Core Insights - Tesla CEO Elon Musk is increasingly confident in a transformational idea involving a distributed AI inference network utilizing Tesla's fleet of electric vehicles (EVs) [1][2] Concept and Technical Foundation - Tesla vehicles are equipped with advanced hardware for autonomous driving, including the latest AI inference chips, with each car having up to one kilowatt of inference capability [3] - The collective compute power of tens of millions of cars could reach 100 gigawatts, surpassing current centralized data centers [3] - This power can be harnessed when cars are not actively driving, particularly during charging, converting unused CPU resources into productive assets [3] Strategic and Economic Implications - By leveraging cars for distributed inference, Tesla could avoid the high costs and energy demands associated with building centralized AI data centers [4] - Vehicle owners may choose to participate in this network, potentially generating significant annual revenue and profit even with a small fraction of Tesla's U.S. fleet involved [4] Concerns and Practical Considerations - Practical implementations of this distributed AI network would need to address concerns about battery drain and memory usage, likely prioritizing vehicles that are charging and equipped with sufficient hardware [5] Tesla's AI Future - Analysts, such as Dan Ives from Wedbush, emphasize that Tesla's future narrative is centered around AI transformation, particularly through autonomous and robotics initiatives [6] - Musk's vision for distributed inference positions Tesla as more of an AI and robotics company rather than a traditional automaker [6]