Why AI hasn’t replaced software engineers, and won’t

为什么人工智能没有取代软件工程师，未来也不会

Coding agents as normal technology 作为常规技术的编程智能体

Arvind Narayanan and Sayash Kapoor | Jun 11, 2026 Arvind Narayanan 和 Sayash Kapoor | 2026年6月11日

There is great anxiety and uncertainty about AI replacing jobs. How can we move past vague warnings and bombastic predictions and bring data to bear on this question? One good way is to look at the profession where AI capabilities are furthest along and adoption has been exceptionally rapid: software engineering. 人们对于人工智能取代工作岗位感到极大的焦虑和不安。我们该如何超越那些模糊的警告和夸张的预测，用数据来审视这个问题呢？一个很好的切入点是观察那个 AI 能力发展最成熟、应用最迅速的行业：软件工程。

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. 在这篇文章中，我们认为已有足够的证据反驳这样一种论调：即一旦 AI 能力达到某个阈值，就会引发大规模裁员。考虑到即使在监管壁垒极低的软件行业也是如此，大多数其他职业受到的冲击很可能更小。

We also have a good understanding of why this is the case. We can think of many kinds of knowledge work, including software development, as a “decide-execute-deliver sandwich”. AI compresses the “execute” layer — the middle of the sandwich — but the other two layers resist automation in a way that will not be overcome by capability improvements alone. 我们也清楚其中的原因。我们可以将包括软件开发在内的许多知识工作视为一个“决策-执行-交付”的三明治结构。AI 压缩了中间的“执行”层，但另外两层具有抗自动化特性，仅靠能力的提升是无法克服的。

We conclude on a note of cautious optimism about the future trajectory of demand for software engineering. This essay is the first in a series, and the next one will look at reasons why individual software engineers’ careers might be rocky even if overall demand is healthy. The series is based on the published literature in economics and software engineering, our own evaluations and observations of AI agents, and many software engineers’ reflection on the present and future of AI impacts on their profession, gleaned both from published writings and our interactions with the community. 我们对软件工程需求的未来轨迹持谨慎乐观的态度。本文是系列文章的第一篇，下一篇将探讨为何即便整体需求健康，个别软件工程师的职业生涯仍可能面临动荡。本系列基于经济学和软件工程领域的已发表文献、我们对 AI 智能体的评估与观察，以及许多软件工程师对 AI 影响其职业现状与未来的反思——这些反思来自已发表的文章以及我们与社区的互动。

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The stories of AI-driven mass layoffs in software seem to be classic “AI washing”

软件行业“AI 驱动大规模裁员”的故事似乎是典型的“AI 洗白”

Consider three stories that made the headlines and how they contrasted with reality: 让我们看看三个登上头条的故事，以及它们与现实的对比：

In February, fintech company Block (maker of Cash App, Square, Afterpay, and other such apps) announced layoffs of 4,000 employees because, according to founder Jack Dorsey, AI is “enabling a new way of working” with “smaller and flatter teams”, specifically citing late-2025 improvements in model capabilities. But subsequent reporting revealed a radically different picture. After growing headcount more than threefold during the pandemic, the company was under massive financial pressure. A data scientist on the Cash App team, Naoko Takeda posted that Block “shoved AI down everyone’s throats” yet she saw “very limited gains in productivity.” She refused a 75% retention raise and quit. Other employees interviewed had a sharply different understanding of what AI was capable of at Block and whether Dorsey had a competent understanding of the issues. As Aaron Levie has pointed out, CEOs are uniquely prone to delusions about AI’s usefulness because they can build quick prototypes but can’t see the 90% of work it takes to turn it into a finished product. Dorsey’s public statements about AI seem to fit exactly this pattern. 2月，金融科技公司 Block（Cash App、Square、Afterpay 等应用的开发商）宣布裁员 4000 人。创始人 Jack Dorsey 声称，AI 正在通过“更小、更扁平的团队”实现“一种新的工作方式”，并特别提到了 2025 年底模型能力的提升。但随后的报道揭示了截然不同的情况：在疫情期间员工人数增长了两倍多之后，该公司正面临巨大的财务压力。Cash App 团队的数据科学家 Naoko Takeda 发文称，Block “强行向所有人推销 AI”，但她看到的“生产力提升非常有限”。她拒绝了 75% 的留任加薪并选择了辞职。其他受访员工对 Block 的 AI 能力以及 Dorsey 是否对这些问题有充分理解，有着截然不同的看法。正如 Aaron Levie 所指出的，CEO 们特别容易对 AI 的效用产生错觉，因为他们能看到快速构建的原型，却看不到将其转化为成品所需的 90% 的工作量。Dorsey 关于 AI 的公开言论似乎完全符合这一模式。

In April, Snap laid off about 1,000 people, with CEO Evan Spiegel primarily citing AI as the reason in his layoff memo. He also said that AI generated 65% of new code. In reality, the layoffs followed a campaign by an activist investor demanding cost cuts. (Snap has posted a net loss every full year since its 2017 IPO and shares were down over 30% in 2026). Tellingly, the nature of the cuts, such as 150 jobs spanning various roles in the augmented reality division, don’t correlate with the cuts we would expect to see if they were driven by AI (i.e. programming and other “AI-exposed” jobs across the board, not concentrated in any unit). 4月，Snap 裁员约 1000 人，CEO Evan Spiegel 在裁员备忘录中主要将 AI 列为原因，并称 AI 生成了 65% 的新代码。实际上，此次裁员是在一位激进投资者要求削减成本的压力下进行的。（自 2017 年 IPO 以来，Snap 每年都出现净亏损，且 2026 年股价下跌超过 30%）。耐人寻味的是，裁员的性质——例如增强现实部门中跨越各种职能的 150 个岗位——与我们预期中由 AI 驱动的裁员（即全面削减编程及其他“受 AI 影响”的岗位，而非集中在某个部门）并不吻合。

In May, Intuit announced 3,000 cuts, alongside deals with Anthropic and OpenAI. The press connected the two, framing the layoffs as AI-driven restructuring. For once, the CEO actually pushed back on this easy narrative, saying that “none of it had to do with AI” and that the cuts targeted “coordination-heavy roles” and too many management layers. 5月，Intuit 宣布裁员 3000 人，同时宣布与 Anthropic 和 OpenAI 达成合作。媒体将两者联系起来，将裁员描述为 AI 驱动的重组。难得的是，CEO 实际上反驳了这种简单的叙事，称“裁员与 AI 无关”，裁员针对的是“协调密集型岗位”和过多的管理层级。

We did not cherry-pick these examples. In every story about AI-driven software engineering layoffs that we examined, the same narrative violation emerged. It turns out that “AI washing” of job cuts is an economy-wide phenomenon, evidenced by many surveys: 我们并非刻意挑选这些例子。在我们调查的每一个关于 AI 驱动软件工程裁员的故事中，都出现了同样的叙事违和感。事实证明，裁员的“AI 洗白”是一种全经济范围的现象，许多调查都证明了这一点：

59% of U.S. hiring managers admitted they emphasize AI when explaining hiring freezes or layoffs because it plays better with stakeholders than citing financial constraints. 59% 的美国招聘经理承认，在解释招聘冻结或裁员时，他们会强调 AI，因为这比引用财务限制更能让利益相关者接受。

Forrester principal analyst J. P. Gownder says of companies preparing supposedly AI-driven layoffs: “When we ask if they have a mature, vetted AI app ready to fill in those jobs, nine out of 10 times, the answer is no—and they haven’t even started.” Forrester 首席分析师 J. P. Gownder 在谈到那些准备进行所谓“AI 驱动裁员”的公司时表示：“当我们询问他们是否有成熟、经过验证的 AI 应用来填补这些岗位时，十分之九的回答是否定的——他们甚至还没开始。”

In a HBR survey of over 1,000 global executives, 21% had made large headcount reductions “in anticipation of” AI, with another 39% having made low or moderate anticipatory headcount reductions. In contrast, only 2% had already made large reductions in headcount related to actual AI implementation. The 10x gap suggests that executives, like everyone else, are highly prone to succumbing to the misleading narratives about AI replacing jobs. 在《哈佛商业评论》（HBR）对全球 1000 多名高管的调查中，21% 的人“为了迎接”AI 而进行了大规模裁员，另有 39% 的人进行了小规模或中等规模的预期性裁员。相比之下，只有 2% 的人是因为实际的 AI 部署而进行了大规模裁员。这 10 倍的差距表明，高管们和其他人一样，极易屈服于关于 AI 取代工作的误导性叙事。

Another interesting data point comes from the WARN Act, which requires certain disclosures of plant closings and mass layoffs affecting over 100 workers. 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. We reached out to the NY Department of Labor who confirmed that as of late May, only one company, Nespresso, checked the box. If these filings are accurate, only 46 out of about 25,000 laid off workers in New York State in the relevant period, or about two-tenths of a percent, were affected by AI. 另一个有趣的数据点来自《工人调整和再培训通知法》（WARN Act），该法案要求对涉及 100 名以上工人的工厂关闭和大规模裁员进行披露。2025 年 3 月，纽约州成为美国第一个在 WARN 法案备案中增加“AI 披露”复选框的州。在完整的第一年里，有 160 多家公司提交了 WARN 通知，但没有一家勾选 AI 选项。我们联系了纽约州劳工部，他们确认截至 5 月底，只有一家公司（Nespresso）勾选了该选项。如果这些备案准确，那么在相关时期纽约州约 2.5 万名被裁员工中，只有 46 人（约 0.2%）是受 AI 影响的。

Even more damning for the AI-driven-mass-layoffs narrative: layoffs are the wrong signal of AI’s potential productivity benefits in the first place! The research is clear that the effect operates through “slower hiring rather than i… 对于“AI 驱动大规模裁员”的叙事来说，更具毁灭性的一点是：裁员本身就不是衡量 AI 潜在生产力收益的正确信号！研究清楚地表明，其影响是通过“放缓招聘而非……”（原文截断）