China's new five-year plan puts the AI model on the device
Beijing wrote its AI hardware ambitions into the 2026–2030 industrial blueprint on Tuesday, a former FTC chair told the labs they are already breakable under old law, and open-weight models went head-to-head with the frontier on real coding work.
China's industry ministry and the National Development and Reform Commission issued the 15th Five-Year Plan for electronic information manufacturing on September 15, and its AI chapter is about silicon under the model, not the model itself. The plan names on-device large models, compute chips, memory chips and operating systems as one compatibility problem to be solved together, orders adaptation work between AI chips and large models plus databases, and starts drafting a unified specification for high-speed computing interconnect buses under the name CLink. It pushes cloud training equipment alongside end-side inference chips for embodied AI and autonomous driving, keeps iterating AI phones, PCs, smart glasses, in-vehicle and industrial terminals, and sets up a grading and evaluation standard for how intelligent a terminal is. The headline targets: above-scale electronics revenue above 30 trillion yuan (roughly $4.47 trillion) by 2030, and R&D intensity of 3.5%. The read: this is a standards-and-procurement mandate that tells Chinese model labs their deployment target is domestic silicon in a device, not a cloud accelerator — which is a structural reason the open-weight 4B-to-30B race keeps accelerating there. We tracked the ministry's companion application push — China's industry ministry lays out its AI application playbook for the next five years.
Lina Khan's message to the frontier labs on Monday was that no new statute has to pass for them to be charged. The former FTC chair argued that law enforcement "already ha[s] authority to charge companies and their CEOs for creating and releasing dangerous, unvetted, or defective products," pointing to consumer-protection and unfair-deceptive-practices law for shipping agents without adequate measures to detect rogue behaviour, and to the ban on unfair methods of competition — citing a 1934 Supreme Court precedent — for labs that "pursue dangerous behavior, aware that doing so may compel rivals to do the same." Her sharper point was structural: OpenAI could face liability over the Hugging Face intrusion, she wrote, but Hugging Face now belongs to Nvidia, which has "a strong incentive to see OpenAI continue full speed ahead." One tech-law partner told the Register federal action is unlikely anyway. That gap is the story — the statutes exist and the incentives to use them don't, at least not federally. The states are the exception so far: California AG opens an OpenAI probe over the Hugging Face hack.
"Almost objectively better than Opus 5." That was Baseten post-training engineer Charles O'Neill's framing on Dwarkesh Patel's episode debating how close AI is to recursive self-improvement, and it sat on top of r/LocalLLaMA for a day. The benchmark receipts are real but narrower than the quote: Together AI's DeepSWE run had Moonshot's Kimi K3 beating GPT-5.6 Sol on pass@4 at 64% lower cost, and a cascade that tries Z.ai's GLM-5.3 first and escalates only on test failure cleared 85.9% of tasks at $6.61 each. The top reply was a working engineer's counter: 2.7 billion tokens of GLM-5.3 in a week, and still a $100 Codex subscription, because the open models repeat failed approaches on genuinely unusual bugs.
What to watch: whether CLink becomes a procurement requirement or stays a working group document — the difference between a standard and a slogan.
If a cheaper model finishes the job two minutes later, is there any reason to pay frontier prices? Tell us in the comments.
