What does this equation mean?

This is not a scaling law in the sense of a fitted curve; it is closer to an accounting identity, and its value is in what it rules out. Because $P_{\text{budget}}$ is nearly constant for a given device class, essentially all of the growth in on-device model capability that anyone can expect by 2035 has to come from growth in $\eta(t)$ — from architecture, from process, from numerical precision, and, in the discontinuity scenario below, from a different physical mechanism entirely. Reuther and colleagues’ survey of commercial AI accelerators, which compiles peak-performance and power figures across dozens of parts and computes efficiency relative to that peak, documents exactly this pattern: efficiency, not raw throughput, is the axis on which parts aimed at constrained power budgets actually compete, and the gap between digital and mixed-signal or in-memory approaches on that axis is large enough to matter [ 8 ] .