While prominent investors like Jeremy Grantham, Rob Arnott, and Bill Smead have warned that the stock market is in a dangerous bubble reminiscent of the dot-com peak, Morgan Stanley’s global head of research Katy Huberty presents a contrasting bullish view: the AI-driven rally may just be getting started.
With the S&P 500’s Shiller CAPE ratio at around 38 following a 67% rally since October 2022, market valuations have reached historically expensive levels. However, Huberty argues that current conditions differ significantly from previous bubbles when examining the AI capital expenditure and adoption cycle.
Huberty estimates that $10 trillion in capital expenditure will ultimately be required to fully develop AI infrastructure. Remarkably, mega-cap hyperscaler companies have only invested approximately $300-$400 billion over the past two years, representing single-digit penetration of the total investment needed. This suggests the AI buildout is still in its infancy.
Comparing current market conditions to the dot-com bubble reveals important differences. The median 12-month forward PE ratio for the top five stocks by market cap was 65.3 in 1999, compared to just 31.1 in 2024, despite similar revenue and earnings trajectories. This indicates that while valuations are elevated, they haven’t reached the extreme levels seen during the late 1990s tech bubble.
Drawing parallels to the mobile internet cycle that began in 2010, Huberty notes that earnings revisions for top tech companies still have substantial upside potential. According to her analysis, “we’re only 15% of the way through the typical estimate revisions and market cap expansion of a new computing cycle.”
Each technology wave over recent decades has been approximately 10x larger than the previous cycle in terms of both user adoption and business investment. This historical pattern supports the thesis that AI development and deployment has years of growth ahead.
For investors looking to capitalize on the next phase, Huberty recommends focusing on AI beneficiaries—companies whose profits will be enhanced by implementing AI technology rather than just AI developers. Her team’s analysis suggests financial sector stocks are particularly well-positioned to benefit first from AI adoption. Investors can gain exposure through ETFs like the Financial Select Sector SPDR Fund (XLF), Vanguard Financials ETF (VFH), and iShares US Financial Services ETF (IYG).
Huberty acknowledges potential volatility, noting that “you can’t rule out a quarter or six- to 12-month period where the market digests” gains before reaching the next inflection point. However, she maintains that substantial alpha generation opportunities remain as AI adoption becomes more broad-based across industries.
Key Quotes
If we’re talking about ultimately a $10 trillion AI infrastructure investment, we’re at single-digit penetration today.
Katy Huberty, Morgan Stanley’s global head of research, emphasizes how early we are in the AI investment cycle, with mega-cap companies having spent only $300-$400 billion of an estimated $10 trillion total needed for AI infrastructure development.
We’re only 15% of the way through the typical estimate revisions and market cap expansion of a new computing cycle.
Huberty draws on historical patterns from previous technology cycles to argue that earnings growth and market capitalization increases for AI companies have substantial room to run, contradicting bubble warnings from other prominent investors.
We’re at this tipping point where adoption is now much more broad-based. None of this is priced. To justify that $10 trillion of infrastructure investment, you have to see the impact on efficiency. And we think it’s coming here pretty quickly.
Huberty explains why she recommends investing in AI beneficiaries rather than just AI developers, arguing that productivity gains from widespread AI adoption will materialize soon and aren’t yet reflected in current valuations.
Our Take
Morgan Stanley’s contrarian perspective deserves serious consideration despite justified concerns about elevated valuations. The key distinction Huberty makes—between bubble psychology and fundamental technology adoption cycles—is crucial. Historical technology waves do follow predictable investment and adoption patterns, and the $10 trillion capex estimate, while staggering, may prove conservative if AI delivers on its transformative promise.
However, investors should remain cautious about timing. Even if the multi-year thesis proves correct, significant corrections can occur within longer bull markets. The 2000 dot-com crash was devastating despite the internet ultimately transforming society. The recommendation to focus on AI beneficiaries rather than pure-play AI companies is particularly astute—these businesses may offer better risk-adjusted returns as they capture efficiency gains without the extreme valuations of leading AI developers. The financial sector call is interesting and worth monitoring as an early indicator of whether AI productivity gains materialize as promised.
Why This Matters
This analysis from Morgan Stanley challenges the prevailing bubble narrative and has significant implications for investors and the broader AI industry. If Huberty’s thesis proves correct, we’re witnessing the early stages of a multi-year, multi-trillion-dollar investment cycle that could reshape the global economy.
The $10 trillion infrastructure investment estimate underscores the massive scale of AI transformation ahead, dwarfing current spending levels. This suggests continued strong demand for semiconductors, cloud infrastructure, data centers, and energy resources—creating opportunities across multiple sectors.
The shift from AI infrastructure buildout to practical business applications represents a critical inflection point. As companies move beyond experimentation to implementation, productivity gains and efficiency improvements should become measurable, justifying the enormous capital investments. The financial sector’s positioning as an early AI beneficiary could signal which industries will lead the next wave of adoption.
For workers and businesses, this timeline suggests AI disruption and opportunity will accelerate over the coming years rather than plateau. Organizations that delay AI integration risk falling behind competitors who capture efficiency gains earlier. The extended investment cycle also implies sustained job creation in AI-related fields, though with continued displacement in roles susceptible to automation.
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