2026-06-14原文2 min readUpdated: 2026-06-15

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

Arvind Narayanan and Sayash Kappor argue that AI will not cause mass unemployment, even in software engineering, citing NY WARN Act data and the real bottlenecks of the profession: deciding what to build, verifying deliveries, and deep human understanding.

SourceSimon Willison's Weblog

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.