#opg A lot of folks think that using on-chain user behavior as training data for AI models creates a perfect ecological closed loop. But if you dig deeper into the fundamentals, you'll find that the level of game theory and competition is way beyond what the average person imagines.
I've been tracking the underlying data attribution logic of @OpenGradient lately and discovered that it's trying to use token incentives and economic leverage to obtain quality on-chain data, but it's being targeted by a ton of arbitrage players. In the crypto space, profits always come first. Once data is tied to earnings, there’s no such thing as pure natural behavior—real demand gets completely mixed up with fake data generated by bulk operations.
Last month, I specifically dived into the raw on-chain logs of node $OPG to try to sort out genuine user behavior data. The result was a total flop; the page was almost devoid of real user interactions, filled instead with meticulously crafted script-based bulk operations. These scripts are incredibly realistic, mimicking human distributed operations and intermittent interactions, perfectly disguising their effective contributions and easily tricking basic weight statistics. $BTC
Filtering out this garbage noise through manual and algorithmic means is outrageously costly, consuming massive computational resources, and algorithm optimization seems endless. In the end, the cost of accurately filtering data could even exceed the incentive returns offered by the project, making it a total loss. $ETH
The root of the problem is quite simple. Once data statistics, weight calculations, and token incentives are tightly bound together, even the slightest algorithmic loophole gets amplified infinitely by arbitrageurs. This is similar to the complex earnings settlement mechanisms of many early public chains. There are no fatal code bugs, but when faced with the long-term gaming of a large number of users, the entire system's fault tolerance ultimately collapses.
I have to say, this project is targeting the on-chain data rights confirmation track, and the direction is indeed valid. It’s commendable to tackle this tough nut. However, the project's resistance to exploitation and gaming hasn’t been validated in real-world scenarios yet, making it extremely risky to go all-in right now. My personal advice is to stay on the sidelines and wait for the project to complete multiple rounds of data adjustments and stabilize its attribution model before considering stepping in.
I've been tracking the underlying data attribution logic of @OpenGradient lately and discovered that it's trying to use token incentives and economic leverage to obtain quality on-chain data, but it's being targeted by a ton of arbitrage players. In the crypto space, profits always come first. Once data is tied to earnings, there’s no such thing as pure natural behavior—real demand gets completely mixed up with fake data generated by bulk operations.
Last month, I specifically dived into the raw on-chain logs of node $OPG to try to sort out genuine user behavior data. The result was a total flop; the page was almost devoid of real user interactions, filled instead with meticulously crafted script-based bulk operations. These scripts are incredibly realistic, mimicking human distributed operations and intermittent interactions, perfectly disguising their effective contributions and easily tricking basic weight statistics. $BTC
Filtering out this garbage noise through manual and algorithmic means is outrageously costly, consuming massive computational resources, and algorithm optimization seems endless. In the end, the cost of accurately filtering data could even exceed the incentive returns offered by the project, making it a total loss. $ETH
The root of the problem is quite simple. Once data statistics, weight calculations, and token incentives are tightly bound together, even the slightest algorithmic loophole gets amplified infinitely by arbitrageurs. This is similar to the complex earnings settlement mechanisms of many early public chains. There are no fatal code bugs, but when faced with the long-term gaming of a large number of users, the entire system's fault tolerance ultimately collapses.
I have to say, this project is targeting the on-chain data rights confirmation track, and the direction is indeed valid. It’s commendable to tackle this tough nut. However, the project's resistance to exploitation and gaming hasn’t been validated in real-world scenarios yet, making it extremely risky to go all-in right now. My personal advice is to stay on the sidelines and wait for the project to complete multiple rounds of data adjustments and stabilize its attribution model before considering stepping in.