- Over half of engineering teams now consistently use AI coding tools
- Top adopters report double pull request throughput compared to low adopters
- Autonomous agents now handle an increasing share of routine coding tasks
The integration of AI tools into software engineering has shifted from experimental to operational, with more than half of engineering teams now consistently relying on AI, new research has claimed.
A report from Jellyfish claims that nearly two-thirds (64%) of companies generate the majority of their code with AI assistance, showing a clear increase in adoption across the industry.
If current trends continue without interruption, this share could reach 90% within a single year.
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AI adoption drives productivity gains
The incentive for this shift appears to be associated with measurable productivity gains rather than improvements in code quality.
“AI coding tools are now the default setting for engineering teams, and the productivity gains are real,” said Nicholas Arcolano, Ph.D., director of research at Jellyfish.
This trajectory suggests that AI is no longer an ancillary tool, but rather the primary driver of software development for organizations that choose to adopt it aggressively.
Although AI does not automatically improve code maintenance, the volume gains alone have made it the default tool for many teams.
Top-performing companies in AI-driven sectors have seen significant increases in output, and companies adopting AI most aggressively report twice the pull request throughput compared to low adopters over three months.
In practical terms, these teams produce and ship code at a pace that leaves the competition behind.
A rapidly growing trend within this adoption is the use of autonomous agents, which generate pull requests entirely without human intervention – although these agents currently represent a small portion of overall code production, their presence is expanding rapidly.
In the 90th percentile of enterprises, contribution from autonomous agents increased from 10% of pull requests in January 2026 to 14% in February.
This indicates that AI-powered automation is not only supplementing human developers, but is gradually taking over a larger portion of routine coding tasks.
Despite these productivity gains, AI adoption does not guarantee fewer bugs or improved code quality, so the focus of organizations has shifted towards monitoring operational output rather than assuming that faster production equals better code.
For top engineering teams, AI’s value lies in its ability to accelerate development cycles and increase throughput.
As AI coding tools become standard in engineering workflows, top teams complete tasks faster and autonomous agents take on an increasing share of pull requests.
This shift is impacting how engineering teams plan, execute and scale their work, and no team wants to be left behind for not following the trend.
For managers, the focus is on integrating AI strategically to maintain high throughput, streamline operations and maintain a competitive edge.
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