a16z: Privacy, the Most Important Moat in Crypto by 2026

比推Pubblicato 2026-01-06Pubblicato ultima volta 2026-01-06

Introduzione

Privacy will become the most important moat in crypto by 2026, as it enables chain lock-in effects and strengthens network effects, making migration between chains difficult and creating winner-take-all dynamics. Decentralized, quantum-resistant communication protocols will gain prominence, moving beyond encryption to eliminate reliance on centralized servers and ensure user control over data and identity. Secrets-as-a-Service will emerge as critical infrastructure, providing programmable, on-chain access controls and decentralized key management to ensure privacy and compliance for sensitive data in sectors like finance and healthcare. DeFi security will evolve from "code is law" to "spec is law," with AI-assisted tools enabling systematic verification of global invariants and runtime enforcement of security properties to prevent exploits.

Source: A16z

Original Title: Privacy trends for 2026

Compiled and Edited by: BitpushNews


1. Privacy will become the most important moat in crypto this year

Privacy is a key feature for the global migration of finance on-chain. However, almost all existing blockchains lack this feature. For most chains, privacy is merely an afterthought. But now, privacy itself has become attractive enough to make a chain stand out among numerous competitors.

Privacy also plays a more important role: it creates a "Chain Lock-in" effect; you can call it the "privacy network effect" if you will. Especially in a world where pure performance is no longer sufficient.

Thanks to cross-chain bridge protocols, migrating from one chain to another is easy as long as everything is public. But once privacy is involved, the situation is completely different: transferring tokens is easy, but transferring secrets is difficult. There is always risk when moving in and out of privacy zones—those monitoring the chain, mempool, or network traffic may identify you. Crossing the boundary between privacy chains and public chains (or even between two privacy chains) leaks various metadata, such as transaction timing and size correlations, making it easier to track users.

Compared to many homogeneous new chains (whose transaction fees may be driven down to zero due to competition, as block space has largely become commoditized), blockchains with privacy features can have stronger network effects. The reality is, if a "general-purpose" chain lacks a thriving ecosystem, killer applications, or unfair distribution advantages, users or developers have little reason to use or build on it, let alone remain loyal.

On public blockchains, users can easily transact with users on other chains, and the choice of chain doesn't matter much. But on privacy blockchains, the chain users choose becomes crucial because once they join, they are less likely to move and risk exposing their identity. This creates a "winner-takes-all" scenario. Since privacy is a necessity for most real-world use cases, a few privacy chains may capture the majority of the crypto market.

Ali Yahya (@alive_eth), General Partner, a16z crypto

2. This year's proposition for social apps: Not just quantum-resistant, but also decentralized

As the world prepares for quantum computing, many crypto-based social applications (like Apple, Signal, WhatsApp) have been leading the way. The problem is, all mainstream instant messaging tools rely on our trust in private servers run by a single organization. These servers are highly vulnerable to government shutdowns, backdoor installations, or forced handovers of private data.

What's the point of "quantum-resistant encryption" if a country can shut down your server, if a company holds the keys to a private server, or even just if a company owns the private server?

Private servers require "trust me," while no private server means "you don't need to trust me." Communication doesn't need a single intermediary company. Instant messaging requires open protocols that let us trust no one.

The path to achieving this is network decentralization: no private servers, no single application, fully open-source code, and top-tier encryption (including resistance to quantum threats). In an open network, no individual, company, non-profit, or country can deprive us of the ability to communicate. Even if a country or company shuts down one application, 500 new versions will appear the next day. Shut down one node, and new nodes will immediately replace it, thanks to economic incentives provided by technologies like blockchain.

When people own their messages through private keys, just like they own money, everything changes. Applications may come and go, but people will always control their information and identity; end users can own their messages, even if they don't own the application.

This is more important than quantum resistance and encryption; it's about ownership and decentralization. Without these two, we're just building an "indestructible" encryption system that can be shut down at any time.

Shane Mac (@ShaneMac), Co-founder and CEO, XMTP Labs

3. "Secrets-as-a-Service" will make privacy core infrastructure

Behind every model, agent, and automation lies a simple dependency: data. But today, most data pipelines—whether data input to or output from models—are opaque, mutable, and unauditable.

This is fine for some consumer applications, but many industries and users (like finance and healthcare) require companies to keep sensitive data confidential. This is also a major obstacle for institutions currently seeking to tokenize real-world assets (RWA).

So, how do we enable secure, compliant, autonomous, and globally interoperable innovation while protecting privacy?

There are many methods, but I'll focus on data access control: who controls sensitive data? How does it move? And who (or what) can access it? Without data access control, anyone wanting to maintain data confidentiality currently must use centralized services or build custom setups. This is not only time-consuming and expensive but also prevents traditional financial institutions from fully unleashing the potential of on-chain data management. As AI agent systems begin to autonomously browse, trade, and make decisions, individuals and institutions across industries will need cryptographic guarantees, not "best-effort trust."

This is why I believe we need "Secrets-as-a-Service": providing programmable, native data access rules through new technologies; client-side encryption; and decentralized key management that enforces who can decrypt what, under what conditions, for how long... all executed on-chain.

Combined with verifiable data systems, secrets can become part of the internet's fundamental public infrastructure, rather than an application-layer patch applied after the fact. This will make privacy core infrastructure.

— Adeniyi Abiodun (@EmanAbio), Chief Product Officer and Co-founder, Mysten Labs

4. Security testing will evolve from "code is law" to "spec is law"

Last year's DeFi hacks affected some battle-tested protocols with strong teams, rigorous audits, and years of operation. These incidents revealed an unsettling reality: today's standard security practices are largely heuristic and handled on a case-by-case basis.

To mature this year, DeFi security needs to move from "finding vulnerability patterns" to "design-level properties," from "best-effort" to a "principled" approach:

In the static/pre-deployment phase (testing, auditing, formal verification): This means systematically proving "Global Invariants" instead of verifying manually selected local variables. AI-assisted proof tools currently being developed by multiple teams can help write specifications (Specs), propose invariants, and take on the expensive manual proof engineering work of the past.

In the dynamic/post-deployment phase (runtime monitoring, runtime enforcement, etc.): These invariants can be translated into real-time guardrails—the last line of defense. These guardrails are written directly as runtime assertions that every transaction must satisfy.

Now, instead of assuming every vulnerability is caught, we enforce critical security properties in the code itself, automatically reverting any transaction that violates these properties.

This isn't just theoretical. In practice, almost every exploit to date would have triggered these checks during execution, stopping hacks at the source.

Thus, the once-popular "Code is Law" evolves into "Spec is Law": even novel attacks must satisfy the security properties that keep the system intact, making any remaining attacks either trivial or extremely difficult to execute.

— Daejun Park (@daejunpark), a16z crypto Engineering Team


Twitter:https://twitter.com/BitpushNewsCN

Bitpush TG Discussion Group:https://t.me/BitPushCommunity

Bitpush TG Subscription: https://t.me/bitpush

Original link:https://www.bitpush.news/articles/7600550

Domande pertinenti

QWhy does a16z believe privacy will be the most important moat in crypto by 2026?

APrivacy creates a 'chain lock-in' effect, making it difficult for users to migrate between chains without risking identity exposure through metadata leaks. This strengthens network effects and could lead to a winner-takes-all scenario for privacy-focused blockchains.

QWhat is the 'chain lock-in' effect mentioned in the article?

AThe 'chain lock-in' effect refers to the difficulty of migrating assets from a privacy-focused blockchain to another chain without exposing transactional metadata (e.g., timing, size correlations), which could compromise user anonymity and deter users from leaving.

QHow does Shane Mac argue that decentralized communication protocols are superior to centralized ones like Signal or WhatsApp?

ADecentralized protocols eliminate reliance on private servers controlled by single entities, which are vulnerable to government shutdowns, backdoors, or data seizures. Open, serverless networks with economic incentives ensure resilience and user ownership of messages and identity.

QWhat is 'Secrets-as-a-Service' and why does Adeniyi Abiodun think it is necessary?

A'Secrets-as-a-Service' is a proposed infrastructure layer offering programmable data access rules, client-side encryption, and decentralized key management to ensure privacy and compliance for sensitive data (e.g., in finance and healthcare), enabling secure, interoperable innovation on-chain.

QHow does Daejun Park suggest evolving DeFi security from 'code is law' to 'spec is law'?

ABy shifting from ad-hoc vulnerability hunting to enforcing global invariants through AI-assisted proof tools and runtime assertions. This ensures transactions violating critical security properties are automatically reverted, making attacks trivial or extremely hard to execute.

Letture associate

Breaking: Google Earth Urgently Pulls Back Nano Banana 2 Image Generation Feature!

Google Earth's newly launched "Create image" feature, powered by the Nano Banana 2 AI image generation model, was abruptly withdrawn shortly after its release due to being "played" by users. The feature allowed users to generate and overlay AI-created visuals directly onto real-world satellite and 3D maps in Google Earth. The tool enabled creative applications like historical recreations (e.g., visualizing ancient Pompeii), generating informational graphics for landmarks, and envisioning architectural projects or futuristic cityscapes on real terrain. It operated under "geospatial grounding," meaning the AI respected the underlying geography, topography, and perspective of the chosen map view. The model also integrated with Gemini to retrieve relevant factual information. However, upon release, users quickly tested its limits. A prominent example involved reimagining Philadelphia's historic Independence Hall as a post-apocalyptic ruin overrun by "happy" zombies, evil clowns, and giant alien mechs. This highlighted both the feature's playful potential and its risks regarding the generation of inappropriate or misleading content on realistic maps, leading to its swift temporary removal. Google stated it would re-release the feature after implementing "enhanced guardrails." Analysts note this move strategically leverages Google's vast proprietary geospatial data, positioning its AI not just for artistic generation but for spatially accurate world visualization—a unique advantage in the competitive AI image generation landscape.

marsbit1 h fa

Breaking: Google Earth Urgently Pulls Back Nano Banana 2 Image Generation Feature!

marsbit1 h fa

Altman Admits: Overestimated AI Snatching Jobs! Huang Renxun: The Unemployment Narrative Is Completely Backwards

Sam Altman has revised his earlier predictions about AI rapidly replacing jobs, admitting he overestimated the speed at which AI would eliminate entry-level white-collar roles. Speaking on the "Invest Like the Best" podcast, he stated that people do not truly want an AI CEO, as accountability and human connection remain critical. He found that individuals prefer interacting with people who can be held responsible for decisions. Similarly, NVIDIA's Jensen Huang argued that the narrative of AI destroying jobs is misguided. He distinguishes between tasks and jobs, noting that while AI can automate specific tasks, entire jobs—encompassing communication, judgment, coordination, and accountability—are not eliminated. He cited examples like radiologists and software engineers, where demand for these roles has increased as AI handles repetitive tasks, allowing for business expansion and the creation of more positions. Data from a University of Maryland and LinkUp study supports this, showing that U.S. job postings for new graduates have actually risen, countering the fear of vanishing entry-level roles. However, a significant shift is occurring: the traditional entry-level tasks that help newcomers gain experience are being automated, making initial career access more challenging. The key insight is that as AI takes over standardized tasks, the enduring value of human work shifts toward areas of responsibility, trust-building, and final decision-making—aspects that AI cannot replicate. The real "moat" for professionals lies in these irreplaceable human elements.

marsbit1 h fa

Altman Admits: Overestimated AI Snatching Jobs! Huang Renxun: The Unemployment Narrative Is Completely Backwards

marsbit1 h fa

Weekly Editor's Picks (0725-0731)

Weekly Editor's Picks (0725-0731) provides a curated selection of deep analysis, filtering out market noise. Key themes from this week include: **Macro & Policy:** The Federal Reserve's upcoming meeting is marked by high uncertainty, balancing cooling inflation data against persistent price pressures. Meanwhile, the U.S. crypto regulatory Clarity Act faces critical political hurdles, with its 2026 passage probability seen as low. **Investing & Crypto:** Analysis suggests long-term crypto success depends on conviction through volatile cycles, focusing on assets like Bitcoin and core smart contract platforms. A trend noted is the increasing similarity between global equity markets (especially tech) and crypto, driven by narrative and leverage. Several major crypto protocols show strong revenue growth, but this isn't always translating to token price appreciation due to sell pressure and structural factors. **AI & Semiconductors:** Nvidia's rising credit default swap rates signal market concern over AI infrastructure financing risks. The storage sector experienced volatility as markets began pricing in potential 2027 oversupply. Despite a record profitable quarter, SK Hynix's results were deemed "below expectations," reflecting heightened investor demands for future growth visibility. **Markets & DeFi:** TradeXYZ demonstrated remarkable accuracy in pre-market pricing for a major A股 listing. The token ONDO saw gains, linked to its growing role in the on-chain tokenized stock ecosystem. **Ethereum:** Post-Pectra upgrade, a major structural shift is underway as Lido begins migrating millions of ETH to new validator architectures designed for capital efficiency. **Also Highlighted:** Butian's bullish stock market move; OpenAI's Altman promising major advances; Samsung and SK Hynix securing large AI chip deals; Apple reaching a $5T market cap; and ongoing discussions around exchange security following Poolin's bankruptcy case.

marsbit1 h fa

Weekly Editor's Picks (0725-0731)

marsbit1 h fa

Low Investment Isn't Apple's Immunity Pass

While Meta and Google face investor scrutiny over ballooning AI capital expenditures, Apple's minimal AI investment has paradoxically become a strength. Its market cap recently reclaimed the global top spot, surpassing $5 trillion. The irony is deep: Apple's own AI efforts have lagged, with "Apple Intelligence" delayed and core talent lost, forcing reliance on partners like Google Gemini and Alibaba's Qianwen. Its Q3 FY2026 (Q2 CY) earnings initially seemed stellar. Revenue hit $109.4B (up 16% YoY), with iPhone and Mac sales, growing 22% and 29% respectively, driving most of the growth. However, the stock fell over 8% post-earnings. The primary concern was a weaker Q4 revenue growth forecast of 9-11%, below expectations, due to looming supply chain constraints. Apple is feeling the indirect cost of the AI boom. Soaring memory and chip prices, fueled by massive data center investments from Microsoft, Amazon, and others, are forcing Apple to raise Mac and iPad prices significantly. The upcoming iPhone launch is also expected to see substantial price hikes. Despite avoiding heavy AI infrastructure spending—its capital expenditures are actually down 28%—Apple cannot escape the industry-wide supply and cost pressures. While Apple's operating cash flow remains robust, its substantial R&D spending (up 32% YoY) has yet to yield major AI breakthroughs. As Tim Cook prepares to step down as CEO, Apple faces a challenging transition: balancing its premium hardware success against the strategic and cost pressures of the AI era it has so far cautiously navigated.

marsbit2 h fa

Low Investment Isn't Apple's Immunity Pass

marsbit2 h fa

Trading

Spot
活动图片