Data Residency
Sovereignty Is an Architecture Decision, Not a Philosophy
Sovereignty in AI isn’t about ideology. It’s about control surfaces.
When you use Claude or GPT-4, you’re renting intelligence. When you run Llama locally, you own the compute but not the training data provenance. When you fine-tune a model on someone else’s infrastructure, you own the weights but not the execution environment. These are different failure modes, different points where control dissolves.
I spent 10 months building infrastructure that closes these gaps. Not because sovereignty sounds good, but because every missing control surface is a future problem. Data residency isn’t paranoia—it’s knowing exactly where your context lives and who can access it. Model ownership isn’t about open source zealotry—it’s about running inference in January 2027 even if an API shuts down. Compute sovereignty isn’t about self-hosting everything—it’s about degrading gracefully when Tier 1 hits rate limits.