Daniel Ek suggests that instead of focusing on flawed metrics like training flops, AI regulation should consider the amount of compute power being used. Access to massive GPU clusters is a more durable chokepoint and a better indicator of potentially powerful, large-scale AI operations.

The rapid pace of new AI models makes model-level regulation futile. The next regulatory frontier will be controlling physical infrastructure, with governments potentially allocating GPU and data center access for national security and critical industries.

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.

The immense resources needed for powerful AI, dictated by scaling laws, limits frontier development to a few well-funded, responsible actors. This centralization, while concerning, provides a temporary buffer against widespread misuse and allows for focused alignment efforts, as these few players are more easily monitored and engaged.

The fear of runaway Recursive Self-Improvement (RSI) is tempered by economic reality. Training frontier models is becoming more, not less, expensive and complex. Each new model requires more GPUs, time, and brittle engineering, creating a logistical barrier that naturally slows progress.

The primary obstacle to meeting AI's future compute demand is not a failure of technology or capital markets. Instead, it's a regulatory and public alignment problem that slows the construction of necessary infrastructure like data centers and nuclear power plants.

A senior frontier lab executive acknowledged there is likely a level of intelligence humanity should not exceed and expressed openness to hard compute constraints, such as capping pre-training runs at 10^27 FLOPs. While operationalizing compute limits remains difficult due to synthetic data enrichment, frontier insiders increasingly view pacing mechanisms and compute input restrictions as viable policy considerations.

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.