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.
These kind of limits happen all the time for big clients.
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.
If you allow me a bit of pedantry, it's infinite "for all intents and purposes". It doesn't mean you can request civilizational levels of compute, but for a blog, a crud, an ETL and such, that is regular use cases with sensible scale you can absorb any elastic demand.
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.
Yeah but you don't need cloud for a blog. Cloud was sold as effectively infinite resources - capacity isn't infinite, or effectively infinite, it's 20% more than you are currently using and you pay 300% more for that.
There has to be a name for this deceptive marketing tactic where you say something is unlimited and then it is only unlimited as long as you don't use very much.
It would be one thing if you occasionally got a "no more capacity" error when requesting large amounts of resources but it doesn't work that way. They confine you to a relatively small amount of resources the entire time you have an account. If you want more you have to request it.
A blog for your product, if your product is already on the cloud, is a very sensible use case for the cloud. Static one deployed to a bucket and a CDN, fast, cache on the edge, high availability.
The tiny blog sure isn't for the cloud, but also it's not the main client of the cloud.
> it's 20% more than you are currently using and you pay 300% more for that.
Compared to what, self-hosting? Then you need to account for things that are difficult to put a price like your time maintaining a physical infrastructure and the lessons you will learn with it.
Sounds like I'm defending the big cloud, but there is a valid use that is disconsidered because it's trendy to hate on the cloud.
> They confine you to a relatively small amount of resources the entire time you have an account. If you want more you have to request it.
I do believe this will be the norm from now on to get access to top frontier model. Computing capacity plus state restrictions plus KYC will be imposed to organisations to get access, individuals will be served last on the queue with degraded performance. Once the Chinese models catch up, nobody (at least individuals) will turn back again to frontier labs.
This seems less about frontier models and restriction and more just lack of compute capacity to meet demand. This has always been an issue for large clients running on cloud, though not to this extent.
It's interesting that Meta is heavily using Google's models (as opposed to Anthropic or OpenAI) given that they are not SOTA for coding. I wonder if this for some strategic/competitive reason, or maybe for cost saving?
I would imagine there are many situations within Meta's applications where relatively small models can do a good job — sentiment analysis, abusive language detection, characterising users based on their posts, summarising a user's complaint so it can be ignored more efficiently, assessing whether ads are likely to be fraudulent so they can be run more often, etc.
Hmm ... I was assuming they were using these models for development, but I wonder if any of it might be for production instead - perhaps using vision models to analyze posted content? That would certainly be massive scale, but I'd have thought that scale would require them to be running in their own datacenters.
Misleading title on HN but an interesting article, a reminder of why the hyper scalers are investing heavily in infrastructure.
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.
This seems to be a bit of a misleading headline.
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.
These kind of limits happen all the time for big clients.
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.
If you allow me a bit of pedantry, it's infinite "for all intents and purposes". It doesn't mean you can request civilizational levels of compute, but for a blog, a crud, an ETL and such, that is regular use cases with sensible scale you can absorb any elastic demand.
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.
Yeah but you don't need cloud for a blog. Cloud was sold as effectively infinite resources - capacity isn't infinite, or effectively infinite, it's 20% more than you are currently using and you pay 300% more for that.
There has to be a name for this deceptive marketing tactic where you say something is unlimited and then it is only unlimited as long as you don't use very much.
It would be one thing if you occasionally got a "no more capacity" error when requesting large amounts of resources but it doesn't work that way. They confine you to a relatively small amount of resources the entire time you have an account. If you want more you have to request it.
A blog for your product, if your product is already on the cloud, is a very sensible use case for the cloud. Static one deployed to a bucket and a CDN, fast, cache on the edge, high availability.
The tiny blog sure isn't for the cloud, but also it's not the main client of the cloud.
> it's 20% more than you are currently using and you pay 300% more for that.
Compared to what, self-hosting? Then you need to account for things that are difficult to put a price like your time maintaining a physical infrastructure and the lessons you will learn with it.
Sounds like I'm defending the big cloud, but there is a valid use that is disconsidered because it's trendy to hate on the cloud.
> They confine you to a relatively small amount of resources the entire time you have an account. If you want more you have to request it.
It's a form of KYC, nothing wrong with that.
Meta builds its own models. How similar is this to a story with the headline “OpenAI limits Anthropic’s use of its ChatGPT AI models.”?
Google is the only LLM frontier that can supply huge enterprise grade AI, yet still struggle, the other one is spacex but their LLM is Grok
also the only cloud platform, the only workspace, the only cloud drive... it's just standard Google fare
I do believe this will be the norm from now on to get access to top frontier model. Computing capacity plus state restrictions plus KYC will be imposed to organisations to get access, individuals will be served last on the queue with degraded performance. Once the Chinese models catch up, nobody (at least individuals) will turn back again to frontier labs.
This seems less about frontier models and restriction and more just lack of compute capacity to meet demand. This has always been an issue for large clients running on cloud, though not to this extent.
It's interesting that Meta is heavily using Google's models (as opposed to Anthropic or OpenAI) given that they are not SOTA for coding. I wonder if this for some strategic/competitive reason, or maybe for cost saving?
I would imagine there are many situations within Meta's applications where relatively small models can do a good job — sentiment analysis, abusive language detection, characterising users based on their posts, summarising a user's complaint so it can be ignored more efficiently, assessing whether ads are likely to be fraudulent so they can be run more often, etc.
Google tends to be very good at vision and smaller/ edge
Hmm ... I was assuming they were using these models for development, but I wonder if any of it might be for production instead - perhaps using vision models to analyze posted content? That would certainly be massive scale, but I'd have thought that scale would require them to be running in their own datacenters.
Facebook does seem to be falling behind. Does anyone here use Llama over more recent options for any technical reasons?
if you use this as a rough gauge: https://openrouter.ai/models?order=top-weekly
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!
Meta's latest model is Spark Muse and not available outside of its products.
https://ai.meta.com/blog/introducing-muse-spark-msl/
Misleading title on HN but an interesting article, a reminder of why the hyper scalers are investing heavily in infrastructure.
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.