Summary
Everywhere you look, AI is changing the world. Half the world says we can’t comprehend the changes about to manifest, and the other half says there’s too much hype. Azul Deputy CTO and Java Champion Simon Ritter made four Java predictions for 2026, which you can read in our ebook, Future-Proof Your Enterprise: 8 Java Predictions for 2026.
In this post you will learn:
- AI will become the glue of enterprise AI
- AI will accelerate compute demand for Java workloads
- AI-assisted “vibe coding” will explode – but enterprise Java developers will more critical than ever
- Application modernization will shift from “cloud migration” to “Java modernization” driven by cost, risk, and AI demands
AI will become the "glue" of enterprise AI
Enterprise applications by their very nature tend to require massive scalability, low latency and high throughput. The Java platform, with its JVM-managed runtime environment, has become the de facto choice for deployments both on-premises and in the cloud.
One of the reasons Java is so popular is that it scales to internet-sized workloads. Over the 30 years of development, the Java platform has continued to deliver better and better support for making effective use of computing resources, especially in how concurrent execution can be used. Conversely, Python still uses the Global Interpreter Lock (GIL), which makes Python applications inherently single-threaded.
| Highlight: A recent article examines a real-world example showing that Java handled five times as many requests per day compared to Python. Read it here. |
Scalability is more important now than ever before, with artificial Intelligence (AI) taking the world by storm, driven primarily by large language models (LLMs) and generative AI. Tools like ChatGPT, Claude, and Gemini are reducing the time required to complete many tasks that require research and composition. Much of the development of AI code uses Python. The popularity of Python is a consequence of early data scientists looking for an easy-to-learn language. This has resulted in many of the popular libraries, like PyTorch, being written in Python.
The trend toward scalability in enterprise application development will naturally see Java become the “glue” (or really, the business logic) between big data and AI tools like LLMs.
| Highlight: 3 reasons Java could overtake Python for AI development. Read it here. |
AI will accelerate compute demands for Java workloads
A natural consequence of the growing use of AI in enterprise Java workloads is increased demand for compute resources. Adding AI capabilities to existing customer relationship management (CRM) or Enterprise Resource Planning (ERP) applications will increase their value. It will be possible to extract more focused insights from vast existing datasets.
So what's the catch? It will require more processing power and memory resources.
Enterprises will face a balancing act between the increased cost of adding AI capabilities and the value those insights deliver. As a result, we will see an exponential increase in the demand for compute resources dedicated to Java workloads.
DevOps teams will need to optimize the performance of Java runtimes, effectively doing more with less to ensure new AI functionality delivers true return on investment.
| Highlight: 10,000 JVMs collaborate in one production environment. Read it here. |
AI-assisted “vibe coding” will explode – but enterprise Java developers will be more critical than ever
Vibe coding is only a year old, but many predict it will lead to the end of traditional software development as we know it. The theory is that software developers with years of training and experience will be obsolete, as anyone will be able to use natural language to tell tools like Claude and Co-Pilot what the application needs to do.
| Highlight: A recent article demonstrates how overwhelming the hype over AI's encroachment on software developers. Read it here. |
The reality is likely to be somewhat different.
Natural language is inherently ambiguous, which makes it difficult, if not impossible, in many situations to specify precisely what you want. Here’s a trivial example.
Q: What does the phrase “The chicken is ready to eat” mean?
A. A live chicken is hungry
B. The chicken I put in the oven earlier is now cooked
The answer, of course, is C, all of the above.
Vibe coding is very suited to hobby projects or prototyping. Still, when you need a rock-solid mission-critical application to work every time, you won’t be relying on an LLM trained on code you have no knowledge of.
| Highlight: Vibe coding is a question of chicken feed or food. Read it here. |
GenAI and LLM coding will, however, make experienced developers more productive. From better, smarter predictive code completion in IDEs to using prompts to describe and generate individual components, AI will play a more significant role.
The experienced developer will still be in control and will still ensure that the code works the way it is expected to; they’ll just get the projects completed much more quickly.
Application modernization will shift from “cloud migration” to “Java modernization” driven by cost, risk, and AI demands
We’ve seen a lot of time, effort and budget devoted to moving workloads from data centeres to the cloud. Many migrations have involved re-architecting applications to fit a cloud environment, switching to a microservice architecture, allowing cost-effective dynamic scaling.
However, there have also been lots of examples of a “lift-and-shift” approach, which simply moves an application from a machine in the data center to an instance in the cloud. Frequently, these migrations result in higher costs than before, rather than the anticipated savings. In 2026 we will see further modernization of Java applications in this category.
Achieving this will require careful analysis and thought. Since many of these applications are many years, if not decades, old, there is a high likelihood that substantial amounts of unused (or dead) code exists. Being able to create an inventory of what code is actually being used will be essential to modernization that has value.
| Highlight: 3 ways to improve Java-based microservices. Read it here. |
Read all the Java predictions
Azul collected Java predictions from some of the most renowned Java Champions worldwide to outline where the industry is headed. The verdict is that Java’s influence across the enterprise is only growing stronger. Read all eight predictions in one easy-to-read ebook.
