- More than half of engineering teams now routinely use AI coding tools
- High users report double throughput of pull requests compared to low users.
- Autonomous agents now handle a growing share of routine coding tasks
The integration of AI tools into software engineering has moved from experimental to operational, with more than half of engineering teams now relying on AI systematically, according to new research.
A report from Jellyfish claims 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 uninterrupted, this proportion could reach 90% within a single year.
Article continues below
AI adoption drives productivity gains
The incentive for this change appears to be tied to measurable productivity gains rather than improvements in code quality.
“AI coding tools are now the default option 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 ancillary tool but rather the primary driver of software development for organizations that choose to aggressively adopt it.
While AI doesn’t automatically improve code maintainability, the volume gains alone have made it the standard tool for many teams.
Top-performing companies in AI-driven industries have seen significant increases in production, and companies adopting AI most aggressively report double the pull request throughput compared to the least adopting companies over three months.
In practical terms, these teams are producing and shipping code at a pace that leaves their competitors behind.
A rapidly growing trend in this adoption is the use of autonomous agents, which generate pull requests entirely without human intervention. Although these agents currently represent only a small portion of overall code production, their presence is growing rapidly.
In the 90th percentile of companies, contributions from autonomous agents increased from 10% of pull requests in January 2026 to 14% in February.
This indicates that AI-based automation is not only complementing human developers, but is gradually taking over a larger share of routine coding tasks.
Despite these productivity gains, adopting AI does not guarantee fewer errors or better code quality. So organizations are now focusing on monitoring business outcomes 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 throughput.
As AI coding tools become the standard in engineering workflows, high-level teams complete tasks faster and autonomous agents take on an increasing share of pull requests.
This shift is affecting how engineering teams plan, execute, and scale their work, and no team wants to be left behind because they aren’t keeping up with the trend.
For executives, the focus is on strategically integrating AI to maintain high throughput, streamline operations, and maintain a competitive advantage.
Follow TechRadar on Google News And add us as your favorite source to get our news, reviews and expert opinions in your feeds. Make sure to click the Follow button!
And of course you can too follow TechRadar on TikTok for news, reviews, unboxings in video form and receive regular updates from us on WhatsApp Also.
