AI가 소프트웨어 엔지니어를 대체하지 않는 이유와 미래

Why AI hasn’t replaced software engineers, and won’t Coding agents as normal technology Arvind Narayanan and Sayash Kapoor Jun 11, 2026 84 6 19 Share 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. 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. 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. 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. 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: 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: 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 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. 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. 1 We reached out to the NY Department of Labor who confirmed that as of late May, only one company, Nespresso, checked the box. 2 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. 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 increased separations ”. Firing existing workers results in the loss of precisely the tacit knowledge and organizational capital that allows workers to operate AI effectively. Besides, it is expensive in terms of severance, damage to morale, and rehiring risk . Given these costs, it is largely unnecessary given that natural turnover achieves the same result in a few years. So what does the data tell us when we look beyond layoffs to overall employment trends? An important paper from Federal Reserve economists compiles the evidence in the U.S. context. Employment is still growing , but they find that it is growing slower post-ChatGPT compared to a no-AI counterfactual, by about 3 percentage points per year. One important limitation of this study is that the methodology can’t capture self-employment, so it is possible that some of the slowdown in growth is being absorbed by entrepreneurship instead. We do have evidence from other studies that AI makes entrepreneurship easier. So the real picture is probably even healthier than the Federal Reserve study suggests. 3 Finally, it is worth acknowledging two kinds of indirectly-AI-driven job losses in software engineering that are real, but different from AI replacing software engineers. First, AI sometimes decimates demand for the product, in cases like Chegg (homework help) or Stack Overflow (technical help), both of which have laid off workers. AI doesn’t directly do the job that these workers did, but rather obviates the need for it. The historical parallel is strong: Among the 270 jobs in the 1950 U.S. census, only one job was automated away — elevator operator . But many others were rendered obsolete by new technology, like the job of telegraph operator. Another credible AI-driven layoffs story is among companies that sell AI, rather than buy it. So when companies like IBM or SAP announce layoffs because of AI, a more accurate framing is “we reallocated headcount from legacy functions to our f