However, a few notes of caution are necessary regarding the widespread adoption of GPUs. CPUs remain essential for workloads that require flexibility, general-purpose computation, or that are memory-bound. For example, CPUs equipped with HBM are competitive with GPUs in some cases, particularly in memory-limited tasks. Although in the near future, we can expect CPUs to integrate AI acceleration features, they will likely remain complementary to GPUs. In addition, while their computational power is undeniable, the energy consumption of GPUs is a growing concern. While a laptop requires less than 100W to function, the peak energy consumption of a modern supercomputer like Frontier is around 22MW. The community must prioritize energy efficiency as a core design principle for future exascale simulations. Time-to-solution should be scaled by power usage when comparing simulations on different architectures. Re-evaluating the blind pursuit of raw performance at the cost of enormous energy consumption (cf. Figs. 1(b) and 4(b)) is of paramount importance. The focus should shift towards a more balanced approach where algorithmic efficiency, software optimization, and energy-aware hardware design work in synergy.