When I joined Alibaba in 2017, one of the first things that surprised me was how little my Western colleagues understood about what was happening in China’s technology landscape. The prevailing narrative in boardrooms from London to New York was simple – China copies, the West innovates. That assumption was already wrong then. Today, it is dangerously outdated.
As Western enterprises race to integrate generative AI into their operations, many continue to view China’s AI ambitions through a competitive lens shaped by outdated stereotypes. This blind spot is not just an intellectual failing – it is a strategic one, and it is costing companies real opportunities and leaving them vulnerable to disruptions they never saw coming.
It was never about catching up
The most persistent misunderstanding is that China’s AI strategy is about replicating what Silicon Valley has built.
In reality, China’s approach to AI is architecturally different. Where Western tech companies have largely pursued AI as a product category – chatbots, copilots, and standalone tools that can be sold to enterprises – China has treated AI as infrastructure: a utility layer woven into the fabric of commerce, logistics, government services, and daily life.
During my time at Alibaba, I watched generative AI being deployed not as a flashy demo, but as an invisible engine powering everything from real-time product recommendations to automated merchant communications at a scale most Western companies have never attempted.
By 2019, AI-generated product descriptions were already standard practice across the platform. The West did not reach a comparable level of mainstream AI integration in commerce until the ChatGPT era, roughly four years later.
This distinction matters. Chinese companies were not waiting for a perfect model before deploying. They were building deployment-first cultures where AI was tested in live, high-stakes environments from day one.
The Western approach of perfecting the model in the lab before cautiously rolling it out is, to many Chinese AI leaders, an unnecessarily slow way to learn.
Second mover’s advantage
We have all heard of the “first mover’s advantage,” where if you are the first to market, it is easier to establish an affinity for your brand with the customer. But sometimes, it might be the second mover that wins. China follows this strategy that I call second mover’s advantage. Take what has been developed and released and optimise on top of it. China basically did it again in the AI space – the West saw this in action when DeepSeek came out of China and shook the stock markets, sending Nvidia’s share price tumbling.
From the manufacturing of goods to algorithms, no matter if it’s hardware or software, it seems that China is remarkably good at taking something that might have emerged from the West, optimising it, and making it cheaper and more accessible to the masses. Is it better ultimately? That one’s debatable.
The ecosystem, not the algorithm
Western companies tend to fixate on who has the best foundational model. The assumption is that the race for artificial general intelligence will be won by whichever company produces the most capable large language model. China’s strategy suggests a fundamentally different theory of victory.
“Western companies that continue to misread China’s approach are not just underestimating a competitor. They are misunderstanding the game itself”
Sharon Gai
Rather than concentrating resources on a single frontier model, China has invested in building an ecosystem where AI capabilities are distributed across industries. The government’s AI development plans, updated repeatedly since 2017, are explicit about this – the goal is not to produce one dominant AI company, but to create an environment where thousands of companies can apply AI to specific problems in manufacturing, agriculture, healthcare, and urban management.
For Western IT leaders, this has practical implications. When evaluating Chinese competitors or potential partners, looking only at their model benchmarks misses the point. The competitive advantage often lies in the integration layer – in how tightly AI is embedded into supply chains, customer journeys, and operational workflows. A Chinese company with a seemingly modest model but deep application integration will often outperform a Western competitor with a more powerful model that sits in a silo.
Data as a national resource
Another common blind spot is the role of data governance. Western companies often frame data privacy and AI capability as a binary trade-off – either you protect privacy or you build powerful AI. China’s approach complicates this framing.
While the country’s data regulations are different from GDPR, they are neither absent nor unsophisticated. China’s Personal Information Protection Law (PIPL) introduced significant data protections in 2021, and enforcement has been meaningful.
What is different is how data flows between the public and private sectors. In China, data is treated more like a shared national resource than a proprietary corporate asset. This creates feedback loops that simply do not exist in Western markets, where data silos between companies, government, and institutions are often reinforced by regulation and competitive incentives.
What this means for Western leaders
The practical takeaway is not that Western companies should replicate China’s model. The regulatory, cultural, and political contexts are too different for direct imitation. But there are strategic lessons worth absorbing.
First, stop treating AI deployment as something that happens after the model is “ready.” The companies gaining the most ground, in China and increasingly elsewhere, are the ones deploying imperfect AI in controlled but real environments, learning from live data, and iterating rapidly. Waiting for perfection is a luxury that the pace of competition no longer affords.
Second, invest in the integration layer. The model is only as valuable as the ecosystem it connects to. Western organisations that focus exclusively on procuring the best model while neglecting the workflows, data pipelines, and cultural changes needed to make that model useful will find themselves outpaced by competitors who build tighter loops between AI and operations.
Third, develop genuine China literacy within your strategy teams. Too many Western companies rely on surface-level reporting or outdated assumptions about Chinese technology. The executives who will navigate the next decade of AI competition successfully are those who invest in understanding what is actually happening on the ground, not what fits the familiar narrative.
The AI race is not a single sprint with one finish line. It is a complex, multi-front contest where different strategies can win in different domains. Western companies that continue to misread China’s approach are not just underestimating a competitor. They are misunderstanding the game itself.
Sharon Gai is an AI transformation strategist, keynote speaker, and former Alibaba executive. She is the author of How to do more with less using AI and advises Fortune 500 companies on AI adoption and organisational change.
