17 Major Changes in the Crypto Industry in 2026: From Infrastructure Upgrades to Ecosystem Restructuring

Part One: A New Architecture for Financial Infrastructure

Stablecoins are breaking through the limitations of “payment tools”

Last year, the trading volume of stablecoins reached $46 trillion. What does this number mean? Over 20 times the volume of PayPal transactions, three times the global Visa payment network volume, and approaching the annual transfer scale of the US ACH electronic clearing center. With such scale, a stablecoin transfer takes less than 1 second and costs less than a cent.

But the real bottleneck is not on-chain, but in the inflow and outflow between fiat currency and digital dollars. A new wave of startups is solving this problem—some through cryptographic proofs to achieve privacy conversion, some integrated with regional payment networks, and others building global interoperable wallet layers and merchant payment solutions. When these infrastructures mature, new use cases will emerge: cross-border workers can settle salaries in real time, merchants can receive global currencies without bank accounts, and application layers can settle value in seconds. Stablecoins will evolve from niche financial tools to the foundational layer of internet settlement.

From “Asset On-Chain” to “Native Creation”

Financial institutions are increasingly interested in tokenizing assets like US stocks, commodities, and indices, but many existing RWA projects essentially replicate traditional finance—this is called “mimetic design.” In contrast, crypto-native derivative tools like perpetual contracts often have deeper liquidity and simpler implementation. Zero-dated options markets for emerging market stocks are usually more active than spot markets, making these assets particularly suitable for “perpetualization” experiments.

Regarding stablecoins, true innovation by 2026 will not just be on-chain, but in the native generation of credit assets on-chain. Current stablecoins mainly act as “narrow banks”—holding extremely safe liquid assets. While effective, this approach is insufficient to support the entire on-chain economy in the long run. Some emerging asset managers and protocols are experimenting with native on-chain lending, using off-chain assets as collateral. But tokenizing off-chain loans has limited efficiency. The truly efficient approach is to originate debt assets directly on-chain from the source—reducing loan service costs, backend costs, and increasing accessibility. Compliance and standardization are challenges, but teams are already working on solutions.

The banking system is about to undergo a technological reshuffle

Most banks still run software systems from the 1960s-90s. The second-generation core banking systems appeared in the 80s-90s (like Temenos GLOBUS, Infosys Finacle) and remain mainstream today. These systems are aging, and updates cannot keep pace with demand. The vast majority of global assets are stored in these “decades-old ledgers”—mainframe systems written in COBOL, based on batch processing rather than APIs.

While these systems have been long validated, regulated, and deeply integrated into complex business processes, they severely constrain innovation speed. Adding real-time payment features can take months or years, crossing technical debt and regulatory hurdles. Stablecoins and tokenized assets offer another path for traditional institutions—building new products and serving new customers via on-chain infrastructure without modifying the old systems. This has become a new channel for institutional innovation.

Democratizing Wealth Management in the AI Era

For a long time, personalized wealth management was only available to high-net-worth clients because customized advice and multi-asset management are costly. But as more assets are tokenized, blockchain can execute and rebalance strategies in real time, and AI advice costs approach zero, the situation will change.

By 2025, traditional financial institutions will increase allocations to crypto assets, but this is just the beginning. By 2026, more platforms focused on “wealth accumulation” (not just custody) will emerge—especially fintechs like Revolut, Robinhood, and exchanges like Coinbase, which can maximize their technological advantages. DeFi tools like Morpho Vaults can automatically allocate assets to the most risk-adjusted yield markets, forming the foundation of investment portfolios. Holding liquidity assets in stablecoins rather than fiat, or replacing traditional money market funds with tokenized equivalents, can further expand yield possibilities. Retail investors will also find it easier to access illiquid assets like private equity, pre-IPO companies, and private credit—tokenization improves accessibility while maintaining compliance. When all asset classes in a balanced portfolio (from bonds to stocks to private and alternative investments) are tokenized, rebalancing can be automated without bank transfers.

Part Two: AI, Identity, and Value Flows

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

The bottleneck of the AI economy is shifting from intelligence to identity. In financial services, “non-human identities” already outnumber human ones at a ratio of 96:1, but these identities are still “ghosts without bank accounts” and cannot be accepted. The most urgent fundamental capability gap is: how to truly recognize an AI agent (KYA). Just as humans need credit scores to get loans, AI agents need cryptographic signatures as credentials to operate—these credentials must link the agent to its delegatee, behavioral constraints, and accountability boundaries.

Until this infrastructure appears, merchants will continue to block agents at the firewall layer. The industry that spent decades building KYC infrastructure now has only a few months to solve KYA.

The internet is becoming the new “banking system”

Once AI agents are deployed at scale, more and more commercial activities will no longer depend on user clicks but will be automatically completed in the background, changing the way value flows. In a system based on “intent” rather than step-by-step instructions, when AI agents automatically move funds to meet needs, fulfill obligations, or trigger outcomes, value should flow as quickly and freely as information.

This is where blockchain, smart contracts, and new protocols come into play. Smart contracts can settle USD in seconds globally. By 2026, new primitives like x402 will make these settlements programmable and reactive: agents can instantly and permissionlessly pay each other for data, GPU time, or API costs—no invoices, reconciliation, or batch processing needed; developers can embed payment rules, caps, and audit trails directly into software updates—no need to integrate fiat systems, open merchant accounts, or connect to banks; prediction markets can self-settle in real time when events occur—quotes update, agents trade, and global profit settlements happen within seconds, without custodians or exchanges.

When value can flow this way, “payment streams” will no longer be a separate operational layer but part of network behavior itself: banks will become the foundational pipes of the internet, and assets will become infrastructure. If money becomes a “data packet” routable over the internet, then the internet will not only support the financial system—it will become the financial system itself.

Making substantial research with AI has become a reality

As a mathematical economist, in January this year I was struggling to get consumer-grade AI models to understand research workflows, but by November I could command models as if instructing PhD students, sometimes even obtaining entirely new correct answers.

More broadly, AI is being used for real research tasks—especially in reasoning, where models can assist discovery and autonomously solve math problems at the Putnam level. Which disciplines will benefit most, and how, remains unclear, but AI is rewarding and fostering a new “polyhedral” research style: the ability to form hypotheses across different ideas and quickly extrapolate from intermediate results.

These answers are not always accurate, but they may point in the right direction (at least topologically). It’s akin to leveraging the model’s “hallucination” ability: when models are sufficiently “smart,” their collisions in the abstract space can produce nonsensical content, but sometimes, like nonlinear human thinking, they lead to genuine discoveries.

This reasoning requires new AI workflows—not just collaboration between agents, but “agent stacks”: multi-layer models evaluating attempts of previous models, distilling out truly valuable parts. Some use this method to write papers, others for patent searches, creating new art forms, or (unfortunately) designing new smart contract attacks. To make this “wrapped reasoning agent cluster” truly useful for research, two problems must be solved: interoperability between models, and fair recognition and compensation for each model’s contribution—all solvable with cryptography.

The “Invisible Tax” on Open Networks

The rise of AI agents is imposing an invisible tax on open networks, eroding their economic foundation. This dilemma stems from the separation of the internet’s “context” and “execution layer”: AI agents extract data from ad-supported content sites, providing convenience to users but systematically bypassing the revenue sources of that content (ads and subscriptions).

To prevent the erosion of open networks (and thus the content ecosystem that AI depends on), we need large-scale deployment of technical and economic mechanisms: new sponsorship content models, micro-ownership systems, or other fund distribution schemes. Existing AI licensing agreements have proven unsustainable—content providers’ payments often account for only a small fraction of traffic loss caused by AI.

Open networks require new technical-economic frameworks to enable value to flow automatically. The key shift next year will be from static licensing to real-time, usage-based compensation models. This involves testing and expanding systems—possibly based on blockchain-supported nano-payments and fine-grained attribution standards—to automatically compensate every entity contributing to successful agent tasks.

Part Three: Privacy, Security, and Cryptographic Primitives

Privacy is becoming the strongest “moat” in cryptography

Privacy is the key capability that enables the transfer of global finance onto the chain, and almost all existing blockchains lack this feature. For most chains, privacy has long been an “add-on.” But now, privacy itself is enough to distinguish one chain from all others.

More importantly, privacy can create lock-in effects at the chain layer—a “privacy network effect,” especially today when performance competition is no longer a differentiator. Due to cross-chain protocols, if everything is public, moving assets between chains costs nearly nothing. But privacy changes this: cross-chain token transfers are easy, but transferring “secret” data is hard. Any move from a privacy chain to a public chain can allow observers to infer your identity by analyzing blockchain, mempool, or network traffic. Even transfers between privacy chains may expose metadata related to time or amount, enabling tracking.

Conversely, new chains that lack differentiation (costs approaching zero due to competition, as block space becomes homogenized) will see privacy as a true network effect. The reality is: a “general-purpose chain” without a thriving ecosystem, killer apps, or distribution advantages has little reason to attract users or developers, and cannot build loyalty. When users are on public chains, if chains are interoperable, chain choice is irrelevant. But once in privacy chains, choice becomes critical—because once inside, users are less willing to migrate or expose risks. This leads to a “winner-takes-all” pattern. Since privacy is crucial for most real applications, only a few privacy chains may dominate most of the crypto economy in the end.

The Quantum Future of Decentralized Communication

As we move toward the era of quantum computing, many encrypted communication applications (Apple, Signal, WhatsApp) have made great progress. But the problem is: all mainstream communication tools rely on privately managed servers controlled by a single organization. These servers are vulnerable points for government shutdowns, backdoors, or data delivery demands.

If governments can directly shut down servers, or if companies hold server keys, or if “private servers” even exist, what’s the use of quantum-level encryption? Private servers require “trust me”; but without servers, it means “you don’t need to trust anyone.” Communication does not need centralized intermediaries. It requires open protocols that do not require trust in anyone. To achieve this, the network must be decentralized: no private servers, no single application, all code open source, with the highest level of encryption (including quantum-resistant).

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, 500 new apps will be born the next day. Even if a node is shut down, due to blockchain’s economic incentives, new nodes will immediately replace it. When people control their information with their own keys, just like controlling money, everything changes. Apps can come and go, but users always control messages and identities—own the messages, not the apps. This is not only about quantum resistance or encryption but about ownership and decentralization. Without these, we are merely building “unbreakable but still blockable encryption.”

“Secrets as a Service”: A New Paradigm for Data Management

Behind every model, agent, and automation system is a common point: data. But most current data pipelines—inputs and outputs of models—are opaque, modifiable, and non-auditable. This may suffice for some consumer applications, but for industries handling sensitive data (like finance and healthcare), it’s far from enough.

This is also the main obstacle preventing institutions from fully tokenizing real assets. How to innovate securely, compliantly, autonomously, and globally interoperably while protecting privacy? It must start with data access control: who controls sensitive data? How does data move? Who (or which system) can access it? Without access control, those who want privacy rely on centralized services or build complex systems—costly, slow, hindering financial institutions from fully utilizing on-chain data management.

As intelligent agents autonomously navigate, trade, and make decisions, users and institutions need not “trust to the best of their ability,” but cryptographic guarantees. Therefore, “secrets as a service” is needed: new technologies providing programmable, native data access rules; client-side encryption; decentralized key management—specifying who can decrypt what, under what conditions, and for how long… all implemented on-chain.

Combining with verifiable data systems, “secrets” will become the foundational public infrastructure of the internet, not just emergency patches. Privacy will become part of the infrastructure, not an add-on.

From “Code is Law” to “Norms are Law”

Recent DeFi attacks, even on mature protocols with strong teams and audits, reveal an unsettling reality: current security practices are still empirical and “case-by-case.” To mature DeFi security, we must shift from bug-fixing to design properties, from “do your best” to systematic, “principled” approaches:

Static security / pre-deployment (testing, auditing, formal verification) The future involves systematically proving global invariants, not just manually selected local properties. Many teams are building AI-assisted tools to help write specifications, propose invariants, and automate most formal verification work that previously required manual effort and high costs.

Dynamic security / post-deployment (runtime monitoring, runtime enforcement) After deployment, these invariants become active guards: the last line of defense. Encoded as runtime assertions, they require every transaction to satisfy safety conditions. In other words, no longer assuming “all bugs are caught before deployment,” but letting the code itself enforce safety properties, automatically reverting violating transactions.

This is not just theoretical—has real impact. Almost every past attack could have been prevented by these runtime checks. So, the old “code is law” philosophy is evolving into “norms are law.” Even new types of attacks must respect the same security properties; attack surfaces are reduced, leaving only a few or nearly impossible vectors.