Recently, @OpenGradient has become wildly popular in the crypto circle. Many people are chasing it and rushing to set up positions. Some believe that $OPG , which is powered by on-chain large models, can outperform the broader market. Today, combining on-chain original data with real-world testing experience, let’s break down the pros and cons and take a rational view of this AI + public chain hype.
The biggest highlight of OpenGradient is that it directly moves machine-learning compute power into smart contracts. With the underlying architecture built on the Walrus storage network and TEE trusted environments, AI inference computations are done off-chain with offline storage, while on-chain only retains hash indexes. This avoids block congestion. The entire compute-to-settlement workflow has already been implemented. With each model call or file parsing, the wallet deducts the compute fee in real time. Only nodes that stake OPG can participate in mining and block production. The tokenomics design is indeed quite forward-looking.
But beneath the glossy surface are two fatal issues. First is the dark-pool “blood-sucking” effect: once on-chain AI compute becomes mature, institutional market makers can complete transactions and arbitrage privately through private solvers. As a result, all the high-quality liquidity gets absorbed within the circle, and slippage in the public order book will keep getting worse. The arbitrage opportunities available to retail traders will become fewer and fewer.
Second is a weakness in the underlying architecture. Although, so far, more than 2.7 million inference records have run stably, Walrus nodes still suffer from address lookup latency, and there is severe stuttering when calling niche models. Moreover, the inference proof files are stored offline. If storage nodes go offline in large numbers, historical compute records can’t be verified or traced, and node reward settlement may become abnormal—any time, the market could face selling pressure.
Practical advice for everyone: do not make large, long-term token pledges or hoard coins. Use only small positions to trade and capture sentiment-driven opportunities. Also, monitor the number of online nodes in real time and track the progress of underlying storage upgrades.
Is on-chain AI truly a new round of market hype, or just a new concept used to harvest retail traders? Will you be planning to position in OPG? Share your thoughts in the comments.
#opg $OPG
The biggest highlight of OpenGradient is that it directly moves machine-learning compute power into smart contracts. With the underlying architecture built on the Walrus storage network and TEE trusted environments, AI inference computations are done off-chain with offline storage, while on-chain only retains hash indexes. This avoids block congestion. The entire compute-to-settlement workflow has already been implemented. With each model call or file parsing, the wallet deducts the compute fee in real time. Only nodes that stake OPG can participate in mining and block production. The tokenomics design is indeed quite forward-looking.
But beneath the glossy surface are two fatal issues. First is the dark-pool “blood-sucking” effect: once on-chain AI compute becomes mature, institutional market makers can complete transactions and arbitrage privately through private solvers. As a result, all the high-quality liquidity gets absorbed within the circle, and slippage in the public order book will keep getting worse. The arbitrage opportunities available to retail traders will become fewer and fewer.
Second is a weakness in the underlying architecture. Although, so far, more than 2.7 million inference records have run stably, Walrus nodes still suffer from address lookup latency, and there is severe stuttering when calling niche models. Moreover, the inference proof files are stored offline. If storage nodes go offline in large numbers, historical compute records can’t be verified or traced, and node reward settlement may become abnormal—any time, the market could face selling pressure.
Practical advice for everyone: do not make large, long-term token pledges or hoard coins. Use only small positions to trade and capture sentiment-driven opportunities. Also, monitor the number of online nodes in real time and track the progress of underlying storage upgrades.
Is on-chain AI truly a new round of market hype, or just a new concept used to harvest retail traders? Will you be planning to position in OPG? Share your thoughts in the comments.
#opg $OPG