A $25 pipeline producing publishable economic theory and 700 experiments running in two days look like productivity stories. They're actually stress tests for organizations that still measure AI value by what gets generated rather than what gets used. The legibility piece named the terminal form of this problem: AI-for-science will produce discoveries faster than labs, regulators, and clinical infrastructure can absorb them, and the bottleneck was never generation. That dynamic was already visible in week one, where the BCG data showed cognitive load spiking as oversight demands increased. The human-in-the-loop model assumes a human with enough bandwidth to loop, and that assumption is failing in practice. The tokenmaxxing story closes the arc: when consumption volume becomes the proxy for productivity, every measurement framework in the organization is now optimized for the wrong thing. What all three weeks surface, read together, is that the generation layer is effectively solved and the evaluation layer: scoring architecture, provenance infrastructure, translation tooling between machine output and institutional deployment, is where the next competitive advantage will be built. The companies that treat evaluation as an engineering problem now, rather than a governance afterthought, will hold a position in 18 months that no amount of inference spend can replicate.
