Regulation

Prior reading: Competitive Dynamics and Safety | P-Hacking and Benchmarks The Premise The competitive-dynamics post explains why motivation for AI safety is lacking. This post assumes the opposite: everyone is motivated. Every nation, every company, every researcher genuinely wants to regulate AI well. It's still incredibly hard. Here's why. The Object You're Regulating Keeps Changing Capability Jumps Are Unpredictable Regulation assumes you can define what you're regulating. But AI capabilities change discontinuously. A model goes from "can't do X" to "can do X fluently" between training runs, sometimes between scale thresholds no one predicted. ...

Prior reading: Game Theory for AI Safety | The AI Threat Landscape | P-Hacking and Benchmarks The Core Problem AI safety is short-term costly and long-term valuable. Every actor faces pressure to defect. This post makes two claims. First, the motivation to prioritize safety is structurally lacking — everyone has reasons to cut corners. Second, even if we could fix motivation entirely, the regulatory problem is so hard that good intentions wouldn't be enough. Both have to be true for the situation to be as bad as it is. Unfortunately, both are. ...