While the market is buzzing about storage hardware investments and staking thresholds, there's a rarely mentioned aspect: how much electricity is actually burned by continuously running storage proofs.

Take Walrus as an example. Its proof design is much simpler than some other storage networks, but looking at it from a different perspective reveals another issue. The high-frequency random challenges and rapid response requirements mean nodes must always be ready with computing resources. This isn't a traditional storage server setup; in fact, nodes are acting as some kind of "lightweight proof machines."

What does this consumption look like specifically? Verifying erasure coding fragments and quickly retrieving data segments needed to respond to network challenges constantly gnaw at CPU resources. In AI training data storage scenarios, if the data itself is encrypted or encoded, the computational complexity of each proof generation jumps to a higher level.

More critically, this consumption isn't a one-time shovel work; it's a continuous overhead that grows linearly with the amount of stored data. Node operators must spread the electricity costs and hardware depreciation across their pricing; otherwise, the books won't balance.

Where are the current risks? If the network only prices and rewards based on storage capacity and bandwidth, completely ignoring the cost of proof computation, operators will eventually resort to using underperforming computing units to cut costs. The result? The entire network's response speed and reliability will decline, creating a clear shortcoming.

Looking further ahead, there's an even more subtle issue. To optimize proof computation efficiency, nodes might gradually standardize on the same hardware configuration—CPUs or accelerators all coming from the same mold. While this indeed improves overall network efficiency, the cost is a reduction in hardware diversity, which weakens the foundation of decentralization.

This means protocol design must be especially cautious, finding a true balance between cryptographic security and accessibility of ordinary hardware. Pursuing perfect mathematical design should not push node operation into a professionalized, centralized computing race. This is not just a technical issue; at its core, it's a philosophical choice about how the entire network should be governed.