English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This study examines the impact of GitHub Copilot on a large sample of Copilotusers (n=934,533). The analysis shows that users on average accept nearly 30%of the suggested code, leading to increased productivity. Furthermore, ourresearch demonstrates that the acceptance rate rises over time and isparticularly high among less experienced developers, providing them withsubstantial benefits. Additionally, our estimations indicate that the adoptionof generative AI productivity tools could potentially contribute to a $1.5trillion increase in global GDP by 2030. Moreover, our investigation shedslight on the diverse contributors in the generative AI landscape, includingmajor technology companies, startups, academia, and individual developers. Thefindings suggest that the driving force behind generative AI softwareinnovation lies within the open-source ecosystem, particularly in the UnitedStates. Remarkably, a majority of repositories on GitHub are led by individualdevelopers. As more developers embrace these tools and acquire proficiency inthe art of prompting with generative AI, it becomes evident that this novelapproach to software development has forged a unique inextricable link betweenhumans and artificial intelligence. This symbiotic relationship has thepotential to shape the construction of the world's software for futuregenerations.

Sea Change in Software Development: Economic and Productivity Analysis of the AI-Powered Developer Lifecycle