Not referring to the Indonesian island or the coffee bean – but the programming language!

In 2026, compliance and cybersecurity concerns could well overtake cost savings as the primary driver for modernizing Java runtimes, largely die to key considerations for investment in and adoption of AI.

2026 has already seen major regulatory updates for data privacy and security in China (CSL amendments for AI/supply chain), Hong Kong (Critical Infrastructure Ordinance, effective Jan 2026), Singapore (Cybersecurity Act amendments for CII/outsourcing), and Australia (Privacy Act reforms for biometric/algorithmic accountability). These laws aim for stronger data governance, broader breach reporting, and alignment with global standards.

Meanwhile, AI regulations in the Asia Pacific region are rapidly evolving with diverse approaches, from China’s strict rules and South Korea’s upcoming AI Act (2026) to Singapore’s principles-based frameworks like AI Verify and Japan’s focus on innovation, with recent developments including Kazakhstan’s AI law, Vietnam’s draft law, and Malaysia’s focus on data officers, all pushing for risk-based frameworks, transparency, and accountability.  

With the enforcement of regulations like the EU’s Digital Operational Resilience Act (DORA), unsupported Java versions and unpatched JVM vulnerabilities will become board-level concerns. These technical risks will no longer be confined to IT — they’ll be treated as material threats to operational resilience, regulatory compliance, and brand reputation.

Against this backdrop, Simon Ritter, Deputy CTO, Azul, shares two key Java-related predictions that would impact organizations in the region in an era of AI-powered developments.

Prediction 1: Java becomes the “glue” of enterprise AI

In 2026, Java will emerge as the foundation for enterprise-grade AI applications, moving beyond its traditional role into a critical layer for deploying, scaling, and securing machine learning in production. 

While Python will remain dominant for AI research, prototyping, and building the AI models used in applications, enterprise-scale AI deployments will increasingly shift to Java as the “glue”. Java’s long-standing strengths — performance, reliability, and robust security — make it an ideal backbone for production environments where AI needs to integrate tightly with complex, mission-critical systems.

Expect to see major growth in:

  • Java-based inference engines optimized for low-latency, real-time workloads
  • AI-powered Java frameworks integrated with cloud-native services like vector databases, streaming platforms, and model registries
  • AI observability and governance tooling embedded into the Java ecosystem for compliance and performance monitoring

There will also be a surge in Java developers contributing to AI-native tooling, frameworks, and accelerators, further closing the gap between experimentation and production. Java’s role will be critical in bridging AI and traditional enterprise software stacks. In short, AI innovation may begin in Python, but when it comes to production applications, Java will prevail as the underlying “glue” that binds AI infrastructure.

Prediction 2: AI Will Accelerate Compute Demand for Java Workloads 

In 2026, the proliferation of AI-enabled features within enterprise software will drive a significant increase in compute demands for Java workloads. As AI becomes more deeply embedded in transactional systems — enhancing personalization, automation, and real-time decision-making — Java applications that power core business functions will require greater CPU, memory, and throughput to keep pace. From intelligent supply chains to AI-assisted financial analysis, Java’s role as a mission-critical runtime will expand in both volume and complexity.

This shift will lead enterprises to rethink performance optimization, infrastructure strategy, and scalability. Java platforms that support low-latency execution, high throughput, and massive heap sizes will become essential. To meet AI-era requirements, organizations will prioritize:

  • High-performance Java platforms that minimize GC pauses and latency.
  • Cloud-agnostic platforms to support distributed AI workloads.
  • Real-time observability and tuning to handle dynamic AI inference loads

As AI adoption intensifies across industries, the infrastructure underpinning AI workloads must scale accordingly. Java’s ubiquity, performance advantages, and cloud-native readiness position it to meet this surge — making Java platforms a strategic cornerstone of the AI-powered enterprise.