Legacy Systems Meet Crypto: How COBOL and KYA Shape 2026's Five Major Narratives

As 2025 draws to a close, the crypto industry narrative cycle has noticeably quieted. Yet beneath this surface calm, a consensus is building among 30+ leading institutions—Galaxy Research, a16z, Bitwise, Hashdex, and Coinbase—about the trends that will dominate 2026. The convergence reveals something profound: the industry is solving not just technological problems, but structural economic barriers rooted in legacy systems like COBOL. Meanwhile, establishing frameworks like Know Your Agent (KYA) has become essential. These aren’t competing narratives—they’re interconnected pieces of a larger infrastructure upgrade. Understanding why banks still run on decades-old COBOL systems, and why AI agents need cryptographic identity frameworks similar to KYC, unlocks the real story of 2026.

Why COBOL Systems Drive Stablecoins From Niche to Mainstream

The first and most widely agreed prediction concerns stablecoins, but the real insight lies deeper than transaction volume. The conventional wisdom says stablecoins will finally transition from “cryptocurrency tools” to “mainstream financial infrastructure” by 2026. Yet the mechanism driving this shift reveals everything about what’s blocking traditional finance.

Consider the numbers: stablecoins have completed approximately $46 trillion in transactions over the past year—roughly 20 times PayPal’s annual volume and nearly 3 times Visa’s. This isn’t a failure of adoption; it’s evidence that the infrastructure already works at scale. The bottleneck isn’t demand. It’s integration.

Here’s where COBOL systems become central to understanding 2026. a16z researcher Sam Broner explained it from an engineer’s perspective: most banks’ core ledger systems still run on mainframe computers using COBOL programming language, with batch-file interfaces rather than modern APIs. These systems are stable, trusted by regulators, and embedded in the real world. But they can barely evolve. Adding a real-time payment function might take months or years while teams navigate technical debt and regulatory complexity. Stablecoins aren’t just alternative currencies—they’re architectural workarounds for a 50-year-old infrastructure problem.

This recognition has spawned an entire generation of startups directly solving the integration challenge. Some use cryptographic proofs to convert local account balances into digital dollars without exposing privacy. Others directly integrate regional banking networks and real-time payment systems, making stablecoins function like local transfers. Still others are building globally interoperable wallet layers and card issuance platforms.

As these onramps and offramps mature, a new behavioral pattern will emerge. Workers can receive wages across borders in real-time. Merchants can accept global stablecoins without traditional bank accounts. Applications can instantly settle value with users anywhere. Galaxy Research’s forecast is direct: 30% of international payments will flow through stablecoins by end-2026, driven largely by the implementation of the GENIUS Act in early 2026, which removes remaining regulatory constraints.

AI Agents Need Identity: Why KYA Will Define the Smart Agent Economy

The second consensus narrative predicts that AI agents will become primary participants in on-chain economic activity. This shift is being driven by a fundamental mismatch between how AI systems operate and how traditional finance handles transactions. AI agents need value transfer that is instant, cheap, and permissionless—exactly what traditional payment systems were never designed to provide.

But here’s the critical bottleneck that few have articulated clearly: establishing “Know Your Agent” (KYA) frameworks. Sean Neville, a16z researcher and co-founder of Circle (the institution behind USDC), framed this bluntly: the financial industry now faces more non-human digital identities than human employees—a 96:1 ratio—yet nearly all of these identities are “ghosts without bank accounts.” The financial system spent decades building KYC (Know Your Customer) processes for humans. It has only months to build equivalent systems for AI.

What does KYA actually require? Cryptographic signatures proving who an agent represents, who holds it accountable, and who bears responsibility if problems arise. Without these, institutions must simply block AI agents at the firewall level. This isn’t a policy problem—it’s an infrastructure problem. The x402 payment standard has emerged as the solution, designed specifically for frictionless micropayments between agents. It enables:

• Instant settlement for agent-to-agent transactions
• Programmable payment logic for automated workflows
• Permissionless operation across service boundaries
• Micropayment precision for fine-grained resource allocation

Galaxy Research’s Lucas Tcheyan provided specific quantitative predictions: by 2026, x402 standard payments will represent 30% of Base’s daily transaction volume and 5% of Solana’s non-voting transactions. Base gains advantage through Coinbase’s backing, while Solana benefits from its massive developer base. Emerging payment-focused chains like Tempo and Arc will also accelerate during this period.

The real asset class in this economy isn’t the AI model itself—it’s scarce, high-quality real-world data (DePAI). Projects like BitRobot, PrismaX, Shaga, and Chakra are building the data infrastructure that AI agents will require. This marks a fundamental reorientation: from models as proprietary moats to data quality as the scarce resource.

RWA Shifts From Hype to Feasibility: When Banks Accept Tokenized Collateral

After years of “everything-can-be-blockchain” hype, the RWA narrative has matured dramatically. Institutions are no longer asking “how big can this market become?” They’re asking “what structural breakthroughs would make this feasible?”

The distinction matters. Most current “tokenizations” are still skeuomorphic—they’ve been given a technological shell while retaining traditional finance’s design logic, trading methods, and risk structures. They don’t leverage crypto’s inherent capabilities; they just replicate traditional assets in a new format.

Galaxy Research’s structural prediction cuts to the core: within the next year, a major bank or brokerage will begin accepting tokenized shares as formal collateral. This threshold event would be symbolically far more significant than any single product launch. To date, tokenized shares have remained experimental—either small-scale DeFi experiments or private blockchain pilots within single institutions. None has substantial connection to mainstream financial systems.

But the situation is shifting. Core infrastructure providers in traditional finance are accelerating blockchain migration. Regulators are demonstrating clear support. For the first time, Galaxy predicts a major financial institution will treat on-chain tokenized shares as fully equivalent to traditional securities within legal and regulatory frameworks.

Hashdex projects the most aggressive growth: a tenfold increase in tokenized real-world assets. This is grounded in three forces: increased regulatory clarity, traditional financial institutions’ readiness, and technological infrastructure maturity. The path from experimental to standard becomes clear when real collateral backing enters the system.

Prediction Markets: From Gambling to Information Infrastructure

Prediction markets are experiencing consensus enthusiasm, but for a reason that surprised many observers: they’re transforming from “decentralized gambling” into critical information aggregation tools.

a16z’s Andy Hall, a Stanford professor of political economy, argues that prediction markets have crossed the threshold of “whether they can go mainstream.” In 2026, as they intersect deeply with cryptocurrencies and AI systems, they will become simultaneously larger, more widespread, and more intelligent. But this expansion comes with complexity: higher trading frequency, faster information feedback, and more automated participant structures.

The data supports bullish predictions. Polymarket’s weekly trading volume is approaching $1 billion and is forecast to consistently exceed $1.5 billion in 2026. Galaxy Research’s Will Owens attributes this growth to three simultaneous forces: deepening capital efficiency amplifying market liquidity, AI-driven order flow increasing transaction frequency significantly, and Polymarket’s distribution capabilities accelerating capital inflow.

Bitwise’s Ryan Rasmussen projects that Polymarket’s open interest will surpass records set during the 2024 US presidential election. New US user access has brought in fresh capital (approximately $2 billion injected), while market expansion beyond politics into economics, sports, and culture broadens the user base.

More striking is the adoption trajectory. Researcher Tomasz Tunguz predicts US adoption of prediction markets will rise from current 5% to 35% by 2026—approaching the 56% adoption rate of traditional gambling. This signals evolution from niche financial tool to mainstream information product.

However, Galaxy Research issued a clearly cautionary corollary prediction: a federal investigation into prediction markets is highly likely. As trading volumes and open interest surge with regulatory clarity, insider trading scandals have begun surfacing. Sports insiders have exploited undisclosed information. Because on-chain prediction markets allow pseudonymous trading (unlike regulated betting platforms with strict KYC), the temptation for information abuse is dramatically amplified. Rather than being triggered by traditional regulated gambling anomalies, investigations will likely focus on suspicious price fluctuations in on-chain markets themselves.

Privacy: From Optional Feature to Essential Infrastructure

As capital, data, and automated decision-making migrate on-chain, privacy is transitioning from idealistic preference to institutional necessity. This shift was already visible in late 2025, when privacy tokens became significant outperformers. The three major privacy coins displayed striking gains: Zcash rose approximately 800%, Railgun by 204%, and Monero by 53% in the same quarter.

Galaxy Research’s Christopher Rosa made a striking prediction: the total market capitalization of privacy tokens will exceed $100 billion by end-2026. The underlying logic is profound. Early Bitcoin developers, including Satoshi Nakamoto himself, researched privacy technologies extensively. Initial Bitcoin design discussions included mechanisms for shielded or fully private transactions. But at that time, zero-knowledge proof technology was too immature for practical deployment.

Today, the situation has inverted. Zero-knowledge technology is becoming engineering-ready. Simultaneously, the value flowing on-chain has increased dramatically. More users—particularly institutional users—are examining a previously accepted fact: are they willing to permanently disclose their entire asset balances, transaction paths, and capital structures to anyone?

Privacy has thus transformed from “idealistic need” into “institutional-level real-world problem.” Adeniyi Abiodun, co-founder of Mysten Labs, extended this logic into the data layer. Every AI model, every agent, and every automated system depends on data. Yet most data pipelines—both inputs to models and outputs from them—are opaque, variable, and unauditable. This might be acceptable for consumer applications but represents an insurmountable obstacle in finance and healthcare.

The solution Adiodun proposed is “secrets-as-a-service”—not post-application privacy features bolted on, but native, programmable data access infrastructure. This includes: enforceable data access rules, client-side encryption mechanisms, and decentralized key management systems that cryptographically enforce who can decrypt what data, under what conditions, and for how long. These rules should be enforced on-chain rather than through manual organizational processes.

Combined with verifiable data systems, privacy becomes a component of internet infrastructure itself—not an application add-on.

2026 Signals Organizational Metamorphosis: AI Agents Replace Positions, Compliance Commands Premium Salaries

Beyond these five narratives, institutions identified shifts in organizational structure and talent allocation that signal deeper ecosystem maturation.

AI Agents Become Cost-Effective Replacements: a16z projects that companies will pay more for AI agents than human employees for routine tasks. This is already observable at the consumer level: Waymo’s autonomous rides cost 31% more than Uber yet see growing demand as users pay for safety premiums. The same logic applies internally. When companies factor recruitment, onboarding, training, and management costs into total expense models, AI agents become more cost-effective for routine workflows.

Current AI task duration roughly doubles every seven months (per METR data). Cutting-edge models already complete tasks requiring about an hour of human work. Extrapolating this trajectory, by end-2026, AI agents will autonomously execute workflows exceeding eight hours—fundamentally reshaping corporate staffing and project planning.

Real-World Risk Experience Becomes More Valuable Than “Crypto-Native” Backgrounds: Hiring preferences are quietly reversing. Founding teams increasingly prefer 42-year-old former risk officers from second-tier banks with full credit-cycle experience over 23-year-old DeFi natives who’ve only known bull markets. Real-world risk cycle expertise is once again commanding premium compensation, displacing the previous era of “native crypto narratives.”

Compliance Becomes the Highest-Paid Function: Perhaps most tellingly, compensation structures are shifting toward roles addressing regulatory and anti-money-laundering requirements. Talent in compliance, stablecoins, and AML is receiving total contracts exceeding $400,000—surpassing even protocol-layer engineer salaries, which have already fallen below this threshold.

These organizational shifts reveal what the five narratives suggest: 2026 represents a transition point where crypto infrastructure becomes serious financial infrastructure. The infrastructure bottleneck isn’t technical anymore—it’s organizational, regulatory, and operational. That’s why understanding COBOL systems matters: they’re not just historical artifacts. They’re metaphors for the deep operational debt that cryptocurrency is being deployed to solve. And that’s why KYA matters: establishing identity frameworks for non-human agents isn’t sci-fi—it’s the institutional prerequisite for scaling.

The consensus, when examined carefully, describes not five separate narratives but one integrated infrastructure upgrade: replacing legacy operational bottlenecks with crypto-native alternatives while building the institutional frameworks that traditional finance requires.