In everyday life, you see millions of testnet users, billions of on-chain transactions, and TVL of unprecedented magnitude enough information in mind-blowing marketing copy to create FOMO for anyone who reads it. However, insiders clearly know that many of these numbers are simply illusions. After numerous sleepless nights analyzing real on-chain numbers and testing for myself, today I would like to speak about the upcoming mainnet OpenLedger and share what I consider its true strength, possible opportunities in the market, and potential pitfalls.The testnet data has long been a war zone for airdrop hunting.
Today, almost all web3 projects offer their users incentives in the form of airdrop points while being still at the testnet stage. Their goal is clear to check out product processes and establish the core audience base; however, what they've created is a thriving industry of fake volumes. $REQ I've seen several studios specializing in airdrop hunting, possessing hundreds of wallets that participate in testnet activities day and night.
Insider market trends show that such bulk fake accounts can occupy 30% to 50% of the testnet data volume of most projects. They may appear crowded, but ultimately they’re akin to giving out samples; people form long lines for free things, but they all disappear once the benefits end. In order to enhance the valuation of the fundings and discuss listing conditions with exchanges, the project team will deliberately beautify the on-chain data; this is an open secret now. To summarize, one can take a look at the testnet data, but it’s not wise to believe everything in it. At best, it merely shows the operational capabilities of the project; it does not mean anything about the users’ consensus or potential of the mainnet.Some gimmicks, but the anti fraud system is truly outstanding.
@OpenLedger Prior to the mainnet launch, its testnet data was extremely popular, and optimism could be seen from the community. Now, I’ll peel through its on-chain data layer by layer to see the gimmicks behind it.
Firstly, let us examine node data; there really is a lot of fluff here. In the project's case, the barrier for the light nodes is relatively low, and therefore, studios can easily arrange node setups with minimal costs to farm points. Therefore, the amount of publicly available nodes does not have much meaning. As for model training counts, the reward gradient design is quite conservative – no one can go wild and create tons of accounts, but still, there are retail investors hanging around in interactions, generating somewhat small fluff.
But the best thing about the project is undoubtedly the data contribution aspect. Indeed, OpenLedger has implemented its special "data attribution" algorithm which filters out duplicates and invalid data. Therefore, useless interactions do not make it on-chain. All of the above makes it extremely expensive to use the airdrop strategy and therefore gives rewards that are extremely low. When compared with the majority of AI+blockchain projects on the market, it is definitely a hardcore benefit.
Furthermore, I've been an active member of multiple official communities, and by comparing with similar tracks, I can say that there aren't that many users who everyday ask about the airdrop in the OpenLedger community.
Moreover, I’ve been a part of several official communities myself, and comparing identical tracks, one can see that the community of OpenLedger doesn’t see many questions about the airdrop per day people are more keen on talking about technical stuff such as on-chain attribution of data and compliance of the AI model. The percentage of hardcore users is significantly greater compared to peers, and the native consensus within the ecosystem is rather stable.
The core track has been identified: the auditing of AI compliance is the true opportunity for OPEN.While ignoring the fuss around testnets, the key driver for me to be optimistic towards OpenLedger is that it addresses the burning issue in the AI world after 2025 compliance and attribution of training data.
But as global laws against AI become stricter, with EU’s AI Act high risk provisions being implemented and many US states introducing their AI data transparency regulations, this kind of data scraping by top companies is no longer possible. More copyright infringement cases are waiting, and the whole industry now understands: auditability, traceability, and legal authorization for training data will be the bar that all future AI companies must cross.
Yet, most AI giants have not come up with a full solution for training data sources. There lies an enormous gap in industrial compliance. But OpenLedger hasn’t tried to compete directly with the top AI players, grabbing customers from them; instead, it focuses on the foundation the AI data supply chain.
Through the on-chain data attribution solution, all data used in the process of training the origin of the data, who uploaded it into the blockchain, how frequently the data was called upon, and how much the data contributed to the advantages gained from the model would be visible on chain. $XPL When the model started earning money, profits would automatically be shared according to the contribution ratio of the data provided. Previously, I tried uploading non-sensitive on-chain transaction data to the platform, and I got my share of profits after a month, along with an on-chain certificate of data usage legality.$OPEN
