The Problem
Current machine learning infrastructure faces fundamental challenges that limit accessibility, scalability, and innovation.
Centralized Control
Today's AI infrastructure is dominated by a handful of large tech companies. This centralization creates gatekeepers who control access to computing resources, limiting who can train and deploy machine learning models.
Prohibitive Costs
Training large machine learning models requires expensive GPU clusters that cost millions of dollars. This high barrier to entry prevents researchers, startups, and smaller organizations from participating in AI development.
Limited Scalability
As models grow larger, they require exponentially more compute power. Centralized systems struggle to scale efficiently, leading to bottlenecks and resource waste. There's untapped computing power worldwide that could be utilized.
Trust and Verification
When compute is distributed, there's no way to verify that workloads are executed correctly. Without trustless verification mechanisms, distributed ML training becomes unreliable and vulnerable to malicious actors.
The Impact
These challenges create a world where AI development is restricted to well-funded organizations with access to massive computing infrastructure. This limits innovation and concentrates power in the hands of a few.
As machine intelligence becomes increasingly important for knowledge creation and decision-making, the need for open, permissionless, and decentralized infrastructure becomes critical.