Every Muse User Gets 2 CPUs and 8GB in Meta's Cloud. Your Phone Already Has That

About this episode

The push to run AI agents on edge devices will not be driven by model labs or hardware vendors, but by service providers like Meta whose ad-based business models create a powerful financial incentive to offload massive cloud compute costs onto the user's own powerful-but-idle hardware. Austin Lyons and Vik Sekar break down why the failed "AI PC" push has given way to a new, economically-driven strategy for on-device AI. They discuss how Qualcomm's acquisition of Modular provides the key software layer and why the ultimate vision is an "ecosystem of you" where the device acts as a context-aware orchestrator between local and cloud compute. This episode is presented by Crusoe. Try Crusoe's managed inference for open models with $5 in free credits: https://semidoped.com/crusoe "[Model Labs] business model is we make money when we do inference in the cloud. And yet we are saying, actually, there's a world where we would really like to just run this stuff locally... Meta's like, bro, I want to help you at the edge." — Austin Lyons, Chipstrat Key Takeaways: - The driver for edge AI is a business model conflict. Model labs like OpenAI sell cloud inference; service providers like Meta