Challenges and Pathways for Real-World Implementation of ZK

Performance and Cost Bottlenecks: Proof Generation Remains Expensive

Despite significant optimizations over the past three years (such as Plonky2, Halo2, Boojum, RISC-V ZK circuits), ZK proof generation remains one of the most computationally expensive operations in blockchain.

1. Proof generation time is still lengthy

• For complex circuits (DeFi states, game logic), generating proofs often takes anywhere from hundreds of milliseconds to several seconds.
• On mobile devices or lightweight hardware, proof generation is nearly impossible and still relies on cloud services or validator nodes.

2. High hardware requirements

• Some ZK systems require a GPU/FPGA to achieve usable speeds.
• Cloud-based generation introduces new trust assumptions and centralization risks.

3. On-chain verification is not free

• SNARKs have low verification costs but require a trusted setup.
• STARKs do not need a trusted setup, but proofs are larger and verification costs exceed those of SNARKs.

Conclusion

ZK is best suited for separating privacy and verification from “real-time logic,” making it ideal for settlement, compliance checks, and batch processing, rather than for all business logic.

Auditability vs. Regulatory Requirements

ZK inherently provides privacy, but excessive privacy can clash with global compliance frameworks (AML/KYC/anti-terrorist financing).

Typical regulatory concerns

• On-chain private assets make it hard to track fund flows.
• Participant identities are obscured.
• Transaction mixing may conceal suspicious activity.

Regulatory requirements

As a result, regulators often require:

• Selective disclosure
• Regulatory exception access (Regulator Backdoor, not a universal backdoor)
• Transaction audit proofs

Compliance solutions for ZK are emerging

Including:
• ZK-KYC (proving you meet requirements without exposing your identity)
• Auditable private accounts (regulator-readable proofs)
• On-chain flow-of-funds proofs

However, divergent regulatory stances across countries make it difficult for projects to meet global standards in one go.

High Development Complexity: Shortage of Talent and Toolchains

ZK engineering is far more challenging than traditional smart contracts due to:
• Required expertise in cryptography, circuit design, compilers, and distributed systems
• Each ZK framework uses its own DSL (Circom, Noir, Leo, etc.)
• High auditing thresholds and costly errors

Result: Development is expensive, audit cycles are long, and tooling cannot fully abstract underlying complexity.

Key future directions

• More mature ZK compilers (zkVM, zkEVM)
• Higher-level abstractions (Rust → Circuit)
• Standardized privacy compliance protocols

User Experience Is Still Underdeveloped

User experience remains one of the biggest obstacles to ZK adoption:

1. Complex wallet interactions

• Users must understand what “proof generation” means
• Proof generation can take several seconds, impacting UX

2. High and volatile transaction fees

• Proof generation typically costs more than standard transactions
• Batch processing experiences are still inconsistent

3. Privacy vs. recovery mechanism conflict

• Full privacy makes account recovery harder
• Social recovery mechanisms require new ZK process designs

4. High user education costs

Most users don’t understand:
• What is a circuit?
• How are proofs generated?
• Why does privacy require computation?

This leads to low user migration and adoption willingness.

Unclear Commercialization Path: Bridging the Gap from Technology to Product

ZK represents advanced technology, but this does not guarantee its commercial viability. Current projects commonly face:

1. No clear payment model

• Ordinary users have a low willingness to pay for privacy.
• Developers hesitate over high proof-generation costs.

2. Slow enterprise adoption

• High compliance demands and integration costs.
• Poor compatibility with existing systems.
• Enterprises are unwilling to shoulder proof-generation expenses.

3. Lack of quantifiable ROI (Return on Investment)

Privacy, compression, and security are hard to directly translate into revenue.

Potential commercial opportunities are emerging

• On-chain identity (ZK-ID)
• Compliance-focused finance (ZK-RegTech)
• Enterprise data collaboration (ZK data exchange)
• AI × ZK: verifiable AI inference
• ZK computation outsourcing

But these remain in early validation stages.

Future Trends: Key Drivers for ZK’s Real-World Adoption

1. Verifiable AI will be the biggest catalyst

• Making AI models “provable”
• Ensuring AI outcomes are trustworthy and traceable

This drives industrial-scale demand for ZK models.

2. Proliferation of hardware acceleration (GPU/ASIC)

Apple, Samsung, and Nvidia are integrating ZK acceleration capabilities, which will drastically lower ZK costs.

3. Standardization and formation of ZK compliance frameworks

• Standardized ZK-KYC
• Audit proofs readable by financial institutions

• “Private yet regulatale” infrastructure

Maturity of ZK Rollups and zkEVMs

More L1/L2s will adopt ZK as their default settlement mechanism.

1. Improved toolchains and developer education

• Low-barrier ZK DSLs
• Circuit visualization tools
• Modular proof architectures

2. Experiences closer to everyday users

• Wallets automatically generate proofs
• Asynchronous proof generation (no waiting for completion)
• Modular privacy toggles

ZK will evolve from a “technical capability” to an “infrastructure-level capability.”

Course Summary

Zero-Knowledge Proofs are becoming a cornerstone of blockchain, AI, and fintech’s future. However, real-world implementation still faces:
• Computational performance bottlenecks
• Conflicts between compliance and auditability
• A complex developer ecosystem
• Immature user experience
• Unclear commercialization models

Nevertheless, the industry is actively seeking solutions. With hardware acceleration, maturation of zkVM technology, emerging compliance frameworks, and surging demand for AI verifiability, ZK will gradually transition from cutting-edge technology to a large-scale real-world application.