The future of quantum mechanical simulations at the exascale level hinges on effectively navigating the shifting landscape of high-performance computing hardware and software. A critical aspect of this adaptation is the development of algorithms that go beyond simply exploiting the brute force of massive parallelism provided by GPUs. While GPUs offer significant acceleration, the impressive performance gain is limited to algorithms using matrix-matrix multiplies with reduced precision. Designing alternative algorithms for QM simulations leveraging this particular strength of GPUs is a must. Furthermore, given the complexity of adapting legacy codes to hybrid architectures, the community must unite in embracing and standardizing modular libraries for quantum chemical calculations, providing reusable, optimized, and versatile implementations of complex algorithms . Some specialized libraries have already demonstrated significant success in enabling efficient implementations on diverse hardware platforms, and their continued development and community-wide adoption are crucial for simplifying code development and ensuring long-term code Finally, AI has already proven its broad potential, generating machinelearning force fields with ab initio accuracy, faithfully describing quantum states, and speeding up various tasks within quantum simulations . As a result, the growing integration of AI with quantum simulations offers a promising path for future breakthroughs.