The first generation of mainstream AI lived in the cloud. Every model call happened in a datacenter. Computing constraints were widespread and we entered an age of scarcity. Cloud companies addressed high demand with usage limits. Rising task complexity and agent capability introduced larger prompt sizes, token counts, and costs. Productivity became a function of access to AI.
Edge AI changes the optimization problem. Edge AI lives on devices at home and in the workplace, and the narrative evolves from scarcity to ownership. Benefits compound around data security, interoperability, and regulatory compliance. Barriers disappear, but so does capacity. The limiting constraint shifts from access to model capability. The missing artifact that solves this is a software framework that connects fragmented edge devices into a unified inference layer for greater model capability.
Distributed computing isn't new. Researchers and companies have explored ways to aggregate idle computers into useful distributed systems for decades. Those efforts offered valuable lessons about coordination, incentives, and reliability. One thing was consistently missing: an essential application that naturally benefits from a global network of idle compute.
Aquaduck is a distributed inference platform for AI. We provide the software, networking, orchestration, and edge device management required to create and operate inference networks. Customers decide how those networks evolve, whether that means powering internal AI, serving production applications, extending products, monetizing available capacity, or building entirely new capabilities on top of the network.
One expression of the platform is Aquaduck's public inference network, which serves as elastic capacity for customers that choose to extend beyond their own infrastructure, while demonstrating the capabilities of the platform itself. Anyone can contribute idle compute to the public network and earn revenue for the inference they provide.
We believe the next generation of AI won't be defined solely by datacenters, but by intelligent networks built from the devices people already own.
Talk to us
We're builders from Apple and Together AI and have been thinking about this problem space for a while. If you're interested in distributed inference, shared compute, or where AI is headed, reach out to us at team@aquaduck.ai.
Want to work on this problem? Tell us why: careers@aquaduck.ai.