#AI交易应用 CZ's statement touches on the core contradiction of AI trading applications—that's precisely the question I've been pondering frequently when tracking fund flows on-chain recently.

The logic is clear: if an AI algorithm is truly profitable, why package it as a service to sell to others? If a strategy is effective enough, raising funds for a high-quality team is no longer a challenge, and the profit ceiling from personal trading far exceeds subscription revenue. This means that most AI trading products on the market are essentially selling users a "suboptimal solution."

A deeper issue lies in the opposite of network effects—when the same algorithm is adopted by many users, the strategy itself becomes ineffective. Markets are a game; the more participants and the higher the homogeneity, the more obvious the pattern of early movers profiting and latecomers losing. This also explains why, when monitoring whale movements and large fund flows, I observe that strategies tend to quickly fail after widespread replication.

However, CZ's final statement is worth noting—applying AI as a tool layer to help traders quickly customize and optimize personalized strategies is a relatively feasible approach. But its persuasiveness is limited because, in the high-frequency trading arms race, speed and performance are the trump cards, and these are precisely what cannot be equally obtained through generic tools.

The key still depends on actual on-chain fund allocation changes, not being misled by marketing narratives.