17 Cryptocurrency Trends Changing the Financial Ecosystem in 2026

I. The Fusion of Stablecoins and Payment Infrastructure

From Edge Tools to Payment Hubs

Last year, stablecoin trading volume reached $46 trillion—this number is 20 times PayPal, nearly 3 times Visa, and approaching the scale of the US ACH electronic clearing network. But this is just the beginning.

Currently, stablecoin transfers are completed within 1 second at a cost of less than one cent. The real opportunity lies in integrating with traditional financial systems. A new wave of startups is building these bridges: some use cryptographic verification technology to exchange local account balances for digital dollars; others integrate regional payment networks, utilizing QR codes and real-time payment systems for bank-to-bank transfers; still others are creating globally interoperable digital wallets and card platforms, enabling users to pay with stablecoins at everyday merchants.

These innovations collectively expand the participation in the digital dollar economy. As deposit and withdrawal channels mature, companies are beginning to operate directly with stablecoins—instant cross-border payroll, merchants receiving globally recognized digital currencies without bank accounts, and real-time settlement between payment apps and users. Essentially, stablecoins are evolving from peripheral financial tools into the foundational settlement layer of the internet.

II. Native Tokenization of RWA (Real-World Assets)

Real Assets on Chain Require Real Applications

Observing the wave of traditional asset tokenization, US stocks, commodities, and indices are all going on-chain. But the reality is: most projects’ tokenization is superficial, not leveraging the native properties of cryptography.

In contrast, synthetic products like perpetual contracts can provide deep liquidity at lower costs. Perpetual contracts also come with an easily understandable leverage mechanism, making them the most suitable native derivatives for market needs. Emerging market stocks are even more worth perpetualizing—liquidity in some zero-option markets surpasses that of spot markets.

The core question boils down to: “Perpetualize or tokenize?” Regardless of the path chosen, by 2026, we will see more native-style crypto RWA tokenization.

Similarly, stablecoins are transforming—they are no longer just tokenized assets but are moving toward genuine “native issuance.” Stablecoins are expected to become fully mainstream by 2025. However, stablecoins lacking strong credit infrastructure resemble “narrow banks,” holding only ultra-safe liquid assets. Long-term, this model cannot support on-chain economic pillars.

The most interesting change is that emerging asset managers, curators, and protocols are starting to do off-chain asset backing and on-chain issuance of lending activities. This form of tokenization offers limited benefits—mainly facilitating distribution to on-chain users. The truly efficient approach is to initiate lending directly on-chain—reducing management costs, minimizing backend burdens, and increasing usability. The biggest challenges are compliance and standardization, but builders are already working on solutions.

III. Stablecoins Driving Modernization of Banking Systems

Centuries-old Banks Meet Blockchain Innovation

Banking software is almost alien technology to modern developers. In the 1960s-70s, banking systems pioneered the era of mainframe computers. In the 1980s-90s, second-generation core systems emerged (like Temenos GLOBUS, Infosys Finacle), but these systems are now outdated, with painfully slow update cycles.

Most assets’ ledgers still run on COBOL-based mainframes, relying on batch file communication—often without APIs. These decades-old systems are stable and trusted by regulators but have also completely locked in innovation. Adding real-time payment features can take months or even years, crossing significant technical debt and regulatory complexity.

This is where stablecoins come in. In recent years, not only have stablecoins found product-market fit and gone mainstream—they are also being adopted by traditional financial institutions on an unprecedented scale. Stablecoins, tokenized deposits, tokenized government bonds, and on-chain bonds enable banks, fintechs, and financial institutions to launch new products and serve new clients—without rewriting those old but stable systems. Stablecoins open a new avenue for institutional innovation.

IV. AI Reshaping Value Flows

When Automation Meets Blockchain

After the widespread emergence of AI agents, business operations will be executed automatically in the background rather than triggered by user clicks. This requires a fundamental change in how value and capital flow. In a world driven by intent rather than step-by-step instructions, AI agents can recognize needs, execute commitments, and trigger outcomes—demanding that capital flows be as fast and free as information flows.

Blockchain, smart contracts, and on-chain protocols play key roles here. Currently, smart contracts can settle global USD payments within seconds. By 2026, new primitives like x402 will make settlements programmable and reactive: agents can instantly and permissionlessly pay for data, GPU compute, or API calls—no invoices, reconciliation, or batch processing needed. Software updates can embed payment rules, limits, and audit trails, without needing fiat currency integration, merchant onboarding, or reliance on financial institutions.

Prediction markets can settle in real-time as events unfold—price dynamics change, agents trade freely, and global payouts are completed within seconds, without custodians or exchanges. When value flows this freely, “payment flow” ceases to be a separate operational layer—it becomes part of network behavior itself. Banks become the foundational pipes of the internet, assets become infrastructure. When money turns into information packets routed over the internet, the internet will not only support the financial system—it will be the financial system.

V. Democratization of Wealth Management

From Exclusive High-Net-Worth to Universal Customization

Traditionally, personalized wealth management services have been exclusive to high-net-worth clients: providing professional advice and customized portfolios across asset classes is costly and complex. But as more asset classes are tokenized and accessible via crypto channels, AI-driven personalized strategies and collaborative systems can execute and rebalance portfolios instantly and at low cost. This is more than robo-advisors—everyone can now access active portfolio management, not just passive.

By 2025, traditional financial institutions will increase their crypto exposure (directly or via ETPs). But this is just the beginning. By 2026, platforms designed for “wealth growth” rather than “wealth preservation” will emerge. Fintech companies like Revolut and Robinhood, leveraging their technological advantages, will capture larger market shares; centralized exchanges like Coinbase will expand. Meanwhile, DeFi tools like Morpho Vaults can automatically allocate assets to the most risk-adjusted, return-optimized lending markets, forming the core of income portfolios.

Holding excess liquidity in stablecoins rather than fiat, and investing in RWA money market funds instead of traditional money market funds, can further boost potential returns. Finally, retail investors will find it easier to access less liquid private market assets, such as private loans, pre-IPO companies, or private equity—tokenization helps unlock these markets’ potential while meeting compliance and reporting requirements. When various assets (bonds, stocks, private investments, alternatives) are tokenized within a portfolio, it can rebalance automatically without manual fund transfers.

VI. From “Know Your Customer” to “Know Your Agent”

Identity Verification Crisis in the AI Era

The bottleneck limiting AI agent economy growth is increasingly not intelligence but identity verification. The number of “non-human identities” in finance exceeds human employees by 96 times, yet these identities are still “ghosts without accounts.” The missing infrastructure: KYA (Know Your Agent).

Just as humans need credit scores to obtain loans, AI agents require cryptographically signed certificates to transact—certificates must bind the agent to the authorized party, operational limits, and responsibilities. Until this mechanism is in place, merchants will block agents at firewall levels. Decades of KYC infrastructure now must solve the KYA problem within months.

VII. AI-Enhanced Research Paradigm

When Models Think, Science Changes

From a mathematical-economics perspective, in January this year, general AI models struggled to understand my workflow. By November, I could give the model abstract commands like guiding a PhD student—and sometimes it produces novel, correct answers.

More broadly, AI applications in research are becoming increasingly common, especially in reasoning. Current models not only support scientific discovery but can autonomously solve problems like Putnam’s math competitions (arguably the hardest university-level math contests worldwide). Which fields will benefit most, and how they will be applied, remain open questions.

I expect AI research will create and reward new types of scholars: those who can foresee connections between concepts and quickly extract insights from imprecise answers. These answers are not always exact but may point in the right direction (at least topologically). Interestingly, this is similar to leveraging the model’s “hallucinations”: when the model is sufficiently “smart,” giving it space to think can produce nonsense, but sometimes it leads to breakthroughs—much like human creativity in nonlinear, counterintuitive thinking.

This kind of reasoning requires new AI workflows—not just single-agent interactions but nested models of “agent-sets”—multi-layered models that help researchers evaluate previous ideas, gradually distinguish between superficial and core insights, until valuable content emerges. I use this method to write articles, others use it to search patents, create new art forms, or (unfortunately) discover new attack vectors in smart contracts.

Running such systems requires better interoperability across models and mechanisms to recognize and fairly reward each model’s contribution—precisely two key problems that cryptography can help solve.

VIII. The “Invisible Tax” of Open Networks

How AI Agents Drain Content Creators

The growth of AI agents imposes an invisible tax on open networks, fundamentally undermining their economic foundations. The problem is the increasing disconnection between the network’s context layer and execution layer: AI agents pull data from ad-driven websites (context layer), providing convenience to users but systematically bypassing revenue channels that support content creation (like ads and subscriptions).

To protect open networks and promote diverse AI-driven content, we must deploy large-scale technological and economic solutions. This could include new sponsorship models, attribution systems, or other innovative financing mechanisms. Existing AI licensing contracts are at best stopgaps, often only compensating a small fraction of lost revenue. The network needs a new economic model where value flows automatically.

The key change next year will be shifting from static licensing to real-time settlement based on actual usage. This involves testing and deploying systems—possibly leveraging blockchain—to enable micropayments and precise source tracking, automatically rewarding everyone who provides necessary information for AI agents.

IX. Privacy as the Strongest Fortress in Crypto

Interoperability Era: Privacy Creates Lock-in

Privacy is a necessary condition for global on-chain finance but is a flaw in nearly all existing blockchains. For most chains, privacy is an afterthought feature. But now, just by having privacy, a chain can be distinguished from all others. More importantly, privacy creates a “chain-internal network lock-in” effect—“privacy network effect.”

When all information is public, bridge protocols make cross-chain transfers easy. But when sensitive data is involved, everything changes: token cross-chain is simple, but secret cross-chain is extremely difficult. When entering or leaving privacy zones, there is always a risk of deanonymization by blockchain observers, mempool watchers, or network traffic sniffers. Crossing between private and public chains, or even between two private chains, leaks metadata like timing and transaction size, enabling tracking.

Compared to many new, homogeneous chains (where block space is undifferentiated and fees tend toward zero due to competition), privacy chains can build stronger network effects. In fact, if “general-purpose” public chains lack a thriving ecosystem, killer apps, or distribution advantages, users and developers have no reason to use or stay loyal. Public chain users can easily transact with anyone else’s users—choice is irrelevant. But private chains are different: once joined, migration is harder, and privacy leaks are more risky—“winner-takes-all” effects emerge. Since privacy is critical for most applications, a few private chains may dominate the entire crypto market.

X. The Future of Communication: Not Just Quantum-Resistant, But Decentralized

Why Key Management Matters More Than Cryptography

As the world prepares for the quantum era, many communication apps (Apple iMessage, Signal, WhatsApp) have set standards and contributed significantly. The problem is: all mainstream communication tools rely on private servers managed by a single entity. These servers are soft targets for governments—they can be shut down, backdoored, or forced to hand over private data.

If governments can shut down servers, companies hold the keys to private servers, or even just own the servers, then post-quantum cryptography is irrelevant. Private servers require “trust me,” whereas no private servers mean “you don’t have to trust anyone.” Communications should not depend on corporate intermediaries. We need open protocols, trustless systems—decentralized networks with no private servers, no reliance on single applications, fully open-source, employing state-of-the-art cryptography (including quantum-resistant).

In an open network, no individual, company, NGO, or state can seize control of our communication capabilities. Even if governments or corporations shut down an app, new versions will appear within 24 hours. Even if nodes go offline, blockchain’s economic incentives ensure new nodes immediately replace them. When people can control data and identities via private keys as easily as controlling money—everything changes. Apps come and go, but users always retain control over their data and identities, even without owning the app itself. This is not just about quantum resistance and cryptography; it’s about ownership and decentralization. Without both, we only build seemingly unbreakable but still easily shut down systems.

XI. Privacy as a Service

Data Control Is Everything

Behind every model, agent, and automation process is a simple element: data. But today, most data flows—inputs and outputs—are opaque, variable, and hard to audit. Acceptable for some consumer applications, but in finance, healthcare, and other industries, sensitive data privacy must be protected. This is also the main obstacle for institutions tokenizing RWA.

So how to promote innovation that is secure, compliant, autonomous, and globally interoperable while protecting privacy? Many solutions exist, but I want to emphasize data access control: who controls sensitive data? How does it flow? Who (or what) can see it? Without access control mechanisms, privacy-conscious users can only rely on centralized platforms or self-built systems. This is time-consuming, expensive, and limits traditional financial institutions from leveraging on-chain data management advantages.

As autonomous agents browse, trade, and make decisions, users and institutions need cryptographic verification mechanisms, not just “trust me” approaches. Therefore, I believe in “Privacy-as-a-Service”: new technologies providing programmable native data access rules, client-side encryption, and decentralized key management—precise control over who, when, and under what conditions data can be decrypted—all executed on-chain. Coupled with verifiable data systems, privacy protection will become a core part of internet infrastructure, not just an application-layer patch, but a fundamental infrastructure.

XII. From “Code Is Law” to “Rules Are Law”

Evolving Defense: From Passive Reaction to Active Defense

Recent verified attacks on DeFi protocols, despite strong teams, rigorous audits, and years of stable operation, reveal an unsettling reality: industry security standards are still based on case-by-case and experiential approaches. To mature, DeFi security must evolve from vulnerability response to proactive design, from “do your best” to principle-based approaches.

In the static phase (pre-deployment testing, auditing, formal verification), this means systematically verifying global invariants rather than just hand-selected local properties. Many teams are developing proof-supporting AI tools to help write technical specifications and state invariants, greatly reducing manual proof costs.

In the dynamic phase (post-deployment monitoring, real-time enforcement), these invariants can be transformed into dynamic guard rails—the last line of defense. These guard rails are encoded as conditions that each transaction must satisfy in real time. This way, we no longer assume all vulnerabilities are known—on the contrary, we enforce key security properties in code, and any violating transaction is automatically reverted.

This is not just theoretical. In practice, almost every exploit attempt triggers one of these security checks, potentially preventing the attack. Thus, the once-popular “code is law” philosophy has evolved into “rules are law”: even new attack vectors must meet the system’s security requirements, making remaining attacks either trivial or exceedingly rare.

XIII. Intelligent Upgrades for Prediction Markets

From Niche to Mainstream, From Unidimensional to Multidimensional

Prediction markets are gradually mainstreaming. Next year, with integration of crypto and AI, they will become larger, broader, and smarter—but this presents new challenges for startups. First, more contracts will be added. This means we will not only get prices for major elections or geopolitical events but also niche outcomes and complex cross-asset events. These new contracts will become part of the information ecosystem (which is already happening), raising important social questions: how to price this information? How to design them to be more transparent, auditable, and open to new possibilities—precisely what crypto can enable?

With the explosion of contract numbers, new consensus mechanisms will be needed to verify authenticity. Centralized decision platforms (Did something happen? How to verify?) are critical but controversial. Cases like Zelensky’s and Venezuela’s elections expose their limitations. To address these issues and expand prediction markets into more practical applications, new decentralized governance mechanisms and large language models as oracles can help establish facts amid disputes.

AI has already demonstrated impressive predictive potential. For example, AI agents running on these platforms can scan global trading signals, gaining advantages in short-term trading, and helping us discover new cognitive dimensions and improve event predictions. These agents are not just political advisors—they analyze strategies to better understand factors influencing complex social events. Will prediction markets replace polls? Not entirely, but they can improve them (poll data can also serve as market inputs). As a political scientist, I am most interested in how prediction markets can collaborate with rich polling ecosystems, but we must improve poll experiences with AI and crypto, ensuring respondents are real humans, not bots.

XIV. The Rise of “Betting Media”

Using Money to Prove Beliefs

The objectivity of traditional media has long been questioned. The internet has empowered everyone to speak, and more operators, practitioners, and creators are directly communicating with the public. Their opinions reflect interests, and counterintuitively, audiences respect and appreciate this honesty.

Innovation is not just in social media growth but in the emergence of crypto tools that enable public, verifiable commitments. AI can generate unlimited content cheaply, with any viewpoint or identity (real or fictitious), so relying solely on words (human or bot) is insufficient. Tokenized assets, programmable lockups, prediction markets, and on-chain history provide a more solid trust foundation: commentators can express opinions while proving they support with real stakes. Podcasts can lock tokens to show they are not market manipulators. Analysts can bind predictions to publicly settled markets, creating auditable track records.

I see this as the early stage of “betting media”: such media not only acknowledge conflicts of interest but can also prove them. In this model, credibility does not come from false neutrality or empty promises but from willingness to bear open, verifiable risks. Betting media do not replace other forms but complement them. They provide new signals: not “trust me because I am neutral,” but “this is the risk I am taking—you can verify.”