吴恩达谈软件工程未来：AI不会带来行业末日，反而藏着这些新机会

导读

This reading note summarizes Andrew Ng's perspectives on the future of software engineering amid the AI wave, debunking the myth of mass AI-driven unemployment, analyzing clear industry trends, and listing core open questions waiting to be explored. It helps practitioners in tech fields sort out development directions, adjust career planning, and understand the underlying logic of industry changes.

这篇笔记整理了吴恩达对AI浪潮下软件工程行业未来的判断，既破除了AI导致大规模失业的焦虑，也梳理了已经明确的行业趋势，还列出了有待探索的核心开放问题，能帮助科技领域从业者理清发展方向、调整职业规划，看懂行业变化的底层逻辑。

As AI agents accelerate coding, what is the future of software engineering? Some trends are clear, such as the idea that we are more constrained by deciding what to build rather than the actual building. But many implications, like AI’s impact on the job market, how software teams will be organized, and more, are still being sorted out.

随着AI智能体不断提升编码效率，软件工程的未来走向成了行业热议的话题。现在有一些趋势已经非常明确：比如当下最大的限制不再是“怎么把产品做出来”，而是“到底要做什么”，但还有很多问题尚未有答案，比如AI到底会怎么影响就业市场、未来的软件团队会用什么形式组织等等，整个行业都还在摸索阶段。

It is currently trendy in some technology and policy circles to forecast massive job losses due to AI. Even if they have not yet materialized, these losses certainly must be just over the horizon! I have a contrarian view that the AI jobpocalypse — the notion that AI will lead to massive unemployment, perhaps even rioting in the streets — won’t be nearly as bad as dire forecasts by pundits, especially pundits who are trying to paint a picture of how powerful their AI technology is.

现在科技圈和政策圈很多人都在唱衰，说AI会导致大规模失业，哪怕现在还没看到明显的失业潮，他们也觉得这一天马上就要来了。但我反而不这么认为：所谓的“AI就业末日”，也就是AI导致大量人失业、甚至引发社会动荡的情况，根本不会像那些专家预测的那么夸张，尤其是很多专家这么说，本质是为了吹嘘自己的AI技术有多强大而已。

In software engineering, I see a lot of exciting work ahead to adapt our workflows. It is already clear that: (i) As AI makes coding easier, a lot more people will be doing it. (ii) Writing code by hand and even reading (generated) code is not that important, because we can ask an LLM about the code and operate at a higher level than the raw syntax (although how high we can or should go is rapidly changing). (iii) There will be a lot more custom applications, because now it’s economical to write software for smaller and smaller audiences. (iv) Deciding what to build, more than the actual building, is becoming a bottleneck. (v) The cost of paying down technical debt is decreasing (since AI can help you refactor code).

在软件工程领域，我们已经能看到不少确定的工作流变革方向：第一，AI降低了编码门槛，以后会有越来越多的人能参与开发；第二，手写代码、甚至阅读生成的代码都不再那么重要了，我们可以直接问大语言模型代码的功能，站在比底层语法更高的层面做开发，当然这个层面的上限还在快速提升；第三，未来会出现更多定制化应用，因为现在哪怕是给很小的用户群体做软件，成本也已经很划算；第四，“决定做什么”已经比“怎么实现”更卡脖子；第五，偿还技术债务的成本会下降，毕竟AI可以帮你做代码重构。

At the same time, there are also a lot of open questions for our profession, such as: In the future, what will be the key skills of a senior software engineer? And for junior levels, what should be the new Computer Science curriculum? If everyone can build features, what skills, strategies, or resources create competitive advantage for individuals and for businesses? What are the new building blocks (libraries, SDKs, etc.) of software? How do we organize coding agents to create software? What should a software team look like? For example, how many engineers, product managers, designers, and so on. What tooling do we need to manage their workflow? How do AI agents change the workflow of machine learning engineers and data scientists? For example, how can we use agents to accelerate exploring data, identifying hypotheses, and testing them?

但同时，这个行业还有很多悬而未决的问题：比如未来高级软件工程师的核心技能会是什么？面向新人的计算机科学课程应该怎么调整？如果人人都能做功能开发，个人和企业的竞争优势又要靠什么来建立？未来软件开发的新基础组件（比如库、SDK这类）会是什么样？我们要怎么组织编码智能体来完成开发工作？未来的软件团队架构会变成什么样？工程师、产品经理、设计师的配比会有什么变化？需要什么样的工具来管理工作流？AI智能体会怎么改变机器学习工程师和数据科学家的工作流程？比如我们要怎么用智能体加速数据探索、提出假设、验证假设的过程？这些问题都等着整个行业一起摸索答案。

来源：https://www.deeplearning.ai/the-batch/open-questions-about-the-future-of-software-engineering