The AI infrastructure landscape faces significant challenges in cost, scalability, and latency as the demand for efficient AI inference continues to grow. Enterprises, startups, and developers are constrained by expensive cloud-based GPUs and limited deployment flexibility, while sensitive sectors like healthcare and finance grapple with privacy and compliance issues. These gaps are amplified by the underutilization of latent compute in everyday devices, creating a significant opportunity for decentralized solutions.

Current solutions focus heavily on centralized cloud infrastructure, which results in bandwidth bottlenecks, vendor lock-in, and suboptimal cost structures for smaller players. Existing systems fail to address the growing need for privacy-preserving, real-time AI solutions across diverse use cases.

The opportunity lies in building an aggregated compute network that harnesses the power of underutilized compute from personal and enterprise devices. By enabling distributed AI inference at the edge, this approach not only reduces costs and latency but also provides privacy-first solutions for regulated industries. This platform could help enterprises scale AI adoption, empower startups with cost-effective tools, and foster new ecosystems for AI-powered innovation.