Java at 30: What's next for the world's most enduring programming language? [Q&A]
As Java marks its 30th anniversary on May 23rd, it's a powerful reminder that few technologies have had the longevity -- or the impact -- of a language first launched in 1995.
Today, Java continues to underpin much of modern software development, from cloud-native systems to enterprise-scale applications.
To mark this milestone, we caught up with Dewan Ahmed, principal developer advocate at Harness. Dewan, whose work focuses on empowering DevOps and engineering teams to deliver reliable, efficient, and secure software. He has seen first-hand just how much Java has changed over the past decade.
We talked to him about why Java has remained at the heart of enterprise engineering for so long -- evolving to meet today’s software demands while maintaining the stability and reliability it's long been known for. Dewan also looks to the future, discussing how Java is likely to adapt to the demands of AI, cloud services, and edge computing in 2025 and beyond.
BN: What has allowed Java to stay relevant for 30 years while many other languages faded?
DA: Java may be turning 30, but it's far from showing its age. Java's longevity lies in its ability to evolve while maintaining backward compatibility. The JVM's 'write once, run anywhere' principle remains valuable today, while modern Java has embraced concise syntax with records and pattern matching, without sacrificing its stability. Enterprise frameworks such as Spring Boot and Jakarta EE -- with its latest Jakarta EE 10 release and Jakarta EE 11 in development -- have also ensured Java stays aligned with the needs of modern cloud-native and microservices architectures. This vast ecosystem of libraries and tools, plus widespread enterprise adoption, give Java staying power that newer languages struggle to match.
BN: How has Java's design shaped the way software is built, tested, deployed, and maintained?
DA: Java pioneered practices that are now standard across software engineering. For one, JUnit established the unit testing paradigm that is used in all modern languages. As Java evolved, build tools progressed from Ant to Maven to Gradle, each advancing dependency management practices that are now essential in DevOps. Meanwhile, deployment evolved from application servers to containerization with optimized JVM images. The Spring ecosystem perfectly demonstrates Java's evolution -- from XML configuration, to annotations, to functional programming models -- showing how Java adapts while maintaining its core design principles.
BN: Where does Java fall short for modern software engineering teams, and how are those challenges being addressed?
DA: While verbosity, startup latency, and memory usage have long been cited as drawbacks, the Java community is consistently working to address these challenges. Java's traditional verbosity has improved with recent features like records and pattern matching. Startup latency and memory usage -- which can be problematic for serverless systems -- are also being addressed by Quarkus and GraalVM native image compilation, which significantly reduces startup time and memory requirements. Meanwhile, build times for large projects have improved through advanced build caching and incremental compilation techniques, while developer experience has advanced thanks to better IDE support and more responsive language servers. Tools such as these address Java's pain points while preserving its core strengths in reliability and performance.
BN: What does Java's role look like in a world increasingly shaped by AI-assisted development?
DA: As artificial intelligence reshapes how code is written, tested, and maintained, a programming language’s adaptability to AI tooling will be central to its future. Right now, it’s true that Java's AI landscape is not as vast or mature as Python's ecosystem, which remains dominant for machine learning (ML) development. Indeed, whilst libraries like Deep Java Library (DJL) and Deeplearning4j bring ML capabilities to Java environments, they often serve as wrappers around Python-based frameworks rather than native implementations.
Java is also carving out its place in enterprise AI adoption. Its strong static typing makes it well-suited for AI-powered development tools that leverage type information for more accurate code suggestions. These tools are particularly valuable when maintaining or modernizing legacy Java codebases, as they assist with tasks like refactoring and enforcing consistency. Java's explicit structure provides helpful context for such AI assistants, even though the Java ecosystem is still maturing compared to Python’s more established presence in machine learning.
BN: How will Java need to adapt technically for AI workloads, cloud services, and edge computing in the next 10 years?
DA: Looking to the future, Java's long-term relevance will hinge on how effectively it adapts to the asks of AI, cloud, and edge computing in 2025 and beyond. Initiatives like Project Panama, which enables AI workloads to run more efficiently on hardware resources, and Project Loom, which helps to meet parallel processing needs at scale, are designed to future-proof Java for these emerging demands.
In the cloud, ahead-of-time compilation via GraalVM is poised to become more mainstream, while advanced garbage collectors like ZGC and Shenandoah promise to minimize pause times for latency-sensitive applications like real-time analytics. And when it comes to edge computing, Project Leyden and Valhalla will optimise Java through static images and memory-efficient value types. These capabilities will ensure Java remains performant in modern, distributed environments and suggest it will remain so long into the AI-centric future.
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