Why developers using AI are working longer hours

AI boosts worker productivity — but does that translate to final outputs? | MIT Sloan

A new study found that developers using AI tools can write much more code than those working without AI, but they don’t release as much new software.

Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

We conduct a randomized controlled trial to understand how early-2025 AI tools affect the productivity of experienced open-source developers working on their own repositories. Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower.

Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools

How do the productivity effects of AI evolve across successive generations of tools, and to what extent do task-level gains ultimately translate into final output? We study these questions in the context of software development, using data on more than 100,000 GitHub developers combined with their AI usage telemetry. In a matched event study design, we find that autocomplete, interactive coding agents, and autonomous coding agents each significantly increase coding activity ("commits"), with respective cumulative effects of 40%, 140%, and 180%. These gains, however, attenuate sharply across the production hierarchy: the 180% cumulative effect falls to 50% for the number of projects, and to 30% for actual releases. This pattern is consistent with the weak-link hypothesis: the strong productivity gains from AI are attenuated by human bottlenecks in the production chain, with an estimated elasticity of substitution of 0.25 between AI and human effort, which indicates strong complementarities. We further confirm these results across four major app marketplaces, finding a moderate increase in the number of new apps but no increase in total usage. Large task-level AI productivity gains have therefore translated only partially into shipped and used software thus far.

Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools

How do the productivity effects of AI evolve across successive generations of tools, and to what extent do task-level gains ultimately translate into final outp

Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools

How do the productivity effects of AI evolve across successive generations of tools, and to what extent do task-level gains ultimately translate into final outp

Study finds AI tools made open source software developers 19 percent slower

Coders spent more time prompting and reviewing AI generations than they saved on coding.

Software engineering may no longer be a lifetime career

I don’t think there’s compelling evidence that using AI makes you less intelligent overall1. However, it seems pretty obvious that using AI to perform a task means you don’t learn as much about performing that task. Some software engineers think this is a decisive argument against the use of AI. Their argument goes something like this:

The Real Reason We Still Need Software Developers in the World of AI

The dream of AI churning out perfect production-ready code doesn’t hold up against the reality of modern software development.

Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the February-June 2025 frontier affect the productivity of experienced open-source developers. 16 developers with moderate AI experience complete 246 tasks in mature projects on which they have an average of 5 years of prior experience. Each task is randomly assigned to allow or disallow usage of early 2025 AI tools. When AI tools are allowed, developers primarily use Cursor Pro, a popular code editor, and Claude 3.5/3.7 Sonnet. Before starting tasks, developers forecast that allowing AI will reduce completion time by 24%. After completing the study, developers estimate that allowing AI reduced completion time by 20%. Surprisingly, we find that allowing AI actually increases completion time by 19%--AI tooling slowed developers down. This slowdown also contradicts predictions from experts in economics (39% shorter) and ML (38% shorter). To understand this result, we collect and evaluate evidence for 20 properties of our setting that a priori could contribute to the observed slowdown effect--for example, the size and quality standards of projects, or prior developer experience with AI tooling. Although the influence of experimental artifacts cannot be entirely ruled out, the robustness of the slowdown effect across our analyses suggests it is unlikely to primarily be a function of our experimental design.

What AI coding costs you | Tom Wojcik

What's the effect of the prolonged AI usage among coders and is it tracked correctly, if it all?

The Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study

AI coding assistants have become prolific in recent years. Through a longitudinal mixed-methods investigation, we examined how professional software engineers perceive the effects of AI coding assistants in regard to task focus, developer experience, and productivity. Two questionnaires were administered six months apart, yielding 158 eligible participants at the first time point, 101 at the second, and a matched longitudinal cohort of 95. Participants reported spending less time on most development tasks, with 82% reporting less on writing code. We find broader shift in focus from creation to verification activities. We propose a new category of work we term supervisory engineering work, encompassing the direction, evaluation, and correction of AI output. We also identified a productivity-experience paradox: productivity perceptions held stable, with 84% reporting improvement at both time points, yet among matched participants, the proportion reporting worsened developer experience in at least one dimension nearly doubled from 14% to 27%, with flow state and cognitive load eroding while feedback loops improved. These findings suggest that AI coding assistants are impacting both the nature of software engineering work and how engineers experience it.

AI research speeds up. Monitoring gets harder. - Sensemaker

OpenAI says coding agents now run 3.1 days for every human research day, while its chief scientist warns the lab’s main monitoring method is weakening.

AI Doesn’t Reduce Work—It Intensifies It

One of the promises of AI is that it can reduce workloads so employees can focus more on higher-value and more engaging tasks. But according to new research, AI tools don’t reduce work, they consistently intensify it: In the study, employees worked at a faster pace, took on a broader scope of tasks, and extended work into more hours of the day, often without being asked to do so. That may sound like a win, but it’s not quite so simple. These changes can be unsustainable, leading to workload creep, cognitive fatigue, burnout, and weakened decision-making. The productivity surge enjoyed at the beginning can give way to lower quality work, turnover, and other problems. To correct for this, companies need to adopt an “AI practice,” or a set of norms and standards around AI use that can include intentional pauses, sequencing work, and adding more human grounding.

Developers say AI coding tools work—and that's precisely what worries them

Ars spoke to several software devs about AI and found enthusiasm tempered by unease.

AI coding agents generate more code, but not more software

Study finds coding efficiency gains get "absorbed" by human review "bottleneck."