ETH2030 Experiment Overview
Ethereum’s long-range protocol roadmap may advance faster than anticipated as AI tools improve, according to Vitalik Buterin. He referenced a recent experiment by developer Jiayao Qi, posting as YQ on X, who unveiled ETH2030—an experimental client targeting the network’s draft “2030+” roadmap. This project spans 702,000 lines of Go code, covers 65 roadmap items across eight phases, passes 36,126 official Ethereum state tests, and syncs with mainnet through integration with go-ethereum v1.17.0. Qi constructed it in about six days using Claude Code at a cost of roughly $5,750 and 2.77 billion tokens.
“Such a thing built in two weeks without even having the EIPs has massive caveats. Almost certainly lots of critical bugs, and probably in some cases ‘stub’ versions of a thing where the AI did not even try making the full version. But six months ago, even this was far outside the realm of possibility, and what matters is where the trend is going.”
AI's Role in Development and Assurance
Buterin views AI as more than a tool for compressing development time; it could transform how Ethereum engineers handle assurance. He proposes allocating half of AI's speed gains to acceleration and the other half to security measures like generating more test cases, formal verification, and multiple implementations. This aligns with ongoing work in Lean Ethereum, where one collaborator used AI to produce a machine-verifiable proof of a complex theorem underpinning STARK security. Buterin noted: “A core tenet of @leanethereum is to formally verify everything, and AI is greatly accelerating our ability to do that. Aside from formal verification, simply being able to generate a much larger body of test cases is also important.”
Roadmap Targets and Challenges
ETH2030 serves primarily as a stress test for the roadmap rather than production software. Qi emphasized its role in surfacing hard engineering questions early. The implemented roadmap envisions Ethereum achieving over 10,000 TPS on L1, finality in seconds instead of 15 minutes, solo staking for 1 ETH, stateless nodes on a $7 Raspberry Pi, and more than 1 million TPS across L1 and L2. However, the experiment exposed deep interdependencies among upgrades like block access lists, gas repricing, PeerDAS, native rollups, and fast finality. Gaps persist: pure-Go cryptographic implementations trail production code by 10x to 100x, consensus logic lacks live beacon chain testing, and scaling from 5 million to 1 billion gas per second remains speculative under real-world MEV and contract patterns.
“Probably, the right way to use it, is to take half the gains from AI in speed, and half the gains in security. Generate more test-cases, formally verify everything, make more multi-implementations of things.”
Implications for Ethereum's Future
Buterin cautioned that AI will not eliminate issues like bugs or inconsistencies but can make addressing them 5x faster and 10x more thorough. The focus for Ethereum researchers and client teams lies in AI's potential to expedite both implementation and verification, potentially realizing the roadmap much faster and at higher security standards than expected.
