- More than half of engineering teams now consistently use AI coding tools
- Top users report double pull request performance compared to lower adoption users
- Autonomous agents now handle an increasing proportion of routine coding tasks.
The integration of AI tools into software engineering has gone from experimental to operational, with more than half of engineering teams now consistently relying on AI, new research claims.
A Jellyfish report states that almost two-thirds (64%) of companies generate the majority of their code with the help of AI, showing a clear increase in adoption across the industry.
If current trends continue unabated, this proportion could reach 90% in a single year.
Article continues below.
AI Adoption Drives Increased Productivity
The incentive for this change appears to be related to measurable productivity gains rather than improvements in code quality.
“AI coding tools are now the default choice for engineering teams and the productivity gains are real,” said Nicholas Arcolano, Ph.D., head of research at Jellyfish.
This trajectory suggests that AI is no longer an auxiliary tool but rather the primary driver of software development for organizations that decide to aggressively adopt it.
While AI doesn’t automatically improve code maintainability, the increase in volume alone has made it the standard tool for many teams.
Top-performing companies in AI-driven sectors have seen significant increases in production, with companies that adopt AI most aggressively reporting double the pull request throughput compared to those adopting it less frequently over three months.
In practical terms, these teams produce and ship code at a pace that leaves their competitors behind.
A rapidly growing trend within this adoption is the use of autonomous agents, which generate pull requests completely without human intervention; Although these agents currently represent a small portion of total code production, their presence is expanding rapidly.
At the 90th percentile of companies, contributions from self-employed agents increased from 10% of pull requests in January 2026 to 14% in February.
This indicates that AI-driven automation is not only complementing human developers but is gradually taking over a greater proportion of routine coding tasks.
Despite these productivity gains, AI adoption does not guarantee fewer bugs or better code quality, so organizations’ focus has shifted toward monitoring operational production rather than assuming that faster production equals better code.
For top engineering teams, the value of AI lies in its ability to accelerate development cycles and increase performance.
As AI coding tools become the default in engineering workflows, better teams complete tasks faster and autonomous agents take on an increasing proportion of pull requests.
This shift impacts how engineering teams plan, execute, and scale their work, and no team wants to be left behind by not following the trend.
For leaders, the focus is on strategically integrating AI to maintain high performance, optimize operations, and maintain a competitive advantage.
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