The two AI strategies shaping the future

Artificial General Intelligence vs. Applied Intelligence

The AI race is no longer just technological, it has become strategic. Europe seeks to cast itself as the global regulator, shaping norms and standards rather than setting the pace. Yet the defining contest is now between the United States and China.

The United States views AI as an engineering endeavor of historic scale, a pursuit of Artificial General Intelligence (AGI), where machines might one day emulate human thought and creativity.

China, by contrast, approaches AI as an organizing principle: not a single breakthrough, but a distribution capability to be woven through its economy, infrastructure, and governance.

The US model: The pursuit of Artificial General Intelligence

The American approach is rooted on capability supremacy: the conviction that whoever achieves AGI first will command the global economy, setting standards, shaping industries, and defining strategic power.

This philosophy has yielded an ecosystem dominated by private-sector giants, OpenAI, Anthropic, Google, Meta, Nvidia, and others, competing in an escalating race for scale. Massive venture funding, trillion-dollar valuations, and hyperscale compute clusters have become the currency of progress and instruments of national ambition.

The logic is straightforward: build the most capable models, attract the most capital, and value will cascade downstream. US firms seek to control the cognition layer of the digital economy, a winner take-most race to make intelligence the ultimate platform.

The Chinese model: Applied Intelligence as strategy

China’s strategy begins from a different premise: intelligence emerges not from model scale but from national integration of data, compute, and deployment. Its tech ecosystem - spanning commerce, finance, logistics, and public services - functions as one vast feedback loop. Where US privacy laws fragment data, China’s infrastructure consolidates it.

A senior adviser on to the EU AI Office added: “This has given Chinese firms a distinctive strength: contextual intelligence, the ability to train models that mirror social, linguistic, and behavioral nuance. As a result, China’s AI strategy is less about achieving AGI and moreabout turning its data networks into distributed intelligence systems, where cognition operates wherever data is generated. This approach transforms data abundance into applied intelligence, linking the nation’s information infrastructure directly to economic productivity.”

Demography reinforces China’s AI strategy. Facing an aging workforce, Beijing sees AI as a productivity multiplier, a digital demographic dividend. Under its AI Plus plan, most citizens are expected to use AI tools by 2030 as part of a national shift toward an “intelligent economy.”

To accelerate adoption, China has made AI radically affordable. In 2024, Alibaba cut the price of its Qwen models by 97 percent, Baidu released free versions of ERNIE models, and Tencent joined the price war. The aim: make AI a public utility rather than a premium technology. Local governments further amplify this through compute vouchers and chip subsidies, giving startups access to processing power without costly hardware. Export restrictions have pushed China to build regional compute hubs and shared infrastructure.

Together, these moves signal a deeper transformation: the AI race is no longer just about technology leadership, but about sovereignty itself - chips as instruments of statecraft, and access as new form diplomacy.

Open source as a geopolitical lever

When constrained, China innovates in the open. The “DeepSeek moment” illustrated how openness can serve strategy: spreading intelligence across networks and accelerating adoption at scale.

Open-sourcing advances several objectives simultaneously: it offers credible alternatives to US systems, reduces dependence on foreign platforms, and draws on global compute and talent as the community iterates on Chinese base models. It also exerts downward pressure on prices, squeezing Western margins and redistributing influence across the AI ecosystem.

A former senior NSA official added: “This is not zero-sum competition but game-theoretic equilibrium. By flooding the market with capable open models, China transforms competition into collaboration, globalizing its influence while eroding US exclusivity.”

The next frontier: Edge AI

If the US builds AI in the cloud, China is building it at the edge. By embedding models in devices and infrastructure, it turns hardware into distributed intelligence. Huawei’s Pangu Edge, Alibaba’s Qwen Lite, and DeepSeek’s compact models exemplify this shift. With domestic chips and vast manufacturing capacity, China aims to rapidly standardize on-device AI across phones, sensors, and industrial systems.

As intelligence moves to the edge, power shifts from model owners to device makers, and China’s integrated hardware ecosystem is uniquely positioned to dominate that layer.

Two service economies, two horizons

The US, as the world’s preeminent service economy, can evolve steadily, layering AI onto existing industries while monetizing its innovation globally. Its strength lies in reach and control of platforms, networks, and the digital infrastructure that underpins global commerce.

China’s service economy remains largely domestic, but edge AI offers a path outward. By embedding intelligence in devices and infrastructure rather than platforms, it can export capability instead of content, transforming industrial scale into technological influence. In essence, the US is building an AI economy of minds; China, an AI economy of machines. One centralizes cognition, the other distributes it. And their trajectories will shape not only the future of technology, but the architecture of global power.

The race that shapes the century

AI is not a single race but a clash of paradigms. The United States is wagering that whoever owns intelligence will command the digital economy; China is wagering that whoever embeds it everywhere will rule the physical one.
China’s strategy, driven by demographic urgency, industrial strength, and open diffusion, is advancing quickly. Yet the balance still tilts westward. The US controls the clouds, platforms, and financial networks through which digital value moves. For now, the world still runs on American infrastructure — and under American terms.

Christian Karam is a senior adviser to private equity firms on TMT opportunities and an expert in cybersecurity, artificial intelligence, and defence technology. He led Research & Innovation at INTERPOL and was a founding member of the INTERPOL Global Complex for Innovation. He subsequently joined UBS as a Managing Director and advised the bank on TMT investments, and held the role of Deputy Group CISO, where he founded the bank’s Intelligence and Fusion Centers.