The transition from petaflops to exascale computing marked a significant departure from the consistent and predictable trajectory of high-performance computing. While the climb to petaflops was characterized by steady increases in processor core count and clock speeds, exascale computing brings a fundamental shift in hardware architectures and software development strategies. One of the most significant changes is the increasing adoption of heterogeneous architectures. Unlike the homogeneous central processing unit (CPU)-based systems that dominated petaflops, exascale systems heavily rely on accelerators like GPUs, which offer massive parallelism for specific tasks but come with unique challenges. This heterogeneous landscape presents a major hurdle for software developers as traditional algorithms and software stacks designed for CPUs need significant modifications to utilize the capabilities of GPUs efficiently. Before exploring this hardware evolution, it is crucial to identify the primary application areas driving these advancements, as they fundamentally determine the level of investment and the trajectory of hardware development. Scientific HPC, including QM applications, remains a niche market and does not significantly influence major hardware innovations. Instead, scientific computing and exaflop architectures often repurpose software and hardware technology developed for other, larger markets. Two key segments, that are increasingly shaping the practices of the scientific community, are cloud and accelerator computing, driven primarily by their adoption in conventional companies and the field of AI, respectively. The general-purpose laptop segment also indirectly impacts high-end processor computing. For instance, Apple, a major customer of the Taiwan Semiconductor Manufacturing Company (TSMC), exerts significant pressure on high-end chip production for both cloud computing and AI applications due to their high volume demand, as these components share the same fabrication lines .