A new Brookings Institution report released Monday suggests the global AI competition isn’t playing out the way many in Washington assumed. Rather than a two-sided sprint to a single prize—artificial general intelligence (AGI)—the picture looks more like divergent strategies: the U.S. chasing scale and superintelligence, China quietly optimizing for efficiency, distribution, and real-world deployment.
Brookings argues that U.S. tech firms are doubling down on massive compute builds—data centers packed with hundreds of thousands of chips—in hopes of producing AGI-style systems that can match or surpass human performance across broad cognitive tasks. By contrast, Chinese AI labs are pursuing multiple parallel tracks: making models run faster on less hardware, pushing open-source models into global circulation, and embedding AI into consumer and industrial products at speed.
Hamza Chaudhry, AI and National Security Lead at the Future of Life Institute, told Decrypt this gap reflects different incentives. “AI development is not a story about two nations racing towards AGI,” he said. “It’s a story of a handful of Silicon Valley companies obsessed with AGI, while companies in China are focused on getting products into as many hands and devices as possible.” That distribution-first approach, he added, explains China’s rapid integration of AI into phones, vehicles, wearables, robotics, robotaxis, delivery drones and humanoid robots—deployment in the physical world rather than waiting for breakthroughs in superintelligence.
Brookings highlights another technical distinction: efficiency. While U.S. teams scale compute, Chinese labs are “hyperfocused on squeezing greater performance out of limited compute and memory resources.” That emphasis dovetails with widespread use of open-source models in China, which can accelerate adoption but also raises security and military concerns. Chaudhry noted public reporting that some open models have been used by the Chinese military, and warned that broad access to model weights and training recipes complicates global AI governance.
A related technical risk is model distillation—extracting a more efficient model by training on the outputs of a stronger one. The Brookings report touches on efficiency innovations but, Chaudhry says, under-weights the role of distillation “attacks.” In February, Anthropic alleged that several Chinese labs, including DeepSeek, Moonshot and MiniMax, used thousands of fraudulent accounts to generate millions of responses from its Claude model and train competing systems. Distillation in that form is effectively a shortcut to replicate capabilities without the original compute investments.
The differing priorities between U.S. and Chinese AI ecosystems may open new geopolitical options, Chaudhry suggested: instead of only trying to outpace each other, Washington and Beijing could negotiate “arms-control-style” red lines on certain kinds of dangerous AI development. That concept reframes AI policy from a pure race to a set of mutual constraints and norms.
What this means for the crypto and Web3 space
- Open-source models and efficiency gains lower the barrier for integrating AI into decentralized apps, smart contracts, and on-device wallets—accelerating new tokenized services, AI-driven market makers, and edge inference for IoT-blockchain applications.
- The prevalence of distillation and public models raises IP and security questions for projects that rely on proprietary models for competitive advantage or foracles (on-chain oracles using LLM outputs).
- Geopolitical friction and concerns about dual-use (civilian-to-military) uses of open models could prompt regulation or export controls that affect crypto projects building cross-border AI services or compute marketplaces.
- Finally, the contrast between compute-heavy AGI bets and lightweight deployment strategies suggests a bifurcated ecosystem where large cloud/compute providers and decentralized compute networks both find roles—one supplying raw scale, the other maximizing efficiency and broad distribution.
Bottom line: Brookings paints a world where AI leadership can come from two very different plays—supercomputing scale or lean, ubiquitous deployment. For builders in crypto and Web3, both approaches create opportunities and risks: faster, cheaper AI on more devices, but also new vectors for misuse and fresh policy headwinds as governments grapple with how to govern powerful, widely available models.
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