> Even if future amendments try to address test-time compute, the proposed regulation seems premature. There are too many unknowns in future AI development to justify using a fixed compute-based threshold as a reliable indicator of potential risk.

I'm disinclined to let that be a barrier to regulation, especially of the export-control variety. It seems like letting the perfect be the enemy of the good: refusing to close the barn door you have, because you think you might have a better barn door in the future.

> Instead of focusing on compute thresholds or model sizes, policymakers should focus on regulating specific high-risk AI applications, similar to how the FDA regulates AI software as a medical device. This approach targets the actual use of AI systems rather than their development, which is more aligned with addressing real-world risks.

How to you envision that working, specifically? Especially when a lot of models are pretty general and not very application-specific?