Large language model (LLM) based coding agents increasingly act as autonomous contributors that generate and merge pull requests, yet their real-world effects on software projects are unclear—especially compared with widely adopted IDE-based AI assistants. We present a longitudinal causal study of agent adoption in open-source repositories using staggered difference-in-differences with matched controls. Using the AIDev dataset, we define adoption as the first agent-generated pull request and analyze monthly repository-level outcomes spanning development velocity (commits, lines added) and software quality (static-analysis warnings, cognitive complexity, duplication, and comment density). Results show large, front-loaded velocity gains only when agents are the first observable AI tool in a project; repositories with prior AI IDE usage experience minimal or short-lived throughput increases. In contrast, quality risks are persistent across settings, with static-analysis warnings and cognitive complexity rising by roughly 18% and 39%, indicating sustained agent-induced technical debt even when velocity advantages fade. These heterogeneous effects suggest diminishing returns to AI assistance and highlight the need for quality safeguards, provenance tracking, and selective deployment of autonomous agents. Our findings establish an empirical basis for understanding how agentic and IDE-based tools interact, and motivate research on balancing acceleration with maintainability in AI-integrated development workflows. The replication package for this study is publicly available at https://github.com/shyamagarwal13/agentic-coding-impact.

Large language models (LLMs) have demonstrated the promise to revolutionize the field of software engineering. Among other things, LLM agents are rapidly gaining momentum in software development, with practitioners reporting a multifold increase in productivity after adoption. Yet, empirical evidence is lacking around these claims. In this paper, we estimate the causal effect of adopting a widely popular LLM agent assistant, namely Cursor, on development velocity and software quality. The estimation is enabled by a state-of-the-art difference-in-differences design comparing Cursor-adopting GitHub projects with a matched control group of similar GitHub projects that do not use Cursor. We find that the adoption of Cursor leads to a statistically significant, large, but transient increase in project-level development velocity, along with a substantial and persistent increase in static analysis warnings and code complexity. Further panel generalized-method-of-moments estimation reveals that increases in static analysis warnings and code complexity are major factors driving long-term velocity slowdown. Our study identifies quality assurance as a major bottleneck for early Cursor adopters and calls for it to be a first-class citizen in the design of agentic AI coding tools and AI-driven workflows.

The proliferation of Automatic Speech Recognition (ASR) systems has revolutionized translation and transcription. However, challenges persist in ensuring inclusive communication for non-native English speakers. This study quantifies the gap between accented and native English speech using Wav2Vec 2.0, a state-of-the-art transformer model. Notably, we found that accented speech exhibits significantly higher word error rates of 30-50%, in contrast to native speakers’ 2-8% (Baevski et al. 2020). Our exploration extends to leveraging accessible online datasets to highlight the potential of enhancing speech recognition by fine-tuning the Wav2Vec 2.0 model. Through experimentation and analysis, we highlight the challenges with training models on accented speech. By refining models and addressing data quality issues, our work presents a pipeline for future investigations aimed at developing an integrated system capable of effectively engaging with a broader range of individuals with diverse backgrounds. Accurate recognition of accented speech is a pivotal step toward democratizing AI-driven communication products.