OORT’s DataHub has approximately 350,000 human contributors who contribute data for AI model training. This raises important questions about data provenance: • Who created the data? • Under what conditions was it collected? • Was consent obtained? • Can the origin and permitted use of the data be verified? These questions matter because trustworthy AI depends not only on the quantity of available data but also on its quality, origin, and the conditions under which it can be used. @oortech approaches this through its DataHub, Deimos infrastructure, and Layer 1 blockchain. DataHub brings human contributors into the process of collecting, labeling, and verifying AI training data. The goal is to help build datasets that are not just large, but useful, diverse, and reliable. But collecting good data is only part of the story. 𝗬𝗼𝘂 𝗮𝗹𝘀𝗼 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘄𝗵𝗲𝗿𝗲 𝗶𝘁 𝗰𝗮𝗺𝗲 𝗳𝗿𝗼𝗺 𝗮𝗻𝗱 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝗶𝘁𝘀 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 𝗰𝗮𝗻 𝗯𝗲 𝘁𝗿𝗮𝗰𝗲𝗱. That's where OORT's blockchain infrastructure comes in. Through its Olympus Protocol, OORT's Layer 1 blockchain, contributions can be recorded to support traceability and accountability across the data process. Meanwhile, Deimos 1 supports decentralized storage, while Deimos 2 provides distributed computing infrastructure. Together, these layers support the broader system that handles data and AI workloads. Why does all this matter? Because poor data can teach an AI model the wrong things. Data collected without proper consent can create serious ethical and legal problems. And when nobody can establish where training data came from, trust becomes difficult to earn. Data provenance helps address these problems by making the origin, history, and permitted use of data easier to establish. As AI becomes more powerful, knowing what a model learned from will matter just as much as knowing what it can do.