Computing Power is Power: Deep Breakdown 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 faces monopolization, the distributed computing network provided by Web3 is transitioning from concept to implementation. This article deeply analyzes the productivity revolution in the computing power track and examines the core differences among various technological pathways.

I. Breaking Through: AI's Endpoint is Computing Power, and the Problem is Monopoly
The iteration speed of AI large models far exceeds hardware output. Currently, developers face two major survival challenges:
1. Resource hegemony: Top-tier computing cards are prioritized for supply to tech giants, while small and medium teams struggle to acquire even a single card.
2. Cost bottleneck: Centralized cloud providers charge severe premiums, while vast amounts of idle computing power globally cannot be effectively utilized.
Core logic: Through blockchain protocols, scattered hardware resources worldwide are aggregated into pools. This is not only a liberation of productivity but also a redistribution of computing power pricing power.

II. Track Breakdown: Three Mainstream Technical Implementation Pathways
1. Transformation from professional rendering to general-purpose computing:
Some established leading projects are upgrading their protocols to fully pivot their mature node networks originally used for image processing into the AI computing sector. Their advantage lies in possessing a massive ecosystem foundation.
2. Decentralized general-purpose cloud services:
A model similar to "cloud-sharing spaces" that provides general computing resource rental. These projects have extremely high cost performance, typically only three to five times lower than traditional big tech platforms, making them very developer-friendly.
3. High-concurrency cluster interconnection technology:
This is currently the most cutting-edge direction, utilizing the characteristics of high-performance underlying chains to achieve ultra-large-scale hardware cluster interconnection. It solves the extremely difficult communication latency problem in distributed computing and supports large-scale model training.

III. Value Capture: Tokens Are More Than Just Payment Tools
To measure whether a distributed computing power project has depth, examine its economic model:
• Supply-demand balancing mechanism: As computing power demand increases, can the system incentivize holders through buyback or burning mechanisms?
• Proof of Useful Work (PoUW): How are cryptographic methods used to ensure remote nodes genuinely complete computational tasks? This is the key distinction between legitimate tech projects and vaporware.

IV. Conclusion: The Second Half of the Computing Power Track
The hype phase of the AI track has ended; future market dividends will go to projects with genuine TVL (Total Value Locked) and real computational loads. Web3 not only provides computing power for AI but also provides transparency and fairness to AI's production relationships.

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