What does the future hold for Java? In this article, together with Dmitry Kvartalny, Head of Java at Godel, we’ll explore key predictions for the language in 2025, examining upcoming features, emerging use cases, and how Java plans to stay competitive in the face of other languages.

What are your tech predictions for Java for 2025?

The buzzword of 2024 was undoubtedly AI, and we anticipate this trend to continue into 2025. The integration of Generative AI and LLM support is set to significantly influence Java’s evolution. AI tools will automate routine coding and testing tasks, while frameworks and IDEs will be evolving to incorporate more and more intelligent assistance features. This synergy between human expertise and AI-driven automation will foster more efficient, robust, and innovative Java development practices, thus strengthening Java’s role in enterprise, cloud-native, and emerging technology landscapes. At the same time, all non-AI related trends will continue to advance steadily, including the adoption of cloud-native architectures, microservices, the latest Java language features, and performance enhancements. Let’s dig even deeper into all highlighted above. 

AI-Assisted Development 

The rising popularity of AI-powered tools like GitHub Copilot, Codex, and other domain-specific assistants is streamlining software development and Java ecosystem. These tools can generate boilerplate code, suggest optimisations, write comprehensive test suites based on high-level requirements, and significantly aid the code review process. As a result, engineers and businesses getting acquainted with a lot of enhancements in various areas: 

  • Integrated Development Environments (IDEs): well-known tools like IntelliJ IDEA, VS Code will further integrate AI features, offering smarter code completions, context-aware suggestions, and real-time bug detection using LLMs. 
  • Efficiency Gains: Developers can focus on business logic rather than repetitive coding tasks, as AI handles mundane boilerplate creation. 
  • Improved Testing: AI-generated tests can cover edge cases and reduce human error, improving overall code quality. 
  • Rapid Prototyping: With AI assistance, prototype development becomes quicker, enabling faster iterations and innovation. 
  • Enhanced Toolchains: with wider adoption of AI tools in various CI/CD products the focus will shift towards improving the developer experience (DX) and providing powerful observability functionalities that offer useful insights and suggestions for improvements.

Evolving Frameworks with LLM Integration 

As Java frameworks adapt to leverage LLM capabilities, we can expect: 

  • Smarter APIs and Libraries: Frameworks like Spring, Quarkus, and Micronaut may incorporate features that dynamically generate code templates, configuration setups, or even adapt to new APIs based on LLM insights. 
  • LLM Agents: Emerging libraries and agents designed to work with LLMs will facilitate tasks such as natural language query processing, automated documentation generation, and intelligent debugging assistance. These agents will be continuously updated to keep up with the latest advancements in language models and their applications in Java. 

How might the role of Java evolve next year?  

The role of the Java engineer will gradually transition from writing routine code to supervising AI-generated outputs, refining complex algorithms, and emphasising creative problem-solving: 

  • Code Reviews: Developers will increasingly review and validate AI-generated code, meticulously examining code suggestions, and ensuring that the solutions meet high standards of quality and security. 
  • Design and Architecture: Human insight will drive architectural decisions, while AI assists by providing suggestions based on extensive code analysis and industry best practices. 

What new features in recent or upcoming Java versions are expected to have the most significant impact on developers and businesses? 

Next year Java will celebrate its 30th birthday and there will be the next LTS release Java 24. But as this comes in September 2025, it’s very unlikely that it will influence 2025, rather we should expect the impact in 2026 and onwards. In 2025 it’s, as usual, expected to see wider adoption of a wide range of Java features finalized after Java 17, especially taking into account that Java 21 adoption is still quite marginal, despite the fact it’s been released in September 2023. Here are the most prominent features worth mentioning: 

Virtual Threads (Project Loom) 

This is undoubtedly the most significant change. Virtual threads dramatically reduce the effort of writing, maintaining, and observing high-throughput concurrent applications. They allow developers to write concurrent code using familiar sequential programming models, significantly improving application performance and scalability. Even though Structured Concurrency and Scoped Values are still under development, currently available as preview features and will not be released even in Java 24, nevertheless, virtual threads are a giant leap towards improved Java application’s concurrency and performance. 

Pattern Matching for switch 

Extends pattern matching to switch expressions and statements, allowing for more concise and readable code when dealing with complex data structures. This improves code clarity and reduces boilerplate.    

Foreign Function & Memory API 

This API allows Java programs to interoperate with code and data outside the Java runtime. It enables efficient access to native libraries and off-heap memory, which is crucial for performance-sensitive applications and integration with native components.    

Sealed Classes 

While introduced in Java 17, sealed classes gained more usage and recognition in subsequent releases. They enhance type safety and code clarity by allowing developers to control which classes can inherit from a given class or interface. This is particularly useful for creating well-defined class hierarchies and improving pattern matching.    

Record Patterns 

This feature enhances pattern matching to deconstruct record classes, making it easier to extract data from records in a concise and readable way. This further improves data processing and code clarity.

Resources Optimisation 

Java applications’ startup time and memory footprint continue to be significant concerns and are closely monitored within the ecosystem. In cloud-native environments, where resources can be dynamically scaled up or down—even to zero—it is crucial to initiate workloads swiftly. Several tools can help achieve this, each requiring varying levels of effort, including CDS, Project CRaC, Graal’s AOT compilation, and native images. While precise usage statistics are hard to obtain, these solutions are generally considered niche at present. However, in 2025, it is anticipated that reliance on these technologies will become much more widespread. 

From the business perspective, the adoption of the aforementioned features delivers: 

  • Improved developers’ productivity  
  • Enhanced performance and scalability 
  • Better code readability and maintainability 

As Java continues to evolve, these features are expected to have a lasting impact on how developers build and businesses deploy Java applications.