daily publicationmay 28, 2026can models prove their own work?Models are making it cheaper to generate more attempts: faster kernels, possible protein binders, new data-center demand, and software output from coding agents. The harder question is whether models can prove their own work, or where tests, labs, buyers, infrastructure, and human judgment still have to decide what is real.
daily publicationmay 26, 2026ai’s new cost curveAI is shifting scarcity from raw capability to the costs around it: inference budgets, chip data movement, reliable evaluation, durable memory, human supervision, capital substitution for labor, and weak-link institutions. This issue maps where leverage moves once models become useful enough that the hard question is no longer only what they can do, but who can allocate the remaining compute, trust, context, labor, power, and access.
daily publicationmay 21, 2026who owns the bottleneck?AI is moving from model competition into an allocation stack: compute, wafers, power, deployment capacity, inference speed, trusted workflows, distribution surfaces, and capital markets.
