Navigating the next wave: Five tech predictions for 2026

Australia is experiencing an unprecedented level of interest in AI, with the potential to reshape industries and accelerate digital transformation. As AI capabilities expand, the Australian government's new National AI Plan underscores the critical importance of building the infrastructure needed to seize the opportunity – placing data centres at the heart of the nation's AI ambitions.

The plan reinforces the essential role that data centres have played, and will continue to play, as the backbone of AI infrastructure, enabling the performance, scalability, and security modern technologies require. With policy and investment aligned around digital capacity – including more sustainable facilities, streamlined approvals and workforce development – Australia is positioning itself as a potential regional hub for AI innovation.

This rapid growth in AI is matched by a pressing need for robust digital infrastructure – networks, data centres, and interconnection – capable of supporting soaring workloads, demanding performance requirements and emerging risk profiles. The ability of organisations to capitalise on AI will depend on the strength and adaptability of these digital foundations. For Australian leaders, keeping pace with this shift will be essential to long-term competitiveness, resilience and growth.

Against that backdrop, here are five trends likely to shape Australia's business and technology landscape in 2026.

1. Sovereignty demands a global-local approach

Data sovereignty has moved beyond compliance to become a strategic priority. As data increasingly underpins business value, organisations must maintain control over where sensitive information is stored, processed, and accessed – particularly as regulatory expectations evolve.

At the same time, organisations still need to collaborate, analyse and innovate across borders. The emerging challenge is balancing local data control with global insight. In 2026, the most successful organisations will adopt hybrid approaches that allow sensitive data to remain onshore while still enabling global learning and collaboration. Done well, this global-local model turns sovereignty from a constraint into a competitive advantage.

2. The AI pendulum swings from training to inference

Much of AI conversation to date has focused on the enormous computational demands of training huge, large models. That focus is now shifting. As organisations look to extract real business value from AI investments, inference – the ability to deploy models for real-time decision-making – is becoming the priority.

Inference places very different demands on infrastructure. Low latency, proximity to users and secure access to data are critical. Recent industry moves to bring high-performance inference platforms closer to customers, such as Groq deploying in Equinix's Sydney data centre, reflect this shift, enabling production AI workloads that require immediate response times while maintaining compliance and control. In 2026, inference-optimised infrastructure will be central to scaling the capability of AI.

3. Agentic AI will test network resilience

The next phase of AI evolution is already emerging: Agentic AI. Unlike today's models, agentic systems are designed to act - making autonomous decisions and coordinating with other AI agents to achieve complex goals.

These multi-agent systems will place unprecedented strain on networks. Continuous, real-time communication between agents demands ultra-low latency, high-bandwidth connectivity that traditional architectures struggle to support. To enable agentic AI at scale, organisations will need highly interconnected, distributed environments capable of supporting constant data exchange. Network resilience will become as important as compute power itself.

4. Liquid cooling becomes the standard for high-density AI

As AI workloads grow more compute-intensive, thermal management is becoming a defining challenge. The high thermal output of modern GPUs and processors is pushing air-cooling approaches to their practical limits.

By 2026, liquid cooling will become the defacto standard for new high-density AI deployments. Beyond performance, liquid cooling offers efficiency and sustainability benefits, enabling greater compute density while reducing energy consumption. For organisations scaling AI infrastructure, adopting advanced cooling technologies will be essential to operating reliably and responsibly.

5. The rise of specialised and verticalised LLMs

The era of one-size-fits-all large language models is giving way to more specialised approaches. Increasingly, organisations are developing and deploying verticalised LLMs tailored to specific industries or use cases – from healthcare and finance to software development and customer support.

These models deliver greater accuracy, relevance and security but require flexible infrastructure capable of supporting both training and inference, often close to end users. Secure access to proprietary data, strong interconnection and scalable deployment environments will be critical. As specialised AI becomes mainstream, infrastructure will play a decisive role in determining how effectively organisations can turn models into measurable business outcomes.

Australia stands at a pivotal moment in its AI journey. The convergence of surging demand, government policy, and infrastructure investment is creating a foundation for long-term innovation and competitiveness. Data centres, networks, and interconnection will remain the critical enablers of this transformation, supporting everything from sovereignty-driven strategies to inference-optimised deployments and agentic AI ecosystems. As organisations embrace specialised models and advanced cooling technologies, those that prioritise resilient, scalable, and sustainable digital infrastructure will be best positioned to turn AI potential into measurable business outcomes. In short, the strength of Australia's digital backbone will define its ability to lead in the next era of AI.