1.4 Current Inefficiencies of Decentralized Solutions

Decentralization is often seen as the ideal solution to the above centralization challenges, offering a method to distribute computational resources across a network composed of numerous relatively independent nodes, thereby aiming to eliminate the monopolistic control of centralized providers.

Modern decentralized networks also introduce additional economic incentive mechanisms, such as issuing new native digital currencies and providing rewards for participating in network maintenance (e.g., validating transactions). These subsidies can effectively reduce the net cost for users to acquire hardware and computational resources during the initial stages of network development. Theoretically, a well-designed decentralized system should offer higher cost-effectiveness, operational flexibility, and systemic resilience.

However, the architecture of modern decentralized systems in reality exhibits significant inefficiency problems. A large portion of block subsidies and the network's total computational power is consumed on tasks that do not directly produce meaningful real-world outputs, primarily focused on maintaining network security itself rather than executing actual productive work. This inefficiency stems from two main factors: the high cost of achieving distributed consensus and the built-in excessive redundancy to ensure the accuracy of computational results.

Maintaining blockchain security through mechanisms like Proof of Work (PoW) or Proof of Stake (PoS) requires consuming vast computational or capital resources. The basic assumption of these consensus models is that the majority of participants are honest, but they require all participants to invest substantial resources, with the core purpose being to prevent a malicious minority from manipulating the network by masquerading as the majority. Beyond protecting blockchain security, in many decentralized AI projects, the same computational task is distributed to multiple nodes for redundant execution for verification purposes. Similarly, decentralized storage projects might replicate the same file across multiple nodes. This redundant design leads to considerable resource waste, particularly pronounced in the computationally intensive AI field, where even the repeated execution of small tasks can drastically increase overall computational costs.