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

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Why AI hasn’t replaced software engineers, and won’t<br>Coding agents as normal technology

Arvind Narayanan and Sayash Kapoor<br>Jun 11, 2026

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

Consider three stories that made the headlines and how they contrasted with reality:<br>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.

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).

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.

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:<br>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.

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.”

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...