Today, a handful of tech giants dominate the landscape, holding vast amounts of user data in their control. For years, this centralized control seemed inescapable—users had little choice but to surrender their information or lose access to essential services. Privacy, security, and fairness took a backseat. But now, a shift is underway.
Decentralized AI is emerging as a bold challenge to this status quo, aiming to put power back into the hands of people. Vana and Pundi AI are two projects that challenge this control. Both these projects shift power back to users, but tackle it differently.
Why Decentralized AI Matters: The Bigger Picture
To understand Vana and Pundi AI, lets understand why today’s AI systems face serious hurdles:
- Data Silos: Tech giants hoard data, limiting access for smaller innovators and slowing progress.
- Privacy Risks: Users rarely know how their data is exploited, fueling distrust.
- Biased Models: Without diverse, high-quality data, AI often mirrors societal biases, leading to flawed outcomes.
So basically, you grant tech giants access to your personal data, which may be at risk of exploitation and the AI itself may not be as accurate when it comes to the end result—this is a recipe for disaster. Let’s explore how Vana and Pundi are turning things around.
Vana uses Data Liquidity Pools (DLPs)—big, open systems where data from millions gets collected, verified, and used to train AI models. Their core idea is simple: data should be free-flowing, and users should own the AI it powers, not corporations. Binance, with major funding, boosted Vana’s treasury to $1 billion in fully diluted value.
In 2025, Changpeng “CZ” Zhao joined as an advisor, bringing his financial and strategic know-how to push them further. Vana’s aim is to onboard 100 million users by 2027, and with steady investments, they’re gaining traction as a decentralized data player.
But Pundi AI takes a broader approach, building a complete AI economy. They don’t just pool data—they validate it, trade it, deploy it, and use it with precision. They’ve gathered 90,000 datasets from sources like Hugging Face and Kaggle, creating the largest AI data layer in Web3, supported by 3.5 billion AI tokens.
Unlike Vana, Pundi AI isn’t tied to centralized investors; its wealth is fully unlocked and community-owned. They operate across Base, EVM, and Cosmos chains for seamless cross-chain functionality, and their system includes data tagging, marketplaces, AI deployment tools, and a liquidity council to keep everything running smoothly.
Let’s compare the two projects and understand their key differences:
Feature Comparison: Vana vs. Pundi AI
| Feature | Vana | Pundi AI |
| Data Platform |  No tagging platform |
 “Tag-to-Earn” data validation |
| Data Marketplace | Yes |
Yes |
| AI Agent Deployment | No |
Pundi Fun AI Agent |
| Market-Making Agent | No |
AI-driven MM agent |
| Cross-Chain Support | Partial |
Full EVM + Cosmos |
| Exchange Presence | Limited |
Coinbase, Bitmart, Huobi |
| Token Unlock Status | Likely vested |
Fully unlocked |
| FDV (Fully Diluted Valuation) | $1B | $250M |
- Tag-to-Earn: Pundi AI’s validation process creates better data, critical for accurate AI—something Vana lacks.
- Cross-Chain Support: Pundi AI’s broader compatibility makes it easier to use across ecosystems.
- Deployment Tools: Pundi AI empowers users to build AI, while Vana stops at data contribution.
The Two Approaches: Data Liquidity vs. Holistic AI Infrastructure
Vana has gained traction due to its model being centered around Data Liquidity Pools. A data liquidity pool is a decentralized system where individuals voluntarily contribute their personal data, which is verified and used to train AI models, enabling users to retain control and earn rewards.
This approach gives individuals ownership of their data, allowing them to contribute to and be rewarded for AI development. While this concept is valuable in liberating data from walled gardens, it primarily functions as a passive mechanism, aggregating, verifying, and storing information for AI model training.
Pundi AI, in contrast, takes a different route to the same problem, designing a fully integrated AI data ecosystem. Pundi provides a complete infrastructure that actively supports AI tagging, dataset trading, model deployment, and liquidity solutions. This multi-layered architecture not only ensures a seamless flow of high-quality AI training data but also positions Pundi AI as an indispensable hub for decentralized AI development.
Why This Matters: Broader Implications
Pundi AI’s approach addresses key challenges in AI development, such as the need for high-quality, structured data and the ability to deploy models without centralized intermediaries. In contrast, Vana’s focus on data liquidity is valuable for breaking down silos, but it may not meet the needs of users seeking active engagement in AI creation. This unexpected detail—that Pundi AI caters to a more hands-on audience—could help it advance decentralized AI, especially given its lower FDV, suggesting room for growth.
With a valuation of $250M FDV, full cross-chain compatibility, a fully unlocked token economy, and established exchange listings, Pundi AI represents a significantly undervalued opportunity. As demand for high-quality, decentralized AI data continues to grow, Pundi AI’s full-stack approach positions it as the definitive AI data powerhouse, ready to meet the rising needs of the industry.
Conclusion and Future Considerations
Pundi AI’s fully integrated AI data ecosystem means it offers an end-to-end platform for decentralized AI data management, covering collection, validation, trading, and deployment, unlike Vana’s narrower focus on data pooling and governance.
This approach positions Pundi AI as a potential leader in the space, but its success hinges on overcoming scalability and adoption challenges, which it aims to cover in due time.
