The two directions of on-chain AI: Talus_Labs' computational parallelization and irys_xyz's data immutability The MoveVM architecture of Talus Network and Irys' Dual-Ledger structure show distinct philosophical differences in how they handle AI workloads. Talus maximizes parallelism at the execution layer through an object-centric parallel execution structure, while Irys secures scalability for data-intensive tasks through a dual ledger structure that separates storage and execution. Talus executes non-conflicting transactions in real-time parallel using the Block-STM parallel processing mechanism, making it suitable for concurrent decision-making and transaction execution by AI agents. In contrast, Irys achieves processing speeds of over 100,000 transactions per second and low latency by handling data validation and permanent storage in parallel through the separation of the submission ledger and the publication ledger. Talus' MoveVM inherits Sui's object-based model, automatically analyzing transaction dependencies at runtime and processing operations on independent objects in parallel. This structure is optimized for parallel inference and real-time collaboration among AI agents, with over 95% of simple transactions not going through the consensus process, allowing for the potential of millions of transactions per second. On the other hand, Irys implements parallelization centered around the data layer, where data that has undergone light validation in the submission ledger is promoted to the publication ledger, gaining immutability and permanence. This process eliminates contention between storage and execution, fundamentally resolving bottlenecks in block space. Talus' resource model is based on the linear type system of the Move language, managing all assets and states at the object level, preventing replication or implicit creation. This ensures that resource ownership transitions and access control are explicitly managed, maintaining safety even during parallel processing. In contrast, Irys secures the integrity and immutability of stored data through Merkle tree-based cryptographic proofs and a global data replication structure. Data must complete sufficient replication proofs before being promoted to the publication ledger, and the dual ledger structure satisfies both the permanence and access speed of the data. While Talus performs static verification at the compilation stage, Irys verifies integrity through cryptographic proofs after execution. This difference indicates that Talus prioritizes security and formal stability, while Irys emphasizes efficiency and flexibility in large-scale data environments. The former is optimized for high-frequency, low-volume operations such as real-time decision-making by autonomous agents, while the latter is optimized for low-frequency, high-volume operations such as storing model training data and sharing large-scale inference results. In terms of resource management, Talus adopts a fine-grained gas charging structure at the object level to keep parallel operation costs predictable, while Irys stabilizes long-term storage costs through a fixed pricing system based on physical storage units (GB, TB). The average cost per operation for Talus is around $0.001, while Irys' cost for permanent storage is $0.05 per GB, making it more than 20 times cheaper than competing protocols. The philosophy of the verification system is also different. Talus blocks the possibility of errors at the pre-deployment stage through formal verification using the Move Prover, while Irys secures continuous reliability of data through post-verification methods using Merkle roots and storage proofs. The former is summarized as a security-centric proactive model, while the latter is a post-verification model centered on large-scale data. As a result, Talus is suitable for environments with low tolerance for errors, such as autonomous trading agents or real-time DeFi orchestration, while Irys shows strengths in areas that require continuous verification of large datasets, such as dataset management, distributed learning, and DePIN networks. The development ecosystems are also differentiated. Talus focuses on development tools and formal verification frameworks based on the Move language, while Irys can utilize Solidity and existing EVM toolchains, lowering the entry barrier. Talus has an advantage in security but has a steep learning curve, while Irys is advantageous in development speed and accessibility. The former is on-chainifying AI workflows through integration with Sui and the Nexus framework, while the latter allows for direct data verification and inference calls from EVM contracts through the scalable storage operations of IrysVM. Overall, both Talus and Irys show performance improvements of over 100 to 1000 times compared to traditional sequential execution models, but their optimization directions differ. Talus is specialized in computation-centric parallel inference and real-time agent collaboration, while Irys focuses on data-centric permanent storage and accessibility of large-scale AI models. Therefore, Talus is suitable for environments where execution bottlenecks are the main constraint, while Irys is suitable for environments where data availability is the key constraint. When the two systems are combined complementarily, they can form an ideal combination that efficiently covers both the execution layer and data layer of the on-chain AI ecosystem.