01 太長(zhǎng)不看版- 核心觀點(diǎn)提煉

核心論斷：AI產(chǎn)品經(jīng)理的黃金時(shí)代來(lái)臨

1?? 經(jīng)濟(jì)學(xué)互補(bǔ)品原理：編程成本↓ → 產(chǎn)品規(guī)劃需求↑

● 就像汽車(chē)變便宜導(dǎo)致汽油需求增加，AI讓編碼成本驟降10倍，將引發(fā)"決定做什么"的人才需求激增

● 預(yù)測(cè)：工程師與PM比例將從傳統(tǒng)的6:1向更平衡方向轉(zhuǎn)變

2?? AI產(chǎn)品經(jīng)理的五大核心能力（區(qū)別于傳統(tǒng)PM）

● AI技術(shù)理解：懂得技術(shù)可行性邊界，理解AI項(xiàng)目生命周期（數(shù)據(jù)→訓(xùn)練→監(jiān)控→維護(hù)）

● 迭代開(kāi)發(fā)管理：AI開(kāi)發(fā)需要更頻繁的方向修正

● 數(shù)據(jù)能力：AI產(chǎn)品從數(shù)據(jù)中學(xué)習(xí)，能生成更豐富的數(shù)據(jù)形式

● 模糊性管理：AI性能難以預(yù)測(cè)，需要應(yīng)對(duì)不確定性的策略

● 持續(xù)學(xué)習(xí)：技術(shù)快速演進(jìn)，必須保持知識(shí)更新

3?? 當(dāng)前瓶頸與機(jī)會(huì)

● 供需失衡：工程師因技術(shù)背景更快擁抱AI，但懂AI的PM奇缺，缺口將持續(xù)擴(kuò)大

● 未來(lái)要求：AI PM需掌握負(fù)責(zé)任AI實(shí)踐（如防護(hù)欄）、快速收集用戶反饋、甚至能自己構(gòu)建原型

4?? 行業(yè)趨勢(shì)預(yù)測(cè)

● 可構(gòu)建的有價(jià)值產(chǎn)品幾乎無(wú)限，軟件團(tuán)隊(duì)組成將發(fā)生結(jié)構(gòu)性變化

● 部分工程師可能承擔(dān)更多產(chǎn)品管理工作

● DeepSeek-V3案例印證：500萬(wàn)美元就能訓(xùn)練GPT-4o級(jí)模型，MoE架構(gòu)訓(xùn)練成本比傳統(tǒng)模型低5-10倍

02原文內(nèi)容（中英對(duì)照）

Andrew Ng的來(lái)信

Dear friends,

親愛(ài)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(xiě)軟件（尤其是原型）的成本正在降低。這將導(dǎo)致對(duì)"能決定構(gòu)建什么"的人才需求增加。AI產(chǎn)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(kāi)發(fā)通常由團(tuán)隊(duì)完成，包括產(chǎn)品經(jīng)理（PM）——決定構(gòu)建什么（如為哪些用戶實(shí)現(xiàn)哪些功能），以及軟件開(kāi)發(fā)人員——編寫(xiě)代碼來(lái)構(gòu)建產(chǎn)品。經(jīng)濟(jì)學(xué)表明，當(dāng)兩種商品互為補(bǔ)充品時(shí)（比如汽車(chē)和汽油），一種商品價(jià)格下降會(huì)導(dǎo)致另一種商品需求上升。例如，隨著汽車(chē)變得更便宜，更多人購(gòu)買(mǎi)汽車(chē)，從而增加了汽油需求。軟件領(lǐng)域也會(huì)發(fā)生類(lèi)似的事情。在有明確構(gòu)建規(guī)格的前提下，AI正讓構(gòu)建本身變得更快、更便宜。這將顯著增加對(duì)"能提出清晰且有價(jià)值構(gòu)建規(guī)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(duì)產(chǎn)品管理（開(kāi)發(fā)和管理軟件產(chǎn)品的學(xué)科）的未來(lái)感到興奮。我尤其對(duì)AI產(chǎn)品管理（開(kāi)發(fā)和管理AI軟件產(chǎn)品的學(xué)科）的未來(lái)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(yè)差異很大，4:1到10:1都很常見(jiàn)）。隨著編碼效率提升，我認(rèn)為團(tuán)隊(duì)將需要更多產(chǎn)品管理工作（以及設(shè)計(jì)工作）占總勞動(dòng)力的比例。也許工程師會(huì)介入做一些這類(lèi)工作，但如果它仍然是專(zhuān)業(yè)產(chǎn)品經(jīng)理的職責(zé)范圍，那么對(duì)這些角色的需求將會(huì)增長(zhǎng)。

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(kāi)發(fā)團(tuán)隊(duì)組成的這種變化尚未全速推進(jìn)。阻礙這一轉(zhuǎn)變的一個(gè)主要因素是，軟件工程師因具備技術(shù)背景，比產(chǎn)品經(jīng)理更快地理解和接受AI。即使在今天，大多數(shù)公司也很難找到既懂產(chǎn)品開(kāi)發(fā)又理解AI的人才，我預(yù)計(jì)這種短缺會(huì)加劇。

AI產(chǎn)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(chǎn)品管理需要與傳統(tǒng)軟件產(chǎn)品管理不同的技能組合。它需要：

1. Technical proficiency in AI（AI技術(shù)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(chǎn)品在技術(shù)上可行。他們還需要了解AI項(xiàng)目的生命周期，如數(shù)據(jù)收集、構(gòu)建、監(jiān)控和AI模型維護(hù)。

2. Iterative development（迭代開(kāi)發(fā)）

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?yàn)锳I開(kāi)發(fā)比傳統(tǒng)軟件迭代性更強(qiáng)，需要更多中途調(diào)整，PM需要懂得如何管理這樣的過(guò)程。

3. Data proficiency（數(shù)據(jù)精通）

AI products often learn from data, and they can be designed to generate richer forms of data than traditional software.AI產(chǎn)品通常從數(shù)據(jù)中學(xué)習(xí)，并且可以設(shè)計(jì)生成比傳統(tǒng)軟件更豐富的數(shù)據(jù)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?yàn)锳I的性能難以提前預(yù)測(cè)，PM需要適應(yīng)這一點(diǎn)并擁有管理策略。

5. Ongoing learning（持續(xù)學(xué)習(xí)）

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(shù)快速發(fā)展。PM和所有想充分利用該技術(shù)的人一樣，需要跟上最新技術(shù)進(jìn)步、產(chǎn)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(chǎn)品經(jīng)理需要知道如何確保AI負(fù)責(zé)任地實(shí)施（例如需要實(shí)施防護(hù)欄以防止不良結(jié)果），并擅長(zhǎng)快速收集反饋以保持項(xiàng)目推進(jìn)。我還越來(lái)越期待優(yōu)秀的產(chǎn)品經(jīng)理能夠自己構(gòu)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(duì)優(yōu)秀AI產(chǎn)品經(jīng)理的需求將是巨大的。除了將AI產(chǎn)品管理發(fā)展為一門(mén)學(xué)科外，也許一些工程師最終也會(huì)承擔(dān)更多產(chǎn)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(gòu)建的有價(jià)值事物幾乎無(wú)限。這是構(gòu)建的大好時(shí)代！

來(lái)源 | DataFunTalk（ID：datafuntalk）

作者 | DataFunTalk ; 編輯 | 蝦餃

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