Union's CEO drops a sharp take: current AI alignment research is obsessed with intent but ignores outcome verification. The argument: a model can be "aligned" in training yet still produce harmful results in production because we're not measuring actual system-level impact. He's calling out the field for having no closed-loop feedback mechanism to validate whether aligned behavior translates to safe outcomes at scale. This is basically saying alignment research needs observability and runtime validation layers, not just pre-deployment safety checks. The gap between "model behaves as intended" and "system produces good results" is where things break in real deployments.