Data Revolution in the AI Era: How DATA Builds a Decentralized Data Economy?

July 2, 2026 (Beijing time), according to Gate price data, DATA (Data Network) is trading at $0.3028, with a 24-hour increase of 3.73%, a market cap of approximately $107 million, and a neutral market sentiment rating. Behind this price performance is an accelerating industry narrative—the infrastructure layer of the decentralized data economy is moving from concept to actual deployment.

Just a week ago, Story Protocol officially rebranded as the DATA Foundation, with its strategic focus fully shifting to AI training data services. This transformation is not an isolated event. In the second quarter of 2026, capital attention in the crypto market has shifted from general AI tokens to underlying data infrastructure protocols. Projects such as Pyth Network, Ocean Protocol, and JasmyCoin are showing logically complementary development trends within their respective tracks. In the architectural evolution of modular blockchains, the data availability layer has emerged as one of the four core modules of public chains.

These signals point in the same direction: data is becoming the most critical production factor in the AI era, and blockchain technology is providing a new infrastructure solution for the circulation, pricing, and governance of this factor.

The global big data and AI market size is expected to grow from $454.5 billion in 2025 to $536.48 billion in 2026, with a compound annual growth rate of 18.0%. The AI training dataset market is expected to grow from $3.19 billion in 2025 to $3.87 billion in 2026. Meanwhile, China's daily token consumption has grown from approximately 100 billion at the beginning of 2024 to 140 trillion in March 2026. The speed of data generation and AI's data consumption are reshaping the underlying logic of data infrastructure at an unprecedented pace.

From four dimensions—the explosive growth of AI data demand, the trend of data assetization, the implementation path of on-chain data marketization, and the integration direction of AI and data infrastructure—this article systematically analyzes why the decentralized data economy has become one of the most structural narratives in the crypto industry in 2026.

Exponential Explosion of AI Data Demand

AI models' reliance on data is climbing at a nearly uncontrollable speed. Training large language models requires PB-level corpora; multimodal AI needs to simultaneously process heterogeneous data such as text, images, audio, and video; and every autonomous decision by an AI Agent generates new data records.

From a market size perspective, the AI Data Contracts for AI market is expected to grow from $1.28 billion in 2025 to $1.57 billion in 2026, with a compound annual growth rate of 23.1%, potentially reaching $3.64 billion by 2030. The AI data management market was valued at approximately $44.71 billion in 2025 and is expected to grow to $54.8 billion in 2026, with a compound annual growth rate of 22.98%, potentially reaching $190.29 billion by 2032.

These numbers reflect a fundamental supply-demand contradiction: AI's demand for data is growing exponentially, but the supply of high-quality, verifiable, and traceable data is severely insufficient.

The traditional data supply model faces three bottlenecks. First, the data silo problem. Leading tech companies and institutions hold massive amounts of data, but due to commercial competition and privacy compliance considerations, this data is difficult to be legally and efficiently utilized in AI training. Second, the data quality issue. According to a Precisely survey from November 2024, 64% of respondents ranked data quality as the top data integrity challenge, a significant increase from 50% in 2023; data governance concerns rose from 27% in 2023 to 51% in 2024. Third, the data traceability and compliance issue. The EU's Artificial Intelligence Act will enter its enforcement window in August 2026, and institutions that fail to prove the data sources behind high-risk AI decisions could face fines of up to €35 million or 7% of global annual turnover.

It is against this backdrop that blockchain-based decentralized data networks have entered the evaluation scope of infrastructure leaders. Their core value proposition lies in: through cryptographic verification and distributed ledger technology, providing verifiable on-chain records for the source, quality, and usage permissions of AI training data.

Data Assetization: From Information to Tradable Assets

The core proposition of data assetization is: how to transform data from a "byproduct" into a priced, tradable, and auditable asset?

In the traditional internet model, data is collected, stored, and used by platforms. Users are both producers of data but have no right to participate in the distribution of data value. This model faces increasing legal and ethical pressure in the AI era. Unclear ownership of data property rights, lack of standards for value assessment, and lack of transparency in transaction processes collectively constitute the core obstacles to the marketization of data elements.

Blockchain technology provides a technical path to solve these problems. Smart contracts can automate the programming and execution of data usage permissions; non-fungible tokens (NFTs) can provide unique on-chain identifiers and ownership proofs for datasets; and decentralized storage ensures the security and availability of data during the transaction process.

In June 2026, the DATA Foundation completed its integration with Kled, a user-consent-based AI training data marketplace with approximately 1.1 billion data records. The DATA Foundation provides a blockchain-based registration, licensing, and provenance network. The industrial significance of this integration lies in: it is the first time that large-scale, user-authorized AI training data has been systematically connected with a blockchain-based property rights management network.

Another path for data assetization comes from decentralized storage protocols. In November 2025, Filecoin announced a full shift to the "Onchain Cloud" strategy, positioning itself as "verifiable, developer-owned infrastructure." As of early 2026, over 100 teams are building on Filecoin Onchain Cloud, processing more than 6,500 payment routes. Decentralized storage is evolving from a "backup solution" into a strategic digital sovereignty infrastructure supporting enterprise intelligence, scientific computing, and global knowledge preservation.

On-Chain Data Marketization: Infrastructure Taking Shape

The realization of on-chain data marketization depends on the coordinated maturity of three layers of infrastructure.

Layer 1: Data Availability Layer. In 2026, public chains are fully transitioning from monolithic architectures to modular designs that decouple consensus, execution, data availability, and settlement into separate layers. After the data availability layer became independent, solutions like Celestia, EigenLayer, and Polygon CDK have matured, reducing the deployment cycle of new chains from six months to two weeks and cutting costs by 85%. The global data availability layer market is expected to grow from $1.97 billion in 2025 to $2.41 billion in 2026, with a compound annual growth rate of 22.4%.

Layer 2: Data Indexing and Query Layer. The Web3 data indexing platform market is expected to grow from $2.12 billion in 2025 to $2.68 billion in 2026, with a compound annual growth rate of 25.9%, potentially reaching $6.77 billion by 2030. In 2026, The Graph published a detailed technical roadmap, planning to transform the protocol from an indexing-centric network into a modular, multi-service data backbone. SubQuery Network has provided decentralized data indexing and dRPC services for thousands of DApps on nearly 300 blockchain networks.

Layer 3: Data Value Distribution Layer. This is the newest layer currently taking shape. Decentralized data networks allow data contributors to set permissions via smart contracts, enabling notification, sharing, and monetization of datasets. Users can directly participate in the value creation of the AI data economy, with their contribution rights transparently tracked on the blockchain and ultimately converted into rewards and settlements.

The coordination of the three-layer infrastructure makes it possible to complete a closed loop for on-chain data from "queryable" to "verifiable" to "tradable."

Integration of AI and Data Infrastructure: Formation of a New Track

In the second quarter of 2026, capital attention in the crypto market has shifted from general AI tokens to underlying data infrastructure protocols. The logic behind this shift is: the competitive landscape of the AI model layer has essentially been locked by a few tech giants, but the data infrastructure layer supporting AI operations is still in the "greenfield" stage.

The integration of AI and data infrastructure is unfolding across multiple dimensions.

On the data collection side, decentralized data networks allow users to authorize personal data for AI training and receive corresponding compensation, breaking the pattern where data value was unilaterally captured by platforms in the traditional model. On the data preprocessing side, blockchain-based data labeling and quality verification markets are emerging, leveraging distributed crowdsourcing and cryptographic incentives to reduce the cost of acquiring high-quality training data. On the data invocation side, the decentralized memory layer for AI Agents has become a new infrastructure track—as AI Agents evolve from single chat tools to autonomous digital entities capable of cross-platform collaboration, long-term memory, identity management, and inter-agent communication are becoming key bottlenecks.

Decentralized computing networks have become the backbone of the AI token industry. These platforms incentivize global participants to contribute spare computing power, not only lowering the barrier for developers but also reducing the concentration of AI power in the hands of a few tech giants. As the upstream of the computing layer, the strategic value of the data layer has been reassessed by the market in 2026.

From the perspective of institutional capital, decentralized storage and data infrastructure are being analogized as the digital equivalent of "basic utilities," with long-term valuation models moving away from short-term price fluctuations. The basis for this judgment is: regardless of how the AI model layer evolves, the demand for data storage, verification, indexing, and trading will be persistent and increasing.

Conclusion: From Data Sovereignty to Data Economy

The industrial logic of the decentralized data economy can be summarized into a clear evolutionary chain: the explosion of AI data demand → institutional and technical needs for data assetization → the formation of on-chain data infrastructure → deep integration of AI and the data layer.

On July 2, 2026 (Beijing time), DATA (Data Network), at a price of $0.3028, a market cap of $107 million, and neutral market sentiment, is in the early commercialization stage of this evolutionary chain. The Web3 data infrastructure market is expected to grow from $5.41 billion in 2025 to $7.55 billion in 2026, with a compound annual growth rate as high as 39.6%. The overall Web3 infrastructure market is expected to grow from $14.12 billion in 2026 to $194.52 billion by 2036.

These numbers point to a deterministic industrial trend: data is evolving from a "byproduct" of the internet to a "core asset" in the AI era, and blockchain technology is providing unprecedented infrastructure for the circulation of this asset.

The return of data sovereignty, the redistribution of data value, and the transparency of data transactions—these are not just technical propositions but structural changes in digital economy governance. Whether decentralized data networks can complete the leap from technical verification to large-scale deployment between 2026 and 2030 will depend on three key variables: the sustained growth intensity of AI training data demand, the compatibility of regulatory frameworks with on-chain data transactions, and whether the user experience and cost competitiveness of the infrastructure layer can reach levels comparable to traditional cloud services.

Regardless of the answer, one certainty is: the decentralized paradigm of the data economy is no longer a distant vision but an ongoing industrial restructuring.

FAQ

Q1: What is the relationship between DATA (Data Network) and the decentralized data economy?

DATA (Data Network) is a decentralized data infrastructure protocol dedicated to building an on-chain data sharing and AI collaboration network, providing developers with data storage, verification, and cross-application access services. Its predecessor, Story Protocol, underwent a brand upgrade and strategic transformation in June 2026, focusing on the AI training data market, using blockchain technology to track data contributors' rights and distribute value.

Q2: How do decentralized data networks solve the quality and compliance issues of AI training data?

Decentralized data networks use the immutable nature of the blockchain to provide verifiable on-chain provenance records for each data unit. Data contributors, collection times, usage authorizations, and quality scores can all be recorded on-chain. This is particularly critical after the EU's Artificial Intelligence Act enters its enforcement window in August 2026—institutions must be able to prove the source and compliance of data underlying high-risk AI decisions.

Q3: What is the market size of on-chain data infrastructure?

The Web3 data indexing platform market is expected to grow from $2.12 billion in 2025 to $2.68 billion in 2026 (CAGR 25.9%), potentially reaching $6.77 billion by 2030. The data availability layer market is expected to grow from $1.97 billion in 2025 to $2.41 billion in 2026 (CAGR 22.4%). The overall Web3 infrastructure market is expected to grow from $14.12 billion in 2026 to $194.52 billion by 2036.

Q4: What are the main directions of AI and blockchain data layer integration?

It mainly includes three directions: first, decentralized data collection and labeling markets, allowing users to authorize personal data for AI training and receive compensation; second, the decentralized memory layer for AI Agents, providing long-term memory and identity management for cross-platform autonomous AI entities; third, blockchain-based data contracts, which use machine-readable protocols to automatically execute data quality verification, usage authorization, and compliance checks.

Q5: What are the main risks facing the decentralized data economy?

Main risks include: the performance of decentralized storage and indexing services cannot yet fully compete with centralized cloud services like AWS; some projects' low-price strategies include subsidy components, and long-term sustainability needs verification; regulatory requirements for compliant cross-border flow of on-chain data remain unclear; and the user adoption rate of the infrastructure layer may be lower than expected, making it difficult to form network effects.