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

1) The narrative of 'parallel EVM' has always been characterized as a critical battle to bridge the gap between the outdated EVM ecosystem and high-performance chain technologies like Solana and Sui. Therefore, the previous market hype around Sei and the $225 million funding from @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. There has been no hype about funding, ICOs, or market expectations; they have simply been showcasing their testnet's stable thousands of TPS data. Recently, they announced a snapshot, clearly aiming to take the lead 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 and serial transaction order execution of EVM is an inherent limitation. Parallel EVM leverages the parallel computing power of modern hardware (CPU, GPU) and implements large-scale batch transaction execution by utilizing some I/O asynchronous storage processing, state access optimization, and so on.

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 a CUDA 'code translator', it converts general EVM opcodes into complex, computation-intensive tasks that can be executed on GPUs, along with other I/O asynchronous storage optimizations, state access management optimizations, and optimistic concurrency control, thereby achieving the capability of parallel transaction processing.

3) Since parallel EVM essentially exploits the performance advantages of 'hardware', AI application scenarios naturally require large-scale parallel computing and intensive processing. A powerful hardware setup can simultaneously utilize both parallel EVM and AI application scenarios. Thus, another layer of narrative imagination space for parallel EVM + AI has been opened.

The parallel EVM chain can facilitate the deployment of large AI models on-chain and allow smart contracts to directly control and schedule AI, while also applying ZK, TEE, and other related data privacy and verifiability capabilities, achieving a native integration of blockchain and AI. For example, real-time AI inference, AI Oracles, off-chain AI trading strategy optimization, and so on.