In simpler terms: We’re trying to make it possible for people to produce, remix, operate, distribute and consume AI systems the way we use the internet— openly, collaboratively, and without needing to centralize everything into one mega-model owned by one mega-entity. Just like internet of intelligence.
Today’s AI landscape is: Centralized, resource-heavy systems demand vast funding, compute, and talent—excluding much of the world. Controlled by a few powerful actors prioritizing profit over public good. Participation is limited, deepening inequality in AI benefits. Fragmented and siloed, with no open protocols for AI coordination We believe it's time to reimagine AI as collective intelligence, as shared commons — poly-centric, collaborative, composable, inclusive, and guided by values beyond profit.
What’s different about our approach is that we’re not trying to build “the one true model” — we’re trying to make it easier for people to build, remix, run, and govern their own AI systems, together. We want a world where AGI doesn’t have to be monolithic — where different models, agents, and collectives can evolve side by side, coordinate, and even argue if they need to. Plural, by design.
At the core of AIGr.id is OpenOS.AI, a distributed AI operating system. It is a full stack AIOS that spans everything from low-level compute orchestration to higher-level cognition, coordination, governance and economic policy. Think of it as a programmable substrate for building and running decentralized AI systems — across any infrastructure, in any topology.
Developers can use shared protocols, primitives, and templates to compose AI systems — models, agents, cognitive workflows — and plug them into running grids. These grids can be public, private, federated, or even permissionless. Each grid can maintain its own sovereignty (values, rules, trust mechanisms), but still remain interoperable with others. It's designed for a world where we expect many intelligences to coexist, rather than one model to rule them all.
We’re in beta and will be kicking off more extensive scale testing during our upcoming testnet phase. If this scratches an itch for you, or just want to jam on open systems — we’d love your feedback. If you're interested in joining the testnet, you can join our discord @ https://discord.gg/W24vZFNB — we’d be excited to have you involved early.
Docs, GitHub, and the paper are all linked at https://www.aigr.id Curious what you think — critiques, weird use cases, edge cases, counterpoints — all welcome.
Our own background is what pushed us into this problem. Before this, we were a 4-person crew running one of the largest real-time AI inference workloads in India. We were doing around 500K inferences/sec across 80–90 models simultaneously, supporting 35+ public-sector use cases — mostly video analytics. We were operating across federated and private infrastructure in real time, processing millions of frames per second. We didn’t rely on cloud providers or commercial frameworks.
Our market was distorted by deprioritized infrastructure investment and choosing to grow within our earnings means the only way to survive was by being ruthlessly efficient: creating frameworks that automated end to end production, operation, distribution and maintenance life cycle of AI -- everything at scale reliably without or with minimal human intervention — so four of us could actually live our lives, too.
So in a way, AIGr.id was born out of necessity. It's the system we wish we had — one that treats intelligence as something modular, networked, composable, orchestratable, shareable, and governable – in a collective way.