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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?
Don't know, but Gemini 3.1 flash lite is available for free under relatively generous limits, and it had lots of random interruptions like when I was testing it. (Intermittently responding with errors due to high load.)
My curiosity is not the free AI summaries (which they can opaquely tune as necessary), but instead the renting of TPUs to Anthropic and OpenAI. Many of these contracts were announced last minute and seemed to involve a very desperate Anthropic. Based on the Anthropic/xAI data center contract, they’re willing to pay crazy markup to get immediate access to compute.
I want to know how impacted Gemini has been by that, because that will reveal a lot about their margins and revenue generating first party demand. Each MSFT earnings report they discuss the balance they’re dealing with between supplying GPUs to Azure customers and first party demand.
My pet theory is that Gemini is “losing” the LLM race because they’re preferentially selling the TPUs to competitors, while keeping just enough for themselves to stay competitive and build their own products.
Yeah I had a trial for AI Pro or whatever it's called and could never use Gemini CLI (when it still existed) because it was constantly "overloaded". Using the API directly (wihtout a subscription) sometimes works but the models are so buggy and the endpoints constantly spew errors that it's not usable. See this forum thread for example: https://discuss.ai.google.dev/t/frequent-503-errors-service-... it started with 503 errors since JANUARY and it's still not fixed. These are "stable" GA models!
I HIGHLY doubt that Gemini is overloaded, Google has been bullshitting with their crap models since release. Waste of everyone's time.
"Traffic too high" is a load shedding decision someone made on purpose. Means their capacity planning assumes you'll retry or go away. Fine for a free tier, brutal if you're paying for an SLA.
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.
It's worse than OpenAI or Anthropic. However their lower tier consumer offerings can sometimes be had for <$10/mo on offer and come bundled with other Google services like cloud storage.
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.
Ran Gemini Flash for exactly this kind of thing on a content pipeline last year, classification tasks where you're calling it millions of times a day. Cost per call matters way more than raw capability at that volume, and Flash's latency after warmup was consistently lower than anything comparable from OpenAI at the time.
Minor correction: Gemini 2.5 Pro briefly topped the LMArena coding leaderboard, so "not SOTA" isn't quite accurate. I could be wrong on the exact ranking now, but cost and infra ties into Google Cloud probably matter more than raw benchmark scores anyway.
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.
OTOH, if they are stressing Google's capacity then it seems it has to be for production use, which would relfect a massive failure on Meta's side given their investment in datacenters and AI. If they can't utilize their own models and datacenters, then maybe they should just rent the excess capacity to Google! :)
Disagree that Google's uniquely positioned here. Azure/OpenAI has way more enterprise AI deployments than Gemini right now, and Bedrock gives AWS customers access to half a dozen frontier models including Anthropic's. "Only" is doing a lot of work in that sentence.
Facebook is ethically challenged and that's putting it very very very mildly. Yes, they have unlimited money, but at a certain point, it comes across like a rich dude at a bar telling a beautiful woman that he'll buy her a diamond bracelet if she will just come over to his place right now. They make my skin crawl.
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
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.
I've criticized Antigravity in this same conversation, but Google Gemini is good at coding. Even Flash 3.5 low is good at coding. The problem is that Google isn't hungry anymore and it really really really shows in how much they've botched everything to do with Antigravity.
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.
I am a huge fan of Google Gemini, but Antigravity is not a good product. Just recently I've had issues with:
* Repeated instances of incorrect code insertion that the agent cannot clean up. Sure, version control, but this is often happening in new files that aren't even in version control yet.
* Lost chat history when I close and restart the app.
* Not being able to restore a chat from the history (just saw this last week).
* Overly broad searches that waste time and tokens.
* No vertical scroll bar arrows. WTF?? Doesn't the interface look "flat" enough already? This feels arbitrary and stupid.
* The previous chat prompt takes up a large portion of the vertical space of the chat window, even on a high res display.
When it works Antigravity is excellent. When it doesn't work, it's absolutely horrible. If you check the update history, there are usually just a few items and they're super generic things like "Fixed a bug with text entry.".
I don't see it improving at any kind of reasonable pace, even over the last 6 months As a result, I've mostly relegated Antigravity to a planning tool and it does an excellent job. Or I use it to write prompts that I give to Codex. It definitely can do an excellent job writing code sometimes, but sometimes it also does an absolutely horrible job with not breaking the code when it inserts it. It seems to be terrible at understanding C++ braces. How often? Way too often. I always know it's happening because it prompts me to run Git while it's doing something. LOL, that's how I know that it's broken something.
Codex is definitely way, way, way better. It's not even a contest at this point. Codex never breaks my code. It might not always do what I want, but it's just an order of magnitude better than Antigravity. Antigravity really feels like a comedy of errors at this point. ESPECIALLY from a company with Google's resources.
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.
The framing as competitive gatekeeping seems too clean. Serving external API traffic at scale competes directly with capacity Google wants for its own products, and inference cost still dominates most deployment budgets. Trimming a heavy customer during a demand spike doesn't need any strategic subplot beyond basic queueing constraints.
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.