The Future of Java Development: What Developers Need to Know in 2026

If you'd asked developers a decade ago whether Java would still be relevant today, you'd have gotten a mixed room—and yet, here we are. Java has been around for over 30 years, and it's evolving in ways that matter for enterprise software right now. 

In a recent webinar, we broke down where Java stands, how the language has changed, and what developers need to stay ahead. This blog covers the highlights, but if you want the full picture, the webinar is worth your time. 

Watch the Full Webinar 

Where Java Stands Today 

Java's staying power runs deep, technically and strategically. 

According to the Stack Overflow Developer Survey in 2025, Java is still the third most popular programming language, and the JRebel 2025 Java Developer Productivity Report found that 61% of respondents are actively using Java. 

On the technical side, Java's security model, including bytecode verification, type safety, automatic garbage collection, and the Java Cryptography Architecture, makes it the language of choice for many industries where security is non-negotiable. 

The business case for Java is just as strong as the technical one. Java is deeply embedded in banking, insurance, healthcare, retail, telecoms, and government systems. As organizations need to connect new AI services to existing APIs, databases, and compliance controls, Java is already there. 

Additionally, Java remaining one of the most widely used languages means it's easier to hire, onboard, and maintain systems over the long haul. That's a strategic advantage most CTOs aren't in a hurry to give up. 

How Java Has Evolved 

Sixteen releases over the last eight years — four of them long-term support — add up to a language that looks and performs meaningfully differently than it did in 2017. These changes can be summarized in four themes: the language got smaller, concurrency got cheap, runtime got cheaper, and the maintenance tax emerged. 

The Language Got Smaller 

Records, sealed classes, text blocks, switch expressions, and pattern matching have dramatically reduced boilerplate code. A class that once required 40 lines of constructors, getters, equals, hashCode, and toString methods is now a single line with a record. Less code to write means less code to maintain and fewer places for bugs to hide. 

Concurrency Got Cheap 

Virtual threads, introduced in Java 21 through Project Loom, are a genuine turning point. Historically, Java threads mapped directly to OS threads, which were scarce, expensive, and the reason reactive programming became the default for high-concurrency workloads. 

Virtual threads flip that calculus. You can have many more threads running simultaneously, and your code stays simple and blocking-style rather than reactive. It doesn't make individual operations faster, but it makes writing scalable concurrent code dramatically more straightforward. 

Runtime Got Cheaper 

Improvements landing between Java 21 and Java 25 such as better garbage collection defaults, smaller memory footprints, class data sharing, and ahead-of-time caching mean Java applications cost less to run. 

Class data sharing alone can deliver a real reduction in startup time with almost no configuration changes. 

And if you're still pasting in JVM settings copied from a runbook a decade ago, removing them might be the cheapest performance improvement available to you right now. 

The Maintenance Tax Emerged 

From Java 16 onwards, Java closed off internal APIs that were previously open by default. That's the most common reason older applications break during upgrades. 

Workarounds exist, but they're debt, not fixes. The old Security Manager is permanently gone as of Java 24, and skipping releases doesn't save time, it just stacks the breaking changes together so they all land at once. 

The four factors worth evaluating when deciding whether to upgrade to a new version: what it costs to run, how you're handling concurrency, your framework support situation, and your security posture. Syntax improvements alone aren't a reason to move a production system. 

The Future: Cloud Costs, AI, and the New Programming Model 

Runtime and the Cloud Bill 

Cloud costs are making startup time a finance problem. 

Containers and serverless pricing charge you for the two things Java has historically struggled with: startup time (paid every time you scale or deploy) and memory (paid all day, whether the application is busy or idle). 

In the Perforce 2025 Java Developer Productivity Report, 41% of respondents said they rely on high-performance Java platforms specifically to bring down cloud compute costs. 

Three approaches exist, and they layer rather than compete: 

• GraalVM native compilation gives you the fastest startup and the smallest memory footprint. The trade-offs are significant build times, less runtime flexibility, and changes to how JVM monitoring tools work. For a small, well-understood service, it's excellent. For a large application with years of history, it's a project. 
• CRaC (Coordinated Restore at Checkpoint) lets you start your application, warm it up, take a snapshot, and start future instances from that snapshot. Startup becomes nearly instant.  
• Class data sharing and ahead-of-time caching gives you less than either of the above, but costs almost nothing to implement. 

Start with the cheapest option, measure what you get, then decide whether you need the expensive one. Teams that jump straight to native compilation often spend months on configuration issues they could have avoided. 

The Programming Model 

For programming, the simpler approach is becoming the right approach. 

Structured concurrency is still in preview but planned to finalize in Java 28. Project Valhalla — over a decade in development — will change how Java objects sit in memory, allowing value types that don't carry the per-object overhead of identity tracking. 

The JVM will be able to store them more efficiently, and the language itself will be making that promise rather than the JVM optimizing it quietly. 

Java in the AI Era 

AI is reshaping Java development in two major ways: developers are increasingly using Java to build AI-powered applications, and they are using AI tools to write Java code more efficiently. 

Building AI Applications in Java 

Java is becoming a popular choice for adding AI capabilities to enterprise applications, largely because business-critical data and systems already live within Java environments. 

In the Perforce 2025 Java Developer Productivity Report, 62% of organizations said they're now using Java to build AI features. That’s up from around 50% the year before. 

Frameworks such as Spring AI simplify AI development by providing a single API for multiple model providers, along with support for retrieval-augmented generation (RAG) and tool calling. For teams that don't use Spring, LangChain4j offers similar AI integration capabilities without requiring the Spring ecosystem. 

The rise of the Model Context Protocol (MCP) further expands possibilities by allowing AI agents to securely interact with systems and services organizations already operate, making existing enterprise data more accessible to AI-driven workflows. 

Using AI to Write Java Code 

AI coding tools have evolved far beyond simple code completion. Modern agent-based tools can compile code, run tests, and execute development workflows automatically. 

These tools generally fall into three categories: IDE-native assistants, AI-first development environments, and terminal-based agent harnesses. 

While AI excels at accelerating coding tasks and large-scale refactoring efforts, debugging remains a more challenging area that still benefits heavily from developer expertise. 

As AI models continue to improve, developer productivity is increasingly constrained not by the model itself, but by the time required to build, deploy, and test applications. 

The Productivity Bottleneck: Redeploy Time 

For Java teams adopting agentic development workflows, reducing feedback loops is critical. The JRebel Skills file helps AI agents work more efficiently by eliminating redeploy delays, allowing developers and AI assistants to validate changes faster and maintain momentum. 

Real Teams Are Already Making the Shift 

The conversation around AI and Java development isn't theoretical. A dental technology provider with 90+ Java developers was working with a legacy monolithic application and had redeploy times of 7-10 minutes or more — long enough to break developer flow, and a serious drag when using AI tools that require rapid iteration to refine prompts and validate output. 

The team found that pairing an AI code generation tool with JRebel dramatically accelerated their development feedback loop. 

By eliminating redeploys with JRebel, the team could test AI-generated code changes instantly, without the wait. The workflow spread quickly across the broader engineering team, and developers freed up time previously lost to redeploys to focus on higher-value work, including migrating from Java 11 to Java 17. 

Read the Full Case Study 

Skills Java Developers Need Now 

Looking ahead, the advantage will go to developers who know how to pair the Java ecosystem with AI tools, containers, observability, and cloud platforms. Here's where to focus: 

Modern Java Features 

Records, pattern matching, virtual threads, and sealed classes aren't optional extras — they're idiomatic Java now. If you're not using features from Java 17 and Java 21, you're writing more code than you need to. 

Spring Boot and the Broader Ecosystem 

Spring Boot remains the dominant framework for enterprise Java. Spring AI, Spring Cloud, and Spring Native show how Spring is evolving for modern enterprise needs, and understanding this ecosystem still matters. 

Microservices Architecture 

Domain-driven design, database isolation per service, asynchronous messaging with Kafka or RabbitMQ, resilience patterns, and distributed tracing are all practical skills, not just architectural theory. 

Cloud Native Development 

Java applications need to run in containers and scale across AWS, Azure, and Google Cloud. A typical cloud native stack today looks like Spring Boot on Java 17 or 21 with Docker, Kubernetes, Helm, Terraform, Prometheus, and Grafana. Containers, Kubernetes, CI/CD, and cloud security are all fair game. 

AI-Assisted Tooling 

Tools like GitHub Copilot, Cursor, JetBrains AI, and Claude are already changing how Java code gets written day to day. The developers who thrive won't be the ones who avoid these tools. They'll be the ones who know how to use them critically and effectively, keeping humans in ownership of design, security, and correctness. 

Security 

Secure coding practices, dependency vulnerability management, API authentication, cloud and container security, and software supply chain security are increasingly part of the core job description. Threat modeling and AI security will become more important for developers who want to stay competitive. 

Want the Full Picture? 

This blog covers the highlights, but we went deeper on all of it, including a live Q&A covering whether AI can handle Java version migrations, how to standardize on AI tooling, and what single thing to focus on if you're not using AI yet. 

Watch the Full Webinar