Tokenization of real-world assets (RWAs) is gaining traction in the crypto industry, with experts identifying it as the next promising use case. A report by Citi projected that the market for tokenized RWAs could reach $4 trillion to $5 trillion by 2030. While blockchain technology has the potential to transform the tokenization of RWAs, mass adoption is not yet widespread. Despite this, industry experts believe that the integration of artificial intelligence (AI) solutions could drive the advancement of tokenized RWA use cases.
RWA tokenization has evolved significantly since the era of security token offerings (STOs) in 2018. Today, tokenized RWAs are characterized by a degree of tangibility, according to Dave Hendricks, CEO of Vertalo. The assets being tokenized include high-quality liquid assets like art, diamonds, and real estate. Through tokenization, investors can own a fraction of these assets and earn income from their use. Tokenization use cases are considered integral to the decentralized finance (DeFi) ecosystem, offering benefits such as increased tradability and transparency in asset management functions.
The application of AI in tokenized RWAs is seen as a key driver for advancing use cases in the industry. AI solutions can enable asset value prediction, particularly for venture capitalists managing diverse portfolios. By predicting the future values of assets and assessing demand for services, AI can enhance traders’ understanding of asset valuation. Platforms like RealCap are leveraging AI to determine the prices of tokenized RWAs, especially for assets with limited pricing information. AI tools like predictive pricing algorithms can improve the tradability and valuation of tokenized RWAs.
AI is also being utilized to streamline workflows and automate processes in the tokenization of RWAs. Platforms like Propy use AI for transaction analysis, contract reviews, and borrower risk assessment. By automating transaction timelines and evaluating customer credibility based on on-chain activities, AI enhances the efficiency and security of property investments. However, challenges such as limited data access and privacy concerns may hinder the widespread adoption of AI in tokenized RWAs. Addressing these challenges will be crucial for AI to realize its full potential in advancing tokenization use cases.
Beyond the integration of AI, tokenized RWAs face primary challenges related to regulatory compliance and asset verification. Tokenizing assets like buildings may attract regulatory scrutiny and turn them into securities, impacting the incentives for RWA tokens. Verifying the authenticity of assets backing RWA tokens remains a critical issue, with crypto traders expressing doubts about the validity of claims. Successful tokenization of RWAs necessitates careful structuring, ensuring compliance with regulations and the creation of secured interests in underlying assets. Despite the challenges, the potential of tokenized RWAs and AI to transform the financial landscape is promising, with ongoing developments aiming to overcome obstacles and drive innovation in the industry.
