The Next AI Race May Be Won at the Application Layer

I have been thinking about this for a while: AI competition is not only about who has the best foundation model.

Models matter. Chips matter. Infrastructure matters. But the next stage may be more about who can turn AI into real products, real companies, and real workflows faster.

That is why I am increasingly optimistic about China’s AI application layer.

This is not a prediction that China will “beat” the US. The US will continue to lead in many frontier areas, including research, chips, cloud platforms, the developer ecosystem, and globally distributed AI products. The more interesting question is what kind of AI-native ecosystem China will build for its own market—and what that ecosystem might teach the rest of the world.

The model layer is only the beginning

The foundation-model race gets most of the attention because it is easy to measure: benchmark scores, context windows, training runs, and model launches. Those things are important, but they are not the same as building useful products.

As models become cheaper, more capable, and easier to integrate, the application layer becomes more important. The advantage may shift toward the teams that can identify valuable problems, design better workflows, and iterate quickly enough to keep up with the technology.

The companies that emerge from this wave will not simply add an AI feature to an existing product. Many will be designed around AI from day one:

• smaller teams with more leverage;
• faster product cycles;
• more automation built into everyday operations;
• workflows designed around what AI can do, rather than around older software boundaries.

The model is a building block. The product is where users experience the value.

Why China’s application layer is interesting

China has a large pool of technology talent, a huge consumer market, a strong hardware supply chain, and a culture that is generally open to new technology. It also has a history of fast product iteration and intense competition.

We saw this pattern with mobile internet, social networks, mobile payments, and crypto/web3. China did not simply copy the US market. It developed its own product patterns, user behaviors, and business ecosystems in response to local conditions.

AI may follow a similar path.

The key opportunity is not just access to a powerful model. It is the combination of capable models, local distribution, user behavior, engineering execution, and a willingness to redesign how work gets done. Those factors can create products that look different from their American counterparts even when they are built on similar underlying technology.

A lively model ecosystem

Chinese foundation-model companies are already serious participants in the global conversation. DeepSeek, Z.ai, MiniMax, Qwen, Kimi, and others are building a diverse ecosystem. They may not all win, but the ecosystem is clearly alive.

That diversity matters. Competition at the model layer can push costs down and capabilities up. More importantly, it gives application builders more options: different model personalities, deployment choices, pricing models, and trade-offs between speed, quality, and control.

When the cost of intelligence falls, more ideas become economically viable. A small team can test a product that would previously have required a much larger organization. An existing company can automate a process that used to be too expensive to improve. Entirely new workflows become possible.

From AI-enabled to AI-native

There is a meaningful difference between an existing company that uses AI and a company designed around AI from the beginning.

An AI-enabled company may add a chatbot, generate marketing copy, or automate a few internal tasks. An AI-native company can rethink its operating model: how it serves customers, how it makes decisions, how its employees collaborate, and which parts of the business need to exist at all.

That shift could produce companies with very different shapes from the software companies we are used to. They may have fewer employees, rely on more automated processes, and move from idea to product much faster. Their advantage may come less from owning a large organization and more from designing a system in which people and models work together effectively.

This is where application-layer competition becomes especially interesting. The winners may not be the companies with the biggest training budget. They may be the ones that understand a specific market deeply and move quickly enough to turn that understanding into a useful product.

A more useful question

The US–China framing is tempting, but “who will win AI?” is probably too simple a question.

The US and China have different strengths, markets, constraints, and product cultures. The US may continue to lead in many frontier capabilities and global platforms. China may build a very large and distinctive AI-native ecosystem for its own market. Both can be true at the same time.

The more useful question is: what kinds of companies and workflows will each ecosystem create when AI becomes cheap, capable, and ubiquitous?

In the last wave, mobile internet changed how people live. This wave may change how companies are built.

That is why I am watching the application layer so closely. The next important AI story may not be another model release. It may be the company that uses existing models to build an entirely new way of working.