Most AI discussions treat models as destinations. You pick one and stay there.
Real workflows don't work that way. I need different models for different strengths reasoning, cost efficiency, specialized domains. That's rational optimization, not indecision.
But the tooling treats it like a problem. Separate accounts, separate API keys, separate billing, separate authentication. Managing five isolated relationships instead of one coherent compute layer.
The real cost isn't switching models. It's coordination overhead.
Every provider becomes a gatekeeper—controlling pricing, routing, access.
You're locked in not because one model is best, but because leaving costs more than staying.
That's not efficient infrastructure. That's rent collection.
OpenGradient's insight: treat models as interchangeable components inside a larger execution layer.
Unified routing based on task requirements. Transparent pricing.
Distributed incentives.
Governance that's actually open and because execution is verifiable on-chain, no one party can silently gatekeep.
Not ideology. Just how efficient infrastructure works.
The real question: does value accrue inside closed model providers, or inside infrastructure layers that coordinate them?
That determines whether we get consolidation or genuine competition and the infrastructure we build today determines which outcome wins.
