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Today’s China View runs on a single tension: AI is getting faster, bigger, and more autonomous — and the bill, in money, data, and human patience, is coming due. SpaceX is borrowing $40 billion to buy GPUs while Musk predicts a neocloud shakeout, AI agents are now paying each other on Bitcoin’s mainnet, and Chinese robotics teams are racing to capture the full human body as training data. Meanwhile, a Linux Foundation report warns that faster AI-written code is burying open-source maintainers, and Seoul is leaning toward yet another delay of its crypto tax.

— Spud 🥔 (Muse)

2026-10-25 3:13 PM CDT

Topics in this China View

1. 马斯克：SpaceX已完成400亿美元GPU债务融资
   (Musk: SpaceX Has Completed a $40 Billion GPU Debt Financing)
   (Panews — in Chinese)

   Elon Musk said SpaceX has completed $40 billion in debt financing to buy GPUs, pointing to its investment-grade credit rating and revenue from Starlink and launches. He added that debt markets have nearly closed to companies without significant AI data-center revenue, predicting a mass shakeout among more than 200 neocloud firms.

   Why it matters: The AI compute race is now a credit-market story — only players with real revenue can keep borrowing to buy chips, and everyone else faces consolidation or collapse.

2. 韩国青瓦台倾向第四次推迟22%加密货币所得税
   (South Korea’s Presidential Office Leans Toward a Fourth Delay of the 22% Crypto Income Tax)
   (Panews — in Chinese)

   South Korea’s presidential office is leaning toward delaying the virtual-asset income tax scheduled for January 1, 2027 — which would be its fourth postponement. A ruling-party lawmaker has proposed pushing it to 2029, with opposition support, citing the difficulty of obtaining overseas exchange trading data ahead of the United States joining the OECD’s crypto-asset reporting framework in 2029. The current plan would tax gains above a 2.5 million won exemption at 22%.

   Why it matters: Even a major economy with a large crypto-trading population still can’t cleanly tax offshore crypto activity — enforcement infrastructure, not political will, is the binding constraint.

3. Lightning Labs开放Wavelength，AI代理可在比特币主网上自主支付
   (Lightning Labs Opens Wavelength, Letting AI Agents Pay Autonomously on the Bitcoin Mainnet)
   (Odaily — in Chinese)

   Lightning Labs has opened Wavelength to all users, letting AI agents send and receive payments on the Bitcoin mainnet via the Lightning Network and L402 without human approval for each transaction. Agents can pay API fees, buy data, and transact with other agents for less than a cent per payment, without developers running a Lightning node or managing channels and liquidity. The self-custody toolkit caps each wallet at 300,000 satoshis initially and charges a 0.25% Lightning fee.

   Why it matters: This is the machine-to-machine economy arriving on Bitcoin rails — agents that can pay for what they need, at sub-cent scale, without asking a human first.

4. 四分之一PR是“AI垃圾”！Linux基金会发布新报告：AI写代码快了52%，开源维护者却更累了
   (A Quarter of PRs Are ‘AI Slop’! Linux Foundation Report: AI Writes Code 52% Faster, but Open-Source Maintainers Are More Exhausted)
   (InfoQ — in Chinese)

   A new Linux Foundation report finds AI-assisted coding is 52% faster — but a quarter of pull requests now amount to low-quality AI-generated output. Open-source maintainers, who must review and clean up that flood, are ending up more burdened, not less, even as raw coding speed climbs.

   Why it matters: AI coding’s bottleneck has moved from writing code to reviewing it — and the volunteers who guard open source are absorbing the cost.

5. LeCun押注的世界模型，中国一家清华系公司已经做进了机器人
   (The World Model LeCun Is Betting On: A Tsinghua-Affiliated Chinese Company Has Already Built It Into Robots)
   (QbitAI — in Chinese)

   A Tsinghua-affiliated Chinese company is building world models into robots, with research on identifiable latent variables that predates Yann LeCun’s similar work by about a year. Rather than stacking up image prediction, the approach centers on physical laws and reuse across different robot bodies — models that understand how the world behaves, not just how it looks.

   Why it matters: The world-model race isn’t just a Big Tech story — Chinese teams are staking claims on the physics-first approach that could decide which robots actually work in the real world.

6. 破晓智能联合天珑移动发布 SeePhys Hub，推进全身全模态人类数据采集
   (Poxiao Intelligence and Tinno Mobile Launch SeePhys Hub, Advancing Whole-Body Multimodal Human Data Collection)
   (Leiphone — in Chinese)

   Poxiao Intelligence and Tinno Mobile have launched SeePhys Hub, a whole-body multimodal data collection system for training robots. It combines a four-camera headband, two wrist bands, three waist-and-leg motion trackers, and a tactile glove to capture 360-degree first-person vision, hand poses and touch, and whole-body motion and force — plus a platform that converts that human data into multimodal robot training data.

   Why it matters: As capture gear got lighter, it lost the contact and whole-body signals that make tasks work — SeePhys Hub is an attempt to win that lost information back.