Ethereum scaling pitfalls: Blob surge ends up crushing the network

Ethereum’s Fusaka upgrade was supposed to be a boon for Layer2, but three months later, an awkward reality has been exposed: increasing Blob capacity for scalability has made the network more prone to failures under high load. A recent report from research firm MigaLabs points out that Ethereum still faces physical and network bottlenecks when handling large-scale data throughput, and blindly increasing capacity may have the opposite effect.

The “Reverse Trap” of Blob Scaling

Rapid Iteration from 9 to 21

The Fusaka upgrade was deployed in December 2025, with the core goal of providing more efficient data channels for Layer2. Before the upgrade, each Ethereum block could carry up to 9 Blob data packets. According to the roadmap, this capacity could eventually be increased to 72 (an 8x increase).

However, the pace of expansion after the upgrade was unexpectedly fast:

Ethereum Foundation executive Alex Stokes openly admitted that this is a very new technology, and the network’s performance under extreme conditions is uncertain. But market enthusiasm seemed to outweigh this caution.

Emerging Problems: Higher Capacity, More Fragile Network

MigaLabs’ findings shatter this dream. The organization observed that when a block approaches its Blob limit, it often causes subsequent block propagation failures or delays. In other words, to allow Layer2 to process more data, Ethereum becomes more unstable at certain times.

Leonardo Bautista Gomez, founder of MigaLabs, straightforwardly states that this is not alarmism but a genuine warning to core developers: before fully understanding network feedback, capacity should not be blindly increased.

The Root Cause: Physical Bottlenecks and Incentive Games

Propagation Pressure on Distributed Nodes

Under high data loads, distributed nodes face real physical and network bottlenecks when synchronizing large amounts of information. Simply put, when a block contains 21 Blob packets, thousands of nodes need to download and verify this data in a very short time, revealing limitations in network topology and bandwidth.

“Time Game” Exacerbates Instability

Sam Calder-Mason, an engineer from PandaOps under the Ethereum Foundation, pointed out another issue: validators motivated to maximize MEV have an incentive to delay block publication. In the case of high-Blob blocks, such delays are amplified, further destabilizing the network.

This presents an incentive-layer contradiction: scaling requires higher throughput, but existing MEV incentives conflict with stability goals.

Current Status and Future Directions

Network Still in Safe Zone, but Needs Shift

Sam Calder-Mason emphasizes that the overall network is not in danger at present. But this is a critical moment: before further scaling, Ethereum needs to deploy more efficient data propagation mechanisms.

What does this mean? Possibly:

• Optimizing data synchronization protocols between nodes
• Improving validator incentive structures to reduce MEV-induced delays
• Gradually rather than aggressively increasing Blob capacity
• Enhancing monitoring and emergency response mechanisms

From a Layer2 Perspective

Related information indicates that Ethereum is gradually transforming into a settlement and coordination layer. Bitfinex’s report notes that Ethereum’s daily transaction volume has hit a record high (about 2.88 million transactions), yet average fees remain low, demonstrating the effectiveness of Layer2 scaling.

But this transition depends on the stability of the mainnet. If high-Blob blocks frequently cause propagation failures, Layer2 advantages could be undermined.

Future Outlook

The ongoing technical game around Blob and Layer2 scaling has become a key topic in Ethereum’s 2026 roadmap. The developer community needs to strike a balance in three areas:

1. Throughput: Meeting the increasing data demands of Layer2
2. Stability: Ensuring network reliability under high load
3. Decentralization: Avoiding sacrificing node participation thresholds for scaling

Failing to find a balance among these could make future Ethereum data layer expansion more challenging than expected. The current technical dilemma shows that scaling is not just about parameter adjustments but requires systemic optimization across infrastructure, incentive mechanisms, and network topology.

Summary

Ethereum’s Fusaka upgrade was well-intentioned, but three months of practice have revealed a paradox: higher capacity leads to greater network pressure. Warnings from MigaLabs and PandaOps deserve attention because they point to a deeper issue—the current Ethereum infrastructure is not yet capable of supporting aggressive throughput increases.

The key is not the raw number of Blob capacity but whether Ethereum can maintain decentralization while solving issues like data propagation and validator incentives across multiple dimensions. This may be more challenging than any single technical upgrade.