09-07-2025
Why Is The AI Engine 'Data' The Most Overlooked Real World Asset?
The New York Stock Exchange during morning trading in New York City. (Photo by Michael M. ...)
Mainstream real world asset (RWA) discussions are dominated by traditional finance: U.S. treasuries, private credit, gold-backed tokens, and tokenized real estate. The logic is straightforward: digitize what the financial world already values, and port it to blockchain rails for better access, transparency, and liquidity. But what if this narrow focus is a blindspot? This article explores why the current RWA discourse may be overlooking one of the most valuable asset classes: data. As we move deeper into the decentralized AI era, data deserves an even more important seat at the RWA table.
What Are Real World Assets？
Real-world assets are tangible or intangible assets that originate in the physical or traditional economy, such as property, bonds, or commodities, and are represented on-chain through tokenization. These tokens reflect ownership rights, revenue claims, or other forms of economic utility and are designed to bring off-chain value into decentralized finance (DeFi). RWAs serve as the connective tissue between the real economy and the digital world, unlocking liquidity for traditionally illiquid assets while enabling programmable finance.
Right now, the RWA conversation largely mirrors the financial system it aims to disrupt. Tokenized U.S. treasuries are growing fast. Private credit markets are getting a Web3 facelift. Even real estate and commodities have their on-chain equivalents. However, this narrow focus could portray potential blindspots. It limits innovation by only applying blockchain to pre-existing financial structures, rather than exploring new paradigms of value creation. It can also create an echo chamber, reinforcing traditional financial thinking instead of pushing the boundaries of what constitutes a valuable, tokenizable asset. This restricts the potential for RWAs to truly revolutionize global markets and unlock vast pools of economic worth currently inaccessible or underutilized.
Why Data Real World Asset Carry Value？
Think of RWAs as the new form of equities that are not just tied to companies, but to asset classes with long-term economic utility. In that context, data is not just valuable, it is strategic, and is the next key battleground for the global AI race following chipsets.
In prior articles, we've explored how quality datasets are quickly becoming the 'digital gold' in the AI arms race. Companies today are not just competing on computing power, but also battling over access to clean, human-generated, diverse and global data that can fuel model training and fine-tuning. In addition, the Big Data market size was valued at USD 325.4 billion in 2023 and is expected to grow to USD 1035.4 billion by 2032, underscoring its immense economic weight.
Just as ETFs backed by gold have become mainstream in capital markets, RWAs backed by data have the potential to open up a trillion dollar market. The logic is similar to how public markets value AI companies based on their proprietary data assets. In this light, tokenizing high-quality datasets creates a new, investable asset class.
Another crucial element ensuring data's value is: its scarcity. In an AI-saturated world, high-quality, human-generated data is becoming more rare and valuable. As synthetic content floods the internet, training models on clean, real, diverse data becomes harder, making it more scarce and critical. Furthermore, data comes from real-world behaviours and human activities, meaning it has clear utility. You may not be able to touch it, but you can tokenize it, trade it, license it, and earn from it.
Unlike tokenized bonds, which may sit idle in wallets, data is meant to be used. Its utility is embedded in its very existence, and demand is growing by the day, across industries from healthcare to autonomous driving to climate analytics. The more unique, verified, and structured a dataset is, the higher its potential value. Whether it's granular consumer behavior patterns, high-resolution satellite imagery, or anonymized medical records, data provides the bedrock for informed decisions across virtually every sector.
How Datasets Can Be Tokenized as Real World Asset？
The core mechanism of RWAs allows data to be represented as a digital token on a blockchain. This enables clear ownership, granular access control, fractionalization, and seamless transfer. Imagine a research institution tokenizing specific scientific datasets, allowing other researchers to buy fractional access or contribute to a collective data pool.
Data tokenization refers to the process of representing datasets as blockchain-based assets, making them tradable, divisible, and verifiable. Just like gold or property titles, tokenized data can be backed by access rights, licensing revenue, or AI model utility.
Challenges and Considerations
Tokenizing data as RWAs is going to be a complex and prolonged journey, as readily available framework, technology, and infrastructure for this novel idea are non-existent yet. Key considerations include:
Final Thoughts: Real World Asset's Missing Piece?
If real world assets are meant to bring the most valuable parts of the real world into Web3, then data cannot be left out of the equation. It is the fuel of the AI economy, the invisible layer behind every smart system, and potentially the most liquid, programmable, and global RWA we've yet to fully explore. As decentralized AI grows, the market will demand decentralized, permissionless access to high-quality datasets, and tokenized data offers the most elegant infrastructure for that future. Data RWAs might not just be a niche, they could be the next dominant RWA narrative. And that story is only beginning to unfold.
