Why Prover Efficiency Gains Are Hitting a Wall of Diminishing Returns

Parallelization of proof generation is hitting a wall. The serial component of the proving algorithm (e.g., FFTs, MSMs) cannot be infinitely parallelized, creating a hard floor on latency.\n- Key Constraint: Even with infinite GPUs/ASICs, a ~30% serial fraction limits max speedup.\n- Real Impact: Moving from ~10 seconds to ~1 second is exponentially harder than the initial gains.

The ROI on specialized hardware (ASICs, FPGAs) for zkEVMs is diminishing. The cost to shave off the next 100ms of proof time is growing non-linearly.\n- Economic Reality: A $10M ASIC investment for a 15% speed boost is a poor trade for most L2s.\n- Market Shift: Focus moves from raw speed to cost-effective throughput (proofs/hour/$).

Recursive proofs (proofs of proofs) are the endgame for scaling, but their aggregation efficiency peaks. Each recursion layer adds fixed overhead, creating a logarithmic, not linear, scaling benefit.\n- Bottleneck: Verifier circuit complexity grows, offsetting gains.\n- Strategic Implication: Projects like Polygon zkEVM, zkSync, and Scroll must optimize for batch economic finality, not just single-proof latency.

Major proving system families (PLONK, Groth16, STARKs) have seen marginal improvements post-2022. The low-hanging fruit in polynomial commitments and constraint systems is gone.\n- Current State: Research is incremental, not revolutionary.\n- Next Frontier: Requires breakthroughs in cryptography (e.g., Binius, new hash functions) not just engineering.

Sequential proof generation is hitting physical limits. Doubling hardware spend yields <20% speedup. The industry's ~$1B+ investment in GPU/ASIC farms is hitting a wall of diminishing returns.\n- Amortization is linear: Each new proof is a new cost.\n- Hardware is a commodity: No sustainable moat.

Aggregate many proofs into one. A single recursive proof can verify thousands of transactions or even entire block batches. This changes the economic model from pay-per-tx to pay-per-batch.\n- Sub-linear cost scaling: Final proof cost grows slower than batch size.\n- Enables L3s & Hyperchains: Recursion is the bedrock for scalable sovereignty.

Move complexity off-chain. Let a solver network compete to fulfill user intents, batching liquidity and settlement. This shifts the prover's job from computing all paths to verifying a single optimal outcome.\n- Proves outcomes, not paths: Drastic reduction in circuit complexity.\n- Leverages existing liquidity: Aggregators like 1inch and CowSwap become data sources.

Decouple proof generation from execution. A decentralized network of provers sells compute as a commodity, creating a market for proving power. This turns a capital-intensive fixed cost into a variable, competitive utility.\n- Capital efficiency: No single protocol bears full hardware cost.\n- Fault tolerance: Redundant proving via networks like Succinct.

Parallelizing proof generation hits diminishing returns as serial components (e.g., FFTs, MSMs) become the bottleneck. Throwing more GPUs yields sub-linear speedups, capping cost reductions.

• Amdahl's Law dictates max theoretical speedup.
• Serial bottlenecks like MSM tree accumulation remain.
• Cost per proof plateaus despite more hardware.

Proof systems like Plonky2 and Halo2 are memory-bound, not compute-bound. GPU VRAM bandwidth is the primary constraint, not raw FLOPs.

• Data shuffling between CPU/GPU dominates latency.
• VRAM size limits circuit complexity per batch.
• Bandwidth costs don't follow Moore's Law scaling.

Major breakthroughs like FFT (Fast Fourier Transform) and MSM (Multi-Scalar Multiplication) optimizations are largely exhausted. Future gains require quantum-resistant or novel polynomial commitments with steep R&D timelines.

• Plonk, Groth16, STARKs have mature toolchains.
• Recursive proofs add logarithmic overhead.
• New paradigms (e.g., Binius, Lasso) are years from production.

ASICs/FPGAs for ZK (e.g., by Cysic, Ingonyama) offer 10-100x gains but create centralization vectors and obsolescence risk. A new proof system can render a $10M hardware investment worthless.

• Vendor lock-in to specific proof systems (e.g., Groth16).
• High Capex creates economic moats for large players.
• Algorithm agility is sacrificed for raw speed.

For many L2s, prover costs are a secondary expense dominated by L1 data posting fees (e.g., Ethereum calldata). A 50% reduction in proving cost may only lower total operational cost by 5-15%.

• Data availability is the primary cost driver.
• Proof cost must fall orders of magnitude to dominate economics.
• Profit margins for prover services are thin.

Efficiency gains often require centralized, high-end hardware, undermining the permissionless validator set. A network secured by a few AWS instances is not meaningfully decentralized.

• Consumer hardware (e.g., M2 Mac) cannot compete.
• Proposer-Builder Separation (PBS) for provers is nascent.
• Trust assumptions shift from math to hardware operators.