Fusaka Upgrade: How Ethereum’s New Release Solves Old Problems and Benefits Crypto Investors

Fusaka Upgrade: How Ethereum’s New Release Solves Old Problems and Benefits Crypto Investors

Ethereum’s Fusaka upgrade, activated in early December, is one of the most consequential changes to the network since the Merge and Dencun. Rather than a cosmetic tweak, Fusaka directly targets Ethereum’s most persistent structural problems: data availability bottlenecks, mispriced Layer 2 (L2) costs, validator hardware pressure, and under-monetized network usage.

The upgrade bundles a set of Ethereum Improvement Proposals (EIPs) focused on:

• Radically improving data availability via PeerDAS
• Making blob capacity dynamically scalable through Blob Parameter Only (BPO) forks
• Fixing the blob fee market so Ethereum actually captures value from L2 usage
• Increasing Layer 1 (L1) capacity and hardening it against denial-of-service (DoS) vectors
• Laying groundwork for better settlement assurances and future “based rollups”

For crypto investors, Fusaka is not just a technical milestone. It reshapes Ethereum’s economic model, reinforcing ETH’s role as a yield-bearing, fee-burning asset at the center of a rapidly scaling rollup ecosystem.

This article unpacks:

• What was broken in Ethereum’s pre-Fusaka design
• How Fusaka’s core components work and what they change
• On-chain and economic implications, especially for L2s
• Competitive positioning vs other L1s
• Key risks and negative scenarios
• Bull / base / bear scenarios for Ethereum post-Fusaka

1. Pre-Fusaka Ethereum: What Needed Fixing

1.1 Data Availability as the True Scaling Bottleneck

Since the Dencun upgrade (EIP‑4844), Ethereum scaled L2s through blobs: temporary data packets where rollups post compressed transaction data to L1 for settlement.

Pre-Fusaka, this design had a major flaw:

• Every full node had to download and verify the entire blob data
• Blob capacity was capped (via target and max blobs per block), and raising it would have linearly increased hardware and bandwidth requirements
• As L2s like Arbitrum, Optimism, and Base scaled to hundreds of millions of monthly transactions and tens of billions in TVL, this model pushed validator requirements toward centralization: only well-capitalized operators could keep up

Research estimates showed validators facing tens to hundreds of gigabytes of additional storage over relatively short windows if blob usage ramped, even under target usage assumptions. This created a hard ceiling on sustainable scaling.

In practice, this meant:

• L2 fees could not fall indefinitely; they were constrained by L1 data availability costs
• Ethereum risked becoming a bottleneck for its own scaling strategy

1.2 Blob Fee Economics: A Broken Market

The second structural problem was blob pricing.

Under the Dencun regime:

• Blob base fees often fell to 1 wei (effectively free) during low demand
• This did not reflect the real cost of verifying and propagating the data
• L2s enjoyed heavily subsidized data availability, while validators and ETH holders did not capture proportional value

Historical analysis (e.g., from Fidelity Digital Assets Research) retroactively applied a minimum blob fee floor to the Dencun–October 2025 period and found:

• On 93% of days, blob fees would have been higher with a floor
• An estimated $78.6 million (around 24,641 ETH) in cumulative revenue was not captured by the protocol in that timeframe

In other words, Ethereum was providing a valuable service (secure data availability) at a steep discount, undermining ETH’s value accrual.

1.3 L1 Execution Constraints and DoS Exposure

Beyond blobs, L1 itself had notable constraints:

• The block gas limit was relatively conservative, constraining throughput
• There was no strict cap preventing a single transaction from consuming nearly a full block’s gas, leaving room for DoS-style attacks and worst‑case latency spikes
• Some cryptographic precompiles (e.g., MODEXP) were mispriced, allowing attackers to stress the network with underpriced operations

This limited both:

• The number of ordinary L1 transactions that could be processed, and
• The amount of L2 settlement activity that could safely be handled

1.4 Sequencer Centralization and Weak Settlement Assurances

The rollup-centric roadmap introduced another structural weakness: centralized sequencers.

Most major rollups used:

• A single sequencer (often run by the rollup team or a single entity)
• That sequencer captured all L2 MEV and controlled transaction ordering

This created:

• A centralization risk and single point of failure
• A mismatch between Ethereum’s decentralization and the more centralized infrastructure of its L2s
• Limited ability for Ethereum L1 validators to participate in or capture value from L2 ordering

Moreover, validators lacked deterministic knowledge of future block proposers, which:

• Complicated advanced settlement assurances
• Limited the ability to implement fast, trust-minimized preconfirmations

2. Core Fusaka Innovations: How the Upgrade Works

Fusaka addresses these pain points through a combination of protocol changes. The most important are PeerDAS, Blob Parameter Only forks, the blob fee floor, L1 execution improvements, and deterministic proposer lookahead.

2.1 PeerDAS: Scaling Data Availability Without Centralization

PeerDAS (Peer Data Availability Sampling), introduced via EIP‑7594, is the centerpiece of Fusaka.

What PeerDAS Changes

Pre-Fusaka:

• Every node downloaded all blob data to verify availability

With PeerDAS:

• Nodes sample only small, random pieces of blob data
• They rely on a peer-to-peer sampling protocol and coding-theory techniques to verify that all data is available with very high statistical certainty
• No node needs to store the entire blob; instead, data is sharded across peers

The impact:

• Bandwidth and storage per node drop by roughly 85–90% relative to a naïve “download everything” model
• The network can safely increase blob capacity without making node operation prohibitively expensive

This directly addresses centralization pressure: scaling no longer requires every validator to scale hardware linearly with blob throughput.

Capacity Roadmap via PeerDAS

Fusaka is not a one-shot jump to massive capacity; it is a staged rollout:

• Before Fusaka, blob targets were modest (e.g., 3–6 blobs per block with a max of 9)
• After Fusaka, the network can progressively move toward much higher blob counts, with a roadmap that conceptually points toward full danksharding (e.g., long-term targets like 128 blobs per block appear in roadmap discussions)

Practically, PeerDAS enables:

• Incremental increases in blob capacity (see BPO forks below)
• A path to orders-of-magnitude more data throughput without sacrificing decentralization

For L2s, more blobs mean:

• More space to post compressed transactions
• Lower per-transaction data availability cost, as the same demand is spread over more capacity

Industry estimates cited in the research suggest that, combining PeerDAS and progressive capacity increases, L2 fees could fall by 40–90% versus pre-Fusaka levels, depending on usage and competition.

2.2 Blob Parameter Only Forks: Fast, Targeted Scaling

Blob Parameter Only (BPO) forks, introduced via EIP‑7892, are a governance and upgrade mechanism innovation.

The Problem They Solve

Previously:

• Changing blob-related parameters (like target blobs per block) required a full hard fork, bundled with many other changes
• This made Ethereum slow to adjust to real-world L2 demand

With BPO forks:

• Ethereum can perform small-scope upgrades that change only blob parameters
• These can be scheduled more frequently and with lower coordination overhead

How They’re Used

Post-Fusaka, the network follows a staged plan:

• BPO1: Activated shortly after Fusaka, increasing blob targets and maxes
• BPO2: Scheduled about a month later, raising them again

Between each BPO:

• Core devs and client teams monitor blob utilization, fee behavior, validator performance, and client stability
• If the network remains healthy, blob capacity is increased again

This creates a feedback loop:

1. Increase capacity modestly
2. Observe network behavior
3. Adjust again if safe

For investors, this means:

• Ethereum can respond to demand much faster than in the old “one big fork per year” model
• There is a credible, observable path to progressively cheaper L2 fees and higher throughput

2.3 Blob Fee Floor: Turning L2 Usage into ETH Revenue

EIP‑7918 (“Blob base fee bounded by execution cost”) tackles the mispriced blob market.

How the Fee Floor Works

• Blob base fees are now anchored to the L1 execution base fee
• The mechanism sets a minimum blob base fee as a fraction of the L1 base fee (e.g., a ratio on the order of 1/15.258 as described in research)
• This prevents blob fees from collapsing to effectively zero

Consequences:

• L2s can no longer externalize data availability costs onto Ethereum for free
• The protocol now captures meaningful revenue from blob usage

Historical Backtest and Revenue Potential

Backtesting the fee floor over the Dencun–October 2025 period showed:

• On 93% of days, the fee floor would have increased blob fees
• The protocol would have captured an estimated $78.6 million more in revenue (about 24,641 ETH)
• Average additional cost per blob would have been around $6

For a large rollup like Base, which submitted over 13 million blobs in that period:

• The research estimated that annual blob costs would have risen from roughly $5.2 million to about $30.6 million under the new floor

Forward-looking analysis in the same research suggests that:

• Combining PeerDAS scaling with the blob fee floor could generate on the order of hundreds of millions of dollars in annual ETH burn (e.g., in the $550–850 million range by 2026 under conservative L2 growth assumptions)

This is crucial for investors:

• More L2 activity → more blobs → higher blob fee revenue → more ETH burned and higher staking rewards
• Fusaka aligns Ethereum’s economic incentives with the growth of its L2 ecosystem

2.4 L1 Execution Upgrades: More Capacity, Less Risk

Fusaka also upgrades Ethereum’s base layer execution environment.

Key changes include:

Higher Block Gas Limit (EIP‑7935)

• The default block gas limit is raised (e.g., from 45M to 60M gas)
• This is roughly a 33% increase in per-block computational capacity

Implications:

• More user transactions can fit into each block
• There is more room for L2 settlement and complex smart contract operations
• Congestion pressure is reduced, which can moderate L1 gas spikes

Per-Transaction Gas Cap (EIP‑7825)

• Each transaction is capped at a maximum of about 2²⁴ gas (~16.77M gas)

This prevents:

• A single transaction from monopolizing an entire block’s gas, a common DoS vector
• Unpredictable worst-case block times and throughput

It also lays groundwork for:

• Parallel execution, where multiple transactions can execute concurrently without risk of one transaction dominating the block

Cryptographic and DoS Hardening

Several EIPs tighten cryptographic operations and block size constraints:

• EIP‑7823: Caps input sizes for the MODEXP precompile, preventing huge, underpriced operations
• EIP‑7883: Reprices MODEXP gas costs to better match actual computation
• EIP‑7939: Adds a Count Leading Zeros (CLZ) opcode, making certain bit operations more gas-efficient and reducing bytecode size
• EIP‑7934: Enforces a maximum RLP block size (e.g., 10 MB), limiting oversized payloads that could stress propagation

Net effect:

• A more predictable and robust L1 environment
• Reduced attack surface for gas mispricing and block-size exploits
• Better performance for contracts relying on cryptographic primitives

2.5 Deterministic Proposer Lookahead and Preconfirmations

EIP‑7917 introduces deterministic proposer lookahead:

• At the start of each epoch, the protocol can compute who will propose which blocks in that epoch
• This schedule is known in advance to validators and potentially to users

Why this matters:

• It enables preconfirmations: validators that know they will propose an upcoming block can commit in advance to include specific transactions
• This can deliver sub-second settlement assurances comparable to centralized systems, but anchored in Ethereum’s consensus

It also paves the way for:

• Based rollups, where Ethereum L1 validators themselves act as L2 sequencers
• In such architectures, MEV and ordering fees from L2s can flow directly to Ethereum validators, further integrating L2 economics into ETH staking returns

3. On-Chain and Market Metrics: What Fusaka Changes in Practice

While the research block does not provide a full live dataset of post-Fusaka on-chain metrics, it does give several important quantitative anchors and projections.

3.1 Historical Blob Revenue Gap

• An estimated $78.6 million (24,641 ETH) in revenue was left uncaptured between Dencun and late October 2025 due to near-zero blob fees
• On 93% of days, a fee floor would have increased revenue

Post-Fusaka, with EIP‑7918 live:

• This leakage is plugged
• As L2 usage continues to grow, a larger share of that activity translates into ETH-denominated revenue (and burn)

3.2 L2 Cost Structure Impact

The research highlights:

• L2s’ primary cost driver is L1 data availability (blobs)
• PeerDAS and higher blob capacity can cut L2 fees by 40–90% versus pre-Fusaka levels

However, this interacts with the fee floor:

• Capacity increases push unit costs down
• The fee floor prevents prices from collapsing to unreasonably low levels

The net result is:

• L2 fees likely trend downward in absolute terms, especially as more blobs per block are enabled
• Ethereum captures more revenue per unit of L2 activity than in the Dencun era, even if per-transaction L2 fees fall

3.3 ETH Value Accrual and Burn

The research projects:

• The combined effect of PeerDAS scaling and the blob fee floor could yield hundreds of millions of dollars in annual ETH burn (e.g., $550–850 million by 2026 under conservative assumptions)

For ETH holders:

• This enhances the deflationary pressure introduced by EIP‑1559 and the Merge
• It strengthens the narrative of ETH as a productive asset, where staking yields are backed by real economic activity (L1 + L2)

4. Ethereum’s Positioning After Fusaka

4.1 Ethereum as a Rollup-Centric Settlement Layer

Fusaka accelerates Ethereum’s transition from a “smart contract chain” to a global settlement and data availability layer for a rollup ecosystem.

Key positioning elements:

• Scalability: PeerDAS and BPO forks provide a credible path to massive throughput via L2s
• Economic alignment: The blob fee floor and preconfirmations align L2 success with ETH value accrual
• Security: L1 remains the final arbiter of settlement, with improved DoS resistance and execution capacity

The result is a layered model:

• L1: High‑value settlement, state roots, and data availability
• L2: High-throughput execution, user-facing applications, cheaper transactions
• ETH: The asset tying it all together via staking, gas, and fee burn

4.2 Comparison with Alternative L1s

Many alternative L1s (e.g., high-throughput monolithic chains) compete on:

• Higher raw TPS at L1
• Lower transaction fees
• Simpler developer experience (no need to think about L2s/blobs)

Fusaka does not turn Ethereum into a monolithic chain; instead, it doubles down on modularity. The trade-off:

• Ethereum L1 remains relatively resource-conservative, but
• The combined capacity of Ethereum + L2s can exceed monolithic competitors, while preserving Ethereum’s decentralization and security assumptions

Below is a conceptual comparison based on the research narrative (not specific numeric TPS):

From an investor’s perspective, Fusaka strengthens Ethereum’s modular moat:

• L2s can be extremely cheap and fast
• Ethereum remains the trusted settlement and DA layer, now with better economics

5. Who Benefits and How: Investor-Centric View

5.1 ETH Holders and Stakers

Fusaka improves ETH’s investment profile in several ways:

• Higher fee capture from L2 activity via the blob fee floor
• Increased burn from higher blob volumes and L1 gas usage
• Potentially higher staking yields, as validators share in a larger fee pool, especially if future based-rollup designs route L2 MEV to L1 validators

For long-term holders:

• ETH becomes more clearly a “network equity-like” asset, whose cash flows (burn + staking yield) scale with usage

5.2 L2 Ecosystem and dApp Builders

For L2s and builders:

• Cheaper DA from PeerDAS and higher blob capacity lowers operating costs
• This enables lower end-user fees, making rollups more competitive with alternative L1s and centralized services
• The improved L1 execution environment and preconfirmations enhance UX and reliability

However, there is a trade-off:

• The blob fee floor raises minimum costs versus the Dencun-era “almost free” regime
• Large L2s will pay substantially more in absolute blob fees, but will likely offset this via higher volume and lower per-transaction costs

5.3 Institutions and Enterprise Users

Fusaka addresses several institutional concerns:

• Predictable performance: DoS hardening and per-tx gas caps reduce tail-risk events
• Clearer economics: The blob fee floor and improved value accrual make ETH’s economic model easier to underwrite
• Settlement assurances: Deterministic proposer lookahead and preconfirmations allow for stronger SLAs and faster confirmations

As institutional adoption of Ethereum-based infrastructure grows, these features reduce friction in:

• On-chain finance and tokenization
• Enterprise rollups and private L2s anchored to Ethereum
• Regulated entities’ risk assessments of Ethereum as core infrastructure

6. Risks and Negative Scenarios

Fusaka is a major upgrade with clear benefits, but it also introduces new risks and trade-offs. Investors should be aware of several categories of downside scenarios.

6.1 Technical and Implementation Risks

• Client bugs or consensus issues: PeerDAS and BPO forks are non-trivial changes. Any implementation bug could cause chain splits, downtime, or unexpected behavior
• Sampling assumptions: PeerDAS relies on probabilistic guarantees. If assumptions about peer behavior or network topology fail, data availability guarantees could be weaker than expected
• Complexity creep: Each new mechanism (PeerDAS, BPO, fee floor, proposer lookahead) increases protocol complexity, potentially raising the risk of subtle exploits

While Ethereum’s client diversity and testing culture mitigate these risks, they cannot be eliminated.

6.2 Economic and Incentive Risks

• L2 cost shock: The blob fee floor significantly increases costs for large rollups compared to the artificially cheap Dencun era. If L2s misprice their fees or cannot pass costs to users, their economics could be stressed
• Fee market dynamics: If L2 demand is weaker than expected, the fee floor might not generate the projected revenue or burn. Conversely, if demand is extremely strong, fees could spike more than anticipated
• Validator incentives: As more revenue comes from blobs and potentially L2 MEV, validator incentives may become increasingly tied to L2 activity. This could create new forms of MEV competition or cartelization risks

6.3 Competitive and Strategic Risks

• Alternative L1s: High-throughput competitors may attract users and developers by offering simpler, monolithic scaling and lower cognitive overhead (no blobs, no L2 bridging)
• Other DA solutions: Specialized data availability layers (outside Ethereum) may offer cheaper DA, luring rollups away from Ethereum’s blob market if they can provide comparable security for specific use cases
• Rollup fragmentation: If some L2s choose alternative DA or settlement layers, Ethereum’s share of rollup value capture could be diluted

6.4 Governance and Coordination Risks

• BPO fork mis-tuning: Aggressive blob capacity increases via BPOs could outpace what the network can safely handle, leading to instability
• Coordination failures: While BPOs are simpler than full hard forks, they still require coordination among clients, validators, and infrastructure providers. Misaligned incentives or communication failures could delay or fragment upgrades

6.5 Regulatory and Macro Risks

• Regulatory classification of ETH: As ETH becomes more clearly tied to fee revenue and staking yields, regulators may scrutinize its status more closely
• Macro environment: If global liquidity tightens or risk appetite falls, even strong fundamental improvements may not translate into positive price action

7. Scenario Analysis: Bull, Base, and Bear

Without giving price targets, we can outline qualitative scenarios for Ethereum’s trajectory in the Fusaka era.

7.1 Scenario Table

7.2 Bull Case: Ethereum as the Modular Standard

In the bullish scenario:

• PeerDAS operates smoothly, and successive BPO forks safely increase blob capacity
• L2s pass on lower DA costs to users, driving mass adoption of rollups for payments, DeFi, gaming, and enterprise use cases
• Institutional players increasingly use Ethereum as their primary settlement and DA layer
• Based-rollup architectures mature, routing a significant share of L2 MEV and fees to Ethereum validators

Under this scenario:

• ETH’s fee-derived cash flows (burn + staking rewards) grow strongly
• ETH is perceived as a core infrastructure asset for global on-chain activity
• Ethereum’s competitive moat against monolithic L1s deepens as the ecosystem and tooling around rollups expand

7.3 Base Case: Steady Growth and Consolidation

In the base case:

• Fusaka works as intended, but adoption of higher blob capacities is gradual
• L2 usage grows, but not explosively; some segments remain on alternative L1s or centralized platforms
• Ethereum retains its position as a leading smart contract and settlement platform, but with a more competitive landscape

Here:

• ETH continues to benefit from structurally improved economics, but the realized revenue and burn are closer to the mid-range of projections
• Investors treat ETH as a blue-chip crypto asset with robust fundamentals but not necessarily outsized returns relative to the broader sector

7.4 Bear Case: Technical or Strategic Missteps

In the bear scenario:

• PeerDAS or related changes encounter technical issues, such as unexpected edge cases or client bugs
• BPO forks are delayed or rolled back due to instability
• L2s explore alternative DA layers or other L1s, diluting Ethereum’s role in the modular stack
• Regulatory or macro shocks dampen on-chain activity broadly

Under such conditions:

• The projected revenue and burn uplift from Fusaka fails to materialize
• ETH’s narrative as a “productive, deflationary asset” weakens
• Competing ecosystems may gain relative traction, especially if they can offer simpler, more predictable environments

8. Synthesis: What Fusaka Really Means for Ethereum

Fusaka is best understood as Ethereum’s pivot from subsidized scaling to sustainable scaling:

• Pre-Fusaka, Ethereum subsidized rollups with almost-free blobs and imposed rising hardware demands on validators
• Post-Fusaka, Ethereum offers massively scalable data availability via PeerDAS, but at a fair market price enforced by the blob fee floor

The upgrade:

• Solves old problems:

  • Data availability bottlenecks and validator centralization pressure
  • Blob fee mispricing and under-monetized L2 usage
  • L1 execution constraints and certain DoS vectors

• Creates new opportunities:

  • Lower L2 fees and better UX via higher blob capacity
  • Stronger ETH value accrual through fee burn and staking rewards
  • Institutional-grade settlement assurances via deterministic proposer lookahead and preconfirmations

For crypto investors, the key takeaway is not a specific short-term price move, but a structural shift:

• Ethereum is now architected to scale with its own success rather than be constrained by it
• As L2s grow, ETH’s economic engine strengthens instead of being diluted

The ultimate outcome will depend on real-world adoption, execution quality, and competition. But Fusaka significantly improves the fundamental alignment between Ethereum’s technology, its economic model, and the interests of long-term ETH holders.