That\u2019s even more of an advantage when it comes to AI, where any budget spent on runtime is budget unavailable for tokens and API calls.\u00a0Efficient Java runtimes also allow you to write\u00a0efficient, scalable agents: something that\u2019s going to become more important as agents become useful for many more tasks than just writing code. If you have hundreds or thousands of AI agents running in your enterprise, you want them to use as few resources as possible.<\/p>\n
\u201cNow that it\u2019s easy to write code with AI, there is really no excuse to not use languages that provide the best runtime performance and great ecosystem.\u201d<\/p>\n
You get those same advantages for creating AI features because that Java ecosystem now includes first-class AI frameworks and SDKs for connecting to LLMs. LangChain4j and Spring AI simplify integrating AI models into Java applications and using powerful patterns like RAG while working with familiar Java frameworks; agentic frameworks like\u00a0embabel\u00a0<\/a>add agentic flows to Spring and the JVM. Building chatbots, generating images, summarizing text or creating search services: Java is ready for generative AI as well as the machine learning and big data workloads developers are already familiar with.<\/p>\n Java\u2019s traditional strength in integration is even more relevant as you start adding more AI features to applications, whether that\u2019s MCP or large-scale event-driven architectures. \u201cYou want context for AI: you want tools, you want databases, you want MCP servers and Java is great for that because Java has always been great for\u00a0integrating with third party solutions,\u201d points out JHipster author and lead of Microsoft\u2019s Java Developer Relations team\u00a0Julien Dubois<\/a>.<\/p>\n The language constructs and the ecosystem of libraries and frameworks for Java make it a good fit for AI, he argues: \u201cit\u2019s not at all difficult for developers to add intelligent capabilities to their existing applications.\u201d<\/p>\n Harder to write, easier to read<\/p>\n Java\u2019s explicitness and verbosity turn into a strength when it comes to using AI code assistants, because it\u2019s easier to read and understand the Java code they suggest adding to your critical, highly-optimized enterprise apps.<\/p>\n When an AI agent is doing most of the typing, language choice should come down to readability, argues Borges: \u201cnot the shortest smallest piece of code.\u201d<\/p>\n \u201cAI writes the code, the developer can understand and read their code, and the runtime runs the best performance possible for that particular code with an amazing ecosystem around it.\u201d<\/p>\n Java\u2019s popularity and the convergence around a small set of frameworks have given LLMs plenty of open source Java code to learn from. The latest versions of AI coding tools like GitHub Copilot, Claude Code and Cursor are extremely good at writing Java code, Dubois notes. \u201cIf you\u2019re a Java developer, you\u2019re probably using frameworks such as Spring Boot, Hibernate or Elasticsearch: because of the available training data, GitHub Copilot will be excellent at writing this code for you.\u201d<\/p>\n That\u2019s not just useful for adding AI features. The combination of efficient coding assistants and code that developers can quickly understand and review, makes it far less expensive to modernize older Java applications you want to update and migrate to the cloud. \u201cBig enterprises have a lot of older Java applications which have been complicated to update as they require large budgets, and developer motivation is quite low on those projects. AI can drastically reduce that effort, and make those projects possible\u201d , Dubois notes.<\/p>\n Continuous modernization is coming<\/p>\n Just analyzing a legacy Java codebase that might have dozens of deprecated APIs across hundreds of classes in millions of lines of code can take months. AI tools can map dependencies and discover which ones are out of date, migrate applications to newer framework and runtime versions, and then generate tests for the code that refactors your monolithic code into microservices or serverless patterns.<\/p>\n In fact, it\u2019s so effective you can make modernization a regular part of the software development lifecycle instead of a painful one-off project that gets postponed until systems are at breaking point, Borges argues. \u201cThat\u2019s never happened, because the cost of modernization was so high and the return on investment was unpredictable at the very least.\u201d<\/p>\n Now AI agents have already made that a reality for some Microsoft customers, he says:\u201d they\u2019re adopting AI to constantly keep up with the changes in all the tools and services and libraries and frameworks, languages and runtimes in their applications by using AI agents.\u201d<\/p>\n These early adopters are getting a head start on an emerging architecture where AI models become the intelligence layer for production systems, with Java providing both the execution and the integration layer: one of the traditional strengths of Java proving just as useful in the era of AI. With the efficiency of the Java runtime, this might even free up some budget you can use for further AI experimentation.<\/p>\n If you want to see what this looks like in practice, Microsoft\u2019s\u00a0JDConf<\/a> brings together Java practitioners, open\u2011source maintainers and tooling teams to talk about how AI in Java is ready for production.<\/p>\n
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