FIL is indeed following the logic of "selling shovels" in AI, but the core reason for not reaping the benefits is: it sells "cold storage shovels," while NVIDIA sells "production line shovels."
🔧 The "temperature" of the shovel determines its value
• NVIDIA (hot shovels): sells HBM (High Bandwidth Memory) and GPUs. AI training and inference involve processing "hot data," requiring millisecond-level responses; NVIDIA's hardware is directly bottlenecked by computing power, indicating a rigid demand.
• FIL (cold shovels): sells distributed storage. Currently, AI mainly stores data locally or in centralized clouds (AWS); FIL excels at storing cold data (like training set backups, historical data archiving). AI has not yet reached the stage where core hot data is being thrown into decentralized networks on a large scale, so demand is lagging.
📉 Why hasn’t FIL risen?
1. The narrative is cashing in too slowly: issues of compliance, speed, and cost in AI data storage have not been fully resolved, and the "necessity" of decentralized storage has not been fully accepted by the market.
2. Oversupply: The FIL network has a vast storage capacity, but the proportion of real and effective data orders is low, leading to an oversupply of "shovels" and preventing prices from rising.
💡 What about your FIL?
Don’t worry, wait for the rotation. TAO and NEAR are the "vanguard" of AI computing power, rising quickly; FIL is the "guard" of data infrastructure, strong in defense. Funds rotate, and when computing power is driven up to high levels, capital will naturally flow back to seek "data lowlands." As long as the FVM ecosystem and AI data demand rise, the value of FIL's "shovels" will eventually be priced.
🔧 The "temperature" of the shovel determines its value
• NVIDIA (hot shovels): sells HBM (High Bandwidth Memory) and GPUs. AI training and inference involve processing "hot data," requiring millisecond-level responses; NVIDIA's hardware is directly bottlenecked by computing power, indicating a rigid demand.
• FIL (cold shovels): sells distributed storage. Currently, AI mainly stores data locally or in centralized clouds (AWS); FIL excels at storing cold data (like training set backups, historical data archiving). AI has not yet reached the stage where core hot data is being thrown into decentralized networks on a large scale, so demand is lagging.
📉 Why hasn’t FIL risen?
1. The narrative is cashing in too slowly: issues of compliance, speed, and cost in AI data storage have not been fully resolved, and the "necessity" of decentralized storage has not been fully accepted by the market.
2. Oversupply: The FIL network has a vast storage capacity, but the proportion of real and effective data orders is low, leading to an oversupply of "shovels" and preventing prices from rising.
💡 What about your FIL?
Don’t worry, wait for the rotation. TAO and NEAR are the "vanguard" of AI computing power, rising quickly; FIL is the "guard" of data infrastructure, strong in defense. Funds rotate, and when computing power is driven up to high levels, capital will naturally flow back to seek "data lowlands." As long as the FVM ecosystem and AI data demand rise, the value of FIL's "shovels" will eventually be priced.