The Fight for the Future of Artificial Intelligence
summary
AI agents perform multi-step plans, run code, and now sustain tasks for 16+ hours (Claude Mythos) because of scaffolding like MCP and agent-to-agent protocols rather than new frontier models. Memory becomes the bottleneck, inference decentralizes compute to phones and CPUs, and export controls tied to training GPUs miss the risk (Nvidia H20 inference controversy). Harnesses and openclaw show agents demand deep data access and enable surveillance; Claude Fable’s foreign cutoff demonstrates immediate chokepoint weaponization. GLM 5.2 and open-source small-model mixes offer an alternate sovereignty path.
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chapters (11)
- 0:00AI agents and geoeconomics
- 1:49How AI agents work
- 5:01Emerging agentic economy
- 7:54AI agents as weapons
- 13:13AI agents and regulation
- 18:06Chokepoint and surveillance risks
- 20:35AI sovereignty challenges
- 23:23One big model approach fails
- 26:57Alternative AI sovereignty paths
- 31:08Open standards for AI sovereignty
- 34:19AI sovereignty vision
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