Until recently, the trajectory of top-performing computers has been fairly predictable, guided by a handful of leading hardware vendors . These vendors have consistently provided scientists with clear paths (via stable languages, compilers, libraries, and tools) to harness escalating computational power, culminating in the era of petaflops . The move to exascale computing comes with unique challenges, including a rapidly changing array of hardware options and the once-reliable software stack that is now becoming increasingly fragmented and inconsistent . After traditional applications like cryptocurrency mining and gaming, artificial intelligence (AI) has effectively harnessed the power of Graphical Processing Units (GPUs). This success is due to a strong alignment between AI algorithms and the architectural strengths of GPUs, coupled with the development of specialized libraries . The transition to exascale for quantum mechanical simulations uncovers an intricate web of prospects and predicaments for physicists and chemists and typically requires large community efforts (e.g. US Exascale Computing Project , EU Centers of Excellence and National Competence Centers ) well beyond individual capabilities. This review explores the uneven path toward exascale simulations, highlighting both promising developments and persistent challenges and offering a nuanced understanding of this important shift in computational science.