# The AI-pilled Software Factory
**作者**: Ann Miura-Ko 🦖
**日期**: 2026-09-08T19:12:13.000Z
**来源**: [https://x.com/annimaniac/status/2097402645078229456](https://x.com/annimaniac/status/2097402645078229456)
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AI-pilled is not a vibe. It’s a factory.
The most AI-pilled companies aren’t simply adopting AI or making their existing employees more productive. They are building software factories.
I have spent the past nine months studying over 30 companies and 50 individuals, from three-person startups to large public companies, trying to understand what separates individual AI use from a company that has actually reorganized itself around AI. That work originally led me to an [L0–L5 maturity model ](https://x.com/annimaniac/status/2050225284277026990)based on four questions: What can AI see? What can it do? Who can extend it? And how has the organization changed?
But after spending more time with the companies at the frontier, I think there is a bigger idea hiding inside the maturity model.
The mistake is to confuse an AI-enabled workforce with an AI-native company. Giving everyone access to Claude, ChatGPT, or Copilot can make employees dramatically more productive. It does not, by itself, change what the organization is capable of.
The engineering organizations have gone far beyond coding faster with more agents. These companies are creating the infrastructure that allows the entire company to continuously build software. Marketing can build. Sales can build. Finance can build. A product manager can identify a problem, create an agent to solve it, and propagate that capability to every other product manager.
That changes the role of engineering. In a traditional software company, the engineering organization largely built the software product. Increasingly, we are seeing engineering teams build the harness that gives the rest of the organization the context, permissions, evaluations, model routing, and guardrails they need to safely create software themselves.
Ramp Glass and Ramp Inspect are good examples of what this looks like in practice. Glass functions as the context layer and everyday agent: employees across the company can talk to it, it understands the context of their work, and they can use it to create agents of their own. Inspect is the execution layer: a background coding agent equipped with Ramp’s development environment, tools, and company-specific standards so that what employees want to build can become production-quality software.
## What does the factory actually unlock?
The L0–L5 model tells me something important about the architecture of a company: how much context AI has, how much agency it has, who can extend it, and how deeply the organization has changed around it. But it doesn’t necessarily tell me how much value that architecture is producing. A company can have agents everywhere and still accomplish very little.
This is where I think many companies are measuring the wrong thing. AI adoption is relatively easy to measure: seats, tokens, agents, AI-generated code. What matters is whether any of that has created a capability the organization did not have before.
That has led me to a different set of questions. The Software Factory Test comes down to five questions:
See: What becomes visible?
Build: Who can create software?
Propagate: Does what they build spread?
Remember: Does experience accumulate?
Notice: What does the machine catch on its own?
These are the capabilities the factory is supposed to produce.
## See
One product manager we spoke to described giving her AI access to her product documentation, usage data, future roadmap, strategy, and the company’s customer conversations in Gong. Previously, she depended on the sales organization to tell her what customers were saying about her product. Inevitably, that meant getting anecdotes: a salesperson would hear something important and remember to pass it along.
Once the AI could listen to every call, find every reference to her product, and connect those conversations with the rest of her context, she could get a complete picture of what customers were actually saying. That picture was sufficiently different from the one she had before that in some cases it changed her product roadmap and in others influenced her product line strategy.
This is why the ability to see is much more powerful than simply “better search.” A human product manager cannot listen to thousands of Gong calls, remember every customer comment, connect those comments to every piece of product telemetry, and simultaneously hold the company’s strategy in her head. The machine can. The employee is not merely faster at gathering information. She has access to a picture of the business that she literally could not assemble before.
## Build
One salesperson I spoke with showed me a simple example. When she contacted an account, other salespeople were not supposed to contact that account for thirty days. Historically, she could have complained about violations or convinced someone in engineering or sales operations to build a solution.
Instead, she built an agent herself. The agent detects when another salesperson contacts one of her protected accounts, alerts her, and gives her a way to alert the other salesperson.
The breakthrough isn’t that AI allowed an engineer to write that software ten times faster. It is that the person who understood the problem could now create the software to solve it.
This changes the economics of software inside a company. When software was expensive to create, organizations needed mechanisms for deciding which problems deserved scarce engineering resources. A salesperson might recognize a small problem every day, but almost none of those problems could justify an engineering ticket. A marketer might have an idea for a useful internal tool but never even articulate it because she knew it would not be built.
When the cost of creating software collapses and the person closest to the problem can build against company context directly, thousands of previously uneconomic pieces of software become viable. The bottleneck migrates from the ability to build and toward the ability to recognize which problems are worth solving.
Vercel’s internal data system, d0, provides another window into this transition. d0 allows anyone at Vercel to ask questions of the company's data warehouse in natural language. Employees now use it to ask tens of thousands of questions each month.
The more interesting story is what happened next. Once employees experienced how easy it was to interrogate company data themselves, asking questions began to turn into building. What started as a way to make data visible became part of a broader proliferation of internal agents and applications across the company including finance and product.
The ability to see created a desire to build. And a community building created more builders.
## Propagate
Earlier, I described the product manager who had given her AI enough context to develop a much richer picture of her customers. She then took the system she had created for herself and packaged it into a reusable skill so that other PMs could have the same capability without reconstructing everything she had done.
Building turns an employee into a software creator but propagation turns what one employee creates into an organizational capability.
Companies have always tried to spread the skills of their best people. The exceptional salesperson teaches the rest of the team how she prepares for meetings. The best product manager writes a playbook. Managers coach people. Companies create training programs. But all of these are lossy ways of transferring tacit knowledge.
In a software factory, some portion of what the exceptional person does can become executable. The best product manager’s research process becomes a skill. The best salesperson’s account preparation becomes an agent. The best marketer’s analysis becomes a workflow. Instead of merely documenting excellence, you can begin to distribute it.
The gap between the best person on a team and the median person is often enormous. Companies have spent decades trying to close that gap through hiring, training, management, and process. AI gives them another mechanism: encode some portion of what the best people do and make it available at the moment everyone else needs it.
Companies have always been able to accumulate knowledge. A software factory gives them a new way to accumulate capability and potentially compound it.
## Remember
Remember is easy to confuse with See, but the distinction is important. See is about making what a company already knows accessible. Remember is about whether experience changes what the company knows next.
Companies are surprisingly bad at this. A team tests something and discovers it doesn’t work. Six months later, another team tries essentially the same thing. A strategy changes, but the reasoning behind the change lives in the heads of the three people who were in the room. An agent takes a bad path, a human corrects it, and the next time the agent encounters the same situation it makes essentially the same mistake.
The most sophisticated system I have seen does something different. It records what it got wrong, what it believed and then believed less, what evidence caused it to change its mind, and which approaches failed. That record isn’t simply an audit trail. It becomes an input into the next attempt.
This distinction matters enormously. You can give an AI access to every Slack message, Gong call, document, and database in the company and still have a system that does not develop judgment. It may be able to retrieve the postmortem from six months ago, but that is different from having incorporated the lesson from that postmortem into how it behaves today.
See gives the machine access to the company’s memory. Remember allows experience to change that memory, enabling the company to become smarter.
## Notice
The final capability feels most different from how companies have traditionally operated because the human no longer has to initiate the work.
One company connected its AI systems to Snowflake, Postgres, meeting transcripts, town halls, and other sources of company context. During one town hall, a product manager made a passing comment that perhaps shorter job shifts would be accepted more frequently within the marketplace they operate. He did not ask the AI to investigate it.
The system noticed the hypothesis, went into the company’s data, tested whether it was true, concluded that it was, built the product, and wrote a memo explaining what it had found and what it had done. Only then did it bring the entire package back to the team for approval.
That is a qualitatively different capability. Much of management is not actually making decisions; it is noticing what deserves a decision. Someone has to see that a metric moved, remember a comment from last week’s meeting, realize that the two might be connected, assign someone to investigate it, and remember to check back later.
When the machine can notice, some of that burden disappears. It can continuously watch the company, surface anomalies and hypotheses, investigate them, and bring back work that deserves human judgment. Humans still decide. Every consequential action in the most advanced system I observed requires explicit human approval. But humans no longer need to personally notice every potentially important thing that deserves a decision.
Taken together, these five capabilities attack five very old constraints on organizations: no person can know everything the company knows; excellence is hard to transfer; software creation is bottlenecked by technical talent; organizations repeatedly forget what they learn; and humans cannot continuously pay attention to everything.
But there is an important trap here. Building more sophisticated AI infrastructure does not necessarily mean you have unlocked more value. A company can have proactive agents everywhere and still be surprisingly bad at seeing, propagating, building, remembering, and noticing.
## ROI rot
I have started to think of that problem as ROI rot.
The company has agents everywhere. It consumes an enormous number of tokens. Employees generate analyses constantly. Automations are firing. AI usage statistics look fantastic. Perhaps there are even demos that make the company look extraordinarily advanced.
But very little changes in the actual business. The analysis doesn’t alter a decision. The automation doesn’t remove meaningful work. The agent creates output nobody uses.
Many companies are likely accumulating AI debt: agents nobody uses, workflows nobody evaluates, generated software nobody maintains, and token consumption disconnected from business outcomes.
AI is particularly dangerous because the marginal cost of producing more activity has collapsed. We have never had a technology that makes it this easy to produce sophisticated-looking work nobody needs. The fact that something would have been expensive to produce three years ago does not make it valuable today.
A software factory optimized for output can produce waste at unprecedented scale. AI can make a poorly designed organization worse faster.
This gives us a simple way to think about ROI rot: if AI usage is exploding but your organization is not materially better at seeing, propagating, building, remembering, or noticing, what exactly are all those tokens buying?
A business is fundamentally a loop: observe something about the world, decide what to do, act, and then learn from the result. AI can collapse enormous portions of that loop. Observation that once required a data request can become instantaneous. Building something that once took months can take hours. A loop that once took a quarter can happen in days.
But making the loop spin faster does not guarantee that the company learns. If nothing records what worked, what failed, what the organization changed its mind about and why, every trip around the loop begins from roughly the same place. Consumption goes up, but capability does not compound.
A great factory is not measured by how fast its machines run. It is measured by whether it reliably produces something valuable, gets better at producing it, and stops producing things that don’t work.
Otherwise you have built a factory optimized for production rather than outcomes.
## The Software Factory Test
So rather than asking a CEO how many agents the company has, how many tokens it is consuming, or how much code is AI-generated, I would ask:
See: What can your employees see today that they couldn’t see a year ago?
Build: Can the person closest to the problem build the solution?
Propagate: When one person discovers a better way to work, does everyone inherit it?
Remember: When the company learns something, does the system learn it too?
Notice: Can the machine notice something important nobody thought to ask?
The defining asset isn’t access to the best model. Everyone will have access to roughly the same frontier models. It isn’t the sheer number of agents either, because agents themselves are becoming cheap to create.
The durable asset is the factory: the context that makes the company legible, the infrastructure that lets employees build against that context, the mechanisms that turn individual excellence into shared capability, the memory that prevents the organization from repeatedly relearning the same lessons, and the evaluation and governance systems that determine which machine-generated work deserves to propagate.
We have spent the last fifty years building software companies. The next generation of great companies will increasingly be software factories: organizations that continuously turn their own context, judgment, and experience into software. The companies that learn to do this will not merely operate more efficiently. They will learn faster than their competitors and increasingly encode what they learn into the company itself.
If you are building the harness layer (context, permissions, evaluations, or tools that let non-engineers create software safely) or your company has the shape of a software factory, I want to hear from you.
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---
*导出时间: 2026/9/9 10:17:53*
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## 中文翻译
# 沉迷AI的软件工厂
**作者**： Ann Miura-Ko 🦖
**日期**： 2026-09-08T19:12:13.000Z
**来源**： [https://x.com/annimaniac/status/2097402645078229456](https://x.com/annimaniac/status/2097402645078229456)
---
沉迷AI不是一种氛围，而是一座工厂。
那些最沉迷AI的公司并不是简单地采用AI或让现有员工更高效。它们在打造软件工厂。
过去九个月里，我研究了30多家公司和50多位个人，从三人初创公司到大型上市公司，试图理解个体使用AI与真正围绕AI完成组织重构的公司之间有何区别。这项研究最初让我得出了一个基于四个问题的 [L0–L5 成熟度模型](https://x.com/annimaniac/status/2050225284277026990)：AI能看到什么？它能做什么？谁可以扩展它？组织发生了怎样的变化？
但在与前沿公司进行更多深入接触之后，我认为成熟度模型内部还隐藏着一个更大的理念。
错误在于把“AI赋能的员工队伍”与“AI原生公司”混为一谈。让每个人都能使用 Claude、ChatGPT 或 Copilot，可以大幅提升员工的生产力。但这本身并不会改变这个组织的能力边界。
那些工程组织早已超越了“用更多智能体更快写代码”的阶段。这些公司正在打造基础设施，让整个公司能够持续不断地构建软件。市场部门能构建，销售部门能构建，财务部门能构建。一位产品经理可以发现问题，创建一个智能体来解决它，并把这种能力传播给所有其他产品经理。
这改变了工程的角色。在传统软件公司中，工程组织的主要工作是构建软件产品。而如今，我们看到工程团队越来越多地构建一个“挽具”（harness），为组织的其他部门提供他们安全自行创建软件所需的上下文、权限、评估、模型路由和护栏。
Ramp Glass 和 Ramp Inspect 就是这种实践形态的绝佳例子。Glass 充当上下文层和日常智能体：全公司的员工都可以与它对话，它理解他们工作的上下文，他们可以用它创建自己的智能体。Inspect 是执行层：一个后台编码智能体，配备了 Ramp 的开发环境、工具和公司专属标准，使员工想构建的东西能够成为生产级软件。
## 这座工厂究竟解锁了什么？
L0–L5 模型告诉我关于公司架构的重要信息：AI 拥有多少上下文、拥有多大自主权、谁能扩展它、组织围绕它发生了多深的变化。但它并不一定能告诉我这套架构产出了多少价值。一家公司可以到处都是智能体，实际成就却寥寥无几。
这就是我认为许多公司度量了错误指标的地方。AI 采用率相对容易度量：席位数、token 数、智能体数、AI 生成的代码量。真正重要的是，这些是否创造了组织以前不具备的能力。
这让我转向另一组问题。“软件工厂测试”归结为五个问题：
看见：什么变得可见了？
构建：谁能创建软件？
传播：他们构建的东西会扩散吗？
记忆：经验会累积吗？
察觉：机器能自己发现什么？
这些就是这座工厂理应产出的能力。
## 看见
我们访谈的一位产品经理描述了她如何让 AI 访问她的产品文档、使用数据、未来路线图、战略，以及公司在 Gong 中的客户对话录音。此前，她只能依赖销售组织告诉她客户对其产品的评价。不可避免地，这意味着只能得到一些零散轶事：某位销售员听到了重要信息，并记得转告她。
一旦 AI 能够听取每一通电话、找到每一处提及她产品的内容，并将这些对话与她的其他上下文关联起来，她就能获得客户实际想法的完整图景。这张图景与她以前的认知截然不同，以至于在某些情况下它改变了她的产品路线图，在另一些情况下则影响了她的产品线战略。
这就是为什么“看见”的能力远比“更好的搜索”强大得多。一位人类产品经理不可能听几千通 Gong 电话、记住每一条客户评论、把这些评论与每一份产品遥测数据关联起来，同时还在脑中把握公司战略。机器可以。这位员工不仅仅是收集信息更快了。她获得了一幅她以前根本无法拼凑出来的业务图景。
## 构建
我访谈的一位销售人员给我展示了一个简单的例子。当她联系某个客户账户后，其他销售人员在三十天内不应再联系该账户。以往，她可以抱怨违规行为，或者说服工程部门或销售运营部门的人构建一个解决方案。
而这次，她自己构建了一个智能体。该智能体能检测其他销售人员何时联系了她的受保护账户，提醒她，并让她能够提醒对方。
突破之处不在于 AI 让工程师写这套软件快了十倍，而在于那个理解问题的人现在能够自己创建解决问题的软件。
这改变了公司内部软件的经济性。当创建软件的成本高昂时，组织需要一套机制来决定哪些问题值得占用稀缺的工程资源。一位销售员可能每天都会发现一些小问题，但这些问题几乎都撑不起一张工程工单。一位市场人员可能想做一个实用的内部工具，但甚至不会说出来，因为她知道不会被开发。
当创建软件的成本崩塌，而最贴近问题的人能够直接基于公司上下文进行构建时，成千上万种以前“不经济”的软件变得可行了。瓶颈从“构建的能力”迁移到了“识别哪些问题值得解决的能力”。
Vercel 的内部数据系统 d0 为这一转变提供了另一个观察窗口。d0 允许 Vercel 的任何人用自然语言查询公司的数据仓库。现在员工每月用它提出数万个问题。
更有意思的是接下来发生的事。一旦员工体验到自行查询公司数据如此容易，提问开始演变为构建。原本只是让数据可见的工具，成为了公司内部智能体和应用更大规模普及的一部分，涵盖财务和产品等部门。
看见的能力催生了构建的欲望。而一个构建社区又创造了更多构建者。
## 传播
前面我提到那位产品经理，她给 AI 提供了足够的上下文，从而对客户形成了丰富得多的认知。随后她把自己创建的系统打包成一个可复用的技能，让其他产品经理无需重复她的工作就能获得同样的能力。
构建让一位员工成为软件创造者，而传播则把一个员工创造的东西变成组织级的能力。
公司一直在尝试推广最优秀员工的技能。顶尖销售员会教团队其他人她如何准备会议。最好的产品经理会写一份打法手册。管理者进行辅导。公司开办培训项目。但这些都是传递隐性知识的有损方式。
在软件工厂中，顶尖人才所做的一部分工作可以变得可执行。最好的产品经理的研究流程变成一项技能。最好的销售员的客户准备流程变成一个智能体。最好的市场营销人员的分析变成一个工作流。你不再只是记录卓越，而是开始分发卓越。
团队中最优秀者与中等水平者之间的差距往往巨大。几十年来，公司一直试图通过招聘、培训、管理和流程来弥合这一差距。AI 给了他们另一种机制：把最优秀员工所做的一部分编码下来，并在其他人需要的那一刻提供给他们。
公司一直都积累知识，而软件工厂给了它们一种积累能力并让其复利增长的新方式。
## 记忆
“记忆”很容易与“看见”混淆，但二者的区别很重要。“看见”是让公司已知的东西变得可访问。“记忆”则是关于经验是否改变了公司接下来所知道的东西。
公司在这一点上出奇地糟糕。一个团队测试了某样东西，发现行不通。六个月后，另一个团队尝试了基本相同的东西。一项战略变了，但改变背后的理由只存在于当时在场的三个人的脑子里。一个智能体走了错误的路径，一个人纠正了它，而下次遇到同样的情况时，它基本上会犯同样的错误。
我见过的最精妙的系统则有所不同。它会记录自己错在哪里、曾经相信什么后来又减少了相信什么、什么证据让它改变了想法，以及哪些方法失败了。这份记录不仅仅是一条审计轨迹，它会成为下一次尝试的输入。
这个区别至关重要。你可以让 AI 访问公司所有的 Slack 消息、Gong 电话、文档和数据库，但得到的系统仍然不会形成判断力。它也许能检索出六个月前的复盘报告，但这与把那次复盘的教训融入它今天的行为方式是两回事。
“看见”让机器得以访问公司的记忆。“记忆”让经验能够改变这份记忆，从而使公司变得更聪明。
## 察觉
最后这项能力与公司传统运作方式的差异最大，因为人不再需要发起工作。
有一家公司将其 AI 系统连接到 Snowflake、Postgres、会议记录、全员大会等公司上下文的各种来源。在一次全员大会上，一位产品经理顺口提到，在他们运营的市场中，更短的工作班次或许会被更频繁地接受。他并没有要求 AI 去调查这件事。
系统注意到了这个假设，进入公司数据，检验它是否成立，得出结论认为成立，构建了产品，并撰写了一份备忘录，说明它的发现和所做的事。直到这时，它才把整个成果带回团队等待批准。
这是一种性质上不同的能力。管理工作的很大一部分其实并不是做决策，而是察觉什么值得决策。必须有人注意到某个指标发生了变化，想起上周会议上的某句评论，意识到这两者可能相关，指派某人去调查，并且记得之后回来跟进。
当机器能够察觉时，这部分负担中的一些就消失了。它可以持续观察公司，浮现异常和假设，进行调查，并带回值得人类判断的工作成果。人类依然做决策。在我观察到的最先进系统中，每一个有影响的行动都需要明确的人类批准。但人类不再需要亲自注意到每一个值得决策的潜在重要事项。
综合来看，这五项能力直击组织五个非常古老的约束：没有人能知道公司知道的一切；卓越难以传递；软件创建受制于技术人才；组织不断遗忘所学；人类无法持续关注一切。
但这里有一个重要的陷阱。构建更复杂的 AI 基础设施并不一定意味着你解锁了更多价值。一家公司可以到处都是主动的智能体，却仍然在看见、传播、构建、记忆和察觉方面表现得惊人地差。
## ROI 腐烂
我开始把这个问题称为“ROI 腐烂”。
这家公司到处都是智能体。它消耗着天文数字的 token。员工不断生成分析。自动化在持续触发。AI 使用统计数据看起来美妙极了。甚至可能还有一些让公司看起来无比先进的演示。
但实际业务几乎没有任何改变。分析没有改变任何决策。自动化没有消除有意义的工作。智能体产出了没人使用的输出。
许多公司可能正在积累“AI 债”：没人使用的智能体、没人评估的工作流、没人维护的生成式软件，以及与业务成果脱节的 token 消耗。
AI 的危险性尤其突出，因为产生更多“活动”的边际成本已经崩塌。我们从未有过这样一种技术，能如此轻易地生产出看起来精妙却无人需要的成果。三年前生产某样东西很昂贵这一事实，并不能让它今天变得有价值。
一座以产出为导向优化的软件工厂，能够以前所未有的规模制造废物。AI 会让一个设计糟糕的组织更快地变得更糟。
这给了我们一个思考 ROI 腐烂的简单方法：如果 AI 用量在爆炸式增长，但你的组织在看见、传播、构建、记忆或察觉方面并没有实质性地变强，那么这些 token 到底买来了什么？
企业的本质是一个循环：观察世界的某个方面，决定做什么，行动，然后从结果中学习。AI 可以压缩这个循环的很大一部分。曾经需要提交数据请求的观察可以变得即时。曾经需要数月构建的东西可能只需数小时。曾经需要一个季度的循环可能几天就能完成。
但让循环转得更快，并不能保证公司真的在学习。如果没有任何东西记录什么有效、什么失败、组织改变了什么想法以及为什么改变，那么每一次循环都从大致相同的地方重新开始。消耗上升，但能力并不复利增长。
一座伟大的工厂，其衡量标准不是机器运转得多快，而是它能否可靠地产出有价值的东西、能否在产出上不断进步，以及能否停止生产无效的东西。
否则，你建造的就是一座为生产而非成果而优化的工厂。
## 软件工厂测试
所以，与其问一位 CEO 公司有多少个智能体、消耗多少 token、多少代码是 AI 生成的，我会问：
看见：你的员工今天能看到什么一年前看不到的东西？
构建：最贴近问题的人能否亲手构建解决方案？
传播：当一个人发现了更好的工作方式时，所有人都能继承它吗？
记忆：当公司学到某件事时，系统也会学到它吗？
察觉：机器能否察觉到没人想到去问的重要事项？
决定性的资产不是能否使用最好的模型。每个人都将能使用大致相同的前沿模型。它也不是智能体的绝对数量，因为智能体本身正变得容易廉价创建。
持久的资产是工厂本身：让公司变得可被理解的上下文，让员工能基于这一上下文进行构建的基础设施，把个人卓越转化为共享能力的机制，防止组织反复重学同一教训的记忆，以及决定哪些机器生成的工作值得传播的评估与治理系统。
过去五十年，我们一直在建设软件公司。下一代伟大的公司将越来越多地成为软件工厂：持续把自身的上下文、判断和经验转化为软件的组织。学会这样做的公司不仅仅是运营得更高效，它们将比竞争对手学得更快，并日益把所学编码进公司本身。
如果你正在构建挽具层（让非工程师能够安全创建软件的上下文、权限、评估或工具），或者你的公司已经具有软件工厂的形态，我想听听你的故事。
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*导出时间: 2026/9/9 10:17:53* 
