#AI与加密货币结合 The AVA case is worth paying attention to. 23 wallets concentrated to snatch 40% of the supply at launch, with the source of funds pointing to large transfers from Bitget and Binance. The timing was very tight—funds were deposited just before the launch, and immediate buying occurred at release. This is a typical internal coordination sniping pattern.

Looking at the data logic: the deployer’s associated wallet cluster, pre-arranged funds, and precise timing execution—this level of coordination is beyond what retail FOMO can achieve. The issue is that such operations expose two risks in the AI token track—first, liquidity concentration, with 40% of the supply locked in a few addresses early on; second, transparency flaws in the token distribution mechanism, significantly compressing early participants’ profit potential.

From the perspective of tracking on-chain signals, this suggests we should focus on: the distribution of wallet clusters for newly issued tokens, the on-chain flow of large funds over time, and the correlation between deployer addresses and early holdings. When organized capital aggregation and rapid position building are observed within a short period, it can be generally judged that the initial risk premium of such projects has already been internally absorbed.

The AI and crypto integration track itself has opportunities, but it’s important to distinguish whether it’s driven by technology or by fundraising.