Java in the age of AI: Platform evolution or ecosystem extinction?
One of the Java platform’s enduring strengths has always been its ecosystem.
Since the early days of the JVM, the classpath has served as an unsung hero, enabling seamless integration of libraries that extend Java’s capabilities in every direction. If a developer needs something, odds are high that someone has already built it—and built it well.
This extensibility, coupled with open-source licensing and a massive developer base, means Java offers not just solutions, but options. Hibernate or jOOQ? JDBC or Spring Data (or JDBC via Spring Data)? The best tool is often just a Maven dependency away.
And even when Java lagged in language evolution (think of Java 8’s long tenure), it still outperformed newer competitors like Go or Rust in many enterprise contexts, because the ecosystem simply made Java work.
The AI era and “vibe coding”
But we live in a different era now. The rise of AI services like ChatGPT, Claude, and Ollama has fundamentally changed how code gets written.
Enter “vibe coding.”
Vibe coding is the practice of describing what you want in natural language and letting the AI generate the code for you. Instead of using AI as a helper, the AI becomes the primary implementer. You focus on outcomes; it fills in the technical blanks.
Critics dismiss vibe coding—and often with good reason. It’s dangerously easy to produce something that looks correct but is riddled with subtle bugs, outdated dependencies, or misunderstood requirements. But when used well—when the developer knows how to define the problem clearly and stays actively engaged with the results—it can be a powerful tool for rapid prototyping or deployment.
Does the Java ecosystem still matter?
So here’s the question: if AI can write workable code in any language, does Java’s ecosystem still matter?
With AI’s broad training base, you can just as easily ask it to build a persistence layer in Go, Python, or Rust. Even if a language lacks a rich ecosystem or a solution for the problem you’re facing, the AI can help you find—or create—one. The constraints that used to steer developers toward Java are weaker now than they were.
But that’s only part of the picture.
AI is not always up to date
First, AI tools are (nearly) always out of date.
Most AI models don’t know about the latest versions of frameworks like Spring Boot or Jakarta EE. They’ll offer working solutions, but often using deprecated libraries, outdated idioms, or insecure defaults. (And when technologies like Spring Security evolve significantly, that’s a problem! You want state-of-the-art.)
That’s not a deal-breaker, but it is a burden—especially in industries that require security audits and regulatory compliance.
The developer must actively vet and adapt what’s produced, even if the AI model is up to date and generates good code. Awareness is everything.
Bad inputs lead to bad outputs
More importantly, most developers aren’t great at writing precise, complete requirements.
AIs inherit this weakness and amplify it. You get bad code not because the AI is flawed, but because the inputs are. The result is a false sense of productivity: code that compiles, maybe even runs, but isn’t right. And since the AI can lead you down a rabbit hole of an almost-working solution, it can end up wasting more time than it saves.
Java libraries and frameworks
This is where Java’s ecosystem still shines. The tools and libraries it offers are not only robust but also battle-tested. When an AI writes Java code, it’s often drawing from proven patterns. That’s not always true for newer or more niche languages. (Watching an AI attempt to generate good Rust code is amusing, for example—and Rust is hardly “niche.”)
In that sense, the AI doesn’t flatten the field—it elevates Java’s ecosystem even further. It enables more people to access it faster and with fewer barriers, while still relying on the deep knowledge embedded in the platform.
The Java platform still matters
So yes, ecosystem still matters. Perhaps more than ever.
AI can scaffold solutions, but it can’t evaluate requirements, enforce standards, or validate correctness. That’s still up to the programmer. And Java, with its mature ecosystem and institutional wisdom, remains one of the best places for developers to turn even AI-generated code into real-world applications that work.