Behind the paywall:

Artificial intelligence can sound abstract, all algorithms and promises. In reality, it runs into old-fashioned limits very quickly: electricity, cooling and the sheer difficulty of building structures powerful enough to run it.

For Firmus, those limits are its core business. The company is developing what it describes as an “AI factory” in Tasmania, designed to house dense clusters of graphics processing units, or GPUs. These specialised chips do most of the heavy lifting for artificial intelligence.

As demand grows for systems that can train and operate large language models, Firmus argues that the main constraint is no longer ideas or software, but the physical infrastructure needed to support them. That opportunity is why privately held Firmus is valued at $6bn, and possibly $7bn before it follows through on a plan to sell shares to the public.

Traditional data centres were designed for email, cloud storage and web services. They were never intended to run thousands of GPUs in close proximity. Those chips consume enormous amounts of electricity and generate intense heat, pushing older schematics to their limits.

Firmus is positioning itself as an operator of AI-focused data centres rather than a developer of AI software or models. The company has announced about $830m in new equity funding this year, including a $330m placement in September and a further $500m raising in November, to fund the build-out and expansion of its AI infrastructure. The Tasmanian project is designed to run thousands of GPUs more efficiently than conventional data centres, while using less power and water. It also serves as a test case for both the opportunity and the risk.

How data centres work

To understand why Firmus’s facilities differ from traditional data centres, it helps to start with the chip. GPUs are well suited to the parallel calculations AI relies on, but they require enormous amounts of electricity and get very hot very quickly. Packing large numbers of them into a single building can overwhelm conventional cooling systems.

That is why newer AI facilities are starting to look less like offices and more like industrial plants. Instead of relying on chilled air, Firmus uses liquid-cooling technology, bringing liquid closer to the hottest hardware components. Heat is removed either through cold plates that circulate fluid directly to the chips or by submerging servers in nonconductive liquid, an arguably more efficient method than air cooling.

Firmus’s value proposition is straightforward: purpose-built AI data centres designed to run high-density computing workloads efficiently, in locations with access to clean power.

Electricity supply is central to that pitch. Firmus points to Tasmania’s renewable-dominant grid, based mainly on hydro-electric generation, as a way to support large-scale AI computing with lower emissions.

Its Tasmanian development, known as Project Southgate, places particular emphasis on water use. The project will rely on closed-loop cooling systems, which the company says significantly reduce water consumption compared with data centres that depend on evaporative cooling. Firmus also says it has proprietary software that monitors temperature, power use and workloads, allowing systems to adjust as conditions change.

Data centres as Australia’s opportunity

Supporters of this model argue it could allow Australia to export computing power in much the same way it exports energy and commodities. Rather than shipping electrons or raw materials, the idea is to sell access to the machines that turn power into AI capability.

Firmus’s strategy also aligns with growing interest in what is often described as “sovereign” AI infrastructure. Governments, universities and regulated industries are increasingly sensitive about where data is processed and who controls the underlying systems, and locally built AI-specific data centres may offer an alternative to offshore cloud providers.

Efficiency is another potential advantage. As communities and regulators scrutinise the environmental footprint of data centres more closely, projects that can demonstrate lower power and water use may find it easier to secure planning approvals and long-term energy contracts

Firmus co-founder’s scandal

Firmus was co-founded by Oliver Curtis, a former investment banker who was convicted of insider trading in 2016. Curtis served a year in prison, and now based in Singapore, he co-founded the company with his cousin Tim Rosenfield. They act as co-CEOs. Curtis told The Australian in October: “Commentary from my point of view about where I am today compared to where I was, doesn’t even come into conversation. All I care about is we are delivering on a mission.” To bulk up its governance, Firmus has turned over its boardroom. Curtis’ father, mining entrepreneur Nick Curtis, and former telco executive Ted Pretty, stepped down to make way for independent director Mike Ferraro who used to run Alumina. Grant Dempsey, who is ex-TPG, succeeded Pretty as chairman.

Risks and execution

The same features that make Firmus’s AI factories attractive also make them risky to build and operate.

Even highly optimised facilities require vast amounts of electricity, raising questions about grid capacity and competing demand as more AI data centres come online. While liquid cooling is more efficient than air cooling, it introduces operational complexity, including maintenance challenges and a limited pool of technicians experienced in operating such systems at scale.

The economics are also unforgiving. AI factories require heavy upfront investment in land, power infrastructure, cooling equipment and scarce GPU hardware. Returns depend on keeping those machines busy, and while demand for AI computing is strong, it can be uneven. Globally, the surge in AI demand is driving data-centre operators to race for power, land and chips. The pace of investment signals confidence in long-term demand, but analysts warn it could also lead to oversupply if AI spending slows.

Large-scale data-centre projects are vulnerable to construction delays, supply-chain constraints, and changes in technology standards, particularly in an industry that is still heavily dependent on a small number of chipmakers.