吴恩达来信：AI产品经理将成为最稀缺人才

01 太长不看版- 核心观点提炼

核心论断：AI产品经理的黄金时代来临

1️⃣ 经济学互补品原理：编程成本↓ → 产品规划需求↑

● 就像汽车变便宜导致汽油需求增加，AI让编码成本骤降10倍，将引发"决定做什么"的人才需求激增

● 预测：工程师与PM比例将从传统的6:1向更平衡方向转变

2️⃣ AI产品经理的五大核心能力（区别于传统PM）

● AI技术理解：懂得技术可行性边界，理解AI项目生命周期（数据→训练→监控→维护）

● 迭代开发管理：AI开发需要更频繁的方向修正

● 数据能力：AI产品从数据中学习，能生成更丰富的数据形式

● 模糊性管理：AI性能难以预测，需要应对不确定性的策略

● 持续学习：技术快速演进，必须保持知识更新

3️⃣ 当前瓶颈与机会

● 供需失衡：工程师因技术背景更快拥抱AI，但懂AI的PM奇缺，缺口将持续扩大

● 未来要求：AI PM需掌握负责任AI实践（如防护栏）、快速收集用户反馈、甚至能自己构建原型

4️⃣ 行业趋势预测

● 可构建的有价值产品几乎无限，软件团队组成将发生结构性变化

● 部分工程师可能承担更多产品管理工作

● DeepSeek-V3案例印证：500万美元就能训练GPT-4o级模型，MoE架构训练成本比传统模型低5-10倍

02原文内容（中英对照）

Andrew Ng的来信

Dear friends,

亲爱的朋友们，

Writing software, especially prototypes, is becoming cheaper. This will lead to increased demand for people who can decide what to build. AI Product Management has a bright future!

编写软件（尤其是原型）的成本正在降低。这将导致对"能决定构建什么"的人才需求增加。AI产品管理前景光明！

Software is often written by teams that comprise Product Managers (PMs), who decide what to build (such as what features to implement for what users) and Software Developers, who write the code to build the product. Economics shows that when two goods are complements — such as cars (with internal-combustion engines) and gasoline — falling prices in one leads to higher demand for the other. For example, as cars became cheaper, more people bought them, which led to increased demand for gas. Something similar will happen in software. Given a clear specification for what to build, AI is making the building itself much faster and cheaper. This will significantly increase demand for people who can come up with clear specs for valuable things to build.

软件开发通常由团队完成，包括产品经理（PM）——决定构建什么（如为哪些用户实现哪些功能），以及软件开发人员——编写代码来构建产品。经济学表明，当两种商品互为补充品时（比如汽车和汽油），一种商品价格下降会导致另一种商品需求上升。例如，随着汽车变得更便宜，更多人购买汽车，从而增加了汽油需求。软件领域也会发生类似的事情。在有明确构建规格的前提下，AI正让构建本身变得更快、更便宜。这将显著增加对"能提出清晰且有价值构建规格"的人才需求。

This is why I'm excited about the future of Product Management, the discipline of developing and managing software products. I'm especially excited about the future of AI Product Management, the discipline of developing and managing AI software products.

这就是我为何对产品管理（开发和管理软件产品的学科）的未来感到兴奋。我尤其对AI产品管理（开发和管理AI软件产品的学科）的未来充满期待。

Many companies have an Engineer:PM ratio of, say, 6:1. (The ratio varies widely by company and industry, and anywhere from 4:1 to 10:1 is typical.) As coding becomes more efficient, I think teams will need more product management work (as well as design work) as a fraction of the total workforce. Perhaps engineers will step in to do some of this work, but if it remains the purview of specialized Product Managers, then the demand for these roles will grow.

许多公司的工程师与PM比例约为6:1（不同公司和行业差异很大，4:1到10:1都很常见）。随着编码效率提升，我认为团队将需要更多产品管理工作（以及设计工作）占总劳动力的比例。也许工程师会介入做一些这类工作，但如果它仍然是专业产品经理的职责范围，那么对这些角色的需求将会增长。

This change in the composition of software development teams is not yet moving forward at full speed. One major force slowing this shift, particularly in AI Product Management, is that Software Engineers, being technical, are understanding and embracing AI much faster than Product Managers. Even today, most companies have difficulty finding people who know how to develop products and also understand AI, and I expect this shortage to grow.

软件开发团队组成的这种变化尚未全速推进。阻碍这一转变的一个主要因素是，软件工程师因具备技术背景，比产品经理更快地理解和接受AI。即使在今天，大多数公司也很难找到既懂产品开发又理解AI的人才，我预计这种短缺会加剧。

AI产品管理的五大核心能力

Further, AI Product Management requires a different set of skills than traditional software Product Management. It requires:

此外，AI产品管理需要与传统软件产品管理不同的技能组合。它需要：

1. Technical proficiency in AI（AI技术精通）

PMs need to understand what products might be technically feasible to build. They also need to understand the lifecycle of AI projects, such as data collection, building, then monitoring, and maintenance of AI models.PM需要理解哪些产品在技术上可行。他们还需要了解AI项目的生命周期，如数据收集、构建、监控和AI模型维护。

2. Iterative development（迭代开发）

Because AI development is much more iterative than traditional software and requires more course corrections along the way, PMs need to understand how to manage such a process.因为AI开发比传统软件迭代性更强，需要更多中途调整，PM需要懂得如何管理这样的过程。

3. Data proficiency（数据精通）

AI products often learn from data, and they can be designed to generate richer forms of data than traditional software.AI产品通常从数据中学习，并且可以设计生成比传统软件更丰富的数据形式。

4. Skill in managing ambiguity（模糊性管理技能）

Because AI's performance is hard to predict in advance, PMs need to be comfortable with this and have tactics to manage it.因为AI的性能难以提前预测，PM需要适应这一点并拥有管理策略。

5. Ongoing learning（持续学习）

AI technology is advancing rapidly. PMs, like everyone else who aims to make best use of the technology, need to keep up with the latest technology advances, product ideas, and how they fit into users' lives.AI技术快速发展。PM和所有想充分利用该技术的人一样，需要跟上最新技术进步、产品理念及其如何融入用户生活。

Finally, AI Product Managers will need to know how to ensure that AI is implemented responsibly (for example, when we need to implement guardrails to prevent bad outcomes), and also be skilled at gathering feedback fast to keep projects moving. Increasingly, I also expect strong product managers to be able to build prototypes for themselves.

最后，AI产品经理需要知道如何确保AI负责任地实施（例如需要实施防护栏以防止不良结果），并擅长快速收集反馈以保持项目推进。我还越来越期待优秀的产品经理能够自己构建原型。

The demand for good AI Product Managers will be huge. In addition to growing AI Product Management as a discipline, perhaps some engineers will also end up doing more product management work.

对优秀AI产品经理的需求将是巨大的。除了将AI产品管理发展为一门学科外，也许一些工程师最终也会承担更多产品管理工作。

The variety of valuable things we can build is nearly unlimited. What a great time to build!

我们能构建的有价值事物几乎无限。这是构建的大好时代！

来源 | DataFunTalk（ID：datafuntalk）

作者 | DataFunTalk ; 编辑 | 虾饺

内容仅代表作者独立观点，不代表早读课立场