Privacy AI narrative gains momentum as OpenClaw promotes Venice

Original | Odaily Planet Daily (@OdailyChina)

Author | DingDang (@XiaMiPP)

The hot new project OpenClaw has begun endorsing privacy AI, and desperate crypto retail investors seem to have found a new炒作 direction again.

It is within this narrative context that a number of projects related to privacy computing and AI Agent infrastructure are re-entering the market spotlight. Odaily Planet Daily has identified that, during this surge in discussion, several projects have already emerged as potential beneficiaries.

Venice is an AI generation platform focused on censorship resistance and privacy, positioned as a decentralized alternative to ChatGPT. The hype around privacy-focused AI began with Venice, after OpenClaw previously highlighted it in official documentation—only to remove the recommendation within 24 hours. Although the recommendation was taken down, this action drew more attention to Venice and its privacy-first approach.

Unlike most AI projects, Venice’s core narrative is not about the capabilities of AI models, but about privacy itself. Amid growing content moderation on mainstream AI platforms and accumulating controversies over AI data leaks and model training, this “no logging, no censorship” product positioning resonates precisely with the most sensitive values of the crypto community.

In the era when the AI Agent trend is rapidly gaining momentum, Venice has perfectly capitalized on this "tailwind." Even more coincidentally, the Venice team is actively reducing the supply of VVV tokens to curb inflation. Increased demand coupled with reduced supply further strengthens the positive feedback expectations for the VVV token.

Read more: OpenClaw Backs Venice.ai, Token VVV Surges Over 500% in January

Near Protocol, a longstanding public chain project once renowned for its high performance, is actively seeking self-renewal amid the AI wave. It is no longer solely focused on traditional L1 metrics like TPS and low gas fees, but is gradually shifting its narrative toward becoming the execution and settlement infrastructure for the AI Agent era, aiming to find a new growth story in this next technological cycle.

Since 2025, we have strongly promoted NEAR Intents, a system that allows users or AI agents to simply express their “desired end result,” while the backend automatically executes complex operations across 35+ chains without requiring manual bridging, wallet switching, or route management.

On February 25, 2026, NEAR officially upgraded its intent system with the launch of Confidential Intents. This version introduces privacy-preserving computation to the existing intent execution framework, leveraging NEAR’s privacy sharding mechanism combined with Trusted Execution Environments (TEEs) to conceal critical details during cross-chain transactions—such as swap paths, transaction sizes, or specific strategies. However, unlike Zcash or Monero, it does not enforce privacy for all transactions; instead, it adds an optional layer of privacy protection for intent execution. Its primary goal is not to anonymize transactions, but to prevent on-chain arbitrage practices such as MEV, front-running, and sandwich attacks, making transaction execution significantly more secure.

In the future, AI agents may become the primary "users" of blockchains, autonomously owning assets, conducting cross-chain transactions, executing strategies, and even coordinating with each other. Under this vision, blockchains must not only handle high-frequency transactions but also provide capabilities such as verifiable execution, privacy-preserving computation, and cross-chain coordination.

Near's current layout is precisely built around this vision. It aims to construct an open network that can support AI agents in autonomously executing complex tasks while ensuring verifiability and security. Against the backdrop of the ongoing AI wave, this transformation can be seen as both an active embrace of a new narrative and a reimagining by a seasoned public chain in a new cycle.

The core objective of Sahara AI is to build a decentralized, transparent, and secure AI ecosystem that makes the development, training, deployment, and commercialization of AI more fair and trustworthy. The project is committed to addressing current challenges in the AI industry, including data privacy, algorithmic bias, and unclear model ownership.

The rise of AI Agents is bringing a new issue: who owns the data, models, and capabilities used by these Agents? In today’s AI industry structure, this question has not been adequately addressed. The data required to train models often comes from a large number of decentralized contributors, yet the profits are highly concentrated in the hands of a few AI companies; even model developers with technical capabilities are often dependent on platform ecosystems; and as AI Agents begin to autonomously invoke models, data, and tools, the entire value chain becomes even more complex. Without a clear framework for ownership and revenue sharing, the future AI economy risks repeating the Web2 pattern—where data belongs to users, but value is captured by platforms.

Sahara AI is attempting to establish new rules in this area. Its ClawGuard security system provides verifiable safety guardrails for AI agents, ensuring they operate securely within predefined rules, while the Data Service Platform (DSP) allows users to earn token incentives by annotating and contributing AI training data, gradually forming a decentralized data marketplace. Under this mechanism, data contributors not only participate in the AI model training process but also receive ongoing rewards when their data is used, while the platform ensures data quality and privacy protection through on-chain mechanisms.

Phala Network is a privacy-preserving smart contract platform built on Substrate, designed to provide verifiable privacy-preserving computation services for Web3 applications. To understand why Phala stands to benefit from the AI Agent boom, one must first answer a more fundamental question: What infrastructure do AI Agents truly rely on to operate?

If we break down the current Agent ecosystem, its technology stack can be roughly divided into several layers. At the top is the model layer, consisting of various large language models or reasoning models, such as OpenAI, Claude, and a series of open-source models; beneath that is the Agent framework layer, including tools like LangChain, AutoGPT, and OpenClaw, which are responsible for organizing tasks, scheduling models, and invoking external tools; below that is the execution environment layer, where Agents actually run code, call APIs, and perform automated tasks; additionally, there is a payment and identity layer that handles payments, identity, and reputation systems between Agents; and at the lowest level is the compute and privacy layer, which ensures trustworthy computation and secure data that is not leaked.

From this structure, Phala sits precisely at the intersection of the execution environment layer and the compute privacy layer. Its core technology—a confidential computing network based on TEE (Trusted Execution Environment)—enables AI agents to securely run programs off-chain while ensuring verifiable computation and protection of data from external exposure. This is especially critical in the agent economy.

In terms of practical ecosystem applications, Phala has already begun integrating with AI Agent projects. For example, Phala collaborated with ai16z to build a TEE component for its Eliza multi-agent framework, directly integrating trusted execution technology into the Agent runtime environment. Meanwhile, some AI Agent token projects (such as aiPool) have also adopted Phala’s TEE technology to manage private keys and on-chain assets.

In the future, as AI Agents evolve from chat tools into digital entities capable of holding funds, executing trades, and even operating protocols, secure execution environments will gradually become an essential infrastructure layer for the entire Agent ecosystem, and Phala is striving to occupy this position.

When reviewing these projects, an interesting observation is that these tokens actually began rising well before the recent recommendations. In other words, before Venice brought “privacy AI” into the spotlight, a portion of market capital had already noticed this direction—there was simply no clear narrative trigger at the time. The OpenClaw recommendation was merely the spark that ignited attention.

In fact, whether it's a16z or Delphi Digital, both have identified privacy and AI as key focus areas for 2026 in their 2025 annual research reports. However, when such macro-level insights translate into the market, they often require a specific event to spark consensus. In early 2026, privacy and AI arrived before us precisely in this combined form.

It will still take time to determine whether this becomes the next long-term trend or merely another brief thematic rally.