OpenClaw "selling" Venice, what other targets are there in the privacy AI sector?

Original | Odaily Planet Daily (@OdailyChina)

Author | Dingdang (@XiaMiPP)

As the popular OpenClaw begins to endorse privacy AI, it seems that the “desperate crypto retail investors” are once again finding a new hype direction.

In this narrative context, a group of projects related to privacy computing and AI Agent infrastructure have started to re-enter the market spotlight. Odaily Planet Daily has found that during this wave of increased discussion, several projects have already become potential beneficiaries.

VVV (#133)

Venice is a decentralized AI generation platform focused on censorship resistance and privacy, positioning itself as a decentralized version of ChatGPT. The origin of the hype around privacy AI began with Venice. Because OpenClaw once highlighted Venice in its official documentation, but then quickly removed it within 24 hours. Although the recommendation was removed, this move 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 about privacy itself. Amid the gradual strengthening of content moderation on mainstream AI platforms and ongoing controversies over data leaks and model training, this “no logging, no censorship” product positioning hits the most sensitive values in the crypto community.

In the era of rapid AI Agent hype, Venice just happened to ride this “wave of the times.” Coincidentally, Venice’s team is actively reducing the supply of VVV tokens to curb inflation. Increased demand combined with reduced supply further reinforces positive feedback expectations for the VVV token.

Read more: 《OpenClaw strongly supports Venice.ai, VVV token surges over 500% in January》

NEAR (#43)

Near Protocol, a veteran public chain known for high performance, is also actively seeking self-rescue amid the AI wave. It is no longer just pursuing TPS and low gas fees as a “traditional L1,” but is gradually shifting its narrative focus toward execution layer and settlement infrastructure for the AI Agent era, trying to find new growth stories in this new technological cycle.

Since 2025, NEAR has been vigorously promoting NEAR Intents, a system that allows users or AI agents to express their “desired end result,” with the backend automatically completing complex operations across 35+ chains, eliminating the need for manual bridging, wallet switching, or routing.

On February 25, 2026, NEAR officially upgraded this intent system to introduce Confidential Intents. This version adds privacy computing capabilities to the existing intent execution framework, utilizing Near’s privacy sharding mechanism combined with Trusted Execution Environment (TEE), enabling cross-chain transactions to hide key details during execution, such as exchange paths, transaction sizes, or specific strategies. However, it does not enforce privacy on all transactions like Zcash or Monero but adds an optional privacy layer to intent execution. Its main goal is not to anonymize transactions but to prevent MEV, front-running, and sandwich attacks, making transactions safer during execution.

In the future, AI agents may become the main “users” of blockchain, autonomously holding assets, conducting cross-chain transactions, executing strategies, and even coordinating with each other. Under this scenario, blockchains will need to handle high-frequency trading, provide verifiable execution, privacy computing, and cross-chain coordination.

Near’s current layout is precisely aligned with this vision. It aims to build an open network capable of supporting AI agents to automatically execute complex tasks while ensuring verifiable and secure processes. In the context of ongoing AI waves, this transformation can be seen as an active embrace of new narratives or as a self-reinvention of an established public chain in a new cycle.

SAHARA (#295)

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

The rise of AI Agents has brought a new problem: who owns the data, models, and capabilities these agents use? In the current AI industry structure, this issue is not well resolved. Training data often comes from numerous dispersed contributors, but the profits are highly concentrated among a few AI companies; model developers, even with technical skills, are often dependent on platform ecosystems; and as AI Agents begin to autonomously call models, data, and tools, the entire value chain becomes more complex. Without clear rights and profit-sharing mechanisms, the future AI economy may repeat Web2’s path—data owned by users, but value captured by platforms.

Sahara AI is attempting to establish new rules in this area. Its ClawGuard security system provides verifiable safety barriers for AI agents, ensuring they operate within preset rules. Data service platforms (DSPs) allow users to earn tokens by labeling and contributing training data, gradually forming a decentralized data marketplace. Under this mechanism, data contributors can participate in AI model training and receive ongoing rewards when their data is used, while the platform ensures data quality and privacy protection through on-chain mechanisms.

PHA (#601)

Phala Network is a Substrate-based privacy smart contract platform designed to provide verifiable privacy-preserving computation services for Web3 applications. To understand why Phala benefits from the AI Agent boom, we need to answer a fundamental question: what infrastructure does AI Agent operation rely on?

Breaking down the current Agent ecosystem, its tech stack can be roughly divided into several layers. The top layer is the model layer, including large language models or inference models like OpenAI, Claude, and open-source models; below that is the Agent framework layer, including tools like LangChain, AutoGPT, OpenClaw, responsible for task organization, scheduling, and external tool calls; further down is the execution environment layer, where Agents run code, call APIs, and perform automated tasks; there are also payment and identity layers for handling transactions, identity, and reputation; at the bottom are the compute and privacy layers, ensuring trusted computation and data security.

From this structure, Phala’s position spans the execution environment and privacy compute layers. Its core technology—TEE (Trusted Execution Environment)-based confidential computing network—allows AI Agents to run programs securely off-chain while ensuring verifiability and data confidentiality. This is especially critical in the Agent economy.

In practical applications, Phala has already begun integrating with AI Agent projects. For example, Phala partnered with ai16z to build TEE components for its Eliza multi-agent framework, embedding trusted execution tech into the Agent runtime environment; some AI Agent token projects (like aiPool) also use Phala’s TEE tech to manage private keys and on-chain assets.

As AI Agents evolve from chat tools to digital entities capable of holding funds, executing trades, and operating protocols, secure execution environments will become an indispensable infrastructure layer, and Phala aims to occupy this position.

Conclusion

A fascinating discovery when reviewing these projects is that their tokens started to rise well before the recent hype events. In other words, before Venice pushed “privacy AI” to the forefront, some market participants had already noticed this trend early, but lacked a clear narrative trigger. OpenClaw’s endorsement was just a spark that ignited attention.

In fact, both a16z and Delphi Digital’s 2025 annual research reports listed privacy and AI as key focus areas for 2026. But when these macro judgments materialize in the market, they often require a specific event to trigger consensus. Early 2026, privacy and AI combined in this way, presenting themselves directly to us.

Whether this will become the next long-term trend or just another short-lived hype remains to be seen.