After using coprocessors, I naturally ask: who will calculate so many proofs? If there is only one 'super server', both security and availability are very fragile. Lagrange's answer is to make 'proof generation' into a network as well—ZK Prover Network. This network is deployed on EigenLayer's restaking framework, with a group of market-validated node operators handling the tasks; the official statement is 'supports any proof type (AI, Rollup, applications, coprocessors, etc.), and has computing power provided by over 85 first-line operators', aiming to ensure proof generation has decentralization and censorship resistance. For developers, this means I don't have to build a bunch of machines to run large proofs but can use them on demand like 'buying cloud'.
The design focus of this network has two layers. The first layer is horizontal expansion: when large partners need a lot of bandwidth, they can 'cut a piece on demand' within the network, avoiding the bottlenecks faced by traditional monolithic proof systems; they write this as 'dynamically expanding horizontally based on demand, partners can obtain dedicated proof bandwidth'. The second layer is the specialization of operators: entities like Coinbase, Kraken, OKX, Ankr, etc., have already participated as AVS (Active Verification Service) operators, bringing stable infrastructure and also staking their 'reputation costs'. For someone like me who has done operations, both layers are critical.
I understand it as 'verifiable computing cloud': applications submit tasks to the Gateway, the network distributes tasks to Workers, and after the Worker generates proofs, they are returned for verification on-chain by the caller. The open-source repository describes this very clearly—operators need to deploy Worker binaries, continuously listen for tasks, and generate proofs; this makes the addition of 'new computing power' a replicable SOP. As more AVS join, the production capacity of proofs can expand on demand. For activities with significant traffic fluctuations (such as cross-chain queries, leaderboard verifications, batch signature verifications), this is more stable than 'building a GPU farm'.
Why did I specifically write an article only about the 'proof network'? Because this is precisely the premise for coprocessors to step out of the laboratory: if proofs can only be generated in a 'centralized computing pool', then applications still end up back at the old path of 'I need to trust a certain institution'. By placing proof generation in the restaking network, it both increases the attackers' costs exponentially (to do evil, they must attack a large number of nodes) and gives developers a 'portable' choice—today using official bandwidth, tomorrow switching to third-party operated bandwidth, or even temporarily renting more during peak business times. Replaceability and elasticity are the soul of the cloud.
Of course, networking also brings new governance issues. For example: how to fairly distribute tasks? How to randomly check and punish the operators' online rates and SLAs? How to quickly isolate malicious or faulty proofs? These issues cannot be solved with just a slogan. I tend to view 'monitoring and auditing' as a productized capability—publicly traceable panels for task distribution and completion, exposing 'P95 latency and failure rate for each operator over the past X days', and disclosing the reasons for exclusions. As long as these metrics can be public and transparent, developers' choice of who computes the proofs will no longer be a black box.
From the application's perspective, the Prover Network also has a very practical benefit: making the cost structure clear. In the past, it was difficult for us to provide an explicit quote for 'doing complex things on-chain'; now we can separate the 'proof computing cost + on-chain verification Gas' for accounting, and use bandwidth packages to reduce the unit price during the activity period. Predictability and budgeting are key to upgrading the business from 'experimental' to 'product'. For someone like me who needs to pull budgets, this is more important than any 'cool technology' description.
Whether you are a Lagrange user or not, a decentralized proof network will be a keyword in Web3 in the coming years. Those who solidify and make this layer transparent will be more qualified to be called upon as 'standard components' by first-line applications. Currently, I will continue to monitor three things: whether the scale of operators continues to grow, whether the latency/failure rate of tasks is steadily declining, and whether SLA and penalties are truly implemented. As long as these three things continue positively, the application of coprocessors will naturally expand, rather than relying on hard marketing.
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