Open source software hits show chance and lasting skill unchanged by generative AI
Chance, Persistent Advantage, and the Generative-AI Era in Open-Source Package Careers
Social and Information NetworksComputers and Society
Summary
The paper looks at how open-source software developers find success across their careers. It finds that when a developer’s biggest success happens is mostly random, similar to patterns seen in science and the arts. Some developers consistently create more impactful work, but that personal advantage only partly explains success, with a momentum effect also playing a role. The arrival of tools like ChatGPT did not change these success patterns in open-source development careers. The authors suggest that generative AI has not disrupted how long-term success forms in this field.
What this means in practice
- •For open source project managers: Adjust evaluation of contributor impact by considering randomness and momentum in success timings discovered in this study.
- •For ai tool developers: Design AI coding assistants knowing they do not fundamentally change longstanding patterns of developer career success.
Authors
Hazem Ibrahim, Yasir Zaki
Abstract
Studies of careers in science, film, music, and books report a common pattern. When a person's most successful work arrives is close to a random draw over the works they produce. How large their successes tend to be, in contrast, follows a stable, person-specific factor. We test whether this pattern holds for open-source software careers and whether it changed when generative AI coding tools arrived. From the complete public record of GitHub push events (2015-2025), we reconstruct 102.2M career works by 6.15M contributors, and for the 908k contributors whose repositories publish packages, we measure each work's impact by how many downstream packages come to depend on it. First, we find that the timing of a career's biggest hit is close to a lottery over their works, as in science and the arts, with a small, replicable lean toward early career that grows as careers get longer. Second, some coders reliably produce higher-impact work than others, but this lasting personal factor accounts for only part of why impact persists (about a fifth in our primary specification); the rest behaves like momentum, success feeding on itself for a period of time. Third, within the same contributors, this structure did not change after ChatGPT's release. The stable factor's weight grew by about as much as it grew for an earlier cohort that simply aged, and subtracting the effect of aging from the effect of generative AI puts the shift at +0.03 (95% CI [-0.22, +0.23]), indistinguishable from zero. The success pattern documented in science and the arts therefore describes open-source careers too, and it shows no detectable break across the arrival of generative AI. These results have implications for how track records on open platforms should be read and on what to expect from generative AI for the careers built on them.