The Evolution of the Cryptocurrency Ecosystem in 2026: 17 New Trends to Watch

Part One: Payments and Financial Infrastructure

Stablecoins Breakthrough: From Marginal to Mainstream Payment Layer

Last year, stablecoin trading volume surpassed $4.6 trillion, a number that is truly staggering—over 20 times the size of PayPal’s payment volume, and approaching the scale of the world’s largest payment networks. Currently, on-chain stablecoin transfers are confirmed within seconds, costing less than a cent.

However, this high efficiency has not yet been fully integrated into daily financial systems. The core issue lies in connectivity—how to enable smooth conversion between digital dollars and local fiat currencies, making stablecoins truly usable as payment tools. A new generation of startups is filling this gap. They leverage cryptographic verification, local payment network integration, QR code solutions, and more, allowing users to complete transactions with stablecoins at everyday merchants.

Once these foundational entry and exit points mature, stablecoins will undergo a transformation: evolving from mere trading tools into the very settlement layer of the internet itself. Cross-border salaries paid in real-time, merchants able to receive payments without bank accounts, and global fund flows—these are no longer just ideas.

RWA and the Chain-Native Evolution of Stablecoins

The wave of traditional asset tokenization continues to rise, but most projects only scratch the surface, not fully utilizing the native features of blockchain. In contrast, synthetic products like perpetual contracts can better unlock liquidity potential, and users find leverage mechanisms easier to understand. Especially in emerging markets, where options markets often surpass spot markets in liquidity, perpetualization has become an interesting experiment.

The key question becomes: should we pursue perpetualization or tokenization? Regardless of the path chosen, by 2026, we expect to see more chain-native RWA tokenization solutions, rather than just wrapping traditional assets.

For stablecoins to truly become mainstream, having trading functions alone is not enough—robust credit infrastructure is needed. Many new stablecoins today resemble “narrow banks,” holding only ultra-safe assets. In the long run, such projects lack vitality. The real breakthrough comes from on-chain native lending: asset managers and curated protocols financing off-chain assets through on-chain lending. Compared to “off-chain initiated tokenization,” directly starting lending on-chain can significantly reduce management costs and compliance complexity.

New Opportunities for Banking Modernization

Most traditional banking core systems date back to the 1960s and 70s, and these mainframes running on COBOL still control most of the world’s assets today. Upgrading them is extremely difficult—adding real-time payment capabilities can take months or even years.

Tools like stablecoins, tokenized deposits, and on-chain government bonds open new avenues for banks and fintech firms: they can launch innovative products without dismantling legacy systems. This “parallel architecture” of old and new allows traditional financial institutions to bypass technical debt.

Payment Automation in the AI Agent Era

With large-scale deployment of AI agents, many business activities will be executed automatically in the background, rather than through manual clicks. This means that fund flows need to become as rapid as information flows. Smart contracts can already settle global USD transactions within seconds. By 2026, new primitives like HTTP 402 will make settlements programmable and instant: AI agents can perform millisecond payments for data, GPU computations, and API calls, without invoices or manual confirmation.

Prediction markets can settle in real-time—odds dynamically change as events unfold, and global payments are completed within seconds. All this will fundamentally change how value flows: payments will no longer be a separate layer but a natural part of the network itself. Banks become internet pipelines, assets turn into infrastructure, and money will be routed like data packets.

Part Two: Democratization of Wealth Management

From Elite Service to Universal Wealth Growth

Traditionally, professional wealth management has been reserved for high-net-worth individuals. But as more asset classes are tokenized, AI-driven strategies and protocol management become automated and low-cost, transforming the landscape.

It’s not just passive management—anyone can now access active portfolio management. By 2026, platforms designed specifically for “wealth growth” rather than “wealth preservation” will emerge in large numbers. Some fintech platforms and centralized exchanges will gain larger market shares through technological advantages, while DeFi tools will automatically allocate assets into risk-adjusted, high-return lending markets, forming the foundation for core income portfolios.

Holding idle liquidity in stablecoins or investing in RWA money market funds instead of traditional funds can significantly boost returns. When ordinary investors find it easier to access private equity, pre-IPO companies, private credit, and other traditionally hard-to-enter assets, tokenization unlocks the potential of these markets.

The ultimate effect: a diversified multi-asset portfolio (bonds, stocks, private investments) can be automatically rebalanced without manual transfers—this is the true democratization of wealth management.

Part Three: AI and Agents

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

The bottleneck limiting AI agent-driven economies is no longer intelligence but identity verification. The number of “non-human identities” in financial services exceeds human employees by 96 times, yet they remain “ghost accounts” without proper identification.

The key infrastructure missing is KYA (Know Your Agent). Just as humans need credit scores to borrow, AI agents need cryptographic signatures as proof to transact—proofs must bind the agent’s authorized entity, operational limits, and accountability chain. Without this mechanism, traders will block agents at firewalls. Decades of KYC frameworks have been built, but solving KYA within months is now urgent.

The Evolving Role of AI in Research

Mathematical economists have already felt the leap in AI research capabilities: from being unable to understand workflows at the start of the year to receiving abstract instructions (like guiding PhD students) by year’s end, sometimes even providing innovative and correct answers. Current models can independently solve the world’s most difficult math competition problems.

Emerging scholars are appearing: those skilled at recognizing connections between concepts and quickly extracting useful signals from fuzzy answers. These “hallucinations” may seem useless, but sometimes point toward breakthroughs—human creativity often arises from nonlinear, counterintuitive reasoning.

This demands new AI workflows: not just single-agent interactions, but “nested agents”—multi-layer models that help researchers evaluate ideas from previous generations, gradually filtering valuable signals. This requires better interoperability between models and fair attribution of each model’s contribution—precisely what cryptographic techniques can enable.

The Invisible Tax Facing Open Networks

The surge in AI agents imposes an invisible burden on open networks. The problem lies in the disconnect between the “context layer” and the “execution layer”: AI agents fetch data from ad-based websites (context), providing convenience to users, but systematically bypass the revenue streams supporting content creation.

To protect open networks and support diverse AI-driven content, large-scale deployment of technical and economic solutions is needed—possibly including new sponsorship models, attribution systems, or innovative financing mechanisms. Current AI licensing protocols are only stopgap measures.

A true transformation involves upgrading from static licenses to real-time usage settlement. Blockchain-based micro-payments and precise attribution tracking can automatically reward each contributor, regardless of how their data is ultimately utilized by AI agents.

Part Four: Privacy and Security

Privacy as a Competitive Edge in Encryption

Privacy is at the core of blockchain finance but remains a major weakness of most public chains today. Privacy protocols alone can make a chain stand out.

Privacy creates online lock-in network effects. Cross-chain transfers are usually straightforward, but when it involves private data, the situation changes dramatically: transferring assets is simple, but transferring secrets is difficult. When moving in and out of privacy zones, there is always a risk of being tracked by on-chain observers through transaction timestamps, sizes, and other metadata.

Compared to homogeneous public chains (where transaction fees may drop to zero due to no difference in block space), privacy chains can establish stronger network effects. For “general-purpose” chains lacking ecosystem or killer apps, users have little reason for loyalty—they can easily trade between chains. But privacy chains are different: once joined, the risk of leaving is higher, making choice critical—leading to a “winner-takes-all” effect.

Because privacy is crucial for most applications, a few privacy chains may monopolize the entire crypto ecosystem.

The Future of Communication: Quantum-Resistant and Decentralized

As the world prepares for the quantum era, many communication apps (iMessage, Signal, WhatsApp) are already working on quantum-resistant standards. The problem is that all mainstream messaging tools rely on private servers managed by a single organization, which can become targets for governments or corporations.

If nations can shut down servers, companies hold keys, or even just own the servers, what use is quantum cryptography? Private servers require “trust me,” whereas decentralization means “you don’t need to trust anyone.”

Communication does not need corporate intermediaries. We need open protocols—trustless, without reliance on any single party. Achieved through network decentralization: no private servers, no single app dependency, fully open-source, with the strongest cryptography including quantum resistance.

In such a network, no individual—be it a person, company, NGO, or government—can deprive anyone of communication rights. Even if an app is shut down, 500 versions will emerge tomorrow. Even if nodes go offline, economic incentives on the blockchain will immediately spawn replacements.

When people can control their data and identities via private keys as easily as controlling money, everything will change. Apps will come and go, but users will always control 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.

Privacy as a Service

Behind models, agents, and automation workflows is a simple element: data. But today, most data flows—both in and out—are opaque, mutable, and hard to audit. Some consumer applications may accept this, but in finance, healthcare, and other sensitive fields, protecting privacy is essential—and a major obstacle to tokenizing RWA.

So how to foster innovation that is secure, compliant, autonomous, and globally interoperable while protecting privacy? Many approaches exist, but I emphasize access control: who controls sensitive data? How does it flow? Who can see it?

Without access control mechanisms, privacy-conscious users can only rely on centralized platforms or self-built systems. This is time-consuming and limits traditional financial institutions from leveraging on-chain data management advantages.

As autonomous agents emerge, users and institutions need cryptographic verification mechanisms—not just “best effort” trust. That’s why we need “privacy as a service”: new technologies providing programmable native data access rules, client-side encryption, and decentralized key management—precise control over who can decrypt data, when, and under what conditions—all executed on-chain.

Coupled with verifiable data systems, privacy protection will become a core part of internet infrastructure, not just application-layer patches, but fundamental infrastructure.

From “Code is Law” to “Rules are Law”

Recently, several verified DeFi protocols have been hacked despite strong teams, rigorous audits, and years of stable operation. This reveals a harsh reality: industry security standards are still based on case-by-case experience.

To mature, DeFi security must shift from reactive to proactive design, evolving from “maximum effort” to principle-based approaches:

Static phase (pre-deployment: testing, auditing, formal verification) involves systematic validation of global invariants. Many teams are developing AI-assisted proof tools to help write technical specifications and formulate invariant hypotheses, greatly reducing manual proof costs.

Dynamic phase (post-deployment: monitoring, real-time enforcement) turns these invariants into dynamic guardrails—the last line of defense. Guardrails are encoded as conditions, and each transaction must satisfy them. This way, the assumption that all vulnerabilities are known is replaced by enforcing key security properties in code—any violating transaction is automatically rolled back.

In practice, nearly every exploit triggers one of these security checks—potentially preventing attacks. Therefore, “code is law” is evolving into “rules are law”: new attack vectors must also meet system security requirements, leaving only trivial or extremely difficult vectors.

Part Five: Emerging Fields

Prediction Market Expansion: Bigger, Broader, Smarter

Prediction markets are gradually mainstreaming, and next year, with integration of crypto and AI, their scale will continue to grow—but startups face new challenges.

First is contract explosion. We can now price not only major elections or geopolitical events but also niche outcomes and complex cross-conditions. New contracts become part of the information ecosystem (already happening), raising important social questions: how to price this information appropriately? How to design contracts to be more transparent, auditable, and open for innovation—this is where crypto’s advantages lie.

To handle the surge in contracts, new authenticity verification consensus mechanisms are needed. Centralized oracles (did an event happen? How to verify?) are critical but controversial. Some international political events lack clear standards, leading to deadlock.

To resolve such disputes and make prediction markets more practical, decentralized governance mechanisms and large language models as oracle proxies can help establish truth amid controversy. AI has already demonstrated remarkable predictive potential. AI agents operating on these platforms can scan global trading signals, gain short-term trading advantages, and open new cognitive dimensions, improving event prediction. These agents are not just policy advisors—they analyze strategies to better understand drivers of complex social events.

Will prediction markets replace polls? No, but they can improve polling. Data scientists are most interested in how prediction markets and polling ecosystems can collaborate, but AI and cryptography must be used to improve survey experience and ensure respondents are real humans, not bots.

The Rise of “Betting Media”

The objectivity of traditional media has long been questioned. The internet empowers everyone to speak, and more operators directly communicate with the public, reflecting their interests. Paradoxically, audiences respect this honesty and even appreciate its authenticity.

Innovation is not in social media growth but in the emergence of cryptographic tools—allowing open, verifiable commitments. AI can cheaply generate unlimited content with arbitrary viewpoints or identities (real or fictional), but relying solely on verbal promises (by humans or bots) is insufficient.

Tokenized assets, programmable locks, prediction markets, and on-chain history provide a more solid foundation of trust: commentators can publish opinions while proving they support with real stakes. Podcasts can lock tokens to demonstrate they are not speculative in the market. Analysts can link predictions to publicly settled markets, creating auditable track records.

This is an early form 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 hollow statements but from willingness to bear publicly verifiable risks. Betting media will not replace other forms but will complement them. They provide new signals: not “trust me because I am neutral,” but “see, this is the risk I bear—you can verify.”