China AI – The Efficiency Game

China plucks the low-hanging fruit

• Efficiency is spreading like wildfire through the Chinese AI industry indicating that the edge that the US has enjoyed is not as large as many thought, but also indicating that the techniques being employed may be easier than I thought to replicate.
• This is a theme RFM has been keeping an eye on for a couple of years since it became clear that models would be required to run on devices at the edge of the network.
• Ant Group (see here) and Tencent (see here) have released new models that they claimed are as good as the best of what the West has to offer, but tend to be smaller and trained with a fraction of the resources.
• This is important as it indicates that the Chinese are a force to be reckoned with in AI and have not been limited as much as expected by their inability to source the latest and greatest chips from Nvidia and AMD.
  • First, Ant Group Ling-Plus and Ling-Light: which are 290bn parameters and 16.8bn parameters respectively and are available on Hugging Face (see here) for anyone to download and use.
  • Ling-Plus was compared against DeepSeek V2 (not the latest), Llama 3.1 70B (not the latest or largest), Qwen2.5-72B (latest version) and GPT-4o (not the latest).
  • This is a fairly random set of peers which could easily have been selected for their ability to produce a decent chart as opposed to an objective comparison.
  • However, the fact that these models have been made available to open source means that anyone can download and test them and so I suspect that the test data is real.
  • These models are relevant as they represent further efforts by the Chinese AI ecosystem to produce AI that is globally competitive despite not having access to the latest and greatest silicon chips.
  • Ant Group lists a series of techniques that it has used to train its models many of which sound similar to what DeepSeek outlined in its release in January.
  • Second, Tencent Hunyuan T1: which is a “reasoning” model based on Tencent’s in-house foundation model Hunyuan Turbo 5.
  • Tencent claims that T1 performs as well as DeepSeek R1 or OpenAI’s GPT-4.5 and o1 and puts up the usual set of benchmark comparisons (see here).
  • Tencent has yet to make any of its models available to open source which given that they are increasingly powering its ecosystem, is not a big surprise.
  • Consequently, there is no way to test Tencent’s claims but this should change somewhat as Tencent did say that it would make some of its models available sometime this year.
• The net result is that when it comes to efficiency in AI, the Chinese are without a doubt leading the world.
• How much of a lead and how sustainable that lead is are open to debate but for the moment, it is safe to say that in this area of AI, China leads the world.
• I do not think that this is by design but has been caused by the fact that Chinese companies are unable to buy leading-edge silicon chips leaving them with little choice but to do more with less.
• This is why China has developed this niche first but the speed with which this is spreading throughout China implies that the efficiency improvements pioneered by DeepSeek are not that difficult to replicate.
• I also do not think that the Chinese AI companies are cooperating with each other outside of what they are contributing to open source as they also compete aggressively on price for the different services that they offer.
• Hence, I think that the Western peers should be able to reverse engineer many of the savings that the Chinese are making should they feel inclined to do so.
• RFM Research has been forecasting that there are plenty of savings to be had in terms of training and inferencing LLMs more efficiently, but that many companies outside of China have not had to bother.
• This is because there has been a large oversupply of money into the sector meaning that no one has really had to worry very much about efficiency.
• Instead, Western companies have pursued the dream of super-intelligent machines which I have long argued is unlikely to bear fruit any time soon.
• Hence, there is likely to be a correction at some point where the focus in the West shifts from pipe dreams to commercial reality which, even without AGI, is a large and lucrative opportunity.
• This is why I don’t think the correction will be nearly as bad as the Internet bubble of 1999 and 2000, but companies that can’t make money with $300bn+ valuations are going to take a big hit.
• This is why Nvidia is the only direct AI company I would own as its valuation is still in the realm of sanity as it is making money and generating cash from AI right now.
• However, I still prefer the adjacencies of inference at the edge and nuclear power where valuations are even cheaper and sentiment has not affected them yet.