The Same Thing That Happened to AI is Happening to ZK: A $100B+ Opportunity Emerging

The massive opportunity emerging as ZK follows AI's explosive growth trajectory

In our previous post, we traced the evolution of artificial intelligence from academic curiosity to trillion-dollar industry. Now, we turn our attention to a technology exhibiting remarkably similar patterns: Zero-Knowledge (ZK) proofs.

While still unfamiliar to many outside cryptography circles, ZK technology offers a transformative value proposition: the ability to prove something is true without revealing any underlying information. This seemingly magical capability enables verifiable computation with complete privacy—a breakthrough with implications as far-reaching as artificial intelligence.

For founders and investors positioning for the next wave of deep tech value creation, ZK represents what AI was in 2014-2016: a fundamental technology on the verge of exponential growth with the potential to create the next generation of tech giants.

A Recap of the Last Post

The Value Proposition of Zero-Knowledge Proofs

Before diving into the market opportunity, it's worth clarifying why ZK technology matters and the core problems it solves:

Zero-knowledge proofs, first introduced by Shafi Goldwasser, Silvio Micali, and Charles Rackoff in their landmark 1985 paper "The Knowledge Complexity of Interactive Proof Systems," allow one party (the prover) to convince another party (the verifier) that a statement is true without revealing any information beyond the validity of the statement itself. In practical terms, this enables:

• Privacy-Preserving Verification: Proving credentials, identities, or compliance without exposing sensitive data

• Computational Integrity: Guaranteeing that computations were performed correctly without requiring others to re-run them

• Scalability Through Compression: Reducing massive computational problems to succinct proofs that can be verified efficiently

• Trust Minimization: Establishing certainty without requiring trusted third parties

These capabilities address fundamental limitations in our digital infrastructure around privacy, security, and scalability—limitations that have become increasingly apparent as our digital and financial systems grow more complex.

ZK Today: Early Enterprise Days — The $100B+ Opportunity

Currently, Zero-Knowledge technology finds itself at a stage reminiscent of AI in 2014—with a similar trajectory of value creation ahead. For investors and founders, this creates an extraordinary opportunity to position early in what will likely become a $100B+ market within this decade.

ZK Today vs. AI Circa 2014: Parallel Development Paths

Basic tools and limited frameworks characterize ZK's current state, similar to AI's early days. Several ZK proof systems have emerged with different technical approaches and trade-offs:

• zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge): Introduced in 2012 by Alessandro Chiesa and others, these proofs are extremely succinct (typically under 1KB in size) and have constant-time verification, making them ideal for blockchain applications. However, they require a trusted setup ceremony, which creates a potential security vulnerability if the setup parameters are compromised. Implementations like Groth16 (published by Jens Groth in 2016) are widely used in projects like Zcash for private transactions.

• zk-STARKs (Zero-Knowledge Scalable Transparent Arguments of Knowledge): Introduced by Eli Ben-Sasson and others in 2018 to address the trusted setup limitation of SNARKs, STARKs are transparent (no trusted setup) and quantum-resistant. However, they produce larger proof sizes (typically 10-100KB) and have logarithmic verification time. StarkWare's StarkEx and StarkNet leverage this technology for Ethereum scaling.

• Plonk: Developed by Ariel Gabizon, Zachary Williamson, and Oana Ciobotaru in 2019, this universal and updatable trusted setup system offers a middle ground between SNARKs and STARKs, with moderate proof size and verification time. Once a single setup is performed, it can be used for any circuit up to a certain size. This innovation significantly reduced the practical barriers to adopting SNARKs and has been adopted by projects like Aztec and Polygon.

• Bulletproofs: Published in 2018 by Benedikt Bünz and others, this non-interactive zero-knowledge proof protocol has no trusted setup and relatively small proof size. They're particularly well-suited for confidential transactions in cryptocurrencies, as implemented in Monero.

Projects such as Polygon Hermez (founded 2020), StarkWare (founded 2018), and zkSync (launched 2020) are leading the charge in developing foundational infrastructure that implements these proof systems for practical applications. These projects are creating ZK rollups—Layer 2 scaling solutions that batch multiple transactions together with a validity proof that verifies their correctness.

Just as AI once required deep expertise in machine learning algorithms, ZK currently demands specialized knowledge in cryptography, elliptic curve mathematics, and circuit design, limiting its accessibility to mainstream developers. The learning curve for implementing ZK systems involves understanding complex topics like arithmetic circuits, R1CS (Rank-1 Constraint Systems), and polynomial commitment schemes.

Challenges in ZK Adoption: Overcoming Technical Hurdles

The complexity of ZK application development remains a significant hurdle. Creating applications that leverage ZK proofs requires deep cryptographic knowledge and specialized skills. There is an urgent need for better infrastructure, abstraction layers, and development tools to unlock broader use cases and facilitate widespread adoption.

This parallels the challenges faced by early AI, where building effective models required expertise in statistical methods and algorithm design before frameworks like TensorFlow (released 2015) and PyTorch (released 2016) simplified the process.

Emerging Use Cases: Early Signs of Potential

Despite these challenges, ZK technology is already finding concrete applications across several domains:

Privacy-Preserving Cryptocurrencies

• Zcash: Launched in 2016, implements zk-SNARKs to shield transaction information, allowing users to prove they own funds for a transaction without revealing their address or the amount.

• Monero: Released in 2014, uses Ring Signatures and (since 2018) Bulletproofs to provide transaction privacy and fungibility.

• Tornado Cash: Deployed in 2019, a decentralized protocol that uses zk-SNARKs to break the on-chain link between source and destination addresses for ETH and other tokens.

Scalable Blockchain Solutions

• zkSync: First introduced in 2020, an Ethereum Layer 2 scaling solution using zk-Rollups that can process up to 2,000 transactions per second while inheriting Ethereum's security.

• StarkEx: Launched in 2020, powers decentralized exchanges like dYdX and DeversiFi, processing over 20 million transactions and securing billions in value with validity proofs.

• Polygon zkEVM: Announced in 2022 and launched in 2023, the first Ethereum-equivalent zkRollup that executes smart contracts with ZK proofs, maintaining compatibility with existing Ethereum applications.

• Mina Protocol: Launched in 2020, a revolutionary "succinct blockchain" that uses recursive zk-SNARKs to maintain a constant-size blockchain (22KB) regardless of transaction history. This groundbreaking application of proof recursion allows a full node to run on a smartphone, truly democratizing blockchain participation. Mina's approach represents one of the most elegant applications of ZK technology, proving the entire blockchain state with a single succinct proof.

• Celo: Launched in 2020, utilizes recursive SNARKs for ultra-light clients that can efficiently verify blockchain state with minimal resource requirements, making blockchain accessible on mobile devices in regions with limited connectivity.

Digital Identity and Verification

• Polygon ID: Introduced in 2022, a self-sovereign identity solution using zk-SNARKs to enable users to prove personal attributes (age, nationality, credentials) without revealing the underlying data.

• Worldcoin: Launched in 2023, uses ZK proofs with biometric verification to create unique identities while preserving privacy, aiming to establish a global identity network.

• Semaphore: First developed in 2019, an Ethereum protocol allowing users to prove their membership in a group and send signals (like votes or endorsements) anonymously.

Decentralized Finance Applications

• Aztec Protocol: Launched in 2019, a privacy layer for Ethereum that enables confidential transactions and private smart contracts using zk-SNARKs.

• Panther Protocol: Founded in 2020, a privacy protocol for Web3 and DeFi that creates zero-knowledge shielded pools for different asset types.

• Manta Network: Established in 2021, a privacy-preserving DeFi stack enabling private swaps, lending, and borrowing on multiple blockchains.

• Aleo: Founded in 2019, a platform for building fully private applications using a custom programming language (Leo) that compiles to ZK circuits, enabling private computation on public infrastructure.

Enterprise Solutions

• Ernst & Young's Nightfall: First announced in 2019, a privacy-focused Layer 2 protocol allowing enterprises to transfer tokens privately while maintaining regulatory compliance.

• Baseline Protocol: Launched in 2020, an open-source initiative that uses zero-knowledge proofs to enable confidential and complex collaboration between enterprises without putting sensitive data on-chain.

• Linea: Released in 2023, an Ethereum scaling solution developed by Consensys (the company behind MetaMask) that leverages ZK proofs to provide security and scalability for enterprise blockchain applications.

These diverse implementations demonstrate that ZK technology is moving beyond theoretical possibilities into practical, deployed solutions solving real-world problems.

Early Signs of Market Traction

The market is beginning to recognize the potential of ZK technology, with several companies achieving substantial valuations:

• StarkWare reached a $8B valuation in 2022 as an early ZK scaling solution provider

• Polygon acquired multiple ZK companies for $400M+ in 2021-2022, signaling the strategic importance of the technology

• zkSync raised $200M in 2023, with a rumored multi-billion dollar valuation

• Aleo raised $200M in 2022 at a $1.45B valuation for its privacy computation platform

• VCs have deployed over $1B into early ZK infrastructure since 2021, recognizing the parallels to early AI investing

These valuations, while substantial, represent just the beginning of the value creation curve—similar to AI infrastructure valuations circa 2015-2016. As ZK technology matures and applications emerge, we can expect multiple companies to achieve $10B+ valuations, with the potential for at least one $100B+ company in the space within 5-7 years.

zkVMs: The Key Abstraction That Will Drive Mainstream ZK Adoption

Zero-Knowledge Virtual Machines (zkVMs) represent perhaps the most important technological breakthrough for driving widespread ZK adoption. These systems are poised to do for ZK what PyTorch and TensorFlow did for AI—create an accessible abstraction layer that dramatically lowers the barrier to entry.

What Are zkVMs and Why They Matter

A zkVM is a virtual machine that can execute general-purpose code while generating cryptographic proofs that the computation was performed correctly. Unlike traditional ZK systems that require developers to build specialized circuits for each application, zkVMs allow developers to write code in familiar languages and automatically convert it to a ZK-provable representation.

This is a game-changing abstraction that solves the primary obstacle to ZK adoption: the extreme technical complexity of writing ZK circuits directly. With zkVMs, developers can use languages they already know (like Rust, JavaScript, or C) to build ZK applications without needing to understand the underlying cryptographic primitives.

Leading zkVM Projects and Their Potential

Several promising zkVM projects have emerged since 2021, each with unique approaches:

Rust-Based zkVMs

• RISC Zero: Launched in 2022, one of the most advanced zkVM implementations, RISC Zero enables developers to write ZK applications in Rust that can prove arbitrary computations. Its open-source zkVM has seen rapid adoption due to its developer-friendly approach and Rust integration. The RISC Zero approach allows developers to write vanilla Rust code that gets compiled to a ZK-provable form, dramatically simplifying the development process.

• SP1: Released in 2023, a direct competitor to RISC Zero, SP1 also provides a zkVM that enables developers to write ZK applications in standard Rust. The SP1 and RISC Zero approaches are similar in that they both aim to make ZK accessible to mainstream Rust developers.

• Jolt: Announced in 2023, another promising zkVM focused on making Rust programs ZK-provable with minimal developer overhead. Jolt emphasizes developer experience and integration with existing Rust tooling, making it particularly accessible for Rust developers new to ZK technology.

These Rust-based zkVMs represent an especially promising approach for mainstream adoption as they leverage Rust's growing popularity, memory safety guarantees, and performance. By allowing developers to write normal Rust code that can be proven in zero-knowledge, these projects dramatically lower the barrier to building ZK applications.

EVM-Compatible zkVMs

• zkSync Era: Launched in 2023, a zkEVM that allows developers to deploy Ethereum smart contracts with ZK security guarantees. Matter Labs has invested heavily in creating a development environment that minimizes the learning curve for Ethereum developers.

• Scroll: Released to mainnet in 2023, another zkEVM implementation focused on full EVM equivalence, allowing developers to use familiar Ethereum tools like Hardhat and Truffle seamlessly while benefiting from ZK security and scalability.

• Polygon Miden: Introduced in 2021, a WASM-based zkVM that allows developers to build ZK applications using WebAssembly, making it accessible to developers across multiple programming languages. Miden's approach leverages the existing WebAssembly ecosystem and tooling.

While zkEVMs are valuable for compatibility with the existing Ethereum ecosystem, the Rust-based zkVMs (RISC Zero, SP1, Jolt) may have broader potential for mainstream adoption due to their ability to work with general-purpose code rather than just blockchain applications.

zkVMs as the "PyTorch Moment" for ZK

The rise of zkVMs parallels the emergence of PyTorch (2016) and TensorFlow (2015) in AI development. Before these frameworks, building neural networks required deep expertise in back-propagation algorithms and gradient optimization. These frameworks abstracted away the mathematical complexity, allowing developers to focus on model architecture and applications.

Similarly, zkVMs abstract away the mathematical complexity of ZK proofs, allowing developers to focus on the application logic rather than the cryptographic implementation. This abstraction layer is likely to be the catalyst that drives ZK from specialized use cases to mainstream adoption.

Conclusion: The AI Playbook Applied to ZK

The parallels between AI's development trajectory and the current state of ZK technology are striking. Just as AI progressed from complex academic research to mainstream technology through key abstractions and infrastructure developments, ZK is following a similar path.

Looking Familiar?

In our first post, we identified several key patterns from AI's evolution:

1. Infrastructure First: The earliest value accrues to foundational infrastructure providers. In ZK, we're seeing this with companies like StarkWare, Polygon, and the zkVM providers.

2. Abstraction Layers Drive Adoption: In AI, frameworks like PyTorch and TensorFlow created inflection points in adoption. For ZK, zkVMs are emerging as this critical abstraction layer.

3. Early Applications in Specialized Domains: AI initially found traction in computer vision and NLP before expanding. Similarly, ZK is establishing early use cases in blockchain, identity, and privacy-focused applications.

4. Computational Demands Drive Hardware Innovation: The computational requirements of AI led to GPU acceleration and specialized hardware. ZK's intensive proof generation requirements will likely drive similar innovation.

5. Venture Capital as a Leading Indicator: Forward-thinking VCs recognized AI's potential early, just as we're now seeing significant investment in ZK infrastructure.

ZK today closely resembles AI circa 2014-2016—a fundamentally powerful technology just beginning to become accessible to developers beyond specialists. The zkVM developments we're witnessing represent ZK's "PyTorch moment"—the emergence of abstractions that will dramatically accelerate adoption and application development.

For founders and investors, the opportunity is clear: positioning early in the ZK ecosystem offers similar potential to early AI investments. Companies that build the picks and shovels of the ZK revolution—particularly zkVMs and developer infrastructure—stand to capture billions in value as the technology matures.

In our final post, we'll look ahead to the future of ZK, exploring how the technology might evolve beyond its current use cases, the potential market developments we might witness, and specific predictions about how the ZK landscape will unfold over the next 5-10 years.