Centralized AI systems face a critical vulnerability: they're entirely at the mercy of hardware supply chains. When component availability tightens, the whole infrastructure becomes brittle. This dependency exposes the fragility lurking beneath most current AI architectures.

But what if we flip the model? Instead of chasing rare components through controlled supply chains, we could distribute computational workloads across billions of consumer devices already deployed worldwide. These devices exist in staggering quantities—largely untapped as a collective compute resource.

This distributed approach doesn't just solve the scarcity problem. It builds systemic resilience. No single point of failure. No chokepoint bottlenecks. Work automatically flows to available hardware, adapting in real-time. The network becomes more robust precisely because it's decentralized.

That's where the real advantage lies: replacing fragile, centralized infrastructure with resilient, distributed alternatives. It's a fundamental rethinking of how AI systems should be architected.