The Conversation, CC BY-SA

Johanna Lim, University of Sydney

Until recently, data centres attracted relatively little public attention. They were largely treated as invisible pieces of digital infrastructure: essential but rarely discussed outside technical and industry circles.

But over the past 12 months across Australia, data centres have become the subject of intense political debate, community opposition, planning disputes and parliamentary inquiries.

Questions are being raised about how much electricity and water they use, where they should be built, who should pay for the infrastructure needed to support them, and whether Australia benefits from their continued expansion.

These questions are feeding into government policy. Following Prime Minister Anthony Albanese’s speech at the University of Sydney in July, a National Cabinet meeting in August reaffirmed plans to legislate nationally consistent mandatory standards for large data centres by early next year. These will include requirements around their energy, water and land use.

So how did a piece of digital infrastructure that once attracted relatively little public attention become such a prominent policy issue? Data centres themselves are not new. What has changed is their scale, purpose and the resources required to support their growth.

Data centres have existed for decades. But something has changed, and this essential infrastructure is now at the centre of a major policy debate.

This article is part of The Conversation’s series on data centres – what they are, why we need them, and why they’re suddenly so controversial.

The evolution of data centres

Data centres are specialised physical facilities that house servers, networking equipment and data storage systems. They are foundational infrastructure for the modern digital economy, supporting a wide range of services such as streaming, social media, banking, emergency response systems, and artificial intelligence (AI).

The origins of data centres can be traced to the 1940s, when early computers were so large they needed dedicated spaces to house them. As computers became smaller, more powerful and more accessible, governments and businesses increasingly adopted their own IT infrastructure. They often operated on-premises server rooms to manage email, file storage and other internal systems.

The internet then drove a shift towards larger co-location data centres in the 1990s, where multiple customers could rent space for their servers in a single shared facility.

Then came cloud computing. This provided customers with on-demand access to computing resources over the internet, and further accelerated the growth of data centres. In 2006, Google opened its first hyperscale data centre. Amazon Web Services also launched its first cloud computing services in the same year. Cloud service providers subsequently built increasingly large facilities to meet growing demand for these services.

What was once a room or floor serving a single organisation evolved into massive dedicated facilities – often called hyperscale data centres – capable of supporting millions of users. Today, the scale of a data centre is often measured by its power capacity, in megawatts or gigawatts. This reflects how much electricity the facility can draw at any one time.

Australia’s data centre boom

Australia’s own data centre market reflects this shift in scale.

Australia currently has over 160 operational data centres, with most located in New South Wales and Victoria. There are at least another 90 facilities in the development pipeline.

The data centres now being proposed are also considerably larger than many existing data centres.

For example, the proposed 1.2 gigawatt Mamre Road Data Centre in Sydney would cover an area equivalent to the size of 52 rugby fields.

If built to its maximum capacity, it would become Australia’s largest single electricity user.

Why AI is driving bigger data centres

Hyperscale data centres typically contain at least 5,000 servers, occupy at least 10,000 square feet of physical space, and can draw over 100 megawatts of power. That’s enough to meet the annual electricity needs of more than 50,000 households.

In 2025, hyperscale operators accounted for 48% of global data centre capacity.

Cloud computing and the growth of everyday digital activity initially drove the expansion of these large, centralised facilities. But since OpenAI launched ChatGPT in late 2022, AI has rapidly accelerated this growth.

AI workloads are far more computationally intensive than traditional digital services. They are expected to account for approximately 70% of data centre demand by 2030.

Training advanced AI models requires dense arrangements of specialised chips working simultaneously to process large volumes of data. This can run continuously for weeks or months.

Once trained, AI models also require computing power to respond to users. This process is known as inference. While a single interaction requires considerably less computing power than training a model, that demand adds up across millions of users.

Inference represents an increasing share of AI’s energy demands. This will continue to increase alongside AI adoption.

A 2025 survey found 88% of organisations reported regularly using AI in at least one business function – an increase from 78% a year earlier.

The shift toward large-scale data centres is also about efficiency.

Larger facilities tend to be more efficient. They benefit from economies of scale and advances in facility design, such as optimising power distribution and using higher-performance chips and servers.

In Australia, on-premises servers are estimated to consume over seven times more electricity to perform the same computation as hyperscale and co-location data centres.

But while hyperscale data centres can use energy more efficiently for the computing they perform, the sheer scale and growth of demand mean their overall electricity consumption is still significant.

Data centres accounted for around 3% of electricity supplied through Australia’s main grid in 2025–26. This share is projected to reach 13% by 2035–36.

What comes next for Australia?

The growth of Australia’s data centre market is unlikely to slow anytime soon.

Australia remains a competitive destination for data centre investment, with strong government support, continued interest from major technology companies and an estimated A$150 billion in data centre buildouts by 2030.

But community opposition is emerging as a significant risk for new developments. This opposition is, in part, because of the land, power and water data centres consume.

But it’s also because there is uncertainty over whether the significant investment in AI infrastructure will truly generate sufficient economic returns – and who will benefit from those returns.

The future growth of Australia’s data centre sector may therefore depend less on whether there is demand or capital to build them, and more on where they can be built, who bears the costs, and whether communities benefit from hosting them.

Johanna Lim, Research Associate, Strategic Technologies, University of Sydney

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Read More........→September 16, 2026 Categories: Australia,Media,Science-Technology

Mia Cobb, The University of Melbourne

What causes a dog to slink away from a cranky person, when they will quietly approach and lean against someone who is weeping? We’ve all seen it – they can respond to our feelings. And science agrees dogs have emotions too.

These social skills could underpin dogs’ success in living with us. But do you think your dog could tell an angry person’s face from a sad or fearful one?

New research published in the journal iScience explored that question, and revealed interesting findings from magnetic resonance imaging (MRI) scans of dogs’ brains.

Scanning dogs’ brains

Dogs are sensitive to human faces. They look longer in response to our emotional expressions and sounds compared with neutral ones.

Scientists weren’t sure whether dogs were just differentiating “good mood” (happy) from “bad mood” (angry, fearful or sad), or treating these expressions as genuine indicators of different emotions.

The new study, by Raúl Hernández-Pérez, a neuroscientist at the University of Vienna, and colleagues, explored this gap using MRI to scan pet dogs’ brains while they were viewing photos of human faces.

Building on their earlier work, the researchers found evidence that dogs do process images of our distinct emotional expressions differently.

The researchers used machine learning and showed that when looking at a dog’s whole brain, a different brain region was activated to distinguish between fear and sadness (the right rostral suprasylvian gyrus, to be precise), than between fear and anger (this was in the right mid ectosylvian gyrus and left splenial gyrus).

The analysis didn’t detect a difference in the brain areas activated when dogs were shown images of human anger and sadness. Fear stood out from the other negative emotions.

This raises the question: why?

It might be that fear and anger are simply more attention-grabbing than sadness.

Other research has found dogs react to fear and anger faster, and with a bigger physical response, such as a raised heart rate. This is likely because they’re the expressions most likely to call for a quick response from dogs to stay safe.

Sadness is less likely to pose a direct threat to dogs living with people, so they experience less urgency to respond to it. We know some dogs don’t respond with the heroic Lassie behaviour we might like when we are in distress.

Although the numbers in this new research were small (eight and twelve dogs across the two parts of the study), this is the first MRI-based proof-of-concept evidence that dog brains can distinguish between two human facial expressions of distinct negative emotions. It indicates dogs’ neural representation of our emotion goes beyond a simple valence (good/bad) split.

This shows us that perceiving emotion in others (even across species) isn’t handled by one single “emotion centre” in the brain – in dogs, in humans, or in other animal species. It’s spread across a network of regions working together as part of living socially.

A sense-ational result

The authors of this study point out that using still images of humans is a very people-centred way to explore how dogs interpret our emotional states.

We know dogs live in rich sensory worlds where the scent and sound of our speech also convey emotions, shaping how dogs respond to us.

In fact, even wolves who have grown up around people show the same kind of response to the odour of human fear as dogs. This highlights the important role of learning, as distinct from evolutionary differences in canid bodies or how they respond to people. Dogs (and wolves) are learning about us in every interaction we have with them.

Dogs are adept at watching, smelling, and listening to our emotions, learning how these signals predict our behaviours toward them, and using this information to live with people harmoniously.

Returning the favour, learning more about how dogs express their emotions seems like the least we can do.

Mia Cobb, Research Fellow, Animal Welfare Science Centre, The University of Melbourne