The Data Infrastructure Revolution in the AI Era: How Unibase Builds the Web3 Decentralized Data Layer?

In 2026, the global big data and artificial intelligence (AI) market is expected to grow from $454.5 billion in 2025 to $536.48 billion, with a compound annual growth rate of 18.0%. At the same time, China’s average daily Token consumption has surged from approximately 100 billion at the beginning of 2024 to 140 trillion in March 2026—an increase of more than 1,000 times over two years. AI’s data hunger is reshaping the underlying logic of the entire data infrastructure at an exponential pace.

Against this backdrop, the Web3 data layer is undergoing a profound structural transformation. From early decentralized data indexing protocols like The Graph, to the separate modularization of data availability (DA) layers, and then to decentralized memory layers for AI Agents—its evolution path is clearly pointing in one direction: building a verifiable, programmable, decentralized data layer for the AI era.

Unibase (UB) is a representative project along this evolution path. As a decentralized memory layer for AI Agents, Unibase aims to answer a core question: as AI Agents evolve from a single chat tool into autonomous digital entities capable of cross-platform collaboration, how should the data layer be restructured?

The Exponential Growth of AI Data Demand Is Driving a Restructuring of Infrastructure

Data is the most core factor of production in the AI era, but the way data is generated, stored, accessed, and verified is undergoing fundamental change.

From the perspective of market size, the global AI training dataset market is expected to grow from $3.19 billion in 2025 to $3.87 billion in 2026, with a compound annual growth rate of 21.5%, and is expected to reach $8.45 billion by 2030. In 2026, the global memory chip market is expected to expand by more than 4 times compared with the previous year. Gartner predicts that the global database management system (DBMS) market size in 2026 will reach $161 billion, up 18.4% year over year.

Behind these figures is a clear trend: the training, inference, and application of AI models are producing massive amounts of data. Model training requires PB-level corpora, and multimodal AI needs to process heterogeneous data such as text, images, audio, and video. Every autonomous decision made by an AI Agent generates new data records.

But the bigger challenge lies in how data is “accessed.” Traditional AI systems rely on limited context windows and cannot store users’ history, task status, or environmental information for the long term. This means that when AI processes complex tasks, it often needs to retrieve context repeatedly, making it difficult to build sustained learning capabilities. As AI Agents evolve from single-task executors into autonomous entities coordinating across platforms, long-term memory, identity management, and inter-agent communication are becoming key bottlenecks in AI infrastructure.

The Evolution Path of the Web3 Data Layer: From Indexing to Memory

The Web3 data layer did not emerge overnight. Its evolution can roughly be divided into three stages:

First Stage: Decentralized Data Indexing Layer. Decentralized indexing protocols represented by The Graph provide DApps with “search engine” capabilities for blockchain data. In 2026, The Graph released a detailed technical roadmap, planning to transform the protocol from an indexing-focused network into a modular, multi-service data backbone. Projects such as SubQuery and Subsquid (SQD) continue to deepen their efforts in this area, building an open data access ecosystem through data lakes, Worker nodes, and the Portal query layer.

Second Stage: Modular Data Availability (DA) Layer. In 2026, public chains are fully transitioning from monolithic architectures to modular designs that decouple consensus, execution, data availability, and settlement into layers. As the data availability layer becomes independent, solutions such as Celestia, EigenLayer, and Polygon CDK are increasingly mature. The deployment cycle for new chains has been compressed from half a year to two weeks, and costs have been reduced by 85%. The data availability layer is no longer just about storage—it is integrated into verification mechanisms and economic systems.

Third Stage: AI-Native Data Layer. This is the direction of evolution happening right now. The explosive growth of AI Agents imposes entirely new requirements on the data layer: it needs not only to be queryable and verifiable, but also to support long-term memory, cross-platform interoperability, and programmable economic incentives. The decentralized memory layer constructed by Unibase is a typical representative of this stage.

The logic of this evolution path is clear: from “data that can be queried” to “data that can be verified,” and then to “data that can be remembered”—the Web3 data layer is evolving from passive storage and indexing tools into an active AI infrastructure with continuous learning capabilities.

Unibase: Building a Decentralized “Long-Term Brain” for AI Agents

Core Positioning: Memory Layer, Not Storage Layer

Unibase’s core positioning can be summarized in one sentence: If Ethereum provides state information for smart contracts, then Unibase provides memory capabilities for AI Agents.

This distinction is crucial. Traditional blockchain storage focuses on “state”—static information such as account balances and contract data. By contrast, the memory AI Agents need is dynamic: continuously accumulated, and shareable across platforms—covering execution logs, interaction history, learned context, and more.

Unibase achieves this goal through three core modules:

Membase (AI Long-Term Memory System): Stores an AI Agent’s long-term context and historical state, enabling it to continuously call past information at different points in time. This addresses the fundamental limitation of traditional large language models that rely on short-term context windows.

AIP Protocol (Agent Interoperability Protocol): Responsible for Agent identity, permissions, and cross-platform communication. Different AI Agents can exchange information and share state through a unified protocol.

Unibase DA (Data Availability Layer): Responsible for high-throughput data storage and synchronization, providing data availability support for AI workloads. It is based on a DAS (Data Availability Sampling) architecture and combines ZK and fraud proofs to achieve on-chain verifiability.

Together, these three layers form the decentralized infrastructure for AI Agents, enabling AI to run long term on open networks, learn continuously, and collaborate across platforms.

Differentiation from Similar Projects

Compared with AI infrastructure projects such as Virtuals, Unibase is more focused on the AI memory layer and Agent interoperability rather than simply providing GPU compute power or AI model services. Compared with traditional AI cloud platforms, its core features include decentralized data structures, a long-term memory system, communication between Agents, and a Web3-native architecture.

From the perspective of technical evolution, what Unibase builds is not simply storage expansion; it attempts to establish a new data trust mechanism—so that an AI Agent’s memory no longer depends on control by any single platform.

Data Assetization: From “Dead Data” to “Live Assets”

The surge in AI data demand not only increases storage and compute needs, but also accelerates the trend of data assetization.

In 2026, the industry calls it the “year of value release for data elements.” The technological convergence of AI and Web3 is providing targeted solutions to pain points that long affect state-owned data assets, such as information silos and lack of trust.

In traditional models, data is either obtained for free by centralized platforms and commercialized, or it lies dormant on hard drives without generating any value. The Web3 path of data assetization offers another possibility: users contribute anonymized behavioral data in exchange for governance weights or compliance credentials within DeFi ecosystems. Data no longer relies on centralized platforms for pricing and circulation, creating new room for data markets and decentralized AI collaboration.

However, data assetization still faces real-world challenges. What the demand side needs is professional data that is structured, context-dependent, and backed by trust and legal responsibility subjects. For now, Web3 projects find it difficult to provide such data at scale. Solving this contradiction precisely requires infrastructure-layer projects like Unibase—by providing a verifiable memory layer and an on-chain data system, data can be endowed with traceable provenance and integrity, giving it the technical prerequisite to truly become assetized.

Market Performance and Ecosystem Progress

As of July 1, 2026 (Beijing time), according to Gate market data, Unibase (UB) is priced at $0.08298. It is down 21.24% over the past 24 hours, up 19.83% over the past 7 days, down 53.90% over the past 30 days, and up 429.16% over the past year. Its current market cap is approximately $207 million, with a 24-hour trading volume of approximately $52.1772 million and a total supply of 10 billion tokens.

Since May 2026, UB has experienced rapid growth. The renewed interest（ renewed interest ） in the AI Agent market, the launch of the ERC-8183 market, and the expansion of the decentralized memory layer have collectively driven Unibase to become a popular asset in the AI space. Unibase has been listed on Binance Alpha and Binance Futures and has begun trading in the OKX perpetual contract market.

In terms of ecosystem partnerships, Unibase has collaborated with the aelf blockchain, using its multi-layer architecture to advance AI solutions; partnered with 4AI to empower autonomous AI Agent economies on BNB Chain; and worked with AON to promote the development of AI Agents with memory functionality. These collaborations show that decentralized memory layers are becoming increasingly important infrastructure components in the AI Agent ecosystem.

Unibase is also continuously expanding its technical capabilities. The launch of the ERC-8183 market provides a more complete set of trading and collaboration mechanisms for the Agent economy. Its GitHub repository shows that the project is actively under development, with a core goal of enabling AI Agents to have long-term memory and cross-platform interoperability.

Risks and Challenges

Although Unibase has made phased progress on both the technology and market fronts, as an infrastructure project at the intersection of AI and Web3, the challenges it faces are also impossible to ignore.

Risk of Technology Maturity. Decentralized memory layers are a completely new technological direction. The coordinated operation of the three major modules—Membase, AIP Protocol, and Unibase DA—needs to be validated through large-scale real-world scenarios. Technical challenges such as AI Agent memory read/write latency, data consistency, and cross-chain state synchronization have not yet been fully solved.

Uncertainty in Market Demand. AI Agents are still in the early stages of development, and most Agent applications have not yet formed large-scale memory access demand. The speed of infrastructure building may outpace actual demand, which could lead to slower network effects.

Dynamic Changes in Competitive Landscape. Competition in the Web3 data layer sector is fierce. Indexing protocols such as The Graph and SubQuery are evolving toward AI compatibility; modular DA-layer projects such as Celestia and EigenLayer are also expanding the boundaries of data services. Unibase needs to continuously strengthen its differentiated positioning.

Effectiveness of the Token Economic Model. As the native utility token for the Agent economy, UB’s value capture depends on real-world deployment in scenarios such as Agent-to-Agent payments and memory settlement. If the scale of the Agent economy falls short of expectations, the token’s long-term value support will face pressure.

Conclusion

From decentralized data indexing to modular data availability layers, and then to AI-native decentralized memory layers—the evolution of the Web3 data layer is accelerating. The core driving force of this evolution is not technology itself, but the fundamental restructuring of how data is accessed in the AI era.

Unibase’s exploration represents an important direction: when AI Agents are no longer tools limited to a single platform, but autonomous entities that collaborate across platforms, the data layer must evolve from “storage” and “indexing” into “memory” and “interoperability.” The difficulty of this transition is no less than the shift from Web2’s client-server architecture to Web3’s decentralized architecture.

2026 is considered a turning point for the convergence of AI and blockchain—hype gradually settles, and technical capabilities continue to improve. At this turning point, the restructuring of data infrastructure will become a key variable determining whether AI Agents can truly move toward large-scale applications. Whether Unibase can secure a core position in this process depends on its speed of technical deployment, its ability to expand the ecosystem, and its responsiveness to real market demand.

For practitioners and investors focused on Web3 data infrastructure, understanding the logic of this evolution path offers far more long-term value than chasing short-term price fluctuations.

FAQ

Q1: What is the difference between Unibase and data indexing protocols like The Graph?

Unibase is a decentralized memory layer for AI Agents, primarily addressing long-term memory and cross-platform interoperability; The Graph mainly provides indexing and query services for blockchain data. The two are products of different stages of the Web3 data layer—the indexing layer solves “where the data is,” while the memory layer solves “how the data is continuously accessed.”

Q2: What does Unibase’s “memory layer” specifically refer to?

The memory layer is a concept that is more advanced than storage. Storage only solves data preservation; memory also involves the continuous accumulation of context, access across different time nodes, and sharing among multiple Agents. Unibase implements this functionality through the Membase module, enabling AI Agents to “remember” past interactions like humans and learn continuously.

Q3: What role does the UB token play in the Unibase ecosystem?

UB is the native utility token of the Agent economy. It is mainly used for settling Agent memory usage, Agent-to-Agent payments and service pricing, and staking and incentives for long-term network usage. Its value capture depends on the real active level of the Agent economy ecosystem.

Q4: What is the future direction of the Web3 data layer?

From data indexing to data availability to AI-native memory layers, the core logic of evolution is that data shifts from “passive storage” to “active services.” In the future, data layers will place greater emphasis on verifiability, programmability, and cross-platform interoperability, and will be deeply integrated into AI workflows.

Q5: What risks should be considered when investing in Unibase?

Mainly including: technology maturity risk (the decentralized memory layer has not yet been validated at scale), market demand uncertainty (the AI Agent ecosystem is still in the early stage), changes in the competitive landscape (multiple projects entering similar tracks), and the effectiveness of the token economic model (depends on the actual scale of real-world Agent economy deployment).