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plus61 Special feature: Joe Walker x e61 -- The compute economy and Australia's place in it
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Joe Walker x e61
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plus61: Special feature
Joe Walker x e61
The compute economy and Australia’s place in it
The financial returns to Australian data centres are uncertain and could be modest in the long run, but there is still a case for government support.
If Australia wanted to become a data centre capital of the world, it would certainly be able to – Sam Altman, May 2026.
Computing power, or “compute” for short, is emerging as a critical feature of the global economy. Described simply, compute refers to specialised computer chips sitting inside racks of servers that are stacked within warehouses called data centres. Vast amounts of compute are required to both train and run modern AI systems. The compute needed to train frontier models, for example, has been doubling roughly every five months since 2020. Growth in compute demand is driving a boom in data centre investment and has seized the attention of policymakers from the UK to the UAE to Australia.
Since compute is a key intermediate input of the AI age, many people assume it will remain an enormously profitable business. Australia could capture a slice of this value by producing compute at scale and exporting to the world (as Sam Altman has suggested). Yet the long-run financial returns to a compute export industry are uncertain and could turn out to be relatively modest, as we outline in this article. While we cannot predict how technology will unfold, nor are we experts on LLMs or digital infrastructure, economic fundamentals suggest compute profits may ultimately look less like iron ore and more like electricity generation.
Despite this, Australian governments have other reasons to step in to support data centre growth – the most obvious being national security and economic resilience.
The best policy support Australian governments can provide initially is to minimise existing regulatory inefficiencies, balancing the degree of support between data centres and complementary infrastructure. Governments also need contingency plans for compute access for critical activities to manage the risk of disruption to compute facilities overseas.
This article focuses on the narrow but important issue of compute. Compute sits among many other considerations for government in responding to the rise of AI, which we do not address in any detail. These include how and whether to promote AI adoption and skills, support research and access to frontier AI models, support an infant industry of AI-application firms, prepare for new threats to cybersecurity and collect new economic data about technology adoption.
The surge in investment in compute
The world has seen a sudden acceleration in capital expenditure on data centres.
The 5 largest US AI-infrastructure firms are expected to complete about US$700 billion in capital expenditure this year. Globally, property and other consultancies project that AI infrastructure expenditure will be about US$1.4 trillion for the year and installed capacity will rise by about a quarter as a result.
Australia is in the mix. There has been a surge in domestic data centre construction, with ICT capital expenditure reaching well over 1 per cent of GDP in the first part of 2026 versus an earlier average of around 0.3-0.4 per cent of GDP. That said, most of that value is imported – the recent increase in ICT capital expenditure has been substantially offset by a rise in data processing equipment imports, which mechanically offset from GDP growth.
Geography of the global compute industry in the longer run
Global compute production is relatively concentrated at present.
Around three-quarters of data centre compute production resides in just 4 countries, according to estimates of data centre energy usage by country. The US and China account for the vast majority of that figure. This is more concentrated than global exports of computer chips and close to the global concentration of iron ore exports (using exports as a proxy for production, since global trade data is more available than global production data).
Compute may be more widely distributed across the world once the market is mature.
This can be seen when we place the characteristics of compute alongside more familiar reference points, such as oil, computer chips and iron ore. Each of these goods is a more ‘mature’ intermediate factor of production. The principles of economic geography help to explain the shape of each global market. For example, iron ore exports are especially concentrated because of the location of viable deposits, whereas computer chips are concentrated because of large-scale economies to production. Viable crude oil deposits are much more dispersed across countries than is iron ore.
We can extend those principles to compute. The case for a more globally dispersed compute industry in the longer run rests on 4 arguments:
Limits to trade: There are limits to how far compute can be transported for some purposes and so some of it must be positioned near end users. The workloads that cannot be traded are those requiring low latency (that is, quick turnaround) or with data sovereignty protections.
Fewer physical limits to their location: There are fewer geographic or scale constraints on where you can build data centres (in contrast to, say, mines). You need some land (former industrial or greenfield), trade access to chip manufacturers, reliable electricity and water connections, and some construction and operations workers. In principle, this could be many locations.
Scale economies: These exist as AI companies do benefit from larger and coherent clusters for model training, but these economies seem less extreme than for high-end computer chips, where high capital expenditure combines with critical proprietary information learnt by doing.
The inability to store reserves of compute: This points to more demand for data centres themselves as protection against disruptions to trade.
By contrast, concentration forces may dominate for other uses. For model training, and perhaps agentic AI uses, higher latency (i.e. slower turnaround) can be tolerated and compute is most tradable. This is where compute can congregate at its lowest cost location. Another potential force for concentration is the housing of compute in satellites, which according to Elon Musk may be achieved within a few years. Few countries can contribute to that endeavour.
Australia could build compute not just to satisfy its domestic needs, but also to compete in this global export market.
Australia appears to have some comparative advantages in that competition, including the potential to develop widespread low-cost renewable energy, plus the present realities of strong legal institutions, land availability outside the main inner cities, high-quality subsea cable connections, trade access to AI-suitable chips, and stable geopolitical relationships. On the other hand, uncertainties about Australian copyright law are a potential barrier to model training, as the Productivity Commission has highlighted.
A mature global market for compute could see modest financial returns
The surge in demand for compute has made for strong near-term financial returns to data centre owners. But the longer run looks more uncertain, and that is the time frame that matters most for a national strategy on compute.
The bullish argument is that demand for compute will grow rapidly even over the longer run. This assumes AI’s economic value will keep growing as it takes on new tasks, and that shifts in how compute resources are used will outweigh the diminishing returns of scaling a single area like training or inference. Add to this a sense that short-run bottlenecks to delivering more compute will always tend to arise, and it points to continued strong financial returns.
There is also an underappreciated bearish case that today’s returns to the scarcity of compute will diminish for the following reasons:
The barriers to building data centres are modest and build times are relatively short compared to the other intermediate inputs we surveyed, so supply of data centres themselves is relatively elastic.
The supply of computer chips is the more significant bottleneck, and the supplier base for AI-suited computer chips is highly concentrated. This means the returns to scarcity are likely to sit upstream of the data centres themselves.
The product is relatively homogeneous once we are talking about latency-tolerant compute, which limits market segmentation and barriers to entry. (Although data centre chips, structures and legal regimes do amount to some product heterogeneity).
The customer base is relatively concentrated among a few powerful AI company buyers, giving them substantial negotiating power. We have already seen some major US compute consumers flex their purchasing power in Australia in search of preferential tax status or other accommodations.
Economic depreciation rates on computer chips are high, meaning customer willingness to pay will decline once the initial contracted period is complete.
It will be difficult for any country to deliver a much lower cost product because labour costs are a small portion of the compute cost base. Energy costs are more significant to the cost base, and there is less ability to achieve an advantage since energy prices are generally linked to global markets. (Although this is complicated by domestic energy reservation policies, or data centres that vertically integrate with energy generation.)
Put together, this suggests that in a longer-term mature market, pricing of compute may be negotiated down towards the cost of capital. Even AI compute may be commoditised.
There may be sufficient returns to motivate the investments, but there seems little scope for the kinds of large economic rents that have been observed over the past 20 years with Australian iron ore, Saudi Arabian oil or Taiwanese chip production. The construction of data centres itself will offer some economic return, but there are supply constraints in the Australian construction industry, including for housing and infrastructure, suggesting material opportunity costs.
Identifying externalities and reducing inefficient delays
The main game for Australian policymakers is to determine if there are positive externalities – social returns beyond the private returns to firms – from a large domestic compute industry.
Economic theory suggests three rationales for industrial policy intervention, namely positive externalities from an activity, coordination/agglomeration failures, and public good provision. For data centres, the first of these – externalities – is the most relevant.
We see 3 main arguments for externalities from domestic data centres.
For ‘economic resilience and security’, domestic compute capacity is likely to have useful insurance and deterrence externalities.
Amid increasingly volatile geopolitics, governments are talking about building resilience to trade disruptions. Domestic production capabilities can come at an efficiency cost if the private returns are not there, but they can also provide insurance against disruption to critical economic inputs. The case for buying this insurance is strongest where the disruption scenario is more likely and the consequence of that disruption is high.
Domestic compute reduces the threat capability of countries that provide compute or could threaten to disrupt international transmission infrastructure. This is discussed more in a break-out box.
Box: How strategic compute capability could protect Australia's sovereignty
The Australian government may wish to ensure a substantial domestic compute capacity to contribute to its strategic goals, most importantly to protect Australia's sovereignty.
The direct connection: we should control enough compute to ensure that we can continue to run critical functions of government and industry, even in a scenario of global disruption.
Without this domestic guarantee of supply, critical activities will be exposed to loss of access to the satellite connections or, crucially, undersea cables that link us to foreign data centres. This becomes increasingly important the more that domestic activity, and particularly the function of Australia's defence forces and intelligence agencies, rely on compute. Addressing this exposure would reduce an important threat-point for other nations.
Insurance could be fiscally costly if it involves subsidies, but there are reasons to think the costs can be mitigated. Additional data centres can be utilised for other purposes when not in use for critical purposes. Unlike oil reserves, compute does not get 'used up' if it is deployed for other activities. Also, it may be possible to achieve a similar policy goal without designating specific data centres as strategic reserves – government just needs conditional priority access.
The indirect connection: a big role for Australian data centres in global compute can plausibly give us leverage at other layers of the AI stack, especially frontier models, and help us pursue larger national goals. It could help Australia secure continued access to these models in a world where they are rationed, as well as early access to increasingly powerful models that are not released to the public (like Claude Mythos Preview). It may also give the Australian government some influence over such models’ governance and alignment with Australian values. Concretely, this could look like the Australian government striking MoUs with frontier labs that seek to do training runs in Australian-based data centres.
Crucially, these indirect benefits do not arise merely by virtue of data centres being located in Australia, even if those data centres are owned by Australian companies. Rather, they will depend strongly on the particularities of contracts or agreements between frontier labs, the Australian government, and data centre providers. Australian statecraft may need to play a large role in shaping these outcomes.
Demand for electricity from domestic compute capacity could have positive externalities on investment in the electricity grid. However, it could also push up energy costs for other sectors if renewable energy investment is not sufficiently responsive.
The positive externality could arise because investment in the electricity network involves large fixed capital costs. Data centre demand could help spread those costs and so prove an important catalyst of network investment. This is certainly the argument that some grid operators are making.
On the other hand, other industry sources have expressed concern that bottlenecks in renewable energy supply could lead to higher energy prices, undermining both the potential for any cost advantage to Australian data centres and also putting inflation pressure on the rest of the economy. (This may be less problematic for data centres using “behind the metre” generation.)
There is also an argument for innovation or productivity spillovers from domestic AI capacity because some Australian data must be held onshore. But beyond that, arguments for spillovers tend to be vague and deserve more scepticism.
The strongest argument here is that additional domestic compute capacity can help facilitate research on secure Australian data that must be handled domestically. This is similar to the argument for specialised supercomputers that Australian governments already support.
For instance, research on Australia’s rich health data assets will tend not to be value-captured by the researchers, leading to under-research. There is a place for government to further encourage research of this nature. This could be done by encouraging data centre infrastructure directly. An alternative targeted approach could be to expand payments to researchers, who will then have the funds to spend on compute. This will in turn encourage the required data centre infrastructure to be delivered by the private market.
There are also regulatory or self-imposed limits to where many private Australian firms’ data can be stored.
In these cases, it might matter not just whether the data centre is onshore, but also whether the data centre is owned by an Australian provider. Australian firms may be uneasy about the risk that US government authorities claim jurisdiction over data held by US service providers on Australian premises, a question raised by the US CLOUD Act even if its application is currently limited to countering serious crime.
Overall, there is not currently a strong case for additional externalities from ‘the vibes’ of having data centres housed in Australia. Latency reductions could have some positives, but seem unlikely to be the binding constraint to AI adoption and AI-driven innovation. Management quality, other data infrastructure or regulatory uncertainty are more likely to be binding for adoption.
The current focus of Federal and state government policy on data centres has been to reduce existing inefficient regulatory barriers.
The NSW and Victorian governments have set up expedited approval processes for data centres. Existing delays in assessing development applications are an important inefficiency, not specific to data centres. If existing regulatory principles can be applied more quickly, that is a clear policy win.
However, there is an important caveat: data centres and supporting infrastructure need to expand in concert. This means policy support should be applied in concert to energy and water infrastructure. For example, if data centre investment were to streak ahead of electricity generation, this could lead to energy costs rising materially and dragging on the economy as a whole. The Australian Government’s recent statement on expectations of data centres shows recognition of this issue.
Would we regret support for data centres if AI investment is a bubble?
It has been suggested that we are in the midst of an AI bubble. Critics point to the enormous scale of capital expenditure by the leading AI companies, much of it on compute, and the gap to still-modest AI subscription revenues. They also point to the easy financial conditions faced by those AI companies – for example, their extraordinary equity price-to-earnings multiples.
Both of those financial comparisons reflect, in part, the lag between investment and payoff for any new venture. But it's also possible that the winner-takes-most race among AI firms will lead to excessive investment, and that fear of missing out and ‘rational bubble’ dynamics are at play among investors chasing capital gains.
If there is a bubble, then how might we expect it to play out?
There is a useful historical guide to a global investment bubble that made a big imprint on Australia in the 1880s. The technological advance of the time was advances in cheap steel manufacturing. That sponsored a global frenzy of investment in railways, ports, shipping capacity and telegraphs, predominantly financed out of the global finance hub of London. During this period, much of British investment went overseas, including to Australia.
These investments ultimately delivered poor financial returns, and their price crash contributed to a period of depression. But the infrastructure that was built did benefit Australia’s productive capacity.
There is some analogy to the deployment of a new technological advance to Australia in the form of data centres, and how even if the investors take a bath at some point, the infrastructure could remain useful for some time.
In thinking about whether this analogy holds, a key difference that stands out is that economic depreciation rates on data centres are higher than for railways. Even so, economic depreciation is not always as smooth or fast as expected – for example, older computer chips like H100s recently rose in value for a period due to shortages of new higher-performance chips.
The case for more evidence
Many of the arguments made here are speculative, in large part due to the limited evidence available. Based on what we know today and economic principles, there is reason to think compute will become more dispersed globally, and Australia could carve out an important role for itself. Further, financial returns to compute are uncertain in the long run and may turn out to be modest. Yet, governments may still have an important role to play in ensuring sufficient domestic capacity for economic resilience and security reasons, and in supporting positive externalities while minimising negative spillovers through electricity prices.
Substantiating the arguments made here requires filling the current gaps in our knowledge. In particular, there is a great need for more evidence on the externalities from domestic compute capacity. For example, the significance (if any) of domestic compute access for Australian firms’ productivity, including tech startups. There is also a need for clearer evidence on the private returns to compute infrastructure providers, the supply elasticity of renewable energy capacity, and the non-linearities in the impact of electricity demand on investment in the electricity network. We also need to carefully identify the bottlenecks to research on Australian sensitive datasets and the potential remedies.
Sam Altman may be right that Australia could become a data centre capital of the world. But it turns out that the case for achieving this vision may turn less on profits than on sovereignty and resilience.
Greg Kaplan
Greg Kaplan is co-founder and Chair of the e61 Institute, and Alvin H. Baum Professor in the Kenneth C. Griffin Department of Economics and the College at the University of Chicago. His research focuses on macroeconomics, labour economics and applied microeconomics, with particular interests in fiscal and monetary policy, labour markets, inequality, and household behaviour. He is Lead Editor of Journal of Political Economy Macroeconomics and is author of the forthcoming book Macro: The Economic Models that Shape Our World to be published in November 2026.
For more information, please reach out to Greg via email at greg.kaplan@e61.in
Ewan Rankin
Ewan is a research manager focused on our Productivity and Structural Analysis workstream. Ewan has more than a decade of experience in economic analysis to support monetary policy at the Reserve Bank of Australia. He has an Economics honours degree from University of Sydney and an MPA from Princeton University.
For more information, please reach out to Ewan via email at ewan.rankin@e61.in
Joe Walker
Joseph Walker is host of The Joe Walker Podcast, Australia's leading ideas and public policy podcast. He is a two-time Emergent Ventures winner and previously worked in technology start-ups, most recently as director of operations at Y Combinator-backed Forage.
For more information, please reach out to Joe via email at joe@jnwpod.com
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