Your AI Knows Everything About You. ZetaChain Bets on Private AI

Model lock-in keeps hundreds of millions of users tethered to platforms that train on their data, but ZetaChain has built an exit ramp.

The red-hot artificial intelligence (AI) industry has a dirty secret hiding in plain sight: Every prompt you type is feeding a data machine you don’t control.

With OpenAI reporting roughly 900 million weekly active users in February, and Google’s Gemini on its heels with 750 million users, AI assistants have become the fastest-adopted consumer technology in history.

However, the architecture underpinning these tools was never designed with user privacy as a priority, but rather to accumulate data and lock users in.

A recent Malwarebytes survey found that 90% of respondents are worried about how much personal data AI tools are collecting. 88% said they do not freely share personal information with AI assistants, and 43% have stopped using ChatGPT entirely due to privacy concerns.

The backlash has gone beyond passive anxiety. Last month, a grassroots campaign called QuitGPT went viral, urging users to cancel their ChatGPT subscriptions over OpenAI’s political entanglements. The campaign drew celebrity backing and claimed over 1.5 million people took some form of action.

A privacy audit of 15 leading AI chat platforms shows that fewer than half offer end-to-end encryption, and the largest platforms all train on user data by default. Users must manually opt out, and most never do. AI models get better with additional context retained about a user, but under the current model, all that context sits on centralized servers controlled by the model provider, not the user.

Privacy is only one dimension of the challenge. The AI stack is also deeply fragmented.

Consumers overwhelmingly use a single AI assistant at a time, with only about 9% paying for more than one subscription, according to a16z’s State of Consumer AI 2025 report. That’s likely due to lock-in at the model layer; switch from ChatGPT to Claude or Gemini, and you start from zero. Your preferences, conversation history, and accumulated context don’t come with you.

For developers, the situation is worse. Building an AI application today means stitching together custom integrations for each model provider, rebuilding routing, state management, and billing infrastructure from scratch, and navigating a patchwork of data-handling requirements. This results in user data being shared across applications, agents, and model providers with minimal transparency.

ZetaChain: The Private Memory Layer for AI

ZetaChain bills itself as the private memory layer for AI and the infrastructure behind a broader AI consumer layer. Originally built as a cross-chain interoperability Layer 1 network, the team has reoriented around a single goal: a user-owned foundation for memory, identity, permissions, payments, and agents that travels with users across connected models and apps.

Underneath, that means one encrypted memory vault per user, carried across ChatGPT, Claude, Gemini, Grok, DeepSeek and other leading models, with programmable, user-controlled permissions deciding what each app or agent can access. The detail on each component sits a few sections down.

In the conventional AI stack, your context is stored on the model provider’s servers. In ZetaChain’s design, every user receives a cryptographic identity on the ZetaChain network at signup. This is not a traditional crypto wallet, but a key pair that serves as the encryption and authentication layer. Memory is encrypted client-side and stored in a personal vault accessible only to the user.

Meet Anuma

Anuma is the first consumer app built on ZetaChain 2.0 and showcases ZetaChain’s infrastructure in action.

Its ‘Memory Import’ feature enables users to export their full conversation histories from ChatGPT, Claude, or Grok and import them into Anuma’s encrypted Memory Vault. The data is encrypted before upload, and the imported context immediately informs conversations across any model the user switches to.

Anuma handles productivity queries with multi-model switching; a second agent called Coco is built for group chats. Every text exchange feeds into the Private Memory Layer, building encrypted context over time. When a user later opens the Anuma app, their memory vault is already populated from the text conversations.

The platform also includes a model selector that lets users switch models mid-conversation without losing context, an activity dashboard that shows credit usage by model, and a file library that retains all images and documents generated across sessions.

ZetaChain offers developers a software development kit (SDK) that bundles private persistent memory, cross-model routing, and monetization tools, including both on-chain settlement and traditional payment rails via processors like Stripe, into a single toolkit.

Building blocks of the AI consumer layer

ZetaChain is focused on five core components for the AI consumer layer: memory, permissions, identity, payments, and agents.

Memory. Personal context lives inside an encrypted vault powered by ZetaChain and used first through Anuma. Preferences, projects, writing style, goals, files, and conversation history can follow users across connected models and apps, so they don't have to start from a cold conversation each time. As more apps connect to this layer, the memory moves with the user while they stay in control. Users can review, edit, export, or delete the vault at any time.

Permissions. Access control is being built to be programmable and specific, not a binary on/off switch. Users can grant a coding agent read access to their codebase context, give a tax agent one-time access to last year's receipts, and revoke either instantly. Access is designed to be logged on-chain.

Identity. A wallet-derived key becomes the user's AI identity. It is one key, with no separate platform accounts or fragmentation. Agents also get on-chain identity and become addressable, authorizable, and accountable.

Payments. Memory access, agent calls, and cross-model interactions are designed to settle natively on-chain via x402. When approved agents use permissioned access to a user's memory, payment can flow automatically. What used to be extracted can become metered, programmable, and returned to the user by default.

Agents. Memory, permissions, identity, and payments come together to enable agents that can work on a user's behalf across every model. These agents in Anuma are addressable, payable, and accountable, while the user stays in control of what they see and do.

ZETA: the token that powers Anuma

ZETA is the token that powers Anuma and the network underneath it. ZetaChain describes six core uses for the token, each tied to a piece of the stack:

Access. Lock ZETA to unlock the leading AI models and specialized expert agents through Anuma. One stake can unlock access across many models and agents.

Creator Incentives. When the memory patterns or agents a user chooses to publish are used by others, they can earn in ZETA without giving up control of the underlying private data.

Settlement. Memory access and agent calls inside Anuma are designed to settle via the x402 standard.

Decentralized cloud. ZETA is designed to power the encrypted, decentralized storage layer that backs the Anuma memory vault.

Gas. ZETA is designed to pay for on-chain memory updates and permission transactions.

Staking. Lock ZETA to help secure the network that powers Anuma and earn from its activity.

From private memory to the AI consumer layer

With private memory as the foundation, ZetaChain plans to expand into what it calls the AI consumer layer: a stack where models, agents, apps, and data work together around the user. In that vision, Anuma is more than a memory layer: over time, the company says, it will use a user’s context to proactively suggest workflows, spin up personal apps, and create agent-powered tools based on their goals, files, preferences, and history.

The user remains the decision-maker. Apps and agents can work around the memory, but the user decides what they see, what they do, and when access ends.

Why ZetaChain is going all-in on AI

ZetaChain built Anuma as an experiment to test whether its interoperability thesis could extend beyond assets and chains into intelligence itself: memory, context, identity, and agents that move with the user. Anuma crossed 60,000 users in its first month, a signal the company has read as confirmation that private, user-owned AI is more than a feature. It is the start of the next major consumer layer.

The company started by solving interoperability between chains. After four years of building cross-chain infrastructure (a mission that produced 12 million users and 240 million transactions), ZetaChain is now dedicating itself entirely to AI. The same coordination problem it once tackled across chains, the team argues, now exists across intelligence: models, memory, agents, apps, and identity are fragmented, and users are stuck rebuilding context from scratch.