The Unexpected Aftermath of Ethereum's Fusaka Upgrade
Ethereum’s journey toward greater scalability and efficiency recently took a significant step with the **Fusaka upgrade**, activated on December 3, 2025. This eagerly anticipated update aimed to supercharge the network’s **data availability capacity** by incrementally expanding blob targets and maximums. The core idea was simple: make more room for compressed transaction bundles, known as blobs, that **Layer 2 rollups** post to Ethereum for security and finality. By increasing throughput for this crucial data, the expectation was a tangible reduction in rollup costs. However, three months into its operation, the data emerging from the network tells a surprising story: while capacity has grown, utilization has not followed suit, and some unexpected challenges have surfaced at the network’s expanded edges.
Understanding the Fusaka Blueprint and Its Goals
Before Fusaka, Ethereum's baseline, established through EIP-7691, set a target of 6 blobs per block with a maximum of 9. The Fusaka upgrade, however, was designed for flexibility. It introduced a novel mechanism allowing Ethereum to adjust data availability parameters without requiring contentious hard forks. This was achieved through two sequential **Blob Parameter Override** adjustments.
These adjustments were implemented in two key phases:
- December 9, 2025: The first override raised the target to 10 blobs per block and the maximum to 15.
- January 7, 2026: A second adjustment further pushed the target to 14 blobs, with a maximum ceiling of 21.
These changes represented a substantial increase in the network's potential data throughput, a move widely celebrated as a foundational step for Layer 2 scaling. The beauty of this approach lies in its agility, enabling the network to dial capacity through client coordination, adapting more readily to evolving demand and technological advancements.
MigaLabs' Unsettling Findings: The Gap Between Capacity and Usage
Despite this significant expansion, a comprehensive analysis by MigaLabs, covering over 750,000 slots since Fusaka’s activation, paints a picture of underutilization. Their findings reveal that the network is simply not reaching the newly set target blob count of 14. Even more remarkably, the **median blob usage** actually saw a decline after the first parameter adjustment, falling from 6 blobs per block before the override to just 4 afterward. (It is worth noting that while the MigaLabs report's timeline text mentioned a target increase from 6 to 12, official Ethereum Foundation documentation indicates the first adjustment was from 6 to 10. We adhere to the official parameters: a 6/9 baseline, 10/15 after the first override, and 14/21 after the second, while treating MigaLabs' observed utilization data as empirical.) This suggests a curious gap between the available capacity and the actual demand from Layer 2 rollups. Blocks containing 16 or more blobs, which represent the higher end of the new capacity, remain exceedingly rare, appearing only a few hundred times across the entire observation period.
The report’s conclusion is stark:
No further increases in the blob parameter until high-blob miss rates normalize and demand materializes for the headroom already created.
This initial data challenges the assumption that limited blob availability was the primary bottleneck for Layer 2 growth.
The Reliability Riddle: Climbing Miss Rates at Higher Blob Counts
Beyond mere utilization, the MigaLabs report also delves into the network's reliability, measured through "missed slots." These are blocks that fail to propagate or attest correctly, indicating potential stress points. A clear and concerning pattern emerges: while the baseline **miss rate** for lower blob counts hovers around 0.5%, this figure climbs significantly as blocks incorporate more blobs. Once blocks reach 16 or more blobs, miss rates escalate, jumping to between 0.77% and 1.79%. At the maximum capacity of 21 blobs, introduced in the second override, the miss rate hits 1.79%, more than triple the baseline. This gradual degradation curve, accelerating past the 14-blob target, signals that Ethereum's underlying infrastructure, encompassing aspects like validator hardware, network bandwidth, and attestation timing, struggles to consistently manage blocks at the upper echelons of its expanded capacity.
What are the implications of this degradation? Should Layer 2 demand eventually surge to fully utilize the 14-blob target, or even push toward the 21-blob maximum, these elevated miss rates could translate into tangible finality delays or an increased risk of chain reorganizations. The report wisely frames this as a stability boundary: the network possesses the technical capability to process high-blob blocks, but doing so with consistent reliability remains an open question. This highlights that simply expanding capacity isn't enough; the network's ability to process that capacity without compromising stability is equally vital.
Beyond Capacity: The New Blob Economics with EIP-7918
Fusaka wasn't solely about expanding capacity; it also refined blob pricing through **EIP-7918**. This crucial update introduced a **reserve price floor**, a mechanism designed to prevent blob auctions from collapsing to a negligible 1 wei. Before this change, particularly when execution costs were dominant and blob demand remained low, the blob base fee could spiral downwards, effectively rendering it an insignificant price signal.
Why does this matter? Layer 2 rollups pay these blob fees to secure their transaction data on Ethereum. These fees are intended to reflect the actual computational and network costs imposed by blobs. When fees drop to near zero, the economic feedback loop breaks, allowing rollups to consume network capacity without contributing proportionally. This obscures genuine demand, making it harder for the network to gauge true utilization. EIP-7918's reserve price floor tackles this by tying blob fees to execution costs. This ensures that even during periods of soft demand, the price remains a meaningful signal, discouraging wasteful usage and providing clearer data for future capacity decisions. If fees stay elevated despite increased capacity, it signals genuine demand; if they consistently hit the floor, it confirms that headroom exists. Early observations from Hildobby's Dune dashboard suggest that blob fees have indeed stabilized post-Fusaka, halting the downward trend seen previously. This is a quiet but significant success for the upgrade.
Unpacking the Effectiveness: Where Fusaka Succeeded and Where It Stumbled
Reflecting on the initial three months post-Fusaka, the upgrade presents a mixed bag of results. On the one hand, it undeniably succeeded in its technical objectives: expanding data availability capacity and proving the efficacy of the Blob Parameter Override mechanism without necessitating disruptive hard forks. Furthermore, the introduction of the reserve price floor through EIP-7918 appears to be functioning as intended, stabilizing blob fees and ensuring they remain an economically meaningful signal.
However, the chasm between increased capacity and actual utilization remains the most prominent concern. The median blob usage declining after the first override, despite more space being available, strongly implies that Layer 2 rollups are not currently bottlenecked by blob availability. This could be due to several factors: perhaps their transaction volumes haven't yet grown to require more blobs per block, or they are becoming more efficient through advanced compression and batching techniques, fitting more transactions into fewer blobs. Blobscan, a dedicated blob explorer, shows that individual rollups are maintaining relatively consistent blob counts rather than aggressively leveraging the new headroom.
The pre-Fusaka narrative often focused on limited blob capacity as a critical bottleneck for Layer 2 scaling, predicting elevated rollup fees due to competition for scarce data availability. Fusaka addressed this capacity constraint directly, yet the bottleneck seems to have shifted. It now appears that other factors, such as sequencer economics, overall user activity on Layer 2 networks, or fragmentation across various rollups, are currently limiting growth more than the availability of blobs.
Looking Ahead: Infrastructure, Demand, and the Road to PeerDAS
Ethereum’s ambitious roadmap includes future advancements like **PeerDAS**, which promises an even more fundamental redesign of data availability sampling, offering further capacity expansion alongside improvements in decentralization and security. However, the insights gleaned from Fusaka’s early performance suggest that raw capacity isn't the most pressing constraint at this moment. The network possesses ample room to grow into its current 14/21 blob parameters before another capacity increase becomes genuinely necessary.
Moreover, the reliability curve, with its noticeable degradation at higher blob counts, serves as a crucial indicator. It suggests that infrastructure upgrades, including enhancements to validator hardware and network optimization, may need to catch up before the network confidently takes on further capacity expansions. Pushing capacity higher while blocks with 16 or more blobs still exhibit elevated miss rates risks introducing systemic instability, which could manifest acutely during periods of peak demand. The safer, more prudent path would involve allowing utilization to gradually rise toward the existing target, diligently monitoring whether miss rates improve as client software and network participants optimize for higher blob loads, and only adjusting parameters further once the network unequivocally demonstrates its ability to reliably manage these edge cases.
In essence, Fusaka’s success is a nuanced one. It masterfully expanded technical capacity and solidified blob pricing. Yet, it hasn't sparked an immediate surge in utilization, nor has it fully resolved the reliability challenges at its maximum capacity. The upgrade has effectively created a significant reservoir of headroom for future growth, but whether that growth will materialize and truly fill this space remains an open question, awaiting definitive answers from ongoing network activity and infrastructure evolution.
Post a Comment