“Arbitrum,” as the system was called, would have suffered the same fate as most other promising academic computer science projects, if not for two ambitious PhD students, Steven Goldfeder and Harry Kalodner, who approached Felten a few years later with the idea of building out a robust layer 2 solution based upon the initial concept. The objective was to circumvent some of the anticipated scaling challenges of smart contract platforms, and the plan was to design a blockchain that relied on a system of challenges and dispute resolution to lighten the computational workload for traditional miners. On a frigid Princeton morning six and a half years ago, a group of undergraduates working with Professor Ed Felten delivered a presentation on the project they had signed up to build: a blockchain-based arbitration system. As it happens, both have somewhat distinctive origin stories. Early beginningsįirst, some brief historical background about each project is in order.
These are tradeoffs that merit discussion, given that both platforms aim to offer full scaling functionality for Ethereum over the coming months. In particular, the differences in their respective approaches to dispute resolution produce some important performance tradeoffs. While Arbitrum and Optimism, the leading Optimistic Rollups, share much in common, it’s not just tribal loyalties that separate the two. Something similar is true within each category of rollups. Nevertheless, as we saw in the first part of this mini series, the single distinction between Optimistic and Zero Knowledge Rollups-how the “review process” works on each-generates a host of downstream differences in security, usability, and EVM compatibility.
While feigning objectivity is futile, my hope is that this piece helps the reader to grasp some of the crucial differences between the two projects, biased though I may be.Īll rollups follow a similar basic architecture and internal logic. I recently participated in Offchain Labs’ latest fundraising round, as did Mechanism Capital. I want to note my implicit bias at the outset.