It's also very possible that they know their big model underperforms chatgpt 5.6 and fable by too much, so they are focusing on what they can get wins in like speed instead.
That and/or the business case isn’t as clear when serving enormous models? You’re constantly stuck in a red queen’s race where your profitability window is increasingly measured in weeks because the Chinese are right behind you.
For small models (which are probably distilled from their big ones) you can serve them economically all the time and not hemorrhage money.
> For small models (which are probably distilled from their big ones) you can serve them economically all the time and not hemorrhage money.
For smaller models, you're competing with DeepSeek V4 Flash. (Which I think is a 284B A13B?) Subjectively, this feels about as smart as Sonnet 4.5, give or take. And it costs $0.09/$0.18 on Open Router, compared to $1/$5 for the latest Claude Haiku. See https://openrouter.ai/deepseek/deepseek-v4-flash#providers The developer antirez of Redis fame uses this as a local coding model.
DeepSeek did some extremely clever research on hybrid attention to get the prices that low, reducing per-user context cache sizes dramatically.
So, no, when it comes to low-price models, the US models probably can't sustain their current margins there, either.
There are also US based companies like Fireworks serving up the best open weight models with the compliances we need in US enterprise. Depending on the company, they may offer more/different jurisdictions, EU probably needs a Fireworks like company (haven't heard about one, maybe it already exists?)
The AI labs have been subsidising. When they try turn a profit, people will move to the fast followers. The only people that won’t are those that compete on leveraging the very latest models and even then, once spend and scale goes to the cheaper providers, we’ll see deeper research from those providers too. Think “PC compatibles beat IBM, Sun, SGI eventually”.
without any hard data one way or another your comment is worthless. "pile up subscriptions" - based on what? neither company is public. "piling up subscriber counts", "piling up API usage"? cool. how much money are they making? oh you don't know because they're not public.
the reality is one way or another that as long as there exists an alternative that a USA company could serve with the same compute rented from hyperscalers, this represents a threat, even if the extent to which is unknown
Why was this flagged? This is absolutely true, Americans seem to not actually want to use Chinese models at least for coding, maybe for other inference use cases but I haven't seen it. No one I know uses anything but OpenAI and Anthropic even if Chinese models are better or cheaper in many use cases.
First 6 of the most used models on openrouter currently are Chinese; that's true for code generation and other coding-relevant tasks too when ranked by share of tokens.
Of course openrouter is not representative because most users directly go to the model provider but it still proves your claim is very far from "absolutely true".
Even if not majority, it doesn't invalidate the point that just because OpenRouter has them as its top models that they are in general use among the population, rather than localized.
Honestly go use Deepseek v4 Flash, the data hosting in PRC is it's only downfall. It truly is an excellent model at the moment. It is opensource thankfully but there is friction there and this won't be as popular.
Why wouldn't the hyperscalers run these open models since they're much better than OpenAI and Anthropic at operating compute at scale?
I think the reason OpenAI and Anthropic stay ahead in revenues right now is because the models are improving too quickly to reliably compete with them on cost.
But, once model performance reaches a plateau -- they have to at some point, though perhaps years away -- that's when ability to operate compute infrastructure at scale becomes the secret sauce.
The big AI labs are likely safe until models stop improving fast enough to protect them from competition on cost.
This similar pattern has repeated in most technical booms prior to this.
When hard drive technology was improving fast enough that old hard drives were quickly obsolete, IBM could maintain good margins making hard drives. But once hard drives got good enough and advances were slow enough that innovation was not the only factor considered by drive purchasers, commodity hard drives started to take over and IBM had to exit those businesses.
The same is likely to happen once model improvement slows.
Kimi K3 is ahead of Fable 5 on several benchmarks.
So basically the angle went from "China cannot ever compete" to "China is six months behind" to "China is six weeks behind" to "China is six days behind but that's because they're distilling" and now you're saying "Yup sure, Kimi K3 is ahead on several benchmarks but you cannot host it yourself so this thing will go absolutely nowhere".
I mean: is it not a bit early to draw conclusions? It's been days since a chinese model is ahead of the very best / frontier US model on several benchmarks and you compare it to models who were clearly behind on everything.
You’re absolutely right and it’s heartening to see. I maintain a client with ~every provider you can think of and llama.cpp and it was really tiring the last few days to see people laundering other stuff through Kimi and Qwen. They’re not even open yet, the hype was based on their own blog posts, no one’s actually running these locally, the Qwen Max’s have never been open, Kimi’s API was 1/2 the speed the benchmarks was based on, when it was up, and had 60% downtime before they had to stop accepting new accounts, and their EULAs are “your inputs and outputs are ours.” May being clear-eyed benefit us both in the long run.
"You’re absolutely right and it’s heartening to see"
Damnit, I usually don't jump to LLM speech patterns, but this opening had me thinking you were a bot. But after checking your profile, I think you pass as human. I wonder when will be the time, this does not work anymore for me. (Creation date is a strong hint, but abandoned accounts can be hijacked)
Hehe, cheers, it really is funny & odd habit I have (usually when I'm in "everyone is wrong!" mode, haven't bothered to argue that, and see someone else arguing it :p)
That's the only explanation that makes sense. If it was frontier but cost or compute were limiting factors, they'd release it at an obscene price for the bragging rights. Google doesn't care that much about alignment, and I don't think it's likely to be significantly different than 3.5 anyway. The only reason it would need to be soft-canceled is if it's terrible, and has to end up in a ditch like Llama 4 to avoid shareholder panic.
3.5 pro was clearly a miss. It should have been in prod mid may, not MIA in late July. The brain drain at deep mind is a clear indicator that the people who know the most think that they can’t stay at the frontier.
Antigravity NEEDED to be game-changing. Without the stream of data that Claude, Codex, and Cursor enjoy there is little chance of getting an effective reinforcement learning loop. For the first time in its history, GOOG is at a meaningful data disadvantage, and apparently a cultural one as well.
"Without the stream of data that Claude, Codex, and Cursor enjoy there is little chance of getting an effective reinforcement learning loop"
Google literally giving everyone + student 18 month free subscription, those are source of cheap gemini + sonet,opus model that people selling/use with rotator proxy with thousands of account
I don’t think this is necessarily true, did we all forget how much Google cared about alignment that their AI wasn’t able to render a white polar bear?
My view then was they are optimising the models for inference ability on their own hardware AND use cases, which is often speed and time to first token.
They've somehow seemed to end up with terrible compute shortages, which again is surprising given how good Google is at infra deployments AND have their own hardware. From rumors out there they are turning down enterprise deals for Gemini because they don't have the compute.
The problem is they're falling further and further behind on frontier class on coding especially, and since I wrote that article it's got even worse with open weights models undercutting them on price AND intelligence.
They include an LLM response with every single Google search, whether it is warranted or not. That scale is, my guess, many orders of magnitude higher than what OpenAI and Anthropic serve. And for Google none of these are paid interactions since their LLMs do not (YET) insert ads into the responses.
So my guess is that Google will continue having compute shortages until the Gemini enshittification starts.
I don't think so. According to some very basic research there are around 8bn searches a day, or 250bn a month.
Let's assume Google serves AI overviews on every SERP (they don't) and don't cache them (they do, afiak).
And let's assume that each AI overview is 2000 tokens (blended input/output), that's 500T tokens a month.
It's rumoured that anthropic is serving somewhere close to 10Q tokens a month.
Now it may be that AI overviews uses vastly more tokens than that per search, but I doubt it based on speed to render the overview.
My very rough napkin math on this is that maybe AI overviews is consuming 100T tokens/month max (after adjusting for caching and SERPs that don't have them), which would be 1% of Anthropic token volume.
Well I asked the google AI mode thing what it thinks about your comment and it told me this (edited obviously):
"10 Quadrillion tokens a month means: 333 Trillion tokens per day and 3.85 Billion tokens generated/processed every single second, 24/7."
"At an incredibly cheap, subsidized infrastructure cost of $1 per million tokens, serving 10 Quadrillion tokens would cost Anthropic $10 Billion per month ($120 Billion a year) just in inference compute."
It also had this to say about how google's AI overview works: "Google doesn't just feed the LLM your 5-word search query. The system scrapes the top 10–20 web results, feeds thousands of words (tens of thousands of tokens of context) into the model, processes it, and then outputs the result."
Oh, and it does all of that in less than two seconds. Honestly, whatever Google is doing with its infrastructure is so far ahead of everyone else, I can't believe you fell for such an obvious lie.
There are also extremely obvious holes in your comment:
>Let's assume Google serves AI overviews on every SERP (they don't) and don't cache them (they do, afiak).
Try it out for yourself. Add a few random letters or punctuation. They cache nothing.
The tokens served number might include cache tokens which are a huge chunk of agentic token spend - and even with that the estimated burn rate for anthropic doesn't seem wildly off? They spend 1.25 bn per month on their deal with SpaceX alone.
They definitely cache results - I've searched and re-searched an identical query back to back a few times and seen identical results from overview. They are definitely throwing a stupid amount of compute towards these ai results nobody is paying for - changing punctuation and stuff does get you a different response - but they're not doing no caching.
Certainly what they're doing with their infrastructure is impressive but it's not super meaningful at the end of the day for a for profit company to be really impressively good at burning tens of billion dollars on a service nobody pays for while the same tech from their competitors is quickly becoming one of the largest spend categories for many software engineering teams
> focusing on what they can get wins in like speed instead
Speed as a differentiator has always been Google's thing. They (used to?) show the microseconds it took to query & rank web-scale search results. Chrome, notoriously, focused on speed at the expense of resource use. The very many efforts to efficiently speed up Android & its runtime since its inception, and so on...
> their big model underperforms chatgpt 5.6
Possible but TFA claims:
We have started our most ambitious pre-training run yet, for Gemini 4 ...
There was some recent reporting that a July release of the Pro model got pushed back for exactly that reason. Its performance was not good compared to the OpenAI/Anthropic big models. They are having a lot of problems with posttrain.
It would be a shame if they cannot beat Kimi K3 or Qwen3.8 Max, both of which are claimed to be Fable-like. If that is true, it will be [or would be] the first time a major American lab falls behind a Chinese competitor.
Google can't compete with China, neither can Meta. Only two labs in the US can keep chucking billions at the frontier race. Everyone else has a real business to run.
China can keep up because it's cheaper to run a frontier lab there. They also have more researchers and a stronger cultural inclination for this sort of thing. And I guess the business case in China doesn't have to work as well as it does in the US.
> China can keep up because it's cheaper to run a frontier lab there
Not sure if this is what you meant, but their training runs are significantly cheaper. This was one of the big shockers from the Deepseek R1 paper. US foreign policy has helped to ensure that the Chinese are compute constrained, so they literally cannot buy the most expensive and powerful training rigs.
This has led to a steady drumbeat of innovations which are not revolutionary on their own but stack together to make things much more efficient.
China will accelerate on ML research and optimization regardless of US export control policy. They want to win or at least not lose just as much as the US and will pull every reasonable lever at their disposal to do so. NVIDIA and the US government have no say in this.
I’ve often thought it was the cost of electricity and crypto mining being banned a few years ago. China has a lot of “stranded electricity” which fits this usecase well.
I think they've been behind for a while - flash isn't even that competitive with glm 5.2 and from their hype around 3.5 flash at launch - that was certainly not intended to be the case
When it is cheaper, and the "lower quality" model is adequate for the task at hand.
Plenty of problems have a low(er) skill/intelligence floor, anyone who uses the dual-mode agent paradigm (plan, then act) figures out the second phase can be completed by a less capable model. Even when disregarding costs - speed is important here because the agent can rapidly iterate without human supervision, based on compiler errors, lint and test failures
A coding agent driven by a large LLM can delegate smaller tasks to a faster model. For example searching through the codebase for references, examples, or established patterns. They are treated as tools and don't pollute the main agent's context.
