What we’re going to see is as energy becomes a problem; they’ll simply shift to more effective and efficient architectures on both physical hardware and model design. I suspect they can also simply charge more for the service, which reduces usage for senseless applications.
It might only stop once the electricity problem becomes truly unsustainable. Of course, I don’t fully understand the specific situation in the U.S., but I even feel that one day they might flee the U.S. altogether and move to the Middle East to secure resources.
Kimi K2 Thinking is rumored to have cost $4.6m to train - according to "a source familiar with the matter": https://www.cnbc.com/2025/11/06/alibaba-backed-moonshot-rele...
I think the most interesting recent Chinese model may be MiniMax M2, which is just 200B parameters but benchmarks close to Sonnet 4, at least for coding. That's small enough to run well on ~$5,000 of hardware, as opposed to the 1T models which require vastly more expensive machines.
Other reported training costs tend to include rental of the cloud hardware (or equivalent if the hardware is owned by the company), e.g. NVIDIA H100s are sometimes priced out in cost-per-hour.
It would be simply wrong to exclude the staffing costs. When each engineer costs well over 1 million USD in total costs year over year, you sure as hell account for them.
Calculating the cost in terms of GPU-hours is a whole lot easier from an accounting perspective.
The papers I've seen that talk about training cost all do it in terms of GPU hours. The gpt-oss model card said 2.1 million H100-hours for gpt-oss:120b. The Llama 2 paper said 3.31M GPU-hours on A100-80G. They rarely give actual dollar costs and I've never seen any of them include staffing hours.
As with staffing costs though it's hard to account for these against individual models. If Anthropic run a bunch of training experiments that help them discover a new training optimization, then use that optimization as part of the runs for the next Opus and Sonnet and Haiku (and every subsequent model for the lifetime of the company) how should the cost of that experimental run be divvied up?
Honestly curious where you got this number. Unless you're talking about extremely small quants. Even just a Q4 quant gguf is ~130GB. Am I missing out on a relatively cheap way to run models well that are this large?
I suppose you might be referring to a Mac Studio, but (while I don't have one to be a primary source of information) it seems like there is some argument to be made on whether they run models "well"?
An M3 Ultra with 256GB of RAM is $5599. That should just about be enough to fit MiniMax M2 at 8bit for MLX: https://huggingface.co/mlx-community/MiniMax-M2-8bit
Or maybe run a smaller quantized one to leave more memory for other apps!
Here are performance numbers for the 4bit MLX one: https://x.com/ivanfioravanti/status/1983590151910781298 - 30+ tokens per second.
30 tokens per second looks good until you have to wait minutes for the first token
This is much more likely to be an issue in the US than in China. https://fortune.com/2025/08/14/data-centers-china-grid-us-in...
https://www.brookings.edu/articles/how-do-china-and-america-...
The source for China's energy is more fragile than that of the US.
> Coal is by far China’s largest energy source, while the United States has a more balanced energy system, running on roughly one-third oil, one-third natural gas, and one-third other sources, including coal, nuclear, hydroelectricity, and other renewables.
Also, China's GDP is a bit less inefficient in terms of power used per unit of GDP. China relies on coal and imports.
> However, China uses roughly 20% more energy per unit of GDP than the United States.
Remember, China still suffers from blackouts due to manufacturing demand not matching supply. The fortune article seems like a fluff piece.
https://www.npr.org/2021/10/01/1042209223/why-covid-is-affec...
China has been adding something like a 1GW coal plant’s worth of solar generation every eight hours in the past year, and the rate is accelerating. The US is no longer a serious competitor for China when it comes to energy production.
Not quite at 1 GW / 8 hrs, but approaching that figure rapidly!
(I'm not sure where the coal plant comes in - really, those numbers should be derated relative to a coal plant, which can run 24/7)
It works both ways: you have to derate the coal plant somewhat due to the transmission losses, whereas with a lot of solar power being generated and consumed on/in the same building the losses are practically nil.
Also, pricing for new solar with battery is below the price of building a new coal plant and dropping, it's approaching the point where it's economical to demolish existing coal plants and replace them with solar.
Western media still carry strong biases toward China’s political system, and they have done far too little to portray the country’s real situation. The narrative remains the same old one: “China succeeded because it’s capitalist,” or “China is doomed because it’s communist.”
But in reality, barely a few days go by without some new technological breakthrough or innovation happening in China. The pace of progress is so fast that even people inside the country don’t always keep up with it. For example, just since the start of November, we’ve seen China’s space station crew doing a barbecue in orbit, researchers in Hefei working on an artificial sun make some new progress, and a team discovering a safe and efficient method for preparing aromatic amines. Apart from the space station bit—which got some attention—the others barely made a ripple.Also, China's first electromagnetic catapult aircraft carrier has officially entered service
about a year ago, I started using Reddit intensively. what I read more on Reddit are reports related to electricity, because it involves environmental protection and hatred towards Trump, etc. There are too many leftists, so the discussions are somewhat biased. But the related news reports and nuclear data are real. China reach carbon peak in 2025, and this year it has truly become a powerhouse in electricity. National data centers are continuously being built, but residential electricity prices have never been and will never be affected.China still has a lot of coal-fired power, but it continues to carry out technological upgrades on them. At the same time, wind, solar, nuclear and other sources are all advancing steadily. China is the only country that is not controlled by ideology and is increasing its electricity capacity in a scientific way.
(maybe in AI field people like to talk about more. not only kimi release a new model, Xpeng has a new robot and brought some intension. these all happends in a few days )
Have recently noticed a lot of pro-CCP propaganda on social media (especially Instagram and TikTok), but strangely also on HN; kind of interesting. To anyone making the (trivially false) claim that China is not controlled by ideology, I'm not quite sure how you'd convince them of the opposite. I'm not a doomer, but as China ramps up their aggression towards Taiwan (and the US will inevitably have to intervene), this will likely not end well in the next 5-10 years.
Presumably they'd stop doing that once AI becomes a more beneficial use for the energy though.
* China has produced more PV panel capacity in the first half of this year than the US has installed, all in all, in all of its history
* China alone has installed PV capacity of over 1000 GW today
* China has installed battery electrical storage of about 100 GW / 300 GWh today and aims to have 180 GW in 2027
energy generation multiples of what the US is producing. What does AI need ? Energy.
second - the open source nature of the models - means as you said a high baseline to start with - faster iteration.
does outperform
China is absolutely winning innovation in the 21st century. I'm so impressed. For an example from just this morning, there was an article that they're developing thorium reactor-powered cargo ships. I'm blown away.
I guess it depends on how you see it, but regardless, the people putting it to use today doesn't seem to be in the US.
FWIW:
> Thorium was discovered in 1828 by the Swedish chemist Jöns Jacob Berzelius during his analysis of a new mineral [...] In 1824, after more deposits of the same mineral in Vest-Agder, Norway, were discovered [...] While thorium was discovered in 1828 its first application dates only from 1885, when Austrian chemist Carl Auer von Welsbach invented the gas mantle [...] Thorium was first observed to be radioactive in 1898, by the German chemist Gerhard Carl Schmidt
For being an American discovery, it sure has a lot of European people involved in it :) (I've said it elsewhere but worth repeating; trying to track down where a technology/invention actually comes from is a fools errand, and there is always something earlier that led to today, so doesn't serve much purpose except nationalism it seems to me).
https://en.wikipedia.org/wiki/Thorium-based_nuclear_power#Hi...
https://en.wikipedia.org/wiki/Molten-Salt_Reactor_Experiment
Many such cases...
My problem was that it really doesn't, none of the models out there are that great at agentic coding when you care about maintainability. Sonnet 4.5 sometimes struggles and is only okay with some steering, same for Gemini Pro 2.5, GPT-5 recently seems like it's closer to "just working" with high reasoning, but still is expensive and slow. Cerebras recently started offering GLM-4.6 and it's roughly on par with Sonnet 4 so not great, but 24M tokens per day for 50 USD seems like good value even with 128k context limitation.
I don't think there is a single model that is good enough and dependable enough in my experience out there yet, I'll probably keep jumping around for the next 5-10 years (assuming the models keep improving until we hit diminishing returns so hard that it all evens out, hopefully after they've reached a satisfying baseline usefulness).
Don't get me wrong, all of those models can already provide value, it's just that they're pretty finnicky a lot of the time, some of it inherent due to how LLMs work, but some of it also because they should just be trained better and more. And the tools they're given should be better. And the context should be managed better. And I shouldn't see something as simple as diffs fail to apply repeatedly just because I'm asking for 100% accuracy in the search/replace to avoid them messing up the brackets or whatever else.
Neither is being bleeding edge.
I use Mistral's models, I've built an entire internal-knowledge-pipeline of sort using Mistral's products (which involved anything from OCR, to summarization, to linking stuff across different services like Jira or Teams, etc) and I've been very happy with it.
We did consider alternatives and truth to be told none was as cost-effective, fast and satisfying (and also our company does not trust US AI companies to not do stuff with our data).
Most of our hosting is also on European providers, my team's the only one that deploys some services on Azure.
But as you say the rest of it is good too. I use it for research and to me it does a great job, all for a fraction of the price and the carbon of the U.S. players.
Europe doesn't have the infrastructure (legal or energy) and US companies offer far better compensation for talent.
But hey, at least we have AI regulation! (sad smile :))
Moreover, the ultimate competition between models will eventually become a competition over energy. China’s open-source models have major advantages in energy consumption, and China itself has a huge advantage in energy resources. They may not necessarily outperform the U.S., but they probably won’t fall too far behind either.