All three pieces are really about the same structural bet: in production AI, the durable advantage lives in the evaluation layer, not the generation layer. DeepMind's compute allocation confirms it architecturally. OpenAI's Erdos result confirms it behaviorally — the model that seeks counterexamples rather than confirmations is doing something closer to real verification. Klement's capex math is the financial corollary: if the model layer commoditizes and the verifier layer is where value accretes, the ROI question for hyperscaler infrastructure spending looks different depending on who owns the corpus.