A month ago, YZi Labs announced its investment in Vana, with Binance founder CZ joining as an advisor, solidifying Vana’s leading position in the AI data sector. Four days later, during an AMA with Vana, CZ stated that data is the core fuel for AI, public data has been exhausted, and private data remains untapped. He expressed optimism about Vana’s product-market fit (PMF) and user growth.

Why have YZi Labs, Coinbase Ventures, and Paradigm invested in Vana? Why is CZ bullish on Vana’s development?

This report systematically analyzes the challenges of AI data, Vana’s core value proposition, practical applications, and future growth trajectory, revealing how Vana is becoming critical infrastructure for the AI ecosystem.

01 AI and the Data Dilemma: Breaking Through Closed Barriers

According to PitchBook data, the U.S. The AI industry attracted nearly 20 billion in investments in Q1 2025. By 2024, AI startups accounted for one−third global venture capital, totaling 131.5 billion, with nearly a quarter of new ventures focusing on AI. Statista data further confirms this explosive growth—venture funding for AI and machine learning surged from 670 million in 2011 to 36 billion in 2020, a 50-fold increase. This trend clearly indicates that AI has become the shared choice of smart capital and top entrepreneurs.

However, the fundamental architecture of AI—“data + models + compute”—faces structural bottlenecks. The core driver of AI model performance is not compute power or algorithmic breakthroughs but the quality and scale of training datasets. Current large language models are approaching a critical point of data exhaustion. Meta’s Llama 3 was trained on approximately 15 trillion tokens, nearly depleting all high-quality public internet data. Despite the vast volume of public internet data, it represents only the tip of the iceberg. A widely overlooked fact is that high-value data is mostly locked behind proprietary systems requiring authorized access. Public internet data accounts for less than 0.1% of all data. This issue transcends the AI industry’s ability to solve alone and requires blockchain technology to redefine data production relationships, establish new incentive mechanisms, and catalyze the emergence of high-quality data at scale.

On the other hand, today, most data is controlled by Web2 tech companies within closed ecosystems. AI development faces the challenge of data walls, a barrier that exists because these companies recognize the immense value of data. High-quality AI models yield significant economic returns—for example, OpenAI’s annual revenue has reached approximately $3.4 billion. Building superior AI models requires vast amounts of data, often at high acquisition costs.

For instance, Reddit earns about $200 million annually from selling data, photo charges US$1–US$2 per image, and Apple’s news data transactions amount to US$50 million. Data ownership has evolved from a simple privacy preference to a major economic issue. In a world where AI models drive much of the economy, data ownership equates to holding equity in future AI models.

As data commercialization becomes more prevalent, accessing data grows increasingly difficult. Many platforms are adjusting their terms of service and API policies to restrict external developer access. For example, Reddit and Stack Overflow have modified API rules, making data acquisition more challenging. This trend is expanding, with data-rich platforms moving toward greater exclusivity.

Yet, one group retains free access to this data: the users themselves. Many people are unaware that, legally, they retain full ownership of their data. Just as parking a car in a lot doesn’t grant the lot rights to the vehicle, users’ data stored on social platforms remains their property.

When registering, users typically check boxes allowing platforms to “use their data,” which grants platforms authorization to operate services but doesn’t relinquish ownership. Users can request their data at any time. Even if platforms restrict API access for developers, individual users can still legally retrieve their data. For example, Instagram allows users to export account data, including photos, comments, and even AI-generated marketing tags. On 23 and Me, users can request their genetic data, though the process may not be intuitive.

Globally, regulations are improving to ensure users can reclaim their data. As data value grows, users must recognize and exercise their ownership rights.

02 VANA’s Core Concepts

Tech companies are building closed systems to protect their valuable data assets. VANA’s mission is to unlock this data and return control to users, enabling data sovereignty.

In other words, users can extract their data from various platforms and create datasets superior to any existing platform’s offerings.

VANA’s framework is built on two foundational concepts:

• Non-Custodial Data: Users control data access like managing crypto assets in a digital wallet. In VANA’s ecosystem, users authorize apps to access their data via signed transactions, ensuring autonomy and security.
• Proof of Contribution: While a single data point holds negligible value, aggregated data’s worth grows exponentially. This mechanism ensures high-quality data pools and creates value for contributors.

When developers pay to access data, contributors receive governance tokens proportional to their input. This allows contributors to earn ongoing rewards and participate in decision-making, reshaping data market pricing and efficiency.

03 VANA’s Ecosystem Applications

3.1 DataDAO

DataDAO is a decentralized data marketplace within the VANA ecosystem, enabling users to contribute, tokenize, and utilize data. Users can select suitable data mining pools (e.g., fitness data, research data) to contribute their data. The contributed data undergoes validation by Vana’s Proof-of-Contribution mechanism, which assesses its quality and value to ensure fair compensation for contributors.

Once verified, the data is tokenized into digital assets that can be traded or used for AI training, while contributors retain control over its usage. Each time the data is utilized, contributors receive token rewards and governance rights, allowing them to benefit economically and influence the direction of the data pool. By aggregating diverse datasets, DataDAO creates a liquid data marketplace, facilitating secure and efficient data circulation within the VANA ecosystem.

At the core of DataDAO is the Data Liquidity Pool (DLP)—a collection of validated datasets linked to tokens. DLPs are managed by DataDAO members, who hold governance rights. Each DLP clearly defines its data structure and contribution standards. For example, Sleep.com, a sleep-focused DataDAO, has established a well-defined data schema to ensure all on-chain data is structured and usable. The value of data lies not only in its volume but also in its structure and accessibility.

DataDAO places a strong emphasis on data authenticity and validity. Currently, most DataDAOs use Trusted Execution Environments (TEEs) to run Python scripts for data validation, ensuring quality while preserving privacy. For instance, Amazon DataDAO employs browser extensions to generate data quality proofs. All DataDAOs publicly disclose their Proof-of-Contribution mechanisms, allowing users to understand how data quality is ensured.

The top 16 DLPs in the VANA ecosystem receive additional incentives, enabling users to earn rewards by contributing high-quality data. Rewards are distributed based on metrics such as data access frequency, quality, and cost-efficiency. Currently, the Reddit DataDAO is the largest, attracting around 140,000 users and successfully training a community-owned AI model. DLP Labs’ DataDAO allows drivers to connect their DIMO_Network accounts, sharing vehicle data to earn rewards and advance AI innovation in the automotive sector. Meanwhile, 23andWE aims to acquire 23andMe to prevent genetic data from being exploited.

DataDAO represents a groundbreaking approach to data management, empowering individuals to take control of their data and monetize it through tokenization. This rapidly evolving ecosystem introduces more open and democratic possibilities for data governance and AI training.

3.2 DataFi

Building on the foundation of data liquidity pools, DeFi is gradually being applied to the realm of data tokens. Data liquidity pools serve as the foundational layer of the entire ecosystem, upon which various DeFi applications can be constructed using data tokens.

Currently, some early applications have emerged in the Data DeFi ecosystem. For instance, decentralized exchanges like @VanaDataDex and @flur_protocol allow users to trade data tokens and track market dynamics for specific data tokens. The emergence of these platforms has facilitated the free flow of data assets and invigorated the data marketplace.

It is worth noting that most DLP reward mechanisms primarily deposit rewards into the DLP treasury rather than directly burning data tokens or affecting their supply and demand. However, with the introduction of the VRC-13 update, this mechanism has evolved. The new model introduces a more market-driven approach: by incentivizing VANA rewards to promote data tokenization, which are then injected into DEX pools to enhance data token trading and further activate the DeFi ecosystem.

Looking ahead, functionalities currently achievable in the DeFi space—such as lending, staking, liquidity mining, and even insurance—may be introduced into the data token market, creating entirely new application scenarios.

From the perspective of traditional Web2 industries, similar to how companies purchase oil futures to hedge against price fluctuations, the data market may develop data futures, enabling users to lock in future prices for datasets in advance and reduce uncertainty in acquisition costs.

Some trading firms have already begun treating data as a new asset class, researching valuation methods such as assessing the value of specific data tokens, the probability of sales usage, and lifecycle analysis. These factors directly influence the price of data tokens and market liquidity, leaving ample room for innovation.

3.3 Streamlined Data Access

Currently, accessing datasets on the mainnet remains relatively cumbersome. Users must submit detailed requests specifying their needs, payment amounts, and project code, then wait for approval before gaining access. While this ensures transparency and standardization, it creates operational friction.

To improve efficiency, Vana is developing more efficient data access methods that enable automated API access and direct data retrieval across multiple DataDAOs. For example, in the future, users could combine sleep data with Coinbase or Binance trading data to analyze the sleep patterns of specific token holders and uncover new market insights.

Additionally, Vana is advancing a new proposal that implements an 80-20 standard ratio for burning data tokens and VANA in exchange for data access rights.

Vana has also developed a new data query interface that significantly simplifies the data access process. Users can authenticate via wallet login and generate digital signatures to verify their access permissions. Since the Data Liquidity Pools (DLPs) record data formats, users can clearly understand data structures and retrieve needed information using SQL queries. During this process, users may first receive synthetic data samples to test and verify query accuracy. When working with real data, all computations are performed within Trusted Execution Environments (TEEs) to ensure data security. This mechanism effectively prevents the “dual-use data problem” (where users might resell purchased data), thereby protecting the economic value of data and ensuring the sustainable development of the data marketplace.

04 Vana’s Value Analysis

Data is rapidly emerging as the core asset of the digital age. While data collection and storage technologies have reached considerable maturity, the true challenge lies in effectively assessing data quality, maximizing its value, and ensuring privacy protection. Vana elegantly addresses this challenge through its innovative incentive mechanism: Users can stake VANA tokens to support high-value DataDAOs while earning corresponding rewards, creating a virtuous cycle of value creation.

4.1 Breaking Through the “Data Wall”

AI development has hit the “data wall” - high-quality public data resources are nearing exhaustion. Future breakthroughs in AI will inevitably depend on effectively accessing and utilizing high-value private data, such as personal health records, smart device usage data, and Tesla driving videos as potential training resources.

A paradox exists in data value: data maintains its worth through exclusivity, but becomes commoditized and depreciates once widely available. As AI models undergo commoditization themselves, long-term competitive advantage will come from controlling unique datasets that enable superior performance in specialized domains. Once data becomes public, price competition emerges almost immediately, causing rapid value erosion.

Vana’s DataDAO leverages Trusted Execution Environments (TEEs) to enable the transfer of high-value private data while preserving privacy. This breakthrough expands the scope of valuable data assets from limited public datasets to the vast realm of private data, opening new possibilities for AI advancement.

4.2 The Unique Value Curve of Data

Data exhibits a distinctive value curve: individual data points hold negligible worth, but when aggregated to critical mass, their value grows exponentially. This characteristic presents significant challenges for data monetization - substantial returns only materialize after collective datasets are formed.

Vana’s DataDAO mechanism provides an innovative solution to this dilemma. By pooling similar data, DataDAOs create collective bargaining power for contributors. Consider Tesla owners: if all owners collectively share driving data through a DataDAO, they gain strong pricing leverage with any potential buyer. In contrast, if owners individually attempt to monetize their data, it inevitably leads to price competition where buyers can simply acquire sufficient samples from the lowest bidders.

Structured, verified high-quality datasets (like authenticated Tesla driving data) command premium market value, and Vana’s framework enables full realization of this value.

4.3 The Breakthrough of Cross-Platform Data Aggregation

The most powerful aspect of DataDAOs is their ability to achieve cross-platform data aggregation - something nearly impossible in today’s walled-garden ecosystems. Imagine researchers needing access to a user’s combined Facebook messages, iMessage history, and Google Docs content. The traditional approach would require cooperation between Facebook, Apple, and Google - platforms that have neither incentive to integrate user data (which would weaken their data moats) nor the regulatory clearance to do so.

DataDAOs elegantly circumvent this obstacle through user-led data integration, unlocking cross-platform data value and creating unprecedented opportunities for AI training and research.

4.4 New Economic Participation Model

Vana’s vision extends far beyond pure technological innovation—it is pioneering an entirely new economic participation paradigm. Under this model, users can engage in the digital economy without traditional capital requirements, as they already possess the most valuable resource: their personal data. Users don’t need to bring financial capital; sharing their data becomes their capital. DataDAOs provide Web3 users with passive income streams derived from their unique personal data, significantly lowering the barrier to entry for participating in the digital economy.

4.5 Reshaping AI Profit Distribution

This model could fundamentally restructure how value from AI advancements is distributed. Rather than profits primarily flowing to big tech corporations, Vana’s data ownership and governance mechanisms enable broad participation in the AI economy. Early indicators show strong resonance with this approach—over 300 DataDAOs are already in development on testnets.

Looking ahead 3-5 years, we may witness the emergence of fully user-governed AI models built by 100 million data contributors—models that could outperform today’s leading centralized AI systems. These community-owned models create stronger user engagement and connections. Data sovereignty empowers users to selectively support ethical AI development while denying access to unethical companies.

Decentralized AI provides a more democratic framework where society collectively determines what AI should learn and believe, rather than leaving these decisions to a handful of corporations. User data ownership translates not just to economic benefits, but also substantive control over AI model behavior—including addressing critical issues like content moderation policies.

05 Conclusion

At the commercial level, Vana is committed to building a comprehensive data value chain that spans the entire process from data aggregation and AI model training to data sales. Currently, the data market is monopolized by a handful of platforms and data brokers. Vana aims to address this inefficiency by creating a fairer data trading ecosystem.

Vana is more than just a new platform—it represents a fundamental shift in data ownership and the development of AI. By enabling users to participate in collective value creation while maintaining sovereignty over their data, Vana is laying the foundation for a more equitable and innovative AI future.

In today’s AI market, which is rife with conceptual hype, Vana stands out with its innovative mechanisms that directly tackle the industry’s core challenges. It has the potential to become a pivotal force in shaping the future trajectory of AI.

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