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The current AI pricing was always going to go away

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Re: The current AI pricing was always going to go away

#51

Post by _fat_santa »

rirze wrote: post #32
_fat_santa wrote: post #6

I wonder how much of Uber blowing their AI budget and MSFT pulling their claude code licenses can be attributed to "tokenmaxxing". When Meta announced token leaderboards and other followed, I could see this being the logical conclusion. That whole trend is so dumb because it leads to this. Company announces they will measure developer performance by how many tokens they burn and constantly talks about how the best de…

> I'm convinced that most of those folks and their elaborate workflows aren't really for productivity but for bragging rights about how much they use AI. This is quite the reductive, charged statement. Can I ask what subscription plan you're using? My personal experience is unlike this at all-- I work on ever-expanding codebases so I can easily burn tokens. Not to mention, structured agentic coding with adverserial r…

I'm on OpenAI's Pro (200/mo) plan.

Re: The current AI pricing was always going to go away

#52

Post by gruez »

zozbot234 wrote: post #47
gruez wrote: post #43

Earlier quoted context omitted.

>The latest deepseek v4 pro model is 2-5x cheaper than Claude Sonnet 4.6. Cursor's Compose 2.5 that was just recently released is 6x cheaper than Sonnet. It's ironic how in a thread about "AI subsidies" that people don't think free model releases from AI don't count as subsidies. Whatever AI winter that would cause AI companies to stop subsiding tokens, would probably cause other AI labs to stop doing free model rele…

The currently-released models don't really go away. Even if they collectively only release a new model every few years for the sake of influence and public image, that's plenty enough to keep the competitive aspect going.

>Even if they collectively only release a new model every few years for the sake of influence and public image, that's plenty enough to keep the competitive aspect going.

This is unpersuasive. Why would AI companies (American or Chinese) stop subsidizing tokens, but keep doing open model releases? At least for the former you can argue it's a lead generation tool for enterprise contracts (eg. hobbyist uses claude code personal plan, then asks the company to buy claude code enterprise, which are billed at API rates), but what's the business case for doing open model releases? You might get some mindshare, but are also arming your competitors in the process. Moreover what makes you think the model releases will be at all competitive to frontier models? Google released gemma 4 a few weeks ago to acclaim, but it's in no way competitive to even GPT-5.4 or Opus 4.6.

Re: The current AI pricing was always going to go away

#53

Post by dismalaf »

tekacs wrote: post #50

> Anthropic’s CFO testified under oath this March that the company spent $10 billion on compute and made $5 billion in revenue (Ed Zitron has the math). The labs are underwater on inference. They’re raising prices to keep the lights on. 'The labs are underwater on inference' is an absurd thing to say whilst not separating the cost of _compute_ out into training and inference.

I mean, I guess they could just stop training new models and coast, but they ARE training models so you have to include those costs.

Re: The current AI pricing was always going to go away

#54

Post by throwaway613746 »

PiRho3141 wrote: post #8

This is where open source models are important. The latest deepseek v4 pro model is 2-5x cheaper than Claude Sonnet 4.6. Cursor's Compose 2.5 that was just recently released is 6x cheaper than Sonnet. The state of the art models are going to get better and more expensive and smaller models are going to get cheaper. There will be a point where the intelligence of both the cheap and state of the art models are indistin…

[dead]

Re: The current AI pricing was always going to go away

#55

Post by anonymousiam »

Not mentioned in the article/blog was the local alternative. Many applications will run just fine locally and not in the cloud. This is also more secure. Running local will probably eventually become the norm. It makes me wonder about the future of all these VC funded AI companies...

Re: The current AI pricing was always going to go away

#56

Post by shay_ker »

In the three options OP presents, I wonder if there's a fourth: BYO model

Customers give vendors metered access to their model. They can budget tokens per vendor. Vendors selling "AI products" can have a cleaner story and win on the margin.

The first step to is to iron out a reasonable protocol, basically authorizing a, access token, and then the model providers (OpenAI, Anthropic, etc.) do the rate limiting. Theoretically this could be done by OpenRouter too.

But even so - do customers want an "AI product" packaged cleanly, or do they want to manage token capacity? They may be forced to do the latter....

Re: The current AI pricing was always going to go away

#57

Post by JimDabell »

tekacs wrote: post #50

> Anthropic’s CFO testified under oath this March that the company spent $10 billion on compute and made $5 billion in revenue (Ed Zitron has the math). The labs are underwater on inference. They’re raising prices to keep the lights on. 'The labs are underwater on inference' is an absurd thing to say whilst not separating the cost of _compute_ out into training and inference.

According to Dario Amodei, Anthropic are even profitable when including inference as long as you look at it on a per-model basis; it’s just that every model is more expensive to train than the last one.

For instance, if you have already spent $n to train a model and are currently earning $2n selling inference with it; but are concurrently spending $3n training the next model in anticipation of earning $6n with it, then you are already in the hole for $n and are currently also losing $n – but you are doubling your money with each model because your $n investment in the first model returns $2n and your $3n investment in the second model returns $6n.

Also:

> Ed Zitron has the math

Ed Zitron is constantly wrong about AI economics:

https://www.theargumentmag.com/p/ais-biggest-critic-has-lost...

Re: The current AI pricing was always going to go away

#58

Post by saltcured »

tekacs wrote: post #50

> Anthropic’s CFO testified under oath this March that the company spent $10 billion on compute and made $5 billion in revenue (Ed Zitron has the math). The labs are underwater on inference. They’re raising prices to keep the lights on. 'The labs are underwater on inference' is an absurd thing to say whilst not separating the cost of _compute_ out into training and inference.

How is training vs inference any different than other product spaces, where all the costs of bringing a product to market have to be considered for profitability? You can't just look at marginal production cost. You are still underwater if the other development costs are not being recouped by the final sales revenue.

The whole commercial AI enterprise is not economically viable if the inference revenue will not cover both inference and the amortized training costs. Given how fast they are churning through models to compete, you cannot act like the training is an asymptotically low cost.

Re: The current AI pricing was always going to go away

#59

Post by yogthos »

My expectation is that local models will be the default for coding within a year or two. You can already run Qwen 3.6 with MTP at a pretty reasonable speed without needing a huge amount of VRAM. And while it's not as good as current frontier models, it's already quite competent for a lot of tasks.

And there's no sign that people are running out of ideas for how to optimize models further. You see a bunch of papers come out literally every few weeks right now. So, it's entirely plausible to me that we'll see models that are superior to current frontier ones in a year or two that will run on your machine.

Once we get to that point, I don't think it's even going to matter if frontier models keep improving for most people. Being able to run the model on your machine, use it as much as you want in any way you want, without having to worry about it changing from under you or the company changing pricing, and not have to send all your data to the vendor are going to be the deciding factors.

At some point the models are just good enough to do what you need to do. On top of that, I expect tooling around models and coding patterns will evolve as well. That could compensate significantly for the capabilities of the model. We already see this happening with two prime examples here:

https://github.com/itigges22/ATLAS

https://arxiv.org/abs/2509.16198

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