Web3 AI Application Overview: Which Ones Are Making You Money, and Which Are Reshaping the Rules?

Original: Odaily Planet Daily (@OdailyChina)

Author: Asher (@Asher_0210)

In the past few weeks, discussions on AI + Crypto have heated up once again.

From the AI x Blockchain conference in New York to the recently concluded Hong Kong Web3 Conference, almost all major participants are revisiting the same question: How will AI shape the next phase of the crypto industry?

However, unlike previous rounds that revolved more around narratives, this round of discussion is beginning to shift towards a more specific question: What problems can AI actually solve?

In a fireside chat titled “Redefining Convenience: Web3, AI, and the Next Decade of the Smart Economy,” Binance co-CEO He Yi mentioned that as the industry matures, the early dividends of the crypto market are fading. The next critical factor is no longer the technology itself, but whether the product truly has value and whether people are willing to pay for it.

This also means that AI is no longer just a new growth narrative but is being placed back into a more specific context. People are beginning to ask more directly: What practical benefits can it actually bring?

Especially in the user-facing application layer, this change is gradually becoming apparent. In the Web3 space, a batch of applications built around AI are emerging. Some are restructuring how information is accessed, others are redefining ownership of data and memory, and still others are starting to integrate on-chain research, trading, and even economic models with AI.

These projects may not yet be mature, but they are collectively pointing towards a more realistic direction. As dividends fade, the combination of AI and crypto is returning its focus to the product itself.

This article selects several representative Web3 AI projects and reviews the actual progress in this application layer from the perspectives of information, memory, operations, Agent economy, and distribution.

Surf: A Real-time Encyclopedia for the Crypto Market

Surf is a typical information-layer product among this wave of AI applications. It doesn't attempt to restructure the trading process, nor does it focus on creating a new economic system. Instead, it returns to a more fundamental, yet long-overlooked issue – in the crypto market, obtaining information itself remains a high-cost endeavor.

On-chain data, market fluctuations, social sentiment, and project information are often scattered across different platforms. Users need to switch between multiple pages to piece together a relatively complete market assessment. This sense of fragmentation becomes more pronounced when market volatility increases. The problem isn’t a lack of information, but that information is dispersed and has time lags. Surf's approach is to integrate these information sources into a unified AI gateway, allowing users to obtain structured conclusions through simple descriptions, compressing the “data searching” step and allowing them to directly enter the “decision-making” phase.

In practical use, it functions more like an always-on researcher. Users can track the capital flow and sentiment changes of a specific token, analyze the TVL and yield structure of DeFi protocols, monitor whale address movements, or generate a project due diligence report for trading decisions or communication preparation in a short time. Compared to traditional tools that require users to filter, piece together, and understand information themselves, Surf more directly outputs organized results, thereby shortening the path from “obtaining information” to “forming a judgment.”

Building on this, Surf has begun to evolve from an “information tool” towards a “workflow platform.” The recently launched Surf 2.0 and Surf Studio allow users to directly build analytical tools and even simple Web Apps using natural language, and deploy them for immediate use, no longer relying on traditional development processes. Simultaneously, Surf integrates multi-model capabilities from providers including OpenAI, Anthropic, and Google, and connects to dozens of data sources and on-chain interfaces, making the generated analysis results not just text but tools for continuous monitoring and decision-making.

At a deeper level, it is also gradually building a capability system for Agents. Through its API and Agent Stack, users can delegate specific tasks (such as whale address monitoring, capital flow tracking, or strategy signal alerts) to AI for continuous execution, rather than manually querying each time. This means Surf is no longer just a passive query portal but is transforming into a research system capable of long-term operation.

However, its capability boundaries are relatively clear. Surf's core remains concentrated in the information integration and analysis layer; it does not truly enter the trade execution stage. Actions like automated order placement or strategy execution still need to be completed by users themselves. This also determines that it is more suitable as a decision-support tool rather than a system capable of independently executing a complete trading loop.

From an industry perspective, this type of product represents an early form of AI application deployment. Compared to directly challenging the complex link of trade execution, first making the task of “understanding the market” more efficient and user-friendly is often more easily accepted by users. Before trading is fully automated, improvements in information processing efficiency remain the most direct and easily perceived value for users.

Anuma: A Sovereign Memory Vault for the AI Privacy Era

Over the past two years, AI has almost become a universal keyword in the global tech scene. From the model race in Silicon Valley to the pursuit of AI applications and capital narratives in New York and Hong Kong, the focus of industry discussion has been shifting rapidly. Previously, the competition primarily revolved around model capabilities – reasoning, multi-modality, Agent execution ability. Almost every product update answered the same question: Whose model is smarter, more accurate, and better at completing complex tasks?

But as model capabilities continue to improve, simply comparing models themselves is increasingly difficult to create a long-term differentiation. Entering a new phase, how AI can remember users long-term and carry these memories through writing, research, decision-making, and daily communication is becoming a new focus of discussion. This means that AI's moat is shifting from model ability to memory capability. Models determine what AI can answer, while memory determines whether AI can truly understand a long-term user.

But today’s AI memory doesn't truly belong to users. In current mainstream AI products, conversations, preferences, and usage habits are continuously recorded, gradually forming an experience that understands the user better. However, these memories are usually locked within their respective platform ecosystems, controlled by the platform, and cannot be freely transferred nor truly controlled by the user.

This means that AI is accumulating the user's digital persona, but the ownership and control of this data often still belong to the platform. The longer the usage time, the more memories accumulate, and the higher the cost for users to switch models. What truly locks users in isn't necessarily the model itself, but the long-accumulated memories that cannot be taken away.

This is precisely the layer Anuma targets. As ZetaChain’s flagship product venturing into the AI direction, Anuma doesn't just serve as an application portal. More accurately, the underlying layer ZetaChain aims to build is a set of AI memory systems controlled by users; Anuma is the user-facing AI interaction interface for this system.

In other words, ZetaChain is responsible for building the underlying memory capabilities, while Anuma brings these capabilities into daily AI usage scenarios. Anuma's goal is to decouple memory from models, allowing users for the first time to invoke, manage, and carry forward their long-term memories in practical use.

Specifically, users can import their complete chat history from ChatGPT, Claude, or Grok into Anuma. After local encryption, this data is stored in a self-controlled Memory Vault. More importantly, this process prioritizes privacy protection *before* data enters the system. Users aren't passively authorizing the platform *after* it has already obtained the data; instead, they retain control from the very beginning.

These memories are no longer tied to a single platform. They can be taken away, reused, and persist across different models. They are locally encrypted, portable, not tied to a single model, and can be continuously accumulated over long-term user usage.

From a practical experience standpoint, Anuma first serves as a unified portal aggregating multiple cutting-edge models. With a single subscription, users can access the latest models like GPT, Claude, and Grok without needing to switch back and forth between different platforms.

More critically, when users switch between different models, their established memories are not reset. Within Anuma, models like GPT and Claude function more as the capability layer, while the user's own memory remains consistent. Regardless of which model is used, past conversation records, expression styles, and preferences can be carried forward instead of being cleared.

Anuma also offers a multi-model Council Mode. Users can have multiple models provide answers to the same question from different perspectives, and then compare the results. For research, writing, and complex judgments, this experience feels more like having multiple AIs participate in a discussion rather than relying on the single-point output of just one model.

Furthermore, Anuma supports users conversing directly with AI via iMessage. Each Agent can be called upon like a contact and even added to group chats. Compared to opening a specific application to initiate a conversation, this method is closer to everyday communication scenarios and makes the AI entry point much lighter. Even in scenarios with weak network signals or when applications cannot be opened, users can still invoke AI, and the related conversations will enter the same encrypted memory system without interruption due to the change in entry point.

From a product perspective, Anuma is not just a multi-model portal; it is building a memory system independent of any single model. In the past, users' conversation records, preferences, and usage habits were often attached to specific platforms. But as AI becomes a long-term tool, these continuously accumulated memories will become the foundation for understanding the user.

This is also the reason ZetaChain is trying to enter the next generation of AI infrastructure, with Anuma serving as the user portal. Models can be continuously upgraded and replaced, but the long-term memories accumulated by users should not be locked up with the platform. Future competition for AI products may not just be about who has the stronger model, but also about who allows users to truly own, invoke, and carry forward their own memories.

In the AI era, memory is becoming part of identity. And identity should belong to the user.

Nansen AI: Turning On-Chain Research and Trading into “Conversational Operations”

When the question shifts from “who owns the data” back to “how to use the data,” another category of product focuses on the more concrete operational layer. What Nansen AI does is compress the originally dispersed steps between on-chain research and actual trading into a single path as much as possible.

In traditional on-chain research, users often need to switch back and forth between multiple dashboards, manually querying capital flows, address behaviors, and token data, before combining their own judgment to execute operations. This process isn't necessarily complex, but it's cumbersome, with a clear disconnect between information and execution. Nansen AI's approach is to reconnect these two parts.

Users can ask questions directly using natural language to obtain information like on-chain capital flows, Smart Money movements, token trends, etc., without needing item-by-item queries. For example, querying the reason for a token’s price increase, analyzing the profit/loss of a specific address, or directly parsing a transaction – the entire process can be completed within a conversation. This method essentially abstracts “research” from the operational workflow, compressing it into a continuous conversational process.

Going further, Nansen AI is attempting to connect information acquisition with actual operations. In some scenarios, users can directly perform on-chain interactions like transfers or Swaps through conversation, thereby compressing the originally separate research and execution processes into the same path. This also means that Nansen AI is no longer just providing explanations but is gradually moving closer to the operational stage.

The prerequisite for this extension comes from its long-term accumulation of on-chain data capabilities. Based on a vast number of labeled addresses and real-time data, the system can identify fund sources, track large capital flows, and combine holding positions to provide more targeted analytical results. Precisely because it possesses this data foundation, it can undertake specific operations beyond conversation.

Under this structure, Nansen AI's positioning also changes. It is no longer just an information tool, but becomes more of a connection point between the data input layer and the operational interface within the trading decision process. However, such “conversational trading” is still in its early stages. AI is primarily reducing operational barriers and information acquisition costs, rather than replacing users in making strategic judgments. Whether it's asset allocation or risk control, the final decision still rests with the user.

Overall, Nansen AI represents another path for AI applications – extending further towards the execution layer on top of the information layer. It doesn't change the logic of trading itself, but offers a lighter, more direct way of “how to complete a trade.” Compared to pure information tools, this ability to connect “research” and “operations” has a higher chance of entering real usage scenarios first.

Virtuals Protocol: Turning AI Agents into “Tradeable Economies”

Once AI begins to participate in operational processes, the question extends further – if these Agents are not just auxiliary tools but can independently provide services and continuously create value, can they be incorporated into a complete economic system?

The attempt by Virtuals Protocol unfolds precisely along this direction.

In traditional AI products, Agents are primarily seen as tools without independent economic attributes. They can complete tasks but cannot directly participate in value distribution, nor easily form a sustainable business model. Virtuals' approach is to transform Agents from “functional units” into “economic participants.”

Within this system, each Agent can be tokenized, thereby possessing the ability for funding, incentivization, and profit distribution. Developers are no longer just releasing an AI tool; they can build a complete economic model around an Agent, allowing it to continuously generate value while being used. In this way, AI is no longer a one-time delivered product, but more akin to a long-term operational asset.

Structurally, Virtuals provides a full suite of infrastructure including collaboration, settlement, and issuance. Agents can collaborate with users or other Agents to complete tasks and perform value exchange through on-chain mechanisms. Simultaneously, through a Launch mechanism, Agents themselves can gain liquidity support, forming a path for pricing and capital formation.

Compared to the previous projects which mainly focus on “how to use AI better,” Virtuals is more concerned with “how AI itself participates in economic activity.” It attempts to push AI from the tool layer into the realm of production relations, making Agents subjects capable of independently creating value.

However, looking at the current stage, this direction is still in its infancy. On one hand, there aren't many Agents with stable usage demand and revenue-generating capabilities yet; real-world applications within the ecosystem are still being validated. On the other hand, mechanisms for collaboration, pricing, and trust between Agents also require more time to establish.

From an industry perspective, Virtuals represents a more long-term oriented path within AI + Crypto. It doesn't directly optimize the user's current experience but is attempting to build a new foundational structure, enabling AI to possess more complete economic attributes in the future. This direction may not be the most easily perceived in the short term, but if successful, it could fundamentally change AI's role within the entire system.

Warden: Making AI Agents Usable, Distributable, and Monetizable

As the number of Agents increases, the difficulty often lies not in their capabilities, but in whether the use case can become viable. Compared to model capabilities or single-point functions, the challenge for most Agents isn't “can it be done?” but “will anyone use it?” They are scattered across different frameworks and entry points, lacking a unified distribution channel and clear mechanisms for payment and collaboration. This is where Warden steps in.

Its approach isn't overly complex but builds a complete, usable infrastructure around Agents. For users, it provides a unified portal where they can invoke different Agents, performing operations like trading, cross-chain, and queries via natural language, consolidating originally fragmented functions into a continuous workflow. For developers, it allows for the quick creation and launch of Agents, directly providing services to users and handling payments and settlements through on-chain mechanisms.

At a deeper structural level, Warden uses a dedicated chain to manage Agent identities and invocation processes, giving each Agent an independent form of existence. It can not only charge fees but also invoke other Agents, gradually forming collaborative relationships. Simultaneously, through a distribution portal akin to an app store, Agents have the