Adrian Bertagnoli

This talk proposes a shift from homogeneous AI systems, which rely on scaling single models across identical hardware, to a new paradigm of heterogeneous intelligence. This approach leverages the inherent complexity of real-world problems by decomposing them into sub-problems that can be addressed by diverse models, workflows, and hardware working in concert. The core thesis is that this heterogeneity, when properly orchestrated, leads to more efficient, faster, and cheaper AI systems.