The 17 Major Changes in the 2026 Crypto Ecosystem: A Comprehensive Forecast from Infrastructure to Applications

Cryptocurrency is experiencing a pivotal shift from the fringe to the mainstream. According to the latest research by top industry investment firms, 2026 will be a critical year for reshaping the crypto ecosystem. This study outlines 17 key development directions—ranging from payment settlement layers to AI empowerment, from privacy protection to market innovation—each capable of redefining the industry.

Phase One: Innovation in Payments and Financial Infrastructure

Stablecoin Breakthrough—The Last Mile Connecting On-Chain and Off-Chain

Last year, global stablecoin transaction volume reached an astonishing $46 trillion. How exaggerated is this number? It’s more than 20 times PayPal’s annual transaction volume, nearly three times the world’s largest payment network Visa, and approaching the scale of the US ACH electronic clearing system.

But behind these figures lies a core issue: while stablecoins can complete transactions on-chain at less than a penny and in under a second, they have yet to truly integrate into everyday financial life. The reason is simple—there are no truly user-friendly deposit and withdrawal channels.

New-generation startups are filling this gap. Some use cryptographic technology to allow users to directly convert fiat in bank accounts into digital dollars; some connect with regional payment networks to facilitate cross-border transfers via QR codes and real-time payment systems; others are building global digital wallet and card networks, enabling consumers to spend stablecoins anywhere as easily as using a credit card.

As these infrastructures mature, stablecoins will no longer be just tools within exchanges but will become the underlying protocol of the internet settlement layer—employees can receive cross-border salaries in real time, merchants can accept globally recognized digital dollars without bank accounts, and payment apps can instantly settle value for users.

Localized Thinking on RWA—Not Just On-Chain Mapping

Tokenization of traditional assets has become a trend. Banks, fintech firms, and asset management institutions are tokenizing assets like US stocks, commodities, and indices. But most approaches are crude: simply transferring assets onto the chain without leveraging the native features of crypto to innovate.

True breakthroughs should come from two directions. The first is synthetic assets—such as perpetual contracts. They not only provide deeper liquidity but are easier to implement than direct asset tokenization. In fact, liquidity for perpetual contracts on some emerging market stocks even surpasses that of spot markets, opening space for innovation. The second is “native issuance” rather than post-hoc tokenization—stablecoins and RWAs should be designed on-chain from the start, not created off-chain and then brought on-chain.

By 2026, we expect to see more crypto-native asset innovation models emerging, rather than mere copies of traditional products.

Software-Driven Upgrades to Banking Systems

This is an easily overlooked but profoundly impactful change. Most banks still run systems from decades ago—mainframe systems programmed in COBOL, communicating via batch files rather than APIs. These systems are stable and reliable, but adding real-time payment features can take months or even years, compounded by massive technical debt and compliance complexities.

Stablecoins, tokenized deposits, and on-chain bonds offer new solutions to this dilemma. Traditional financial institutions don’t need to overhaul old systems but can build new products and serve new clients on top of them. This accelerates innovation and finally allows the financial system to breathe.

Democratization of Wealth Management in the AI Agent Era

Once, professional wealth management services were only available to high-net-worth individuals. Custom portfolios required extensive manual work and were costly.

Now, with increasing tokenization across asset classes, AI-driven personalized strategies and automated systems can achieve instant adjustments and rebalancing at very low costs. This means everyone can access active management once reserved for the wealthy.

Earning yields from stablecoins, investing in RWA money market funds instead of traditional funds, automatically optimizing yields with DeFi tools, and even connecting to private equity and private credit—these once complex investments will become as simple as clicking a button this year.

Coupled with the technological advantages of decentralized exchanges (DEX) and traditional fintech platforms like Revolut and Robinhood (, the wealth management landscape in 2026 will be fundamentally transformed.

Phase Two: New Order of AI and Autonomous Agents

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

With the explosion of AI agents, non-human identities are surpassing humans in number. But most of these “identities” are “ghost accounts”—they have no foothold in the financial system.

The solution is to establish cryptographically verifiable credentials that bind agents to authorized entities, operational limits, and accountability chains. Just like human credit scores, AI agents need such “ID cards” to operate within financial networks. Without them, firewalls will block all of them.

Nested Layers: A New Paradigm for Collaboration Among AI Models

AI applications in academic research are deepening. An economic mathematician at the start of this year still had to teach models manually to understand his workflow; by November, he could give the models abstract commands like supervising PhD students—sometimes the answers they produce are both novel and correct.

Further, models are beginning to solve problems at the level of Fields Medal winners independently. But this raises new questions: how to allocate credit and rewards among multiple models?

The solution is “nested agents”—a model evaluates the output of the previous layer, progressively filtering and optimizing. Interoperability of cryptographic techniques and incentive mechanisms can precisely address coordination and fair compensation among models.

The Invisible Tax of Open Networks

The rise of AI agents introduces a new problem for open networks: agents extract contextual information from ad-based websites but bypass those sites’ revenue channels. This constitutes systemic plunder of content creators.

Past AI licensing agreements were mere stopgaps, often compensating only a small part of revenue loss. True solutions require shifting from static licenses to real-time settlements based on actual usage.

Imagine: every time an AI agent uses a piece of information to complete a task, it automatically triggers a micro-payment, precisely tracking information sources and rewarding each contributor based on contribution. This demands blockchain-level transparency and automation.

Phase Three: Fundamental Revolution in Privacy and Security

Privacy Becomes the Strongest Competitive Moat in Crypto

Privacy is not an afterthought of encryption but should be a native design feature. This creates a unique network effect.

In traditional public blockchains, all information is transparent, allowing users and developers to migrate easily via cross-chain bridges. In privacy chains, the situation is entirely different. When crossing privacy boundaries, metadata such as transaction time and size are exposed, greatly increasing tracking risks. This means that once you enter a privacy chain, migration costs spike sharply.

Unlike homogenous public chains competing on throughput and cost, privacy chains can establish a true network lock-in effect—what we call the “privacy network effect.”

In a world where performance is no longer the main competitive focus, privacy could become a key factor that allows only a few chains to dominate the entire crypto market.

The Future of Communication: Quantum-Resistant and Truly Decentralized

The world is preparing for the quantum era. Many communication apps )Apple iMessage, Signal, WhatsApp( are upgrading to quantum-resistant cryptography. But these efforts have a fundamental flaw: they all rely on centralized private servers.

Governments can shut down these servers; companies may already hold backdoor keys. Even if not, they own the infrastructure itself—so what’s the use of quantum encryption?

The real solution is complete decentralization. Do not trust any single entity but rely on open protocols, open-source code, and cutting-edge cryptography )including quantum resistance(. In such systems, no one can prevent you from communicating. Even if an app is shut down, 500 versions will appear tomorrow. Even if a node goes offline, blockchain economic incentives will immediately add new nodes.

When people can own data as they own money—controlled by private keys—everything changes. Apps can come and go, but users always control their data and identities.

“Privacy as a Service” Becomes Infrastructure

Behind every AI model and automation process is data. But today, most data flows are opaque, unstable, and difficult to audit. This may be acceptable for consumer apps but is deadly for sensitive sectors like finance and healthcare.

This is also the main obstacle preventing traditional financial institutions from tokenizing RWAs.

The solution is a new tech stack: programmable native data access rules, client-side encryption, and decentralized key management. Who can decrypt data, when, and under what conditions—everything is precisely coded and executed on-chain.

Combined with verifiable data systems, privacy protection upgrades from an application-layer patch to a core part of internet infrastructure.

From “Code is Law” to “Rules are Law”

Recent years have seen several battle-tested DeFi protocols suffer hacks, despite strong teams, rigorous audits, and years of stable operation. This exposes an unsettling fact: industry security standards are still based on case-by-case experience.

True maturity requires shifting from passive vulnerability response to proactive security design. Before deployment, systems must verify global invariants, not just local properties. AI-assisted proof tools are accelerating this process.

Post-deployment, these invariants become dynamic guardrails—the last line of defense. Every transaction must satisfy these conditions in real time. The result: we no longer assume all vulnerabilities can be discovered; we enforce critical properties in code, and any violating transaction is automatically reverted.

This has been validated in practice: nearly every known attack triggers one of these protections, often preventing it.

Thus, “Code is Law” is evolving into “Rules are Law”: new types of attacks must also conform to the system’s security requirements. This means the remaining attack surface is either negligible or extremely difficult to execute.

Phase Four: Emerging Markets and Frontier Applications

Explosive Growth of Prediction Markets—From Niche to Mainstream

Prediction markets are breaking free from niche constraints. Next year, with the integration of crypto and AI, this market will be larger, broader, and smarter.

On the supply side, the number of contracts will far exceed current levels. Not only for presidential elections and geopolitical events but also for niche outcomes and complex cross-asset scenarios. These new contracts will gradually integrate into the information ecosystem, raising social questions: how to price this information properly? How to design more transparent, auditable, and innovation-friendly markets?

On the verification side, new consensus mechanisms are needed to judge event truthfulness. Centralized oracles are key but controversial—cases like the Ukraine president incident and Venezuela elections reveal their limitations. The solution is decentralized governance combined with large language models as oracles to help establish facts in contentious cases.

On the efficiency front, AI agents have demonstrated powerful predictive potential. These agents can scan global trading signals, profit from short-term trades, discover new cognitive dimensions, and improve forecasts. They are not only policy advisors but can analyze their strategies to reveal factors influencing complex social events.

Prediction markets will complement polls rather than replace them—data can serve as input for polls. But we need to improve survey experiences with AI and encryption to ensure respondents are real humans, not bots.

“Guaranteed” Media Revolution

The myth of “objectivity” in traditional media has long been shattered. The internet has empowered everyone to speak, with more practitioners directly engaging with the public, reflecting interests—audiences respect sincerity.

Innovation lies not in social media growth but in new capabilities brought by encryption tools: making open, verifiable commitments.

AI can generate unlimited content from any viewpoint and identity, and it’s impossible to distinguish truth from falsehood based solely on speech. But tokenized assets, programmable locks, prediction markets, and on-chain histories provide a more solid trust foundation: commentators can publish opinions while proving they support them with real money; podcasts can lock tokens to prove they won’t speculate in markets; analysts can link forecasts to public settlement markets, creating auditable histories.

This is the early form of “guaranteed media”—these media do not shy away from conflicts of interest but can prove them. In this model, credibility does not come from pretending to be neutral or making hollow promises but from willing to bear open, verifiable risks. Guaranteed media will not replace other forms but will complement them. It offers new signals: not “Trust me, I’m neutral,” but “See the risks I bear—you can verify.”

Zero-Knowledge Proofs Step Out of Blockchain

For years, zero-knowledge proof )SNARK( technology has been confined within blockchains. The cost is too high: generating proofs requires work millions of times more than actual computation. It’s reasonable when distributed across thousands of nodes, but impractical elsewhere.

But this will change. By 2026, the cost of zero-knowledge virtual machine )zkVM( proofs will drop from millions of times to about ten thousand times, with memory usage reduced to a few hundred MB—making it feasible to run on smartphones with minimal deployment costs.

Ten thousand times is a magic number because GPU performance is roughly ten thousand times that of a laptop CPU. By the end of 2026, a single GPU can generate real-time proofs for CPU computations.

This unlocks an ancient scientific vision: verifiable cloud computing. If you already use cloud CPUs )due to lack of GPUs, knowledge, or legacy systems(, you can now obtain cryptographic proofs of computational correctness at reasonable costs. The proof generation will be optimized for GPUs, requiring no changes to your code.

Reconstructing the Business Logic of Crypto Startups

An often-overlooked trend: transactions should not be the goal but just a docking point.

Today, aside from stablecoins and infrastructure, almost every growing crypto company is transitioning or planning to transition into trading. But what happens if “all crypto companies become trading platforms”? The industry will fall into fierce competition on the same track, with only a few winners surviving.

This means companies rushing into trading are sacrificing the opportunity to build more defensible, sustainable business models. While understanding founders’ survival pressures, chasing short-term product-market fit at all costs has a price. This problem is especially severe in crypto—speculation around tokens often tempts founders to seek immediate gratification rather than long-term stickiness.

True builders should focus on “products,” not “trades.”

Phase Five: Regulation and Future Frameworks

Aligning Law and Technology to Unlock Blockchain’s True Potential

The biggest obstacle to blockchain development in the US over the past decade has been legal uncertainty. Securities laws have been misused and selectively enforced, forcing founders to apply frameworks designed for traditional companies rather than the unique needs of blockchain.

As a result, many companies have prioritized legal risk minimization over product strategy, with engineers playing secondary roles and lawyers taking the lead. This has led to bizarre phenomena: founders encouraged to adopt opaque, arbitrary token distributions, fake governance mechanisms, organizational structures designed for compliance rather than efficiency, and even tokens or business models with no economic value.

Ironically, projects operating in gray areas often outperform honest builders.

But the regulatory framework is closer than ever—potentially changing everything next year. If key legislation passes, it will promote transparency, establish clear standards, and provide explicit pathways for fundraising, issuance, and decentralization, replacing the current “regulatory roulette” with a reasonable framework.

After the passage of the GENIUS Act, stablecoins have experienced explosive growth. The Market Structure Act will bring even greater change—this time targeting the network ecosystem.

In other words, proper regulation will enable blockchain to truly operate as a network: open, autonomous, composable, neutral, and decentralized.

Looking Ahead to 2026

These 17 trends are interconnected. The maturation of stablecoins will empower AI agents; privacy infrastructure will protect autonomous systems; zero-knowledge proofs will expand application boundaries; prediction markets will improve information flow…

By 2026, the crypto ecosystem will no longer be a casino for speculators but a foundational infrastructure for internet finance. What is considered cutting-edge today will become the standard configuration of the next-generation financial system.

The true transformation has already begun.