机遇、持久优势与开源软件包职业生涯中的生成式人工智能时代
Chance, Persistent Advantage, and the Generative-AI Era in Open-Source Package Careers
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中文总结 AI 辅助
本研究基于GitHub数据验证了开源软件职业生涯中成功时机随机性与个人持久优势并存，且生成式AI的出现未显著改变这一模式。
中文摘要 AI 辅助
对科学、电影、音乐和书籍领域职业生涯的研究报告了一个共同模式。一个人最成功作品出现的时间，接近于对其所创作作品的一次随机抽取。相比之下，其成功作品的规模大小则遵循一个稳定的、因人而异的因素。我们检验了这一模式是否适用于开源软件职业生涯，以及当生成式人工智能编码工具出现时，这一模式是否发生了变化。基于GitHub推送事件的完整公开记录（2015-2025年），我们重建了615万贡献者的1.022亿个职业作品，对于其中90.8万名其仓库发布了软件包的贡献者，我们通过有多少下游软件包依赖于每个作品来衡量其影响力。首先，我们发现职业生涯中最重大成功的时机接近于对其作品的一次抽签，正如在科学和艺术领域一样，且存在一个微小、可复现的向职业生涯早期倾斜的趋势，该趋势随职业生涯变长而增强。其次，一些编码者确实能比其他人更稳定地产生高影响力的作品，但这种持久的个人因素仅解释了影响力持续存在的一部分原因（在我们的主要设定中约占五分之一）；其余部分表现得像动量，即成功在一段时间内自我强化。第三，在相同的贡献者中，这种结构在ChatGPT发布后并未发生变化。稳定因素的权重增长幅度与一个仅因年龄增长而变化的早期队列的增长幅度大致相同，将年龄效应从生成式人工智能效应中减去后，变化量为+0.03（95%置信区间[-0.22, +0.23]），与零无法区分。因此，在科学和艺术领域记录的成功模式也适用于开源职业生涯，并且在该模式在生成式人工智能到来之际未显示出可检测的断裂。这些结果对于如何解读开放平台上的过往记录以及对于生成式人工智能对建立在其上的职业生涯的预期具有启示意义。
英文摘要
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.
