💡 The collision of Web3 and AI brings both opportunities and challenges!
1️⃣ Decentralized AI training: The bottleneck of on-chain training lies in the GPU communication delay and increased bandwidth cost. Although decentralized networks have potential, there are still challenges in efficiency.
2️⃣ AI data iteration: AI requires a large amount of data iteration, but in a decentralized environment, data processing is difficult, and the lack of mature tools makes this process more complicated.
3️⃣ Consensus problem of reasoning results: In order to ensure the accuracy of AI reasoning results, repeated calculations are proposed, but the current shortage of high-end AI chips makes this solution expensive and difficult to popularize.
4️⃣ Web3's AI use case market: The market is not yet mature, demand is small and unstable, and business expansion faces many difficulties.
5️⃣ Consumer-grade GPU DePINs: Although decentralized computing networks are suitable for low-end AI reasoning tasks, data centers are still the first choice for companies that require high reliability and high-end GPUs.
🔮 The integration of Web3 and AI is still in the exploratory stage, with challenges and opportunities coexisting, and the future is promising!
