17 Key Development Directions in the 2026 Crypto Ecosystem

01 Stablecoins, Asset Tokenization, and Payment Innovation

Explosive Growth in Stablecoin Trading Volumes and Infrastructure Development

Over the past year, stablecoin trading volume has reached $46 trillion, continuously setting new records. This figure has profound implications: it exceeds the annual transaction volume of payment platforms by more than 20 times, is nearly three times the annual transaction scale of major global payment networks, and is approaching the processing scale of the US Automated Clearing House (ACH)—the infrastructure for handling direct deposits and other electronic financial transactions.

Currently, stablecoin transfers can be completed within 1 second, with fees below one cent. However, the real bottleneck lies in how to effectively connect these digital assets with the financial systems used daily by people. In other words, establishing a conversion channel between stablecoins and traditional currencies is necessary.

A new wave of startups is filling this gap. They use cryptographic verification technologies to allow users to exchange local account balances for digital dollars; connect regional payment networks to facilitate cross-bank transfers via QR codes, real-time payment systems, and other tools; and build truly interoperable global digital wallet layers and card issuance platforms, enabling users to spend stablecoins in everyday retail scenarios.

These innovations broadly expand the coverage of the digital dollar economy. As channels for entry and exit are improved, stablecoins will no longer be marginal financial tools but will become the internet settlement layer. Cross-border workers can receive salaries in real-time, merchants can accept global digital assets without bank accounts, and payment apps can instantly settle value with users worldwide.

The Evolution of Asset Tokenization in Its True Form

Traditional assets (US stocks, commodities, indices) are increasingly being tokenized on blockchains, but most tokenization schemes remain superficial and fail to fully leverage native crypto features. In contrast, synthetic derivatives like perpetual contracts offer deeper liquidity, easier execution, and understandable leverage mechanisms—these derivatives may be the most market-compatible crypto-native financial products today. Emerging market equities are particularly suitable for perpetual contracts, with some zero-option liquidity surpassing spot markets.

Looking ahead to 2026, the market will see more crypto-native asset tokenization initiatives rather than mere on-chain transfers of existing assets. As stablecoins move from the fringe to the mainstream, the number of newly issued stablecoins will also increase. However, stablecoins lacking strong credit infrastructure are essentially limited-scale banks holding perceived super-safe specific liquid assets.

Emerging asset managers, curators, and protocols are beginning to offer lending services supported by off-chain assets but operated on-chain. These loans often originate off-chain and are subsequently tokenized. However, the tokenization value of such schemes is limited, mainly distributed to existing on-chain users. A true upgrade involves originating debt assets on-chain, rather than originating them off-chain and then tokenizing. On-chain origin reduces loan management and backend costs and enhances accessibility. Compliance and standardization will be challenges, but industry efforts are underway.

Stablecoins Driving Ledger Updates and New Payment Scenarios

Banking software systems are largely unfamiliar to modern developers: in the 1960s-70s, banks pioneered large-scale software; in the 1980s-90s, second-generation core banking systems emerged. But these systems are now outdated and update slowly. Most global asset management still relies on decades-old centralized ledgers running on mainframes, programmed in COBOL, communicating via batch files rather than APIs.

Stablecoins have become game-changers. Last year marked not only the phase where stablecoins found market entry and mainstream adoption but also unprecedented institutional acceptance. Stablecoins, tokenized deposits, government bond tokenization, and on-chain bonds create new channels for banks, fintechs, and financial institutions to develop new products and serve new clients. More importantly, this does not require rewriting those old but stable systems, opening new avenues for institutional innovation.

02 Artificial Intelligence and Autonomous Agents

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

The limitations of the intelligent agent economy are shifting from technical to identity verification issues. In financial services, the number of “non-human identities” is 96 times that of human employees, yet these identities often have no accounts and are like ghosts. The key gap is KYA (Know Your Agent)—agents need cryptographically signed credentials to execute transactions, linking the agent to authorized entities, operational limits, and responsibilities. Until this mechanism is perfected, merchants will still block agents at firewalls. KYC infrastructure has taken decades to develop; now, the KYA problem must be solved within months.

New Paradigm for AI-Assisted Scientific Research

As a mathematical economist, I initially struggled to get general AI models to understand my workflows; by November, I could give them abstract instructions like guiding PhD students, sometimes even receiving novel and correct answers. More broadly, AI applications across research fields are expanding, especially in reasoning—current models can directly assist scientific discovery and autonomously solve the world’s most difficult university math competition problems.

Which areas these tools are most useful in and how they operate remain open questions. But I believe AI-assisted research will give rise to new academic types: those that value rapid extraction of conjectural answers and insight into concept relationships. Answers may be imprecise but point in the right direction. Ironically, this leverages the “hallucination” power of models: sufficiently intelligent models sometimes produce absurd content in divergent thinking spaces but can also generate innovative discoveries—similar to human creativity in nonlinear, non-predefined thinking.

This requires new AI workflows, not just interactions between individual agents but nested agent models—using multi-layer models to help researchers evaluate and refine ideas step-by-step. I have used this approach to write articles, others for patent searches, art creation, and even (unfortunately) discovering new smart contract vulnerabilities. Executing such nested agent research systems requires better interoperability and mechanisms to recognize and fairly compensate each model’s contribution. These are two core issues that cryptography can help solve.

The “Hidden Tax” Faced by Open Networks

The rise of AI agents is imposing a hidden tax on open networks, fundamentally changing their economic foundation. This stems from the growing disconnection between the contextual layer and execution layer on the internet: AI agents scrape data from ad-supported websites (context layer), providing convenience to users but systematically bypassing revenue channels that sustain content creation (ads, subscriptions).

To prevent erosion of open networks and protect content diversity that drives AI, large-scale deployment of technical and economic solutions is needed. These include new sponsorship models, attribution systems, or other innovative financing methods. Existing AI licensing agreements only mitigate the problem, often compensating content creators only a small fraction of revenue lost due to AI-driven traffic reduction. The network needs new economic models that enable value to flow automatically.

A key shift will be from static licensing to real-time, usage-based compensation mechanisms. This involves testing and deploying systems that may leverage blockchain-enabled micro-payments and precise, traceable standards to automatically reward information providers who help AI agents complete tasks.

03 Privacy and Security

Privacy: The Strongest Competitive Barrier in Cryptography

Privacy is a core requirement for global financial operations on blockchain but is almost absent in most current blockchains. For many chains, privacy is just an afterthought patch. Today, privacy itself can differentiate one chain from all competitors. It also plays a deeper role: creating on-chain lock-in effects, i.e., privacy network effects.

In a world where performance no longer guarantees a competitive edge, this is especially critical. Cross-chain bridging makes transferring information easy if it’s public, but when privacy-sensitive data is involved, it’s entirely different: transferring tokens is easy, transferring secrets is hard. When moving in and out of privacy zones, there’s always a risk that monitored chains, mempools, or network traffic reveal identities through metadata like transaction time and size. Crossing boundaries between private and public chains, or between private chains, can leak metadata such as transaction timing and scale, aiding tracking.

Compared to countless homogeneous new chains (whose fees may drop to zero due to space competition), privacy chains generate stronger network effects. The reality is that if a public chain lacks a thriving ecosystem, killer apps, or distribution advantages, users and developers have no reason to use or build on it—let alone stay loyal. Users can easily trade between public chains; the choice is less critical. But with private chains, choice is crucial because migrating after joining involves high risks and potential privacy exposure, leading to a “winner-takes-all” scenario. Since privacy is vital for most real-world use cases, a few privacy chains can dominate the entire crypto market.

Future Communications Must Be Quantum-Resistant and Decentralized

As the world prepares for the quantum era, many cryptography-based communication applications (like Apple iMessage, Signal, WhatsApp) have led the way, making significant contributions. But the problem is that mainstream communication apps rely entirely on privately operated servers controlled by a single organization. These servers are easy targets for government shutdowns, backdoors, or privacy data demands. If governments can shut down a user’s server, or if companies hold private keys or servers themselves, what’s the point of quantum cryptography?

Private servers require users to “trust me,” but without private servers, the principle is “you don’t have to trust me.” Communications should not depend on corporate intermediaries. They need open protocols; we shouldn’t trust anyone. Achieving this through a decentralized network: no private servers, no reliance on single applications, fully open-source, and employing the best cryptography, including quantum resistance. In an open network, no individual, company, nonprofit, or state can deprive us of communication. Even if a country or company shuts down an app, new versions will emerge the next day—potentially hundreds. Even if a node goes offline, blockchain incentives allow new nodes to instantly replace it.

When individuals can own their information via private keys as they do money, everything will change. Apps can come and go, but people will always control their information and identities; end-users can truly own their data even if they don’t own the app itself. This involves not only quantum-resistant cryptography but also ownership and decentralization. Both are indispensable; without them, we only build systems that appear unbreakable but can be shut down at any time.

Privacy as a Service

Behind every model, agent, and automated process is a simple element: data. But most current data input/output channels are opaque, volatile, and hard to audit. This may be acceptable for some consumer applications, but for finance, healthcare, and many other industries and users, sensitive data privacy must be protected. It’s also a major obstacle for many institutions aiming to tokenize RWA (real-world assets).

How to advance secure, compliant, autonomous, and globally interoperable innovations while protecting privacy? Many approaches exist, but I want to focus on data access control: who controls sensitive data? How does it flow? Who or what can access it? Without data access control mechanisms, users seeking data confidentiality rely solely on centralized platforms or custom-built systems. This is costly, time-consuming, and hinders traditional financial institutions from fully leveraging blockchain data management advantages.

As autonomous agents begin to navigate, trade, and make decisions independently, users and institutions across fields will need cryptographic verification mechanisms, not just “trust-but-verify” models. Therefore, I believe “Privacy as a Service” is needed: this new technology can provide programmable, cryptography-native data access rules, client-side encryption, and decentralized key management, precisely controlling who can decrypt what, under what conditions, and when—all executed on-chain. Coupled with verifiable data systems, privacy-preserving data management will become a core element of internet infrastructure, not just an application-layer patch, making privacy a true foundational infrastructure.

From “Code is Law” to “Rules are Law”

Recently, several well-established DeFi protocols have been hacked despite strong teams, rigorous audits, and years of stable operation. This highlights a worrying reality: current security standards in the industry are still based on case-by-case and experience-based judgments. To mature DeFi security, we must shift from vulnerability-driven to design-driven approaches, evolving from “do your best” to “principle-driven.”

In the pre-deployment phase (testing, auditing, formal verification), this means verifying system invariants globally, not just local invariants manually selected. Many teams are developing AI-assisted verification tools to help draft technical specifications, propose invariant hypotheses, and significantly reduce the manual effort that has made such verification costly.

In the post-deployment phase (runtime monitoring, runtime enforcement), these invariants can serve as dynamic barriers—the last line of defense. They can be encoded as runtime assertions that each transaction must satisfy. This way, we no longer assume all vulnerabilities can be discovered but enforce key security properties in code, with any violations automatically rolling back.

This is not just theoretical. In practice, almost all exploit attacks trigger one of these security checks during execution, preventing the attack. Therefore, the popular notion of “code is law” has evolved into “rules are law”: even new attack methods must adhere to security properties that maintain system integrity, making residual attack vectors negligible or extremely difficult to execute.