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How the AI Bubble Bursts

martinvol.pe

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Re: How the AI Bubble Bursts

#231

Post by WarmWash »

guzfip wrote: post #143
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…

> Software is or was one of the few remaining arenas wherein a person can find a consistently. I want to add something additional to this: it is one of the few fields that can afford middle or upper middle class lifestyle and is accessible . I have no doubt if I could redo my life with the necessary resources I’d be more than capable of putting myself through med school and gone with a secure career that paid more th…

It was kind of a flash in the pan moment where you could leave your retail floor manager job, crash course this thing called "javascript" in a 3 month class, and then get hired for a six figure remote job if you could choke out a mildly competent github repo.

Re: How the AI Bubble Bursts

#232

Post by dist-epoch »

malfist wrote: post #52
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 How can you possibly say that? Everyone knows that's not the case, these companies are losing money every day selling tokens. Revenue is not the same thing as profit.

There are private companies which rent/buy GPUs, run open-weight LLMs on them and sell the tokens. They absolutely make profit, and their clients think they get a good deal and are buying the tokens.

Re: How the AI Bubble Bursts

#233

Post by hyperpape »

> Magnificent 7 companies are increasing capex to their biggest ever to differentiate their tech from each other and the big AI labs, but the key realization is that they don’t have to spend it to win. It’s a defensive move for them, if they commit $50B, OpenAI and Anthropic need to go raise $100B each to stay competitive, which makes them reliant on investors’ money.

Stay competitive how? If the Magnificent 7 aren't spending the money, then how could it possibly hurt OpenAI/Anthropic to not raise equal amounts of money? Maybe you can pull together an explanation, but this author didn't even try to do so.

This piece seems poorly thought-out, but well designed to get shared.

Promote writers who will actually explain their claims carefully.

Re: How the AI Bubble Bursts

#234

Post by adventured »

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…

LLMs haven't remotely begun to be integrated into the lives of the typical person. Not even close. The typical person is using LLMs not at all as it pertains to their daily life tasks. They're using them almost entirely for limited discussion matters (eg having a discussion with GPT about a medical issue, or a work related matter).

This is the first or second inning in the LLM rollout. It'll take 15-20 more years for full integration of AI agents into the life of the typical person.

The claw experiments for example can just barely be considered alpha stage. They're early AI garbage unfit for the average person to utilize safely. That new world hasn't gotten near the typical person yet.

The compute requirements to get to full integration of AI agents into the life of the average person - billions of them - is far beyond 10x where we're at now.

Re: How the AI Bubble Bursts

#235

Post by lnfromx »

Okay lets suppose all those companies are profitable if training would stop today. What if token demand is shrinking ? I think big parts of the current demand is artificially build by e.g. FOMO and marketing without real value generated by them. There is no indication in economic data about some productivity boom resulting from AI usage. Next thing is Energy costs - that will soon eat into profitability too. I don't see how this bubble can't burst.

Re: How the AI Bubble Bursts

#236

Post by 9rx »

lotsofpulp wrote: post #220
9rx wrote: post #154

Earlier quoted context omitted.

> This is a really concerning perspective: people were paid what they were worth. The parent comment doesn't discount that, only pointing out that "what they were worth" was inflated due to a speculative environment. Wherein lies your concern?

That prices change from one point in time to another is a trivial fact. “Inflated due to a speculative environment” is not an accurate way to frame labor prices that held for many years. At that point, the prices were simply high due to high demand relative to supply (compared to other types of labor).

> At that point, the prices were simply high due to high demand relative to supply

That goes without saying. The investigation here is into demand. Which was said to be overinflated due to speculation. As noted, many of the companies hiring the developers did not have viable businesses.

Re: How the AI Bubble Bursts

#237

Post by cwillu »

martinvol wrote: post #222
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…

RAM prices haven't crashed yet and it'll take time because it has to propagate within the supply chain. Micron is -20% from the top already https://www.investing.com/equities/micron-tech Stock price is the best forward indicator I can think of

That might be true, but it's still straightforwardly wrong to say that RAM prices have crashed, and it calls into question everything else they write.

Re: How the AI Bubble Bursts

#238

Post by [deleted] »

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…

[deleted]

Re: How the AI Bubble Bursts

#239

Post by skeeter2020 »

Aperocky wrote: post #58
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…

Demand of tokens is absolutely skyrocketing. And unlike the traditional "this will replace humans right away", I think what this introduce is a lot of incentive to spread those token in places where there was never any incentive to hire a software engineer for previously. In turn, that will drive a lot of business activity in those area that will potentially fail given the current quality of the output. This feels li…

Maybe we need to focus on a better definition of "bust" but we will surely see something along the lines of the hype-cycle graph in AI; what technology has not fallen into the trough before (best case) reaching a more steady-state of use and growth?

Re: How the AI Bubble Bursts

#240

Post by yobbo »

wongarsu wrote: post #166
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?

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…

> We can assume they are doing so at a profit

This is false. We may assume it's the most efficient way of generating revenue given their GPUs, but their overall profitability will just be a guess. They would still have incentives to run hardware at maximum, even when it's uncertain to eventually recoup costs.

> a world where those API prices aren't profitable

A lab with employees and models in training has other costs than the operating expenses of a GPU farm.

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