AI为何未取代软件工程师——且不会
Why AI hasn’t replaced software engineers, and won’t
Arvind Narayanan与Sayash Kapoor反驳“AI能力达阈值即会导致大规模裁员”的说法。2025年3月，纽约州成为美国首个在WARN法案申报中增设AI披露复选框的州，超160家公司提交通知，无一家勾选AI。他们认为软件工程瓶颈并非编码速度，而是决定构建什么、验证并交付责任、以及深度理解代码库/业务/环境。AI可辅助前两步，但深度理解仍是人类工程师不可替代的核心价值。
Simon Willison’s Weblog
14th June 2026 - Link Blog
Why AI hasn’t replaced software engineers, and won’t. Arvind Narayanan and Sayash Kappor take on the question of AI job losses through the lens of a profession that is uniquely suited to AI disruption - software engineering.
In this essay, we argue that there is enough evidence to reject the narrative that once AI capabilities reach a certain threshold, it will cause mass layoffs. Given that this is true even in a sector with very few regulatory barriers, most other professions are likely to be even more cushioned.
The first good news is that the data still doesn't support the idea that AI is causing mass unemployment.
In March 2025, New York became the first U.S. state to add an AI disclosure checkbox to WARN Act filings. In the full first year, more than 160 companies filed WARN notices. Not a single one checked the AI box
AI speeds up the typing-code-into-a-computer phase, but it turns out software engineering is about a whole lot more than that:
If writing code isn’t the bottleneck, what is? The task-breakdown surveys point at things like meetings or debugging. This just leads to more questions: what are developers doing in those meetings and why can’t it be done by AI? Won’t debugging get automated as capabilities improve? To understand the real bottlenecks, we have to get qualitative, and dig into software engineers’ own understanding of what it is they do that resists automation.
When we did this analysis, it revealed three things as the real bottlenecks (1) deciding and specifying what to build, (2) verifying and being accountable for what is delivered, and (3) the deep human understanding — of the codebase, the business, and the environment — required to carry out both of these.
I'm finding AI assistance also helps me with the deciding and verifying steps, but it's the "deep human understanding" that remains key to the value I provide. Give me all of the AI assistance in the world and the value I produce will still be reliant on how deeply I understand both the problems and the solutions that the agents are building for them.
14th June 2026
来源：Simon Willison 博客 · simonwillison.net