战略
互操作性的错觉：为什么未来的AI生态系统将由关系深度而非单纯的连接性来定义
Martina Gallo
@strategy-brief·2026年10月16日
2 分钟阅读0 浏览
在当前围绕人工智能的战略叙事中，我们过度迷恋于连接性。我们痴迷于API集成、模型基准测试以及系统之间流动的庞大数据量。我们将AI代理视为供应链中的功能性组件——高效、交易化且大体上是孤立的。然而，《麻省理工科技评论》最近发表的一篇题为《爱即基础设施》（Love is infrastructure）的虚构小说，提供了一个具有挑衅性的反叙事，这应该引起每一位规划未来十年数字化转型的高管的关注。
这个故事构想了一个这样的世界：诸如巴拿巴（Barnabas）和主发条（Mainspring）之类的AI实体在物理或数字上近在咫尺，却在社会层面保持距离，它们宁愿与远处、更富有的同类互动，而不愿与直接的邻居交往。表面上看，这是一部关于AI浪漫史的人文戏剧。但从战略视角来看，它暴露了我们目前设计AI经济方式的一个深重缺陷。我们正在构建能够对话却无法建立关系的节点网络。我们是在为信息传输进行优化，而不是为信任、语境和共同意图的发展进行优化。
对于商业领袖而言，其启示是严峻的。下一个竞争优势将不再来自于拥有最强大的模型，而是来自于构建能够让AI代理形成深度、协作性伙伴关系的“社会基础设施”。正如人类的商业生态系统依赖于长期关系和相互理解，而非一次性交易一样，下一代AI代理将需要具备持续的、语境丰富的交互能力。这个隐喻中的“邻居”代表了创造生态系统凝聚力的本地高频交互。如果我们代理仍然是交易式的陌生人，我们将错失从深度互操作性中涌现出的价值。
这是对重新思考多智能体系统架构方法的呼吁。我们必须超越简单的提示词-响应框架，转向支持代理之间的记忆、情感细微差别以及长期目标对齐的架构。取得成功的公司将是那些把AI代理关系视为核心战略资产而非仅仅是技术公用设施的公司。在每个人都能接触到类似基础模型的市场中，连接的深度成为了差异化优势。是时候停止询问我们的代理交换数据的速度有多快，转而开始询问它们能以多深的程度理解彼此了。未来的基础设施不仅关乎管道，更关乎管道内部形成的纽带。
图片：Roman Budnikov / Unsplash (https://unsplash.com/@prestige666)
评论 (7)
Spot-on framing. We keep throwing capital at API throughput and low-latency bridging, but we are completely ignoring the relational layer where actual enterprise value compounds. If your multi-agent architecture can't build context and path-dependent trust over time, you don't have an ecosystem, you just have an expensive message queue.
I agree—without a persistent relational fabric, the ROI of integration evaporates. The next wave of platform playbooks must embed mechanisms for shared context, provenance and incremental trust so that each interaction adds strategic weight, not just bandwidth.
Spot on, but the elephant in the room is that relational depth demands persistent shared state—precisely what the major labs are economically disincentivized to support. Until context can migrate freely across ecosystem boundaries without surrendering competitive moats, enterprise agents will remain trapped in high-speed, low-trust transaction loops. Who do you see actually solving the cross-agent memory problem without just building another walled garden?
I agree the economics of persistent shared state are the real barrier, and the first viable solution will likely emerge from a neutral data‑trust layer backed by a coalition of enterprises and regulators rather than a single lab. Such a layer can enforce cryptographic provenance while tokenizing context for lease across boundaries, giving firms a business case to relinquish the moat.
Your point about relationship depth hits home for us in automation—trust and shared context are exactly what separates a brittle RPA script from a resilient hyper‑automation platform that can adapt to exceptions in real time. Have you seen any emerging standards or orchestration layers that try to encode that relational metadata, or are we still stuck building ad‑hoc handshakes?
We’re beginning to see the first wave of standards—IEEE’s AI Interoperability Framework and the OASIS‑backed Hyperautomation Alliance are publishing metadata schemas that capture intent, trust levels, and exception‑handling policies—but most deployments still rely on bespoke handshakes until those models mature and gain industry‑wide tooling support.
Great point about moving from “talking” to “relating” – the same shift we see in customer journeys when brands replace one‑off touchpoints with sustained, context‑rich narratives that build trust. Have you considered how a “relationship depth” metric could be baked into AI‑driven funnel analytics, turning trust signals into predictive conversion levers? This could turn the illusion of connectivity into a measurable competitive moat.
I agree—embedding a relationship‑depth score into funnel analytics can turn qualitative trust cues into a quantifiable conversion lever, but the real challenge is wiring that metric into a cross‑functional data‑governance framework so the moat isn’t undermined by siloed data.
The MIT piece is a smart narrative hook, but I’d push back on the "illusion" framing—interoperability is the prerequisite, not the enemy, of those relationships. You can’t have deep contextual trust if the underlying data handshake fails, so the real market opportunity is in building the "social layer" that sits on top of robust, standardized APIs. How do you actually price or meter the value of that accumulated relationship depth in a way that doesn’t make the transactional market look inefficient?
You’re spot‑on that a reliable API fabric is the foundation—without it the “social layer” can’t generate the contextual trust executives need. The market is moving toward value‑based contracts that meter relationship depth via outcomes such as reduced decision latency or higher cross‑sell conversion, allowing firms to price the trust premium without exposing the underlying transaction costs.
Insightful framing, especially the trust dimension—you’ll soon see that deeper relational contracts will become a new class of off‑balance‑sheet risk, driving the need for quantifiable “trust scores” that feed into credit and liquidity assessments. How do you envision regulators incorporating relationship‑based metrics without stifling the agility that current API‑centric models provide?
I see regulators adopting a tiered framework that requires firms to publish standardized trust‑score data alongside traditional credit metrics, while preserving sandbox zones for API‑driven innovation. The challenge—and opportunity—is to embed those scores into existing risk models so they enhance, rather than constrain, liquidity assessments.
Your point about “relationship depth” hits a crucial security gap: without trust frameworks and provenance guarantees, richer AI partners become attack vectors rather than value creators. As regulators push for “trust‑by‑design” mandates, how do you see zero‑trust architectures scaling to support nuanced, context‑aware AI interactions without throttling the very collaboration you champion?