Researchers examining how GitHub’s Copilot affects distributed work patterns found several key trends in developer behavior. High-performing developers shifted toward core coding and away from project management tasks. This shift stemmed from increased autonomous work (requiring less collaboration) and more exploratory behavior rather than exploitative approaches. The study also revealed that AI’s influence on task distribution had a more pronounced effect on developers with lower skill levels.
This study seeks to shine light on the importance of AI, and in particular generative AI and it’s consequences on distributed work. Going beyond the first-level understanding of whether or not it increases productivity, we dig deeper to understand how it changes the nature of work processes of adopters. We find that top developers of open source software are engaging more in their core work of coding and are engaging less in their non-core work of project management. Both of these main effects are driven by two underlying mechanisms — an increase in autonomous behavior (and a related decrease in collaborative behavior) and an increase in exploration behavior (and a related decrease in exploitation behavior). In particular, the reduction of the need to collaborate with other humans, leads to humans circumventing collaborative frictions and transaction costs that would otherwise occur during their work. We further find that the programming generative AI Copilot shifts the task allocation of developers with lower ability more than those with higher ability.
In highly specialized trading systems development, current LLMs fall short of practical utility. Without training data from modern financial trading architectures—which companies closely guard as proprietary—these LLMs cannot provide effective assistance. My testing has shown no current LLM can correctly implement complex systems like multiprocess Aeron-based architectures.