AI Is Now Contributing 25% of US GDP Growth — What Morgan Stanley's Infrastructure Buildout Data Means for Investors

There is a number buried in Morgan Stanley's latest economic research that deserves far more attention than it has received. According to the bank's analysts, AI-related infrastructure spending, the data centers, chips, power plants and cooling systems being built to run artificial intelligence, is now contributing approximately 25 percent of all US GDP growth in 2026. That is not a forecast about some distant future. It is a description of what is happening in the economy right now, and it means that one in every four dollars of economic growth the United States generates this year traces back, directly or indirectly, to the AI buildout. For investors trying to figure out where to put their money, that statistic carries an unusually direct message: this is no longer a story about an exciting technology theme sitting off to one side of the economy. It has become one of the central engines of the economy itself.

How AI Became a Quarter of America's Growth Story

Morgan Stanley's research team describes what is happening as something that looks more like an industrial buildout than speculative technology spending, and the comparison is deliberate. The bank estimates that nearly $2.9 trillion will be spent globally on data center construction alone through 2028, a figure driven by demand for computing power that continues to vastly exceed available supply. That spending feeds directly into industrial output, power investment and a wide range of supporting services, which is precisely the mechanism through which it has come to represent such an outsized share of US economic growth this year.

This is not happening in isolation from the rest of the economy either. Morgan Stanley's broader 2026 outlook frames the current moment using the phrase capex over consumption, capturing a structural imbalance in which massive corporate investment in AI infrastructure is providing crucial economic momentum even as American households face diminished purchasing power from elevated energy costs. The bank forecasts nonresidential fixed business investment expanding 7 percent in 2026 and 8 percent in 2027, with the five dominant technology infrastructure companies, Amazon, Alphabet, Meta, Microsoft and Oracle, expected to deploy roughly $805 billion in capital expenditure in 2026 alone, a figure Morgan Stanley projects will surpass $1 trillion in 2027. Crucially, the bank characterizes this AI-driven investment as structural rather than cyclical, meaning it expects this spending to continue regardless of swings in oil prices or consumer confidence, a distinction that matters enormously for how durable today's growth contribution is likely to prove.

From Mentions to Monetization: The Earnings Story Behind the Spending

Alongside the macroeconomic contribution, Morgan Stanley's research has tracked a parallel shift happening inside corporate earnings reports, and the shift is significant. According to the bank's most recent mapping of 3,600 stocks for AI exposure, 21 percent of S&P 500 companies now mention at least one AI benefit in their disclosures, up sharply from just 10 percent in 2024. That kind of jump signals how rapidly AI references have spread beyond the technology sector and into the broader corporate landscape.

What makes this data genuinely useful for investors, rather than simply a curiosity, is the distinction Morgan Stanley draws between companies that merely mention AI and those that are actually monetizing it. The market, in the bank's own words, is not paying for AI mentions alone. The companies that matter are the ones showing measurable results, and on that front the data is striking: AI adopters are seeing cash-flow margin expansion outpacing the global average by a factor of two. Morgan Stanley Investment Management has found that this effect extends beyond the companies building AI infrastructure directly, with second-order AI beneficiaries, businesses that apply AI to improve their own operations rather than sell AI products themselves, showing similarly strong efficiency gains and margin expansion. This pattern echoes something Thomas Kamei of Counterpoint Global, part of Morgan Stanley Investment Management, has pointed to directly: history shows that in major technology waves, equity value accrues not only to the companies supplying the technology, but to the companies that apply it most effectively.

The Energy Bottleneck Shaping Where the Money Goes

Underlying all of this spending is a constraint that is becoming impossible to ignore: power. Morgan Stanley's research forecasts that US data center demand could reach 74 gigawatts by 2028, against a projected shortfall of roughly 49 gigawatts in available power access. Global power consumption is rising at the fastest pace in more than a decade, with annual demand set to climb by more than one trillion kilowatt-hours per year through 2030, and AI-driven data centers alone are expected to account for nearly 20 percent of that growth, with their own power consumption increasing by nearly 126 gigawatts annually through 2028.

Morgan Stanley describes this energy bottleneck as simultaneously a constraint and a catalyst, one capable of triggering what the bank calls a trillion-dollar energy buildout layered directly on top of the data center spending itself. Large technology companies are likely to commit more than $1 trillion of spending in just the 2025-to-2026 period, and securing the financing for that scale of investment is expected to be a major focus for credit markets throughout 2026, with hyperscalers relying on their own substantial cash flows to fund roughly half of that spending and credit markets covering much of the remainder. For investors, this energy dimension is not a side note. It represents an entire additional category of the AI buildout, spanning natural gas, nuclear power, battery storage and increasingly off-grid power solutions, that sits adjacent to but distinct from the chips and data centers that usually dominate AI investment conversations.

Morgan Stanley's Four-Part Strategy for 2026

Rather than simply describing the scale of this transformation, Morgan Stanley's research has translated it into a specific, four-part recommended strategy for investors navigating 2026. The first element is to focus on companies positioned to benefit as nations pursue self-sufficiency in energy, critical materials, manufacturing capacity and AI capabilities, reflecting how thoroughly geopolitical competition, particularly between the United States and China, is now shaping where AI infrastructure investment flows. The second is to invest directly in AI infrastructure itself, the data centers, power generation and supporting physical assets, rather than focusing exclusively on the software and application layer built on top of that infrastructure.

The remaining two elements extend into credit markets and portfolio construction more broadly. Morgan Stanley's fixed-income strategists have pointed investors toward opportunities in structured credit and asset-backed financing tied to contracted AI infrastructure, alongside the diversification benefits available through highly rated, cash-generative hyperscaler bonds in investment-grade credit markets, a reflection of how central debt financing has become to funding this buildout. Underlying all four elements is a consistent caution from Lisa Shalett, Morgan Stanley's Chief Investment Officer for Wealth Management, who has explicitly warned investors against simply chasing broad technology exposure and urged them instead to differentiate the genuine AI winners from companies merely benefiting from association with the theme, noting how difficult genuine portfolio diversification has become given how correlated so many sector themes now are to the scale and scope of the data center buildout.

What This Means for Ordinary Investors Choosing Where to Look

For everyday investors trying to translate this research into actual decisions, the most important takeaway is that the AI value chain is considerably broader than the handful of household names that typically dominate headlines. Morgan Stanley's own framework points toward at least four distinct categories worth understanding separately: the companies building the physical infrastructure itself, including data center developers, real estate investment trusts and construction firms; the energy companies and utilities positioned to supply the enormous and growing power demand this buildout requires, spanning natural gas, nuclear and renewable generation alongside battery storage; the credit markets and structured financing vehicles increasingly used to fund this infrastructure, which offer a different risk and return profile than equities; and the broader universe of companies across every sector that are genuinely monetizing AI through measurable productivity and margin gains, rather than simply mentioning the technology in passing.

This is precisely why Morgan Stanley's distinction between mentioning AI and monetizing it matters so much for stock selection. A company that discusses AI prominently in its earnings calls without showing the kind of cash-flow margin expansion the bank's research has identified among genuine adopters is a meaningfully different investment than one demonstrating real, measurable efficiency gains. Investors without the time or expertise to evaluate individual companies across all of these categories can gain broader exposure through diversified technology and infrastructure-focused exchange-traded funds, though even that approach requires some awareness of which underlying holdings represent genuine AI monetization versus companies riding the theme's coattails without yet showing it in their financial results.

The Risks Layered Beneath the Opportunity

It would be incomplete, and frankly irresponsible, to present this data without acknowledging the risks Morgan Stanley's own research flags alongside the opportunity. The bank explicitly states that the AI trend is large enough to trigger valuation resets and sector rotation, as markets weigh the substantial benefits of this buildout against its potential to disrupt existing workers and industries. Recent weeks have already seen exactly this kind of rotation play out, with capital shifting away from some of the most expensive mega-cap technology names toward value stocks and smaller companies with accelerating growth, even as the underlying infrastructure spending itself continues uninterrupted. Investors have, in some cases, rotated specifically away from AI infrastructure companies whose operating earnings growth has come under pressure relative to their debt-funded capital expenditure, a pattern Morgan Stanley's research describes as the market punishing companies whose massive spending has not yet translated into robust, profitable growth.

This nuance matters enormously for anyone tempted to treat the 25 percent GDP contribution figure as a simple, uncomplicated reason to buy anything with AI exposure. The same research documenting AI's outsized economic contribution also documents a market that is becoming increasingly discerning about which companies within that broader theme actually deserve premium valuations, rewarding genuine monetization while becoming less forgiving of capital expenditure that has not yet produced commensurate earnings growth.

The Bottom Line

Morgan Stanley's finding that AI infrastructure spending now contributes roughly a quarter of US GDP growth transforms how this entire investment theme should be understood. This is no longer a speculative bet on a promising future technology sitting at the margins of the economy. It is, by the bank's own description, a central macro variable shaping growth, earnings, geopolitics and capital markets activity simultaneously, with nearly $3 trillion in infrastructure spending still ahead and more than 80 percent of that total yet to be deployed. For investors, the practical lesson is not simply to buy more exposure to anything labeled AI, but to follow Morgan Stanley's own guidance and differentiate carefully between companies genuinely monetizing this transformation through measurable productivity and margin gains, and those merely benefiting from proximity to a theme the market has, for now, been willing to reward generously. Given how much of America's current economic growth now runs directly through this buildout, understanding that distinction is no longer optional for anyone trying to navigate where their money should actually go.

*This article is for informational purposes only and does not constitute financial or investment advice. Always conduct your own research or consult a licensed financial advisor before making investment decisions. Data and quotes are sourced from Morgan Stanley Research and Morgan Stanley Institute publications as of June 2026.*