AI’s new competitive battleground: Long-term memory has become a pain point—how can users protect their own context ownership?

Author: Zen, PANews

You spent half a year teaching ChatGPT to understand your work habits, writing style, and long-term projects. It knows how you usually revise articles, which companies you often keep an eye on, and has gradually come to understand your preferences for content structure, tone, and information density.

But one day, a stronger new model appears. You open Claude, Gemini, or DeepSeek and realize everything has to start over. The new model doesn’t know you—nor does it understand the work context you accumulated over the past few months, how you think, how you write, or how you make decisions.

For the past two years, the most important competition in the AI industry has revolved around “model capability.” Whoever has stronger reasoning, a longer context window, and better coding ability almost determines everything. But now, a new question is emerging: AI understands you more and more—so who exactly does this “understanding” belong to?

A role shift: AI moves from a chat tool to a personal digital assistant

In November 2022, the AI chatbot ChatGPT burst onto the scene. After it launched, it sparked a global chat craze; within just two months, its monthly active users exceeded 100 million, becoming the fastest-growing consumer application in history. At the time, large models were more like “advanced search.” You asked the AI a question, it generated an answer instantly, and once the conversation ended, the relationship was cut off as well.

But over the past two years, the role of AI has been changing noticeably. As reasoning ability, coding ability, and tool-calling capabilities continue to improve, AI has begun to move deeper into real work processes. More and more people are using it to write code, organize materials, analyze data, plan trips, manage schedules, and even participate long-term in content creation and business decision-making.

In many cases, users are no longer merely “asking AI questions,” but instead collaborating with AI over the long term. It starts to understand your working style, communication habits, and long-term goals, and begins to continuously participate in the same project and the same workflow—sometimes even gradually taking on part of the execution tasks. To some extent, AI is evolving from a one-off Q&A tool into a long-term private digital assistant.

And as model capabilities improve substantially, top products’ capabilities become increasingly close, and AI sees long-term and widespread use, new questions are beginning to surface.

Once AI starts collaborating for long periods, the “memory” that stores and retrieves past experiences to improve decisions and overall performance is no longer just a harmless database. In many application scenarios, the bottleneck is no longer the model’s reasoning level—it is the ability to manage long-term memory and context. Cloudflare even directly refers to “agentic memory” as the biggest challenge facing AI infrastructure today, while also calling it one of the fastest-growing areas.

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 stores information that users want to keep 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 dialogues. Claude has introduced memory and supports memory import and export.

Platform silos make AI “memory” the industry’s new battleground

But the problem is that these memory capabilities, overall, are still confined to their own platforms. They belong to separate account systems and product environments, remaining isolated “islands.” Although Anthropic has enabled memory import and export, for now it still looks more like a migration tool for Claude rather than a universal memory standard adopted across different platforms.

And what ZetaChain wants to break into is precisely this gap. After fully shifting to AI, ZetaChain begins extending the concept of “ownership”—originally belonging to 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 independent of model platforms (Private Memory Layer), so that users can truly own their long-term memory, behavioral preferences, and AI context.

ZetaChain’s consumer-grade AI product Anuma advocates giving users an encrypted set of private memories and enabling seamless handoff across mainstream AI models such as ChatGPT, Claude, Gemini, and others. Users don’t have to rebuild background, preferences, and work habits every time they switch models. Instead, users control access permissions, bringing their historical memories into different models and agents.

As AI gradually accumulates users’ usage preferences, writing habits, work workflows, and historical conversations, the so-called “memory” will increasingly resemble a “personality mirror.” It can determine whether the model’s responses match user preferences, and it may also influence 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 selecting models with different strengths for different tasks, Anuma is also building a programmable, auditable, and revocable permission system. It allows an AI agent to read records once, and permissions can be revoked at any time—while all permission changes can be recorded and tracked on-chain.

What’s more, users’ memories and knowledge graphs can become shareable, licensable, and monetizable assets without exposing raw data. This allows professional users—such as investors, doctors, lawyers, and developers—to package their expertise into agents and publish them on an Agent Marketplace, earning revenue when others use them.

From cross-chain to cross-AI platforms: Why is ZetaChain pivoting?

What enables Anuma to achieve the above functions is ZetaChain’s underlying infrastructure, the Private Memory Layer. As an infrastructure for private memory, identity, permissions, payments, and agents for AI, its purpose is to let applications and agents collaborate across models while ensuring users always retain control.

ZetaChain has long focused on cross-chain interoperability infrastructure, with the core goal of solving asset and message transmission issues between different blockchains. In terms of “a unified multi-chain entry,” it has built a network and narrative at a considerable scale. According to its official data, the blockchain has 11.9 million unique addresses and 241 million transactions.

But after Anuma publicly launched on April 27 of this year and surpassed 50,000 users within its first month, ZetaChain began deciding to fully pivot to AI and gradually shut down its cross-chain interoperability business. Behind this pivot, there is also a relatively clear internal logic.

Previously, ZetaChain mainly handled the issue that chains could not interoperate. In today’s AI world, similar fragmentation still exists. To some extent, digital assets on blockchains are like memory and context for AI. Different models have their own closed memory systems. Once users switch platforms, the long-term accumulated context and behavioral preferences often get interrupted.

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

a16z crypto also previously mentioned in analysis articles that agents have begun becoming economic participants, but they lack portable identities, programmable payments, verifiable authorizations, and a public coordination layer needed for cross-environment collaboration. Therefore, compared with many AI+Crypto projects that awkwardly look for application scenarios, ZetaChain’s pivot logic is much more seamless.

In business history, successful pivots by infrastructure companies are not rare. These companies often don’t simply switch tracks; instead, they pursue new bottlenecks based on product logic. Nvidia’s initial and most important narrative focused on graphics computing and gaming GPUs, but with the rise of AI, its GPU architecture ultimately became the core infrastructure of the entire AI industry. Infrastructure never revolves forever around the same constraint point. The true winners are often those who recognize that the “next constraint point” is emerging first.

From the privacy memory layer to the AI consumer layer

With AI’s explosive development, it’s clear that the future form of AI will not remain limited to chat windows. It will gradually evolve into large numbers of long-lived AI assistants that collaborate with each other. Based on this judgment, ZetaChain proposed a “privacy memory layer” and attempted to solve not only how AI can understand users over the long term, but also further introduced the concept of an “AI Consumer Layer,” aiming to redefine the relationship between users and AI after AI has long represented users at work.

In ZetaChain’s vision, future AI will not only answer questions—it will deeply participate in users’ workflows and everyday decisions. Different AI assistants will handle different tasks: some will deal with code, some will handle financial organization, some will be responsible for travel planning, and others will participate long term in content creation and research analysis. For these AIs to truly collaborate, they will need to share the same set of long-term context, identity, and permission systems.

Therefore, the so-called “AI Consumer Layer,” at its core, is an attempt to integrate originally scattered capabilities into a unified framework. Memory manages long-term context, Permissions handle access control, Identity defines the identity system, Payments cover invocation and payments between AIs, and Agents are the AI networks that ultimately execute tasks on behalf of users.

This is also why “ownership” becomes a core concept ZetaChain repeatedly emphasizes.

Because in this system, whether users still own their context, permissions, and identity becomes the most important question. For example, a future AI responsible for code review can be temporarily authorized to read a GitHub repository. A future AI responsible for tax preparation can read tax filing materials in one go. A future AI responsible for travel arrangements can only access permissions for travel history and calendar information. Permission control is no longer unified by platforms; instead, it is dynamically allocated by users and can be revoked at any time.

And this is also the reason blockchain is starting to reconnect with AI.

As more and more AI works on behalf of users, issues like “who can access what,” “whether permissions can be revoked,” and “whether invocations are traceable” will gradually become new infrastructure problems. On-chain permission systems are naturally well-suited to handle such multi-party collaboration relationships.

“AI infrastructure token” ZETA: utility growth brought by the pivot

Along with the strategic shift of ZetaChain, the functions and utility of the ZETA token are also being adjusted. Previously, ZETA was more like a traditional layer-1 token, mainly serving Gas, verification, and cross-chain network security functions, with no particularly novel mechanism design. But under the new narrative, ZETA will become an “AI infrastructure token,” and its utility will be significantly expanded.

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

First, access permissions for AI models and agents. Some advanced models, professional AI tools, or agent services need to unlock or pay invocation fees through ZETA.

Second, payment settlement between agents. ZetaChain states that future interactions between different AIs and applications will be completed via the x402 protocol for on-chain payments. Its goal is clear: if AI will automatically invoke other AIs in the future, then machines will also need a native payment system.

Third, on-chain operations for permission and memory updates. In the future, users’ modifications to permissions, access control, and memory status may all be recorded on-chain.

Fourth, the creator economy. ZetaChain hopes that professionals such as developers, researchers, lawyers, and doctors can encapsulate their knowledge into AI tools or agents, earn income through invocation, and that ZETA will play the role of facilitating value flows.

However, it should be noted that this portion is still largely in the narrative stage for now. Because the AI agent economy itself is far from mature. Real large-scale “AI calling AI” and “agent autonomous payments” have not yet appeared. Concepts such as x402, on-chain permissions, and AI identity are still, for the most part, infrastructure groundwork rather than already-verified large-scale demand.

But ZetaChain and its product logic are worth paying attention to not only because it built an infrastructure and bundled AI products, but because it is trying to redefine whether future users’ memories, identities, contexts, and AI permissions belong to the platform or belong to the user themselves. In essence, what ZetaChain wants to do is to ensure that these things are no longer controlled by platforms, but return to users.

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