The Question Commercial AI Development Ignores
This research domain addresses questions that commercial AI development largely ignores: how do you build AI systems that remain coherent, capable, and trustworthy without continuous connectivity to cloud infrastructure? Concretely, that means model update strategies without network access, mitigating operational drift over long deployment cycles, and maintaining system capability without external refreshes.
Why Drift Is a Real Problem
A system running for months or years without external model updates has to learn differently than one being continuously retrained. Henri's answer is the continuous self-reflection cycle and the five-database memory architecture: the system accumulates operational experience specific to its installation, rather than relying on a generic, externally-refreshed model.
Update Strategy Without a Network
Where updates are still needed, they're delivered via encrypted physical media, cryptographically signed and verifiable offline — a direct translation of this research into deployment practice, not just a theoretical possibility.