Why China's AI Advantage Came From Getting Its Hands Dirty

For much of the past twenty years, people in Silicon Valley spoke about scale. It was a doctrine built on the idea that the most powerful companies were those that could grow without growing heavy.

The most admired businesses didn’t own cars, factories, or stores.
They owned code.

• Uber became the world’s largest transportation company without owning vehicles.

• Airbnb became the largest hospitality brand without building a single hotel.

These were companies that lived in the cloud and left the physical world to others.

That mindset shaped an entire generation of founders. Their goal was to stay lean, asset‑light, and endlessly scalable, to build something that multiplied without friction.

But while Silicon Valley was racing to get lighter, China was doing the opposite.

The Weight of Progress

The Chinese business environment has rarely rewarded purity of idea. It rewards execution.

When an opportunity appears, hundreds and at times even thousands of entrepreneurs chase it at once.

In this kind of battlefield, a feature released in the morning can be copied by the evening. The way to win is to build huge moats around the business.

Chinese firms built delivery fleets, payment networks, and logistics centers. They burned billions and mastered the unglamorous details that Western startups dismissed as “operations.”

Their progress was not clean, but it was fast.

A Living Map of Urban Life in China

Take Meituan. It began as another Groupon clone in 2010. A simple deal‑of‑the‑day website, indistinguishable from thousands of others in what local investors called the “thousand‑Groupon war.”

When that business model collapsed, Meituan didn’t fold. It evolved, first into food delivery, then movie tickets, hotels, and travel.

To Western analysts, this looked inefficient. Too many business lines. Too little focus. But what looked like chaos from afar was in fact a system that became harder and harder to replicate. Every restaurant partnership, every delivery route, every user review became a part of the system that a newcomer would not be able to replicate fast enough.

By 2017, Meituan was handling more than twenty million orders per day. It had become not just a company, but a living map of urban life in China.

The Price of Elegance

In the United States, elegance is a virtue. Founders celebrate building “pure” software systems that scale globally.

But that purity has limits.

Airbnb cannot control the cleanliness of an apartment or how well the photos reflect the actual place. Uber cannot guarantee the behavior of a driver. I personally had issues with Uber drivers to the point that I was afraid for my life. One time, an Uber driver was talking about death and how life is meaningless while driving at high speed on a highway. Another time, a driver was screaming that she would leave me on the side of the road on a highway after I very politely asked her if it would be possible to turn off the music. Uber got away with this till now, but only because they didn't have a more formidable competitor that would deliver a better service.

These are the physical edges where what they may think of as "perfect" software meets a very imperfect, and even sometimes scary, reality.

Chinese firms chose to cross that line.

• Tencent built a payment rail inside WeChat.

• Alibaba built warehouses and delivery drones.

Instead of staying light, they built anchors. They built systems that are harder and harder to replicate the bigger the business becomes and the more time passes, because they are not resting on their laurels, they are building and building. It reminds me of a quote that is frequently attributed to Walt Disney. When he was asked how they managed to build a park in 365 days, he said, "We used every one of them."

To an engineer in Silicon Valley, this approach looked messy and grimy.

But to strategists like members of our community, it revealed something very important: control of the physical layer creates a bigger moat and more data. A bigger moat makes a business more resilient to withstand competitive forces, and more data allows companies to build better products and have more loyal customers.

China’s founders learned that to move fast, you must first go deep. And we have something to learn from them on this.

Yet, China’s rapid implementation edge faces a severe human‑capital bottleneck. As of mid‑2025, industry reports estimate a shortage of more than five million qualified AI professionals across the country, despite rapid university expansion and state incentives. This growing gap limits the pace at which China can sustain AI innovation.

The World’s Richest Dataset

Much of the modern world’s data in the United States reflects what people see online: clicks, searches, likes.

China’s data reflects what people do offline, an advantage in volume and behavioral depth, though privacy law reforms since 2021 now limit unrestricted reuse and narrow that edge.

Millions pay for lunch through a QR code, hail a ride, buy groceries, transfer money, book doctors, and pay utility bills all from within a single app. Every one of those actions generates information that can be accessed by a single company.

When engineers call China the “Saudi Arabia of data,” they do not exaggerate. And we, in the US, should take note, because data is critically important for the advancement of AI tools, and we are behind when it comes to collecting data.

Of course, there is also a question of privacy and data being collected without our consent, but that is another story. Now, we are focusing on a situation from the perspective of how we remain competitive as a country.

Back to our discussion about data collection, each interaction fuels a continuous learning cycle between human behavior and algorithmic prediction, and we in the US lack the type of data our Chinese counterparts have, despite the fact that when Google launched, only 0.2-0.3% of Chinese citizens had access to the internet.

However, U.S. export controls have sharply limited China’s access to advanced Nvidia chips such as the A100 and H100. In response, Chinese firms now use downgraded A800/H800 chips while accelerating innovation on locally designed processors like Huawei’s Ascend 910B and Alibaba’s Yitian 900, which partially narrow the gap.

But the point is, they do have more data.

More data trains better models, which improve service quality, which attracts more users, which creates still more data.

This feedback loop, born from China’s willingness to handle physical complexity, now fuels its strength in large-scale applied AI deployment, especially across logistics, retail, finance, and urban infrastructure, though the United States still leads at the frontier research level.

The Discipline of Doing

The marketplace that produced these results was brutal. Products were copied within weeks. Competitors spread rumors, poached employees, and fought on price until only the most aggressive survived.

But that environment also served as a training ground.

It forged a generation of entrepreneurs fluent in fast iteration.

While their Silicon Valley counterparts spent years refining one product, China’s founders launched, adjusted, and relaunched a dozen.

Speed of learning became their true competitive edge.

Over time, the culture of endless iteration produced something resembling collective intelligence: millions of experiments running in parallel, all feeding the same national ecosystem of technological adaptation.

What looked chaotic from abroad was in fact an accelerated form of trial‑and‑error learning.

And on top of it, the Chinese government was pushing “mass entrepreneurship and mass innovation” initiatives since 2015, heavily backing venture capital funds, entrepreneurs, and infrastructure projects.

Local governments competed to offer subsidies, startup zones, and public‑private venture funds. Universities opened entrepreneurship programs.

As part of launching an electric‑vehicle startup during that period, we ourselves secured 20 million dollars in government funding.

The message from the Chinese government was clear: innovation was not a slogan for them, it was a national priority.

Implementation as Innovation

China’s story is no longer one of imitation. It is one of implementation.

In earlier decades, copying Western business models was a means of survival. But as the domestic market expanded, the most successful firms began building services with no Western equivalent.

WeChat became more than a messenger; it became a platform for life.

Didi became more than a ride‑sharing app; it became an engine of urban mobility policy. In fact, this was one service that was better than Uber, and I used it over Uber when visiting Mexico in 2022.

Meituan became more than a delivery platform; it became part of the city infrastructure.

China’s entrepreneurs didn’t win by inventing new technologies. They won by integrating existing ones faster and deeper into daily life.

That distinction, from discovery to implementation, defines the new economic balance of power. And we have to take note and act accordingly.

A New Kind of Superpower

When we think of technological leadership, we often picture research labs or university breakthroughs. But the next era may very likely belong not to the inventors of technology, but to those who can deploy it at scale across messy human systems.

That shift favors China. And that represents a formidable competitive challenge for the United States. China's vast mobile infrastructure, its data abundance, and its relentless entrepreneurial base form an ecosystem uniquely suited to the age of AI implementation.

Silicon Valley may remain the workshop of new ideas.

But China has become the factory of applied intelligence.

The Lesson in All of This

The companies that will do well in the next decade will not be the ones that stay light, but the ones that learn fast.

They will embrace the friction between digital models and physical reality instead of avoiding it.

They will build systems that touch the real world and feed lessons back into the loop.

If Uber did this, they would have probably retained my business. If Airbnb did this, they would have probably retained my business as well. They didn't.

China’s story is no longer foreign. It’s a preview.

Yet this entrepreneurial dynamism in China now collides with subdued venture‑capital optimism. Private venture funding in China slowed through 2025 amid stricter regulation and slower growth, but total AI investment remained high as state‑linked and hybrid funds filled the gap.

In essence, China’s AI rise reflects execution under constraint: a system that does well because of iteration and state support, yet must now overcome talent shortages, chip limits, and investor fatigue to maintain momentum.

Yet, the future may very well belong to those who operate like Chinese entrepreneurs: relentless, pragmatic, insanely hardworking (meaning they "fight tonight"), and willing to build what no one else wants to touch.