The move to understand AI, it's global implications and the ability to keep the AI lights on around the world without reliance on particular global companies or nations has come back to the forefront with the recent activity around allowing models or phasing their rollouts. "Sovereignity" is again a key topic. 1. Layer 0 - Sovereignity of location, easy to achieve. 2. Layer 1 - Hardware (has to be bought by global companies but can be deployed and managed locally. 3. Layer 2 - Staffing - can use locals to staff. 4. Layer 3 - Services and in the future AI models (usually delivered by global cloud and AI players) - this is where the challenges begin, what tradeoffs do you need to make, can you run services independently of the mother ship? Or can you build them locally - usually creates a technological delta in innovation, usually local companies are scarce at this layer and some tradeoffs are needed for innovation and scalability. 5. Layer 4 - Applications - Can usually be built and maintained locally, as long as they are needed only in the local context. 6. Layer 5 - Data - This can also be segregated and held locally and can be restricted from global movement. The risks arrive in the services layer and associated tradeoffs, application isolation, especially if companies are global in nature originating out of the sovereign state. Ultimate sovereignity also causes a resilience issue - requires a backup outside the country. But there is a strong use case for nation state services, classified data and local services with citizen data. Looking forward to @ValarianHQ and Max taking on the problem.