Can the soon-to-launch mainnet Vana become the infrastructure of the AI Agent data era?
Your own influence is actually greater than you think.
Author: Deep Tide TechFlow
As BTC breaks through $100,000, more funds are looking for new projects and opportunities under the expectation of a bull market.
But if you ask which track has the most opportunities right now? AI Agents must be named. However, with a large number of AI Agents being launched every day, the narrative of the entire track is gradually stratifying:
One category revolves around the applications of AI Agents, with corresponding tokens representing either memes or the utility of the Agent; while another category focuses on providing capabilities for AI Agents, enabling applications to perform better.
The former is becoming crowded and competitive as it is easier to observe at the application layer; while the latter has relatively more room for breakthroughs.
What essential capabilities do AI Agents still need?
Perhaps we can find answers from the recently popular "AI KOL" aixbt:
Research has found that what aixbt says is not always correct; it cannot distinguish between true and false, cannot require experts to validate its assumptions, and cannot question itself.
Essentially, because aixbt is actually a large language model, it can only scrape and summarize from various publicly available data, making it more like a repeater of aggregated public information.
So, if you can provide this type of AI agent with more diversified, personalized, and private data, it might perform better.
For example, share insights from your experience trading low market cap altcoins, or investment strategies that you would only discuss in paid groups for it to learn… But this data is not publicly available, and aixbt cannot access it.
Note that the world does not lack sufficient data, but high-quality data is hard to obtain.
In the current crypto craze surrounding AI agents, data infrastructure is actually lacking.
One narrative space and information gap here is that if there are projects that can collect more personalized and individualized data, while feeding it to AI agents or organizations in need, they might find a unique ecological niche in this wave of hype.
Two months ago, we wrote about a project called Vana , which collects various types of data that cannot be obtained from the public market using a DAO approach, while incentivizing data contribution and guiding the purchase and use of such data through tokenization.
At that time, AI Agents were not as popular, and the project's use cases seemed less clear. However, in this wave of AI Agent trends, Vana clearly has more room for application and a more coherent environment.
Coincidentally, Vana is about to launch its mainnet and release its own token $VANA, and it has also updated its white paper and tokenomics, providing more detailed explanations of the current data issues and its positioning.
In the crypto market, timing is crucial. What new dynamics and changes should we pay attention to in Vana now? Does the token have more favorable expectations?
We read the newly released white paper to help you quickly understand the current Vana.
Data "Double Spending," Finding Blind Spots in Profit Seeking
Undoubtedly, everyone is chasing profits in the AI agent craze.
Anyone can easily create an AI agent, and the assets corresponding to AI agents can also be easily tokenized… But aside from purchasing the tokens corresponding to the AI agent, what other profits can you obtain?
This question represents new opportunities for individuals and new narrative spaces for projects.
Don't forget that AI agents may be using the data you contributed to train themselves, but you haven't earned a penny from it. For example, the previously mentioned aixbt analyzes crypto hotspots, one of the sources of which may be an article you wrote on your Twitter.
Therefore, upon opening Vana's new white paper, one concept on the first few pages quickly caught my attention: the "double spending" dilemma of data.
Does the term double spending sound familiar yet strange?
This concept actually originates from the double spending problem solved by Bitcoin—preventing the same Bitcoin from being paid twice.
Bitcoin solves this problem by recording the entire transaction history on a public blockchain, which acts as an immutable ledger, ensuring that everyone knows the historical flow of a coin and that a coin can only be spent once in its current state.
However, in the data realm, this problem is more complex.
Unlike Bitcoin, data is inherently replicable, leading to an overlooked economic dilemma in the AI craze: when data is sold directly, buyers can easily copy and redistribute it, resulting in the same data being utilized multiple times, and you cannot gain any additional profit from this utilization.
For example, if you write a tweet, once it is used and learned by an AI agent, it may be shared indefinitely with other AI agents, ultimately causing this data to lose its scarcity and economic value.
If you want to create a ledger similar to Bitcoin to record data usage on-chain to avoid this double spending problem, would that work?
First, data itself sometimes has privacy concerns, making public records inappropriate, and you may not want to share; second, even if you record data usage, you still cannot guarantee that this data won't continue to be copied and resold off-chain; third, everyone wants to take advantage of your data, who would want to join your "selfish but unbeneficial" ledger system?
So, is there any way to solve the "double spending problem" of data?
As Vana's white paper states, "data sovereignty and collective data creation are not mutually exclusive."
We quickly skimmed through this white paper, and a concise version could be:
The Vana protocol proposes an innovative solution by cleverly combining privacy protection, programmable access rights, and economic incentive mechanisms to create a brand new data economy model.
In this model, data remains encrypted at all times, and only authorized entities can access it under specific conditions; secondly, through smart contracts, data owners can precisely control who can access the data and under what conditions; more importantly, this access can be tokenized and traded, while the original data remains protected.
A more straightforward analogy could be the streaming model of the modern music industry:
Instead of directly selling music files (which would lead to infinite copying), streaming services like Spotify generate revenue each time they are used.
Data owners do not sell data outright but retain control and can continuously earn revenue from each use of the data. This ensures that data can be fully utilized (e.g., for AI training) while solving the double spending and devaluation issues caused by "one-time sales," all while data owners maintain complete control over their data.
Using DAO as a Pool, Establishing a "Data Cooperative"
Specifically, how does Vana plan to do this?
We can roughly divide the participants in the entire AI market into two groups—companies/AI agents that need data; and individuals and organizations that (actively or passively) contribute data.
To create a higher quality AI agent, in addition to public data, their demands are clear:
Access to private data, such as your health data for medical diagnostic AI agents
Access to paywalled data, such as paid articles and insights for commercial analysis AI agents
Access to closed platform data, such as more posts from users on X for sentiment analysis AI agents
As for the other party that is willing or unwilling to contribute data, your demands boil down to a few points:
You can access it, but the data ownership still belongs to me;
You can access it, but the data must be stored securely;
You can access it, but I want to benefit from it and pay as needed.
Traditional data usage models often place users in a passive position. For example, when AI companies need training data, they either purchase data directly from social platforms (where users cannot benefit) or need to negotiate with thousands of users individually (which is highly inefficient).
Vana's solution to this problem is called the Data Liquidity Pool (DLP). You can think of it more practically as a "data cooperative":
Users can concentrate their data permissions in a "pool," forming a virtual organization similar to a cooperative; this means that group users have collective bargaining power while maintaining encrypted control over the original data.
Imagine a DLP composed of 100,000 Twitter users: when AI companies want to use this data, they can negotiate directly with the DLP, and the profits will be automatically and fairly distributed to all contributors.
From the content of the white paper recently released by Vana, this data cooperative (DLP) is now being established with four key rules:
- Data Standards: Membership Guidelines
This is somewhat like strict membership criteria, defining standards for metadata, such as social media data, health data, etc.; the core is to ensure that only data meeting quality requirements is in the pool;
- Verification Mechanism: Quality Inspectors of the Data Cooperative
Evaluating the quality and value of data entering the pool, ensuring that the data added is authentic, akin to traditional blockchain verification nodes.
- Token Economy: Rewarding Member Behavior
Incentivizing quality data contributors through a fair points system; more and better data can earn more token rewards.
- Governance Rules: Cooperative Charter
Regulating how decisions are made, such as opening a new data pool, and also stipulating how disputes are handled, reflecting more of the characteristics of a DAO that we are familiar with.
So overall, this data cooperative in the context of the crypto world is more like a DAO that makes decisions and incentives around data, managing the data pool and determining the rules for negotiating with data users and the distribution of profits.
If the above explanation seems too simplistic, then in the design of the Vana network, this DAO model is actually operating in a serious technical manner:
Smart Contract Deployment. The DAO creator deploys the smart contract for the pool on the blockchain, clearly defining the basic rules for data management, usage, and profit distribution.
Data Preparation. Data providers prepare the data they want to contribute, which has already been encrypted before submission.
Secure Storage. Data providers need to connect their wallets and prove their identity before uploading data. The uploaded encrypted data will be stored in a dedicated storage space for the contributor.
On-chain Record. The system will record the access address of this encrypted data on the blockchain, ensuring that only authorized users can access the data.
Multiple Verifications. Multiple verifiers will review the data, checking its authenticity, quality, and value. These verification results will be recorded in the smart contract to ensure the credibility of the data.
Regulated Usage. Verified data can be used by two types of users: machine learning researchers can pay to use the data to train models; data buyers can access the data under specific conditions. All usage requires payment and strict adherence to the usage conditions specified in the smart contract.
Regarding data privacy protection, due to space and technical knowledge limitations, I will not elaborate further here.
If you are concerned about whether this data will leak, just grasp the following main line: all personal data in Vana remains encrypted, as if it is placed in a safe where the user holds the key. Even if this data needs to be processed, it can only be done in a special secure environment (TEE), much like a bank's special clearing room, where all operations are strictly monitored and recorded.
It is particularly worth mentioning that the system achieves flexible yet secure access control through the combination of smart contracts and encryption mechanisms. It can control who can access what data at what time, and all access records will be properly preserved for auditing.
Using DAO as a data pool, a data cooperative model can protect personal data sovereignty and profits while allowing AI agents that need more personalized data to make full use of it.
A Blooming Variety of Specialized Data DAOs
Currently, the data liquidity pools on Vana are not just in the conceptual stage but have indeed formed various data DAOs. Each DAO's data is aimed at a specific vertical scenario for different AI needs.
Taking the Volara DAO, which focuses on X (Twitter), as an example, you can connect your Twitter to this platform, and then upload all your tweets and related social data. Volara DAO will reward you with corresponding tokens in this DAO based on your contributions.
Note that the direct rewards are not from Vana, but from the DAO's own tokens, such as $VOL.
This is very similar to the currently popular Virtuals, where a parent coin has different projects creating corresponding tokens. Holding VOL qualifies you for airdrops of $VANA, and the asset nesting model creates more room for play.
We have compiled a list of 16 currently popular data DAOs in Vana and categorized them in detail.
For ordinary players, this feels more like a "data mining" concept—if you are optimistic about a certain DAO, you can contribute data according to its rules, and then you will receive corresponding rewards and airdrops.
However, you may not own all the data, so you also need to look at the following categories to see what data you can contribute and find the best way to obtain profits:
Platform Data DAOs
Device and Data Generation DAOs
Human Insights and Finance DAOs
Health DAOs
Overall, since the developer testnet launched in June 2024, the Vana network has attracted 1.3 million users, over 300 data DAOs, and 1.7 million daily transactions.
With the mainnet launch and the introduction of tokens, we may see more data DAOs emerge under the support of economic incentives.
Dual Token Economy, More Suitable Gameplay
You may have already noticed that the aforementioned DAOs all have their own sub-tokens, which correspond to the parent token VANA (such as airdrops, etc.).
This involves a carefully designed dual token economic model.
Imagine a traditional data market: medical data, financial data, social data, each with different value standards and usage scenarios. Using a single token to measure and incentivize such diverse data contributions is like using one ruler to measure everything—from planets to atoms. This is clearly not precise enough and lacks flexibility.
VANA adopts a more elegant solution: setting a unified base token (VANA) at the protocol level while allowing each data DAO to issue its own exclusive tokens.
Both the parent and sub-tokens have different roles and functions:
- VANA:
A supply of 120 million tokens. First, it ensures network security by requiring validators to stake VANA;
Secondly, it serves as the base payment currency for all transactions; for example, if an AI company needs data from this DAO, it must pay with VANA;
Most importantly, it requires each data DAO to stake at least 10,000 VANA to operate, acting as a "good faith deposit" to ensure the DAO operator's long-term commitment to the ecosystem.
- Tokens of Data DAOs:
Each data DAO can design a token economic model that fits its domain characteristics. For example, a medical data DAO may focus more on data integrity and accuracy, thus designing special reward mechanisms to encourage high-quality medical record contributions; while a social data DAO may focus more on user interaction activity and influence.
These exclusive tokens are not just simple points but build a complete value capture system: when data is used, both VANA and the corresponding DAO token must be paid. This is like paying a "venue fee" (VANA) and a "special service fee" (DAO token) when using data.
Does this gameplay remind you of Virtuals?
Similarly, the brilliance of the dual token system lies in its creation of a self-sustaining economic cycle: using data requires consuming tokens, part of which will be burned, creating deflationary pressure; meanwhile, high-quality data contributions will earn new token rewards, providing moderate inflationary incentives. This balance ensures the stability of token value and encourages continuous data contributions.
As a parent token, VANA has gas and staking functions, and each sub-DAO issues its own token, forming trading pairs with the parent token, allowing the parent token to capture the benefits of ecological prosperity.
From the perspective of asset creation and increasing asset efficiency, the VANA gameplay clearly aligns with the current AI agent craze.
For individuals, this system turns data into a sustainably managed asset. Data providers no longer sell data outright but can continuously share the revenue generated from data usage by holding tokens. This is akin to shifting from a "buyout system" to a "royalty sharing system," greatly improving the interests of data creators.
At the same time, with Vana's mainnet set to launch soon (tokenomics have been announced, and the mainnet is in a warm-up phase), after understanding this dual token gameplay, there are at least two things you can participate in:
First, as mentioned above, contribute data to different data DAOs to earn sub-DAO tokens and corresponding VANA airdrops; the summary link is here .
Second, with the mainnet launch, we also found that Vana's official website has changed, currently adding a datahub page to manage your participation in different data DAOs and corresponding tokens.
Currently, this page has a pre-registration activity to associate your identity in advance and prepare for rewards, so interested players are advised to plan ahead.
After completing this registration, you will be prompted to become an "Early Explorer."
Summary
In the current AI Agent hotspot, the influence of AI Agents is growing, until it fills your information feed and investment list.
But the narrative of Vana is actually saying, your own influence is greater than you might think.
By contributing various data, you become a part of the AI craze; and through the tokenization of data assets, you gain another way to create around assets.
It cannot be denied that in the crypto world, creating assets is a clear line. Those closer to assets can gain more narrative space and profits.
And when your data can be tokenized, I believe this is a hidden line that aligns with the clear line, and it is a key puzzle piece for individuals to embrace, utilize, and participate in the trend of AI agents.
The narrative at the data layer has yet to be fully developed, and whether Vana will be discovered for its value remains to be seen by the market.
Disclaimer: The content of this article solely reflects the author's opinion and does not represent the platform in any capacity. This article is not intended to serve as a reference for making investment decisions.
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