6 AI Product Lessons to Steal from China

China’s AI scene is evolving fast—and it’s no longer just about scale or speed. From open-source LLMs like DeepSeek to AI-powered appliances and billion-user super-apps like WeChat, the country is forging a unique path that’s turning heads in Silicon Valley. This isn’t a copycat story anymore. It’s a masterclass in consumer-first product strategy, fast iteration, and turning “good enough” models into billion-dollar outcomes. Here’s what global founders, PMs, and AI builders need to know—and what they should steal.

About the Guest: Grace Shao is a Hong Kong–based writer, analyst, and author of the Substack newsletter AI Proem, where she covers China’s AI ecosystem with insider clarity and nuance. Formerly with Alibaba and active across Asia’s tech scene, she brings a rare cross-border perspective on how China builds—and competes—in the global AI race.

1. DeepSeek: China’s Unexpected Inflection Point

DeepSeek wasn’t supposed to happen—at least not this fast. But when it launched, it didn’t just perform well; it rewrote expectations about Chinese AI.

“DeepSeek completely changed how the world viewed our scaling laws.”
— Grace Shao

Its founder, Liang Jiufang, is a math prodigy turned quant trader who grew up in a rural village. He funded the project himself, trained the models, and returned home a national hero. His village literally renamed the road to his home "Top Scholar Road."

What DeepSeek represents is bigger than one model:

• It marked the first time a Chinese LLM was considered globally competitive on core benchmarks.
• It signaled a cultural shift from profit-first to mission-first entrepreneurship.
• It sparked a wave of new founders, most born in the '80s and '90s, educated in China, with global ambitions.

This new generation isn’t just trying to clone OpenAI. They’re chasing influence, technical legitimacy, and national pride. In short, they’re playing a different game.

2. Why China Went All-In on Open Source

When DeepSeek dropped for free, it wasn’t an outlier—it was a signal. Open source has become the default play in China’s LLM race.

Alibaba’s Qwen model? Open. Baidu? Heading there. Even ByteDance is feeling the pressure to follow suit.

Why?

"Enterprise software never really took off [in China]."
— Grace Shao

A few drivers:

• Weak enterprise demand: China’s economy is still industrial-heavy. Knowledge workers don’t dominate GDP the way they do in the U.S. SaaS never became a cultural or business norm.
• Piracy legacy: Even major companies used bootleg Microsoft Office. IP protection has historically been soft, reducing the incentive to sell software the traditional way.
• Open-source as status: Developers want reputation and reach. Being quoted in open-source communities carries weight—and it’s feeding a rising generation of builder-energy.
• Strategic pragmatism: If models are good enough, value shifts to application and distribution. Open-sourcing isn’t a liability—it’s a growth hack.

The result? In China, there's a growing expectation that LLMs should be free. And the real game is: who can build the most useful, most widely adopted products on top of them?

"The real innovation and use cases are in the application."

3. Why China’s AI Monetization Is All-In on Consumer

In China, the real money isn’t in selling models to enterprises. It’s in getting them into apps.

“Everyone in China right now is like—why should I pay for this? Let me just build on it.”

Here’s the contrast:

• U.S. companies monetize LLMs via enterprise APIs and SaaS platforms.
• Chinese companies embed LLMs directly into massive consumer platforms.

Take Tencent:

• They skipped building their own LLM.
• Instead, they plugged DeepSeek into WeChat—the country’s all-in-one messaging, payment, and service platform with 1.3 billion DAU.
• Result: instant scale, no friction, and a compelling user experience.

ByteDance took a different route:

• They launched Doubao, a standalone chatbot app.
• Despite heavy marketing, adoption stalled.
• Without adjacency, even ByteDance’s reach couldn’t offset the user acquisition cost.

“It’s about functional adjacency. If you’re already using WeChat for everything, trying out the AI feature is natural.”

China’s strategy is clear: use existing distribution to push AI at scale. The monetization? Ads, upsells, virtual services. But the power lies in reach, not recurring revenue.

4. Productization > Research: The Real AI Battlefield

Frontier model research? Still largely dominated by the U.S. But products? China’s playing offense.

"If your LLM is good enough, the competition is at the product layer."

Here’s what that looks like:

• CapCut (ByteDance): AI-native video editing with massive global usage.
• Doubao: Multimodal chatbot with themes like Love Guru, Fake Elon Musk, and therapy assistants.
• Smartphones: Huawei and others integrating LLMs to power AI companions.
• EVs: 20+ Chinese auto brands embedding DeepSeek for voice, infotainment, and navigation.
• Home appliances: Midea’s smart ACs understand natural phrases like “I feel cold."
• Hospitals: Over 100 institutions using DeepSeek for diagnostics and record analysis.

These aren’t prototypes—they’re shippable, usable, and already live. The takeaway?

In China, LLMs aren’t products. They’re ingredients. The products are what consumers touch.

5. China’s Edge in Robotics: Hardware + AI = Embodied Advantage

When it comes to AI in the physical world, China is already out of the lab and into homes, factories, and hospitals.

“This is where China’s legacy in manufacturing becomes a superpower.”

Let’s talk Unitree:

• 70% global share in humanoid robots.
• 40% share in quadruped robots.
• $16,000 price point—1/4 the cost of Boston Dynamics.

And it’s not just about the hardware:

• China’s engineers are trained in mechatronics.
• The supply chain is local, fast, and cheap.
• Talent clusters in regions like the Greater Bay Area (home to DJI, SenseTime, and more).

Even NVIDIA CEO Jensen Huang agrees: "The GBA has all the talent in the world to lead in this technology."

From robotic nannies to warehouse automation, China’s physical AI is already running.

6. What the West Gets Wrong About Chinese AI

If you think China’s AI strength is all government subsidies and copycat hustle, think again.

“Entrepreneurship was always vibrant. It just looked different.”

Here’s what Western narratives often miss:

• Speed and adaptation: Chinese teams iterate faster, copy quicker, and ship sooner. Even Uber’s founder admitted he couldn’t keep up when launching in China.
• Copy as iteration: Yes, some ideas are borrowed. But in China, they get localized, rebuilt, and optimized within weeks.
• From policy chill to startup spring: After years of tech crackdowns, AI has reignited optimism. The government is back in support mode. Founders are back in builder mode.
• Culture of scale: When you launch a product in China, your sandbox is 1 billion users. That pressure breeds sharp instincts, not timid innovation.

Bottom line? China’s AI isn’t just catching up. It’s running a different race—one that increasingly favors scale, speed, and scrappy execution.

7. What’s the Deal with Manus?

While DeepSeek made headlines for its technical leap, Manus is stirring buzz for something else: exclusivity.

“Invites to Manus are going for $10,000 on the black market.”

Manus is an AI agent platform making waves in China’s tech scene, but very few have actually used it. It’s tightly gated, access is scarce, and the experience is still largely secondhand. Some are calling it the next DeepSeek moment—but not everyone agrees.

“I don’t think Manus has disrupted anything we already believe in.”

Here’s what we know:

• It showcases the rise of AI agents and end-user applications in China.
• It hasn’t redefined technical limits like DeepSeek, but it hints at growing user appetite.
• It reinforces the trend toward LLMs becoming assistants, copilots, and task agents.

The hype signals something bigger: as inference costs fall, smaller teams can build high-quality, personalized AI layers. Whether Manus lives up to its mystique or not, it shows that the agent era in China is already here—and people are willing to pay for it.

8. Lessons for AI Product Leaders: What to Steal from China

You don’t need to build in China to learn from it. Here are six things global product teams should take seriously:

• Ship faster: Speed matters more than perfection. Tencent embedded DeepSeek into WeChat and immediately dominated distribution.
• Open early, monetize later: In China, open-source LLMs build developer ecosystems and unlock surface area. Consider what you gain by giving more away.
• Start with distribution, not models: ByteDance had a good model but poor distribution. Tencent had a good channel and won.
• Design for adjacency: Features that live where users already are—like WeChat search or Meta Messenger—get more traction than standalone apps.
• Use LLMs as infrastructure, not end products: The Chinese mindset: the model is fuel. The real value is what you build on top.
• Cultural UX matters: Chinese AI interfaces lean into personality, emotion, and interactivity. Think Love Guru bots, not just AI spreadsheets.

The next great AI product may not come from the lab—it might come from a super-app, a consumer device, or a robot that does your dishes.

Watch the products. Not just the papers.

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