We argue that the next era of scientific capability will be measured less by peak floating-point rates and more by time-energy-fidelity trade-offs across end-to-end pipelines. The most plausible path to “effective zettascale” is not brute-force FP64, but certified mixed-precision algorithms, communication-avoiding methods, AI-augmented reduced-order models, and hybrid AI+simulation workflows with rigorous error control and uncertainty quantification. We also outline an emerging reference architecture for platforms comprising integrated simulation, AI, and data/workflow partitions, linked and coordinated across multiple separate resources with secure cloud resources and instruments.
