Executive Summary
The global financial system is undergoing a fundamental transformation from a centralized, intermediated model to a programmable, decentralized, and increasingly autonomous architecture. The synthesis of blockchain infrastructure, distributed ledger technology (DLT), real-world asset (RWA) tokenization, and agentic artificial intelligence has progressed from experimental pilots to institutional operationalization. As of January 2026, regulatory frameworks—most notably the U.S. GENIUS Act signed into law in July 2025—have established federal oversight for payment stablecoins. However, the emergence of agentic AI that can autonomously execute multi-step tasks poses novel challenges for market liquidity, systemic risk, and algorithmic contagion that remain inadequately addressed by current policy frameworks.
I. Introduction: A Historical Narrative of Financial Evolution
Financial systems have never been static. They evolve through successive waves of innovation in record-keeping, settlement, and coordination, each designed to mitigate what may be termed “trust friction”—the time delays, informational asymmetries, costs, and counterparty risks inherent in economic exchange. From the emergence of double-entry bookkeeping in fifteenth-century Italy to the digitization of financial ledgers in the 1970s, every major transformation has sought to render transactions more legible, auditable, and scalable across expanding networks of commerce.
Double-entry bookkeeping did more than standardize accounting practices; it enabled the rise of modern capitalism by making complex enterprises governable at a distance. Centuries later, the digitization of ledgers replaced paper with electronic records, dramatically increasing processing speed and reducing clerical error. Yet despite these advances, the architecture of modern finance remained institutionally fragmented. Banks, clearinghouses, custodians, and exchanges each maintained their own proprietary ledgers, linked only through reconciliation processes that were operationally complex, legally layered, and temporally slow.
As a result, even in the digital age, cross-institutional transactions continued to rely on deferred settlement mechanisms. The T+2 settlement cycle—long accepted as a structural constraint of capital markets—became emblematic of this fragmentation. While computation accelerated, settlement lagged; while data moved in milliseconds, legal finality still took days. The persistence of these delays underscored a deeper truth: digitization alone did not eliminate trust friction—it merely repackaged it within siloed infrastructures.
The emergence of Bitcoin in 2009 represented a conceptual rupture with this paradigm. For the first time, a shared, append-only ledger could be maintained without reliance on a central authority. Bitcoin introduced the idea of a “trustless” system, not in the sense that trust disappeared, but in that trust was reallocated—from institutions and intermediaries to cryptographic verification and consensus mechanisms. However, Bitcoin itself was limited in scope: it functioned primarily as a peer-to-peer value transfer system, not a generalized financial platform.
It was the subsequent development of smart contracts, programmable tokens, and distributed ledger platforms that expanded this breakthrough into a broader financial toolkit. These technologies made it possible to represent real-world assets digitally, encode contractual logic directly into software, and automate execution without continuous human intervention. Tokenization transformed assets into composable digital objects, while smart contracts enabled conditional, self-executing financial relationships. Together, they laid the foundation for a financial system in which logic, settlement, and ownership could converge within a single computational layer.
The Programmable Inflection Point
The financial system now stands at what can be described as the Programmable Inflection Point—a stage at which financial infrastructure is no longer merely digital, but natively programmable. This shift is not driven by blockchain technology alone, but by its convergence with artificial intelligence, cloud computing, and interoperable data architectures. According to Gartner, by 2026 approximately 40% of enterprise applications are expected to incorporate task-specific AI agents, a dramatic increase from less than 5% in 2025. This rapid diffusion signals a transition from passive analytics to active, agent-based execution.
Generative AI has accelerated this transition by enabling agentic commerce—a paradigm in which AI systems do not simply advise human decision-makers but autonomously initiate, negotiate, and execute transactions. These agents can operate continuously, interact across multiple protocols and blockchains, optimize for cost and speed, and adapt dynamically to market conditions. When combined with tokenized assets and programmable settlement layers, AI agents transform financial activity from episodic human intervention into continuous machine-mediated coordination.
For the economies of the G7, this transformation presents a profound dual-mandate challenge. On one hand, programmable finance and AI-driven automation are essential to maintaining global competitiveness, capital efficiency, and technological leadership. On the other hand, systems characterized by high velocity, algorithmic autonomy, and cross-platform composability introduce new systemic risks. These include feedback loops between similarly trained AI agents, correlated strategy execution at machine speed, and the potential amplification of shocks across interconnected markets.
Regulatory authorities have begun to recognize these dangers. The U.S. Securities and Exchange Commission, alongside other supervisory bodies, has initiated scrutiny of AI’s expanding role in trading, asset management, and market-making. A central concern is that AI systems trained on comparable datasets and optimization objectives may behave synchronously under stress, triggering flash crashes, liquidity vacuums, or rapid price dislocations. In such an environment, instability does not emerge from malice or error, but from the very efficiency and homogeneity of automated intelligence.
Thus, the Programmable Inflection Point is not merely a technological milestone; it is a structural turning point in the political economy of finance. It compels a rethinking of how trust, accountability, and systemic resilience are embedded within increasingly autonomous financial systems—an issue that lies at the heart of this project.
II. The Crypto Ecosystem: Mainstream Infrastructure or Illicit Shadow?
By 2026, the crypto ecosystem occupies an ambiguous position within the global financial order—simultaneously approaching infrastructural maturity while remaining burdened by enduring legitimacy concerns. This bifurcated status reflects the technology’s dual-use nature: the same attributes that make blockchain systems efficient, programmable, and globally accessible also render them attractive for illicit activity. As such, crypto’s evolution cannot be assessed merely in technical terms; it must be evaluated as a contested institutional space at the intersection of innovation, regulation, and enforcement.
From Experimental Networks to Payments Infrastructure
On the infrastructure front, progress has been substantial and measurable. Layer-2 scaling solutions have transformed blockchain performance characteristics, addressing the throughput and cost constraints that once rendered public blockchains unsuitable for high-frequency economic activity. Platforms such as zkSync now process over 100 transactions per second at sub-cent fees, while zero-knowledge rollups are projected to achieve throughput exceeding 15,000 transactions per second by mid-2026. Finality times are expected to fall below one second, with transaction costs approaching $0.0001 per transfer.
These are not marginal gains. By late 2025, average Layer-2 transaction costs had already dropped below $0.01, with rollup throughput surpassing 5,600 transactions per second. Such performance metrics render micro-payments economically viable for the first time on public blockchain infrastructure, enabling use cases—machine-to-machine payments, streaming payments, real-time settlement—that were previously infeasible under traditional financial rails.
As a result, blockchain infrastructure has become functionally competitive with established payment networks for specific categories of transactions. While it does not yet rival legacy systems in universality or consumer familiarity, it increasingly matches—and in some dimensions exceeds—them in speed, cost efficiency, and programmability.
Institutionalization and Compliance Convergence
Parallel to these technical advances, the institutional perimeter of the crypto ecosystem has matured. Regulated Virtual Asset Service Providers (VASPs) now operate under compliance regimes that increasingly resemble those of traditional fintech firms. Know-Your-Customer (KYC), Anti-Money Laundering (AML), and transaction monitoring standards have converged toward established financial norms, particularly within jurisdictions aligned with the Financial Action Task Force (FATF).
This institutionalization is reflected in capital concentration and usage patterns. By October 2025, Total Value Locked (TVL) across Layer-2 networks had reached approximately $47 billion, while daily transaction volumes peaked at roughly 1.9 million transactions—exceeding activity on the Ethereum mainnet itself. The migration of economic activity from base layers to scalable execution environments underscores the ecosystem’s transition from speculative experimentation toward operational financial infrastructure.
Yet legitimacy remains incomplete, not because of infrastructural insufficiency, but because of unresolved governance asymmetries.
Legitimate Versus Illicit Use: The Persistent Shadow
Despite advances in compliance among regulated intermediaries, the persistence of unhosted wallets, decentralized exchanges, and mixing services continues to facilitate illicit financial flows. According to Chainalysis, approximately $40.9 billion flowed into illicit cryptocurrency addresses in 2024, a figure widely regarded as a lower-bound estimate due to attribution limitations. By mid-2025, illicit volumes were on track to meet or exceed the estimated $51 billion recorded in 2024.
More revealing than absolute volume, however, is the composition of illicit activity. Stablecoins have rapidly displaced Bitcoin as the preferred medium for criminal finance. Whereas Bitcoin accounted for roughly 70% of illicit crypto flows in earlier years, its share fell to approximately 20%, while stablecoins surged from 15% to 63% of illicit activity. This shift reflects a rational recalibration by illicit actors, who increasingly prioritize liquidity, transactional speed, and fiat parity over maximal anonymity.
State-sponsored actors have further underscored this trend. In 2025 alone, North Korean hacking groups stole approximately $2.02 billion in cryptocurrency—a 51% year-over-year increase—demonstrating that digital assets remain an attractive vector for sanctions evasion and asymmetric financial warfare.
At the same time, it is critical to contextualize these figures. Illicit transactions accounted for just 0.14% of total cryptocurrency activity in 2024, the lowest proportion in four years. This decline suggests improving surveillance, analytics, and enforcement capabilities. Nevertheless, absolute volumes continue to rise, and laundering techniques are growing more sophisticated, reinforcing regulatory skepticism and delaying universal acceptance.
Thus, the crypto ecosystem in 2026 exists in a state of unresolved tension: increasingly indispensable as programmable infrastructure, yet persistently compromised by governance gaps at the edges of decentralization.
III. U.S. Policymaking: The GENIUS Act and the Payment Stablecoin Framework
Against this backdrop of accelerating programmability and contested legitimacy, U.S. policymakers have moved decisively to define the regulatory contours of digital money. On July 18, 2025, President Trump signed into law the Guiding and Establishing National Innovation for U.S. Stablecoins Act (GENIUS Act)—the most comprehensive federal legislation governing digital assets in American history.
Legislative Architecture and Scope
The GENIUS Act passed with bipartisan support, clearing the Senate on June 17, 2025 by a vote of 68–30 and the House of Representatives on July 17, 2025 by a margin of 308–122. The law is scheduled to take effect on January 18, 2027, or 120 days after the issuance of final implementing regulations, whichever occurs first.
At its core, the Act establishes a federal framework for payment stablecoins, explicitly distinguishing them from unregulated cryptoassets and speculative tokens. It defines three categories of permitted issuers:
Insured depository institutions and credit unions, issuing stablecoins through regulated subsidiaries
Federally qualified nonbank payment stablecoin issuers, overseen by the Office of the Comptroller of the Currency (OCC)
State-qualified payment stablecoin issuers, regulated by approved state authorities under federal standards
This tiered structure reflects a deliberate attempt to integrate stablecoins into the existing financial system without forcing a one-size-fits-all institutional model.
Stablecoins as Infrastructure in an AI-Driven Financial System
Policymakers increasingly view stablecoins not as speculative instruments, but as core infrastructure for digital markets. In 2024, stablecoin transaction volumes surpassed those of Visa and Mastercard combined—a signal that programmable money has already achieved scale, if not yet universality.
This shift is inseparable from the rise of AI-driven finance. In markets where AI agents operate on 5- and 15-minute time horizons, achieving annualized returns with win rates of 65–75% in leading implementations, traditional banking rails such as ACH and Fedwire are structurally inadequate. Settlement delays measured in hours or days are incompatible with autonomous systems executing strategies at machine speed.
The GENIUS Act responds to this reality by imposing a set of guardrails designed to ensure that speed does not come at the expense of stability:
1:1 Reserve Backing
Issuers must maintain full reserve backing with high-quality liquid assets, including U.S. dollars and short-term Treasuries, accompanied by monthly public disclosures of reserve composition.Operational Resilience
Robust cybersecurity and risk-management standards are mandated to prevent AI-accelerated bank runs, systemic outages, and cascading failures.Anti-Money Laundering (AML) and Sanctions Compliance
Stablecoin issuers are explicitly subject to the Bank Secrecy Act, with requirements to implement comprehensive AML and sanctions programs. Critically, issuers must possess the technical capability to freeze, seize, or burn stablecoins when legally required.Consumer Protection and Priority Claims
In the event of issuer insolvency, stablecoin holders receive priority over all other creditors, reinforcing confidence in stablecoins as transactional money rather than speculative exposure.
Strategic Opportunities and Structural Risks
The strategic upside of this framework is clear. By mandating reserve backing in U.S. dollars and Treasuries, the GENIUS Act effectively extends dollar hegemony into programmable financial space. Stablecoins create persistent global demand for U.S. sovereign debt, reinforcing the dollar’s role as the world’s reserve currency in an era where monetary competition increasingly occurs at the infrastructure level.
However, this opportunity is accompanied by a nontrivial systemic risk: deposit flight. As funds migrate from commercial banks into stablecoin ecosystems, the traditional banking system’s deposit base—and by extension, its credit-creation capacity—may erode. This disintermediation raises unresolved questions about monetary policy transmission, liquidity provision, and the future role of banks in an increasingly tokenized economy.
In this sense, the GENIUS Act does not resolve the tensions identified in Sections I and II—it formalizes them. It acknowledges that programmable money is inevitable, while attempting to anchor its expansion within the institutional logic of the U.S. financial system. Whether this balance proves sustainable remains one of the defining questions of the programmable financial era.
IV. Evaluaion of Digital Instruments and the Policy Gap
While payment stablecoins have come to dominate regulatory and political attention, they represent only one component of a rapidly expanding universe of tokenized financial instruments. A growing array of digital assets now replicate familiar financial claims—bank deposits, money market funds, government debt, private credit—yet operate under markedly uneven levels of oversight. This asymmetry reveals a widening policy gap between what regulators focus on and where systemic risk may increasingly reside.
Tokenized Deposits: Familiar Claims, Novel Rails
Tokenized deposits represent digital claims on commercial bank liabilities recorded on distributed ledgers. Unlike stablecoins, these instruments are direct extensions of the traditional banking system: they benefit from existing deposit insurance frameworks, established AML/KYC infrastructure, and prudential supervision. In principle, tokenized deposits offer a low-risk path toward modernization by combining trusted bank money with programmable settlement.
In practice, however, their integration into decentralized finance (DeFi) protocols remains ambiguous. While banks may issue tokenized deposits within closed or permissioned environments, their interaction with open, composable protocols raises unresolved questions regarding custody, finality, liability, and supervisory jurisdiction. Regulators have yet to clearly define how deposit insurance, resolution frameworks, and consumer protections apply once bank money becomes interoperable with autonomous smart contracts.
Thus, tokenized deposits occupy a regulatory gray zone: low-risk in origin, but potentially high-impact in deployment.
Programmable Money Market Funds: Securities as Infrastructure
Tokenized money market funds represent another rapidly expanding category. These instruments exhibit functional similarities to stablecoins—price stability, liquidity, and use as transactional collateral—yet remain legally classified as investment funds rather than payment instruments. Under the GENIUS Act, they are therefore regulated as securities, not as payment stablecoins.
These yield-bearing tokens represent fractional claims on underlying fund assets and increasingly function as what may be termed “securities as a service.” They provide blockchain-native access to cash management strategies, enabling programmable yield, instant settlement, and integration into DeFi liquidity stacks. Despite their growing systemic relevance, they operate with significantly less public scrutiny than payment stablecoins, largely because they fit neatly into existing regulatory categories.
This creates a paradox: instruments that closely resemble money in economic function are regulated as investments, while those explicitly labeled as money attract far more intensive oversight.
Real-World Asset (RWA) Tokenization: Scale Without Symmetry
The tokenization of real-world assets has experienced explosive growth, transforming illiquid, traditionally intermediated claims into programmable, on-chain instruments. As of October 2025, the total value of tokenized RWAs reached approximately $33 billion, with a substantial share concentrated in government debt and stablecoin-adjacent products.
Private credit remains the largest and fastest-growing category, with active on-chain private credit exceeding $18.91 billion by November 2025. Tokenized U.S. Treasury products alone surpassed $9 billion in value during the same period, offering blockchain-native equivalents of traditional money market strategies. More broadly, the total on-chain value of tokenized RWAs exceeded $17 billion in 2025, held by more than 82,000 unique participants.
These instruments promise efficiency, transparency, and global accessibility. Yet their rapid expansion has outpaced the development of coherent supervisory frameworks. Legal enforceability, investor protections, and cross-border jurisdictional issues remain unevenly addressed, even as RWAs increasingly serve as collateral, yield sources, and liquidity anchors within tokenized markets.
AI-Operated Crypto Assets: Delegated Intelligence, Undefined Accountability
An emerging and qualitatively distinct category involves crypto assets managed entirely by autonomous AI agents. These systems dynamically rebalance portfolios, allocate liquidity, and execute strategies without continuous human oversight. Decision-making is delegated to algorithmic systems that adapt in real time to market conditions.
This represents a fundamental departure from traditional asset management. Accountability becomes diffuse, explainability is limited, and conventional fiduciary concepts strain under conditions of full automation. Despite these challenges, AI-operated assets remain largely unregulated, occupying a conceptual blind spot between financial regulation and AI governance.
Why the Policy Gap Persists
The uneven regulatory attention across these instruments is not accidental. Tokenized deposits and money market funds are perceived as “new wrappers for old assets,” allowing regulators to apply familiar frameworks without confronting deeper structural questions. Payment stablecoins, by contrast, directly challenge the sovereign monopoly over money issuance and monetary transmission, making them politically sensitive and symbolically charged.
Meanwhile, AI-operated assets and fully autonomous trading systems pose potentially greater systemic risks but lack a clear regulatory home. As a result, policy attention remains misaligned with the evolving risk landscape—focused on monetary symbolism rather than algorithmic reality.
V. The Critical Oversight: AI-Driven Algorithmic Contagion
Perhaps the most consequential gap in current policy frameworks is the insufficient treatment of AI-driven algorithmic contagion. As financial markets become increasingly autonomous, tokenized, and continuously liquid, systemic risk no longer arises primarily from leverage or credit mismatches alone, but from the interaction of machine intelligence operating at scale and speed.
The Mechanics of Contagion
1. Homogenization of Logic
Empirical research has demonstrated that AI systems interacting within markets may inadvertently converge on correlated strategies. In experimental settings, algorithms have been shown to develop behavior resembling price coordination or tacit collusion without explicit human instruction.
When multiple agentic systems are trained on similar large language models, market datasets, or optimization objectives, they are likely to exhibit logic homogenization. In 2025, real-world deployments revealed that general-purpose models suffered task failure rates approaching 70% in complex financial contexts, underscoring the limits of broad intelligence. Although domain-specific financial models have since emerged, similarity in training data and reward structures increases the probability of synchronized behavior.
Under stress, such correlation may manifest as simultaneous position unwinds, amplifying volatility rather than absorbing it.
2. Velocity of Liquidation
AI agents can execute thousands of transactions per second. In environments characterized by 24/7 liquidity, Layer-2 throughput exceeding 5,600 transactions per second, and automated collateral management, small price movements can cascade into systemic events before human intervention is possible.
Historical precedent illustrates the danger. During the 2010 Flash Crash, U.S. equity markets lost nearly $1 trillion in value within minutes, with the majority of losses occurring in under five minutes. Tokenized markets compress this timeline further, substituting milliseconds for minutes and removing circuit breakers designed for human-paced systems.
3. Cross-Chain Cascades
Tokenized financial systems are deeply interconnected. Stablecoins serve as collateral for tokenized bonds; those bonds back synthetic derivatives; derivatives are auto-liquidated through smart contracts. A disruption in one protocol can propagate instantaneously across chains through pre-programmed liquidation logic.
Research indicates that systemic risk in such environments depends critically on algorithmic trader behavior, leverage thresholds, and network topology. Notably, contagion speed becomes a non-monotonic function of diversification: beyond a certain point, diversification increases interconnectedness and accelerates failure rather than mitigating it.
Historical Signals and Emerging Reality
The lessons of the 2010 Flash Crash remain instructive, but they understate the challenge ahead. The IMF’s October 2024 Global Financial Stability Report explicitly links the rise of AI—and generative AI in particular—to increased volatility and market fragility.
By 2026, AI-powered agents are expected to dominate crypto-native markets. Retail-facing AI agents are entering the mainstream, while decentralized exchanges increasingly offer “agent mode” execution. These agents rebalance portfolios continuously, devoid of fear, greed, or hesitation, processing global information flows in seconds while humans remain structurally slower.
The result is not greater rationality, but compressed reflexivity.
Regulatory Blind Spots and Partial Responses
Regulators have begun to acknowledge these risks, but responses remain fragmented. The UK’s Financial Conduct Authority has warned that deep learning models may evade traditional market surveillance due to their opacity and adaptive complexity. The European Commission has similarly raised concerns about unpredictable AI-driven trading behavior undermining market fairness and integrity.
At the same time, international bodies such as the OECD caution against overstating AI autonomy. Most deployed systems, they note, still operate in hybrid configurations—acting as advisors rather than fully autonomous actors, with humans retaining final decision authority. Such models enhance governance, transparency, and client trust.
Yet the trajectory is clear: autonomy is increasing, not receding. Without proactive coordination between financial regulators and AI governance institutions, the risk is that policy will continue to regulate yesterday’s money while tomorrow’s markets run on unexamined code.
VI. Recommendations for G7 Authorities
To mitigate systemic risks arising from the convergence of agentic artificial intelligence and tokenized finance, G7 authorities should adopt a coordinated, forward-looking policy response that recognizes both the speed and the structural novelty of these systems. Traditional regulatory tools—designed for human-paced markets and institution-centric finance—are no longer sufficient.
1. Circuit Breakers for Autonomous Agents
Regulators should mandate AI-aware circuit breakers tailored to autonomous execution speeds rather than legacy trading velocities. This includes latency floors, dynamic throttling, and mandatory “human-in-the-loop” intervention triggers for transactions exceeding defined size, leverage, or velocity thresholds.
Enhanced pre-trade risk checks should be embedded directly into execution layers, preventing runaway feedback loops before they propagate system-wide. Crucially, these safeguards must operate at machine timescales—milliseconds, not minutes—recognizing that AI-driven instability unfolds far faster than human oversight can react.
2. Model Diversity and Anti-Herding Requirements
To reduce the risk of synchronized logic failures, regulators should encourage—or where appropriate mandate—model diversity across systemically important market participants. This includes heterogeneity in:
Training data sources
Model architectures and optimization objectives
Execution and risk-management strategies
Just as financial regulation discourages excessive balance-sheet correlation, AI governance must address cognitive concentration risk. Incentivizing diversity in algorithmic logic reduces the probability that multiple agents respond identically under stress.
3. Real-Time, Cross-Domain Surveillance Enhancement
Market surveillance infrastructure must evolve from retrospective analysis toward real-time, predictive monitoring. Regulators should invest in systems capable of identifying conditions that may precipitate flash crashes, liquidity droughts, or cross-chain cascades before they fully materialize.
This requires surveillance tools that can track:
Cross-chain asset flows and collateral dependencies
AI agent execution patterns and clustering behavior
Emergent network effects across protocols and platforms
Absent such capabilities, supervisors risk observing crises only after irreversible damage has occurred.
4. Unified Interoperability and Supervisory Standards
To prevent the emergence of fragmented “digital islands,” G7 authorities should pursue interoperability standards that allow U.S.-regulated stablecoins, Euro-area tokenized deposits, and Asian digital assets to interact through secure, supervised bridges.
The Financial Stability Board has already emphasized the need for cross-border regulatory coordination, transparent reserve disclosures, and harmonized AML/CFT compliance. These principles must now be operationalized through shared technical standards and supervisory data-sharing arrangements, ensuring that global programmability does not undermine global stability.
5. Stress Testing for AI-Driven Scenarios
Regulators should require regular, scenario-based stress testing explicitly designed for autonomous markets. These exercises must simulate:
Correlated AI agent behavior
High-frequency liquidation cascades
Cross-chain contagion under extreme volatility
Traditional stress tests—focused primarily on credit, interest rate, or market risk—are inadequate in environments where failure emerges from algorithmic interaction rather than balance-sheet weakness.
6. Transparency, Explainability, and Auditability Standards
In high-risk financial applications, accuracy and reliability are inseparable from trust. G7 authorities should require that AI agents deployed in financial markets maintain:
Verifiable audit trails of decisions and actions
Explainable rationale for material trading or allocation choices
Regular independent testing and certification
Explainability is not merely a technical preference; it is a prerequisite for accountability, enforcement, and public confidence in machine-mediated markets.
VII. Conclusion: Navigating the Convergence
The convergence of decentralized financial infrastructure and agentic artificial intelligence represents one of the most consequential transformations in modern financial history. It offers extraordinary opportunities: programmable efficiency, global accessibility, real-time settlement, and new forms of economic coordination. At the same time, it introduces systemic risks that are novel in speed, scale, and complexity.
The passage of the GENIUS Act in 2025 marked a decisive step forward, establishing a federal framework for payment stablecoins and providing regulatory clarity that is likely to accelerate institutional adoption while reinforcing U.S. dollar dominance in digital commerce.
Yet as we enter 2026, the central question has shifted. It is no longer whether real-world assets can be tokenized, but whether tokenization—combined with autonomous decision-making—can function reliably at institutional scale. Asset classification alone is insufficient. The defining risks of the next financial era arise not from what assets are, but from how intelligent systems interact with them.
Experience from 2025 suggests a nuanced truth: AI agents do not replace human judgment; they augment it. They act as co-pilots in financial modeling, silent auditors in compliance systems, and invisible architects of portfolio construction. The challenge ahead is ensuring that as these agents become increasingly autonomous, the financial system remains resilient, transparent, and aligned with the public interest.
The G7 now faces a defining moment. It can foster innovation in programmable finance while proactively guarding against algorithmic contagion, cross-chain cascades, and AI-driven instability—or it can allow regulation to lag until the first major AI-induced financial crisis forces reform under duress.
The former path demands international coordination, technical sophistication, and regulatory agility. The latter would impose far greater economic, political, and geopolitical costs.
Appendix: Plain-Language Definitions for Quick Briefing
Below are clear, non-technical explanations of key terms used throughout this project:
Stablecoin
Digital Cash. A cryptocurrency designed to keep a steady value—usually tied 1:1 to the U.S. dollar—by holding real dollars or government bonds in reserve.Tokenized Deposit
Your Digital Bank Balance. A blockchain-based version of money held at a regulated bank, with the same legal protections, but easier to move and program.Money Market Fund (MMF)
A Low-Risk Savings Tool. A fund that invests in very safe, short-term debt. When tokenized, its shares can be held in digital wallets and traded 24/7.Virtual/Crypto Asset Service Providers (VASPs/CASPs)
The Gatekeepers. Companies that help users buy, sell, store, or manage digital assets. They are required to verify identities and prevent financial crime.Blockchain and Blockchain Infrastructure
Blockchain: A shared, tamper-resistant digital record of transactions.
Infrastructure: The computers, software, and rules that keep the blockchain running securely.
Distributed Ledger Technology (DLT)
A Shared Record Book. Any system where multiple parties keep synchronized copies of the same ledger. Blockchain is one type of DLT.Real-World Asset (RWA) Tokenization
Turning Physical Assets into Digital Tokens. Converting assets like real estate, bonds, or art into blockchain-based tokens that can be easily traded or divided.Digitization of Ledgers
From Paper to Computers. The shift from handwritten records to electronic databases—fast, but still mostly siloed and institution-specific.Counterparty Risk
“What if they don’t pay?” The risk that the other side of a transaction fails before it is completed.T+2 Settlement Cycle
Transaction Plus Two Days. The traditional delay between buying an asset and officially completing the transaction. New systems aim for instant (T+0) settlement.Layer-2 Scaling Solutions
Express Lanes for Blockchains. Secondary networks that process transactions faster and cheaper, then record summaries on the main blockchain.
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