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How the AI Bubble Bursts

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Re: How the AI Bubble Bursts

#61

Post by shafyy »

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.

Not counting training models as part of your gross margin is just creative accounting. It's an inherent part of being able to provde the service for OpenAI, Anthropic etc.

Even so, their subscriptions are significantly cheaper than the token pricing via API. So at some point they will need to get rid of subscriptions or increase the subscription prices dramatically... And that's assuming their current token pricing is actually profitable. Which it probably isn't.

Lastly, I would not trust one word that comes out of an executive of an AI company (or any other large company, for that matter).

Re: How the AI Bubble Bursts

#62

Post by Aperocky »

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…

Sub par is not the right word, the right word is feature creep.

markdown have much less of that brilliance and thankfully I also needed none of it.

Last time I authored a word document is probably 2 years ago for a government interaction.

Re: How the AI Bubble Bursts

#63

Post by EternalFury »

If somehow recovering the capex expenditure is not counted, if somehow the cost of developing future models is not counted, then yes, inference costs of current leading models allow a profit.

But those things are tied together.

Even xAI, that now has a reasonably competitive model, is struggling to achieve PMF. Meta is in shambles because their models have underperformed for years now.

Re: How the AI Bubble Bursts

#64

Post by elzbardico »

nickphx wrote: post #44
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…

step change? how? profitable? where did you read that? people want tokens? really? who are these people?

Yeah, if we just ignore R&D, fixed costs, depreciation, and the fact that there's a high likelyhood investor were expecting a return, yeah, ignoring all of that, and trusting their number we may say inference turns a profit.

In accounting, almost anything you want can be true, at least for some time.

Re: How the AI Bubble Bursts

#65

Post by sigmoid10 »

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…

Yeah, I also stopped reading at that point. If I want a bunch of random, made up facts to sell lukewarm opinions or steer the uneducated masses, I'll tune in on a Trump press conference. Why does this feel like someone is desperately trying to make reality mirror his flailing market bets?

Re: How the AI Bubble Bursts

#66

Post by jqpabc123 »

netdevphoenix wrote: post #42
jqpabc123 wrote: post #7

Another possibility not really addressed here --- local LLMs. AI on hardware you own and control --- instead of a metered service provider. In other words, a repeat of the "personal computing" revolution but this time focused on AI. TurboQuant could be a key step in this direction.

Local LLMs don't sound profitable at all for those building them. If you really wanted a SOTA model, you would be paying eye watering amounts to own it unless you got an open sourced one.

unless you got an open sourced one.

Ding, ding, ding --- we have a winner.

https://techstartups.com/2026/03/26/nvidia-backed-ai-startup...

https://tiiny.ai/

Re: How the AI Bubble Bursts

#67

Post by sandworm101 »

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…

There is also demand for ram in others areas of data centers. As we are all pushed deeper into clouds, i can see the rise of ram for data storage (ram drives) continue to eat into the supply. A module of ddr5 will be more useful in a netflix rack streaming movies 24/7 than in a gaming PC where it may only be used an hour or two every day.

Re: How the AI Bubble Bursts

#68

Post by dgb23 »

general_reveal wrote: post #15
myspy wrote: post #9

Earlier quoted context omitted.

Why?

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

Aren't you conflating the technical side of it with the economic one?

A bubble doesn't necessarily mean that the the underlying tech/innovation isn't useful. It's a financial and economic phenomenon that is pretty well understood and researched:

- During the hype cycle, investors tend to overestimate the short to mid term effects and underestimate the long term effects.

- It's near impossible to pick the winners in advance, and research has shown that investors underestimate how many losers there will be.

- The financial system/market works very well when there are localized issues with debt. Those get seemingly automatically detected and repaired. But broad increases in credit not so much. Those spread into the whole system in non-obvious and complex ways and destabilize the whole system, which can lead to very large corrections.

etc.

Re: How the AI Bubble Bursts

#70

Post by thereitgoes456 »

shimman wrote: post #47
baq wrote: post #32

Earlier quoted context omitted.

If they shut down all training today they’d be absolutely printing money for the next couple quarters and then die with a bang once the other lab releases the next frontier to the public.

How? They're already burning $2 bills to make $1, court documents shown that Anthropic has already been lying around revenue (claimed to have made $19 billion when it's actually $5 billion to date [1]). Not hard to believe they're lying about other things when they've been lying about the capability of their products since inception. [1] https://www.reuters.com/commentary/breakingviews/anthropic-g...

That is not what the article says, it says $19B ARR.

I don’t necessarily see a contradiction. $19B run rate, achieved very recently, is actually consistent with $5B lifetime earnings, because their growth curve is so sharp. Zitron is not good at math.

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