A.I. and the Economy: Lessons from the U.S. for Australia
Artificial Intelligence is driving a major shift in the U.S. economy, fuelling rapid demand growth—particularly in infrastructure like data centres and chip production. Yet its long-anticipated productivity gains remain elusive. In a recent interview, Harvard economist Jason Furman noted that much of the current GDP growth and equity market strength is concentrated in just a handful of tech giants, raising important questions about sustainability, valuation, and the broader economic balance.
Furman, a former Chair of the Council of Economic Advisers under President Obama, likens today’s A.I. surge to earlier cycles of “productive bubbles” such as the railroads and the early internet—periods of overinvestment that nonetheless laid foundations for lasting transformation. The distinction, he argues, lies in whether these investments ultimately deliver on their promise of innovation-driven growth.
A.I.’s Role in the Economy
A.I. is currently powering the demand side of the economy, not the supply side. In early 2025, 92 percent of U.S. GDP demand growth came from just two categories: information processing equipment and software—a direct result of the A.I. build-out, including data centres, chip purchases, and digital infrastructure.
However, these figures primarily reflect increased capital expenditure, not gains in productivity. Furman argues that the expected supply-side benefits of A.I. have not yet materialized. This may be due to a J-curve effect, where businesses are still learning how to integrate A.I. effectively. During this phase, productivity may decline temporarily as organisations reallocate resources and retrain staff without yet achieving efficiency gains.
What does this mean for Australia?
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Investment Risk and Sectoral Balance
Like the U.S., Australia may face economic distortion if capital concentrates in A.I. infrastructure at the expense of manufacturing, housing, or mining technology. -
Lagging Productivity
A J-curve effect is likely. Firms investing in A.I. integration may see initial drops in productivity before longer-term gains materialise—testing the patience of policymakers and investors alike. -
Tech Market Concentration
The risks of concentrated equity exposure apply to Australian super funds and tech-heavy portfolios, particularly those over-indexed to global A.I. giants or local tech leaders like Atlassian or NextDC. -
Human Capital and Immigration
A successful A.I. transition requires skills and talent. Policy on skilled migration, STEM education, and digital infrastructure will shape Australia’s ability to compete in a global A.I. economy. -
Financial Oversight
As venture and non-bank financing grows, regulators must monitor for signs of speculative funding bubbles—particularly in early-stage A.I. ventures lacking robust business models. -
National Security and Sovereign Capability
With growing strategic interest in A.I., targeted public investment may be justified. But Furman cautions against hype-driven subsidies and calls for transparent, outcome-linked investment criteria.
Unlike the U.S., where a trillion-dollar private equity sector plays a dominant role in funding emerging technologies, Australia’s A.I. investment landscape is far more reliant on government funding—placing the public sector at the centre of shaping the country’s technological future. -
Monetary and Fiscal Policy Sensitivity
If A.I. exuberance inflates asset prices, Australian monetary and tax policy may need to account for financial stability risks, beyond traditional inflation and employment mandates.
Wrap
Whether A.I. becomes a cornerstone of long-term productivity or the next boom-bust cycle will depend not just on technological advances, but on how well markets and governments manage expectations, investment flows, and economic resilience. Australia has the opportunity to learn from the U.S. experience—and avoid repeating its missteps.