Is "Data" the most overlooked RWA as an AI engine?

Original Article Title: Why Is The AI Engine's

Data

The Most Overlooked Real World Asset?

Original Article Author: Dr. Max Li, Founder of OORT

The current mainstream Real World Asset (RWA) discussion is dominated by traditional financial products: U.S. Treasury bonds, private credit, gold-backed tokens, and on-chain real estate assets. The logic behind this is simple: digitize assets that the financial world already values, move them to the blockchain to enhance accessibility, transparency, and liquidity. But what if this narrow focus is actually a blind spot? This article will explore why the most valuable type of asset, data, may be overlooked in the current RWA discourse. As we enter the era of decentralized AI, data should occupy a more important place on the RWA table.

What is RWA?

Real World Assets are tangible or intangible assets from the physical world or traditional economy, such as real estate, bonds, or commodities, represented on-chain in tokenized form. These tokens can represent ownership, revenue rights, or other forms of economic utility, with the aim of bringing off-chain value into the decentralized finance (DeFi) system. RWAs serve as a bridge between the real economy and the digital world, unlocking liquidity for traditional illiquid assets and enabling programmable finance.

Currently, most RWA discussions are still replicating the financial system they were supposed to disrupt. For example, the tokenization of U.S. Treasury bonds is rapidly advancing; the private credit market is undergoing Web3 transformation; even real estate and commodities have their on-chain counterparts. However, this focus may lead to a blind spot: it limits the space for blockchain innovation, merely refreshing the existing financial structure with technology rather than truly exploring new value carriers. At the same time, this path is prone to falling into a closed-loop mindset, continuously reinforcing traditional financial logic rather than driving the development of a new paradigm, thereby restricting the potential of RWA to disrupt global markets and unleash economic potential.

Why is "Data" a Valuable RWA?

RWAs can be seen as a new type of "stock," no longer tied solely to enterprises but anchored in asset categories with long-term economic utility. In this framework, data is not only valuable but also strategically significant—a next major battleground in the global AI competition following chips.

As we have discussed in previous articles, high-quality datasets are quickly becoming the "digital gold" in the AI arms race. Today, the competition between enterprises revolves not only around computing power but also the race for clean, real, diverse, and globally sourced human data, which serves as the fuel for training and fine-tuning AI models.

In addition, according to statistics, the big data market is projected to reach $325.4 billion in 2023 and is expected to grow to $1,035.4 billion by 2032, indicating a significant underlying economic value.

Similar to how Gold ETFs have become a mainstream capital market tool, data-backed RWAs also have the potential to open up a trillion-dollar new market. The underlying logic is consistent with how the capital market evaluates AI companies' proprietary data assets: high-quality data itself constitutes an investable asset class.

Another key point to ensure that data has value is its "scarcity." In an AI-driven era, high-quality human-generated data is becoming scarce and valuable. As synthetic content floods the internet, the "real, clean, diverse data" required for training models becomes increasingly rare, amplifying its value.

More importantly, data originates from real-world human behaviors and activities, possessing clear practicality. You may not be able to touch it, but you can tokenize, trade, license, and earn income from it.

Unlike bond tokens "lying flat" in wallets, data is inherently meant to be used. Its utility is embedded in its existence, and the demand for it continues to grow across various industries: from healthcare, autonomous driving to climate analysis, almost every industry requires insightful data support. The more unique, validated, and structured a dataset is, the higher its value. Whether it's detailed consumer behavior patterns, high-resolution satellite images, or anonymized medical records, data has become the cornerstone of decision-making across industries.

How to Tokenize Data Sets into Real-World Assets?

The core mechanism of RWAs can allow data to be expressed in the form of blockchain tokens, enabling clear ownership, precise permission control, divisibility, and easy transfer. For example, a research institution can tokenize its specific scientific dataset, allowing other researchers to purchase partial access rights or participate in data pool development.

Data tokenization refers to expressing a dataset in the form of blockchain assets, allowing it to be traded, divided, and verifying its source. Just as ownership of gold or real estate can be put on the chain, tokenized data can also anchor access rights, licensing revenues, or model invocation rights.

Challenges and Considerations

Tokenizing data as an RWA process is destined to be long-term and complex, with almost no mature frameworks, technical standards, or infrastructure in the current market. The main challenges include:

· Smart Contract Design: The technical implementation is relatively simple, but designing a contract structure that transparently reflects data ownership, permission rights, and revenue distribution will be a major challenge.

· Yield Flow and Utility: The value of data tokens depends on whether they are actually being used by AI developers, for example, payment based on usage. Mechanisms are needed to bring yield into the contract and distribute it while preventing system abuse.

· Valuation Challenge: How to objectively value a dataset? Value may depend on its uniqueness, timeliness, quality, relevance, and ability to generate insights. Developing a widely accepted valuation mechanism will be key.

· Source and Quality Verification: Ensure that tokenized data is always genuine, accurate, and timely, especially for dynamic datasets, which pose a significant technical challenge.

· Privacy and Security: When data is tokenized and propagated on the chain, how can its sensitivity be protected? State-of-the-art encryption schemes and access control mechanisms are needed.

· Privacy Regulation Compliance: Tokenizing human-generated data may raise a series of issues regarding data privacy regulations (such as GDPR, HIPAA). The existing legal framework needs to evolve to accommodate decentralized data ownership and consent-based authorization mechanisms.

Conclusion: The Missing Puzzle Piece of RWA?

If RWA's mission is to bring the most valuable elements of the real world into Web3, then "data" cannot be overlooked. It is the fuel of the AI economy, the intangible foundation behind all intelligent systems, and may also be the most liquid, programmable, and globalized type of RWA currently.

With the rise of decentralized AI, the market will increasingly need open, permissionless high-quality data access channels, and tokenized data is the most elegant infrastructure to realize this future. Data RWA may not only be a fringe direction; it has the potential to become the next core theme driving the RWA narrative. And this story is just beginning.