Original | Odaily Planet Daily (@OdailyChina)
Author | Dingdang (@XiaMiPP)
The popular sensation OpenClaw has started to endorse privacy AI, and the "desperate cryptocurrency investors" seem to have found a new direction for speculation.
In this narrative context, a batch of projects related to privacy computing and AI Agent infrastructure have started to re-enter the market view. Odaily Planet Daily found that several projects have already become potential beneficiaries during this wave of heated discussion.
VVV (#133)
Venice is an AI generation platform focusing on no censorship + privacy, positioned as a decentralized version of ChatGPT. The starting point of speculation around privacy AI comes from Venice. OpenClaw once highlighted Venice in official documents, but removed it rapidly within 24 hours. Although the recommendation can be removed, this action made more people start to pay attention to Venice and its privacy-first characteristics.
Unlike most AI projects, Venice's core narrative is not the capability of AI models, but privacy itself. Amid the background of mainstream AI platforms gradually strengthening content censorship, accumulating controversies over AI data leakage and model training, this "no recording, no censorship" product positioning happens to hit the most sensitive values of the cryptocurrency community.
In the rapidly fermenting era of AI Agents, Venice happens to seize this "era dividend." Coincidentally, the Venice project team is actively reducing the supply of VVV tokens to decrease inflation. The increase in demand coinciding with the reduction in supply further reinforces the positive feedback expectation for the VVV token.
Reading reference: “OpenClaw strongly supports Venice.ai, VVV token skyrockets over 500% in January”
NEAR (#43)
Near Protocol, an established public chain project known for its high performance, is actively self-rescuing under the impact of the AI wave. It is no longer just pursuing TPS and low gas fees as a "traditional L1," but gradually shifting its narrative focus to the execution layer and settlement infrastructure in the AI Agent era, attempting to find new growth narratives in a new round of technology cycles.
Since 2025, it has been vigorously promoting NEAR Intents, a system that allows users or AI agents to express "the final desired result," automatically completing complex operations across over 35 chains in the background, without the need for manual bridging, wallet switching, or router 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, allowing cross-chain transactions to hide key details during execution, such as exchange paths, transaction scales, or specific strategies, through Near's privacy sharding mechanism combined with Trusted Execution Environment (TEE). However, it does not implement mandatory privacy for all transactions like Zcash or Monero but adds an optional privacy protection layer for intent execution. Its main goal is not the anonymization of transactions, but to prevent on-chain arbitrage activities like MEV, front-running, and sandwich attacks, making transactions safer during execution.
In the future, AI agents may become the main "users" of blockchain; they will independently own assets, engage in cross-chain transactions, execute strategies, and even coordinate with each other. In this scenario, blockchain needs to handle high-frequency trading while also providing verifiable execution, privacy computing, and cross-chain coordination capabilities.
Near's current layout revolves around this imagination. It aims to build an open network that can support AI agents in automatically executing complex tasks while ensuring verifiability and safety throughout the process. Against the backdrop of the ongoing AI wave, this transformation can be viewed as an attempt to actively embrace a new narrative or as a self-reconstruction 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 the processes of AI development, training, deployment, and commercialization fairer and more trustworthy. The project is dedicated to solving current issues in the AI industry, such as data privacy, algorithm bias, and unclear model ownership.
The rise of AI Agents brings a new question: Who actually owns the data, models, and capabilities used by these agents? In the current AI industry structure, this question has not been well addressed. The data required to train models often comes from numerous dispersed contributors, but the final benefits are highly concentrated in a few AI companies; even if model developers possess technical capabilities, they often have to rely on platform ecosystems; and as AI Agents begin to autonomously utilize models, data, and tools, the entire value chain becomes more complex. Without a clear rights confirmation and profit sharing mechanism, the future AI economy may repeat the path of Web2, where data belongs to users but value is captured by platforms.
Sahara AI attempts to establish new rules in this link. Its ClawGuard security system provides verifiable safety barriers for AI agents, ensuring they operate safely within preset rules, while the Data Service Platform (DSP) allows users to obtain token incentives by labeling and contributing AI training data, gradually forming a decentralized data market. In this mechanism, data contributors can participate in the AI model training process and earn ongoing profits when their data is used, while the platform also ensures data quality and privacy protection through on-chain mechanisms.
PHA (#601)
Phala Network is a privacy smart contract platform built on Substrate, designed to provide verifiable privacy-preserving computing services for Web3 applications. To understand why Phala would benefit from the rise of AI Agents, one must first answer a more fundamental question: What infrastructure do AI Agents 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 it is the Agent framework layer, including tools like LangChain, AutoGPT, OpenClaw, which are responsible for organizing tasks, scheduling models, and calling external tools; further down is the execution environment layer, which is where Agents actually run code, call APIs, and execute automated tasks; additionally, there is a payment and identity layer for handling payments, identity, and reputation systems between Agents; at the bottom layer, there is the computing power and privacy layer, responsible for ensuring that the computing process is trusted and data is securely not leaked.
From this structure, Phala occupies a position that spans the execution environment layer and the computing power and privacy layer. Its core technology—confidential computing network based on TEE (Trusted Execution Environment)—enables AI Agents to run programs securely off-chain while ensuring the computing process is verifiable and data is not externally monitored. This is particularly crucial in the Agent economy.
In terms of specific ecological implementation, Phala has also begun to collaborate with AI Agent projects. For example, Phala partnered with ai16z to build TEE components for its Eliza multi-agent framework, directly integrating trusted execution technology into the Agent operating environment; at the same time, some AI Agent token 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" to 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 trying to occupy this position.
Conclusion
While reviewing these projects, an interesting discovery is: the time when these tokens truly began to rise was actually earlier than the recommendation event of the past few days. In other words, before Venice brought "privacy AI" to the forefront, a portion of funds in the market had already noticed this direction early on; however, at that time, there was a lack of a sufficiently clear narrative trigger point. The recommendation event by OpenClaw was merely a fuse to ignite attention.
In fact, both a16z and Delphi Digital in their annual investment research reports for 2025 listed privacy and AI as key focus tracks for 2026. However, when these macro judgments are truly reflected in the market, they often require a specific event to trigger consensus. In early 2026, privacy and AI presented themselves to us in this integrated manner.
As for whether this will become the next long-term trend or yet another brief thematic speculation, time will likely provide the answer.
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