"AI is everywhere right now. Every conference, keynote, and internal meeting has someone showing a prototype powered by a large language model. It looks impressive. You ask a question, and the system answers in natural language. But if you are an enterprise Java developer, you probably have mixed feelings. You know how hard it is to build reliable systems that scale, comply with regulations, and run for years."
"Java became the backbone of enterprise systems for a reason. It gave us strong typing, memory safety, portability across operating systems, and an ecosystem of frameworks that codified best practices. Whether you used Jakarta EE, Spring, or later, Quarkus and Micronaut, the goal was the same: build systems that are stable, predictable, and maintainable. Enterprises invested heavily because they knew Java applications would still be running years later with minimal surprises."
"This history matters when we talk about AI. Java developers are used to deterministic behavior. If a method returns a result, you can rely on that result as long as your inputs are the same. Business processes depend on that predictability. AI does not work like that. Outputs are probabilistic. The same input might give different results. That alone challenges everything we know about enterprise software."
AI adoption has become pervasive across conferences and internal meetings, often demonstrated with prototypes powered by large language models that answer questions in natural language. Enterprise Java emphasizes strong typing, memory safety, portability, and frameworks that produce stable, predictable, maintainable systems that can run for years. Enterprise systems rely on deterministic behavior, where identical inputs produce the same outputs, while AI generates probabilistic outputs that can vary for the same input. Most AI work starts as prototypes wired to APIs and chat interfaces; prototypes are useful for exploration but reveal issues like latency and scalability when pushed toward production.
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