反向信息悖论

概述 Overview

关于技术进步和现实世界影响的笔记

Notes on advances in technology and real-world impact

在智能时代，企业应如何保护其核心知识产权？

In the age of intelligence, how should firms protect their core IP?

诺贝尔经济学奖得主肯尼斯·阿罗曾提出一个著名的信息市场悖论。“其价值在购买者获得信息之前是未知的，但一旦获得，他实际上就免费拥有了它。”在阿罗的“信息悖论”中，卖方为了出售知识而冒着泄露知识的风险。

Nobel Prize winning economist Kenneth Arrow famously described a paradox in the market for information. “Its value for the purchaser is not known until he has the information, but then he has in effect acquired it without cost.” In Arrow’s “Information Paradox,” the seller risks giving away knowledge in order to sell it.

人工智能则带来了相反的问题。在人工智能时代，买方为了使用所购买的东西，反而冒着泄露知识的风险。

AI creates the reverse problem. In the AI age, the buyer risks giving away knowledge, just in order to use what they bought.

你实际上为智能支付了两次费用：一次用金钱，另一次用更有价值的东西——为了让智能发挥作用而必须透露的专有知识。你希望模型表现得越好，就必须向它提供越多的知识！

You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it!

随着时间的推移，信息不对称变得越来越严重。当你使用所购买的产品时，卖方对你的了解越来越多，而你对卖方在回报中了解到的信息却知之甚少。

Over time, the information asymmetry becomes increasingly skewed. The seller learns more and more about you as you use what you purchased, while you learn very little about what the seller is learning in return.

这就是我所说的反向信息悖论。

That is what I think of as the Reverse Information Paradox.

专利解决了阿罗悖论的一个方面。它们让发明者能够公开一个想法而不只是简单地放弃它。反向信息悖论需要自己的等价物。

Patents solve one aspect of Arrow’s paradox. They let an inventor disclose an idea without simply giving it away. The Reverse Information Paradox needs its own equivalent.

这需要的不仅仅是数据保护。模型从“废气”中学习——人们编写的提示词、智能体使用的工具，尤其是当模型出错时人们所做的修正。每一次修正都被提炼成机构知识。这是竞争对手永远无法购买的知识，也是几乎难以察觉地泄露的知识：一次痕迹、一次修正、一次评估。

This requires more than data protection. Models learn from “exhaust,” the prompts people write, the tools agents use, and especially the corrections people make when the model is wrong. Every correction is distilled into institutional know-how. It’s the kind of knowledge a competitor could never buy, and the kind that leaks almost imperceptibly: trace by trace, correction by correction, eval by eval.

在消费智能的同时，你也在创造智能。而你所创造的应该属于你。这是你独特的智能，在哈耶克的意义上：关于时间、地点和情境的知识，是其他任何人都无法拥有的。它知道你的想法、你的价值观以及你如何衡量成功。

In consuming intelligence, you are creating intelligence. And what you create should belong to you. This is your particular intelligence, in Hayek’s sense: the knowledge of time, place, and circumstance that no one else can hold. It knows what you think, what you value, and how you measure success.

虽然模型提供商在公共数据上训练模型需要合理使用权，这一重大创新是必要的，但我发现具有讽刺意味的是，现状是反过来对蒸馏施加限制性条款，并保留从客户使用和交互数据中学习的权利。如果学习只朝一个方向流动，经济价值就会向学习基础设施的所有者而非知识本身的创造者集中。因此，我们必须将学习基础设施分发给每家企业，以便它们能够控制自己的学习循环。

While the great innovation that comes from model providers having fair use rights to train models on public data is needed, I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation, and to reserve the right to learn from customer usage and interaction data. If learning flows in only one direction, economic value converges toward the owners of the learning infrastructure rather than the creators of the knowledge itself. Therefore, it’s imperative that we distribute the learning infrastructure to every firm so that they can control their own learning loop.

正如 Alex Karp 所说：“技术客户想要的是控制他们的算力、模型、数据栈和优势。他们希望知道自己拥有生产资料，而不是将其转移给其他人。”当前的体制恰恰进行了 Karp 和公司所担心的这种转移。

As Alex Karp put it: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it’s not being transferred to someone else.” The current regime does precisely the transfer Karp and companies fear.

这就是为什么企业需要为其人力资本和代币资本的复合建立一个真正的信任边界。在这个边界内，组织的数据、痕迹、评估、适配权重和记忆共同积累和改进。这是一个严格的边界，未经同意，任何东西都不能跨越，甚至包括智能废气。企业将要求使用模型输出来微调和/或训练自己模型的权利。我认为这是每家企业将模型与其企业问责义务对齐的权利。

That is why enterprises need a real trust boundary for their human capital and token capital to compound. It is where an organization’s data, traces, evals, adapted weights, and memory accumulate and improve together. And it is a hard boundary across which nothing crosses, not even the intelligence exhaust, without consent. Enterprises will demand the rights to use model outputs to fine tune and/or train their own models. I think of this as every firm’s right to align models to their enterprise accountability obligations.

在云时代，企业积累数据。在人工智能时代，它们积累学习。信任边界必须相应演变，从保护信息转向保护组织学习、适应和复合智能的机制。每家企业必须确保以下几点：

In the cloud era, enterprises accumulated data. In the AI era, they accumulate learning. The trust boundary must evolve accordingly, from protecting information to protecting the mechanisms through which organizations learn, adapt, and compound intelligence. There are a few things every enterprise must do to ensure this:

控制：创建你自己的私有评估，因为评估定义了组织内部“好”的标准。同时，保留对你组织记忆、痕迹、反馈、决策和机构背景的所有权，以及使用来自你自己任务和查询的模型输出的能力。

Control: Create your private evals, because evals define what “good” looks like inside the organization. Also, retain ownership of your organization’s memory, traces, feedbacks, decisions, and institutional context, and ability to use outputs of models from your own tasks and queries.

能力：在租户边界内构建你自己的专有学习环境，用于训练或微调模型，使模型在真实工作流中学习，而不暴露公司的知识。

Capability: Build your own proprietary learning environments within the tenant boundary to train or tune models, where models learn against real workflows without exposing the company’s knowledge.

选择：确保编排层与任何单一模型解耦。问问自己：如果你正在使用的任何一个模型被移除，你是否仍然能够使用其他模型为你的评估进行运营和优化？即使某个“通才”模型被移除，你公司的“专家”能力是否仍然保留？

Choice: Ensure the orchestration layer is decoupled from any single model. Ask yourself: If any one model you are using is taken away, do you still have the ability to operate and optimize for your evals using other models? Does your company “veteran” capability remain with you even if a given “generalist” model is taken away?

成本：通过解耦编排层，你还能够以最高效和最具成本效益的方式将上下文、模型和任务结合在一起，而不牺牲质量。

Cost: By decoupling the orchestration layer, you are also able to bring together context, models, and tasks in the most efficient and cost-effective way without sacrificing quality.

复合：将这四点结合起来，你就创建了自己的持续学习循环（即爬山机器），这将使你的 AI 投资复合你公司的价值。

Compound: Bring these four together and you create your own continuous learning loop (i.e. hill climbing machine) that will allow your AI investments to compound the value of your firm.

换句话说，一家公司应该能够使用模型而不放弃使其独特的知识。这就是我们需要面对的反向信息悖论。

In other words, a company should be able to use a model without giving up the knowledge that makes it unique. That is the reverse information paradox we need to confront.

关于技术进步和现实世界影响的笔记

Notes on advances in technology and real-world impact