文章探讨了在人工智能驱动的市场竞争日益激烈的背景下，企业如何转型为高效的学习型组织。核心观点是，企业应将反馈循环作为首要产品，并构建信用归因系统，以便从数据中学习并实时调整运营。文章指出，传统企业浪费大量数据，因为受限于高昂的人力成本，无法有效利用数据进行学习。而AI模型的出现降低了学习成本，使得企业能够大规模学习。未来的企业运营将更像高频交易或军事行动，需要一个集观察、推理和行动于一体的中心化操作系统。企业若能拥抱学习的角色，将更有可能在激烈的市场竞争中生存下来。

🚀 在竞争日益激烈的市场中，企业应将**反馈循环**视为核心产品，因为企业本质上是学习机器，需要不断从环境中获取信息并快速适应。

💰 传统企业在数据利用方面存在瓶颈，尽管投入巨资收集数据，但最终往往只得到一些用于说服而非提供信息的图表。**AI模型的出现**降低了学习成本，使企业能够大规模地从数据中学习。

♟️ 企业需要构建**信用归因系统**，类似于在棋局中理解每一步棋的意义，而不仅仅是知道输赢的结果。通过分析环境，企业可以更有效地进行决策和调整策略。

⚔️ 未来的企业运营将更像**高频交易或军事行动**，需要一个中心化的操作系统，将观察、推理和行动整合在一起，以便实时学习和调整。

🏢 **Conway定律**表明，企业的产出反映了其内部结构。拥抱学习的企业将更有可能生存下来，而拒绝学习的企业将面临被淘汰的风险。

Published on February 25, 2025 8:33 AM GMT

This is a post about how running companies will change. It seems safe to say that markets are becoming more competitive, since AI tools are raising the floor for incumbents and new market entrants alike. But does this shift raise the floor symmetrically? 

And what happens if this shift benefits incumbents? It's easy to imagine a world in which tech giants subsume previously difficult to aggregate assets and edge out new competition due to their ability to resource share under their umbrella. This is a scary world. Social mobility in an ultra-consolidated marketplace is not protected. 

It's my belief that we can create tools to empower even individuals to become competitive with incumbents. I'll make that argument here, and I'd be interested to hear what you think. 

The Feedback Loop is the Primary Product

The purpose of a company is to learn about its environment.^[1] [2] Companies are learning machines, and the best ones focus on extracting more information with less noise as fast as possible. This dynamic has always been true, and promises to become even more true because smaller teams increasingly generate outsized returns. This rising competitive pressure means organizations have an ever-shrinking margin for error in their information uptake rates. If you’re planning on participating in the new world, the feedback loop is your primary product.^[3]

Dry powder ready to explode

Companies waste their data, and it’s not for lack of trying. Companies spend billions of dollars extracting and storing information about user interactions. But at the end of this effort they are left with a few colorful graphs designed to persuade, not to inform. And it’s not their fault. Companies are bottlenecked by intelligence. Learning from data has been a hugely expensive effort until recently because of labor costs. “Until recently” because frontier models solve the cost of labor problem. 

In other words, companies are learning machines, and now it’s possible for the first time to learn at scale. 

Credit attribution systems

Imagine you’re playing chess, you don’t know the rules, and you only get one bit of feedback: whether you won or lost at the end of the game. Over 1,000 iterations of the game, you may improve by classifying every sequence of actions that led to a winning strategy as “good” and every sequence of actions that led to a losing strategy as “bad.” This is a black box RL approach, and the simplest version of credit assignment. However, if you have the ability to figure out why you won or lost, you will become a better chess player in a smaller number of iterations.^[4]

Now imagine you’re running a company. You’re operating in an exponentially more complex environment. It’s not a viable strategy to call the sequence of actions that led to the company going bankrupt “bad”. Instead, we can model the environment itself as a white box that can be cracked open and analyzed. In other words, we’re trading off computational requirements for state of the art sample efficiency. But that’s fine, because the cost of intelligence is dropping by an order of magnitude every year. If indeed a company’s purpose is to learn about its environment, then a company’s purpose is to do credit attribution.^[5] 

Companies are learning machines, it's possible for the first time to learn at scale, and the way to learn at scale is to build credit attribution systems. 

Command and control

This new world will move at a fast clip. Every learning will be extracted and operationalized in real time. Company operations will start to resemble something more akin to high frequency trading or military operations rather than the paper-pushing politics of companies today. 

Data-rich regimes with fast and complex decision-making requirements have a common interface pattern: a centralized operating picture that combines observability, reasoning, and action in a single place. Traders have Bloomberg terminals, and military operations have command and control systems (see Anduril's Lattice OS). Conway (my company) is building a command and control system for companies, powered by a credit attribution engine.

Revisiting Conway’s Law

Conway’s law suggests that the outputs of a company inevitably mirror how the company is internally structured. Companies who refuse to learn have always suffered, and now they’ll be saved from their suffering by the hands of sweet death. The converse is also true. Companies that choose to embrace their roles as learning machines may join the first rarified cohort of companies that last. 

If you want to talk more about this, I'd love to chat. My name is Anne Brandes, and I'm founding Conway, a command and control platform for companies. You can learn more about me here, email me at i>anne@conway.ai</i, or message me directly. 

 

1. ^{^}

   See Patrick McKenzie's discussion here. 

2. ^{^}

   Isn't a company's purpose to make money? Of course. But if you needed to pinpoint the guiding principle of a company, it's to build a machine that makes money, not to opportunistically make money. This is the difference between winning the battle of a favorable quarter and winning the war of conquering a market. How do you build a machine that makes money? You learn about your environment, and you learn about your environment better than your competitors. Companies are learning machines. 

3. ^{^}

   Inspiration for this language comes from Raemon's post a while back Feedback-Loop First Rationality.

4. ^{^}

   Great grounding from Andrei Karpathy's work Deep Reinforcement Learning: Pong from Pixels.

5. ^{^}

   If you want to read more about credit attribution, especially in a company context, read Gwern's Evolution as a Backstop for Reinforcement Learning. 

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