AI market turbulence: Bubble or a correction?

A sharp sell-off in global technology stocks has once again reignited the debate: is the artificial intelligence (AI) boom entering bubble territory, or is this correction simply a pause in an overheated market?

Over the past week, the major US indices have seen broad declines, with the Nasdaq falling sharply as mega-cap leaders Amazon, Microsoft, Nvidia, Meta, and Tesla lost between 2% and 5% from their highs. Even traditionally stable names such as Visa, JPMorgan, and Home Depot joined the downturn, reflecting a wider shift in market sentiment.

At the centre of this pullback is a growing fear that AI has crossed into speculative excess. Bank of America’s latest global fund manager survey shows that 45% of investors cite an AI bubble as the biggest market risk, while more than half believe valuations have already stretched beyond fundamentals. This points to unusually large capital flows, multi-trillion-dollar deal activity, and an investment cycle that resembles previous periods of market mania. The question now is whether this marks the beginning of an actual bubble or merely a healthy correction in an overheated market.

The conversation around an “AI bubble” has intensified because the technology sits at the intersection of unprecedented hype, rapid valuation expansion, and massive capital inflows. Markets are pricing not just current capability but a sweeping transformation that has yet to fully materialise. This mismatch between soaring expectations and still-maturing infrastructure has created pockets of excess, where optimism runs ahead of operational reality. These pressures together form the conditions often mistaken for a bubble — though the underlying story is far more complex.

The parallels are uncomfortable. Today’s “Magnificent Seven” — Nvidia, Microsoft, Apple, Alphabet, Amazon, Meta, and Tesla — now command a market capitalisation greater than the entire Chinese economy. This concentration of value and the “this time is different” narrative are classic hallmarks of a bubble. However, a critical distinction lies in the fundamentals: profitability. During the dot-com bubble, companies with little more than a “.com” in their name achieved staggering valuations despite having no path to profit. Today’s AI giants are profoundly different. While AI stock prices have appreciated strongly, this has been matched by sustained earnings growth, not mere speculation.

The valuation metrics confirm this. The median forward price-to-earnings (P/E) ratio for the Magnificent Seven is around 27. This is nearly half the median valuation of top tech stocks during the 2000 bubble. The current tech leaders are cash-generating behemoths, not speculative startups.

The more compelling argument against a dot-com repeat lies in a growing industry crisis: a severe infrastructure bottleneck. The problem is no longer whether there is demand for AI — demand is overwhelming — but whether the physical world can supply the power and computing capacity to meet it.

This market fall stems from a fundamental misunderstanding of treating AI as traditional software. Unlike static code, AI is a dynamic, energy-intensive industrial process that generates intelligence in real time. The core challenge is not vanishing demand, but a severe physical supply crunch — a struggle to secure the vast computational power and energy required to meet explosive, contract-backed demand. This is not a speculative bubble deflating, but a market confronting the hard physical realities of a new industrial age.

The real risk is structural. AI behaves like an energy-intensive industry, where every improvement increases total consumption — a modern echo of Jevons’ paradox. Any correction ahead is more likely to come from power constraints, grid stress, or delayed chip capacity, not a collapse in demand.

The next AI phase will not be defined by who can spend the most, but by who can execute through constraint. Evidence abounds: AI-cloud company Coreweave, despite a revenue backlog that nearly doubled to $55.6 billion, recently slashed its 2025 capital expenditure guidance by up to 40%, citing delayed power infrastructure. Similarly, Oracle is sitting on a $455 billion revenue backlog but is “still waving off customers” due to capacity shortages.

This “backlog paradox” — where firms have customers, capital, and contracts, but cannot deploy infrastructure fast enough — is triggering a market reassessment. Investors are realising that the AI gold rush’s timeline is being set not by software engineers, but by the pace of building power grids and data centres.

For investors, the priority now is to separate hype from substance. The sell-off is filtering out companies whose AI ambitions lack real business fundamentals, making it essential to focus on firms with strong earnings, clear monetisation strategies, and steady cash flows. While the “Magnificent Seven” dominate headlines, the real opportunities may lie in the infrastructure enablers of AI — power utilities, semiconductors, data-centre developers, and cooling technologies. Above all, a long-term view is crucial. Like the early internet, AI will see phases of excess and correction, but the structural shift is undeniable. Investors who stay selective and patient are best positioned to benefit from the next leg of this transformation.

The current turmoil looks far more like a necessary reset than the start of an AI collapse. Unlike the dot-com era, where belief evaporated overnight, today’s challenge is execution, not demand. AI’s foundations remain solid, its applications are expanding, and the physical constraints slowing its growth underline its real-world scale. Corrections remove excess — they don’t erase transformative technologies. The real question for the investors is whether they have the patience to stay invested as this revolution matures.

(Chirayu Sharma is an independent researcher based in Pune. Jadhav Chakradhar is an assistant professor, Centre for Economic and Social Studies (CESS), Hyderabad)