Your L2 transaction fees are higher because of MEV spam, report
The post Your L2 transaction fees are higher because of MEV spam, report appeared on BitcoinEthereumNews.com.
A new report, titled MEV and the Limits of Scaling, explores the vast volume of MEV spam offsetting improvements in blockchain throughput. Effects of the extra traffic include higher fees for users of popular Ethereum “layer-two” (L2) scaling networks. Similar trends on Solana and other L2s led Flashbots to do their own deep dive into rollups built via the OP Stack (Optimism, Base, Unichain, and World). The findings show how spam transactions take up a significant portion of available blockspace whilst paying disproportionately lower fees for doing so. Today, we introduce a new thesis: MEV has become the dominant limit to scaling blockchains. Spectacularly wasteful onchain searching is starting to consume most of the capacity of most high-throughput blockchains. This is a market failure we can no longer ignore. pic.twitter.com/c92S3Sjznh — @bertcmiller ⚡️🤖 (@bertcmiller) June 16, 2025 Read more: DeFi trader hit by MEV attack swapped 440K USDC for just 10K USDT Maximal extractable value (MEV) is a practice that traditionally involves scanning the “mempool” of pending transactions to insert a profitable trade according to the actions of other users. Frontrunning, backrunning, and sandwich attacks are all common MEV tactics. The process tends to be highly specialised, leading to a dog-eat-dog world of bots battling for peak efficiency and the corresponding rewards. However, on rollups such as those studied, there is no public mempool. The high-throughput, low-fee environment instead allows bots to take a dragnet approach, submitting transactions that read prices across multiple on-chain exchanges. If a profitable price discrepancy is found, they take the arbitrage. If not, the transaction is aborted. The highly competitive winner-takes-all landscape of MEV means that the activity is heavily concentrated, with just two searchers being responsible for over 80% of the spam on Base, for example. Miller highlights one successful example on Base from…
