UB has increased more than 12 times in over a month; why has the AI Agent memory layer become the new focus of capital?

Since May 2026, the market enthusiasm for the AI Agent track has begun to heat up again, and in this round of AI asset rotation, Unibase (UB) has performed particularly prominently. According to Gate market data, UB’s price has steadily risen from around $0.018 near April 10th to about $0.24 near May 15th, with a cumulative increase of over 12 times in just over a month. Compared to the previous market focus on AI chat tools, large model reasoning, and AI application layers, more and more funds are now shifting their attention back to Agent collaboration, long-term memory, on-chain identities, and AI infrastructure. The current emphasis of Unibase on the ERC-8183 Marketplace and decentralized memory layer aligns precisely with this hot intersection of market interests.

From the current market structure, the focus of the AI industry has begun to shift. Previously, many AI projects emphasized the competitive capabilities of individual models, but as model performance converges, the market is now seeking new directions for “how AI systems can collaborate.” Especially in the Web3 environment, as more Agents enter automated task scenarios, the importance of on-chain collaboration, long-term memory, and identity systems has become more apparent. This shift is causing AI infrastructure assets to regain market attention.

Unibase Launches ERC-8183 Agent Marketplace

In May 2026, Unibase officially launched the ERC-8183 Agent Marketplace and began to strengthen its focus on the Agent service market. This move quickly attracted market attention, not just because of the product launch itself, but because the competitive focus in the AI industry has shifted from “single AI tools” to “AI network collaboration.”

Over the past year, most AI projects have competed mainly on model capabilities, including generation quality, reasoning speed, and content processing. However, as general AI approaches homogeneity, the market is beginning to realize that the true ceiling of the AI ecosystem may no longer be determined solely by models, but by whether different Agents can form a collaborative network.

Unibase’s current emphasis on ERC-8183 is more like an attempt to establish a standard for on-chain Agent collaboration. As more AI Agents enter automation, on-chain execution, and service invocation scenarios, the demand for identity verification, permission control, task distribution, and long-term state synchronization has increased significantly. In this context, AI Agents are no longer just standalone tools but systems capable of calling each other and working together to perform tasks.

A recent clear trend is that more AI projects are emphasizing:

• Agent collaboration
• Long-term memory for Agents
• On-chain identity and invocation

These more foundational capabilities indicate that the AI market is transitioning from a “model competition phase” to a “system competition phase.” After recent enhancements to the Marketplace and on-chain collaboration, UB is increasingly categorized by market funds as an AI infrastructure asset.

AI Agent Assets Are Beginning to Strengthen On-Chain Collaboration Needs

As the concept of AI Agents continues to expand, discussions around “on-chain collaboration” have increased significantly. Previously, many AI Agents resembled independent tools mainly responsible for chatting, retrieval, content generation, or executing single tasks. Recently, more projects are attempting to enable multiple Agents to participate in complex workflows. When Agents need to share data, synchronize states, or automatically coordinate tasks, the importance of on-chain structures rises rapidly.

Recent market changes show that more projects are emphasizing:

• Agent task distribution
• Multi-Agent collaboration
• On-chain identity systems

These needs are naturally better suited to be implemented through Web3 structures. Compared to traditional centralized AI systems, on-chain systems can better handle permission verification, data sharing, incentive distribution, and long-term state synchronization. Consequently, market attention to the integration of AI and Web3 is rising again.

Especially as Agent applications become more complex, AI systems are shifting from “single-response” to “long-term execution.” This change means that the future AI ecosystem will no longer be just about model capabilities but about who can build more stable collaboration networks. From a market perspective, the AI Agent track is already showing clear stratification: some projects remain at the application layer, while others are extending into underlying collaboration structures, which tend to have longer-term narrative potential.

Why Decentralized Memory Layers Are Entering Market Focus

Another recent notable change in the AI market is the increasing discussion around “long-term memory.” Previously, most AI tools resembled real-time response systems, with user interactions being relatively independent. As Agent applications grow more complex, the market is beginning to realize that long-term context and persistent memory could become critical for the next stage of AI competition.

In multi-Agent collaboration scenarios, the importance of memory layers is further amplified. When multiple Agents need to work together on a task, sharing long-term context and historical states is essential; otherwise, the entire collaboration system cannot achieve truly continuous execution.

One of Unibase’s current strategic directions is the emphasis on decentralized AI memory layers. Compared to traditional centralized AI systems, on-chain memory layers can better support long-term state storage, shared context among Agents, and autonomous data control. They also align well with Web3’s principles of open invocation and transparent permissions.

Recent market trends show that the AI industry is refocusing on:

• Long-term contextual capabilities
• Persistent state synchronization
• Agent shared memory

These are more foundational directions. Compared to simple chat functions, the market is increasingly concerned with whether Agents can form truly long-running collaborative networks. This shift has brought the memory layer concept back into the market spotlight.

How Short-term Funds Are Reacting to UB’s Volatile Rise

As UB’s price continues to rise, short-term trading sentiment has also increased noticeably. From mid-April to mid-May, UB’s cumulative increase exceeded 12 times, attracting a large influx of short-term capital into the AI Agent track.

A clear recent trend is that more high-risk appetite funds are returning to AI Agents, small-cap AI assets, and AI infrastructure sectors. Compared to the previous long-term focus on mainstream AI concepts, more funds are now seeking small-cap assets with new narratives. Since Unibase covers multiple hot directions—Agent Marketplace, on-chain collaboration, AI memory layers, and ERC-8183—it is more likely to attract short-term speculative interest.

However, from the current market structure, UB remains a highly volatile, small-cap asset. Market attention to UB is driven not only by the project itself but also by the spread of AI Agent sentiment, small-cap rotation, and high-elasticity trading preferences. This indicates that the overall market is still in a sentiment-driven phase rather than a mature fundamental phase. Especially in environments with rapid AI hot-spot rotations, high-volatility assets tend to amplify emotional reactions, increasing market risks.

How User Focus Is Shifting as Agent Applications Expand

As AI Agent applications continue to grow, user attention is also shifting. Previously, discussions about AI projects focused mainly on model parameters, reasoning capabilities, and chat experience. Now, more users are concerned with whether Agents can collaborate, possess long-term memory, automatically execute tasks, and be invoked on-chain.

This change signifies a shift in the competitive logic of the AI industry. Compared to simple chat tools, the market is now re-evaluating whether AI can form truly automated execution networks. During this phase, on-chain structures are becoming increasingly important. When multiple Agents participate in tasks, on-chain systems can better handle identity verification, permission control, data sharing, and incentive distribution. These capabilities are becoming key directions for the next stage of AI infrastructure competition.

From a market perspective, user focus has shifted from “Can AI chat?” to “Can AI collaborate?” This change indicates that the AI industry is moving from an application competition phase to a system competition phase.

Opportunities Brought by AI Infrastructure Asset Rotation

Since 2026, the AI asset market has begun to show clear differentiation. Compared to the previous focus on AI application layers, recent months have seen increasing attention on AI infrastructure, especially on the AI data layer, Agent protocols, long-term memory, and collaboration networks.

As general AI models become more homogeneous, the market is refocusing on underlying structural capabilities. Projects with infrastructure attributes are more likely to develop long-term narratives. Recent market rotations show that AI infrastructure has become a key focus for high-risk capital. Under the ongoing expansion of the Agent concept, the market is seeking the essential underlying components for the next phase of AI networking. Unibase’s current coverage of the Agent Marketplace, decentralized memory layers, and on-chain collaboration networks are all important directions within this hot market segment.

However, the entire AI Agent track remains in an early stage, and market volatility can be intense. The rapid pace of AI hot topics also carries the risk of quick shifts.

Risks to Watch After UB’s Rise

Although UB has recently experienced significant gains, the market still faces considerable uncertainty. First, the AI Agent track is still in the concept expansion phase, with many projects not yet reaching large-scale application, so current enthusiasm is largely sentiment-driven.

Meanwhile, competition in AI infrastructure is intensifying rapidly. As more projects enter the Agent protocol, on-chain collaboration, and AI memory layers, market hotspots could shift quickly. Additionally, UB remains a small-cap, high-volatility asset. During rapid short-term capital inflows, market fluctuations tend to be amplified. If AI hot spots cool down or overall risk appetite declines, such assets are likely to be among the first affected.

Therefore, UB’s recent rise is driven not only by project fundamentals but also by the spread of AI Agent sentiment, high-risk appetite revival, and asset rotation. These factors are inherently volatile.

Summary

Since May 2026, Unibase has surged as the AI Agent market reactivates. The launch of the ERC-8183 Marketplace, increasing demand for on-chain collaboration, and the expansion of decentralized memory narratives have made UB a popular direction among recent AI infrastructure assets.

Compared to the previous focus on standalone AI tools, more funds are now paying attention to how AI Agents can form collaborative networks. Unibase’s core focus aligns with this hot intersection. However, the overall AI Agent track remains in an early stage, with high market sentiment and volatility risks. Whether UB can sustain its market enthusiasm will depend on the continued expansion of the Agent ecosystem and the reinforcement of AI infrastructure narratives.

FAQ

Why did UB rise so quickly recently?

According to Gate data, UB increased over 12 times from mid-April to mid-May 2026. The recent resurgence in AI Agent interest, the launch of the ERC-8183 Marketplace, and the rotation into AI infrastructure are key reasons for increased market attention.

What is the ERC-8183 Agent Marketplace?

It is Unibase’s recently launched AI Agent service marketplace, focusing on enhancing collaboration, service invocation, and on-chain task execution among Agents.

Why is the AI memory layer gaining market attention?

As AI Agent applications grow more complex, the importance of long-term context and persistent memory increases. The market is re-focusing on AI memory infrastructure as a result.

What is the biggest change in the current AI Agent market?

The focus is shifting from “single AI tools” to “multi-Agent collaboration networks.”

What is the biggest risk for UB now?

The AI Agent track is still in an early stage, and UB is a high-volatility small-cap asset. Market sentiment shifts can cause significant fluctuations.