Grassroots demand is creating organic usage in the @usedotai ecosystem The result is a fundamentally different AI inference model… Privacy-first Efficient & cost-optimized by design When you can deliver comparable capabilities at a 10–15%+ pricing advantage, that difference compounds as usage scales Organic adoption is feeding an infrastructure advantage $DOT @usedotai can continuously optimize how models are selected and served Then there’s the ability to sell unused inference credits back to the team Instead of credits simply sitting idle, they can potentially retain utility and liquidity within the ecosystem Turning unused capacity into something economically productive rather than dead weight & credit where it’s due: @stagedhappen , @AlaaDelRey & the entire @usedotai team have seriously exceeded expectations with this direction This thesis is also starting to align with where institutional research is heading… @BlackRock ‘s latest “Machine-Native Economy” paper frames compute as an emerging economic resource and specifically highlights the importance of efficient model routing, compute optimization and programmable infrastructure as AI scales @GoldmanSachs Research estimates AI-agent token usage could multiply 24× by 2030, with enterprise, consumer, generative and physical AI driving enormous increases in inference demand If that trajectory even partially materializes, inference efficiency becomes a critical infrastructure problem As agents execute more tasks, inference becomes less about simply accessing a model and more about optimizing routing, compute, latency, cost and privacy at scale The platforms capable of efficiently serving that kind of demand.. While maintaining privacy and organically compounding usage… Sit directly at the intersection of the next compute cycle @usedotai is changing the way we build Open by design Private by architecture & engineered to push inference costs as low as possible https://t.co/Hud0Fjfmms 🔴