Data validation in decentralized AI computing: A structural shift towards completely flawless artificial intelligence @OpenledgerHQ , @gensynai , @OpenGradient Decentralized AI computing offers a fundamentally different solution to the question of how artificial intelligence is created and validated. In centralized environments, the assessment of where data comes from, whether the learning process was executed accurately, and whether the inference results are trustworthy has relied on the internal management, documentation, and audits of specific organizations. In a decentralized structure, the basis of this trust is placed not on organizations but on the technology itself, treating the entire process of artificial intelligence—data, computation, and inference—as separate verifiable layers. The starting point of this structure is the training data. AI models are built on vast amounts of data, but in traditional environments, it is difficult to verify the source and modification history of the data externally. OpenLedger plays a role in addressing this issue by tracking data integrity and provenance. In OpenLedger, data is not merely stored; it is clearly recorded who provided the data for what purpose and context. Data is registered in a data net organized by domain, and each contribution is recorded on-chain along with version information. This creates a structure that allows tracking which data was actually used for training a specific model and how that data influenced the results. This process prevents data from disappearing into a black box and leaves the relationship between data and model performance as verifiable facts. Once the data is prepared, the next step is the computing resources that perform the actual training. Gensyn provides a decentralized computing network that connects idle computing resources scattered around the world for AI training. The key here is not just to distribute the computation but to prove that the computation was performed correctly. Gensyn verifies the legitimacy of the training process through the Verde verification protocol. Training tasks are delegated to multiple participants, and in case of disagreements on the results, a method is used to identify error points without recalculating the entire process. This is made possible by a reproducible operator structure that strictly fixes the order of operations so that the same computational results emerge from different hardware. Thanks to this, the consistency of training results can be confirmed even in a decentralized environment, and verification costs are minimized. This structure also has clear characteristics in terms of cost and accessibility. Training using high-performance GPUs in centralized cloud environments requires high costs, but Gensyn aims to provide the same level of computation at a lower cost by utilizing idle resources. At the same time, verification that the computation was actually performed is handled through cryptographic procedures and game-based verification, replacing simple trust declarations with confirmations backed by technical evidence. Even after training is completed, verification does not end. There is a need to confirm whether the results are correct when the model actually performs inference. OpenGradient is responsible for the verification at this inference stage. In OpenGradient's structure, AI inference is executed within blockchain transactions, and the inference results are verified according to the selected verification method. The most robust method mathematically proves the accuracy of the computation through zero-knowledge proofs, and methods utilizing hardware-based trusted execution environments are also employed. In relatively low-risk situations, simpler methods relying on cryptoeconomic security may be applied. These various methods are chosen based on the importance, cost, and performance requirements of the inference. A characteristic of OpenGradient is that the inference process is not handled secretly outside the chain but is treated as part of the blockchain state transition. Model files are stored in distributed storage, and inference requests and results are linked through clear identifiers. This allows for post-verification of which model generated which output for a specific input using which verification method. The inference results are recorded not just as simple output values but as products of verifiable operations. In this way, OpenLedger, Gensyn, and OpenGradient each take on roles at different stages of data, training, and inference. In the data stage, provenance and contributions are tracked; in the training stage, the accuracy of computations is verified; and in the inference stage, the legitimacy of results is proven. These three layers are not bound into a single integrated system, but functionally form a continuous verification chain. It is a structure designed to ensure that no stage remains opaque. This data validation structure of decentralized AI computing demonstrates a change in how to make artificial intelligence more trustworthy. As the basis of trust shifts from the reputation or internal controls of companies to technical proof, AI systems inherently embed explainability and accountability. An environment where one can verify where data came from, how learning occurred, and whether inference was accurate treats artificial intelligence not as an opaque tool but as a verifiable computational system. This shows that decentralization goes beyond mere distribution to fundamentally reshape the structure of trust in artificial intelligence. $OPEN