用大模型代理打破平台封闭，实现服务间自动互操作。
LLM Agents Are the Antidote to Walled Gardens
- 用大模型代理自动转换数据格式、操控人机界面
- 实现跨平台数据无缝交换，降低互操作成本
- 适合关注数据自由与反垄断的AI研究者
尽管互联网核心架构本应开放通用，但当前应用层却由封闭的专有平台主导。开放互操作的API需要大量投入，而市场领导者缺乏推动数据共享的动力，以免削弱用户粘性。我们提出，基于大语言模型的智能体从根本上改变了这一格局：它们能自动转换数据格式并操作面向人类的界面，使互操作变得极为廉价且几乎无法避免。我们称之为‘通用互操作性’——任意两个数字服务可通过AI中介适配器实现无缝数据交换。这有助于遏制垄断行为、促进数据可迁移性，但也可能引发新的安全风险、技术债务和法律摩擦。我们的主张是，机器学习领域应积极拥抱这一趋势，同时构建相应框架以缓解负面影响。若能及时行动，我们可借助AI恢复用户自由与市场竞争，且不牺牲安全性。
原文摘要 · Abstract (English)
While the Internet's core infrastructure was designed to be open and universal, today's application layer is dominated by closed, proprietary platforms. Open and interoperable APIs require significant investment, and market leaders have little incentive to enable data exchange that could erode their user lock-in. We argue that LLM-based agents fundamentally disrupt this status quo. Agents can automatically translate between data formats and interact with interfaces designed for humans: this makes interoperability dramatically cheaper and effectively unavoidable. We name this shift universal interoperability: the ability for any two digital services to exchange data seamlessly using AI-mediated adapters. Universal interoperability undermines monopolistic behaviours and promotes data portability. However, it can also lead to new security risks, technical debt, and legal frictions. Our position is that the ML community should embrace this development while building the appropriate frameworks to mitigate the downsides. By acting now, we can harness AI to restore user freedom and competitive markets without sacrificing security.
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