Making Intangible Assets Visible

How DLT and Zero Knowledge Proofs Redraw the Value Boundaries of RWA

Liovest AG
1. Intangible Assets: The Next Trillion-Dollar Market

In the modern digital economy, the core value of companies is undergoing a fundamental shift from tangible assets such as machinery and real estate to intangible assets such as brand equity, data, algorithms, and social influence. According to research from Ocean Tomo, intangible assets accounted for only 17% of the S&P 500’s market value in 1975. By 2020, that figure had risen to more than 90%.

Yet these core assets, often a company’s true competitive moat, remain largely invisible within the traditional financial system. They lack clear ownership rights, standardized valuation frameworks, and established mechanisms for circulation. As a result, they are rarely accepted as collateral, and the market continues to struggle with information asymmetry and valuation inefficiency. Innovators and creators are therefore often unable to convert their invisible value into financial capital.

Today, with the rise of digital trust infrastructure, particularly distributed ledger technology (DLT) and zero-knowledge proofs (ZKPs), we now have the tools and frameworks needed to register, verify, tokenize, and finance intangible assets. This new paradigm is emerging under the Real World Assets (RWA) framework.

DLT and ZKPs provide a pathway that traditional systems could not offer. They allow previously invisible assets to enter the financial ecosystem for the first time by making them registrable, collateralizable, and tradable. In doing so, they are reshaping the structure of capital markets.

 

2. The Structural Dilemma of Intangible Assets

Enterprise competitiveness has shifted from capital-intensive to knowledge-intensive models. The most valuable assets are no longer land and machinery, but brand reputation, algorithms, processes, talent, and data, collectively referred to as intangible capital. The World Intellectual Capital Initiative (WICI) defines this as the foundation of long-term value creation, comprising organizational capital, human capital, relational capital, and innovation capital.

However, intangible assets face several structural constraints:

  • Lack of ownership recognition: Assets such as algorithms, digital content, and social influence often exist without formal legal registration or clearly defined ownership frameworks.

  • No standard valuation framework: Financial reporting frequently relies on historical cost or amortization, which fails to capture market value or future income potential.

  • Limited access to financing: Banks and credit markets generally do not accept data, brand value, or user networks as valid collateral.

  • No effective mechanisms for circulation: Intangible assets are often indivisible and non-standardized, making fractional ownership and secondary trading difficult.

These limitations trap value, distort capital allocation, and constrain innovation.

 

3. DLT and ZKPs: Infrastructure for Ownership and Trust

DLT provides a decentralized, tamper-resistant, and traceable ledger system that fundamentally changes how intangible assets can be managed:

  • Ownership registration: Once recorded on-chain, assets obtain a verifiable timestamp and ownership record, helping establish digital property rights.

  • Asset mapping: Algorithms, creative works, and datasets can be uniquely identified and tokenized.

  • Contract execution: Smart contracts can automate revenue sharing, collateralization, and trading processes for intangible assets.

DLT gives intangible assets access to the same ownership and liquidity infrastructure once reserved for physical assets, expanding efficiency, scalability, and tradability.

Zero-knowledge proofs (ZKPs), a cryptographic method for verifying claims without revealing the underlying data, are essential for protecting the privacy and security of intangible assets:

  • Privacy-preserving verification: Creators can prove authorship without disclosing the content itself.

  • Model integrity verification: AI companies can validate model performance without exposing model architecture.

  • Data compliance verification: Data providers can prove legal compliance and data quality without revealing raw datasets.

  • Trustless execution: ZKPs enable decentralized verification without reliance on intermediaries.

ZKPs fill a critical privacy gap in DLT systems, ensuring that trust and confidentiality can coexist. Projects such as Aztec and Mina are already applying ZKPs to support secure and private on-chain interactions.

 

4. The Evolution of RWAs: From Physical Assets to Knowledge-Based Assets

The RWA narrative began with traditional physical assets:

  • Phase 1: Tokenization focused on real estate, such as Propy and RealT, private equity, such as Securitize, receivables, such as Centrifuge, and commodities such as gold through Paxos Gold. These assets benefited from relatively clear legal frameworks and valuation methods.

  • Phase 2, now emerging: RWA is expanding into the realm of intangible assets, including:

    • intellectual property and content rights, including music, film, and branding;

    • AI models and algorithmic structures;

    • data assets, including user data, medical records, and IoT data;

    • personal reputation and social capital; and

    • ESG metrics and corporate culture indicators.

     

The World Bank’s Tokenization of Assets in Emerging Markets report identifies the tokenization of intangible assets as a major driver of the next wave of Web3-enabled financialization.

To unlock this opportunity, four core pillars must be established:

  1. a DLT-based asset registration system;

  2. ZKP-based verification of ownership and asset attributes;

  3. financing and trading platforms, whether decentralized or centralized; and

  4. legal frameworks and disclosure standards, including those advanced by WICI.

5. Use Cases: From IP Rights to AI Model Tokenization

Intellectual Property NFTs

NFTs were among the earliest forms of intangible asset tokenization. Platforms such as Royal.io for music rights and Async Art for programmable art allow creators to tokenize ownership and revenue rights. These mechanisms improve creators’ bargaining power while enabling fans and investors to acquire fractional ownership and participate in revenue sharing.

Tokenized AI Models

AI models are high-value but highly sensitive assets. ZKPs can validate model performance, for example by confirming accuracy above 95%, without revealing structural details. In this model:

  • AI models are registered on-chain;

  • ZKPs verify functional integrity;

  • revenue-sharing tokens can be issued; and

  • investors can participate in income generated by model usage.

Projects such as SingularityNET and Numerai are building algorithmic marketplaces that treat AI models as tradable assets.

Data Asset Authorization

Data is the crown jewel of the digital economy, but it is also burdened by privacy, compliance, and governance concerns. ZKPs enable providers to validate data integrity and authorize analytical use without exposing raw data. Ocean Protocol is building infrastructure for data asset trading with embedded privacy protections.

Tokenizing Social Capital

Influencers, streamers, and key opinion leaders generate significant commercial value that is rarely monetized directly. Through tools such as reputation tokens and influence NFTs, these individuals can verify engagement metrics with ZKPs and tokenize future commercial potential. Lens Protocol and FANtium are among the early adopters of this model.

 

6. Regulatory and Structural Challenges Ahead

Technological capability does not automatically translate into broad adoption. Several regulatory and systemic issues remain unresolved.

Legal Uncertainty

Most jurisdictions do not yet recognize on-chain ownership as legally binding. Key questions remain:

  • Is an NFT legally equivalent to ownership?

  • Are smart contracts enforceable in court?

  • Are cross-border data assets protected under local law?

Valuation Complexity

Intangible assets are difficult to standardize and benchmark. Building robust markets will require:

  • multi-factor scoring systems that integrate influence, returns, and historical performance;

  • third-party verification frameworks; and

  • baseline credit and risk ratings.

Investor and Regulatory Safeguards

For RWAs to scale, investor protection and compliance frameworks must evolve, including:

  • AML/KYC protocols;

  • smart contract audits;

  • investor qualification standards; and

  • liquidity management mechanisms.

 

7. Reimagining Asset Boundaries, Reimagining Value

Intangible assets are becoming the true core of value creation in the digital era. RWA frameworks, powered by DLT and ZKPs, offer one of the most practical paths to unlocking their financial potential.

We are entering an era in which knowledge, algorithms, reputation, and digital influence can be registered, priced, fractionalized, traded, and financed, moving into the mainstream capital structure.

The financial systems of the future will no longer revolve solely around land, debt, and equity. They will increasingly incorporate softer, knowledge-based asset classes as investable and financeable forms of value. Those who establish the standards, infrastructure, and trust frameworks early will shape the future sovereignty of value in the digital economy.

 

 

References

  1. Ocean Tomo. Intangible Asset Market Value Study.

  2. WICI Global Network.

  3. World Bank. Tokenization of Assets in Emerging Markets.

  4. Ledger. What Is Zero-Knowledge Proof?

  5. Rapid Innovation. Top 10 Blockchain Use Cases of Zero-Knowledge Proof.

  6. Async Art.

  7. Royal.io.

  8. Zero-Knowledge Project Index.

  9. Ocean Protocol.

  10. Investax. Intellectual Property Tokenization.

  11. BlockApps. Case Studies in IP Tokenization.

  12. Blockchain App Factory. RWA Tokenization Use Cases.