I'm Narasimha Prasanna, co-founder of AIGr.id. We're excited to introduce AIGr.id, a decentralized, community-driven global AI network designed as public infrastructure—think of it as the Internet of Intelligence.
Instead of large, closed, monolithic AI systems, AIGr.id interconnects smaller, independent AI clusters through AIOS, a decentralized AI operating system that's 100% open-source.
Today’s AI landscape is:
- Centralized: Resource-heavy systems demand vast funding, compute, and talent—excluding much of the world.
- Controlled: Dominated by a few powerful actors incentivized to prioritize profit over public good.
- Limited Participation: Production and distribution of AI is limited to a small group of people or organizations, which leads to unequal benefits.
- Fragmented: Siloed AI systems with no open protocols for AI coordination.
We believe it's time to re-imagine AI as collective intelligence—as a shared commons that is decentralized, collaborative, composable, inclusive, and guided by values beyond profit. 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.
We believe the future of AI is along the lines of: https://asia.nikkei.com/Business/Technology/Artificial-intel... .
AIGrid enables use cases like these.
Why AIGr.id?
- Global Commons: Built and governed collectively by communities, not corporations.
- Composable AI Blocks: Deploy shared and reusable AI modules like LLMs or vision models.
- Decentralized Control: Supports both controller and coordinator mechanism for resource allocation and task execution - without central control.
- Resource Efficiency: Smart scheduling and GPU sharing to maximize resources.
- Policy-Driven: Governed transparently through Python-based policies.
- Distributed Workflow: Utilize Directed Acyclic Graphs (vDAGs) to manage complex distributed AI workflows.
- Extensible: Easily integrates external tools, frameworks, and models.
Current Status:
Beta phase—Testnet launching first week of May 2025. Actively seeking feedback and contributions from developers, engineers, designers, and governance researchers.
Explore more:
- Source code: https://github.com/OpenCyberspace/AIGr.id/tree/main
- Website: https://aigr.id
- Documentation: https://docs.aigr.id
- Vision Paper: https://resources.aigr.id
We'd love your feedback and ideas - let's build a Sovereign and Networked AI future together!