I watched the latest white paper published by @reddio_com, and it indeed integrates automated AI execution into the grand narrative of EVM, effectively filling the gap in the entire Ethereum ecosystem in the AI track direction. It makes a lot of sense. So, why can parallel EVM seamlessly connect with AI? What is the underlying logic and technical principle? Let me briefly explain my understanding:

1) The narrative of 'parallel EVM' has always been characterized as a key battle to bridge the gap between the old, outdated EVM ecosystem and high-performance chain technologies like Solana and Sui. Therefore, the previous market hype around Sei and the massive $225 million funding from @monad_xyz pushed parallel EVM to unprecedented heights.

In contrast, Reddio, as a parallel EVM public chain led by Paradigm, seems to have kept a low profile, with no hype around funding, ICOs, or KOL rounds, but has instead been consistently showcasing its testnet's stable throughput of over ten thousand TPS. Recently, an official snapshot was announced, clearly aiming to take the stage first and validate the ecological value of parallel EVM in the Ethereum ecosystem.

2) So, why is parallel EVM an effective supplement to the technical capability bottlenecks of the Ethereum ecosystem?

In simple terms, the original single-threaded execution + serial execution in transaction order of EVM is a fundamental limitation. Parallel EVM utilizes modern hardware (CPU, GPU) for parallel computing capabilities, combined with some I/O asynchronous storage processing, state access optimization, etc., to achieve the 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 uses a CUDA 'code translator' to convert general EVM opcodes into complex, compute-intensive tasks that can be executed on GPUs, along with other I/O asynchronous storage optimizations, state access management optimizations, optimistic concurrency control, etc., to achieve 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 computation processing. A powerful hardware setup can simultaneously serve both parallel EVM and AI application scenarios. Thus, another layer of narrative imagination space for parallel EVM + AI has been opened up.

Parallel EVM chains can enable the deployment of large AI models on-chain and allow smart contracts to directly control the scheduling of AI, while also applying capabilities related to data privacy and verifiability such as ZK, TEE, etc., achieving the native integration of blockchain and AI. For instance, AI real-time inference, AI Oracle, off-chain AI trading strategy optimization, etc.