17 Major Shifts Reshaping Crypto's Future in 2026

Compiled from a16z Crypto Research Perspectives

Part 1: The Foundation Layer – Finance Reimagined

On-Ramps and Off-Ramps: Bridging Digital and Traditional Finance

Last year painted a striking picture: stablecoins processed an estimated $46 trillion in transaction volume—a milestone that speaks volumes about crypto’s infrastructure maturity. To put this in perspective, that’s more than 20 times PayPal’s annual throughput, nearly triple Visa’s global payment volume, and rapidly converging on the scale of the US Automated Clearing House (ACH), which orchestrates direct deposits and electronic fund transfers across the traditional financial system.

The technical foundation is already solid. A stablecoin transfer today takes less than a second and costs less than a penny. Yet the real bottleneck remains unresolved: how do we weave these digital rails into the financial infrastructure billions of people rely on daily?

This is where a new wave of infrastructure builders is emerging. These companies are constructing bridges—some leveraging cryptographic verification to convert local bank balances into digital dollars; others integrating with regional payment rails through QR codes and real-time settlement systems; and still others are architecting global digital wallet layers and card-issuing platforms that let users spend stablecoins at everyday merchants.

As these on/off-ramps mature, we’re witnessing the early stages of a fundamental shift. Cross-border salary payments can now settle in real time. Merchants without bank accounts can accept globally-circulating digital dollars. Payment applications instantly reconcile value across borders. Stablecoins are transitioning from a niche financial tool to the internet’s foundational settlement layer.

Reimagining Real-World Assets Through Crypto-Native Thinking

Traditional finance is knocking on crypto’s door, eager to tokenize stocks, commodities, and indices. But most tokenization efforts remain superficial—they replicate existing financial infrastructure rather than leveraging what blockchain actually enables.

Consider perpetual contracts: they function as crypto-native derivatives that often provide superior liquidity and implement leverage mechanisms that users intuitively understand. Emerging market equities represent particularly compelling candidates for this treatment. In certain markets, zero-day options liquidity already dwarfs spot market volume—a phenomenon that warrants experimentation with perpetual contractization.

The emerging fork here is straightforward: perpetualize or tokenize? The answer increasingly favors crypto-native derivatives over tokenization for many asset classes.

Simultaneously, we should expect stablecoins to evolve from mere tokens of existing assets into natively-issued instruments. Stablecoins achieved mainstream status in 2025, but many current issuers resemble narrow banks—holding ultra-safe, liquid assets but lacking robust credit infrastructure. While narrow banking is a legitimate financial model, it won’t serve as the on-chain economy’s backbone.

The next frontier involves protocols and asset managers originating debt on-chain rather than tokenizing it after the fact. On-chain origination reduces loan servicing overhead, eliminates back-office friction, and democratizes access. Compliance and standardization present challenges, but builders are already solving them.

Bank Ledgers Enter an Upgrade Cycle

Modern developers rarely confront legacy banking systems, yet those systems are profound. Banking software pioneered large-scale systems in the 1960s-70s. Second-generation core banking platforms emerged in the 1980s-90s. But here we are, decades later, with the vast majority of global assets still running on mainframe systems programmed in COBOL, communicating through batch file interfaces rather than APIs.

These aging architectures are trusted, tested, and deeply embedded in complex operations. They’re also a drag on innovation. Adding real-time payment capabilities can take months or years, requiring excavation through layers of technical debt.

This is where stablecoins and tokenized assets unlock a solution without requiring wholesale system rewrites. By enabling banks and fintech firms to build new products on-chain while preserving legacy systems, stablecoins provide an innovation bypass. Tokenized deposits, treasuries, and bonds create pathways for institutions to serve new customer segments and develop entirely new offerings—without the apocalyptic task of replacing decades-old infrastructure.

The Internet Becomes the Financial System

As autonomous agents proliferate and business processes shift from user-initiated clicks to background automation, money must flow as freely as information does today. Blockchains and smart contracts already settle global dollar transactions in seconds. Emerging primitives like x402 will make settlement programmable and responsive—agents will trigger instant, permissionless payments for data, GPU cycles, or API calls without invoices or reconciliation.

Picture this: developers ship software updates with embedded payment rules, audit trails, and limits—zero fiat integration required. Prediction markets self-settle in real time as events resolve, with odds updating dynamically and global payouts completing in seconds.

When value moves this way, “payment flow” transforms from a separate operational layer into a network primitive. The internet itself becomes the financial backbone—money becomes an internet-routable information packet, and the internet becomes finance.

Wealth Management at Scale

Historically, personalized wealth strategies have been gatekept by banks for ultra-high-net-worth clients. The cost and complexity were simply prohibitive. But as asset classes tokenize and become accessible through crypto channels, AI-driven recommendations enable instant portfolio rebalancing at negligible cost.

The shift from passive to active management becomes democratized. Platforms emerging in 2026 will position “wealth growth” as their north star, not mere wealth preservation. Advanced yield-generating allocation strategies—like leveraging lending protocols with optimized risk-adjusted returns—become available to retail participants. Assets remain in stablecoins rather than fiat, capturing yield spreads unavailable in traditional money markets.

Tokenization further unlocks previously illiquid categories: private credit, pre-IPO equity, private fund access. Compliance and reporting requirements are met while market accessibility expands dramatically. Once portfolio components span bonds, stocks, private investments, and alternatives—all tokenized—automated rebalancing executes without the friction of fund transfers and manual procedures.

Part 2: Intelligence and Autonomy

From Know Your Customer to Know Your Agent

The agent economy’s bottleneck is shifting from raw intelligence toward identity verification. Financial services today host 96 times more “non-human identities” than human employees, yet these digital identities lack infrastructure.

The missing layer is KYA—Know Your Agent. Humans need credit scores to borrow; agents need cryptographically-signed credentials to transact. These credentials must link agents to their authorizing principals, operational limits, and liability chains. Without this infrastructure, merchants remain skeptical and block agent access at the firewall level.

The KYC infrastructure that took decades to build must now solve KYA in months.

AI as Research Infrastructure

The evolution of AI’s research capability has been remarkable. A mathematical economist observed that in January 2024, general AI models struggled with specialized workflows. By November, those same models could process abstract instructions like PhD advisors and occasionally generate novel, correct insights.

We’re witnessing AI applied across research domains, particularly in mathematical reasoning. Current models independently solve Putnam Mathematical Competition problems—arguably the world’s most demanding university-level mathematics exam. The field most likely to benefit, and how these tools will function, remains open questions.

But a pattern is emerging: AI research will reward a new kind of polymathic approach—one favoring the ability to recognize conceptual connections and extrapolate from speculative premises. Answers may lack precision but provide directional guidance. Interestingly, this mirrors harnessing model hallucinations: sufficiently-advanced models given divergent-thinking space sometimes produce noise, but occasionally spark breakthroughs.

This requires rethinking AI workflows toward agent-nesting-agent architectures: multi-layer models evaluate early-stage hypotheses and gradually distill value. Applications range from academic paper writing to patent research to novel content creation—and, less happily, discovering new smart contract vulnerabilities.

Running such nested systems requires better interoperability between models and mechanisms for crediting and compensating each model’s contribution. These are precisely the problems cryptography is positioned to solve.

The Hidden Tax on Open Networks

AI agents are imposing an invisible tax on open networks, fundamentally destabilizing their economic models. The dynamic: AI extracts data from ad-dependent websites (context layer), benefiting users while systematically circumventing revenue channels—ads, subscriptions—that fund content creation. To preserve open networks and the diverse content fueling AI development, we need scaled technical and economic solutions.

Current AI licensing agreements function only as temporary patches, compensating content providers with mere fractions of traffic-driven revenue losses. The network needs a techno-economic model where value flows automatically.

The critical transition ahead: from static licensing to real-time, usage-based compensation. This involves testing systems that leverage blockchain-enabled nano-payments and precise provenance tracking, automatically rewarding every entity contributing information to successful agent task completion.

Part 3: Cryptography, Privacy, and Security

Privacy as the Ultimate Network Moat

Privacy stands as a prerequisite for global finance operating on-chain, yet most blockchains treat it as an afterthought. Now, privacy itself becomes sufficient differentiation.

More profoundly, privacy generates on-chain lock-in effects—what we might call privacy network effects. In a performance-dominated landscape, migration between chains is trivial through bridging protocols (assuming all data is public). But privately-held information changes everything: tokens bridge easily; secrets do not.

Transitioning private information across chains exposes identity through monitored mempools or network traffic. Metadata leakage—transaction timing, sizes, correlations—enables tracking. For generic public chains lacking thriving ecosystems or killer applications, users face little reason to commit; they easily switch. But privacy chains generate stronger network effects: choosing a privacy chain creates switching costs, because exit involves privacy exposure risk—a winner-takes-all dynamic.

Since privacy protects most real-world applications, a handful of privacy-focused chains may ultimately dominate the ecosystem.

Messaging Demands Quantum Resistance and Decentralization

Mainstream messaging applications (Signal, WhatsApp, iMessage) have invested heavily in quantum-resistant cryptography. Yet they all rely on private servers operated by single organizations—easy targets for state shutdown, backdoors, or coercion.

If a nation closes a server, if a company controls private infrastructure, quantum encryption’s theoretical security becomes irrelevant. The solution requires decentralized messaging: no private servers, no app dependencies, fully open-source code, top-tier cryptography including quantum-resistance.

In open networks, no actor—individual, organization, nonprofit, or nation-state—can sever communications. Close one application; 500 alternatives emerge. Shut down nodes; blockchain incentives immediately spawn replacements.

When users own information through private keys as they own money, everything shifts. Applications appear and disappear; users retain permanent information ownership and identity control. This transcends cryptography to embrace fundamental ownership and decentralization principles.

Privacy-Preserving Infrastructure: From Applications to Infrastructure Layer

Behind every model, agent, and process lies data. Yet most data pipelines—whether feeding models or output by them—remain opaque, volatile, and difficult to audit. For consumer applications this may suffice; for finance and healthcare, sensitivity demands privacy protections.

This represents a major friction point for institutions pursuing RWA tokenization. How do we enable secure, compliant, autonomous, globally-interoperable innovation while protecting data privacy?

Data access control provides the key lever: who owns sensitive data? How does it circulate? Who or what accesses it? Without proper mechanisms, privacy-conscious users today resort to centralized service platforms or custom-built solutions—expensive, time-consuming, and preventing traditional institutions from fully leveraging on-chain data advantages.

As autonomous agents begin browsing, trading, and deciding independently, users and institutions require cryptographic verification, not merely “best-effort trust models.” This demands “privacy as a service”: infrastructure providing programmable data access rules, client-side encryption, and decentralized key management—precisely controlling data decryption rights, conditions, and timeframes, all executing on-chain.

Combined with verifiable data systems and robust defenses against both active and passive attacks in cryptography, data privacy upgrades from an application-layer patch to core internet infrastructure.

Evolution: From Code Is Law to Rules Are Law

Recent years have witnessed sophisticated DeFi protocols—staffed by strong teams, cleared by rigorous audits, operating stably for years—suffer hacker exploits. This exposes a troubling reality: current security standards rely on case-by-case, empirical judgment.

Maturing DeFi demands shifting from vulnerability pattern-matching to design-level, principled approaches. This spans two phases:

Pre-deployment: Systematically verify global invariants, not manually-selected local ones. AI-assisted proof tools help write technical specifications, propose invariant hypotheses, and dramatically reduce the manual engineering that previously made formal verification prohibitively expensive.

Post-deployment: Convert invariants into dynamic guardrails—final defense layers. These encode runtime assertions every transaction must satisfy. Rather than assuming all vulnerabilities are discoverable, this approach enforces critical security properties in code itself. Transactions violating these properties automatically rollback.

This isn’t theoretical. In practice, nearly every exploit triggers one of these security checks during execution, potentially halting the attack. The mantra “code is law” evolves into “rules are law”: new attack methodologies must nonetheless satisfy security properties preserving system integrity. Remaining attack vectors are either trivial or extraordinarily difficult to execute.

Part 4: Prediction Markets, Media, and Innovation

Prediction Markets: Bigger, Broader, Smarter

Prediction markets have achieved mainstream status. In 2026, their merger with crypto and AI will expand their scope, broaden their applications, and sharpen their accuracy—while introducing fresh challenges for builders.

Expect contract proliferation: major geopolitical events and elections alongside niche outcomes and complex multi-event correlations. As these contracts surface, they’ll integrate into news ecosystems—creating social questions around information valuation and design optimization for transparency and auditability (all capabilities crypto enables).

Scaling contracts demands new consensus mechanisms for result verification. Centralized platform adjudication proves essential but controversial; disputed cases expose limitations. Decentralized governance and large-language-model oracles help determine truth in contested outcomes.

AI agents trading these platforms scan global signals and discover new predictive dimensions, revealing complex social event factors. These agents function as advanced analysts for human consultation, with their strategy patterns informing social science research.

Can prediction markets replace traditional polling? Not entirely. But they enhance polling (reciprocally, poll data inputs prediction markets). The optimal path involves prediction markets and rich polling ecosystems working synergistically, enhanced by AI for better survey experiences and crypto for verifying respondent authenticity against bot infiltration.

Bet-Based Media: Credibility Through Verifiable Stakes

Traditional media’s “objectivity” has long shown cracks. The internet democratized voice; operators and builders now speak directly to audiences, views reflecting their interests. Audiences respect this transparency—not despite stakes, but because of them.

The innovation here transcends social media to embrace crypto tools enabling publicly-verifiable commitments. As AI enables infinite content generation claiming any perspective or identity (real or fictional), words alone prove insufficient. Tokenized assets, programmable lockups, prediction markets, and on-chain history provide trust foundations: commentators publish arguments while proving financial commitment. Podcast hosts lock tokens demonstrating resistance to opportunistic pivots. Analysts tie predictions to publicly-settled markets, creating verifiable track records.

This emerging “bet-based media” model doesn’t claim neutrality or make empty assertions. Instead, credibility flows from publicly-verifiable stakes—the willingness to risk real capital on your claims, verifiable by anyone.

This supplements rather than replaces existing media, offering a new signal: not “trust my neutrality,” but “here’s the risk I’m taking—verify I mean it.”

Cryptographic Primitives Beyond Blockchain

SNARKs—cryptographic proof technology verifying computation without re-execution—have historically remained blockchain-confined due to extreme overhead: proof generation required 1 million times more work than computation itself. Acceptable when spreading load across verification nodes; impractical elsewhere.

By 2026, zkVM prover overhead drops to approximately 10,000 times, with memory footprints of only hundreds of megabytes—fast enough for smartphone execution and cheap enough for global deployment.

Why does 10,000 times matter? High-end GPUs deliver roughly 10,000 times the parallel throughput of laptop CPUs. By late 2026, single GPUs generate proofs in real-time for CPU workloads. This unlocks verifiable cloud computing: if you’re already running CPU workloads in cloud environments (whether due to GPU-incompatible compute, expertise gaps, or legacy constraints), you’ll access cryptographic correctness proofs at reasonable cost. The prover optimizes for GPUs while your code remains GPU-agnostic.

Building Defensible Businesses: Trading as Waypoint, Not Destination

Crypto’s current landscape reveals a troubling pattern: aside from stablecoins and core infrastructure, seemingly every established crypto company pivots to trading platforms. But what emerges when “every crypto company becomes a trading platform”? A glut producing identical offerings—followed by bloodbaths leaving sole winners.

Companies rushing toward trading abandon opportunities to build defensible, durable business models. Founders chasing instant product-market fit sacrifice long-term positioning. Crypto’s speculative atmosphere exacerbates this tendency, creating marshmallow-experiment dynamics favoring immediate gratification.

Trading serves important market functions. But it needn’t be the endgame. Founders prioritizing the “product” dimension of product-market fit—building genuine value rather than pure trading infrastructure—position themselves for sustained advantage.

Unlocking Blockchain Potential: Legal Clarity Meets Technical Innovation

The past decade’s major obstacle to US blockchain development was legal uncertainty. Securities laws saw selective, misapplied enforcement, forcing founders into regulatory frameworks designed for traditional companies, not decentralized networks. Legal risk reduction displaced product strategy. Engineers stepped back; lawyers moved center-stage.

Odd consequences followed: founders received opacity advice; token distribution became arbitrary legal avoidance; governance became performative; organizational structures optimized for compliance over effectiveness; token economics deliberately avoided value creation.

Perversely, projects bending rules often outperformed honest builders. But crypto market structure regulation—government approaches passage—could eliminate these distortions in 2026. Clear standards, structured fundraising and token issuance pathways, and explicit decentralization frameworks replace today’s “enforcement roulette.”

Stablecoins saw explosive growth post-GENIUS Act passage. Crypto market structure legislation will effect even greater transformation, primarily for network ecosystems. This regulation enables blockchains to function as genuine networks—open, autonomous, composable, credibly neutral, and decentralized.