The exponential growth in AI model size has created unprecedented energy demands that challenge traditional computing infrastructure. Recent industry reports have estimated that by 2040, AI inference and training will collectively require 600 terawatt-hours annually—equivalent to the energy consumption of a medium-sized industrial nation. Current hyperscaler architectures introduce critical bottlenecks: geographically concentrated energy demands, transmission constraints, and concerning environmental impacts, with some facilities resorting to fossil fuel consumption to meet power requirements.

Greg Osuri, founder and core contributor of Akash Network, will discuss how decentralized marketplaces efficiently allocate resources across geographically dispersed nodes. He will demonstrate how Akash has achieved approximately 70% resource utilization rates across heterogeneous hardware configurations, including recent breakthroughs in distributed training algorithms that overcome previous limitations in heterogeneous compute environments. The presentation will include a technical analysis of small modular data center architectures optimized for distributed AI workloads, including their integration with renewable energy sources. This will highlight how decentralized approaches can address current energy constraints while democratizing access to compute resources, potentially preventing market concentration that threatens open innovation in AI research.