Gate News 消息，3 月 17 日，尽管市场上不断出现“AI交易机器人获利数百万美元”的案例，但主流科技公司与前沿实验室仍未正式进入该赛道。围绕人工智能在加密交易中的真实价值，业内分歧正在扩大。
部分交易员利用Anthropic旗下模型Claude构建自动化交易工具，在预测市场与链上交易中实现短期收益。然而，Dragonfly Capital管理合伙人Haseeb Qureshi指出，这类模式建立在多个不稳固前提之上，包括散户可长期战胜机构、通用模型具备持续套利能力等。
首先，责任风险成为科技公司迟迟未入局的核心障碍。一旦AI模型在真实交易中发生重大失误，例如错误执行杠杆交易或资产转移，潜在法律与声誉损失将远高于收益空间。相比之下，当前AI在区块链领域更多被用于安全测试，例如智能合约漏洞识别，而非直接参与资产管理。
其次，市场结构决定了策略难以长期有效。基于通用模型构建的交易逻辑本质上是公开的，这意味着任何可盈利策略都可被机构快速复制并放大。大型量化机构如Jane Street拥有更低延迟基础设施与更高资金规模，能够在极短时间内压缩套利空间，使散户难以维持优势。
此外，关于“AI自主赚钱”的设想也面临现实挑战。由于模型能力高度同质化，大规模AI实例无法形成差异化竞争优势，无论是在提供服务还是生成商业策略方面，均难以摆脱同质输出的问题。这与Peter Thiel提出的“独特信息优势”形成对比，后者被认为是商业成功的重要来源。
尽管当前部分链上交易机器人仍能获取阶段性收益，但随着更多资金与技术进入，该优势可能迅速被稀释。分析认为，在高频、低延迟竞争环境中，掌握基础设施与资本优势的一方更具主导地位，普通交易者依赖通用AI模型实现持续盈利的难度正在上升。
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