Original Title: Why Is The AI Engine 『Data』 The Most Overlooked Real World Asset?
Original Author: Dr. Max Li, Founder of OORT
Current mainstream discussions about real world assets (RWA) are dominated by traditional financial products: U.S. Treasury bonds, private credit, gold-backed tokens, and real estate on-chain assets. The logic behind this is simple: digitizing assets that the financial world already values and migrating 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 at the RWA table.
What is RWA?
Real world assets are tangible or intangible assets sourced from the physical world or traditional economic systems, such as real estate, bonds, or commodities, represented on-chain through tokenization. These tokens can represent ownership, rights to income, or other forms of economic utility, with the goal of bringing off-chain value into the decentralized finance (DeFi) system. RWA serves as a bridge connecting the real economy and the digital world, releasing the liquidity of traditional illiquid assets on one hand, while also enabling programmable finance.
Currently, most discussions about RWA are still replicating the financial system it was supposed to disrupt. For example, the tokenization of U.S. Treasury bonds is developing rapidly; the private credit market is undergoing a Web3 transformation; even real estate and commodities have found their corresponding counterparts on-chain. However, this focus may lead to blind spots: it limits the space for blockchain innovation, merely providing a technological renovation of the existing financial structure rather than truly exploring new value carriers. At the same time, this path is prone to falling into a closed-loop of thinking, continuously reinforcing traditional financial logic instead of promoting the development of new paradigms, thereby limiting the potential for RWA to disrupt global markets and unleash economic potential.
Why is "Data" a Valuable RWA?
RWA can be seen as a new type of "stock," no longer merely tied to companies but anchored to asset classes with long-term economic utility. In this framework, data is not only valuable but also strategically significant—it is the next battleground in global AI competition after chips.
As discussed in our previous articles, high-quality datasets are rapidly becoming the "digital gold" in the AI arms race. Today, companies are competing not only for computing power but also for clean, authentic, diverse, and global human data, which serves as the fuel for training and fine-tuning AI models.
Moreover, statistics show that the big data market size was $325.4 billion in 2023 and is expected to grow to $1,035.4 billion by 2032, indicating the enormous economic value hidden behind it.
Just as gold ETFs have become mainstream tools in capital markets, RWA supported by data also has the potential to open up a trillion-dollar new market. The underlying logic is consistent with how capital markets assess the proprietary data assets of AI companies: high-quality data itself constitutes an investable asset class.
Another key point ensuring the value of data is its "scarcity." In an age filled with AI, high-quality human-generated data is becoming scarce and precious. With the proliferation of synthetic content online, the "real, clean, diverse data" needed to train models is increasingly hard to come by, and this scarcity further amplifies its value.
More importantly, data comes from real-world human behaviors and activities, possessing clear practicality. You may not be able to touch it, but you can tokenize it, trade it, license it, and profit from it.
Unlike bond tokens that "lie flat" in wallets, data is inherently meant to be used. Its practicality is embedded in its existence, and demand is continuously growing across various industries: from healthcare and autonomous driving to climate analysis, almost every industry requires insightful data support. The more unique, verifiable, and structured the dataset, the higher its value. Whether it’s detailed consumer behavior trajectories, high-resolution satellite images, or anonymized medical records, data has become the cornerstone of decision-making across industries.
How to Tokenize Datasets as Real World Assets?
The core mechanism of RWA allows data to be expressed in the form of blockchain tokens, thereby achieving clear ownership, fine-grained permission control, divisibility, and convenient transfer. For example, a research institution can tokenize its specific scientific dataset, allowing other researchers to purchase partial access or jointly participate in building a data pool.
Data tokenization refers to expressing datasets as blockchain assets, making them tradable, divisible, and verifiable in origin. Just as ownership of gold or real estate can be put on-chain, tokenized data can also anchor access rights, licensing revenues, or model invocation rights.
Challenges and Considerations
The process of treating data as RWA is destined to be long and complex, with almost no mature frameworks, technical standards, or infrastructure currently available in the market. Major challenges include:
· Smart Contract Design: While the technical implementation is relatively simple, designing a contract structure that transparently reflects data ownership, licensing rights, and revenue distribution will be a significant challenge.
· Revenue Flow and Practicality: The value of data tokens depends on whether they are genuinely used by AI developers, for example, paid per invocation. Mechanisms are needed to bring revenue into contracts and distribute it while avoiding system abuse.
· Valuation Dilemma: How to objectively value datasets? Value may depend on their uniqueness, timeliness, quality, relevance, and ability to generate insights. Establishing a widely accepted valuation mechanism will be key.
· Source and Quality Verification: Ensuring that tokenized data is always real, accurate, and timely, especially for dynamic datasets, is technically challenging.
· Privacy and Security: When data is tokenized and disseminated on-chain, how to protect its sensitivity? Cutting-edge encryption solutions and access control mechanisms are needed.
· Compliance with Privacy Regulations: Tokenizing human-generated data may raise a series of issues regarding data privacy regulations (such as GDPR, HIPAA). Existing legal frameworks need to evolve to accommodate decentralized data ownership and consent-based authorization mechanisms.
Conclusion: The "Missing Puzzle Piece" of RWA?
If the mission of RWA is to bring the most valuable elements from the real world into Web3, then "data" must not be overlooked. It is the fuel of the AI economy, the invisible foundation behind all intelligent systems, and it may currently be the most liquid, programmable, and global type of RWA.
With the rise of decentralized AI, the market will increasingly need open, permissionless access channels to high-quality data, and tokenized data is the most elegant infrastructure to achieve this future. Data RWA may not just be a marginal direction; it has the potential to become the next core theme dominating the RWA narrative. And this story is just beginning.
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