17 Major Transformations in the Cryptocurrency Industry by 2026: From Payments to Privacy, From Code to Regulation

This year, the crypto industry is undergoing a profound transformation in both technology and governance. From stablecoin infrastructure to AI agent governance, from privacy protection to prediction markets, the entire sector is redefining how it operates. This is not just a technological upgrade but a rebuilding of paradigms.

Infrastructure Layer: How Stablecoins Are Rewriting Financial Logic

The speed of money flow determines the progress of civilization. And stablecoins are unlocking new possibilities in internet finance.

Last year, stablecoin trading volume reached $46 trillion. How exaggerated is this number? It’s 20 times PayPal’s annual transaction volume, nearly three times Visa’s total yearly volume, and approaching the annual scale of the US ACH electronic clearing system.

Now, transferring stablecoins takes less than 1 second, with fees under a cent. But the real bottleneck isn’t on-chain; it’s at the fiat on/off ramps.

A new wave of startups is solving this problem—using cryptography to prove private exchanges, integrating with regional payment systems, and enabling cross-bank settlement via QR codes and instant payment channels. More aggressive solutions include building a global wallet interoperability layer, allowing users to buy stablecoins directly with bank cards.

Once this infrastructure matures, three new scenarios will emerge:

• Global employees can receive real-time salary payments
• Businesses can receive USD without bank accounts
• Apps can instantly settle with users worldwide

Stablecoins will evolve from “niche financial tools” to the foundational settlement layer of the internet era.

From Tokenization to Native Chain Assets: Rethinking RWA Economics

If last year was the era of “securities on-chain,” this year marks a turning point toward “economic activities on-chain.”

Banks and asset managers have been frantically moving US stocks, commodities, and indices onto blockchains over the past two years. But the problem is: they’re still thinking within traditional finance frameworks.

What should truly crypto-native approaches look like? The perpetual contract market provides clarity. Perpetuals offer deeper liquidity, simpler implementation, and more understandable leverage structures. They represent the real demand for derivatives in crypto markets.

An interesting phenomenon in emerging markets is that 0DTE (zero-day-to-expiry) options have liquidity deeper than spot markets. These assets are ideal candidates for “perpetualization.”

Therefore, the key question in 2026 isn’t “tokenization vs. perpetualization,” but rather that more projects will adopt crypto-native methods to reconstruct asset representations.

The story of stablecoins is similar. Last year, stablecoins fully entered mainstream consciousness, but the current stablecoin ecosystem resembles a “narrow bank”—holding only ultra-safe assets, not engaging in credit expansion.

What’s the key missing piece? On-chain credit infrastructure. We’re seeing some new asset managers, curators, and protocols beginning to support collateralized on-chain lending with off-chain assets. But they’re still using the “off-chain initiation → on-chain tokenization” approach.

A better path is “on-chain debt issuance directly.” The benefits are clear: lower credit service costs, fewer back-end operations, and increased accessibility. The challenges are compliance and standardization, but teams are already working on solutions.

Traditional Banking Systems Face New Challenges: How Stablecoins Drive Technological Innovation

What is the biggest enemy of the banking system? It’s not interest rate changes but the legacy code accumulated over decades.

Most banks still run on systems from the 1960s-70s. Back then, banks were early adopters of large software systems; the second generation of core banking systems (like Temenos GLOBUS, Infosys Finacle) appeared only in the 1980s-90s. But these systems are aging, and updates can’t keep pace with modern demands.

Even more ironically: the world’s most important ledgers—databases recording deposits, mortgages, and financial debts—still run on mainframe systems written in COBOL, using batch interfaces rather than APIs. The vast majority of assets are stored in these “decades-old ledgers.”

While these systems have stood the test of time, gained regulatory approval, and are deeply integrated, they severely limit innovation speed. Adding features like “real-time payments” often takes months or years, entailing multiple layers of technical debt and regulatory hurdles.

Stablecoins act as accelerators here. Over the past few years, stablecoins have found market fit and entered mainstream finance. This year, traditional financial institutions’ acceptance of stablecoins has reached new heights.

Stablecoins, tokenized deposits, tokenized treasuries, on-chain bonds—these enable banks and financial institutions to create new products and serve new clients. Most importantly, they don’t require rewriting those old, solid core systems. Stablecoins have become a bypass for institutional innovation.

When AI Becomes a Trading Partner: The Internet Is Turning Into a Financial System

If last year AI changed information flows, this year AI will change value flows.

As intelligent agents expand, more business operations will be automated in the background, no longer triggered by user clicks. This means the way value moves must also evolve.

Imagine a scenario: systems operate based on “intent” rather than “step-by-step instructions,” with AI agents automatically transferring funds to meet needs, fulfill commitments, and trigger outcomes. Then, value should flow as freely and quickly as information.

This is where blockchain, smart contracts, and new protocols come into play. Smart contracts can now settle global USD transactions in seconds. 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 APIs—no invoices, reconciliation, or batch processing needed
• Developers can embed payment rules, limits, and audit trails directly into software updates—no need to integrate with fiat systems, open accounts, or connect to banks
• Prediction markets can automatically settle when events occur—coefficients update, agent protocols execute, and payments clear globally within seconds, without custodians or exchanges

When value can flow this freely, “payment streams” are no longer just operational layers but become inherent behaviors of the network. Banks become part of the foundational internet infrastructure, and assets become infrastructure itself.

If money becomes routable “data packets” on the internet, then the internet itself will turn into a financial system.

Democratization of Wealth Management: Everyone Can Have a Personal AI Advisor

Why has wealth management been a game for the rich? Because providing personalized advice is costly. Tokenization changes all that.

Traditionally, only high-net-worth clients could access personalized wealth management, because tailoring investment advice and managing diverse asset classes is expensive.

But now, with more assets tokenized and crypto networks enabling AI-assisted execution at near-zero cost, this is changing. It’s not just “robo-advisors”—active management is now within everyone’s reach, no longer limited to passive index funds.

By 2025, traditional financial institutions will have increased allocations to crypto assets (directly or via ETPs), but that’s just the beginning. By 2026, we’ll see more “wealth accumulation” platforms—especially fintech giants like Revolut, Robinhood, and centralized exchanges like Coinbase, leveraging their tech stacks.

Meanwhile, DeFi tools like Morpho Vaults can automatically allocate assets to the highest risk-adjusted yield markets, forming the core of investment portfolios. Holding liquid assets in stablecoins instead of fiat, or replacing traditional money market funds with tokenized equivalents—these further expand yield opportunities.

Retail investors will find it easier to access illiquid private assets: private credit, pre-IPO companies, private equity. Tokenization enhances accessibility while maintaining necessary compliance and reporting.

Finally, as various assets are tokenized (bonds, stocks, private/alternative assets), balanced portfolios can be automatically and intelligently rebalanced without cross-bank transfers.

Agents and AI: From Know Your Customer to Know Your Agent

The next bottleneck in financial services isn’t AI capability but agent identity.

Here’s a startling fact: in finance, the number of “non-human identities” is already 96 times that of human employees—and these identities are still “ghosts of banks.”

What’s the most critical missing infrastructure? KYA (Know Your Agent)—understand your agent.

Just as humans need credit scores to get loans, AI agents need cryptographic-signed certificates to execute transactions. These certificates should link the agent to its operator, behavioral constraints, and accountability boundaries.

Until this infrastructure exists, companies will continue to block agents behind firewalls. The industry that took decades to build KYC infrastructure now has only a few months to solve the KYA problem.

AI Conducting Real Scientific Research

Since the beginning of this year, consumer-grade AI models have made astonishing progress in understanding research workflows.

As a mathematical economist, I struggled to get AI to understand my research process in January; by November, I could give models instructions as if guiding graduate students… sometimes even receiving novel and correct answers.

Overall, we see AI beginning to be used in genuine research—especially in reasoning. Models not only assist discovery but can independently solve Putnam-level math problems (one of the hardest university math competitions).

It’s still unclear which disciplines will benefit most and how to get there. But I believe AI will inspire and reward a new “generalist researcher” style: the ability to form hypotheses between ideas and quickly infer from intermediate results.

These answers may not be perfectly accurate but still point in the right direction (at least in a certain topological sense). In a way, this even leverages the “hallucination” ability of models: when models are smart enough, their wandering in the abstract space can produce nonsense, but sometimes—like human nonlinear thinking—it leads to breakthroughs.

This reasoning style requires new AI workflows—not just agent collaboration but “agent wrapping agents”: multi-layered evaluations of previous models, gradually selecting truly valuable ones.

I’ve used this approach to write scientific papers, some use it for patent searches, others create new art forms, or (unfortunately) design new attacks on smart contracts. But to make this “cluster reasoning agents” truly serve research, two issues must be addressed: model interoperability and how to recognize and fairly compensate each model’s contribution—both solvable with cryptography.

Privacy Paradox: The Invisible Tax of the Open Network

The emergence of AI agents introduces an invisible tax on the open web, fundamentally undermining its economic foundation.

This problem stems from the disconnection between the “context layer” and the “execution layer” of the internet: currently, AI agents extract data from content sites relying on ads, but systematically bypass the revenue sources (ads and subscriptions) that support content.

To prevent erosion of the open web (and protect the content ecosystem AI depends on), we need large-scale deployment of new technical and economic mechanisms: perhaps new sponsored content models, micro-ownership, or other revenue-sharing schemes.

Current AI licensing agreements have proven ineffective—the payments to content providers are often just a small fraction of traffic loss. The open web needs a new technical-economic framework to enable automatic value flows.

The most critical shift is moving from static licensing to real-time, usage-based payments. This requires testing and expanding systems—possibly based on blockchain-supported nano-payments and detailed attribution standards—to ensure everyone contributing to agent tasks is automatically rewarded.

The New Role of Privacy: The Strongest Moat for the Crypto Industry

Privacy isn’t an add-on—it’s the ultimate form of competition among blockchains.

Privacy is a key capability for the global shift of finance onto blockchains, and nearly all existing chains lack it. For most chains, privacy has long been an “optional feature.” But now, privacy itself can be a differentiator.

More importantly: Privacy creates chain-level lock-in—“privacy network effects”—especially when performance is no longer a differentiator.

Cross-chain protocols mean that if everything is public, migrating from one chain to another is almost free. But adding privacy changes everything: transferring tokens is simple; transferring “secrets” is hard.

Any movement from privacy chains to public chains exposes metadata—time, amount—making it easier for observers, mempool listeners, or network traffic analyzers to infer identities. Moving between privacy chains also leaks metadata—timestamps, amounts—simplifying tracking.

Unlike non-differentiated chains (where fees tend toward zero due to competition as blockspace becomes homogenous), privacy chains can generate stronger network effects.

The reality is: without killer apps, ecosystem advantages, or distribution benefits, “general-purpose chains” have little reason to attract users or developers, let alone loyalty. On public chains, free interaction among chains makes chain choice irrelevant.

But when users join privacy chains, choice becomes critical—because after migration, they’re less likely to switch or risk exposing themselves. This creates a “winner-takes-all” scenario.

Since privacy is crucial for most real applications, only a few privacy chains will ultimately dominate the crypto economy.

The Future of Communication: From Quantum Resistance to True Decentralization

As the quantum computing era approaches, apps like Apple, Signal, and WhatsApp are upgrading cryptography. But the deeper issue is even more profound.

All modern apps rely on private servers operated by a single organization. These servers are vulnerable points for government shutdowns, backdoors, or forced data delivery.

If a government can shut down servers; if a company holds the keys; or if “private servers” simply exist… what’s the point of quantum encryption?

Private servers demand “trust me”; serverless means “trust no one.” Communications shouldn’t need a central intermediary. We need open protocols that require no trust.

To achieve this, the network must be decentralized: no private servers, no single application, all code open source, with the strongest encryption (including quantum-resistant).

In an open network, no individual, company, NGO, or government can deprive us of communication. Even if governments or companies shut down an app, new versions will emerge within days. Even if nodes go offline, economic incentives in blockchain will immediately produce replacements.

When people control information with keys as they do money, everything changes. Apps may disappear, but users will always control messages and identities—users own their messages, not the app.

This is not just about quantum resistance or encryption; it’s about ownership and decentralization. Without this, we’re merely building “unbreakable but killable” encryption.

“Secrets as a Service”: A New Primitive for Data Governance

Behind every model, every agent, every automation system lies a truth: data. But most data pipelines are opaque, volatile, and unaudited.

This might suffice for some consumer applications, but for industries handling sensitive data (finance, healthcare), it’s far from enough. It’s also the main obstacle to fully tokenizing real assets.

How to balance privacy, security, compliance, autonomy, and global interoperability? It starts with data access control: who controls sensitive data? How does data flow? Who (or which system) can access it?

Without data access control, anyone wanting privacy is forced to rely on centralized services or build complex systems themselves—costly, slow, and hindering financial institutions from fully leveraging on-chain data management.

As intelligent agents begin to browse, trade, and make decisions automatically, users and institutions need not “trust defaults” but cryptographic guarantees. That’s why we need “secrets as a service”:

New tech offers programmable, native data access rules; client-side encryption; decentralized key management—explicitly defining under what conditions, for how long, who can decrypt what data… all enforced on-chain.

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

From “Code Is Law” to “Norms Are Law”

The past decade’s attacks on DeFi—despite mature protocols, strong teams, and rigorous audits—reveal an unsettling reality: modern security still relies on empiricism and case-by-case handling.

To make DeFi safer, we must shift from vulnerability patterns to property-based design, from “try hard” to principled systems:

Static/pre-deployment security (testing, auditing, formal verification)

The future will focus on proving global invariants systematically, not just manually checking local properties. Teams are already developing AI proof assistants to help write specifications, express invariants, and automate costly manual work.

Dynamic/post-deployment security (monitoring, runtime enforcement)

These invariants can become “live constraints”: the last line of defense. Encoded as runtime assertions, every transaction must satisfy them. In other words, we no longer assume “all vulnerabilities are caught before deployment,” but enforce key security properties directly in code, automatically rolling back violations.

This isn’t just theoretical—almost all past attacks could have been prevented by such runtime checks.

Thus, “Code Is Law” evolves into “Norms Are Law.” Even new attack vectors must conform to embedded security properties; the attack surface shrinks to minimal or nearly impossible scenarios.

Prediction Markets: From Niche to Mainstream to Infrastructure

Prediction markets are now mainstream. Next year, they will grow bigger, cover more, and become smarter.

First, new contract types will emerge. This means we’ll not only get real odds on elections or geopolitical events but also on granular outcomes and complex event combinations.

As new contracts reveal information and integrate into news ecosystems (which is already happening), society must answer: How to value this information? How to create more transparent, auditable prediction systems?

Handling more contracts requires new “reality-aligned” settlement mechanisms. Centralized arbitration (did the event happen? How to verify?) has limitations, as shown by cases like Zelensky’s lawsuit or Venezuela’s elections.

Therefore, decentralized governance and LLM-based oracles will be key to scaling prediction markets and increasing their value.

AI capabilities go beyond LLMs. AI agents can autonomously trade on prediction platforms, scan the world for signals, and seek short-term advantages. This helps us discover new ways of thinking and predicting “what’s next.” Projects like Prophet Arena already show early interest.

Beyond acting as “senior political analysts,” emerging AI agents’ strategies can even help us reverse-engineer the fundamental factors behind complex social predictions.

Will prediction markets replace polls? No, they will improve polls. Poll data might even become input for prediction markets. As a political economist, I look forward to the collaboration between prediction markets and a healthy, diverse polling ecosystem. But this requires new tech: AI can improve polling experience; cryptography can prove respondents are human, not bots, opening new avenues for innovation.

The Rise of “Staked Media”: Creating Economics for Opinions

Traditional media models (especially the “objectivity” assumption) are cracking. The internet enables everyone to speak, and more insiders, practitioners, and builders are directly addressing the public.

Ironically, audiences respect them not “despite conflicts of interest,” but “because of conflicts of interest.”

The real novelty isn’t social media itself but the fact that cryptographic tools enable people to make public, verifiable commitments.

As AI lowers content creation barriers to near zero—any opinion, any identity (real or fictitious) can be infinitely copied—simply “what was said” no longer builds trust. Tokenized assets, programmatic locks, prediction markets, and on-chain history provide a stronger foundation:

• Commentators can express opinions and prove “I’ve invested real money”
• Podcasters can lock tokens to show “I won’t dump in the short term”
• Analysts can link predictions to publicly settled markets, creating auditable trace records

This is the early “staked media”: media that adopts “interested party” principles and provides verifiable proof.

In this model, trust isn’t based on “pretending to be neutral” or “empty words,” but on openly verifiable interests. Staked media won’t replace existing media but will complement them. They offer new signals: not “trust me, I’m neutral,” but “see how much risk I’m willing to bear—you can verify if I tell the truth.”

SNARKs and Zero-Knowledge Proofs: From Blockchain to the World

For years, SNARKs (zero-knowledge proofs) have been used almost exclusively within blockchains. The reason is simple: generating proofs is extremely costly—100,000 times more expensive than direct computation.

It’s reasonable when verifying thousands of validators; it’s not in other scenarios.

This is about to change. By 2026, zkVM proof systems will reduce proof costs to about 10,000 times less than today, with memory usage down to hundreds of MB—enough to run on smartphones and deploy anywhere.

Why is 10,000 times the “magic number”? Because top-tier GPUs have roughly 10,000 times the parallel processing power of a laptop CPU. By 2026, a single GPU will be able to generate proofs for real-time CPU computations.

This opens a long-held dream: Verifiable cloud computing. If your workload already runs on cloud CPUs—due to low compute needs, lack of GPUs, or historical reasons—you’ll soon be able to obtain cryptographic proof of correctness at a reasonable cost. The proof system is optimized for GPUs, and your code remains unchanged.

Builders’ Choice: Trading Is Not the End

Today, aside from stablecoins and a few foundational layers, almost every successful crypto project is shifting toward or preparing for trading.

If “all crypto companies eventually become trading platforms,” what will the market structure look like? Many participants doing the same things will compete, leaving only a few winners.

Jumping into trading too early or too fast might miss the opportunity to build a stronger, more durable business. I fully understand why founders chase viable business models, but pursuing “obvious PMF” comes at a cost.

Especially in crypto, token dynamics and speculative culture tempt founders to “immediately satisfy” and overlook deeper product-market fit issues.

In a sense, this is the “cotton candy test.” Trading is an important market function, but it shouldn’t be the ultimate goal. Those who focus most on “product” PMF are often the biggest winners.

Regulatory Framework: The Final Reconciliation of Technology and Law

One of the biggest obstacles to US blockchain development over the past decade has been legal uncertainty.

Securities laws have expanded and been selectively applied, forcing founders to develop “networks” within frameworks designed for “corporations.” Over the years, “reducing legal risk” has taken precedence over “product strategy,” with engineers replaced by lawyers.

This dynamic has led to many strange distortions:

• Founders avoiding transparency
• Token distributions becoming legally arbitrary and unnatural
• Governance turning into performance
• Organizational structures constrained by legal defenses
• Tokens carrying zero economic value and lacking business models
• Worse, those who comply less often move faster

But now, the US market structure law for crypto is closer than ever to passing. Once enacted, it will:

• Incentivize transparency
• Set clear standards
• Replace “law enforcement lotteries” with understandable, structured financing, token issuance, and decentralized pathways

The GENIUS Act’s passage will trigger explosive growth in stablecoins; changes in crypto market structure law will be even more profound—this time affecting the entire network.

In other words, such regulation can enable blockchain networks to operate as designed: open, autonomous, composable, neutral, and decentralized.

The crypto industry is redefining itself. From payment infrastructure to privacy moats, from AI agent governance to on-chain prediction systems, from traditional legal frameworks to new regulatory paradigms—this is not just technological progress but a paradigm shift across the entire ecosystem. By 2026, we will witness these ideas transforming from visions into reality.