Author: Zen, PANews

You spent six months letting ChatGPT understand your work habits, writing style, and long-term projects. It learned how you usually edit articles, which companies you frequently follow, and gradually understood your preferences for content structure, tone, and information density.

But one day, a more powerful new model emerges. You open Claude, Gemini, or DeepSeek, only to find that everything has to start all over again. The new model doesn't know you, doesn't know the working context you've accumulated over the past few months, and doesn't know how you think, write, or make decisions.

Over the past two years, the most important competition in the AI ​​industry has revolved around "model capabilities." Whoever has stronger reasoning, more contextual knowledge, and better coding skills has almost determined everything. But now, a new question is emerging: AI is increasingly understanding you, but to whom does this "understanding" truly belong?

Role shift: AI transforms from a chat tool into a personal digital assistant

In November 2022, the AI ​​chatbot ChatGPT burst onto the scene. Its launch sparked a global chat craze, reaching over 100 million monthly active users in just two months, becoming the fastest-growing consumer application in history. At that time, the large model was more like an "advanced search." Users asked the AI ​​questions, it generated answers instantly, and the relationship ended after the conversation.

However, in the past two years, the role of AI has been undergoing a significant change. With its reasoning, coding, and tool-invoking capabilities constantly improving, AI has begun to penetrate real-world workflows. More and more people are using it to write code, organize documents, analyze data, plan schedules, manage appointments, and even participate in content creation and business decision-making on a long-term basis.

In many cases, users are no longer simply "asking questions of AI," but rather collaborating with AI over the long term. It's beginning to understand your work style, communication habits, and long-term goals, and is increasingly involved in the same projects and workflows, even gradually taking on some execution tasks. To some extent, AI is evolving from a one-off question-and-answer tool into a long-term, personal digital assistant.

As model capabilities improve significantly, leading products become increasingly similar in strength, and AI is used extensively and for a long time, new problems begin to emerge.

Once AI begins to collaborate over extended periods, its "memory"—which stores and recalls past experiences to improve decision-making and overall performance—is no longer just a trivial database. In many applications, the bottleneck is no longer the model's inference capabilities, but rather the ability to manage long-term memory and context. Cloudflare has directly identified agency memory as one of the biggest challenges and fastest-growing areas in current AI infrastructure.

Leading AI companies have also recognized that long-term memory is becoming an integral part of the product experience. OpenAI has broken down ChatGPT's memories into saved memories and Reference chat history. The former stores information that users want to retain long-term, while the latter allows ChatGPT to extract useful content from past conversations for subsequent personalized responses. Gemini has also begun learning user preferences based on previous conversations. Claude has launched Memory, which supports memory import and export.

Platform silos make AI "memory" a new battleground for the industry.

The problem is that these memory capabilities are still largely confined to their respective platforms, belonging to independent account systems and product environments, remaining isolated islands. While Anthropic supports memory import and export, it currently feels more like a migration tool for Claude than a universal memory standard adopted by various companies.

ZetaChain aims to fill this gap. Having fully embraced AI, ZetaChain is extending the concept of "ownership," originally a part of the crypto world, to AI memory and user context. It hopes to build not just a chat product, but a private memory layer independent of the model platform, allowing users to truly own their long-term memories, behavioral preferences, and AI context.

ZetaChain's AI consumer product, Anuma, aims to give users a set of encrypted private memories and supports seamless use across various mainstream AI models such as ChatGPT, Claude, and Gemini. Users don't need to rebuild their background, preferences, and work habits every time they switch models; instead, they control access permissions and bring their historical memories to different models and agents.

As AI gradually accumulates user preferences, writing habits, workflows, and historical conversations, this so-called "memory" will increasingly resemble a "personality mirror." It will not only determine whether the model's answers align with user preferences, but may also determine whether the model will act in accordance with your habits and values ​​when making decisions for you in the future.

In addition to giving users ownership of their memories and allowing them to choose different strengths for different tasks, Anuma is also building a programmable, auditable, and revocable permission system that allows AI agents to read records at once and revoke permissions at any time, while all permission changes can be recorded and tracked on the blockchain.

Furthermore, users' memories and knowledge graphs can become shareable, licensed, and monetizable assets without exposing the original data. This allows users in professions such as investors, doctors, lawyers, and developers to encapsulate their expertise into agents and publish them on the Agent Marketplace, earning revenue when others use them.

Why is ZetaChain transforming itself from cross-chain to cross-AI platform?

Anuma's ability to achieve these functions is thanks to the underlying infrastructure, Private Memory Layer, developed by ZetaChain. As an AI-oriented infrastructure for private memory, identity, permissions, payments, and agents, it aims to enable applications and agents to collaborate across models while allowing users to maintain control at all times.

ZetaChain has consistently focused on cross-chain interoperability infrastructure, with its core objective being to solve the problem of asset and message transfer between different blockchains. It has built a substantial network and narrative around the concept of a "unified multi-chain entry point." According to its official data, the blockchain hosts 11.9 million unique addresses and 241 million transactions.

However, after Anuma was publicly launched on April 27th of this year and surpassed 50,000 users in its first month, ZetaChain decided to fully shift towards AI and gradually shut down its cross-chain interoperability business. Behind this transformation, there is a relatively clear internal logic.

In the past, ZetaChain primarily addressed the issue of interoperability between blockchains. A similar disconnect exists in today's AI world. To some extent, digital assets are to blockchain what memory and context are to AI. Different models possess their own closed memory systems, and when users switch platforms, the long-accumulated context and behavioral preferences are often disrupted.

With its development in recent years, ZetaChain believes that its biggest challenge today is no longer cross-chain transfers between blockchains, but rather the continuity between different models and different agents, as well as the issue of user ownership of their own context.

As previously mentioned in an analysis article by a16z crypto, agents have begun to become economic participants, but they still lack portable identities, programmable payments, verifiable authorization, and a common coordination layer needed for cross-environment collaboration. Therefore, compared to many AI+Crypto projects that awkwardly seek application scenarios, ZetaChain's transformation logic is much smoother.

In business history, successful transformations of infrastructure companies are not uncommon. These companies often don't simply switch tracks, but rather pursue new bottlenecks based on product logic. Nvidia's initial most important narrative was graphics computing and gaming graphics cards, but with the rise of AI, its GPU architecture ultimately became the core infrastructure of the entire AI industry. Infrastructure never revolves around the same constraint forever, and the real winners are often those who are the first to identify the "next constraint" emerging.

From the privacy memory layer to the AI ​​consumption layer

With the explosive development of AI, the future form of AI will clearly not be limited to chat windows, but will gradually evolve into a large number of long-term, collaborative AI assistants. Based on this judgment, ZetaChain, in addition to proposing the "Privacy Memory Layer" and attempting to solve the problem of how AI can understand users in the long term, has further proposed the concept of the "AI Consumer Layer," hoping to redefine the relationship between users and AI after AI has been working on behalf of users for a long time.

ZetaChain envisions a future where AI will not only answer questions but will also deeply participate in users' workflows and daily decisions. Different AI assistants will be responsible for different tasks: some will handle code, some will manage finances, some will be responsible for travel planning, and others will be involved in content creation and research analysis. For these AIs to truly collaborate, they need to share the same long-term context, identity, and permission system.

Therefore, the so-called "AI consumption layer" is essentially an attempt to integrate previously scattered capabilities into a unified framework. Memory is responsible for long-term context, Permissions for access control, Identity for the identity system, Payments for inter-AI calls and payments, and Agents is the AI ​​network that ultimately performs tasks on behalf of the user.

This is why "ownership" is a core concept that ZetaChain repeatedly emphasizes.

In this system, the most important thing is whether users still retain their own context, permissions, and identity. For example, in the future, an AI responsible for code review may be temporarily authorized to read GitHub repositories; an AI responsible for tax preparation may be able to read tax documents all at once; and an AI responsible for travel arrangements may only have access to travel history and calendar information. Permissions will no longer be uniformly controlled by the platform, but will be dynamically allocated by the user and can be withdrawn at any time.

This is precisely why blockchain is beginning to reconnect with AI.

As more and more AI systems work on behalf of users, questions such as "who can access what," "whether permissions can be revoked," and "whether calls are traceable" will gradually become new infrastructure problems. On-chain permission systems are naturally suited to handle such multi-party collaborative relationships.

ZETA, the "AI infrastructure token," brings utility growth with the transformation.

Along with the ZetaChain strategy, the functionality and utility of the ZETA token have also been adjusted. Previously, ZETA resembled a traditional public blockchain token, primarily serving as a gas, verification, and cross-chain network security provider, with little innovation in its mechanism design. However, under the new narrative, ZETA will become an "AI infrastructure token," significantly enhancing its utility.

According to ZetaChain's current description, ZETA will serve several purposes in the future:

First, there's the access permission for AI models and agents. Some advanced models, specialized AI tools, or agent services require unlocking through ZETA or paying a call fee.

Secondly, there's payment settlement between agents. ZetaChain mentions that future interactions between different AIs and applications will be completed through on-chain payments via the x402 protocol. Its goal is clear: if AI will automatically call other AIs in the future, then a native payment system will be needed between machines.

Thirdly, there are on-chain operations for updating permissions and memory. In the future, user modifications to permissions, access controls, and memory states may all be recorded on the blockchain.

The fourth is the creator economy. ZetaChain hopes that in the future, professionals such as developers, researchers, lawyers, and doctors can encapsulate their knowledge into AI tools or agents and earn income by using them, with ZETA playing the role of value transfer in this process.

However, it's important to note that this aspect is still largely in the narrative stage. The AI ​​Agent economy itself is far from mature, and truly large-scale "AI calling AI" and "Agent autonomous payments" have not yet emerged. Concepts such as x402, on-chain permissions, and AI identity are currently more of an infrastructure pre-installation than a proven large-scale demand.

However, ZetaChain and its product logic are noteworthy not only because it has built an infrastructure with accompanying AI products, but also because it attempts to redefine whether future user memories, identities, contexts, and AI permissions belong to the platform or to the user themselves. Essentially, ZetaChain aims to free these elements from platform control and return them to the user.