Java Trends Report 2025

This report summarizes how the InfoQ Java editorial team and several Java Champions currently see the adoption of technology and emerging trends within the Java and JVM space in 2025. This TensorBlue analysis is based on reporting and source material from InfoQ (https://www.infoq.com/articles/java-trends-report-2025/).

What Happened

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AI on the JVM accelerates: New frameworks like Embabel, Koog, Spring AI, and LangChain4j drive rapid adoption of AI-native and AI-assisted development in Java.

Java 25 anchors a modern baseline: The new LTS improves readability, concurrency, and performance, while frameworks standardize on Java 17+.

Modernization surges: Organizations prioritize updating legacy apps and outdated Java versions, with OpenRewrite emerging as the dominant automation tool.

Enterprise Java advances: Jakarta EE 11 stabilizes with broad adoption, and early work on Jakarta EE 12, especially Jakarta Query, pushes the platform forward.

Community and deployment shifts: Java community engagement is growing, while enterprises increasingly explore hybrid, regional, or on-prem deployment strategies.

This report summarizes the InfoQ Java editorial team's current perspective on the adoption of technology and emerging trends within the Java space. We focus on Java, the language, as well as related languages such as Kotlin and Scala, the Java Virtual Machine (JVM), and Java-based frameworks and utilities. We discuss trends in core Java, including the adoption of new Java versions, as well as the evolution of frameworks such as the Spring Framework, Jakarta EE, Quarkus, Micronaut, Helidon, and MicroProfile.

To assist technical leaders in making mid- to lo

Van Dijk: There is an increasing focus on using Java for AI, and new tools are emerging. Langchain4j continues to evolve. This year, we also saw the introduction of Embabel, a new agent platform for the JVM, created by Rod Johnson of Spring Framework fame. Another addition is Koog, a Kotlin-based framework designed to build and run AI agents entirely in idiomatic Kotlin. It’s great to see the number of options for using AI on the JVM growing. With the use of AI tools for software development, we seem to be (re)discovering software engineering practices. With the trend of spec-driven development, we are integrating good software development practices into the use of AI for software development. As more code is being generated by AI, rather than written by developers, readability continues to be important. That’s why it is good to see that the Java language and tools continue to evolve in ways to make code easier to read and understand.

InfoQ

Implications for Product and Engineering Teams

For TensorBlue readers, the useful question is not just what happened, but how this changes product architecture, engineering priorities, AI delivery, observability, team workflows, or executive decision-making.

• Review whether this changes your AI roadmap, platform architecture, or engineering operating model.
• Identify the specific workflow, reliability, governance, or developer-productivity lesson that applies to your organization.
• Convert the lesson into a small production experiment with measurable quality, latency, cost, adoption, or risk metrics.
• Document source assumptions clearly so teams do not overgeneralize from incomplete public information.

TensorBlue Takeaway

The practical opportunity is to turn this signal into a concrete implementation decision: better AI systems, stronger product instrumentation, more reliable automation, and clearer technical governance. Teams that connect public technology shifts to their own delivery systems will move faster without adding unnecessary complexity.