The 2026 Cryptocurrency Revolution: How 16 Major Trends Will Reshape the Entire Industry

The cryptocurrency industry is about to迎来 another turning point. a16z and its partners recently outlined key development directions impacting the next 12 months. These trends involve not only technological innovation but also the redefinition of business models, regulatory frameworks, and the entire value flow.

From “Know Your Customer” to “Know Your Agent”: Identity Revolution in the AI Era

In financial services, non-human identities have already outnumbered human identities at a ratio of 96:1 — yet these identities are still barred from banking systems, like invisible people. The missing key infrastructure is the concept of “Know Your Agent” (KYA).

Just as humans need credit scores to obtain loans, AI agents require cryptographically signed credentials to operate — credentials that link the agent to its authorized party, constraints, and responsibilities. Until this mechanism is established, traders will continue to block agent access through firewalls.

Decades of building KYC infrastructure have been followed by only a few months to solve the KYA problem. The urgency of this timeline means that whoever first establishes a trusted AI agent verification system will hold the ticket to the future of finance.

Privacy Becomes the Ultimate Moat in Crypto Competition

While everyone is vying for trading volume and user numbers, the true winners are building “privacy barriers.” Privacy is a key feature for bringing global finance onto the chain, and it is also a missing element in nearly all existing blockchains.

For most blockchains, privacy is either a secondary feature or completely overlooked. But today, privacy has become a sufficiently attractive feature to make a chain stand out from competitors. More importantly, privacy can create “chain lock-in effects” or even “privacy network effects.”

Imagine the difference between two scenarios:

Scenario 1 (Public Chain Environment): Due to cross-chain protocols, if everything is transparent, migrating from one chain to another is extremely simple. Users are free to come and go.

Scenario 2 (Privacy Chain Environment): Once privacy is introduced, this convenience disappears. Token migration is easy, but secret migration is difficult. When moving from a privacy chain to a public chain, or between two privacy chains, risks always exist — third parties might identify your identity by observing on-chain transactions, mempools, or network traffic. The correlation of transaction timing and size can leak metadata, making tracking easier.

Compared to homogeneous new chains (whose costs may drop to zero due to competition, as block space becomes similar across chains), blockchains with privacy features can form stronger network effects.

If a “general-purpose” blockchain lacks a thriving ecosystem, killer apps, or asymmetric distribution advantages, users have little reason to use or develop on it. Not to mention user loyalty. Users on public chains can easily interact with users on other chains — which chain they join doesn’t matter. But on privacy chains, chain choice is critical because once entered, users are unlikely to migrate to avoid exposure risks. This creates a “winner-takes-all” dynamic. Since privacy is a necessity in most real-world scenarios, a few privacy chains may dominate most of the crypto market.

Stablecoins Poised for Mainstream and Deep Integration from 2025 to 2026

Last year, stablecoin trading volume was estimated at $46 trillion, continuously hitting new highs. To understand this scale: it’s more than 20 times PayPal’s transaction volume; nearly 3 times the largest global payment network Visa; and rapidly approaching the US Automated Clearing House (ACH) transaction volume — the electronic network handling US direct deposits and other financial transactions.

Today, you can complete stablecoin transactions in less than a second at a cost of less than a penny. Yet, the unresolved question remains: how to connect these digital dollars to the financial systems people use daily — in other words, how to establish stablecoin deposit and withdrawal channels.

New startups are filling this gap, connecting stablecoins to more familiar payment systems and local currencies. Some use cryptographic proofs to privately convert local balances into digital dollars. Others integrate with regional networks, using QR codes, real-time payments, and other features to enable interbank payments. Still others are building truly global interoperable wallet layers and issuance platforms, allowing users to spend stablecoins at everyday merchants.

These approaches expand participation in the digital dollar economy and may accelerate the adoption of stablecoins as mainstream payment methods. As these deposit and withdrawal channels mature, digital dollars will connect directly to local payment systems and merchant tools, creating new behavioral patterns: cross-border workers can receive real-time payments; merchants can accept global dollars without bank accounts; apps can settle with users instantly anywhere, anytime.

Stablecoins will evolve from niche financial tools into the foundational settlement layer of the internet.

Elevating Prediction Markets: From Niche to Mainstream to Intelligence

Prediction markets have moved from niche to mainstream, and in the coming year, at the intersection of crypto and AI, they will become bigger, broader, and smarter, bringing new major challenges for builders.

First, more contracts will be listed. This means we will not only get real-time probabilities of major elections or geopolitical events but also probabilities of minor outcomes and complex event intersections. While these new contracts will reveal more information and gradually integrate into the news ecosystem (an ongoing trend), they will also raise important social issues, such as how to balance the value of this information and how to design these markets to be more transparent and auditable — problems that cryptography can help solve.

To manage more contracts, we need new ways to reach consensus on truth and resolve contract disputes. Centralized platforms are crucial for solving this (did an event really happen? How do we confirm?), but controversy cases like Zelensky’s market and Venezuelan elections have shown their limitations.

To address these edge cases and help prediction markets expand into more useful scenarios, new forms of decentralized governance and large language model (LLM)-based oracle systems can help determine the truth of contentious outcomes. AI can go beyond LLMs in oracle applications. For example, AI agents operating on these platforms can search global signals, provide advantages in short-term trading, and reveal new insights about the world and what might happen.

From Tokenization to Native Crypto Assets: New Paths for Real Asset Integration

Banks, fintech, and asset managers have shown great interest in bringing US stocks, commodities, indices, and other traditional assets on-chain. However, as more traditional assets are tokenized, this often becomes “mimetic” — based on existing understanding of real assets without fully leveraging the native properties of crypto.

But synthetic representations (like perpetual contracts) not only offer higher liquidity but are also generally easier to implement. Perpetual contracts have understandable leverage mechanisms, making them arguably the most suitable derivatives for crypto market needs.

I also believe emerging market stocks are among the most worth “perpetualizing” asset classes. For example, some stocks’ “0DTE” options markets even surpass spot markets in liquidity, providing a very interesting experiment for perpetualization. This is a question of “perpetualization and tokenization”; in any case, we should see more real assets (RWA) tokenized in a crypto-native way next year.

Meanwhile, in 2026, after stablecoins become mainstream in 2025, we will see more “issuance rather than just tokenization” trends, with stablecoin circulation continuing to grow. However, stablecoins lacking solid credit infrastructure are more like “narrow banks,” holding only highly safe liquid assets. While narrow banks are effective products, I don’t believe they will long-term form the backbone of on-chain economies.

We have already seen many new asset management firms, curators, and protocols facilitating on-chain loans collateralized by off-chain assets. These loans are often initiated off-chain and then tokenized. However, I believe the benefits of tokenization in these cases are limited — perhaps mainly for easier asset allocation to on-chain users. Therefore, debt assets should be issued directly on-chain, not initiated off-chain and then tokenized. On-chain issuance can reduce loan service costs and backend infrastructure costs, increasing accessibility. The challenge lies in compliance and standardization, but developers are actively working to solve these issues.

Trading Is Not the End: The True Dilemma of Crypto Business Models

Today, aside from stablecoins and some core infrastructure, almost all successful crypto companies have migrated or are migrating into trading businesses. But what if “every crypto company becomes a trading platform”? What does the industry’s future look like?

When too many players do the same thing, not only does market attention decline, but the risk is that only a few large companies will win. This also means that companies rushing into trading too early lose the opportunity to build more defensive and durable business models.

While I empathize with entrepreneurs seeking breakeven, chasing short-term product-market fit comes at a cost. This problem is especially evident in crypto because the unique dynamics of tokens and speculation push entrepreneurs toward instant gratification when seeking product-market fit — a “cotton candy test” (delayed gratification).

Trading itself is not wrong; it’s an important market function, but not necessarily the ultimate goal of a company. Entrepreneurs focusing on the “product” part of product-market fit may ultimately be the true winners.

AI as Research Assistant: From Calculation to Discovery Leap

As a mathematician and economist, in January this year, it was hard for consumer-grade AI models to understand my workflow; by November, I could give the models abstract instructions like a PhD student… sometimes they even provide new, correct solutions.

Beyond my personal experience, we are beginning to see AI more broadly applied to research, especially in reasoning — models now directly participate in discovery processes, capable of autonomously solving problems like the Putnam competition (arguably the hardest university math exam in the world).

Which fields’ research support is most effective, and how exactly it works, is still unclear. But I predict AI-driven research will cultivate and reward a new “multi-faceted” research style: emphasizing the ability to hypothesize relationships between different ideas and to quickly infer from more speculative answers. These answers may not be fully accurate but can still point in the right direction (at least in some topology).

Ironically, this approach leverages certain aspects of model “hallucination”: when they are smart enough, allowing them to freely explore abstract spaces may produce useless content but also accidental discoveries — much like humans tend to be more creative when working in nonlinear and fuzzy directions.

This reasoning requires a new AI workflow — not just “agent to agent,” but “wrapping agents’ agents.” In this structure, different levels of models help researchers evaluate early methods and gradually refine valuable content. I have used this approach to write papers, others to conduct patent research, create new art forms, and even (unfortunately) find new attacks on smart contracts.

However, for this reasoning agent-based research system to work effectively, better interoperability between models and a fair way to recognize and compensate each contribution are needed — issues that cryptography can help address.

A New Paradigm for DeFi Security: The Era of “Norms as Rules”

Recent DeFi hacks have targeted verified protocols, managed by strong teams, audited thoroughly, and online for years. These events reveal an unsettling reality: current security standards are still based on heuristics and case-by-case management.

To further mature DeFi security, we need to shift from patching vulnerabilities to designing properties at the protocol level, moving from “do your best” to “principled approaches”:

In the static/deployment phase (testing, auditing, formal verification), this means systematically verifying global invariants, not just selected local invariants. Several teams are now building AI-assisted proof tools to help write specifications, propose invariants, and reduce lengthy, costly manual proofs.

In the dynamic/deployment phase (runtime monitoring, runtime enforcement), these invariants can serve as real-time “barriers” — the last line of defense. These barriers are encoded as runtime assertions, ensuring each transaction complies. Therefore, we no longer assume every vulnerability will be discovered in advance but embed critical security properties directly into code, automatically rolling back any violating transactions.

This is not just theoretical. In practice, almost every past attack would have triggered these checks during execution, potentially stopping hackers. Thus, the concept of “code as law” is evolving into “norms as rules”: even the most innovative attacks must respect security properties that maintain system integrity, leaving only minimal or extremely difficult-to-execute attacks.

Zero-Knowledge Proofs Beyond Blockchain: The New Era of Verifiable Computation

For years, SNARKs (Succinct Non-Interactive Arguments of Knowledge) — cryptographic proof systems that allow verifying computations without re-executing them — have been almost exclusively applied to blockchain. This is because the computational cost is very high: generating a proof can require executing the computation 1,000,000 times. This high cost is only reasonable when distributed across thousands of verifiers, but limited elsewhere.

This situation is about to change. By 2026, the overhead for zero-knowledge virtual machine (zkVM) proofs will drop to about 10,000 times, with memory usage in the hundreds of megabytes — fast enough to run on smartphones and economical enough for many scenarios.

Why is “10,000 times” a magic number? Because high-end GPUs have a parallel throughput roughly 10,000 times that of a laptop CPU. By the end of 2026, GPUs will be able to generate proofs of CPU-executed computations in real time.

This technological breakthrough could realize some early research visions: verifiable cloud computing. If you are already running CPU workloads in the cloud — due to workload not utilizing GPUs, lack of expertise, or legacy constraints — you will be able to obtain cryptographic proofs of computational correctness at a reasonable cost. And these proof systems are optimized for GPUs, requiring no code modifications.

The Invisible Tax of Open Networks: Economic Imbalance in the AI Era and Solutions

With the rise of AI agents, open networks face an invisible tax that weakens their economic foundation. This stems from the growing disconnect between the “background layer” (content layer) of the internet and the “execution layer”: today, AI agents extract data from ad-supported content sites (background layer) to provide convenience to users but systematically bypass the revenue sources supporting this content (like ads and subscriptions).

To prevent further erosion of open networks and protect the content ecosystem that drives AI development, large-scale technological and economic solutions are needed. These might include new sponsored content models, micro-attribution systems, or other innovative funding mechanisms.

However, current AI licensing agreements are showing their financial unsustainability — often only compensating content providers a small fraction of the revenue lost due to AI traffic shifts. The internet needs a new technological-economic model that automates value flows.

The key change next year will be shifting from static licensing models to real-time usage-based compensation mechanisms. This requires testing and scaling systems — perhaps using blockchain-supported micro-payments and advanced attribution standards — to automatically reward every entity contributing to AI task success.

The Rise of Bet-Driven Media: Rebuilding Trust with Blockchain

The cracks in the traditional media “objectivity” model have long been evident. The internet empowers everyone to speak, and more practitioners, practitioners, and builders are directly expressing their views to the public. Their perspectives reflect their interests in the world, and surprisingly, the public often respects them precisely because of these interests, not despite them.

The real novelty is not the rise of social media but the advent of cryptographic tools that enable open and verifiable commitments.

In the AI era, generating infinite content is easy and low-cost — from real or fake identities, from any perspective — relying solely on what humans (or bots) say is no longer enough. Tokenized assets, programmable locks, prediction markets, and on-chain histories provide a more solid foundation for trust: commentators can prove they “put skin in the game” when expressing opinions; podcasters can lock tokens to show they won’t “pump and dump”; analysts can link predictions to publicly regulated markets, creating auditable records.

This is what I call the birth of “bet-driven media”: a form of media that not only accepts the principle of “stakeholder interests” but also proves it. In this model, credibility no longer comes from false neutrality or unfounded claims but from explicit, transparent, and verifiable commitments. Bet-driven media will not replace other forms but will complement them. They provide a new signal: not just “trust me, I am neutral,” but “this is the risk I am willing to take, and here is how you can verify if I am telling the truth.”