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.
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?
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.
DeepSeek didn't do to OpenAI and Anthropic what nearly everybody claimed they would.
Every single person on HN that loudly proclaimed the end was nigh for GPT & Co. due to DeepSeek, was wrong. They were humiliatingly wrong, and they'll never own up to it. The reason those people were so very wrong, is the same exact reason the Kimi crowd is wrong now. And it's very obvious that they're wrong, but they have intense emotional blinders on. Their thinking process is hyper emotionalism: they want a certain outcome, regardless of if reality aligns to that or not. They're making emotional wishes about how they want things to turn out, and pretending those magic wishes are grounded in reason.
It takes enormous resources to run something equivalent to GPT 5.6 or Fable. Nobody can or wants to do that outside of very limited situations - if you can just reasonably pay as you go instead. As it turns out, you can just pay as you go with GPT and Fable. Their businesses have gotten radically larger since DeepSeek launched. Get it yet?
Domestic China is the only very large audience for their own models, so long as OpenAI and Anthropic stay top tier.
All the hype online from the forums about Kimi, is worthless: those people hyping it can't even come close to running it locally, which is the fantasy. So why are they hyping it? Why did they hype DeepSeek just the same, and learn nothing from its total failure to actually take down OpenAI and Anthropic? Rhetorical questions with obvious answers.
Kimi poses zero actual threat to OpenAI and Anthropic. Those companies will continue to pile up the subscriptions and API usage. Check out GPT's subscriber base today vs when DeepSeek launched. Get it yet? When Model X launches out of China in a year, we'll have this same conversations all over again, and the hypsters will have learned nothing.
While the Kimi fawning is endless, OpenAI will just keep piling up subscriber counts, and Anthropic will keep piling up API usage. Then OpenAI is going to staple a gigantic ad system onto GPT. China can't compete in the model-as-a-service business globally, for the exact same reason they failed so miserably to compete in search globally.
I don't think so. US models are very expensive, and not available in every country. I am not willing to pay $50/1M tokens for writing my pet projects.
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
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.
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)
On the very link on the top comment of this thread, which I repost here:
https://artificialanalysis.ai/models/gemini-3-6-flash
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.
Give it some time.
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.
So my guess is that Google will continue having compute shortages until the Gemini enshittification starts.
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
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.
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.
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 ...Paywalled article, but the headline is basically all you need: https://www.bloomberg.com/news/articles/2026-07-16/google-ge...
4) googles big model just performs worse than K3 and GLM so they choose not to embarass themself.
Like I love Gemini and use it a lot to one-shot whole MR with huge contexts, but its just much worse when its come to tool use and agentic coding.
Fast, light weight, ok intelligence. Perfect for serving 20B+ prompts per day mostly surrounding banal human things.
OAI and Anthropic's cloud spend can cover the revenue gap, as Google is already capturing a large chunk of those guy's revenue.
https://huggingface.co/microsoft/bitnet-embedding-0.6b
It’s a small multilingual embedding model designed for things like search, RAG, and semantic similarity. It supports a fairly large context window and is designed to run efficiently on a CPU in a GPU starved world.
The interesting part is that it builds on BitNet, using ternary weights of -1, 0, and 1 instead of the usual floating-point weights. That should make indexing and searching large amounts of text much cheaper without giving up too much accuracy.
From the outside they look like they're behind in terms of frontier models, but I think they might be the best positioned to not go out of business when the bubble pops.
Also look at the fact that they've been able to deploy AI-assisted search at google scale. It must be another order of magnitude larger (at least) than the model deployments for OpenAI and Anthropic.
Of course unless you're inside Google it's impossible to know for sure.
Lest we forget, "Attention is All You Need" came from Google.
It also came directly from the university of Toronto, and the university of Toronto seeded all American frontier labs (including Grok (why do you think they could start so fast))
I do not think OpenAI or Anthropic are actively chasing margins - though, Anthropic is supposed to be profitable on some form of non-GAAP accounting...
I suspect Google isn't really interested in seeing how far it can get dragged into a race of selling dollars for $0.25, and is more interested to see if it can stay in the race selling $0.50 for a dollar - when everyone else is losing or barely breaking even.
Maybe they don't want to price war with the other labs so they can comfortably maintain healthy margins on selling them compute?
Porting CUDA-based research, debugging, and overall experimentation speed is likely slower.
The GPU is still king for training.
Yes, subs like codex are heavily subsidized. But API billing has massive margins and that's what enterprises pay.
https://www.bloomberg.com/news/articles/2025-12-21/openai-se...
As for Anthropic, the rumors I remember seeing for their API margins were more like 85-90%, but I don't have a reference at hand for those. But once you know the API is wildly profitable and the subscriptions are roughly break-even and not even a big slice of their income, all of the investment makes a lot more sense.
Rumors say 4) it didn't perform well, especially in coding so has been delayed