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.
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 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.
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.
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.
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
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?
Rather than direct usage, I suspect a lot of Gemini capacity is being use for the AI summary presented with every google search or AI features of android phones etc.
And I'd expect Google will want to prioritize capacity for those - they don't want their google pixel phone to error or google search to barf.
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.
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[ 2.7 ms ] story [ 35.8 ms ] threadIn 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.
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.
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
And I'd expect Google will want to prioritize capacity for those - they don't want their google pixel phone to error or google search to barf.
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.
honestly better than gemini flash