The hyperscalers sit at the intersection of infrastructure and applications, and many have already monetized AI indirectly through higher cloud demand.
In Brief
- The AI trade in 2026 has been more volatile, with technology stock pullbacks and concerns about sustainability.
- Massive capital spending is underway to build AI infrastructure, driven by surging adoption and intensifying workloads; yet supply constraints in data centers and power continue to pose risks and opportunities.
- Achieving acceptable returns in the AI investment theme requires sustained adoption, and thoughtful diversification will be essential as both winners and losers will emerge.
Evaluating AI
The artificial intelligence (AI) trade has stumbled out of the gate at the start of 2026 compared to recent years. In fact, concerns about AI have been brewing since last year, with several distinct pullbacks in technology (tech) stocks over the course of 2025. However, while the AI trade may be more volatile in 2026, there is little reason to doubt the broader AI boom, supported by earnings, valuations, adoption, and strong balance sheets.
As we EVALuate the AI boom, several dynamics underscore the directional momentum in this theme:
(E) Earnings: Tech profits have been spectacular since the launch of generative AI chat in November 2022. The mega-cap tech stocks have enjoyed double-digit profit growth since early 2023, and earnings surprises have exceeded 10% in recent quarters, compared to the market average of 7.5% over the same period. For 4Q251, hyperscaler revenues in key AI segments, whether cloud or apps, grew 35% year-over-year (y/y) on average, providing evidence that AI is being monetized. As we look ahead, mega-cap tech earnings have been revised to 24.3% for 2026, while the S&P 493 have been revised to 13.9%. The past, present, and future of AI profitability is bright.
(V) Valuations: Together, the mega-cap tech stocks sport a price-to-earnings ratio (P/E) of around 28x, not much higher than their collective P/E in the spring of 2023. While that is expensive, it is by no means extreme, and investors have gotten what they’ve paid for, given that valuations have barely budged while profits have soared. It is also dramatically less than information technology’s peak valuation of nearly 70x during the internet boom.
(A) Adoption: In order to justify the massive hyperscaler capex spend, demand must also be robust. 17% of U.S. businesses now report AI adoption, and even more (45%) pay for AI subscriptions. Many of the leading tech CEOs note “insatiable” demand, citing that the bigger risk is underinvesting, not overinvesting.
(L) Leverage: Excessive leverage, followed by a credit crunch, has caused many booms to go bust. However, while the hyperscalers have increasingly been tapping the bond markets and private funding vehicles, they do have the cash to support their investments, but are opting for a more sensible capital structure. Despite taking on more debt, hyperscaler net leverage is 0.9x, compared to 2.6x for the average investment-grade issuer.
How will companies monetize AI?
Hyperscalers are set to spend USD 533billion in capex this year as the race to build AI infrastructure ensues. With some estimates suggesting the AI buildout could eclipse USD 5trillion over the next several years, investors are rightfully questioning whether this scale of investment can ultimately generate acceptable returns.
AI monetization is already underway, but it remains concentrated in the infrastructure layer. Elsewhere, end-user monetization is still early, uneven, and opaque. This pattern is common for an early general-purpose technology, but the stakes are high in today’s world, and how monetization ultimately materializes across the value chain will have significant implications for markets.
Capital spending on semiconductors, data centers, networking equipment, and power infrastructure has surged as companies race to deploy AI at scale. Two forces are driving this scramble for compute:
- Rising adoption: 55% of American adults now use GenAI each week, an adoption rate that took the internet 16 years to achieve2, and 17% of U.S. firms report adoption.
- Intensifying workloads: Compute intensity per task is rising as models scale and become more complex, particularly with the rise of reasoning models.
Despite having hiked capex 170% over the last two years, the hyperscalers still expect a very tight demand-supply environment through 20263. Of the 125 GW of data centers globally, only ~20 GW can currently handle AI workloads4, and at least 100 GW of new power generation will be needed to support projected data center demand5. These constraints pose real risks to companies servicing AI, but they also create unique opportunities across the infrastructure value chain.
Three years into generative AI, there are already unprecedented success stories. AI-native companies are reaching massive scale with lean, capital-efficient teams that would have been unthinkable a decade ago.
That said, the economics of AI services are still under construction. Many leading models lose money on each interaction, and while that’s not unusual for a new service, it's unclear how long it can persist. Subscription revenue has dominated so far, but advertising and alternative pricing models are likely to emerge.
Enterprise software companies are also testing a range of pricing strategies, from bundling AI into existing products to charging per token. Roughly 60% of firms expect to expand AI budgets significantly, but the open question is whether this represents incremental spend or substitution within IT budgets. Large firms already spend roughly USD 1,000-USD 3,500 per employee annually on software6, and productivity gains will ultimately determine how far spend can climb.
Investment implications
The reasons why AI is a compelling investment opportunity persist, but how we invest in AI has evolved. The mega-cap tech companies are no longer synonymous with AI. Instead, the theme has broadened out to other AI innovators within tech, as well as AI enablers in sectors like industrials, utilities, and materials, all of which have boasted double-digit earnings growth in the most recent quarter. Although we do not believe AI is a bubble, it does not preclude one from forming, which is why we don’t want to chase the herd but rather employ thoughtful stock selection and diversification.
The hyperscalers sit at the intersection of infrastructure and applications, and many have already monetized AI indirectly through higher cloud demand7. However, the return hurdles ahead are substantial. Achieving a 10% return on current AI investments could require USD 650 billion in annual revenue, or USD 35 from every iPhone user monthly8. While that may be feasible, it's a high bar that assumes sustained, broad-based adoption.
Finally, an important reminder for investors: even if the aggregate AI returns disappoint, individual winners can still do extremely well, particularly if winner-take-all dynamics emerge. And even if aggregate monetization exceeds expectations, there will almost certainly be losers. This is why diversification remains key—across companies, the AI value chain, and asset classes—as portfolios benefit not just from exposure to innovation, but from balance.