TLDR:
- Computation proves most scalable through parallel processing, hints, and cryptographic proofs without architecture changes.
- Data availability sits in middle tier, allowing erasure coding and graceful degradation despite strict guarantee requirements.
- State management presents hardest scaling challenge as transaction verification demands access to complete network state.
- Buterin advises replacing state with data and data with computation when possible without introducing centralization risks.
Ethereum co-founder Vitalik Buterin recently outlined the fundamental challenges of blockchain scaling through a detailed technical breakdown.
The analysis categorizes three distinct layers of blockchain infrastructure based on their scalability potential. Buterin’s framework addresses computation, data availability, and state management.
His assessment provides clarity on where developers should focus optimization efforts. The hierarchy reveals critical differences in how each component responds to scaling solutions.
Computation and Data Present More Flexible Scaling Options
Buterin identifies computation as the most scalable element in blockchain architecture. Developers can implement parallel processing to handle multiple calculations simultaneously. Block builders can provide hints that streamline verification processes.
Proof systems can replace extensive computational work with cryptographic verification. These methods allow networks to handle increasing transaction volumes without fundamental architectural changes.
Data availability occupies the middle position in this scaling hierarchy. Networks must guarantee data availability when required, leaving little room for shortcuts. However, Buterin notes that data can be split and distributed through erasure coding.
The scaling hierarchy in blockchains:
Computation > data > state
Computation is easier to scale than data. You can parallelize it, require the block builder to provide all kinds of “hints” for it, or just replace arbitrary amounts of it with a proof of it.
Data is in the…
— vitalik.eth (@VitalikButerin) January 27, 2026
PeerDAS represents one implementation of this approach. The framework also supports graceful degradation where nodes with limited capacity can produce proportionally smaller blocks.
The flexibility in scaling computation and data stems from their divisible nature. Block producers can optimize these elements through various technical approaches.
Networks can implement sharding for data without compromising security guarantees. This adaptability makes computation and data more manageable than state as blockchain networks expand.
State Management Remains the Most Challenging Scaling Problem
State represents the hardest element to scale in blockchain systems. Verifying a single transaction requires access to the complete state.
Merkle trees offer partial solutions by storing only the root hash. Yet updating that root still demands knowledge of the full underlying state.
Splitting state across multiple locations requires significant architectural modifications. These changes cannot be applied universally across different blockchain applications.
General-purpose solutions for state scaling remain elusive compared to computation and data. The fundamental requirement for complete state access creates inherent bottlenecks.
Buterin recommends replacing state with data whenever possible without introducing centralization. Similarly, replacing data with computation should be seriously considered when feasible.
This hierarchy guides developers toward the most effective scaling strategies. The framework helps teams identify where their optimization efforts will yield the greatest results while maintaining decentralization guarantees.
