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How the AI Bubble Bursts

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Re: How the AI Bubble Bursts

#41

Post by phito »

Chance-Device wrote: post #18

From the beginning of this I’ve wondered the same question: how do these companies justify spending such massive amounts now (and 3 or 4 years ago) when software and hardware efficiencies will bring down the cost dramatically fairly soon? They basically decided that scaling at any cost was the way to go. This only works as a strategy if efficiency can’t work, not if you simply haven’t tried. Otherwise, a few breakthr…

I think their current goal is to capture as much market as they can while they still have the best models, their only moat. Look at Anthropic, they are clearly trying to lock their users in their ecosystem by refusing to follow conventions (AGENT.md etc) and restricting their tools exclusively to their own services.

Re: How the AI Bubble Bursts

#42

Post by netdevphoenix »

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.

Re: How the AI Bubble Bursts

#43

Post by camdenreslink »

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.

If a major model provider were to just halt progress on developing new and improved models, the open weight alternatives would catch up in a couple years.

They would have a period of great margin, followed by possibly zero margin as enterprises move to free options.

They would have to come up with a lot of great products around the inferior models to justify charging at that point.

Re: How the AI Bubble Bursts

#44

Post by nickphx »

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?

Re: How the AI Bubble Bursts

#45

Post by schnitzelstoat »

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.

Yeah, I don't think local LLM's will keep up with what the massive corporations put out. But they might get to a level of performance where it just doesn't matter for most users.

And people would prefer to run a model locally for 'free' (not counting the energy cost) rather than paying for an LLM subscription.

Re: How the AI Bubble Bursts

#46

Post by irusensei »

franze wrote: post #8

.... so what? the technology exists, the models exist. Even when the bubble bursts things will not go to the state "before AI". Even if model development would stop today (not the worst thing to happen) it would still be the most impactful invention since the printing press

I guess the point is that without the hype subsiding it enshitification will ensue.

Re: How the AI Bubble Bursts

#47

Post by shimman »

baq wrote: post #32
atwrk wrote: post #20

Earlier quoted context omitted.

But are they actually profitable, or do they employ creative accounting where only parts of overhead expenses are counted against all of inference revenue, similar to what Uber did? OpenAI's numbers show that they definitely are not profitable on inference, and even worse, revenue growth scaled linearly with inference cost from 2024 to 2025, which means they can't outgrow this problem. See https://www.wheresyoured.at…

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...

Re: How the AI Bubble Bursts

#48

Post by boriskourt »

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?

Re: How the AI Bubble Bursts

#49

Post by ap99 »

Chance-Device wrote: post #18

From the beginning of this I’ve wondered the same question: how do these companies justify spending such massive amounts now (and 3 or 4 years ago) when software and hardware efficiencies will bring down the cost dramatically fairly soon? They basically decided that scaling at any cost was the way to go. This only works as a strategy if efficiency can’t work, not if you simply haven’t tried. Otherwise, a few breakthr…

They're not just betting on the current tech, they're building out infra like this because probably any future tech currently being researched will also require massive data centers.

Like how the gpt llms were kind of a side project at openai until someone showed how powerful they could be if you threw a lot more parameters at it.

There could be some other architecture in the works that makes gpts look old - first to build and train that new ai will be the winner.

Re: How the AI Bubble Bursts

#50

Post by thereitgoes456 »

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?

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