AI for Systems is "AGI-Complete"

Abstract

Computer systems are increasingly using AI techniques, as evidenced by increasing numbers of publications and several reported deployments in production systems. While early work in this area focuses on relatively small ML models and statistical techniques that replace heuristics, the field is experiencing a paradigm shift towards using agentic techniques to automatically discover new systems algorithms and designs, driven by a rapid increase in model capabilities. We argue that these recent advances in AI capabilities – specifically, models that reason about the world and perform complex sequences of actions through agentic interactions – not only suggest that the OS community should employ AI more widely in its research, but also that it should look at AI for Systems through a new lens. We show that solving systems problems such as resource scheduling and code optimization requires reasoning that is fundamentally as hard as any reasoning problem a human might have to solve. By establishing a strict success criterion for what it means to ''fully solve'' these tasks across open-ended, real-world edge cases, we demonstrate that these problems are ''AGIcomplete''. We argue that this makes systems problems a promising target for AGI research, and that both the AI and systems community should investigate this area more.