Seeing this post from @0G_labs about DeAi, I also have something to say, so I’ll make a popular science post! 1. Why is there a conflict between "TradAI vs. DeAI"? Traditional AI (TradAI) is dominated by a few large cloud service providers and model publishers: model training and inference are centralized in the cloud, with data and access being controlled! However, weak explainability and audit trails, along with high barriers to entry and costs, hinder widespread participation. Decentralized AI (DeAI) reconstructs AI data through distributed storage, peer-to-peer computing, on-chain/off-chain verification, and tokenized economies, allowing for value sharing among more participants through computational incentives. Both academia and industry view DeAI as an important direction to address the "drawbacks of centralization." —— —— ————— ʕ·͡ˑ·ཻʔ ————— —— —— 2. Market opportunities? Currently, there are few blockchain-level infrastructures specifically designed for AI (balancing data availability and verifiable computing power). If 0G can differentiate itself in performance, cost, and developer experience, it can quickly attract AI-centric projects, as DeAI can provide new business pathways for data providers, computing power providers, and application builders! —— —— ————— ʕ·͡ˑ·ཻʔ ————— —— —— ➟ Of course, while competing with @KaitoAI, we also know that TGE is approaching! The biggest risk in choosing the #DeAI track among #AI is the imbalance in token issuance, staking, and distribution, which can lead to price volatility and security issues! This might also be one reason for the delay in TGE, but we certainly look forward to 0G's performance as an AI public chain in the future! #KAITO #Yap