I carefully read the interview with @SaharaLabsAI co-founder Sean @xiangrenNLP over the weekend, which contains many in-depth thoughts about AI+web3. Here are my views around the brilliant points: 1) Sahara AI Captures an Overlooked "Time Window" Sean mentioned that AI development currently faces centralization issues in resources, talent, and data acquisition capabilities, with a few large companies controlling key elements for training larger models. I believe this judgment hits the nail on the head. Currently, the market is chasing innovation in the AI Agent application layer, but Sahara AI chooses to do "production relationship reconstruction" at a deeper level. The timing is quite clever. The AI industry indeed faces a contradiction: technological capabilities are increasingly strong, but the entry threshold is getting higher. The trend of centralization in data, computing power, and talent is becoming more pronounced, which ironically gives blockchain a chance to "attack from another dimension". Essentially, Sahara AI is using the distributed collaborative thinking of Crypto to solve the monopolization problem in the AI industry. This perspective is much more profound than simply working on the application layer of AI+Crypto, and in some ways, offers more imaginative space. 2) "Off-chain Computation + On-chain Trust" Technical Path Sean emphasized the technical architecture of "off-chain computation + on-chain trust", implementing AI's off-chain operation and on-chain evidence through TEE technology. I find this technical approach quite practical and smart. Compared to projects trying to move all AI computation on-chain, using TEE to create a trusted execution environment and then using on-chain smart contracts to handle rights confirmation and profit sharing, this hybrid architecture ensures performance while achieving decentralization. It's essentially leveraging the strengths of each approach. The key insight of this path is: not to reinvent AI, but to redefine AI ownership and value distribution. Technical pragmatism makes business model innovation possible. 3) Data Rights Confirmation and Reshaping of AI Industrial Chain Sean mentioned using smart contracts to automatically and transparently distribute earnings to all participants, establishing a truly "decentralized production relationship". What I find most interesting is Sahara AI's redefinition of data value. In the traditional model, data annotators and model trainers' contributions are often "one-time buyouts", but Sahara AI wants them to continuously benefit from the long-term success of AI applications. In other words, turning "workers" into "partners". Once this model works, it could fundamentally change the value distribution logic of AI. Imagine if every data contributor could receive a share from AI applications using their data - the entire AI industry's incentive mechanism would undergo a fundamental transformation. This isn't a minor improvement, but a restructuring of the entire industry's interest structure. 4) Challenges of DeAI Sean mentioned that instead of completely relying on AI-generated data, the future should be "AI collaborating with humans", with humans handling long-tail scenarios AI cannot cover. This view is indeed forward-looking, because no matter how powerful AI models are, they will have edge cases they cannot handle, and human creativity and contextual understanding can precisely make up for this shortcoming. However, the biggest challenge Sahara AI faces is also here: can decentralized data annotation and model training compete with centralized solutions in terms of quality and efficiency? After all, the reason Microsoft and Google can train powerful AI models is largely due to their centralized resource allocation capabilities. The ideal of distributed collaboration is beautiful, but whether it can overcome the efficiency advantages of centralization in reality remains to be verified. Overall, the path Sahara AI has chosen might be the direction with the most "fundamental transformation" potential in the current DeAI track. Because it's not doing incremental innovation within existing rules, but trying to rewrite the rules themselves. The success probability is unknown, but if successful, the impact would be disruptive. In other words, we've been thinking about how existing chains can adapt to and carry AI, but have we considered starting by reconstructing the chain architecture itself? To some extent, there might be new Crypto species beyond Ethereum's smart contract chain in the future. After all, when technological paradigms fundamentally change, infrastructure often gets reshaped accordingly.