State of the Art of Java in 2026 Ben Evans

The podcast explores Java's enduring relevance and future trajectory, emphasizing its resilience despite repeated predictions of its decline since 1999. It discusses how Java's longevity stems from consistent foundational principles, balancing "dynamism" (adapting to new trends) and "integrity" (maintaining core values), which remain central to its evolution. The talk highlights tensions within Javas ecosystem between innovation and stability, underscoring the complexity of measuring market size due to regional disparities and the reliance on data from JVM analytics. Sustained growth in server-side Java workloads and corporate investment, alongside stable developer wages, reinforces its position as a cornerstone of enterprise and technical domains. The discussion also addresses Javas role in emerging technologies, including AI, where it is increasingly integrating frameworks like LangChain4j and Spring AI, though challenges persist in aligning AI tools with production code and legacy systems.

The podcast outlines Javas ongoing language evolution, including its semi-annual release cycle, the dominance of Long-Term Support (LTS) versions like Java 17 and 21, and modern features such as enhanced garbage collection, functional programming constructs, and concurrency tools like virtual threads. It delves into the JVMs dynamic capabilities, contrasting Javas compiled nature with its runtime flexibility enabled by features like reflection and method handles. The Valhalla Project is highlighted as a transformative effort to introduce value types and identity objects, aiming to improve data efficiency and align Java with modern hardware. Additionally, the talk explores AI's growing influence, noting Java's expanding presence in AI/ML compared to Python, while acknowledging gaps in AI tooling for production systems and the risks of enterprise legacy code. The dialogue also critiques traditional language rankings and survey methodologies, arguing that established ecosystems like Java benefit from sustained, steady growth rather than explosive adoption seen in newer languages.

• What if you adopt Java 21 LTS with Valhalla preview features to future-proof your application?

  • Concrete move: Integrate Java 21's LTS version into your project, experimenting with Valhalla's value types and identity objects in non-critical modules.
  • Why now: Java 21 is a major LTS release with performance enhancements and foundational changes (like Valhalla), aligning with enterprise trends and reducing long-term technical debt.
  • Expected upside: Improved memory efficiency, faster runtime for data-heavy operations, and positioning your codebase to leverage upcoming JVM innovations.

• What if you prototype AI integration using Spring AI or LangChain4j in your existing Java stack?

  • Concrete move: Deploy a small-scale AI-powered feature (e.g., auto-generated documentation or query optimization) using Spring AI or LangChain4j in a non-production environment.
  • Why now: Java's ecosystem is rapidly adopting AI tools for enterprise use, and existing Java codebases can leverage these frameworks without rewriting systems.
  • Expected upside: Early adoption of AI capabilities for competitive differentiation, while avoiding risks of full-scale AI integration in production.

• What if you refactor legacy Java 8 code to Java 17 LTS, prioritizing constrained dynamism and nullability?

  • Concrete move: Migrate a critical module from Java 8 to Java 17, applying strict nullability annotations and constrained dynamism features (e.g., ahead-of-time compilation).
  • Why now: Java 8's market share (~30%) is declining, and Java 17 offers stability, security, and modern language features that reduce maintenance overhead.
  • Expected upside: Reduced runtime errors, better compatibility with future Java versions, and alignment with enterprise security standards.

• Leverage Java's LTS (Long-Term Support) versions for stability and security, prioritizing Java 17 or newer (e.g., Java 21/25) in production environments while avoiding outdated versions like Java 8 unless necessary for legacy compatibility.
• Integrate AI-specific Java tools such as LangChain4j, Quarkus, or Spring AI into your projects to align with enterprise AI adoption trends, even if your codebase is not AI-focused.
• Test Java preview features and incubator modules in development/QA environments to provide feedback before they become production-ready, ensuring smoother transitions during finalization.
• Adopt modern Java frameworks like Spring (for microservices) or Quarkus (for cloud-native apps) to capitalize on their dominance in enterprise infrastructure and reduce long-term maintenance costs.
• Monitor and update legacy Java workloads to address security risks from outdated systems, ensuring compliance with LTS timelines and mitigating vulnerabilities exacerbated by enterprise "lag" and technology debt.