Overview

Overview

AI infrastructure today suffers from a fundamental centralization problem. While artificial intelligence transforms every sector of the global economy, the underlying compute resources remain concentrated among a few major cloud providers who control pricing, access, and availability. This creates significant bottlenecks for innovation, particularly for smaller organizations and individual developers who face prohibitive costs and limited access to high-performance GPU clusters.

The numbers tell a compelling story. Recent market analyses project the global AI ecosystem to reach $1.8 trillion by 2030, with multiple high-growth segments driving this expansion. AI compute infrastructure is expected to capture $334 billion of this market, while broader AI infrastructure including cloud and data centers will reach $394.5 billion. Adjacent markets show equally impressive trajectories: AI agents ($52.6 billion), robotics ($77.7 billion), generative AI ($220 billion), and software platforms ($862 billion by 2033). The cumulative economic impact is projected at $15.7 trillion by 2030 — representing a 14% boost to global GDP.

Despite this unprecedented growth opportunity, access barriers continue to widen. Individual users and smaller organizations face escalating costs for GPU compute, while even blockchain projects committed to decentralization find themselves dependent on centralized cloud infrastructure. This dependency creates operational risks and philosophical contradictions that limit the potential of both AI and crypto ecosystems.

EDITH addresses this infrastructure gap through a fundamentally different approach: community-owned AI compute backed by real-world assets. Rather than renting compute from centralized providers, users can own fractional stakes in the physical infrastructure that powers AI workloads. Our protocol transforms GPU clusters, data centers, and energy infrastructure into tokenized investments that generate yield from actual AI usage.

The result is a sustainable alternative to the current centralized model — one where compute resources are owned by their users, yields are generated by real economic activity, and the benefits of AI growth are distributed to the community rather than concentrated among a few major corporations.