After reading the latest white paper released by @ reddio_com, it is true that automated AI execution has been integrated into the overall narrative of EVM, which is equivalent to filling the gap in the AI track direction of the entire Ethereum ecosystem. Very Make Sense, so why can parallel EVM seamlessly integrate with AI? What is the logic and technical principle behind it? Let me briefly explain my understanding: 1) The narrative of "parallel EVM" has always been characterized as a crucial battle to bridge the gap between the outdated EVM ecosystem and the iteration of high-performance chain technologies such as Solana and Sui. Therefore, the previous market speculation expectations for Sei and the massive $225 million financing of @ monad_xyz have pushed parallel EVM to unprecedented heights. In contrast, Reddio, as a parallel EVM public chain led by Paradigm, seems to be much more low-key. It does not speculate on market expectations such as financing, ICO, KOL rounds, etc., but has been sharing its testing network's stable TPS data of tens of thousands. Recently, the official announcement of the snapshot clearly indicates that we need to take the lead and verify the ecological value of parallel EVM in the Ethereum ecosystem. 2) So, why is parallel EVM an effective supplement to the technological bottleneck of the Ethereum ecosystem? Simply put, the original processing method of single threaded execution and serial execution of transactions in EVM is a congenital limitation. Parallel EVM utilizes the parallel computing capabilities of modern hardware (CPU, GPU), as well as some I/O asynchronous storage processing, state access optimization processing, etc., to achieve simultaneous execution of large-scale batch transactions. The technical implementation logic revealed in the Reddio white paper is roughly as follows: Reddio has an execution network composed of GPU nodes, and through CUDA "encoding translator", ordinary EVM opcodes are transformed into complex and intensive computing tasks that can be implemented within the GPU. In addition, other I/O asynchronous storage optimization, state access management optimization, optimistic concurrency control, and so on, realize the ability to process transactions in parallel; 3) Since parallel EVM essentially leverages the performance advantages of "hardware", AI application scenarios naturally require large-scale parallel computing and intensive computational processing. A powerful set of hardware can simultaneously parallel EVM and AI application scenarios to achieve effectiveness. So, another layer of narrative imagination space for parallel EVM+AI is opened up. Parallel EVM chains can achieve the deployment of AI large-scale models on the chain, allowing smart contracts to directly control and schedule AI, while also applying data privacy and verifiability capabilities such as ZK and TEE, which can achieve the native integration of blockchain and AI. For example, AI real-time reasoning AI Oracle、 Off chain AI trading strategy optimization, etc.