Sat down with my friend Rezaur @intellgenc (CIO / CISO / CAIO at the @usachp) for a long conversation on building frontier AI for federal infrastructure.
Among his projects: "We're working with Google Public Sector and @SnorkelAI on a geospatial deep-research, AI-native system. And since that wasn't challenging enough, a world-simulation system to model real-world impacts on large infrastructure projects."
We get into the limits of frontier models, mechanistic interpretability for applied AI, and why Rezaur wants more entropy from his models (not less).
00:26 Building AI-native, not bolt-on
05:18 Why one model can't do geospatial AI
08:23 "I want more entropy, not less"
10:28 Inside the model: mechanistic interpretability
15:32 Externalizing memory: context, files, graphs
26:01 The RGB-pixel trick for sensor data
28:24 The geospatial benchmark gap
31:11 When a frontier model hallucinated an Iran-backed attack
