OPEN Comparative Analysis with Similar Projects: Bittensor, Fetch.ai, SingularityNET. During my research on OPEN, I am often asked: What is the difference between it and Bittensor, Fetch.ai, SingularityNET? Why does the market give it a separate valuation space? To answer this question, we must place it within the entire AI × Blockchain landscape and make a horizontal comparison. 1. Bittensor (TAO): Network Effects of Computing Power and Models. The logic of Bittensor is to build a distributed AI model market using blockchain, where nodes provide computing power and train models. Its advantage lies in significant network effects, having already gathered a large number of developers and validation nodes in its early stages. Its market value once exceeded $3 billion, nearly a dozen times that of OPEN currently. However, Bittensor's weakness lies in its greater emphasis on computing power and the training process, lacking sufficient focus on data rights confirmation and profit-sharing mechanisms. 2. Fetch.ai (FET): AI Agents and Automation Applications. Fetch.ai focuses on AI agents and automated application scenarios, such as smart travel and decentralized IoT. Its current market value is in the range of $500 million to $800 million. Fetch.ai's advantage is that its application scenarios are intuitive and easily understood by users; however, the problem is that its ecosystem remains primarily experimental and has yet to form a strong universal value settlement network. 3. SingularityNET (AGIX): AI Service Market. SingularityNET proposed the concept of a decentralized AI service market as early as 2017, allowing developers to publish and invoke AI services on the platform. Its current market value is approximately $1 billion to $1.5 billion. Its shortcomings include a slower pace of ecosystem development, with a gap between on-chain invocation volume and narrative. 4. OPEN's Differentiated Positioning. In contrast, OPEN's core lies in: 1. Data Rights Confirmation: Through Datanets, transforming data into on-chain, tradable assets. 2. Model Profit Sharing: Through Model Factory and OpenLoRA, writing model invocation into contracts for automatic revenue distribution. 3. Settlement Layer Logic: OPEN's positioning is more like Ethereum, serving as a foundational settlement network rather than a single-point application. This means that OPEN's value capture logic leans more toward infrastructure. If the future AI industry requires a transparent and fair settlement layer, then its potential valuation space could theoretically align with Ethereum's position in the financial world. 5. My Judgment. In the short term, OPEN's market value is less than $200 million, a significant gap compared to TAO, FET, and AGIX. However, in the medium term, as data and model transactions land on-chain, its value may be gradually reassessed. In my view, OPEN's competitive advantage is not to 'replace' these projects but to fill the gaps in the industry. It addresses the profit-sharing issue of data and models, which existing AI blockchain projects have not fully resolved. In summary: Bittensor has seized computing power, Fetch.ai has seized agents, SingularityNET has seized services, while OPEN has seized the 'settlement layer.' In the AI × Blockchain landscape, this piece of the puzzle is crucial, and it is the reason I continue to be optimistic about it.