Author: Zen, PANews
You spent half a year training ChatGPT to understand your work habits, writing style, and long-term projects. It learned how you like to revise articles, which companies you often follow, and gradually came to understand your preferences for content structure, tone, and information density.
But one day, a newer, more powerful model appears. You open Claude, Gemini, or DeepSeek, and find yourself having to start all over again. The new model doesn't know you, isn't aware of the work context you've accumulated over the past months, and doesn't understand how you think, write, or make decisions.
Over the past two years, the most important competition in the AI industry revolved around "model capabilities." Whose reasoning was stronger, whose context window was longer, whose coding ability was better—these factors almost decided everything. But now, a new issue is emerging: AI is getting to know you better, but who exactly does this "understanding" belong to?
Role Shift: AI Transforms from Chat Tool to Personal Digital Assistant
In November 2022, the AI chatbot ChatGPT debuted, sparking a global chat craze. It reached 100 million monthly active users in just two months, becoming the fastest-growing consumer application in history. At that time, large language models were more like an "advanced search." Users asked AI questions, it generated instant answers, and when the conversation ended, the relationship also ended.
But in recent years, the role of AI has been undergoing a significant shift. As reasoning capabilities, coding abilities, and tool-calling functionalities have continuously improved, AI has begun to deeply integrate into real workflows. More and more people are using it to write code, organize information, analyze data, plan trips, manage schedules, and even for long-term participation in content creation and business decisions.
In many cases, users are no longer just "asking AI questions" but are engaging in long-term collaboration with AI. It starts to understand your working style, expression habits, and long-term goals, begins to consistently participate in the same project, the same workflow, and even gradually takes on some execution tasks. To some extent, AI is evolving from a one-time Q&A tool into a persistently available personal digital assistant.
And as model capabilities have significantly increased, the capabilities of top products have increasingly converged, and with AI being used long-term and extensively, new issues have begun to surface.
Once AI begins long-term collaboration, "memory"—the system storing and recalling past experiences to improve decision-making and overall performance—ceases to be just an inconsequential database. In many application scenarios, the bottleneck is no longer the model's reasoning level, but the capabilities related to long-term memory and context management. Cloudflare has also directly labeled agentic memory as one of the biggest challenges and fastest-developing areas in current AI infrastructure.
Leading AI companies have also realized that long-term memory is becoming part of the product experience. OpenAI has split ChatGPT's memory into saved memories and reference chat history. The former saves information users wish 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 prior conversations. Claude has introduced Memory and supports memory import and export.
Platform Silos Turn AI 'Memory' into a New Industry Battlefield
But the problem is that these memory capabilities are largely still confined to their respective platforms, belonging only to the platform's independent account systems and product environments—they remain isolated islands. Although Anthropic supports memory import/export, it currently functions more like a migration tool for Claude rather than a universal memory standard jointly adopted by various companies.
And this is precisely the gap ZetaChain aims to fill. After fully pivoting to AI, ZetaChain began extending the concept of "ownership," originally from the crypto world, further into AI memory and user context. What it hopes to build is not just a chat product, but a privacy memory layer (Private Memory Layer) independent of model platforms, allowing users to truly own their long-term memories, behavioral preferences, and AI context.
ZetaChain's consumer AI product, Anuma, advocates for users to own a set of encrypted private memory, supporting seamless use across mainstream AI models like ChatGPT, Claude, Gemini, etc. Users don't have to rebuild context, preferences, and work habits every time they switch models. Instead, users control access permissions and bring their historical memory to different models and agents.
As AI gradually accumulates users' usage preferences, writing habits, workflows, and historical conversations, the so-called "memory" will increasingly resemble a layer of "personality mirror." It not only determines whether a model's responses align with user preferences but may also determine whether the model, when making decisions on your behalf in the future, acts according to your habits and values.
Beyond giving users ownership of memory and allowing them to choose models with different strengths for different tasks, Anuma is also building a programmable, auditable, and revocable permission system. It allows AI agents to read records once, with permissions revocable at any time, and all permission changes can be recorded and tracked on-chain.
Moreover, users' memory and knowledge graphs can become assets that can be shared, authorized, and monetized without exposing raw data. This enables professional users such as investors, doctors, lawyers, and developers to encapsulate their expertise into agents, publish them to an Agent Marketplace, and earn revenue when others call upon them.
From Cross-Chain to Cross-AI Platform: Why Did ZetaChain Pivot?
What enables Anuma to achieve these functions is the underlying infrastructure, Private Memory Layer, developed by ZetaChain. As an infrastructure for AI-focused private memory, identity, permissions, payments, and agents, it aims to allow applications and agents to collaborate across models while users retain control.
ZetaChain had long focused on cross-chain interoperability infrastructure, with the core goal of solving asset and message transfer issues between different blockchains. Regarding the "unified multi-chain entry point," it built a considerable network and narrative. According to official data, there are 11.9 million unique addresses and 241 million transactions on its blockchain.
But after Anuma publicly launched on April 27th this year and surpassed 50,000 users in its first month, ZetaChain began deciding to fully pivot to AI, gradually phasing out its cross-chain interoperability business. Behind this pivot lies a relatively clear internal logic.
In the past, ZetaChain mainly dealt with the issue of incompatibility between different chains. In today's AI world, similar fragmentation exists. To some extent, digital assets are to blockchain what memory and context are to AI. Different models have their own closed memory systems; once users switch platforms, the long-accumulated context and behavioral preferences are often interrupted.
With developments in recent years, ZetaChain believes the biggest challenge it faces now is no longer cross-chain transfers between blockchains, but continuity between different models and agents, and the issue of user ownership over their own context.
a16z crypto previously mentioned in an analysis article that agents have begun to become economic participants, but they still lack portable identities, programmable payments, verifiable authorization, and the public coordination layer needed for cross-environment collaboration. Therefore, compared to many AI + Crypto projects awkwardly searching for application scenarios, ZetaChain's pivot logic is much smoother.
In business history, successful pivots by infrastructure companies are not uncommon. Such companies often don't simply switch tracks but chase new bottlenecks based on product logic. NVIDIA's most important initial narrative was graphics computing and gaming GPUs. But with the rise of AI, its GPU architecture ultimately became the core infrastructure for the entire AI industry. Infrastructure never revolves around the same constraint point forever, and true winners are often those who earliest identify that the "next constraint point" is emerging.
From Privacy Memory Layer to AI Consumer Layer
With the explosive development of AI, the future form of AI will clearly not be confined to chat windows but will gradually evolve into numerous long-term, collaborative AI assistants. Based on this judgment, after proposing the "Privacy Memory Layer" and attempting to solve how AI can understand users long-term, ZetaChain further proposed the concept of the "AI Consumer Layer," hoping to redefine the relationship between users and AI after AI works on behalf of users long-term.
In ZetaChain's vision, future AI won't just answer questions but will deeply participate in users' workflows and daily decisions. Different AI assistants will be responsible for different tasks—some handling code, others organizing finances, some planning schedules, and others participating long-term in content creation and research analysis. For these AIs to truly collaborate, they need to share the same set of long-term context, identity, and permission systems.
Therefore, the so-called "AI Consumer Layer" essentially attempts to integrate originally scattered capabilities into a unified framework. Among them, Memory handles long-term context, Permissions handles access control, Identity handles the identity system, Payments handles invocation and payments between AIs, and Agents are the AI networks that ultimately execute tasks on behalf of users.
This is why "ownership" has become the core concept ZetaChain repeatedly emphasizes.
Because in this system, whether users still own their context, permissions, and identities becomes paramount. For example, in the future, an AI responsible for code review could be temporarily authorized to read a GitHub repository; an AI handling tax organization could read tax filing materials once; an AI arranging travel could only access travel history and calendar permissions. Permissions would no longer be uniformly controlled by the platform but dynamically allocated by users and revocable at any time.
And this is precisely why blockchain is starting to reconnect with AI.
As more and more AIs simultaneously work on behalf of users, "who can access what," "whether permissions are revocable," and "whether invocations are traceable" will gradually become new infrastructure issues. And on-chain permission systems are naturally suited to handle this kind of multi-party collaboration.
'AI Infrastructure Token' ZETA Brings Utility Growth Alongside Pivot
Alongside ZetaChain's strategy adjustment, the functionality and utility of the ZETA token have also been adjusted. In the past, ZETA was more like a traditional public chain token, primarily serving Gas, validation, and cross-chain network security functions, with not much innovation in its mechanism design. But under the new narrative, ZETA will become an "AI Infrastructure Token," with its utility significantly enhanced.
According to ZetaChain's current description, ZETA will serve several purposes in the future:
Firstly, access permissions for AI models and Agents. Access to certain advanced models, specialized AI tools, or Agent services may require unlocking or payment of invocation fees using ZETA.
Secondly, payment settlement between Agents. ZetaChain mentions that future interactions between different AIs and applications will be completed via on-chain payments through the x402 protocol. Its goal is clear: if AIs in the future will automatically invoke other AIs, then machines also need a native payment system.
Thirdly, on-chain operations for permissions and memory updates. User modifications to permissions, access controls, and memory states may all become on-chain records in the future.
Fourthly, the creator economy. ZetaChain hopes that in the future, professionals like developers, researchers, lawyers, and doctors can encapsulate their knowledge into AI tools or Agents, earn revenue through invocations, with ZETA facilitating the flow of value.
However, it's important to note that this part currently remains largely at the narrative stage. Because the AI Agent economy itself is still far from mature, and truly large-scale "AI invoking AI" and "Agent autonomous payments" have not yet emerged. Concepts like x402, on-chain permissions, and AI identity currently belong more to pre-built infrastructure than to validated, large-scale demand.
But the reason ZetaChain and its product logic are noteworthy is not just because it's building an infrastructure paired with an AI product. More importantly, it attempts to redefine whether users' future memory, identity, context, and AI permissions belong to the platform or to users themselves. What ZetaChain wants to do, in essence, is to ensure these things are no longer controlled by platforms but returned to the hands of users.
