Lagrange's recent technological advancements in the Web3 space, especially breakthroughs around zero-knowledge proofs (ZKP) and verifiable AI, are drawing considerable attention. This project is not emerging from thin air but is solidly addressing real pain points in the industry—such as the data processing efficiency issues in blockchain and the trustworthiness challenges of AI decision-making.
The DeepProve-1 they launched can now be regarded as the first zkML system that can be deployed in a production environment, capable of cryptographic verification of complete LLM inference. What does this mean? AI is no longer a 'black box'; its outputs become auditable and verifiable. This is especially crucial for ecosystems like DeFi that rely heavily on transparency and trust. For example, a lending protocol can introduce AI for dynamic risk assessment while leveraging DeepProve-1 to prove that its decisions have not been tampered with.
Let’s look at another core technology of theirs, Dynamic SNARKs. It is particularly good at handling continuously changing on-chain data, enabling incremental updates rather than full recalculations. For example, a DeFi project can use this to track collateral value changes in real time, without having to repeatedly process all historical records, resulting in a significant efficiency improvement.
In terms of cross-chain capabilities, Lagrange's approach is also distinctive. It does not rely on traditional models like multi-signature bridges but builds security on the foundation of ZK cryptography. Cross-chain state verification is completed through a decentralized proof network, freeing itself from dependence on additional trust assumptions and significantly reducing common security risks.
From an ecological collaboration perspective, Lagrange has already partnered with Matter Labs, and will take on a significant amount of zkProof outsourcing tasks in the future—potentially up to 75%. This sends a signal that the Rollup ecosystem is shifting key computational tasks to decentralized proof networks.
The progress of the testnet is also worth noting. The Euclid Testnet Phase 1 has already handled over 81,000 queries and generated 610,000 proofs, covering various data call types such as cross-chain, NFT, and SQL. Notably, it takes an average of only 1 minute to verify a million storage bits, demonstrating the project's implementation capabilities.
The economic design of the token emphasizes multi-party participation and incentive compatibility. Nodes earn rewards through staking, developers enjoy fee discounts by paying with tokens, and holders can share in the ecological profits. This design helps maintain network activity and participant engagement during bear markets.
In July, Lagrange completed a $17 million financing round and was also included in the Binance HODLer airdrop program, distributing 15 million $LA tokens to BNB holders. The dual support of funds and the community provides a solid foundation for its subsequent development.
In my view, what Lagrange is doing is essentially laying the foundation for the field of 'verifiable computing.' It has chosen not to create a closed system but instead provides infrastructure for other developers through a modular and composable tech stack. This open approach aligns better with the collaborative spirit of Web3 and makes it easier to accumulate long-term ecological value.
Whether enhancing DeFi's composability or bridging the trust gap between AI and blockchain, Lagrange's explorations indicate that future digital services will not be built solely on a single dimension such as efficiency or security, but must achieve trustworthiness, efficiency, and self-verifiability simultaneously.