Invited Talks

Systems are increasingly being written and optimized by AI systems. This talk focuses on kernel LLMs: models that generate GPU kernels. GPU kernels are a strong target for AI-driven optimization because they are verifiable and commercially interesting to optimize. But despite promising demos, very few AI-generated kernels are reliable enough to be used in production without significant human supervision. We will go through examples of how we made LLM kernel evaluation more robust through open benchmarks, community feedback loops, and infrastructure built in public through GPU MODE. We will close with some thoughts on where ML systems are going, where junior researchers should spend their time, and how to build systems that last in a world where the cost of writing code is approaching zero.

MLSys showed how systems can accelerate AI. The next shift is broader: AI is beginning to reshape the practice of systems itself. This emerging paradigm, which we call system intelligence, goes beyond automating programming tasks. It enables new forms of reasoning, design, validation, and evolution for complex systems while preserving rigor. In this talk, I will argue that system intelligence changes not only what systems we can build, but also how we understand systems as a discipline. It pushes us to rethink systems principles and methodology, shifting attention from code-level complexity to greater rigor in specification, design, and validation. Through our experiences with system verification, I will discuss how this shift may help give systems a stronger scientific foundation.