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
usedot.xyz/
🔴