# Weekly Dose of China Tech [08.24.2026] > Source: > Published: 2026-08-24 15:03:20+00:00 **Hi friends,** **Hope you had a good week.** **China’s AI race is entering a new phase.** **For the past year, the competition was mostly about models: who could build the strongest LLM, who could catch up with OpenAI and Anthropic, and who could achieve frontier performance with fewer resources.** **But the battlefield is moving.** **The next question is no longer just who has the smartest AI. It is who can build the infrastructure, finance the spending, and turn AI capability into a sustainable business.** **Alibaba and Tencent are making two very different bets on that future. One is trying to turn AI into a new infrastructure empire. The other is trying to make AI the next layer of an existing one.** **At the same time, robotics is facing a similar reality check. China has become one of the world’s most aggressive players in embodied AI, but even the industry’s biggest believers admit that the “ChatGPT moment” for robots has not arrived yet.** **Technology is moving fast.** **The harder question is whether the business model can keep up.** **Anyway, let’s take a look.** **This Week Features...** **Tencent vs Alibaba: Two Ways to Bet on China’s AI Future** Everyone is watching China’s AI models. Investors are watching something else: the bill. Tencent and Alibaba are both spending billions building their AI futures, but they are making almost opposite bets. Tencent is trying to bring AI into the empire it already owns. Its strategy is built around Hunyuan, WeChat agents, and productivity tools like WorkBuddy. The company’s biggest advantage is not the model itself, but the ecosystem around it: hundreds of millions of users, businesses, payments, advertising, and one of China’s most valuable consumer platforms. Alibaba is taking the opposite path. Instead of adding AI to an existing business, it is rebuilding itself around AI infrastructure: chips, cloud computing, foundation models, and enterprise services. The difference matters because the AI race may not be won by whoever builds the best chatbot. It may be won by whoever owns the infrastructure layer underneath every chatbot. But both strategies come with a difficult question. Tencent needs to prove AI can create a new growth engine inside an existing empire. Alibaba needs to prove that massive AI spending today can become tomorrow’s advantage rather than tomorrow’s cost. This week’s feature looks at the two biggest bets in China’s AI race — and the very different assumptions investors are being asked to finance. **The News…** **(I) Unitree’s CEO Says Humanoid Robots Aren’t Ready Yet Despite the Hype** The humanoid robot market has one uncomfortable problem: investors are moving faster than the technology. At the World Robot Conference, Unitree founder Wang Xingxing tried to lower expectations, saying humanoid robots are still not ready for widespread factory or home use. The reason is not hardware. It is adaptability. A robot that can perform a fixed demonstration is very different from one that can walk into an unfamiliar environment and complete a new task without extensive retraining. Wang’s benchmark for a true “ChatGPT moment” in robotics is ambitious: robots should complete roughly 80% of tasks in 80% of unfamiliar environments through simple voice or text commands. He expects that breakthrough within 3–5 years. The market, however, is already pricing in that future. Unitree earned around $41 million in profit on $252 million revenue in 2025, but its IPO valuation implies roughly 219x earnings. The robot revolution may happen. The question is whether it arrives before investors run out of patience. **(II) Xiaomi’s Profits Take a Hit From Memory Chip Prices** Xiaomi’s H1 2026 numbers show the squeeze: revenue down 8.4% YoY to $28.9 billion, adjusted net profit down 42.8% to $1.7 billion. Still, Q2 alone beat Wall Street’s expectations, pulling in $15.1 billion in revenue and $790 million in profit. The culprit is memory price inflation, which dragged Q2 phone revenue down 7.5% even as Xiaomi held gross margin at a respectable 8.5%. Management is betting things improve once memory prices normalize, but costs are staying elevated for now. AI spend isn’t slowing down: Xiaomi poured $2.5 billion into R&D, with AI eating up nearly 30% of that. Its in-house model, Mimo, is still playing a supporting role powering HyperOS, smart devices, and eventually autonomous driving rather than being monetized directly. EVs remain the growth story, though Xiaomi looks likely to fall short of its 550,000-unit annual delivery target, currently tracking around 35,000 units a month. Overseas EV expansion is now penciled in for H2 2027, starting with Europe. **(III) China’s Robot Boom Is Arriving Before Humanoid Robots Are Ready** The robotics market in China is expanding rapidly, but the biggest opportunity may not come from humanoid robots first. According to China’s Ministry of Commerce, sales of embodied AI robots on major platforms increased 95.1% year over year in July. Exoskeleton devices, action cameras, and robot vacuum cleaners also recorded strong growth during the same period. The export numbers tell a similar story. In the first seven months of 2026, China exported $1 billion worth of industrial robots, up 13.2% from a year earlier. Meanwhile, exports of cleaning robots and intelligent bionic robots reached RMB 18.09 billion ($2.5 billion) in the first half of the year. **(IV) Alibaba’s AI Strategy Has a Chip Nobody Talks About** Most discussions about Alibaba’s AI ambitions focus on Qwen. But the company’s long-term advantage may come from somewhere else: T-Head. Alibaba CEO Eddie Wu said the semiconductor unit expects its second-generation domestic AI chip to enter tape-out and production in the second half of 2026. The new chip is designed to improve computing performance and interconnect bandwidth, with Alibaba believing it can support large-scale AI model training. This fits Alibaba’s broader AI strategy. The company is not treating AI as only a model competition. It is building a full-stack infrastructure business that combines cloud computing, chips, and foundation models. Alibaba Cloud has already launched its Zhenwu M890 AI supernode, which has served more than 650 customers, while T-Head has shipped 560,000 AI chips across more than 20 industries. The strategic importance is straightforward: owning more of the AI stack could eventually lower costs and reduce dependence on external suppliers. **(V) Xiaomi’s Robot Demo Shows the Real Challenge in Robotics** At the World Robot Conference, Xiaomi’s humanoid robot became one of the most watched demonstrations on the exhibition floor. The company said the robot was powered by a large AI model capable of understanding instructions, making decisions, and executing actions independently. If fully autonomous, that would represent a major step forward. But robotics has always had a gap between impressive demonstrations and reliable real-world deployment. The challenge is not making a robot perform one action in a controlled environment. The challenge is making the same robot complete thousands of different tasks in unpredictable conditions without constant human assistance. This is the same problem autonomous driving faced: perception and intelligence can improve quickly, but reliability in the physical world is much harder. **(VI) Zhipu AI Ships GLM-5.3, Aiming Straight at the Global Frontier** Zhipu AI’s new foundation model, GLM-5.3, is now live via API, built for heavy-duty coding, cybersecurity defense, and long-horizon tasks. It scored 60 on the Artificial Analysis Intelligence Index — enough to land it in the global frontier tier. That puts GLM-5.3 in the same conversation as closed models like Anthropic’s Claude Fable 5 and OpenAI’s GPT-5.6 Sol, while it’s essentially tied with Moonshot AI’s Kimi K3 for the top open-source spot. It’s already baked into Zhipu’s ZCode and GLM Coding Plan, and the model weights are set to open-source next week. **(VII) China Is Trying to Turn AI Usage Into a New Financial Asset** One of the most unusual AI developments this week came not from a model company, but from banks. Chinese financial institutions are experimenting with a new type of lending model that uses AI-related activity, including token consumption and AI contracts, as part of credit assessments. Some pilot loans have reached $3.9 million. The idea reflects a broader question: how should banks value companies whose most important assets are not factories, inventory, or physical equipment, but AI usage and digital activity? Traditional lending depends heavily on historical revenue and tangible assets. AI companies often look different. Their value may come from model usage, developer adoption, and future demand rather than current profits. China’s experiment suggests that the financial system may need to evolve alongside the AI economy. The country is not only trying to build AI infrastructure. It is also exploring the capital system needed to fund the companies built on top of it.