There’s been an important development in the GitHub Copilot space that caught my attention, and I wanted to break it down — not just what was announced, but why it matters and how it fits into the bigger picture.
What’s New
AI coding tools can generate code quickly, but speed alone does not solve the hardest software engineering problems. Developers still need to provide context, validate results, protect maintainability, and remain accountable for what ships.
To learn how experienced developers strike that balance, we spoke with three Java Champions, all with over a decade of experience in using Java. They shared concrete examples of using GitHub Copilot in production and open source, along with the practices they use to turn AI-generated output into reliable software.
Meet the Java Champions
Vlad Mihalcea, founder of Hypersistence and author of the book High-Performance Java Persistence
Patrick Baumgartner, software crafter at 42talents with 20+ years of Java experience
Frank Delporte, senior technical writer at Azul and maintainer of Foojay.io with 15+ years of Java experience
Vlad Mihalcea
How has GitHub Copilot helped you recently?
I’ve been using it to analyze, design, and implement various features in both Hypersistence Utils and Hypersistence Optimizer.
For example, I’ve been wanting for a long time to develop a web application that provides Optimizer event history so we can see how performance improvement efforts are materializing in application performance. Before AI, I kept postponing this task because it would have taken me weeks to develop. But using Copilot is like having a new team member, so I used it to create an app that will ship in the next version of Hypersistence Optimizer. Instead of weeks, this task took just two days to implement.
What’s your typical usage? Are you using GitHub Copilot as an IDE plugin or the GitHub Copilot app?
Key Takeaways
- AI coding tools can generate code quickly, but speed alone does not solve the hardest software engineering problems.
- To learn how experienced developers strike that balance, we spoke with three Java Champions, all with over a decade of experience in using Java.
- I’ve been using it to analyze, design, and implement various features in both Hypersistence Utils and Hypersistence Optimizer.
- For example, I’ve been wanting for a long time to develop a web application that provides Optimizer event history so we can see how performance improvement efforts are materializing in application performance.
- What’s your typical usage? Are you using GitHub Copilot as an IDE plugin or the GitHub Copilot app?.
Why This Matters
Developer productivity is one of the highest-ROI applications of AI today. Copilot is proving that AI can augment — not replace — human expertise, making experienced developers more productive and helping newcomers ramp up faster.
This development is particularly significant because it reflects the broader industry shift toward making AI more accessible, enterprise-ready, and integrated into existing workflows. For teams already invested in the Microsoft ecosystem, this is a clear signal of where the platform is heading.
My Take
The technical evolution of Copilot is fascinating from an AI systems perspective. We’ve moved from completion-based assistance (predict the next line) to agent-mode (understand the intent, decompose the problem, execute multi-file changes, run tests, iterate on failures). This is a fundamental architectural shift — from a stateless token predictor to a stateful reasoning agent with tool use. What’s technically impressive is the context management: Copilot now maintains a working memory of your entire codebase, open issues, PR history, and team conventions. The retrieval augmentation layer that grounds suggestions in your specific codebase (not just generic patterns) is what makes the difference between a toy and a production tool. The agentic workflows — where Copilot can plan, execute, verify, and self-correct — represent the state of the art in applied AI agents today.
Business Translation
For the C-Suite: Developer talent is the #1 constraint in digital transformation. Copilot delivers a measurable 25-55% productivity gain (validated by Microsoft/GitHub research across 1M+ developers). In financial terms: a 500-developer organization saves $15-30M annually in equivalent output — without hiring. More importantly, it reduces time-to-market for features, which directly impacts revenue capture. The strategic play isn’t just efficiency — it’s enabling your existing engineering team to tackle projects that were previously impossible given resource constraints.
📖 Read the original article for the full details and official documentation.
Written by Cyril Bou-Harb — Solution Engineer, Cloud & AI at Microsoft. Opinions and analysis are my own and do not represent Microsoft’s official position.