Afra: There’s one more thread I’ve been following closely: the intersection of China’s vast manufacturing base with AI, and why that combination is particularly potent here. Du Lei walked me through a great example earlier: a Chinese luggage company that sources whole hides of cowhide for every production run. Natural leather is inherently uneven — some areas are uniform enough for the exterior face of a handbag, others have flaws and can only be used for lining. Traditionally, experienced craftsmen would eyeball each hide and sketch out a cutting pattern by hand, trying to maximize the number of usable pieces while minimizing waste.
The problem is that the optimal layout for leather is actually a complex two-dimensional combinatorial puzzle: an irregular curved surface onto which you need to fit dozens of pieces of varying shapes, where the orientation and placement of each piece affects the final yield. After introducing AI, the factory uses image recognition to scan each hide’s texture and contour, then runs an optimization algorithm to compute the optimal cutting path in real time.
This kind of application is simply invisible to Silicon Valley — because Silicon Valley doesn’t have the leather manufacturing substrate. The problem domain doesn’t exist there.
Du Lei: And the significance extends beyond leather. China has an enormous manufacturing base, and every industry within it carries the same accumulated store of “master craftsman’s intuition” waiting to be translated into AI. Steel heat treatment parameter optimization, food factory quality inspection, textile color consistency control — the pattern repeats across sectors. What these problems share is that they used to require expensive bespoke algorithm development. Now a single person equipped with the right AI tools can tackle them independently. These opportunities will keep surfacing across Chinese manufacturing — and China is exceptionally well-positioned to capture them.
Afra: I’ve been watching Bambu Lab along exactly these lines — they’ve deeply integrated AI image recognition into their 3D printers to monitor the print head for clogs in real time, automatically distinguishing normal operation from the dreaded “spaghetti” failure mode where the print spirals out of control, and halting immediately to prevent further material waste. For the 3D printing industry, it’s a meaningful leap.
Afra: …In China, [OpenClaw’s] popularity has far outstripped anything seen in the US. Tencent and Baidu organized OpenClaw configuration workshops in Shenzhen that drew retirees and students alike; developer meetups in Beijing sold out instantly; China’s daily token consumption has surpassed 140 trillion — more than a thousandfold increase from the 100 billion daily tokens of early 2024. According to cybersecurity firm SecurityScorecard, China has now overtaken the US in OpenClaw adoption. So how did this happen?
Du Lei: …There’s a structural reason for this. In Silicon Valley, OpenClaw’s arrival didn’t produce any cognitive shock — because it emerged from a complete evolutionary sequence. We’d already had Devin, various agent frameworks, and a step-by-step progression of Claude and other tools steadily advancing in capability. OpenClaw felt like a natural next layer — a more flexible agentic glue. Most of what it enables, people in the Valley had already approximated six months earlier through other means. Exciting, yes. Directionally significant, yes. But perceived as an organic next step within a familiar lineage.
In China, that entire year of evolution had essentially not happened. The domestic conversation had stayed at the level of “how do I use AI to break down a PowerPoint” — consumer entertainment tracks like image and video generation, extremely sophisticated in their own right, but irrelevant to anyone who needed AI to actually assist with work, anyone who needed a genuine intelligent agent.
So when OpenClaw arrived in China, it compressed an entire year of product evolution into a single moment. The final exam answers appeared on the table overnight. OpenClaw settled the debt that the domestic AI productivity track had been accumulating for a year — all at once, in one decisive strike.
Hua Han: On the supply side: every model company has enormous incentive to accelerate this. AI capability has climbed in distinct steps — first ChatGPT-style single-turn conversation; then reasoning models, where thinking time extended but the interaction remained fundamentally one-shot; then Claude Code and open computer use, where single interaction is no longer the design target and multi-turn, long-horizon task collaboration becomes the paradigm, with token consumption rising exponentially. For model companies, this is an enormous business — their entire imperative is to sell tokens.
Shenzhen is home to vast numbers of hardware and model companies. Every laptop or phone that ships with an OpenClaw client pre-installed becomes a persistent token consumption entry point. Token-maxxing, from a pure business logic standpoint, is entirely rational for every model vendor.
Afra: I have my own theory as well. A lot of local governments in China are desperately hungry for new tech narratives right now. Against the backdrop of economic slowdown and high youth unemployment, keywords like “digital nomad” and “open source” have become the latest in a string of buzzwords that local governments race to adopt. From Liangzhu and Anji in Hangzhou to Huangshan in Anhui, cities and towns are competing to attract AI digital nomads. Local governments are anxiously latching onto each new narrative, trying to shore up their talent pipelines and fiscal resources.
Hua Han: From the user side too, the appeal is real. OpenClaw is a genuine productivity tool. Chinese corporate culture — whether in state enterprises or major internet companies — is notoriously process-heavy. Even if you’re already cycling through DingTalk and Feishu (China’s Slack) all day, a tool that can actually automate your routine work has authentic pull for ordinary users.
Manufacturing x AI:
OpenClaw adoption incentives: