Why Prover Economics Favor Large, Batch-Based Rollups

Proving hardware (e.g., GPUs, ASICs) and specialized engineering talent represent massive fixed costs. A small rollup processing 10 TPS cannot amortize a $1M+ proving setup cost, making its per-transaction fee untenable.

• Economies of Scale: Cost per transaction plummets as batch size increases.
• Natural Monopoly: Large, established rollups like Arbitrum and Optimism have an insurmountable cost advantage over new entrants.

Proof generation cost is sublinear; proving 1M transactions costs far less than 1000x the cost of proving 1000. This makes maximizing transactions per batch the primary economic goal.

• Shared Sequencing: Protocols like Espresso and Astria enable rollups to build larger, cross-rollup batches.
• Proof Aggregation: EigenDA and Avail provide cheap data availability, enabling massive batches without on-chain bloat.

Dedicated provers for each rollup are inefficient. The future is general-purpose proving networks like RiscZero, Succinct, and Lumoz that aggregate proof workloads across many clients.

• Commoditized Security: Rollups rent proving power, eliminating capital expenditure.
• Optimized Hardware: Prover networks can run specialized ASICs or FPGAs at full utilization, driving costs toward the thermodynamic limit.

High fixed costs for proving hardware (e.g., GPU/ASIC clusters) create a natural monopoly. Small rollups cannot afford dedicated provers, creating a market dominated by a few providers like Espresso Systems or EigenLayer AVS operators. This centralizes a critical security function.

• Risk: Single points of failure for dozens of L2s.
• Consequence: Prover collusion or failure threatens the entire batch-based ecosystem.

Prover profitability is a direct function of batch size and fee density. During bear markets or low-activity periods, revenue plummets while fixed costs remain, forcing provers offline.

• Trigger: Sustained low transaction fees or TVL outflow.
• Outcome: Remaining provers raise prices, increasing L2 costs and further depressing activity—a negative feedback loop.

When a major prover serving multiple rollups (e.g., via EigenDA or a shared sequencing layer) fails or is exploited, downtime or invalid proofs cascade across all dependent chains. This systemic risk is analogous to the cloud provider outage problem.

• Vector: A bug in the prover software or hardware.
• Amplification: A single failure can halt $10B+ in aggregated TVL.

The capital required to compete in proving disincentivizes experimentation with novel VMs or proof systems. The ecosystem consolidates around a few optimized, general-purpose VMs (like the EVM or WASM) because that's where the prover economics work.

• Result: ZK-ASICs are built for dominant VMs only.
• Opportunity Cost: More efficient or specialized architectures (e.g., RISC Zero, SP1) are locked out.

Batch-based models are doubly dependent on external DA. High-volume periods strain Ethereum calldata or alternative DA layers like Celestia or EigenDA, creating a bidding war for block space. The rollup's cost and throughput are now tied to another volatile market.

• Dependency: L2 security ≠ L1 security if using a lighter DA layer.
• Bottleneck: Throughput is ultimately capped by the chosen DA layer's capacity.

Batch-based rollups embrace modularity, outsourcing sequencing, DA, and proving. This creates a complex, latency-prone stack where each module takes a profit margin. Integrated monolithic chains (e.g., Solana, Monad) avoid these coordination costs and latency, offering a simpler, potentially more efficient user experience.

• Competition: Modular chains compete on cost, monolithic on performance.
• User Reality: Most users don't care about modularity; they care about finality speed and cost.

Generating a validity proof has a high, fixed computational overhead regardless of transaction count. A single transaction proof costs nearly as much as a batch of 10,000. This makes small, frequent batches economically unviable.

• Key Benefit 1: High-throughput chains amortize the fixed cost over more transactions.
• Key Benefit 2: Creates a minimum viable batch size for profitability, estimated at ~1,000-10,000 tx.

Provers compete for the right to prove the most valuable batches. High-volume rollups like zkSync Era, Starknet, and Polygon zkEVM attract prover pools with higher revenue share and lower marginal costs, starving smaller chains.

• Key Benefit 1: Large L2s can negotiate ~20-30% lower proving fees via competitive markets.
• Key Benefit 2: Creates a liquidity moat; developers follow users, and users follow low fees.

To survive, emerging rollups must aggregate demand. Shared sequencers (e.g., Espresso, Astria) batch transactions from multiple rollups, creating a pseudo-volume pool to hit economic batch sizes and attract provers.

• Key Benefit 1: Enables viable proving for sub-1k TPS chains.
• Key Benefit 2: Turns sequencing into a strategic resource, similar to MEV-boost for Ethereum.

The proving cost curve will be defined by ASIC/FPGA adoption. Chains with predictable, high-volume workloads (e.g., dYdX, Immutable X) will vertically integrate with hardware prover networks for ~100x cost reductions, locking in structural advantages.

• Key Benefit 1: Predictable workloads enable custom silicon, unlike general-purpose L1s.
• Key Benefit 2: Creates a capital-intensive barrier to entry for new prover markets.