AI and Web3 tokens are transforming Real World Assets -- Kathy Tong, Venture Investor at Decasonic
AI x RWA Market Overview
Tokenization is transforming traditional assets—such as treasuries, real estate, and commodities—by enabling fractional ownership, enhanced liquidity and access to global markets. As institutions move on-chain, demand for scalable, secure, and regulated infrastructure continues to rise. In order to access all of Web3, institutions require scalable, secure, and appropriately licensed market infrastructure. Meanwhile, record-high stablecoin AUM of $226 billion and increasing inflows into ETFs highlight the accelerating institutional shift toward digital assets and on-chain economic growth.
RWAs have seen rapid growth, driven by institutional adoption and advancements in blockchain technology. RWAs, which represent physical and financial assets as digital tokens, now hold a total market value of $17.6 billion, up from ~$13 billion at the end of 2024, according to rwa.xyz. Excluding private credit, RWAs across major networks stand at $5.6 billion, with tokenized treasuries comprising 3.9B or 69% of this value.
The data from rwa.xyz shows that RWAs (excluding stablecoins in this graph) are seeing considerable growth and will continue to increase as more institutions come on-chain, with a total RWA category market cap of $35 billion according to CoinGecko. Interest in RWAs is also on the rise with increasing mindshare by Kaito and growth in wallet holdings and cumulative loans in lending and tokenized treasuries from Dune.
Regulatory developments are playing a crucial role in this expansion in the macro view. The European Union's Markets in Crypto-Assets (MiCA) regulation, fully applicable since December 2024, provides a comprehensive framework for crypto assets, including RWAs, aiming to streamline blockchain adoption while ensuring user and investor protection. Brazil also plans to regulate stablecoins and asset tokenization by 2025, reflecting a global trend toward clearer guidelines in the crypto space. We are seeing increasing positive projections for the RWA tokenization market, with Boston Consulting Group estimates the market could reach $16 trillion by 2030, driven by the integration of blockchain technology into traditional financial systems. This growth is further supported by major financial institutions like BlackRock and State Street exploring tokenization initiatives, signaling a shift toward mainstream adoption of digital asset management.
Rise of AI in RWA
The intersection of AI and RWA is still emerging, and AI’s role will reshape how tangible assets—such as farmland, real estate, and collectibles—are digitized, managed, and traded on-chain. While tokenization has long promised liquidity and democratized access, AI can really accelerate real-time optimization, risk assessment, and automation, making RWAs dynamic and responsive financial instruments, as well as provide novel tooling for a new world of robots, autonomous vehicles and drone technology.
In recent developments, we’re seeing a broad trend of enhanced liquidity and efficiency. Platforms like Ondo Finance, with its $95M Blackrock BUIDL allocation in 2024, is now exploring AI-driven yield optimization for its tokenized treasuries.
Mantra, an L1 built for RWA tokenization, has teamed up with UAE-based DAMAC,a conglomerate spanning real estate, hospitality, and data centers, to tokenize at least $1 billion of its portfolio exclusively on Mantra chain. This deal, set to roll out in early 2025, exemplifies the fusion of AI and RWAs in action by enhancing liquidity and unlocking fractionalization of high-value assets and using AI to optimize yields.
AI is also crunching vast datasets- from market trends, weather patterns, regulatory updates and more to enhance RWA valuations. MakerDAO’s growing RWA exposure likely leverages AI for yield analysis. Similarly, AI-powered real-time valuation models are also refining asset pricing accuracy.
Sector-specific tokenization is also becoming more prevalent as one-size-fits-all tokenization is fading, with platforms catering to niche industries. Agrotoken on Algorand is tokenizing agricultural products like soybeans, while Blocksquare’s SPRING protocol is tokenizing real estate at scale—up to 100,000 tokens per property. AI is also playing a role in optimizing tokenized stablecoins like SuperState’s $USDM, which offers 5% APY on Treasury-backed assets.
Decasonic RWA x AI Market Map 2025
Our RWA x AI market map is built on a dual-axis framework that brings clarity to an otherwise fragmented landscape.
X-Axis: Maps products across AI’s standard layers: Compute → Data → Model → Interface → Application.
Y-Axis: Differentiates target audiences between Web3 Natives, Web3 Traders, Web3 DeFi Users, and Mainstream DeFi Users.
This systematic approach ensures that our map not only reflects the current state of the market but also highlights the specific niches where innovation is thriving.
Key Categories in RWA x AI
As this space is emerging, we foresee there to be emerging market categories that may be white-spaces for new projects to carve out. We have identified several distinct categories where AI is making an impact on real-world asset tokenization and management:
Agent and Agent Tooling
Overview: AI-powered agents and the tools that support them are at the forefront of automating data analytics and decision-making in the RWA space. These technologies enable real-time market analysis, risk assessment, and dynamic trading strategies.
Asset Management
Overview: Intelligent asset management platforms are leveraging AI to enhance portfolio management, optimize asset allocation, and provide predictive insights for better decision-making in RWA portfolios.
Infrastructure
Overview: Robust infrastructure is essential to support the seamless integration of AI with tokenized real-world assets. This includes blockchain networks, cross-chain solutions, and execution environments that provide the backbone for RWA transactions.
Tokenization
Overview: The tokenization category represents the digitization of tangible assets across a variety of sectors. AI enhances these protocols by enabling accurate asset valuation, automated compliance, and improved market interoperability.
Sub-Categories:
Agriculture: Facilitates tokenization and management of agricultural assets.
Art: Empowers artists and collectors by tokenizing art pieces.
DePin: Integrates decentralized physical infrastructure networks.
Real Estate: Revolutionize property ownership and transactions.
Stablecoin: Innovate with AI-enhanced stable asset protocols.
Financial: Bridges traditional financial instruments with blockchain technology.
Opportunities and Future Outlook
The integration of AI into RWA tokenization is poised to overcome longstanding challenges in asset liquidity, valuation, and accessibility. For developers, the AI x RWA landscape offers a fertile ground to build next-generation solutions that can automate complex asset management tasks and drive mass adoption. Investors have the opportunity to tap into projects that not only digitize real-world assets but also apply intelligent systems to maximize efficiency and reduce risk.
The future of Physical AI where androids and robots can co-exist with humans are accelerated by RWA technology as more physical data, AI tooling, geospatial data and more can all be tokenized and brought on-chain.
Furthermore, as regulatory frameworks continue to evolve, the combination of AI’s precision with the transparency of blockchain technology could set new industry standards for compliance and security.
Looking ahead, there are so many interesting use cases within AI x RWA, some of which we explore further here.
Rise of Physical AI Enhanced by RWA
The blend of AI and humanoid robotics and intelligent robots could reshape work and economics in many areas. In manufacturing, humanoid robots can take on repetitive or risky tasks—like assembling parts or handling hazardous materials—boosting efficiency and cutting human exposure. In construction, they could tackle labor shortages by building structures or inspecting sites, addressing aging workforce challenges. In healthcare AI-powered robots can aid in eldercare or therapy. In logistics, they could streamline warehouses or deliveries, navigating real-world chaos with ease.
A big future use case for AI x RWA lies in tokenizing assets. RWA means turning physical and digital stuff—like a robot’s data or capabilities—into tradable tokens. Imagine humanoid robots generating data from their actions (say, mapping a factory floor or tracking movements). That data gets tokenized, creating assets that companies or developers can buy, sell, or use to improve AI smarts and robot performance and RWA can really unlock a new layer of growth for robotics down the line through tokenizing assets.
AI-Driven Asset Tokenization
AI models can provide real-time valuation for tokenized assets like real estate, precious metals, or fine art. By analyzing market data, trends, and comparable sales, AI can refine pricing, improve market efficiency, and inform buying or selling decisions.
AI can also enable asset valuation and enhanced liquidity by using real-time data oracles, and real time pricing algorithms to ensure market efficiency in all liquid and illiquid markets such as commodities, treasury loans and real estate valuation. AI-driven liquidity pools optimize RWA-backed stablecoins and DeFi assets, dynamically rebalancing holdings based on market movements.
AI models can provide real-time valuation for tokenized assets like real estate, precious metals, or fine art. By analyzing market data, trends, and comparable sales, AI can refine pricing, improve market efficiency, and inform buying or selling decisions.
In traditional banking, credit scoring can also be enhanced with the use of AI. In the context of RWA, AI can also enhance underwriting tokenized debt markets such as Maple Finance’s RWA lending pool through analyzing borrower history, asset data, and macroeconomic indicators, and Clearpool’s Ozean uses AI to score private credit processed on 2024 loans.
Supply Chain and ESG
AI-powered analytics can help the ESG market through tracking carbon offsets and optimized tokenized renewable energy markets. AI is also enabling dynamic supply chain financing by tokenizing key steps of the supply chain process, processing invoices and assessing real-time logistics risk, with applications in tokenized commodities and real estate-backed lending.
From farmland-backed loans to AI-managed REITs, AI is actively driving liquidity, reducing risk, and scaling tokenized RWAs into institutional-grade investment vehicles. As AI-powered analytics refine asset management and automation streamlines trading, RWAs are evolving from static holdings into dynamic, data-driven financial instruments. The fusion of AI and tokenization is not only unlocking liquidity but also enhancing risk modeling, optimizing capital efficiency, and improving accessibility for both institutional and retail investors. We believe with the integration of AI, the RWA landscape is rapidly shifting toward a more transparent, efficient, and inclusive on-chain economy.
Some trends that are emerging at this intersection are:
Rapid Expansion of AI-Driven Tokenization: As more sectors embrace digital asset management, AI’s role in ensuring accurate, real-time data analysis and automated decision-making will become indispensable.
Increased Cross-Chain and Interoperable Solutions: Enhanced infrastructure solutions will facilitate smoother interactions between disparate blockchain networks, improving the overall liquidity and reach of tokenized assets.
Greater Emphasis on Sector-Specific Applications: Tailored tokenization solutions in agriculture, art, real estate, and stablecoins will address unique industry challenges and unlock new revenue streams.
Conclusion
The AI x RWA market map underscores a transformative phase where real-world asset tokenization is not only about converting tangible assets into digital tokens, it’s about leveraging AI to create intelligent, dynamic ecosystems that redefine asset management. With a structured approach that spans agent tooling, asset management, foundational infrastructure, and specialized tokenization, the AI x RWA sector is poised to lead the next wave of financial innovation. As we continue to monitor this space, it’s clear that the convergence of AI and real-world assets offers unparalleled opportunities for efficiency, liquidity, and growth in the digital asset economy.
The content of this material is strictly for informational and educational purposes only. It is not intended to constitute investment advice, nor should it be considered a recommendation or a solicitation to buy, sell, or hold any asset. Decasonic does not endorse investments in any specific tokens, and nothing in these blog posts should be construed as legal, tax, or financial advice. Please consult with a qualified professional advisor before making any financial decisions. Decasonic provides no warranties, whether expressed or implied, on the content provided in these blog posts, including its accuracy, completeness, or correctness. The opinions expressed here are those of the authors and do not necessarily reflect the views of Decasonic. Please note that Decasonic may hold a position in some of the tokens mentioned, including Virtuals. Decasonic is not liable for any errors or omissions in the content of this material or for any actions taken based on the information provided herein.