Why Performance Requires Sacrificing EVM Dogma

EVM determinism is a bottleneck. The strict, single-threaded execution model that guarantees identical state transitions across all nodes is the primary constraint on throughput. Parallel execution, as seen in Solana or Sui, requires abandoning this guarantee at the execution layer.

State growth is unsustainable. The EVM's global state model, where every node must store all contract data, creates exponential hardware demands. Solutions like Verkle trees or stateless clients are architectural pivots that move away from the original 'everyone verifies everything' dogma.

Gas metering limits innovation. Precise opcode pricing prevents optimizations like precompiles for complex operations (e.g., ZK proofs). High-performance L2s like Arbitrum Nitro and zkSync Era implement custom precompiles and gas rules, creating fragmentation.

Evidence: The EVM's theoretical limit is ~100 TPS. Monad targets 10,000+ TPS by implementing parallel execution, asynchronous I/O, and a custom state database, explicitly breaking EVM dogma to do so.

EVM equivalence is a performance bottleneck. The EVM's single-threaded execution model and 256-bit word size create inherent inefficiencies that limit throughput and latency for high-frequency applications.

High-performance chains must specialize. Networks like Solana and Monad optimize by using parallel execution engines, native fee markets, and custom VMs, sacrificing seamless compatibility for order-of-magnitude gains in speed and cost.

The future is multi-VM. The ecosystem is converging on a modular stack where specialized execution layers (Fuel, Eclipse) run alongside EVM rollups, connected by interoperability protocols like LayerZero and Wormhole.

Evidence: Arbitrum Stylus demonstrates this shift, enabling Rust/C++ smart contracts within an EVM rollup to achieve 10-100x faster computation for specific workloads, proving that hybrid architectures win.

EVM Opcode Inefficiency: The EVM's 256-bit word size and complex opcodes like SHA3 create massive proof overhead. ZK-native VMs like zkSync's zkEVM and Starknet's Cairo use smaller, RISC-style instructions that generate proofs 10-100x faster.

Storage Layout Dogma: The EVM's Merkle-Patricia Trie is a proof-generation bottleneck. High-throughput chains like Polygon zkEVM and Kakarot adopt sparse Merkle trees or custom storage models, reducing proof costs by orders of magnitude.

Synchronous Execution Constraint: The EVM's strict, sequential block execution prevents parallel proof generation. Architectures like zkRollup and validium separate execution from settlement, enabling concurrent proof batching for scalability.

Evidence: A single EVM SSTORE operation in a ZK circuit requires ~20,000 constraints, while a similar write in a custom VM like Cairo uses under 1,000. This 20x difference defines the performance frontier.

EVM compatibility fragments liquidity. Every new L2 with a custom precompile or opcode creates a new, isolated liquidity pool. This forces developers to deploy separate contracts and users to bridge assets across incompatible environments like Arbitrum and Optimism, increasing capital inefficiency.

Tooling requires standardization. Frameworks like Foundry and Hardhat, and indexers like The Graph, are built for a single, stable instruction set. Custom EVM forks force teams to maintain proprietary forks of these tools, a hidden tax on developer velocity and security.

Equivalence enables atomic composability. A truly equivalent EVM, like what Monad is building, allows smart contracts and liquidity to exist in a single, unified state. This eliminates the need for bridging protocols like Across or LayerZero for intra-ecosystem moves.

Evidence: The 30+ EVM L2s have spawned over 100 fragmented liquidity bridges. This fragmentation directly increases slippage and reduces capital efficiency, a measurable performance deficit versus a single, high-throughput equivalent chain.

EVM equivalence is a performance trap. Syntactically matching the EVM's execution model forces chains to inherit its single-threaded bottlenecks and gas overhead, capping throughput at the design level of Ethereum L1.

Semantic compatibility is the escape hatch. Chains like Monad and Sei V2 implement the EVM's application logic but replace its underlying execution engine, enabling parallel processing and native optimizations that break the 30 TPS ceiling.

The trade-off is developer friction. Projects like Aptos Move and Sui demonstrate that a superior VM with different semantics can win, but it requires developers to learn new paradigms, slowing initial adoption.

Evidence: Monad's pipelined execution and MonadDB target 10,000 TPS for EVM applications, a 300x increase over Arbitrum Nitro, by discarding syntactic fidelity for semantic intent.