What makes the AI boom different from the dotcom bubble?

Tech giants are investing heavily in AI, but there’s an important difference between today’s spending boom and the dotcom era of the 1990s.

20 November 2025

An office featuring computers from the dotcom era. Picture: AAP

Key takeaways

  • AI-related investment now makes up around 10 per cent of total US business investment.

  • US hyperscalers are expected to spend US$370 billion this year - a 65 per cent jump from 2024.

  • AI spending could rise to US$500 billion by 2026 - more than Apple, Microsoft, and Google combined spent on R&D last year.

Is this another tech bubble? 

Not quite. While the scale of investment is drawing comparisons to the dotcom era, today’s datacentre giants, known as hyperscalers, are in a much stronger financial position. These large cloud computing service providers, including Amazon Web Services, Microsoft Azure and Google Cloud, are investing billions in artificial intelligence (AI) infrastructure. But unlike the telcos of the 1990s, which borrowed heavily to build the internet, hyperscalers are funding growth with strong cash flow and healthy profit margins.

“Telecommunication companies from the late 90s borrowed heavily to invest in the internet, and they didn't even have profitable business structures. This time around, hyperscalers are starting from a much stronger position, and they're already monetising AI as it grows,” CBA International Economist Samara Hammoud said.

“This is a structural shift in how tech firms invest.”

What are hyperscalers spending on?

Most of the money is going into capital expenditure, or capex, which means long-term investments in physical and digital infrastructure. That includes building massive data centres to power AI models, upgrading cloud networks, buying high-performance chips, and expanding server capacity. Hyperscalers are also investing in software development and tools that help businesses and consumers use AI more easily.

McKinsey estimates global data centre investment could reach US$7 trillion by 2030, equivalent to almost a third of the entire US economy.

What could go wrong?

There’s always a risk that AI hyperscalers may not deliver the profits investors expect. Chips could become more expensive, wages for AI talent may rise, and the growing electricity shortfall could worsen. These pressures could squeeze earnings and force companies to borrow more.

“It’s okay to be cautious about AI, but ultimately we don’t believe we’re living through another dotcom bubble,” Hammoud said.

See Samara Hammoud’s full analysis of the US current account in the AI era here.

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