A robot's flash memory degrades with each write, forming a non-renewable asset. A wear-aware pricing model uses a shadow price $η$ to guide memory placement across RAM, NVM, and cloud, with optimal routing depending on whether task value increases with memory persistence. The sign of the value-write association $χ$ varies by deployment: positive in long-horizon manipulation, null in short-horizon tasks, and negative in teleoperation. The endurance budget is binding only on low-end QLC/eMMC memory, and while wear-aware routing aligns with task value, actual performance improvements remain unverified in data.