# Computing Power is Power: In-Depth Analysis of the Underlying Logic of Distributed Computing Networks

**Abstract:** Behind the explosive growth of AI lies extreme "computing power anxiety."

As traditional centralized computing power becomes monopolized, Web3's distributed computing networks are moving from concept to reality. This article provides an in-depth analysis of the productivity revolution in the computing power sector, examining the core differences among various technological approaches.

**I. Breaking the Problem: AI's Endgame is Computing Power, and the Dilemma is Monopoly**

The iteration speed of AI large models far exceeds hardware supply. Currently, developers face two major survival challenges:

1. Resource Hegemony: Top-tier GPUs are prioritized for giants, making it difficult for small and medium teams to acquire even a single card.
2. Cost Bottleneck: Centralized cloud providers charge exorbitant premiums, while vast amounts of idle computing resources worldwide remain underutilized.

Core Logic: By utilizing blockchain protocols, scattered hardware resources worldwide are pooled into a single resource pool. This not only liberates productivity but also redistributes the pricing power over computing capacity.

**II. Sector Breakdown: Three Mainstream Technical Implementation Paths**

1. Transition from Specialized Rendering to General-Purpose Computing:
Some established leading projects are upgrading their protocols to fully pivot their mature node networks—originally designed for image processing—into AI computing. Their advantage lies in a large existing ecosystem foundation.

2. Decentralized General Cloud Services:
Similar to "cloud-sharing spaces," these projects offer general-purpose computing resource rentals. They are highly cost-effective, often costing only 30-50% of traditional large cloud providers, making them very developer-friendly.

3. High-Concurrency Cluster Interconnection Technology:
This is currently the most cutting-edge direction. By leveraging the capabilities of high-performance underlying blockchains, it enables ultra-large-scale hardware cluster interconnection. This approach addresses the difficult problem of communication latency in distributed computing, supporting large-scale model training.

**III. Value Capture: Tokens Are More Than Just Payment Tools**

To assess whether a distributed computing project has real depth, one must examine its economic model:

• Supply-Demand Balance Mechanism: As demand for computing power increases, can the system use buyback or token burn mechanisms to benefit holders?
• Proof of Useful Work (PoUW): How does cryptography ensure that remote nodes genuinely complete computational tasks? This is the key distinction between serious technical projects and vaporware.

**IV. Conclusion: The Second Half of the Computing Power Sector**

The hype cycle for AI has passed. The next market dividends will go to projects with real TVL (Total Value Locked) and genuine computational workloads. Web3 is not just about providing computing power for AI; it aims to bring transparency and fairness to AI's productive relationships.