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Tokenization 2030 - Wall Street On-Chain

<hr>1. Growth predictions: We forecast a $5.5 trillion base case for tokenized assets by 2030, rising to $8 trillion in a bull case. Public market securities and liquid collateral, particularly U.S. equities and treasuries, are likely to drive early adoption and expand distribution to digitally native investors.<hr>2.&nbsp;Liquid assets on-chain: Modern, digitally native investors increasingly expect 24x7 access to financial assets. Equities, bonds and commodities could move on-chain as younger retail investors drive adoption. If 10% of U.S. retail investors use on-chain solutions by 2030, this could create about $2.6 trillion of demand for tokenized public equities.<hr>3. Institutional catalysts: Major market infrastructure providers, including DTCC, NYSE, and Nasdaq, are beginning to integrate tokenization into their core platforms. As pilot programs transition into production, supported by evolving regulation, adoption among traditional financial institutions could accelerate.<hr>4. Digital money is the foundational enabler: Tokenization of financial assets is the companion to tokenized cash. Regulated stablecoins and tokenized deposits can help engender trust in on-chain settlement for Delivery-versus-Payment (DvP), improving capital efficiency and reducing settlement risk.<hr>5. “Structural orchestrators” emerge: Tokenization could create new revenue pools through programmability, composability and vertically integrated business models. Institutions may look to control issuance, distribution and settlement rails (e.g., select banks, asset managers, stablecoin issuers) in order to capture value.<hr>6. Evolution, not revolution – hybrid models set to dominate: The transition will be gradual. We expect a “messy” period where tokenized and legacy systems operate side by side. Hybrid models and interoperability between on-chain and off-chain worlds are critical to scaling.

Key Takeaways

The tokenization of financial assets – the representation of securities as digital tokens on blockchain infrastructure – is moving from pilot stage toward operational deployment. After years of slow progress held back by regulatory uncertainty, fragmented infrastructure and the absence of on-chain settlement money, adoption is now accelerating.The current global tokenized asset market stands at approximately $17 billion. Citi Institute projects this to reach $5.5 trillion by 2030 in a base case scenario, with a bear case of $2.7 trillion and a bull case of $8.2 trillion. Growth is expected to be led by public market securities – particularly U.S. equities and treasuries – rather than private markets, where adoption remains early-stage and structurally constrained.Three forces underpin this shift. First, major financial market infrastructure providers, including the Depository Trust &amp; Clearing Corporation (DTCC), New York Stock Exchange (NYSE), and Nasdaq, are integrating tokenization into core issuance, trading and settlement workflows, moving well beyond experimentation. Second, the growth of regulated on-chain money, including stablecoins (projected at $1.9 trillion by 2030) and tokenized deposits, is providing the settlement foundation that earlier tokenization efforts lacked. Third, regulatory clarity is improving across key jurisdictions.

Wall Street On-Chain

The tokenization of financial assets – the representation of securities as digital tokens on blockchain infrastructure – is moving from pilot stage toward operational deployment. After years of slow progress held back by regulatory uncertainty, fragmented infrastructure and the absence of on-chain settlement money, adoption is now accelerating.

Tokenization 2030

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The way you interact with market intelligence is changing. In fast moving markets, static information no longer keeps up. The reimagined Market Guide introduces a dynamic, AI-powered experience that transforms insight into real time perspective. Powered by deep local expertise and Citi’s global market network, it delivers clarity, context, and confidence—bringing you closer to the markets that matter. Discover what’s possible when market intelligence moves at the speed of today’s markets. Contact your Citi representative to learn how Market Guide can help your team access the intelligence they need, when and how they need it.

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AI in the Digital Economy - From Experimentation to Global Deployment

Artificial intelligence is entering a new phase. While the past few years have been defined by experimentation, organizations are now focused on the challenge of deploying AI at scale and embedding it into core business operations. In AI in the Digital Economy: From Experimentation to Global Deployment, Citi Institute combines proprietary survey data with perspectives from industry leaders to examine how AI adoption is evolving across regions and sectors.&nbsp;The findings suggest that AI is becoming a strategic business capability rather than a standalone technology initiative. While nearly 60% of surveyed organizations remain in the exploration or pilot stage, more advanced adopters are already realizing significant benefits. Among companies that have reached production or scaling phases, 60% report transformational value creation of more than 15% through improved productivity, cost efficiencies, or revenue growth.&nbsp; Regional differences are emerging. North America leads in enterprise deployment, with more than three-quarters of firms operating AI in production or scaling environments, while Europe shows steady progress and Asia Pacific remains focused on capability building and experimentation.&nbsp;The report also highlights a shift in priorities. Business model transformation has overtaken operational efficiency as the primary objective for AI investment, reflecting growing recognition that AI's greatest potential lies in reshaping products, services, customer experiences, and operating models. Industry executives interviewed for the study consistently emphasized that competitive advantage will depend less on access to AI models and more on proprietary data, organizational expertise, and successful execution.Looking ahead, organizations remain optimistic. Ninety percent of respondents expect artificial general intelligence to have a material impact on their industry within the next decade, underscoring the belief that AI will play a central role in future growth, innovation, and competitiveness.

<hr>1. Nearly 60% of organizations surveyed by Citi remain in the exploration or pilot phase, with only 40% reaching production or scaling. The challenge now isenterprise wide deployment.<hr>2. North America leads AI adoption, with more than three-quarters of firms in the production or scaling phase. By contrast, Asia Pacific remains focused on capability-building and experimentation, while Europe is somewhere in between.<hr>3. Business model transformation is now the leading investment objective, ahead of KPI improvement. Our expert interviewees consistently argued that AI’s biggest opportunity lies in redesigning business models.<hr>4. More than half of organizations in the production or scaling phase report transformational value creation of more than 15%.<hr>5. Organizations at the scaling stage are far more likely to work on investment horizons of less than 12 months. As adoption matures, AI shifts from a long-term innovation to a core business capability.<hr>6. Task automation remains the most common deployment use case across all spending levels. But higher-spending firms are investing in autonomous agents capable of managing more complex workflows.<hr>7. Survey evidence and interviews suggest success will depend less on just access to AI models and more on proprietary data, and expertise.

Artificial intelligence is entering a new phase. While the past few years have been defined by experimentation, organizations are now focused on the challenge of deploying AI at scale and embedding it into core business operations. In AI in the Digital Economy: From Experimentation to Global Deployment, Citi Institute combines proprietary survey data with perspectives from industry leaders to examine how AI adoption is evolving across regions and sectors.&nbsp;

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<ul><li data-list-item-id="e5a3f4058ffcae2de5845a86d41d089dd">Data centers face a multi-trillion-dollar AI-driven capex supercycle constrained by power availability, semiconductor shortages and complex commercial contracts</li><li data-list-item-id="eeeb422c10850b4508e4d69a567a428f5">Trade finance tools such as Standby Letters of Credit, Inventory Finance, Export Agency Finance and Supply Chain Finance help utilities, hyperscalers and suppliers secure critical power and equipment while preserving liquidity</li><li data-list-item-id="e7ce5d481e00366cadbdccf35e69a45ef">Companies that embed trade finance into their data center growth strategy may be better positioned to secure scarce power capacity manage supply constraints and accelerate infrastructure buildout going forward</li></ul>

The rapid expansion of AI, cloud computing and digital infrastructure is driving a multi-trillion-dollar data center buildout, with Citi Research projecting $3.8 trillion in AI-related capital expenditure by 2030. Yet demand alone cannot create capacity: power availability, semiconductor supply constraints and increasingly complex commercial agreements are slowing project timelines and raising costs across the ecosystem.&nbsp;Trade finance solutions—including Standby Letters of Credit, Inventory Finance, Export Agency Finance, and Supply Chain Finance—are emerging as strategic tools that help utilities, hyperscalers, chipmakers and developers secure scarce resources, manage risk and preserve liquidity throughout the project lifecycle. As the data center supercycle accelerates, companies that embed trade finance into their growth strategy may be better positioned to move faster and more competitively than peers relying on conventional funding alone.

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The rapid expansion of AI, cloud computing and digital infrastructure is driving a multi-trillion-dollar data center buildout, with Citi Research projecting $2.9 trillion in AI-related capital expenditure by 2030. Read more.