In the current climate limiting someone's use of AI might be expected to be about restricting access or restricting what someone can do with it, but the story here ostensibly seems to be about capacity constraints, not any limitation on what models or capabilities Google is giving Meta access to.
Cloud services like to present the illusion of an infinite amount of compute available at a fixed price per unit, but the reality is if you try to use too much of any service you'll find you have a quota and requests to increase it will fall on deaf ears if the provider doesn't have more of that resource.
Too much of my working life has been spent shoehorning services into less space/compute/ram/spindles or migrations to other data centers to solve such issues.
Having said that, I agree with you. You have to request limit increases often and can't scale even in those instances if you don't plan ahead.
That said, I expect much of the AI bubble to pop. Google Gemini with Antigravity is a good product, as is a Claude Code subscription but I have switched to using DeepSeek v4 Pro with the Claude Code harness and DeepSeek v4 Flash with the OpenCode harness (when I am not using local models with little-coder/pi) and at least for the foreseeable future I don’t think I am going back. Fast APIs at low cost trumps having to spend a little more time to get the same quality of results.
Zambyte•1h ago
khurs•1h ago
Llama Meta 70b is 50th or so down the list of popular models.
It has 24.1b tokens used in 7 days vs the top models that have trillions or hundreds of billions of tokens.
So practically dead!
dataminded•59m ago
https://ai.meta.com/blog/introducing-muse-spark-msl/