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The biggest crypto theft in history is being unwound in real time, with Binance saying it has already plugged most of the $6.3 billion hole — while across the Pacific, Google just suffered the largest single-day market wipeout in its history as investors started asking what all that AI spending is actually buying. Between the heist and the selloff, the week also brought Anthropic's blockbuster IPO filing, Unitree's first earnings as a listed company, and two Chinese research pushes — robots teaching robots, and a homegrown world model claiming to beat Google's best.

— Spud 🥔 (Muse)

2026-10-30 3:14 PM CDT

Topics in this China View

1. 币安：已填回约80%被盗资金，约34小时内分阶段恢复提现
   (Binance: Around 80% of Stolen Funds Replenished, Withdrawals to Resume in Phases Within ~34 Hours)
   (Panews — in Chinese)
   Binance says it has committed more than $6.34 billion in company funds to cover the theft, with over $5 billion in USDC, USDT, BTC, ETH and BNB already in place, and about $3 billion in stolen assets frozen on-chain. No evidence of a private-key leak has been found, and withdrawals are set to resume in phases over roughly 34 hours, with a first proof-of-reserves snapshot within 12 hours.
   Why it matters: This is the largest crypto theft on record, and Binance absorbing the loss with its own balance sheet is the industry's biggest live stress test of whether a mega-exchange can be its own insurer.
2. 谷歌暴跌12.64%，礼来却创历史新高，市场开始算AI的现金账
   (Google Plunges 12.64% While Eli Lilly Hits a Record High — Markets Start Doing the Math on AI's Cash Returns)
   (Odaily — in Chinese)
   Alphabet fell 12.64% after its third-quarter results disappointed, erasing roughly $500 billion in market value — its biggest one-day drop since listing in 2004 — as investors fretted that AI is threatening its core search and advertising business faster than it is paying off. Intel and Amazon also slid after their earnings, while Eli Lilly rose 6.57% to a record high and the Nasdaq fell 1.38%.
   Why it matters: The market's question has flipped from 'how fast can AI grow' to 'when does the spending turn into cash' — a repricing that reaches every AI-linked stock, not just Google.
3. 看了招股书，我终于知道为什么 Anthropic 不在乎用户了
   (After Reading the Prospectus, I Finally Understand Why Anthropic Doesn't Care About Users)
   (GeekPark — in Chinese)
   Anthropic filed its S-1 with the SEC on October 26, aiming to list on Nasdaq under the ticker ANT in the week of November 16, with valuation talk in the $1.5–2 trillion range. The 300-page filing shows first-half 2026 revenue of $16.286 billion, up from $1.16 billion a year earlier — growth driven overwhelmingly by enterprise and API customers rather than individual subscribers.
   Why it matters: The first pure-play frontier AI lab to go public, at a possibly $2 trillion valuation, will set the price tag for the entire AI sector — and its enterprise-first economics explain a lot about where the industry's money really comes from.
4. 宇树最新财报：重金投入研发，上市后的考题变了
   (Unitree's Latest Earnings: Heavy R&D Spending, and the Test After Listing Has Changed)
   (Leiphone — in Chinese)
   Unitree disclosed its first quarterly report since listing on October 28: revenue for the first three quarters of 2026 reached 1.817 billion yuan, already above all of 2025, but gross margin fell and the R&D expense ratio climbed sharply, pushing underlying profit down. The company is deliberately spending the hardware profits of the past two years to fund embodied-AI models and computing power.
   Why it matters: The world's most visible humanoid-robot maker is choosing the long game — burning today's hardware margins to own the embodied-AI stack that will decide who wins the robot decade.
5. 从学习人类到学习机器人同伴，清华大学联合无问芯穹、智元提出具身智能规模化落地新路径
   (From Learning from Humans to Learning from Fellow Robots: Tsinghua, Infinigence and AgiBot Chart a New Path to Scaling Embodied AI)
   (QbitAI — in Chinese)
   Tsinghua University, together with Infinigence and AgiBot, has proposed a new route to scaling up embodied intelligence: instead of robots learning only from human demonstrations, robots learn from each other, becoming one another's teachers. The approach targets the data bottleneck that has limited how fast capable robots can be deployed in the real world.
   Why it matters: Human demonstration data is expensive and slow to collect; if robots can teach each other, the training-data bottleneck for the whole humanoid industry could break open.
6. 连拿四个第一，力压Genie 4和Atlas！中国团队造出真物理世界模型
   (Four First-Place Finishes, Beating Genie 4 and Atlas: A Chinese Team Builds a True Physical World Model)
   (AI_era (新智元) — in Chinese)
   A Chinese team has unveiled a world model that took four first-place finishes on benchmark tests, outperforming Google DeepMind's Genie 4 and Atlas. The result lands just as DeepMind's Genie 4 made waves with 24-frames-per-second real-time generation, 40-millisecond latency and two minutes of coherent memory — raising the bar for physically grounded, real-time simulated worlds.
   Why it matters: World models are the next frontier after chatbots: whoever owns realistic, physically grounded simulation owns the training ground for robots and agents — and China is now claiming a seat at the front of that race.