Dario Amadei's call to stop selling advanced chips to China is a strategic play to control the pace of AGI development. He argues that since a global pause is impossible, restricting China's hardware access turns a geopolitical race into a more manageable competition between Western labs like Anthropic and DeepMind.

To prevent a reckless race, a proposed solution is a U.S.-China treaty to govern the resources needed for frontier AI. This would involve tracking and monitoring advanced AI chips in data centers and imposing a verifiable cap on the computational power used for any single training run.

A global AI safety regime should learn from nuclear arms control by focusing on the physical infrastructure that enables strategic capabilities. Instead of just seeking promises, it should aim to control access to chokepoints like advanced chip manufacturing and the massive data centers required for frontier models.

Vitalik Buterin suggests that slowing AI progress to buy time for safety is a valid goal. He argues the most feasible and least dystopian method is to limit hardware production. Since chip manufacturing is already highly centralized, it presents a control point that avoids more invasive, freedom-restricting measures.

The global supply chain for cutting-edge AI chips is a major chokepoint, ideal for governance. Three companies design them, one (TSMC) manufactures over 90%, and one Dutch firm (ASML) makes the essential machinery. This concentration makes tracking and controlling compute resources feasible for a global coalition.

Former White House advisor Ben Buchanan argues that contrary to the popular phrase "data is the new oil," computing power is the true bottleneck and driver of AI progress. This physical reality—advanced chips primarily made by democracies—creates a powerful geopolitical lever to influence nations like China.

Enforcing a global pause on superintelligence development is technically possible. Training frontier models requires massive, physically-observable data centers with hundreds of thousands of advanced chips and city-level power consumption. This infrastructure is visible from space and relies on a bottlenecked supply chain, making international tracking and verification achievable. The obstacle is political will, not technical limitation.

While ethical debates about AI's risks continue, the actual slowdown in AI's societal integration is being driven by practical constraints like the limited supply of compute, data centers, and grid power. This physical reality is a more powerful force for gradual adoption than any organized pause.

While energy is a concern, the highly consolidated semiconductor supply chain, with TSMC controlling 90% of advanced nodes and relying on a single EUV machine supplier (ASML), creates a more immediate and inelastic bottleneck for AI hardware expansion than energy production.