Many AI tasks, such as real-time decision-making and large-scale simulations, require substantial computational resources. By spreading computational tasks across multiple nodes, decentralized compute networks can extend the capabilities of machines beyond their onboard hardware and increase resource utilization. In addition, by allocating these tasks to local nodes, decentralized compute can strike a balance between the benefits of on device and remote compute, enhancing energy-efficiency, resiliency and security.

Currently, the design of robots and other autonomous machines is limited by the demand for  onboard processing power to handle computationally heavy processes like object recognition and decision making. This increases the cost and size of these machines as well as limiting scalability. These tasks can be offloaded to centralized, remote compute networks but at the cost of latency. Decentralized compute systems offer an alternative, by allowing tasks to be processed closer to their sources, these systems can reduce latency and support real-time applications.

A significant advantage of decentralized compute is cost efficiency. Often utilizing underused or idle resources, such as personal computers or edge devices, these systems reduce the need for expensive, centralized infrastructure. This distributed architecture also enhances resilience and security by eliminating single points of failure. Blockchain integration makes decentralized compute ideal for machine to machine transactions, crucial for the machine economy.

Decentralized compute networks have the potential to greatly impact the design of future machines, allowing them to extend their computational capabilities beyond their onboard hardware.