北京
01 太長不看版- 核心觀點提煉
核心論斷：AI產品經理的黃金時代來臨
1?? 經濟學互補品原理：編程成本↓ → 產品規劃需求↑
● 就像汽車變便宜導致汽油需求增加，AI讓編碼成本驟降10倍，將引發"決定做什么"的人才需求激增
● 預測：工程師與PM比例將從傳統的6:1向更平衡方向轉變
2?? AI產品經理的五大核心能力（區別于傳統PM）
● AI技術理解：懂得技術可行性邊界，理解AI項目生命周期（數據→訓練→監控→維護）
● 迭代開發管理：AI開發需要更頻繁的方向修正
● 數據能力：AI產品從數據中學習，能生成更豐富的數據形式
● 模糊性管理：AI性能難以預測，需要應對不確定性的策略
● 持續學習：技術快速演進，必須保持知識更新
3?? 當前瓶頸與機會
● 供需失衡：工程師因技術背景更快擁抱AI，但懂AI的PM奇缺，缺口將持續擴大
● 未來要求：AI PM需掌握負責任AI實踐（如防護欄）、快速收集用戶反饋、甚至能自己構建原型
4?? 行業趨勢預測
● 可構建的有價值產品幾乎無限，軟件團隊組成將發生結構性變化
● 部分工程師可能承擔更多產品管理工作
● DeepSeek-V3案例印證：500萬美元就能訓練GPT-4o級模型，MoE架構訓練成本比傳統模型低5-10倍
02原文內容（中英對照）
Andrew Ng的來信
Dear friends,
親愛的朋友們，
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!
編寫軟件（尤其是原型）的成本正在降低。這將導致對"能決定構建什么"的人才需求增加。AI產品管理前景光明！
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.
軟件開發通常由團隊完成，包括產品經理（PM）——決定構建什么（如為哪些用戶實現哪些功能），以及軟件開發人員——編寫代碼來構建產品。經濟學表明，當兩種商品互為補充品時（比如汽車和汽油），一種商品價格下降會導致另一種商品需求上升。例如，隨著汽車變得更便宜，更多人購買汽車，從而增加了汽油需求。軟件領域也會發生類似的事情。在有明確構建規格的前提下，AI正讓構建本身變得更快、更便宜。這將顯著增加對"能提出清晰且有價值構建規格"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.
這就是我為何對產品管理（開發和管理軟件產品的學科）的未來感到興奮。我尤其對AI產品管理（開發和管理AI軟件產品的學科）的未來充滿期待。
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.
許多公司的工程師與PM比例約為6:1（不同公司和行業差異很大，4:1到10:1都很常見）。隨著編碼效率提升，我認為團隊將需要更多產品管理工作（以及設計工作）占總勞動力的比例。也許工程師會介入做一些這類工作，但如果它仍然是專業產品經理的職責范圍，那么對這些角色的需求將會增長。
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.
軟件開發團隊組成的這種變化尚未全速推進。阻礙這一轉變的一個主要因素是，軟件工程師因具備技術背景，比產品經理更快地理解和接受AI。即使在今天，大多數公司也很難找到既懂產品開發又理解AI的人才，我預計這種短缺會加劇。
AI產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產品管理需要與傳統軟件產品管理不同的技能組合。它需要：
1. Technical proficiency in AI（AI技術精通）
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需要理解哪些產品在技術上可行。他們還需要了解AI項目的生命周期，如數據收集、構建、監控和AI模型維護。
2. Iterative development（迭代開發）
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.因為AI開發比傳統軟件迭代性更強，需要更多中途調整，PM需要懂得如何管理這樣的過程。
3. Data proficiency（數據精通）
AI products often learn from data, and they can be designed to generate richer forms of data than traditional software.AI產品通常從數據中學習，并且可以設計生成比傳統軟件更豐富的數據形式。
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.因為AI的性能難以提前預測，PM需要適應這一點并擁有管理策略。
5. Ongoing learning（持續學習）
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技術快速發展。PM和所有想充分利用該技術的人一樣，需要跟上最新技術進步、產品理念及其如何融入用戶生活。
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產品經理需要知道如何確保AI負責任地實施（例如需要實施防護欄以防止不良結果），并擅長快速收集反饋以保持項目推進。我還越來越期待優秀的產品經理能夠自己構建原型。
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.
對優秀AI產品經理的需求將是巨大的。除了將AI產品管理發展為一門學科外，也許一些工程師最終也會承擔更多產品管理工作。
The variety of valuable things we can build is nearly unlimited. What a great time to build!
我們能構建的有價值事物幾乎無限。這是構建的大好時代！
來源 | DataFunTalk（ID：datafuntalk）
作者 | DataFunTalk ; 編輯 | 蝦餃
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