Arthur Hayes’ Privacy Trade: Why NEAR and Zcash Sit at the Center of His AI-Era Crypto Thesis

Arthur Hayes has never been subtle about macro trades. When the former BitMEX chief and Maelstrom CIO sees a narrative forming, he tends to say the quiet part out loud. This time, the trade is privacy. Not privacy as a cypherpunk slogan, not privacy as a niche feature buried in wallet settings, but privacy as a market category that could become one of crypto’s dominant themes in an age of artificial intelligence, platform surveillance, and state-level financial monitoring.

His core argument is blunt: as AI makes blockchain analysis cheaper, faster, and more automated, the value of private digital money should rise. In that framework, Hayes has singled out two assets as central to his thesis: Zcash and NEAR. “Zcash is the first place you go,” Hayes said, arguing that anonymous value transfer across chains is a major unlock. He also framed the pair in risk-adjusted terms, suggesting NEAR has roughly “20x potential,” while Zcash could do “5x over the next year,” with NEAR carrying the larger upside and the higher risk. The Rollup published the remarks, including Hayes’ view that “these two form the core” of his privacy thesis in an “AI, big tech, big government universe.”

Privacy Is Becoming a Macro Trade, Not Just a Crypto Feature

For years, privacy coins were treated as an ideological corner of the crypto market. Monero, Zcash, Dash, and smaller projects existed largely outside the dominant investment narratives of smart contracts, DeFi, NFTs, modular blockchains, or Bitcoin-as-digital-gold. Privacy was important to some users, but it rarely commanded the same speculative oxygen as scalability or yield.

Hayes is arguing that this is changing because the surveillance environment has changed. Public blockchains are transparent by default. That transparency was once marketed as a feature: anyone could verify transactions, audit supply, and inspect flows. But transparency also creates a permanent data exhaust. Every wallet interaction, exchange withdrawal, bridge transfer, token swap, liquidation, NFT purchase, and DAO vote can become part of a behavioral profile.

AI intensifies that problem. Blockchain analytics already links addresses, clusters wallets, and tracks funds across chains. Add modern machine learning, better entity databases, and more aggressive regulatory integration, and public-chain activity becomes easier to classify at scale. In Hayes’ thesis, privacy becomes valuable not because users suddenly become criminals, but because normal financial life cannot function well when every counterparty, competitor, government agency, and machine-learning model can read the ledger.

That is the emotional force behind the trade. The crypto industry spent a decade telling users to “be your own bank.” Hayes is asking what happens when that bank has glass walls.

Why Zcash Comes First

Zcash is the obvious first pillar of Hayes’ trade because it is one of the few crypto assets built specifically around private value transfer. Unlike Bitcoin, where every transaction is visible, Zcash uses zero-knowledge proofs to allow shielded transactions that can hide sender, receiver, and amount while still proving that the transaction is valid.

That distinction matters. Zcash does not merely promise discretion through wallet hygiene or mixer-style obfuscation. Its privacy model is rooted in cryptography. The network allows both transparent and shielded addresses, which has always been a double-edged sword. Optional privacy can make Zcash more flexible and more compatible with regulated environments, but it also means privacy depends on user behavior and shielded-pool adoption.

That adoption appears to have improved materially. Coin Metrics reported that ZEC held in shielded addresses had grown to around 4.9 million coins, roughly 30% of current supply, up sharply from the start of 2025. That figure matters because a privacy system becomes more useful as the anonymity set grows. The more value sitting in shielded pools, the harder it becomes to isolate individual users through simple heuristics.

Hayes’ point about Zcash being “the first place you go” is therefore easy to understand. If the market decides that financial privacy is no longer optional, Zcash is one of the few liquid, recognizable, battle-tested assets that directly expresses that view. It has a Bitcoin-like supply cap of 21 million coins, a long operating history, and a privacy brand that has survived multiple cycles.

But Zcash is not a perfect instrument. Its optional privacy design means transparent usage still exists. Older academic work has shown that careless movement between transparent and shielded addresses can weaken anonymity. Researchers have also warned that privacy can be degraded by usage patterns and network-level observation, even when the cryptography itself is strong.

That is the central tension. Zcash may be the cleanest liquid bet on private digital cash, but its real-world privacy depends on how people use it, how wallets guide behavior, and whether shielded adoption continues rising.

Why NEAR Belongs in the Same Conversation

NEAR is a less obvious privacy trade, which is why Hayes’ inclusion of it is interesting. Zcash is about private money. NEAR is about private execution, cross-chain coordination, and potentially private AI-agent commerce.

The key development is NEAR’s Confidential Intents. NEAR describes Intents as infrastructure that lets users express what they want to do across chains, while solvers handle execution. In plain English, instead of manually bridging, swapping, routing, and settling across networks, a user can state an outcome and let the system find the path. NEAR positions this as a “Universal Transaction Layer for the AI Economy,” connecting chains, assets, and agents.

Confidential Intents add privacy to that architecture. NEAR says the system executes cross-chain transactions in a restricted-visibility environment before settlement, designed to reduce front-running, MEV extraction, and strategy copying. A related NEAR announcement described confidentiality across transfers, deposits, and withdrawals while preserving verifiable on-chain execution.

This is where NEAR’s privacy thesis diverges from Zcash. Zcash protects value transfer at the asset layer. NEAR is trying to make transaction intent, routing, and agentic execution less exposed. That is potentially powerful because the next wave of crypto may not be dominated by humans clicking swap buttons. It may be dominated by agents, wallets, bots, and applications negotiating across chains on behalf of users.

In that world, privacy is not only about hiding payment amounts. It is about hiding strategy. An AI agent managing treasury movements, paying service providers, rebalancing assets, or coordinating DeFi positions may not want every instruction visible before execution. Public intent is valuable information. If the market can see what an agent wants before settlement, searchers and competitors can exploit it.

That is why Hayes can plausibly see NEAR as the higher-upside leg. It is not just a privacy coin. It is a bet that privacy becomes a core primitive for the agentic economy.

Zcash Is the Purist Bet; NEAR Is the Infrastructure Bet

The cleanest way to understand Hayes’ pairing is this: Zcash is the privacy asset, NEAR is the privacy rail.

Zcash offers a direct expression of anonymous digital value. It is simple, legible, and already culturally associated with private money. If investors wake up and decide they need exposure to privacy, ZEC is easy to understand. The thesis does not require a complex ecosystem map. It requires belief that shielded value transfer becomes more important and that Zcash remains one of the leading ways to access it.

NEAR requires a more sophisticated thesis. It depends on cross-chain activity growing, intents becoming a dominant transaction pattern, AI agents needing private settlement, and NEAR capturing enough of that flow to justify a major repricing. That is why Hayes can talk about NEAR’s larger upside while also acknowledging higher risk. A 20x outcome usually demands more than narrative recognition. It requires execution, adoption, liquidity, developer activity, and a market willing to re-rate the asset.

Zcash’s path is narrower but cleaner. NEAR’s path is wider but messier. One is a monetary privacy trade. The other is a private coordination trade.

The AI Angle Is Not Marketing Fluff

The phrase “AI, big tech, big government universe” could sound like standard crypto paranoia. But there is a real structural issue underneath it. AI changes the economics of surveillance. Tasks that once required specialized analysts can be automated. Wallet behavior can be modeled. Transaction histories can be scored. Cross-chain patterns can be stitched together. Off-chain identity leaks can be combined with on-chain flows.

This does not only affect dissidents or whales. It affects ordinary users who may not want salaries, savings, trading histories, donations, business payments, or health-related purchases permanently visible. It affects companies that cannot operate with fully public treasury movements. It affects AI agents that may need to pay, trade, and coordinate without broadcasting every strategic instruction.

Public blockchains are excellent settlement systems, but they are poor confidentiality systems. Hayes is betting that the market eventually notices the difference.

The Competition: Monero, Ethereum L2s, and Privacy Startups

Hayes’ Zcash-and-NEAR framing does not mean the rest of the privacy sector disappears. Monero remains the most established always-private payment coin. Unlike Zcash, privacy is mandatory by design, which gives Monero ideological purity and a strong user base. But that same design has also made Monero more vulnerable to exchange delistings and regulatory discomfort.

Ethereum-based privacy protocols are another competitive front. Zero-knowledge infrastructure, privacy pools, confidential DeFi, and encrypted mempools could all capture parts of the opportunity. The challenge is that Ethereum privacy has often struggled with usability, compliance concerns, and fragmentation. Users may want privacy, but they do not want to manage a maze of specialized tools.

Then there are newer projects focused on fully homomorphic encryption, trusted execution environments, confidential computing, and private AI. These may eventually become important, but many are earlier-stage and harder to underwrite. Hayes’ choices reflect liquidity and narrative clarity as much as technology. Zcash and NEAR are not the only privacy plays, but they are large enough and recognizable enough to become market vehicles.

The Weaknesses in Hayes’ Thesis

The first weakness is regulation. Privacy assets sit in a sensitive zone. Governments may tolerate selective disclosure, view keys, and compliance-friendly privacy, but they remain wary of tools that can obscure flows. Zcash may be better positioned than some rivals because its model allows transparency and selective disclosure, but privacy coins still face listing risk, jurisdictional pressure, and institutional hesitation.

The second weakness is adoption. Privacy is something users claim to value but often fail to use. Convenience regularly beats principle. If shielded wallets are clunky, if bridges are limited, if liquidity is thin, or if exchange support weakens, the thesis can stall. Zcash needs shielded usage to keep rising. NEAR needs Confidential Intents to become more than a compelling demo.

The third weakness is competition from incumbents. If Ethereum wallets, stablecoin issuers, L2s, or major exchanges build acceptable privacy layers, the market may not concentrate value in ZEC or NEAR. Privacy could become a feature rather than a standalone asset category.

The fourth weakness is reflexivity. Hayes is influential. His endorsement can move attention, but attention-driven trades can overshoot. ZEC has already experienced major rallies tied to the privacy narrative. If the trade becomes crowded before usage catches up, volatility could be brutal.

Why the Trade Still Matters

Even with those risks, Hayes’ privacy thesis deserves attention because it reframes a forgotten category around a current problem. Privacy is no longer only a philosophical debate about individual liberty. It is becoming an infrastructure problem for AI-mediated finance.

If agents are going to transact, they need confidentiality. If institutions are going to use public rails, they need protection from strategy leakage. If users are going to hold digital assets directly, they need something better than permanent financial exposure. If governments and platforms continue expanding monitoring capacity, private settlement becomes more valuable as a counterweight.

Zcash and NEAR represent two different answers to that problem. Zcash says the world needs private money. NEAR says the world needs private intent execution across chains and agents. Hayes’ bet is that both answers will matter.

Verdict: A High-Conviction Trade With Very Different Risk Profiles

Arthur Hayes’ privacy trade is not simply a call on ZEC and NEAR prices. It is a call on the next phase of crypto demand. The first era proved that digital scarcity could work. The second era proved that programmable finance could work. The AI era may force the market to confront whether transparent-by-default finance is actually usable at scale.

Zcash is the cleaner trade. It has the brand, the monetary simplicity, the shielded transaction model, and the most direct link to private value transfer. Its upside may be lower than a high-beta infrastructure asset, but its narrative is easier for the market to price.

NEAR is the more ambitious trade. If Confidential Intents become a serious layer for cross-chain transactions, AI agents, stablecoin flows, and private execution, NEAR could be valued less like another Layer 1 and more like infrastructure for confidential digital commerce. That is the 20x version of the thesis. It is also the version with more execution risk.

Hayes’ pairing makes sense because it covers both sides of the privacy stack. Zcash protects the money. NEAR protects the movement, intent, and coordination around that money. In a world where AI watches everything, that combination may be more than a narrative. It may be one of crypto’s most important strategic fault lines.