4 posts tagged with "zk-SNARKs"

In 2022, Vitalik Buterin posed a simple question that would define the next four years of Ethereum scaling: how much Ethereum compatibility are you willing to sacrifice for faster zero-knowledge proofs? His answer came in the form of a five-type classification system for zkEVMs that has since become the industry standard for evaluating these critical scaling solutions.

Fast forward to 2026, and the answer isn't so simple anymore. Proving times have collapsed from 16 minutes to 16 seconds. Costs have dropped 45x. Multiple teams have demonstrated real-time proof generation faster than Ethereum's 12-second block times. Yet the fundamental trade-off Vitalik identified remains—and understanding it is essential for any developer or project choosing where to build.

The Vitalik Classification: Types 1 Through 4​

Vitalik's framework categorizes zkEVMs along a spectrum from perfect Ethereum equivalence to maximum proving efficiency. Higher type numbers mean faster proofs but less compatibility with existing Ethereum infrastructure.

Type 1: Fully Ethereum-Equivalent​

Type 1 zkEVMs don't change anything about Ethereum. They prove the exact same execution environment that Ethereum L1 uses—same opcodes, same data structures, same everything.

The upside: Perfect compatibility. Ethereum execution clients work as-is. Every tool, every contract, every piece of infrastructure transfers directly. This is ultimately what Ethereum needs to make L1 itself more scalable.

The downside: Ethereum wasn't designed for zero-knowledge proofs. The EVM's stack-based architecture is notoriously inefficient for ZK proof generation. Early Type 1 implementations required hours to generate a single proof.

Leading project: Taiko aims for Type 1 equivalence as a based rollup using Ethereum's validators for sequencing, enabling synchronous composability with other based rollups.

Type 2: Fully EVM-Equivalent​

Type 2 zkEVMs maintain full EVM compatibility but change internal representations—how state is stored, how data structures are organized—to improve proof generation.

The upside: Contracts written for Ethereum run without modification. The developer experience remains identical. Migration friction approaches zero.

The downside: Block explorers and debugging tools may need modifications. State proofs work differently than on Ethereum L1.

Leading projects: Scroll and Linea target Type 2 compatibility, achieving near-perfect EVM equivalence at the VM level without transpilers or custom compilers.

Type 2.5: EVM-Equivalent with Gas Cost Changes​

Type 2.5 is a pragmatic middle ground. The zkEVM remains EVM-compatible but increases gas costs for operations that are particularly expensive to prove in zero-knowledge.

The trade-off: Since Ethereum has a gas limit per block, increasing gas costs for specific opcodes means fewer of those opcodes can execute per block. Applications work, but certain computational patterns become prohibitively expensive.

Type 3: Almost EVM-Equivalent​

Type 3 zkEVMs sacrifice specific EVM features—often related to precompiles, memory handling, or how contract code is treated—to dramatically improve proof generation.

The upside: Faster proofs, lower costs, better performance.

The downside: Some Ethereum applications won't work without modification. Developers may need to rewrite contracts that rely on unsupported features.

Reality check: No team actually wants to stay at Type 3. It's understood as a transitional stage while teams work on adding the complex precompile support needed to reach Type 2.5 or Type 2. Both Scroll and Polygon zkEVM operated as Type 3 before advancing up the compatibility ladder.

Type 4: High-Level Language Compatible​

Type 4 systems abandon EVM compatibility entirely at the bytecode level. Instead, they compile Solidity or Vyper to a custom VM designed specifically for efficient ZK proofs.

The upside: Fastest proof generation. Lowest costs. Maximum performance.

The downside: Contracts may behave differently. Addresses might not match Ethereum deployments. Debugging tools need complete rewrites. Migration requires careful testing.

Leading projects: zkSync Era and StarkNet represent the Type 4 approach. zkSync transpiles Solidity to custom bytecode optimized for ZK. StarkNet uses Cairo, an entirely new language designed for provability.

Performance Benchmarks: Where We Stand in 2026​

The numbers have transformed dramatically since Vitalik's original post. What was theoretical in 2022 is production reality in 2026.

Proving Times​

Early zkEVMs required approximately 16 minutes to generate proofs. Current implementations complete the same process in roughly 16 seconds—a 60x improvement. Several teams have demonstrated proof generation in under 2 seconds, faster than Ethereum's 12-second block times.

The Ethereum Foundation has set an ambitious target: proving 99% of mainnet blocks in under 10 seconds using less than $100,000 in hardware and 10kW of power consumption. Multiple teams have already demonstrated capability close to this target.

Transaction Costs​

The Dencun upgrade in March 2024 (EIP-4844 introducing "blobs") reduced L2 fees by 75-90%, making all rollups dramatically more cost-effective. Current benchmarks show:

Platform	Transaction Cost	Notes
Polygon zkEVM	$0.00275	Per transaction for full batches
zkSync Era	$0.00378	Median transaction cost
Linea	$0.05-0.15	Average transaction

Throughput​

Real-world performance varies significantly based on transaction complexity:

Platform	TPS (Complex DeFi)	Notes
Polygon zkEVM	5.4 tx/s	AMM swap benchmark
zkSync Era	71 TPS	Complex DeFi swaps
Theoretical (Linea)	100,000 TPS	With advanced sharding

These numbers will continue improving as hardware acceleration, parallelization, and algorithmic optimizations mature.

Market Adoption: TVL and Developer Traction​

The zkEVM landscape has consolidated around several clear leaders, each representing different points on the type spectrum:

Current TVL Rankings (2025)​

• Scroll: $748 million TVL, largest pure zkEVM
• StarkNet: $826 million TVS
• zkSync Era: $569 million TVL, 270+ deployed dApps
• Linea: ~$963 million TVS, 400%+ growth in daily active addresses

The overall Layer 2 ecosystem has reached $70 billion in TVL, with ZK rollups capturing increasing market share as proving costs continue declining.

Developer Adoption Signals​

• Over 65% of new smart contracts in 2025 deployed on Layer 2 networks
• zkSync Era attracted approximately $1.9 billion in tokenized real-world assets, capturing ~25% of on-chain RWA market share
• Layer 2 networks handled an estimated 1.9 million daily transactions in 2025

The Compatibility-Performance Trade-off in Practice​

Understanding the theoretical types is useful, but the practical implications for developers are what matter.

Type 1-2: Zero Migration Friction​

For Scroll and Linea (Type 2), migration means literally zero code changes for most applications. Deploy the same Solidity bytecode, use the same tools (MetaMask, Hardhat, Remix), expect the same behavior.

Best for: Existing Ethereum applications prioritizing seamless migration; projects where proven, audited code must remain unchanged; teams without resources for extensive testing and modification.

Type 3: Careful Testing Required​

For Polygon zkEVM and similar Type 3 implementations, most applications work but edge cases exist. Certain precompiles may behave differently or be unsupported.

Best for: Teams with resources for thorough testnet validation; projects not relying on exotic EVM features; applications prioritizing cost efficiency over perfect compatibility.

Type 4: Different Mental Model​

For zkSync Era and StarkNet, the development experience differs meaningfully from Ethereum:

zkSync Era supports Solidity but transpiles it to custom bytecode. Contracts compile and run, but behavior may differ in subtle ways. Addresses aren't guaranteed to match Ethereum deployments.

StarkNet uses Cairo, requiring developers to learn an entirely new language—though one specifically designed for provable computation.

Best for: Greenfield projects not constrained by existing code; applications prioritizing maximum performance; teams willing to invest in specialized tooling and testing.

Security: The Non-Negotiable Constraint​

The Ethereum Foundation introduced clear cryptographic security requirements for zkEVM developers in 2025:

• 100-bit provable security by May 2026
• 128-bit security by end of 2026

These requirements reflect the reality that faster proofs mean nothing if the underlying cryptography isn't bulletproof. Teams are expected to meet these thresholds regardless of their type classification.

The security focus has slowed some performance improvements—the Ethereum Foundation explicitly chose security over speed through 2026—but ensures the foundation for mainstream adoption remains solid.

Choosing Your zkEVM: A Decision Framework​

Choose Type 1-2 (Taiko, Scroll, Linea) if:​

• You're migrating existing battle-tested contracts
• Audit costs are a concern (no reaudit needed)
• Your team is Ethereum-native without ZK expertise
• Composability with Ethereum L1 matters
• You need synchronous interoperability with other based rollups

Choose Type 3 (Polygon zkEVM) if:​

• You want a balance of compatibility and performance
• You can invest in thorough testnet validation
• Cost efficiency is a priority
• You don't rely on exotic EVM precompiles

Choose Type 4 (zkSync Era, StarkNet) if:​

• You're building from scratch without migration constraints
• Maximum performance justifies tooling investment
• Your use case benefits from ZK-native design patterns
• You have resources for specialized development

What Comes Next​

The type classifications won't remain static. Vitalik noted that zkEVM projects can "easily start at higher-numbered types and jump to lower-numbered types over time." We're seeing this in practice—projects that launched as Type 3 are advancing toward Type 2 as they complete precompile implementations.

More intriguingly, if Ethereum L1 adopts modifications to become more ZK-friendly, Type 2 and Type 3 implementations could become Type 1 without changing their own code.

The endgame appears increasingly clear: proving times will continue compressing, costs will continue declining, and the distinction between types will blur as hardware acceleration and algorithmic improvements close the performance gap. The question isn't which type will win—it's how quickly the entire spectrum converges toward practical equivalence.

For now, the framework remains valuable. Understanding where a zkEVM sits on the compatibility-performance spectrum tells you what to expect during development, deployment, and operation. That knowledge is essential for any team building on Ethereum's ZK-powered future.

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Zero-knowledge rollups were supposed to be the future of blockchain scaling. Instead, they've become hostages to a $97 million centralized prover market where a handful of companies extract 60-70% of fees — while users wait minutes for proofs that should take seconds.

Boundless, RISC Zero's decentralized proof marketplace that launched on mainnet in September 2025, claims to have cracked this problem. By turning ZK proof generation into an open market where GPU operators compete for work, Boundless promises to make verifiable computation "as cheap as execution." But can a token-incentivized network really break the centralization death spiral that's kept ZK technology expensive and inaccessible?

The Billion-Dollar Bottleneck: Why ZK Proofs Are Still Expensive​

The promise of zero-knowledge rollups was elegant: execute transactions off-chain, generate a cryptographic proof of correct execution, and verify that proof on Ethereum for a fraction of the cost. In theory, this would deliver Ethereum-level security at sub-cent transaction costs.

Reality proved messier.

A single ZK proof for a batch of 4,000 transactions takes two to five minutes to generate on a high-end A100 GPU, costing $0.04 to $0.17 in cloud computing fees alone. That's before factoring in the specialized software, engineering expertise, and redundant infrastructure needed to run a reliable proving service.

The result? Over 90% of ZK-L2s rely on a handful of prover-as-a-service providers. This centralization introduces exactly the risks that blockchain was designed to eliminate: censorship, MEV extraction, single points of failure, and web2-style rent extraction.

The Technical Challenge​

The bottleneck isn't network congestion — it's the mathematics itself. ZK proving relies on multi-scalar multiplications (MSMs) and number-theoretic transforms (NTTs) over elliptic curves. These operations are fundamentally different from the matrix math that makes GPUs excellent for AI workloads.

After years of MSM optimization, NTTs now account for up to 90% of proof generation latency on GPUs. The cryptography community has hit diminishing returns on software optimization alone.

Enter Boundless: The Open Proof Market​

Boundless attempts to solve this problem by decoupling proof generation from blockchain consensus entirely. Instead of each rollup running its own prover infrastructure, Boundless creates a marketplace where:

1. Requestors submit proof requests (from any chain)
2. Provers compete to generate proofs using GPUs and commodity hardware
3. Settlement happens on the destination chain specified by the requester

The key innovation is "Proof of Verifiable Work" (PoVW) — a mechanism that rewards provers not for useless hashes (like Bitcoin mining) but for generating useful ZK proofs. Each proof carries cryptographic metadata proving how much computation went into it, creating a transparent record of work.

How It Actually Works​

Under the hood, Boundless builds on RISC Zero's zkVM — a zero-knowledge virtual machine that can execute any program compiled for the RISC-V instruction set. This means developers can write applications in Rust, C++, or any language that compiles to RISC-V, then generate proofs of correct execution without learning specialized ZK circuits.

The three-layer architecture includes:

• zkVM Layer: Executes arbitrary programs and generates STARK proofs
• Recursion Layer: Aggregates multiple STARKs into compact proofs
• Settlement Layer: Converts proofs to Groth16 format for on-chain verification

This design allows Boundless to generate proofs that are small enough (around 200KB) for economical on-chain verification while supporting complex computations.

The ZKC Token: Mining Proofs Instead of Hashes​

Boundless introduced ZK Coin (ZKC) as the native token powering its proof market. Unlike typical utility tokens, ZKC is actively mined through proof generation — provers earn ZKC rewards proportional to the computational work they contribute.

Tokenomics Overview​

• Total Supply: 1 billion ZKC (with 7% inflation in Year 1, tapering to 3% by Year 8)
• Ecosystem Growth: 41.6% allocated to adoption initiatives
• Strategic Partners: 21.5% with 1-year cliff and 2-year vesting
• Community: 8.3% for token sale and airdrops
• Current Price: ~$0.12 (down from $0.29 ICO price)

The inflationary model has sparked debate. Proponents argue ongoing emissions are necessary to incentivize a healthy prover network. Critics point out that 7% annual inflation creates constant sell pressure, potentially limiting ZKC's value appreciation even as the network grows.

Market Turbulence​

ZKC's first months weren't smooth. In October 2025, South Korean exchange Upbit flagged the token with an "investment warning," triggering a 46% price crash. Upbit lifted the warning after Boundless clarified its tokenomics, but the episode highlighted the volatility risks of infrastructure tokens tied to emerging markets.

Mainnet Reality: Who's Actually Using Boundless?​

Since launching mainnet beta on Base in July 2025 and full mainnet in September, Boundless has secured notable integrations:

Wormhole Integration​

Wormhole is integrating Boundless to add ZK verification to Ethereum consensus, making cross-chain transfers more secure. Instead of relying purely on multi-sig guardians, Wormhole NTT (Native Token Transfers) can now include optional ZK proofs for users who want cryptographic guarantees.

Citrea Bitcoin L2​

Citrea, a Bitcoin Layer-2 zk-rollup built by Chainway Labs, uses RISC Zero's zkVM to generate validity proofs posted to Bitcoin via BitVM. This enables EVM-equivalent programmability on Bitcoin while using BTC for settlement and data availability.

Google Cloud Partnership​

Through its Verifiable AI Program, Boundless partnered with Google Cloud to enable ZK-powered AI proofs. Developers can build applications that prove AI model outputs without revealing inputs — a crucial capability for privacy-preserving machine learning.

Stellar Bridge​

In September 2025, Nethermind deployed RISC Zero verifiers for Stellar zk Bridge integration, enabling cross-chain proofs between Stellar's low-cost payment network and Ethereum's security guarantees.

The Competition: Succinct SP1 and the zkVM Wars​

Boundless isn't the only player racing to solve ZK's scalability problem. Succinct Labs' SP1 zkVM has emerged as a major competitor, sparking a benchmarking war between the two teams.

RISC Zero's Claims​

RISC Zero asserts that properly configured zkVM deployments are "at least 7x less expensive than SP1" and up to 60x cheaper for small workloads. They point to tighter proof sizes and more efficient GPU utilization.

Succinct's Response​

Succinct counters that RISC Zero's benchmarks "misleadingly compared CPU performance to GPU results." Their SP1 Hypercube prover claims $0.02 proofs with ~2 minute latency — though it remains closed source.

Independent Analysis​

A Fenbushi Capital comparison found RISC Zero demonstrated "superior speed and efficiency across all benchmark categories in GPU environments," but noted SP1 excels in developer adoption, powering projects like Celestia's Blobstream with $3.14B in total value secured versus RISC Zero's $239M.

The real competitive advantage may not be raw performance but ecosystem lock-in. Boundless plans to support competing zkVMs including SP1, ZKsync's Boojum, and Jolt — positioning itself as a protocol-agnostic proof marketplace rather than a single-vendor solution.

2026 Roadmap: What's Next for Boundless​

RISC Zero's roadmap for Boundless includes several ambitious targets:

Ecosystem Expansion (Q4 2025 - 2026)​

• Extend ZK proof support to Solana
• Bitcoin integration via BitVM
• Additional L2 deployments

Hybrid Rollup Upgrades​

The most significant technical milestone is transitioning optimistic rollups (like Optimism and Base chains) to use validity proofs for faster finality. Instead of waiting 7 days for fraud proof windows, OP chains could settle in minutes.

Multi-zkVM Support​

Support for competing zkVMs is on the roadmap, allowing developers to switch between RISC Zero, SP1, or other proving systems without leaving the marketplace.

Decentralization Completion​

RISC Zero terminated its hosted proof service in December 2025, forcing all proof generation through the decentralized Boundless network. This marked a significant commitment to the decentralization thesis — but also means the network's reliability now depends entirely on independent provers.

The Bigger Picture: Will Decentralized Proving Become the Standard?​

The success of Boundless hinges on a fundamental bet: that proof generation will commoditize the way cloud computing did. If that thesis holds, having the most efficient prover network matters less than having the largest and most liquid marketplace.

Several factors support this view:

1. Hardware commoditization: ZK-specific ASICs from companies like Cysic promise 50x energy efficiency improvements, potentially lowering barriers to entry
2. Proof aggregation: Networks like Boundless can batch proofs from multiple applications, amortizing fixed costs
3. Cross-chain demand: As more chains adopt ZK verification, demand for proof generation could outpace any single provider's capacity

But risks remain:

1. Centralization creep: Early prover networks tend toward concentration as economies of scale favor large operators
2. Token dependency: If ZKC price collapses, prover incentives evaporate — potentially causing a death spiral
3. Technical complexity: Running a competitive prover requires significant expertise, potentially limiting decentralization in practice

What This Means for Developers​

For builders considering ZK integration, Boundless represents a pragmatic middle ground:

• No infrastructure overhead: Submit proof requests via API without running your own provers
• Multi-chain settlement: Generate proofs once, verify on any supported chain
• Language flexibility: Write in Rust or any RISC-V compatible language instead of learning ZK DSLs

The trade-off is dependency on a token-incentivized network whose long-term stability remains unproven. For production applications, many teams may prefer Boundless for testnet and experimentation while maintaining fallback prover infrastructure for critical workloads.

Conclusion​

Boundless represents the most ambitious attempt yet to solve ZK's centralization problem. By turning proof generation into an open market incentivized by ZKC tokens, RISC Zero is betting that competition will drive costs down faster than any single vendor could achieve alone.

The mainnet launch, major integrations with Wormhole and Citrea, and commitment to supporting rival zkVMs suggest serious technical capability. But the inflationary tokenomics, exchange volatility, and unproven decentralization at scale leave important questions unanswered.

For the ZK ecosystem, Boundless's success or failure will signal whether decentralized infrastructure can compete with centralized efficiency — or whether the blockchain industry's scaling future remains in the hands of a few well-funded prover services.

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What if you could prove your bank account has $10,000 without revealing your balance, transaction history, or even your name? That's not a hypothetical scenario — it's happening right now through zkTLS, a cryptographic breakthrough that's quietly reshaping how Web3 applications access the 99% of internet data trapped behind login screens.

While blockchain oracles like Chainlink solved the price feed problem years ago, a far larger challenge remained unsolved: how do you bring private, authenticated web data on-chain without trusting centralized intermediaries or exposing sensitive information? The answer is zkTLS — and it's already powering undercollateralized DeFi loans, privacy-preserving KYC, and a new generation of applications that bridge Web2 credentials with Web3 composability.

In the rapidly converging worlds of artificial intelligence and blockchain, the demand for trust and transparency has never been higher. How can we be certain that an AI model's output is accurate and untampered with? How can we perform complex computations on vast on-chain datasets without compromising security or scalability? Lagrange Labs is tackling these questions head-on with its suite of zero-knowledge (ZK) infrastructure, aiming to build a future of "AI You Can Prove." This post provides an objective overview of their mission, technology, and recent breakthroughs, culminating in their latest paper on Dynamic zk-SNARKs.

1. The Team and Its Mission​

Lagrange Labs is building the foundational infrastructure to generate cryptographic proofs for any AI inference or on-chain application. Their goal is to make computation verifiable, bringing a new layer of trust to the digital world. Their ecosystem is built on three core product lines:

• ZK Prover Network: A decentralized network of over 85 proving nodes that supplies the computational power needed for a wide range of proving tasks, from AI and rollups to decentralized applications (dApps).
• DeepProve (zkML): A specialized system for generating ZK proofs of neural network inferences. Lagrange claims it is up to 158 times faster than competing solutions, making verifiable AI a practical reality.
• ZK Coprocessor 1.0: The first SQL-based ZK Coprocessor, allowing developers to run custom queries on massive on-chain datasets and receive verifiably accurate results.

2. A Roadmap to Verifiable AI​

Lagrange has been methodically executing a roadmap designed to solve the challenges of AI verifiability one step at a time.

• Q3 2024: ZK Coprocessor 1.0 Launch: This release introduced hyper-parallel recursive circuits, which delivered an average speed increase of approximately 2x. Projects like Azuki and Gearbox are already leveraging the coprocessor for their on-chain data needs.
• Q1 2025: DeepProve Unveiled: Lagrange announced DeepProve, its solution for Zero-Knowledge Machine Learning (zkML). It supports popular neural network architectures like Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The system achieves significant, order-of-magnitude acceleration across all three critical stages: one-time setup, proof generation, and verification, with speed-ups reaching as high as 158x.
• Q2 2025: The Dynamic zk-SNARKs Paper (Latest Milestone): This paper introduces a groundbreaking "update" algorithm. Instead of re-generating a proof from scratch every time the underlying data or computation changes, this method can patch an old proof (π) into a new proof (π'). This update can be performed with a complexity of just O(√n log³n), a dramatic improvement over full re-computation. This innovation is particularly suited for dynamic systems like continuously learning AI models, real-time game logic, and evolving smart contracts.

3. Why Dynamic zk-SNARKs Matter​

The introduction of updatable proofs represents a fundamental shift in the cost model of zero-knowledge technology.

• A New Cost Paradigm: The industry moves from a model of "full-recomputation for every proof" to "incremental proofing based on the size of the change." This dramatically lowers the computational and financial cost for applications that undergo frequent, minor updates.

• Implications for AI:

  • Continuous Fine-Tuning: When fine-tuning less than 1% of a model's parameters, the proof generation time grows almost linearly with the number of changed parameters (Δ parameters), rather than with the overall size of the model.
  • Streaming Inference: This enables the generation of proofs concurrently with the inference process itself. This drastically reduces the latency between an AI making a decision and that decision being settled and verified on-chain, unlocking use cases like on-chain AI services and compressed proofs for rollups.

• Implications for On-Chain Applications:

  • Dynamic zk-SNARKs offer massive gas and time optimizations for applications characterized by frequent, small-state changes. This includes decentralized exchange (DEX) order books, evolving game states, and ledger updates involving frequent additions or deletions.

4. A Glimpse into the Tech Stack​

Lagrange's powerful infrastructure is built on a sophisticated and integrated technology stack:

• Circuit Design: The system is flexible, supporting the embedding of ONNX (Open Neural Network Exchange) models, SQL parsers, and custom operators directly into its circuits.
• Recursion & Parallelism: The ZK Prover Network facilitates distributed recursive proofs, while the ZK Coprocessor leverages the sharding of "micro-circuits" to execute tasks in parallel, maximizing efficiency.
• Economic Incentives: Lagrange is planning to launch a native token, LA, which will be integrated into a Double-Auction-for-Recursive-Auction (DARA) system. This will create a robust marketplace for bidding on prover computation, complete with incentives and penalties to ensure network integrity.

5. Ecosystem and Real-World Adoption​

Lagrange is not just building in a vacuum; its technology is already being integrated by a growing number of projects across different sectors:

• AI & ML: Projects like 0G Labs and Story Protocol are using DeepProve to verify the outputs of their AI models, ensuring provenance and trust.
• Rollups & Infrastructure: Key players like EigenLayer, Base, and Arbitrum are participating in the ZK Prover Network as validation nodes or integration partners, contributing to its security and computational power.
• NFT & DeFi Applications: Brands like Azuki and DeFi protocols like Gearbox are using the ZK Coprocessor to enhance the credibility of their data queries and reward distribution mechanisms.

6. Challenges and the Road Ahead​

Despite its impressive progress, Lagrange Labs and the broader ZK field face several hurdles:

• Hardware Bottlenecks: Even with a distributed network, updatable SNARKs still demand high bandwidth and rely on GPU-friendly cryptographic curves to perform efficiently.
• Lack of Standardization: The process of mapping AI frameworks like ONNX and PyTorch to ZK circuits still lacks a universal, standardized interface, creating friction for developers.
• A Competitive Landscape: The race to build zkVMs and generalized zkCompute platforms is heating up. Competitors like Risc-Zero and Succinct are also making significant strides. The ultimate winner may be the one who can first commercialize a developer-friendly, community-driven toolchain.

7. Conclusion​

Lagrange Labs is methodically reshaping the intersection of AI and blockchain through the lens of verifiability. Their approach provides a comprehensive solution:

• DeepProve addresses the challenge of trusted inference.
• The ZK Coprocessor solves the problem of trusted data.
• Dynamic zk-SNARKs incorporate the real-world need for continuous updates directly into the proving system.

If Lagrange can maintain its performance edge, solve the critical challenge of standardization, and continue to grow its robust network, it is well-positioned to become a cornerstone player in the emerging "AI + ZK Infrastructure" sector.