Author: Zen, PANews
In today's generative AI applications, users often face fragmented conversational experiences. When switching between different models, the context of previous conversations often cannot be carried over, requiring users to start from scratch and repeatedly provide information. For example, details discussed about a project on ChatGPT cannot be directly inherited when switching to Claude or other models, severely impacting efficiency.
Moreover, the conversational data of these large models is typically stored on the servers of various platforms, leaving users without privacy protection and lacking control over their own data. "These real issues not only create a disjointed user experience but also raise concerns about user data sovereignty and security."
To address this pain point, the industry has begun exploring the concept of a "transferable, user-controlled memory layer," and blockchain technology may be key to achieving this goal.
Based on the open interoperability of blockchain, it may be possible to create a privacy memory layer that saves AI context as a digital asset, allowing for seamless transfer across multiple AI platforms, eliminating the worry of "forgetting" past communication every time a tool is changed, while ensuring data privacy and sovereignty.
ZetaChain 2.0 Launches, Creating a Universal Layer for AI and Web3
In response to the above needs, the cross-chain interoperability-focused public chain project ZetaChain has seized the opportunity of the integration of AI and Web3. In a roadmap review at the end of 2025, ZetaChain announced its "2.0" version plan, which will introduce new features aimed at the AI era on top of its existing universal cross-chain architecture.
On January 27, 2026, ZetaChain 2.0 officially launched, debuting alongside its first AI product—Anuma, a large model aggregation application centered on privacy. According to official information, ZetaChain 2.0 focuses on the following three core capabilities:
The Private Memory Layer is a protocol-level memory system designed specifically for AI interactions, aiming to bridge the contextual gap between AI tools, allowing users' digital memories to truly become assets under their control. Based on the privacy memory layer, all of a user's conversation content is encrypted and stored, with only the user holding the key, and the platform itself has no right to view it. Valuable information generated across different models and at different times will also be controlled by the user, allowing for sustainable accumulation and migration to new conversations without being monopolized by any single AI service.
The AI Portal is a unified routing and execution layer that enables applications to access multiple AI model providers without being locked in, and it includes support for availability, fallback, and cost/performance optimization. The AI Portal is responsible for the underlying model routing and contextual connection, allowing users to freely choose different models such as ChatGPT, Anthropic Claude, and Google Gemini to obtain answers, with previous conversation memories supported by the privacy memory layer.
In addition to the protocol itself, ZetaChain 2.0 has also packaged key capabilities into a developer toolkit (SDK). Developers can directly integrate privacy persistent memory, cross-model switching, and monetization components into their products. This SDK allows applications or AI agents to maintain continuous context across different models and call different model capabilities on demand, significantly reducing the cost and complexity of building infrastructure from scratch.
From a mechanism design perspective, the three core modules complement each other. The private memory layer provides privacy-first user memory and data support, the AI portal enables continuous interaction across mainstream large models, and the SDK ecosystem facilitates efficient and rapid participation from third-party developers. This also allows ZetaChain to expand from a foundational cross-chain protocol to a universal platform serving both Web3 and AI.
Centered on Privacy and User Sovereignty, Anuma Launches and Opens Applications
At the official launch of ZetaChain 2.0, the project team presented another major highlight: the first consumer-grade AI product on the platform, Anuma. Currently, Anuma is in the private beta stage, gradually opening up trial access through an invitation waitlist, allowing users to apply for early access via a public waitlist.
As a large model aggregation application, Anuma integrates multiple mainstream large models, allowing users to call different AI engines within a single conversation. It offers the convenience of aggregation tools like Poe while supporting OpenAI's GPT series and Anthropic's Claude.
When users ask questions, they can specify or change the model used for the response, with switching between engines requiring just a click, without needing to migrate to another application. Users can flexibly choose the most suitable model on Anuma based on the type of question, while the entire conversation process continues in the same window.
On a technical level, thanks to ZetaChain's private memory layer, every segment of conversation in Anuma is encrypted and stored as personal memory, seamlessly migrating to new models or new sessions. When users start a new conversation or switch AI models in an existing conversation, Anuma can securely inject the relevant context to the target model, allowing it to understand the previous background and user intent. This means users do not need to repeatedly explain the same background information between different AIs, greatly enhancing the efficiency of cross-model collaboration.
Traditional Web2 companies have long been criticized for exploiting user data due to their centralized advantages. Issues like platform favoritism and data selling have persisted despite prohibitions. This has led users to remain vigilant and concerned about centralized platforms, a sentiment that has carried over into the rapidly evolving AI field.
Anuma places great importance on the privacy of conversation content and user control. The entire platform employs an end-to-end encryption scheme to protect user data. From the moment users input messages on the front end, the content is encrypted using the user's key before being sent to the privacy memory layer for storage. When context needs to be provided to the AI model, it is decrypted on the user side or in a trusted execution environment before being sent to the model. Throughout the entire process, conversation records are always stored in encrypted form on-chain or in transit, making it impossible for even ZetaChain's nodes or servers to view the content.
This stands in stark contrast to traditional AI chat services, where chat records are typically stored in plaintext on servers, raising concerns about being viewed or leaked by operators. Anuma, through blockchain and encryption technology, achieves a security level similar to that of Web3 wallet private key management, where only the user can interpret their data. This provides a more secure option for AI applications in sensitive scenarios such as law and healthcare, encouraging users to engage in more private conversations.
In fact, before Anuma's launch, there were already some multi-model aggregation AI chat products on the market, notably Poe, launched by Quora, and TypingMind from the open-source community.
Compared to the cloud service models of these two platforms and localized deployments, Anuma's on-chain encrypted storage balances privacy and sovereignty. In terms of usability and model richness, Anuma eliminates the cumbersome configuration process of the TypingMind model, allowing users to directly enjoy a convenient multi-model conversation experience similar to Poe.
Behind the Move into AI: ZetaChain's Technical Logic and Natural Evolution
The ZetaChain team chose to launch version 2.0 and Anuma at this time, backed by deep technical accumulation and a clear evolution logic.
As the first universal L1 blockchain project, ZetaChain has focused on tackling the fragmentation problem in the blockchain space since its launch in 2021, aiming to establish a foundational network connecting all public chains. Built on the Cosmos SDK, it naturally supports interoperability with heterogeneous chains like Ethereum, Bitcoin, and Cosmos.
ZetaChain simplifies traditional cross-chain operations that require bridges and wrapping into a single contract call on a single chain through innovations like CAF, providing users with unified liquidity and user experience. By the end of 2025, ZetaChain's mainnet had integrated ten major blockchain networks, including Bitcoin, covering tens of millions of users, with a cumulative on-chain transaction count reaching 225 million.
In terms of ecosystem and capital, ZetaChain has also gained widespread recognition. According to public data, the project has secured $27 million in funding, with investors including well-known institutions like Blockchain.com, Jane Street, and Sky9 Capital. In 2024-2025, global tech and infrastructure giants such as Google Cloud, Deutsche Telekom, and Alibaba Cloud joined the network as validation nodes, endorsing its security and compliance.
Entering the second half of 2025, with the explosion of generative AI, the ZetaChain team realized that there are actually similar pain points between the multi-chain ecosystem of the industry and multi-model AI, both suffering from fragmentation across multiple platforms and systems, necessitating a universal layer for integration. Thus, they proposed the strategic vision of an "AI universal platform," bringing trusted computing and storage from blockchain into the AI field to create blockchain infrastructure for the AI era.
ZetaChain 2.0 is the realization of this vision. It retains and enhances the original cross-chain functionality while adding AI privacy memory and interaction capabilities. This aligns with ZetaChain's consistent vision of making Web3 equally friendly to both people and AI. The natural evolution from "universal blockchain" to "AI universal platform" not only follows the trend of technological integration but also extends the project's mission.
"ZetaChain has achieved large-scale unification at the blockchain experience level." As ZetaChain core contributor Ankur Nandwani stated, ZetaChain 2.0 will extend the same approach to AI, enabling the next generation of applications and agents to operate between models and blockchain, inherently possessing private, authorized memory capabilities and global monetization channels.
A New Paradigm of Deep Integration Between Blockchain and AI: What Does the Future Hold?
The launch of ZetaChain 2.0 and its debut product Anuma represents an important attempt at the deep integration of blockchain and AI. Within this system, we see a new paradigm for multi-model AI applications: privacy-first, user-controlled, and cross-platform flow.
Of course, it is important to objectively point out that Anuma is still in the very early Private Beta stage, and the overall ecosystem is in the initial stages of construction. Many features and details still require feedback from a wide range of testers to improve, such as support for more model integrations, memory layer capacity and performance optimization, and the richness of third-party developer tools. This means that in the short term, Anuma is far from replacing the mature single-platform experience, and some users will need time to adapt to this new interaction model.
However, it cannot be ignored that the direction represented by Anuma is pioneering. In the track of multi-model aggregation experience, Anuma offers a different approach from large company solutions. Instead of being monopolized by centralized platforms for data and model invocation rights, it returns the choice and memory to users, achieving minimal trust coordination through blockchain technology.
With the open public testing of Anuma and the iteration of its features, more innovative applications may emerge on this platform, such as privacy-preserving AI advisors, cross-domain intelligent search assistants, and more. As for how far this privacy-first multi-model experience trend can go, it remains to be seen over time.
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