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Home Insights Macro views The AI boom: Bubble risk or durable cycle?

The AI boom: Bubble risk or durable cycle?

December 17, 2025 | 9 min read
Magdalena Ocampo
Market Strategist
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Few technological revolutions have rivaled AI in scale and speed since the dot-com era of 1995- 2000. The launch of ChatGPT in late 2022 unleashed a wave of excitement around a technology that promises to supercharge productivity and economic growth but also carries echoes of past bubbles.

Nearly 75% of major technological breakthroughs since the 19th century have coincided with speculative bubbles. When rapid expansion outpaced demand, the booms often ended badly and deepened economic downturns.

This cycle, billions have been invested in AI infrastructure—semiconductors, cloud platforms, and data centers—to meet expected computing demand. Yet, investors are increasingly wary of whether the return on investment will be positive. Estimates suggest AI capex will exceed $1 trillion in the coming years, increasing reliance on debt-funded investment. For context, roughly $300 billion in debt was accumulated during the dot-com era, amplifying the downturn when revenues failed to materialize.

Still, despite history’s cautionary take and investors’ growing concerns, markets have rewarded companies deeply entrenched in AI development. Over the past three years, the Magnificent 7 have seen their collective stock prices surge by 300% as of December 16, outperforming the S&P 500’s 76% gain, while the S&P 500, excluding tech and telecom, rose just 40%.

The rally, however, has come with trade-offs: higher valuations and elevated concentration risk. Together, these seven companies now boast a $19 trillion market cap—larger than the combined real GDP of Germany, Japan, India, the UK, and France in USD—and represent 32% of the S&P 500 and 21% of the MSCI World Index. The last time a few stocks dominated the index this much was during the dot-com era.

Whether this is a bubble or a sustainable breakthrough remains one of the largest debates among investors. While today’s AI rally exhibits traits seen in past bubbles, today’s fundamentals and macro backdrop are more supportive.
Elevated valuations are supported by fundamentals, not speculation

Putting today’s valuations in perspective should temper fears of bubble-like conditions. At the peak of the dot-com bubble, tech stocks traded at a 2.5x premium to the S&P 500, with tech leaders—Cisco, Oracle, Microsoft, and Intel—seeing their multiples surge from ~30x to over 80x between 1997 and the peak.

Today, the tech premium to the S&P 500 is closer to 1.3x, with the earnings multiples of hyperscalers—Microsoft, Meta, Google, and Amazon—remaining rangebound around 30x. Importantly, their earnings growth is keeping pace with multiple expansion, suggesting rising prices are grounded in fundamental strength rather than speculation.

While valuations across the Mag 7, the broader tech sector, and the S&P 500 are at their highest since the dot-com era, expectations remain contained and well below prior extremes.
Fundamentals in the AI era cushion the downside

Today, tech fundamentals are stronger than they were during the dot-com era. Profit margins (~28%) and cash flow margins (~22%) outpace those of the late 1990s (~7% and ~4% respectively), underscoring tech companies’ resilience and their ability to generate cash more efficiently. Even if AI monetization disappoints, these firms retain the flexibility to rein in spending and rebuild cash reserves—making a correction plausible, but a deep, dot-com-style bear market less likely.

And while there’s uncertainty about what the return on all this capex investment will be, AI revenue prospects are legitimate. In Q3, one leading tech company noted that integrating advanced AI models into marketing processes, such as smarter ad targeting, helped advertisers reach new customers and unlock revenue opportunities. Furthermore, a recent report by audit tech company AuditBoard also revealed that large audit companies that adopted AI early are already seeing tangible and sizable benefits, with estimated productivity gains of 20–40% and annual savings of $3.7 million.

Capex funding shifts: a contained but rising risk

While tech fundamentals today remain strong and clear of bubble-like conditions, the ongoing AI boom could see risks begin to emerge. One development to keep an eye on is the shift in capex funding for AI infrastructure buildout, particularly data centers, which are capital- intensive. Initially, capex was financed with cash reserves. This year, however, cash drawdowns have become more visible on balance sheets, alongside rising debt financing.

Hyperscalers have issued an estimated $121 billion in debt in 2025, well above the five-year average of $28 billion. Yet despite this surge, credit markets show no signs of strain. After all, tech debt levels relative to earnings power are still contained, particularly when compared to the dot-com era and to the S&P 500. Still, the growing reliance on debt warrants attention. If that trend continues, credit spreads could widen, making this a risk worth monitoring.

Additionally, it is worth considering that not all AI-related infrastructure investments carry the same risk profile. The data center landscape is increasingly differentiated by workload, with cloud and AI inference facilities supported by more durable demand, while generative AI training buildouts are more speculative and, in some markets, contribute to near-term oversupply. As a result, the sustainability of AI capex also hinges on where the spending is directed.
Circularity of deals is also raising concerns about interdependence

Adding to investor concerns are emerging signs of circularity among key AI players. One recent example involved an AI company raising capital for its own data-center buildout from a chipmaker, while simultaneously committing to purchase that same firm’s hardware to build and operate the facility. Arrangements like this risk obscuring true end demand if growth is increasingly supported by circular financing rather than independent revenue generation.

Such interdependence can also amplify vulnerabilities. When leading players become tightly linked through capital, supply, and demand, stress at one firm can quickly spread across the ecosystem—creating the kind of domino effect that has characterized past bubbles and, in extreme cases, led to systemic risk.

That said, not all circularity is inherently problematic. In some cases, these structures reflect deliberate, strategic investments made by market leaders seeking to accelerate their scale and secure supply. Even so, history suggests that when capital flows and demand signals become difficult to disentangle, caution is warranted.
Easing ahead, not tightening, makes the AI rally still look early-stage

Bubbles are often triggered by shifts in macro conditions and monetary policy. In the dot-com era, following Fed easing, short-term rates fell from nearly 8% in early 1995 to 3.8% by late 1998, fostering an environment that helped fuel a surge in tech IPOs and speculation. Nearly 79% of tech IPOs in 1999–2000 were unprofitable, and when the Fed began tightening in mid- 1999 and the economy slowed, the fundamental weakness of those firms became evident.

Today’s AI rally looks very different, unfolding amid lingering macro risks, moderately restrictive policy despite some easing from peak rates, and subdued IPO activity. Importantly, the economy remains in an easing cycle, with a macro backdrop that is likely to stay constructive—rather than entering a tightening phase that has historically popped market bubbles. Corporate profits are near historic highs, underscoring a fundamentally stronger backdrop than during the dot-com era, when profits deteriorated well ahead of the eventual collapse. Taken together, these conditions do not point to peak bubble territory.
Early-cycle dynamics, late-cycle questions

Today’s AI boom is unfolding against a materially stronger foundation—characterized by healthier balance sheets, robust cash flow generation, and broadly profitable companies. Valuations are elevated, but remain well below dot-com extremes, and history suggests that valuations alone rarely trigger major market corrections. Sharp selloffs are more often driven by a deterioration in macro conditions and a breakdown in earnings.

Still, risks are likely to build as the cycle matures. AI’s expanding weight in equity markets and the broader economy increases sensitivity to execution missteps and raises the potential for systemic stress. Rising debt financing and signs of circular capital flows heighten scrutiny around whether returns ultimately justify the scale of investment. For now, AI’s productivity and economic benefits remain prospective rather than assured.

As the AI cycle enters its next phase, selectivity will matter far more than broad exposure. Companies that fail to convert heavy spending into durable earnings growth are likely to be punished, reinforcing the importance of active due diligence. Monitoring leverage, credit conditions, and earnings momentum will be critical—not to time a peak, but to distinguish sustainable leaders from those vulnerable to an eventual unwind.

Investment implications: Cautiously long while diversifying opportunities

As AI becomes increasingly intertwined with markets and the broader economy, diversification will be critical. Investors can maintain exposure to the AI growth story while mitigating concentration risk.

Sector diversification: Broadening exposure beyond technology and identifying segments poised to benefit from an improving macro-outlook could be prudent. Cyclical sectors offer lower valuations and may benefit from tailwinds in 2026, driven by a more constructive economic backdrop and potential efficiency and earnings gains as companies integrate AI into their business models.

Style and size diversification: While maintaining exposure to large-cap and growth, which are the forefront beneficiaries of the AI story, diversifying into other styles and sizes, such as value and small caps, could offer additional benefits. These pro-cyclical segments, which typically have lower valuations, generally perform well in a backdrop of improving economic prospects.

Regional diversification: Europe lags the U.S. in AI development, but the equity index’s smaller exposure to tech makes it a compelling option for investors seeking diversification. The region has higher exposure to industrials, financials, and healthcare—three sectors that are also poised to benefit from AI infrastructure development (industrials) and AI adoption trends (financials and health care). While European equity performance also hinges on an improving economic backdrop, active managers can capitalize on AI-driven gains with reduced concentration risk.
Macro views
Equities

Footnotes

Sorescu, A. et al. (2018, July 12). Two centuries of innovations and stock market bubbles. Kaplan, K., et al. (2002, June 30). Too much, too soon for telecom. Bramwell, J. (2025, October 16). AI adoption in audit is on the rise. CPA Practice Advisor. Ritter, K. et al. (2002). NBER paper 8805: A review of IPO activity, pricing, and allocations.
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