The field of operating systems and accelerator technologies is witnessing a significant shift towards AI-integration and adaptive architectures. Researchers are exploring new ways to design operating systems that can proactively anticipate and adapt to the cognitive needs of autonomous intelligent applications. This includes the development of AI-native environments, neurosymbolic kernel designs, and ML-specialized operating systems. Additionally, there is a growing focus on accelerator technologies, such as programmable chip-to-chip photonic fabrics and GPU-accelerated query processing platforms, to improve the performance and efficiency of machine learning workloads. Noteworthy papers in this area include: Composable OS Kernel Architectures for Autonomous Intelligence, which proposes a new OS kernel architecture for intelligent systems. Morphlux, which develops a server-scale programmable photonic fabric to interconnect accelerators within servers. MaLV-OS, which rethinks the OS architecture to make it specifically tailored to ML workloads. Rethinking Analytical Processing in the GPU Era, which presents a prototype open-source GPU-native SQL engine. Theseus, which presents a production-ready enterprise-scale distributed accelerator-native query engine.