The promise of artificial intelligence transforming software development is not merely hype; it is a measurable reality, albeit one with nuanced dependencies. Nicholas Arcolano, Head of Research at Jellyfish, presented compelling data insights from an extensive dataset comprising 20 million pull requests from 200,000 developers across 1,000 companies, spanning from June 2024 to the present. His analysis reveals that while AI coding tools are rapidly integrating into workflows, the extent of productivity gains is profoundly shaped by an often-overlooked factor: the underlying code architecture.
Across the software industry, the adoption of AI coding tools has surged dramatically. Starting from a modest 2% in June 2024, the proportion of companies generating 50% or more of their code with AI has ballooned to nearly half by October 2025. This rapid embrace extends to individual developers, with median AI adoption rates skyrocketing from 22% to approximately 90% within the same timeframe, indicating a widespread shift in coding practices.
However, not all AI tools are created equal in their current impact. Arcolano highlighted that fully autonomous coding agents remain in their infancy. "Only 44% of companies have used a fully autonomous agent in the past 3 months," he stated, emphasizing that this usage is predominantly for trialing and experimentation, not full-scale production, accounting for less than 0.2% of all pull requests. The real, immediate gains are coming from interactive AI coding tools.
These interactive tools are delivering substantial productivity enhancements. Companies leveraging AI are experiencing a remarkable 2x increase in pull request (PR) throughput and a 24% reduction in cycle times. Arcolano noted, "The average trend here is about a 2x change as you go from zero to full adoption," confirming that doubling PR output is a realistic expectation for widespread AI integration. This acceleration means more code is being shipped faster, a critical metric for any agile development team.
Beyond speed, the nature of the code itself is evolving. Data shows an 18% increase in the average size of pull requests, specifically in net lines of code added, rather than merely reworked or deleted lines. Interestingly, this surge in output has not, to date, demonstrably compromised code quality. Jellyfish’s research found no statistically significant relationship between the rate of AI adoption and an increase in bug tickets created or PR reverts. In fact, teams are increasingly using AI to tackle and resolve existing bug backlogs, suggesting a positive, if indirect, impact on code health.