17 Trends Reshaping the Crypto Network in 2026: From Infrastructure to Ecosystem Transformation

When the Internet Becomes the Settlement Layer: Crypto Redefining Financial Infrastructure

Stablecoins have become the “blood” of on-chain economy. Last year, transaction volume reached $46 trillion, over 20 times PayPal and nearly 3 times Visa, approaching the scale of the US ACH clearing system. Technically, stablecoin transfers now confirm in seconds with fees below one cent—yet the challenge lies in the “last mile.”

How to seamlessly connect digital dollars with traditional finance? A new generation of startups is filling this gap: some use cryptographic proofs to exchange user privacy, some integrate regional networks supporting QR code transfers, and others build globally interoperable on-chain wallets. Once these solutions mature, cross-border workers can receive real-time wages, and small merchants can accept global payments without bank accounts.

The next chapter for stablecoins is no longer “how to connect,” but “how to innovate.” Currently, most stablecoins are simply tokenized dollars—essentially “digital narrow banks.” True breakthroughs involve building native on-chain credit infrastructure. Unlike issuing loans off-chain and then tokenizing, directly launching debt assets on-chain can significantly reduce service costs and backend overhead while increasing accessibility. This requires overcoming compliance and standardization challenges, but developers are actively pushing forward.

Meanwhile, RWA (Real-World Asset) tokenization is moving from “copy-paste” to “crypto-native design.” Merely bringing US stocks and commodities on-chain is just the first step; the real value lies in leveraging on-chain features—such as perpetual contracts and other synthetic derivatives, which can provide deeper liquidity and easier deployment. Emerging market stocks are especially suitable for “perpetualization” (some zero-day options markets already surpass spot liquidity). By 2026, more projects will adopt “crypto-native” RWA solutions rather than simply tokenizing assets.

The Old Banking System Meets the Disruption of On-Chain Finance

Most of the world’s major banks still operate on architectures from the 1960s-90s. COBOL code, batch interfaces, no APIs—these “decades-old” systems control global asset flows but severely constrain innovation. Launching real-time payments can take months or even years.

Stablecoins break this deadlock. They do not require rebuilding these “bulky but stable” legacy systems but instead parallelly build new financial layers on-chain. When stablecoins, tokenized deposits, and tokenized government bonds circulate on-chain, traditional institutions gain a “low-risk innovation pathway”—enabling new products and services for new customers without the risk of core system overhaul.

This “bypassing rather than replacing” strategy will be the core logic of TradFi institutions’ on-chain deployment in 2026.

Identity Challenges and Research Revolution in the AI Agent Era

As AI begins to autonomously execute business logic, the financial system faces an unprecedented question: how can non-human identities participate in economic activities?

Currently, AI agents outnumber human employees by 96 times, yet they remain “ghosts” in the financial system—unable to obtain credit scores or conduct transactions. The missing piece is “KYA” (Know Your Agent)—agents need cryptographic signatures binding delegators, restrictions, and liabilities—this is the foundation of a large-scale AI economy. Without it, merchants will continue to block agents at firewalls. Industries that spent decades building KYC infrastructure now need to solve KYA within months.

On the other hand, AI is reshaping research itself. From being unable to understand complex workflows in early 2025, to executing abstract tasks like a PhD student by mid-year, AI’s reasoning capabilities are rapidly advancing. It supports scientific discovery and can independently solve “Putnam problems”—some of the hardest university math competitions worldwide.

Key innovation lies in “nested agents”—multi-layer models collaborating to evaluate methods of previous models and progressively filter valuable information. But to make this system work, two fundamental issues must be addressed: model interoperability and fair contribution recognition and rewards. Cryptographic techniques can play a crucial role here.

Flow of Value and the Economic Transformation of the Internet

When AI agents shift from passive responses to active execution, the “flow of value” must change—speed must synchronize with information flow.

Blockchain and new foundational protocols (like x402) make this possible. Smart contracts can now enable global payments in seconds; by 2026, new protocols will give settlement “programmable and responsive” features: agents can instantly and permissionlessly pay for data, GPU computation, or API calls, without invoices or batch processing; developers can embed payment rules and audit trails into software updates without integrating with fiat systems; prediction markets can settle in real-time as events unfold—exchange rates updates, agent trades, and global revenue sharing are completed within seconds, without custodians or exchanges.

When value can flow so freely, “payment processes” are no longer separate operational layers but become “network behaviors” themselves. Banks will become part of the internet infrastructure, and assets themselves will be infrastructure. If money can circulate like internet data packets, the internet will no longer “obey” the financial system but become the financial system itself.

Democratization and Automation of Wealth Management

Traditional wealth management is only accessible to high-net-worth clients—personalized advice and cross-asset allocation are costly. Tokenization changes all that.

When all asset types can be traded instantly on-chain, AI-driven automated strategies become “active management” rather than passive index funds. This is not just an upgrade of robo-advisors but “everyone can access professional-grade investment management.”

By 2025, traditional financial institutions will have allocated 2-5% of client portfolios to crypto; by 2026, platforms centered on “wealth accumulation” rather than just “wealth preservation” will explode—fintech giants like Revolut, Robinhood will leverage technological advantages, and exchanges like Coinbase are watching closely.

Meanwhile, DeFi tools (like Morpho Vaults) can automatically seek “risk-adjusted optimal yields” in lending markets, providing a “core yield layer” for portfolios. Idle funds will be converted into stablecoins or tokenized money market funds, further boosting returns. Ultimately, once bonds, stocks, private equity, and alternative assets are all tokenized, asset allocation and rebalancing will be fully automated—no more bank transfers.

Privacy as a “Moat” in Blockchain Competition

Privacy is not just an option but a prerequisite for global on-chain finance. Currently, almost all public blockchains lack privacy—it’s an afterthought, not a native design.

This is the opportunity. Chains with strong privacy capabilities can build “privacy network effects”—because cross-chain transfers become difficult. In traditional competition, users can easily switch; but when privacy is involved, “transferring tokens across chains is easy, but transferring secrets is hard.” When entering or leaving privacy zones, observers can identify user identities; cross-chain transfers leak metadata like timestamps and amounts, increasing tracking risks.

At this stage, many “mediocre new public chains” compete with near-zero fees (on-chain space becomes a commodity); privacy chains can build stronger network effects. For general-purpose public chains, without a unique ecosystem, killer app, or distribution advantage, users and developers lack reasons to migrate. But on privacy chains, choice is key—once joined, the cost to exit skyrockets (due to identity exposure risks), creating a “winner-takes-all” scenario.

Because privacy is indispensable in most real-world scenarios, the future dominance of a few privacy chains may lead the entire crypto market.

The Future of Communication: Quantum Resistance and True Decentralization

The world is preparing for the “quantum computing era,” with Apple, Signal, WhatsApp taking action. The problem: all mainstream messaging apps rely on “single-entity managed private servers.” These servers are easy targets for governments—they can be shut down, backdoored, or forcibly accessed.

If governments can shut down servers, companies hold keys, and companies are the owners, then “quantum resistance” is meaningless. The real solution is decentralized networks: open protocols, fully open-source, no private servers, employing cutting-edge cryptography (including quantum-resistant algorithms).

In such networks, no one can deprive users of communication—if an app is shut down, 500 new versions will appear the next day; if nodes go offline, blockchain and economic incentives will immediately activate replacements. When people “control messages with keys” (like controlling funds), everything changes: apps can be iterated, users always hold their messages and identities—even if the app is discontinued, they remain the owners of their messages.

This is not just about “quantum resistance” and “cryptography,” but about “ownership” and “decentralization.” Without these two, we are merely building “unbreakable but easily shut down cryptography.”

Balancing Data, Privacy, and Automation

The foundation of every model, agent, and automation system is data. But most current data flows—whether input or output—lack transparency, are easily tampered with, and hard to audit.

This may not be a problem for consumer applications, but in critical fields like finance and healthcare, data privacy is a hard requirement and a major obstacle to institutionalized asset tokenization. So how to protect privacy while ensuring innovation, compliance, autonomy, and global interoperability?

The key lies in data access control: who controls sensitive data? How does data flow? Who has access rights?

Without access control mechanisms, any organization wanting to protect data privacy must rely on centralized services or self-built systems—time-consuming, expensive, inefficient, limiting traditional financial institutions from fully leveraging on-chain management. And as agents begin to autonomously browse information, trade, and make decisions, users and institutions need “cryptographic-grade guarantees,” not just “best efforts.”

Future solutions require “Secrets-as-a-Service”: new technologies enabling programmable, native data access rules, client-side encryption, and decentralized key management—explicitly defining who, when, and under what conditions can decrypt data, with all rules enforced on-chain. Coupled with verifiable data systems, data privacy protection will become a core layer of internet infrastructure, not just application-level patches, making privacy a true foundational layer.

Paradigm Shift in DeFi Security: “Standards as Law”

Recent DeFi hacking incidents have targeted protocols with multiple audits, strong teams, and years of stable operation—exposing a worrying truth: current security practices mainly rely on “experience and luck.”

To mature DeFi security, two major shifts are needed: from “patching vulnerabilities” to “design-level property guarantees,” and from “best effort” to “principle-based systemic defense.” This can be achieved in two phases:

Phase 1 (Static/Pre-deployment): Testing, Auditing, Formal Verification
Systematically prove “global invariants” (rules the entire system follows), not just handpick local rules. AI-driven proof tools have emerged to help write specifications and hypothesize invariants, greatly reducing manual effort—though still costly and hard to scale.

Phase 2 (Dynamic/Post-deployment): Runtime Monitoring and Real-time Enforcement
Transform the above “invariants” into protective barriers—the final line of defense in code. These rules are encoded as “runtime assertions” that must be satisfied for transactions to execute. This way, there’s no need to assume “all vulnerabilities are fixed,” but rather, critical security properties are enforced by code—any transaction violating them is automatically rejected.

Empirical evidence: nearly every historical hack has triggered such security checks during execution, potentially preventing the attack.

Thus, the once-popular “code is law” paradigm is evolving into “standards as law”: even against new attack vectors, attackers must adhere to the system’s core security properties, and other attack methods are either limited in harm or extremely difficult.

Three-Dimensional Upgrades to Prediction Markets

Prediction markets are now mainstream, and by 2026, deep integration with crypto and AI will enable a three-dimensional leap in scale, coverage, and intelligence—bringing new developer challenges.

Scale and coverage expansion: traditional prediction markets cover major elections and geopolitical events; now, they will include niche areas and complex cross-issues. As the number of contracts explodes and integrates with news ecosystems (already underway), key questions arise: how to value these information sources? how to enhance market transparency and auditability (cryptography can help)?

Faced with this surge, new consensus mechanisms are essential, especially for contentious cases like “Zelensky lawsuit market” or “Venezuela election market.” Decentralized governance and LLM-based oracle dispute resolution can help resolve disputes and extend prediction markets into more practical scenarios.

AI empowerment: on prediction platforms, AI agents can collect signals broadly, gaining short-term trading advantages, and offering new perspectives and trend forecasts (projects like Prophet Arena have shown potential). These agents can become “advanced political analysts,” with strategic insights revealing key factors influencing complex social events.

Will prediction markets replace polls? No. Instead, they can improve poll quality—polls can also integrate with prediction markets. As political scientists, the most anticipated outcome is “prediction markets coexisting with rich polling ecosystems”—but this requires new tech: AI can optimize survey experiences, and cryptography can enable new human identity verification methods, distinguishing real humans from bots.

The Rise of Stakeholder Media

Traditional media emphasize “objectivity,” but its drawbacks are increasingly evident. The internet empowers everyone to speak, and more practitioners and builders share their views directly with the public—reflecting their own “interest ties.”

The paradox: audiences respect them not “despite” interest ties, but “because of” them. The new factor is not the rise of social media but the emergence of cryptographic tools—supporting “publicly verifiable commitments.”

As AI drastically reduces content generation costs (any opinion, any identity—real or fake), pure speech loses credibility. Tokenized assets, programmable locks, prediction markets, and on-chain history provide stronger trust foundations: commentators can prove “alignment” by “staking funds on their opinions”; podcasts can lock tokens to prove “no change of stance for profit or pump-and-dump”; analysts can link predictions to “public settlement markets” to establish “auditable performance records.”

This is the embryonic form of “Stakeholder Media”: not only accepting interest ties but also providing proof. In this model, credibility does not come from “pseudo-neutrality” or “baseless claims,” but from “public, transparent, verifiable stake commitments.” Stakeholder media will not replace other media forms but complement them—delivering a new signal: “Don’t trust me because I am neutral; don’t believe me without basis, but see the risks I take and how you verify my authenticity.”

The Era of Large-Scale Cryptography Applications

For years, SNARKs (zero-knowledge proofs) have been confined to blockchain applications. The main reason: “costs are too high”—generating proofs requires 1 million times more work than the computation itself. Only when costs can be distributed across thousands of nodes (like blockchains) is the technology feasible; otherwise, it’s impractical.

This is changing. By 2026, the cost of zero-knowledge VM proofs will drop to about 10,000 times (proof generation requiring 10,000x computation), with memory consumption down to hundreds of MB—fast enough to run on smartphones, and low enough to support widespread adoption.

10,000x is a critical threshold: high-end GPUs have roughly 10,000 times the computing power of a laptop CPU. By the end of 2026, a single GPU will be able to “generate proofs in real-time for CPU-executed computations.” This realizes the early vision of scientific papers: verifiable cloud computing.

If, due to “GPU load limitations,” “lack of technical expertise,” or “legacy system constraints,” CPU tasks must run in the cloud, in the future, you will obtain “cryptographic proof of correct computation” at a reasonable premium. Proof generators are already optimized for GPUs, requiring no additional code modifications.

Transactions Are Not the End: Rethinking Business Models of Crypto Companies

Currently, aside from stablecoins and some infrastructure firms, almost all successful crypto companies have shifted to trading or are in transition. But what if “all crypto companies become trading platforms”?

Competition within the same market not only disperses user attention but also leads to “a few giants monopolize, most companies exit.” This means early move into trading will cause companies to miss the opportunity to build “more competitive and sustainable business models.”

Founders eager to profit are understandable, but “pursuing short-term product-market fit” comes at a cost. The unique tokenomics and speculative nature of crypto make founders prone to the “instant gratification” trap—similar to the “marshmallow test” (delayed gratification).

Trading itself is not evil; it is a key market function. But it should not be the ultimate goal of a company. Founders focusing on “product-market fit” at the core of their product are most likely to succeed in the industry.

The Ultimate Alignment of Legal Frameworks and Technical Architectures

Over the past decade, the biggest challenge in building US blockchain networks has been “legal uncertainty.” The scope of securities law has expanded, enforcement standards vary, forcing founders to design companies for regulation rather than the network.

“Avoiding legal risks” has replaced “product strategy,” and engineers have yielded to lawyers. This has led to distortions: founders are advised to avoid transparency, token distributions become arbitrary, governance becomes superficial, organizational structures prioritize legal risk avoidance, and tokens are designed as “without economic value” or “without business models.” Worse, projects operating in legal gray areas often grow faster than honest, compliant builders.

Now, the situation is changing. The US government is close to passing the “Crypto Market Structure Act”—by 2026, it may eliminate all these distortions. The law will encourage more transparency, clear standards, and replace “arbitrary enforcement” with “transparent, orderly financing, token issuance, and decentralization.”

After the GENIUS Act passes, stablecoin trading volume will surge; the Crypto Market Structure Act will bring even greater change—this time focusing on blockchain networks. In other words, new regulations will enable networks to truly operate as networks: open, autonomous, composable, trust-neutral, and decentralized.