Podcast Ep.317——Blockchain in the AI Era: Why 0G Is Merging the "Three Major Fragmentations"

The 0G project for AI workload optimization highlights the growth potential and limitations of AI blockchain. A recent Messari research report shows that 0G is a modular infrastructure stack providing an integrated pipeline for data publishing, storage, and computation. Its main feature is the integration of inference, fine-tuning, storage, and data availability fee settlement into a single system through a Layer 1 “0G chain” compatible with EVM.

Traditional blockchain AI services face structural issues such as difficulty in result verification, low data processing efficiency, and insufficient task execution visibility. Some argue that while blockchain is suitable for sequential recording of state changes, it has limitations in storing massive data or verifying results generated by off-chain computations. Therefore, each project needs to connect separately to data availability networks, file storage networks, and computation networks, leading to unpredictable costs and operational complexity. 0G states that “a single settlement layer will coordinate the three core infrastructures to solve the distributed AI workload problem.”

The core of 0G includes four main components. First, the 0G chain adopts an EVM execution and consensus separation architecture, providing 11,000 TPS per shard and sub-second finality. By deploying data publishing and computation task settlement on distributed shards, it enhances traffic efficiency. Validator set management is handled via Ethereum-based Symbiotic execution, balancing security and flexibility.

Second, 0G DA is a data availability layer that uses erasure coding technology for redundant publishing of massive AI datasets and supports sampling verification. External Rollups can access this system, with actual data retrieved through an independent component, 0G storage. Third, 0G storage distinguishes between a fixed log layer and a dynamic key-value layer, enabling distributed storage from AI training archives to application operational data.

Finally, 0G Compute is based on GPU vendors’ inference and fine-tuning markets, capable of returning signed task results as receipts and settling on the 0G chain. Future plans include expanding to support model pre-training. Given current verification limitations, the project also plans to introduce trusted execution environments. Additionally, 0G employs smart NFTs carrying AI configuration information and a recognizable address system, “.0G domain,” to facilitate interaction mechanisms between AI agents and users.

Messari mentions competitors such as Celestia, EigenDA, and other DA networks, archival storage based on Filecoin and Arweave, GPU markets like Akash, Render, and io.net, as well as smaller bundled DeAI projects like AIOZ and Autonomys. Unlike these solutions, 0G emphasizes completing task execution, storage, verification, and settlement within a single address. However, if any integration layer experiences performance degradation or market rejection, it could hinder the adoption of the entire system, representing a significant risk.

The key in 2026 is whether the ecosystem plan and node incentives, along with initial funding, can be transformed into sustained actual demand. In particular, how to leverage the $88.88 million ecosystem fund to acquire partner applications and convert this demand into sustainable gas consumption and computing costs remains an unresolved challenge. Ultimately, 0G can be seen as “an attempt to compress the massive AI blockchain pipeline into a manageable single workflow.”