Stop fixating on narrative buy orders; when it comes to new AI tokens, I only focus on real consumption.
There are countless AI projects out there that are all just fancy packaging. Strip away the tech facade, and you’ll see that if the token is disconnected from the business, it’s basically destined for zero. My current criteria for evaluating whether a project can succeed is straightforward: I look at whether actual network calls can directly create deflationary pressure or lock up tokens.
Take OpenGradient, which just launched on Binance with a Seed tag, for example. The core of its research lies in its hybrid computing architecture. The network has performed over two million verifiable inferences, along with hundreds of thousands of zkML proofs. Unlike those projects that merely inflate data, it turns inference settlements into hard consumption, burning tokens with every call. At the same time, the supply side has nodes staking to lock up funds. Compared to Bittensor (TAO), which leans towards resource scheduling, this model directly ties developer hosting, paid calls, and revenue sharing into a value loop, actively promoting a healthy supply-demand cycle for the token.
However, the computational costs and high latency brought by zkML are still hurdles. If developers are overly focused on extreme decentralization and widely adopt zero-knowledge proofs, the cost structure can become quite bloated, easily deterring projects with limited budgets. In contrast, while TEE hardware enclaves have a slight risk of centralization, they offer better real-world advantages in terms of efficiency and costs. This technical route's competition will be a key indicator I’ll watch as the entire decentralized computing sector evolves.
@OpenGradient #OPG $OPG
There are countless AI projects out there that are all just fancy packaging. Strip away the tech facade, and you’ll see that if the token is disconnected from the business, it’s basically destined for zero. My current criteria for evaluating whether a project can succeed is straightforward: I look at whether actual network calls can directly create deflationary pressure or lock up tokens.
Take OpenGradient, which just launched on Binance with a Seed tag, for example. The core of its research lies in its hybrid computing architecture. The network has performed over two million verifiable inferences, along with hundreds of thousands of zkML proofs. Unlike those projects that merely inflate data, it turns inference settlements into hard consumption, burning tokens with every call. At the same time, the supply side has nodes staking to lock up funds. Compared to Bittensor (TAO), which leans towards resource scheduling, this model directly ties developer hosting, paid calls, and revenue sharing into a value loop, actively promoting a healthy supply-demand cycle for the token.
However, the computational costs and high latency brought by zkML are still hurdles. If developers are overly focused on extreme decentralization and widely adopt zero-knowledge proofs, the cost structure can become quite bloated, easily deterring projects with limited budgets. In contrast, while TEE hardware enclaves have a slight risk of centralization, they offer better real-world advantages in terms of efficiency and costs. This technical route's competition will be a key indicator I’ll watch as the entire decentralized computing sector evolves.
@OpenGradient #OPG $OPG