OpenClaw "Promotes" Venice: What Other Targets Are There in the Privacy AI Track?

Original | Odaily (@OdailyChina)

Author | Ding Dang (@XiaMiPP)

As the current hot topic OpenClaw begins to endorse privacy AI, the "desperate crypto retail investors" seem to have found a new direction for speculation.

It is precisely within this narrative context that a batch of projects related to privacy computing and AI Agent infrastructure have started to re-enter the market's view. Odaily's review found that during this wave of heated discussion, several projects have already become potential beneficiaries.

VVV (#133)

Venice is an AI generation platform focused on censorship resistance and privacy, positioning itself as a decentralized version of ChatGPT. The starting point of the privacy AI hype originated from Venice. This is because OpenClaw once highlighted Venice in its official documentation, only to remove it hastily within 24 hours. Although the recommendation could be removed, this action drew more attention to Venice and its privacy-first features.

Unlike most AI projects, Venice's core narrative is not about AI model capabilities, but privacy itself. Against the backdrop of mainstream AI platforms increasingly strengthening content moderation, and amidst ongoing controversies over AI data leaks and model training, this "no logging, no censorship" product positioning precisely hits the most sensitive values of the crypto community.

In an era where the AI Agent boom is rapidly fermenting, Venice has just stepped into this "era dividend." More coincidentally, the Venice project team is actively reducing the token supply of VVV, decreasing inflation. Increased demand meeting reduced supply further strengthens the positive feedback expectations for the VVV token.

Read Reference: 《OpenClaw Strongly Supports Venice.ai, VVV Token Surges Over 500% in One Month》

NEAR (#43)

Near Protocol, this veteran public chain project once known for its high performance, is also actively seeking self-reinvention under the impact of the AI wave. It is no longer just a "traditional L1" pursuing TPS and low gas fees, but is gradually shifting its narrative focus towards being the execution layer and settlement infrastructure for the AI Agent era, attempting to find a new growth narrative in the new technological cycle.

Since 2025, it has been vigorously promoting the NEAR Intents system. This system allows users or AI agents to simply express "the final desired outcome," and the backend will automatically complete complex operations across 35+ chains without manual bridging, wallet switching, or routing management.

On February 25, 2026, NEAR officially upgraded this intent system, launching Confidential Intents. This version introduces privacy computing capabilities into the original intent execution framework. By combining Near's privacy sharding mechanism with Trusted Execution Environments (TEE), it enables cross-chain transactions to hide key details during execution, such as swap paths, transaction size, or specific strategies. However, it does not enforce privacy on all transactions like Zcash or Monero, but rather adds an optional layer of privacy protection to intent execution. Its main goal is not to anonymize transactions, but to prevent on-chain arbitrage behaviors like MEV, front-running, and sandwich attacks, thereby making transactions more secure during execution.

In the future, AI agents may become the primary "users" of blockchains. They will autonomously hold assets, conduct cross-chain transactions, execute strategies, and even coordinate with each other. Under this vision, blockchains not only need to handle high-frequency transactions but must also provide capabilities like verifiable execution, privacy computing, and cross-chain coordination.

Near's current layout is precisely centered around this vision. It attempts to build an open network that can both support AI agents in automatically executing complex tasks and ensure the process is verifiable and secure. Against the backdrop of the continuous impact of the AI wave, this transformation can be seen as both an attempt to actively embrace a new narrative and a self-remodeling of a veteran public chain in the new cycle.

SAHARA (#295)

The core goal of Sahara AI is to build a decentralized, transparent, and secure AI ecosystem, making the development, training, deployment, and commercialization of AI more fair and trustworthy. The project is committed to solving current issues in the AI industry such as data privacy, algorithmic bias, and unclear model ownership.

The rise of AI Agents is bringing forth a new problem: Who exactly owns the data, models, and capabilities used by these Agents? In the current AI industry structure, this problem is not well resolved. The data required to train models often comes from a large number of dispersed contributors, but the final profits are highly concentrated in the hands of a few AI companies; model developers, even with technical capabilities, often can only rely on platform ecosystems; and as AI Agents begin to autonomously call models, data, and tools, the entire value chain becomes more complex. Without a clear mechanism for rights confirmation and profit sharing, the future AI economy is likely to repeat the Web2 path where data belongs to users, but value is captured by platforms.

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

PHA (#601)

Phala Network is a privacy-preserving smart contract platform built on Substrate, aiming to provide verifiable privacy-preserving computation services for Web3 applications. To understand why Phala benefits from the AI Agent boom, we first need to answer a more fundamental question: What infrastructure does the operation of AI Agents actually rely on?

If we break down the current Agent ecosystem, its tech stack can roughly be divided into several layers. The top layer is the model layer, consisting of various large language models or inference models, such as OpenAI, Claude, and a series of open-source models. Below that is the Agent framework layer, including tools like LangChain, AutoGPT, and OpenClaw, which are responsible for organizing tasks, scheduling models, and calling external tools. Further down is the execution environment layer, where Agents actually run code, call APIs, and execute automated tasks. Additionally, there is the payment and identity layer for handling payments, identity, and reputation systems between Agents. At the very bottom is the computing power and privacy layer, responsible for ensuring the computation process is trustworthy and data is secure from leaks.

From this structure, Phala's position precisely spans the execution environment layer and the computing power/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 the computation process is verifiable and data is not spied on externally. This is particularly crucial in the Agent economy.

In terms of specific ecosystem implementation, 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 launch projects (like 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 transactions, and even operating protocols, secure execution environments will gradually become an indispensable infrastructure layer for the entire Agent ecosystem, and Phala is attempting to occupy this position.

Conclusion

When reviewing these projects, an interesting discovery is: The actual start of the price increase for these tokens predates the recommendation events of the past couple of days. In other words, before Venice brought "privacy AI" to the forefront, a portion of capital in the market had already noticed this direction, but it lacked a sufficiently clear narrative trigger point at the time. The OpenClaw recommendation event was merely a fuse that ignited attention.

In fact, whether it's a16z or Delphi Digital, their annual investment research reports for 2025 listed privacy and AI as key focus areas for 2026. However, when these macro judgments actually land in the market, they often require a specific event to trigger consensus. And at the beginning of 2026, privacy and AI have come before us in this combined form.

As for whether this will become the next long-term trend or just another short-lived thematic speculation, only time will likely provide the answer.