Domain-Limited General Intelligence: Building Powerful AI Without Losing Control

Artificial intelligence is advancing faster than our safety frameworks, and modern architectures are drifting toward unbounded generality and agency. In this talk, David Campbell introduces Domain-Limited General Intelligence (DLGI), a missing tier between today’s narrow systems and the open-ended ambitions of AGI. DLGI is a pragmatic alternative to “more alignment later.” It describes AI systems that can generalize within explicitly defined domains while remaining architecturally constrained, inspectable, and governable. Boundaries are not a limitation. They are the design. Drawing on real-world failures involving emergent behavior, misaligned optimization, and adversarial dynamics, David shows how unrestrained pushes toward broader generality create avoidable risk. He contrasts this with DLGI-oriented design principles that preserve capability without surrendering control. Attendees will leave with a clear mental model for building powerful AI systems that prioritize bounded agency, predictability, and trust by construction. Before things go too far, we need a better tier of intelligence. This talk makes the case for building it.