put a guess on china doing the money lubrication and extracting a subtle but richly ironic level of control in exchange

Also, its no guarantee that AI will trigger the next bubble popping, my money is on Private Equity.

Re: How the AI Bubble Bursts

#198

Post by throwup238 »

slfnflctd wrote: post #148
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…

> almost as bad as when LLMs link things to prove their point, you visit the link, and find it says nothing of the sort or even the opposite To be fair, they got it from us. This happened to me plenty of times long before modern LLMs.

It learned by reading HackerNews, after all.

Re: How the AI Bubble Bursts

#199

Post by aurareturn »

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…

Even if TurboQuant, which was released a year ago, drastically lower RAM requirements, AI labs will just release bigger models.

Jevons Paradox. When are we going to learn that efficiency gains in AI does not decrease hardware usage?

Re: How the AI Bubble Bursts

#200

Post by NickNaraghi »

> Taking this into account, Google is extremely well positioned to weather the storm. When they announce capex expenditure, they don’t spend it overnight. They can simply deploy month by month until their competitors struggle to raise and get forced to capitulate. At that point they can just ramp down the spending and declare victory in a cornered market. They don’t need capex, they just need to make it very clear for everyone that nobody can outspend them.

Have you tried Gemini 3.1 lately? It is not even close to Opus 4.6 never mind Claude 5.

This post, like many pessimistic takes, seriously discounts innovation and the exponential takeoff of recursive self-improvement.

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