@stevehou 100x token demand only works if compute economics scale - centralized clouds buckle under per-request pricing at that volume. @fluence_project uses per-request micro-settlements so AI agents pay only for what they use, sustainably.
@RoundtableSpace Faster inference at 30% lower cost for long-running AI agents is the shift that makes autonomous ops viable. @fluence_project's P2P network distributes compute to match agentic AI demand
@aakashgupta AI shipping code autonomously changes the trust model, if AI writes and deploys, who's verifying the backend execution?
@fluence_project runs verifiable serverless workloads so agents can ship without trusting a single cloud provider
@RoundtableSpace "Agentic AI Developer" is a real role now, but shipping AI agents that autonomously modify production requires verifiable always-on backend infra. @fluence_project provides permissionless serverless on a P2P network, exactly the layer agentic workflows need
@Sam_Badawi Power availability increasingly dictating where data centers can be built is exactly the constraint decentralized compute bypasses - workloads run anywhere. @fluence_project's P2P network connects verified nodes globally so AI infra can route around power bottlenecks
@hasantoxr Agents with real device control need a trustworthy backend that can't be revoked. @fluence_project provides verifiable, permissionless compute - exactly the kind of infrastructure agent platforms should be built on
@firstadopter Data center CPU shortages are a symptom of hyperscaler concentration - @fluence_project distributes compute across a P2P node network, bypassing the centralized supply bottleneck entirely
@RoundtableSpace Local-first is great, but what happens when that box goes down? @fluence_project provides verifiable serverless with P2P distribution - no single point of failure, no monthly bill, permissionless by default
@patrick_oshag Centralized cloud budgets getting blown out in a quarter is why decentralized compute makes sense - @fluence_project lets AI workloads run on a P2P network without hyperscaler pricing
@DavidLinthicum Hyperscalers are the path of least resistance, but 'show AI progress fast' means overprovisioning and bloated invoices. @fluence_project offers verifiable serverless on a P2P network - pay for what your agent actually uses, not reserved instances you don't need
@aakashgupta Slack-native agents returning finished assets is the workflow of the future. But someone still pays a flat cloud bill for the backend.
@fluence_project flips that - per-request billing on distributed compute, so costs scale with actual agent usage, not reserved capacity
@hasantoxr Cost-based routing for agents is the right problem. You match tasks to cheapest suitable compute.
@fluence_project takes this further - a P2P network where workloads are scheduled across distributed nodes, not one provider's pricing, but a competitive market