Avoiding the question:
Will the incoming Congress use their antipathy to Elon Musk and the AI build-out to cripple the AI expansion, remove the only area propping up the economy, plunge the nation, and thus the world unto a depression, neuter any tech advantage America possesses, hand the emerging future to China and force the U.S. to follow the last-ditch German economic plan, betting everything on re-armament before the final war to end all wars?
That question.
From the High Performance Computing geeks at HPCwire, October 30:
What the AI race is a contest over, and why the cheapest capable model may win
In part 1 of this series, I looked at where the US and China stand after two decades of investment. This installment asks the question the public debate tends to skip: when people say “winning the AI race,” what does winning mean?
There Is No Single Finish Line
The AI race gets described as though there were one finish line: one model, one moment when someone crosses first and the outcome is settled. The United States and China are not running toward the same point. If China is racing toward nine o’clock and the US toward two o’clock, both can run as fast as they possibly can and still not compete for the same prize. The useful question is what each side is trying to get, and what happens to the rest of the world once they get it.Five Things Being Contested
Frontier model capability. This is the headline contest: which model scores highest on the hardest benchmarks. The US leads, narrowly. Stanford’s 2026 AI Index put the gap between the leading US and Chinese models on the Arena leaderboard at about 2.7% as of March. In mid-October, the Artificial Analysis Intelligence Index scored the best closed model at 58 and the best openly downloadable model at 46. The pattern is consistent: close on conversation, wider on hard reasoning and long autonomous tasks.Chip manufacturing. The US holds a wide and, for now, structural lead: advanced chip fabrication sits in a small number of facilities the US and its allies control. Part of that lead is policy. The Institute for Progress estimates that with no H200 exports, the US would hold a 21 to 49 times advantage in AI compute produced in 2026, and that unrestricted exports of Nvidia‘s H200 could cut it to roughly 1.2 to 6.7 times. Commerce approved about 10 Chinese buyers in May, but Reuters reported no deliveries as Beijing steered its companies toward Huawei. That substitution is not one for one. Huawei’s Ascend 910C trails the H200 in computing power and memory bandwidth, and Epoch AI estimates Huawei’s 2026 output at about 880,000 H100-equivalents against about 23 million for Nvidia, under 4%. The tightest constraint is high-bandwidth memory, which China cannot yet make in volume. Huawei is pushing anyway. In September it pulled its Ascend 960DT forward to early 2027, a chip Bernstein estimates will roughly match the H200 with more than twice its interconnect bandwidth, and said a 256,000-card cluster is being deployed. How large the US lead stays depends on Washington’s export decisions and China’s memory production as much as on chip design.
Research volume and open-source distribution. China leads here, and this may be the contest that decides what the rest of the world runs on. Chinese models accounted for about 41% of downloads on Hugging Face over the past year, ahead of US models, and the summer brought a run of Chinese releases: Moonshot‘s Kimi K3, Alibaba‘s Qwen3.8, DeepSeek V4.1-Flash, and models from Z.ai and Xiaomi. In October, Anthropic reported that Z.ai’s open-weight GLM-5.3 came close to its restricted Mythos Preview model on cyber-exploit tests, while NIST’s Center for AI Standards and Innovation put it about four months behind the US frontier. DeepSeek and Huawei have also released open tools for programming Huawei’s chips, aimed at reducing dependence on Nvidia’s CUDA software.
A note on terms. An open-weight model publishes its trained parameters, called weights, so anyone can download it, run it on their own hardware, and modify it. Many people call these open-source models, though the training data and code usually stay private. A closed model, such as those from OpenAI, Anthropic, and Google, can only be used through the company’s own service.
The trade between the two is simple. Closed models are more capable, and open models are much cheaper. DeepSeek’s open-weight V4-Pro costs $0.66 per million input tokens and $1.98 per million output tokens off-peak (a token is about three-quarters of a word), against $5 and $25 for Anthropic’s Claude Opus 4.7 and $5 and $30 for OpenAI’s GPT-5.5. That makes DeepSeek roughly 8 to 15 times cheaper. On SWE-bench Verified, a widely used test of fixing real software bugs, Opus 4.7 solves 87.6% of the problems; DeepSeek reports 80.6%, and an independent US government evaluation measured 74%. Put simply, the open model solves fewer of the hardest problems for about a tenth of the price.
Open models also reach what the best closed models could do only recently. Epoch AI puts the lag at about four months. So a buyer can pay a premium for the top of the range now, or give up some capability on the hardest tasks at a fraction of the cost and expect open models to reach today’s frontier within months. Many will take that trade for customer service, document processing, and routine coding, where the cheaper model already does the job. Open models are not only a Chinese story, either: hosting companies offer OpenAI’s open-weight gpt-oss-120b for well under a dollar per million tokens.
Capital. Stanford’s AI Index puts US AI investment in 2025 at about $286 billion against about $12 billion for China, roughly 23 times. China answers with state capital. Bloomberg reported in June that Beijing has drafted a five-year, roughly $295 billion plan for a national AI data-center grid, funded by state bonds, requiring at least 80%domestic technology and interconnected by 2028. That is about $59 billion a year, less than any one of the largest US cloud companies spends: Oracle, Microsoft, Amazon, Meta, and Alphabet together are expected to spend about $800 billion on capital projects in 2026, according to research by Columbia Business School’s Stijn Van Nieuwerburgh. The Chinese plan’s strength is coordination, bringing state debt, land, power allocation, and domestic chips together under one national grid.
Talent. This trend favors China and compounds over the longest horizon, which makes it the hardest to reverse quickly.
What the Summit Did and Did Not Settle
The most visible diplomacy of the fall was the Trump-Xi summit in Washington, which ended September 25. According to news reports of the readouts, it extended the trade truce to January 10, 2027, eased tariffs on about $30 billion of non-sensitive goods in each direction, and opened a dialogue on AI risks with a channel for AI-related incidents. Rare-earth talks will continue, and advanced chips did not feature. The leaders are expected to meet again at APEC in November and the G20 in Miami in December. The diplomacy is moving on its own track, and the competition in chips, models, and power is moving faster.
....MUCH MORE
