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.
show comments
kouunji
Google makes claims here about high demand for Gemini - does anyone here have insight into how much of the load on Google is paid use vs the load from putting AI summaries into every web search?
show comments
symisc_devel
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.
show comments
netdur
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
show comments
snake_doc
Image/video understanding still quite cost effective from the Gemini flash series models?
Image generation and veo models I’d imagine quite effective for creators; new Instagram accounts with AI content that are garnering millions of followers in spans of weeks are quite common now
HarHarVeryFunny
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?
show comments
Zambyte
Facebook does seem to be falling behind. Does anyone here use Llama over more recent options for any technical reasons?
show comments
gcanyon
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.”?
show comments
sidcool
How do they figure out it's being used by Meta?
show comments
hendersoon
Must be to classify/moderate images for social media. They're pretty good at that. I can't imagine what else you'd want to use Gemini models for, certainly not coding.
show comments
dist-epoch
Demand for tokens raises exponentially, we are in the middle of a compute crisis, and people still think AI is a bubble...
mark_l_watson
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.
show comments
arcatech
Using LLMs for development is not efficient. All of the problems these companies are having trying to provide enough compute and energy are proof.
Understanding the actual problems we are trying to solve with code and efficiently coming up with solutions (essentially, pre-LLM development) will always be better than wastefully brute forcing solutions with LLMs.
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.
Google makes claims here about high demand for Gemini - does anyone here have insight into how much of the load on Google is paid use vs the load from putting AI summaries into every web search?
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.
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
Image/video understanding still quite cost effective from the Gemini flash series models?
Image generation and veo models I’d imagine quite effective for creators; new Instagram accounts with AI content that are garnering millions of followers in spans of weeks are quite common now
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?
Facebook does seem to be falling behind. Does anyone here use Llama over more recent options for any technical reasons?
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.”?
How do they figure out it's being used by Meta?
Must be to classify/moderate images for social media. They're pretty good at that. I can't imagine what else you'd want to use Gemini models for, certainly not coding.
Demand for tokens raises exponentially, we are in the middle of a compute crisis, and people still think AI is a bubble...
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.
Using LLMs for development is not efficient. All of the problems these companies are having trying to provide enough compute and energy are proof.
Understanding the actual problems we are trying to solve with code and efficiently coming up with solutions (essentially, pre-LLM development) will always be better than wastefully brute forcing solutions with LLMs.