- More than half of engineering teams now routinely utilize AI coding tools.
- Leading adopters report double the pull request throughput compared to those who adopt less.
- Autonomous agents are increasingly managing routine coding tasks independently.
The use of AI tools in software engineering has evolved from a trial phase to a standard practice, with over half of engineering teams now consistently employing these technologies, according to recent research.
A report from Jellyfish indicates that nearly two-thirds (64%) of companies create a significant portion of their code with the aid of AI, highlighting a marked increase in adoption across the sector.
Should current trends persist, this figure could soar to 90% within just a year.
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AI Adoption Fuels Productivity Boosts
The motivation behind this transition seems primarily tied to observable productivity improvements, rather than enhancements in code quality.
“AI coding tools have become the standard for engineering teams, and the resultant productivity gains are substantial,” stated Nicholas Arcolano, Ph.D., head of research at Jellyfish.
This trend signifies that AI is no longer merely an auxiliary tool; it has become the core driver of software development for organizations adopting it extensively.
While AI does not inherently enhance code maintainability, the sheer volume of output has made it the go-to tool for many teams.
High-performing companies within AI-centric fields have reported remarkable increases in output, with the most aggressive adopters seeing double the pull request throughput compared to lower adopters over a period of three months.
In practical terms, these teams are producing and deploying code at a speed that outpaces their competitors.
A rapidly emerging trend within this arena is the utilization of autonomous agents, which now generate pull requests without any human involvement—though these agents currently account for a smaller share of total code production, their influence is rapidly expanding.
Among the top 10% of companies, contributions from autonomous agents grew from 10% of pull requests in January 2026 to 14% in February.
This trend shows that AI-driven automation is not only supplementing human developers, but also gradually assuming a greater portion of routine coding responsibilities.
Despite these productivity gains, adopting AI doesn’t necessarily lead to a reduction in errors or an enhancement in code quality. Consequently, organizations’ focus has shifted towards monitoring operational output rather than mistakenly assuming that increased speed translates to superior code.
For elite engineering teams, the real advantage of AI lies in its capacity to quicken development cycles and boost throughput.
As AI coding tools become integral to engineering workflows, leading teams are completing tasks more swiftly, while autonomous agents are taking on a larger share of pull requests.
This transformation influences how engineering teams strategize, implement, and scale their operations, with no team wanting to be left behind in this new landscape.
For leaders in the field, the objective is to integrate AI strategically to maintain high production rates, streamline processes, and uphold a competitive advantage.
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