Morgan Stanley: AI Infrastructure Spending Surges to $1.4 Trillion – Can META's "Computing Power Bet" Pay Off?

TL;DR

• According to a Morgan Stanley research report, the capital expenditures of the five major hyperscale cloud providers could reach $1.4 trillion by 2028.
• Construction cost per GW is being driven up by memory, power, and construction, with computing capacity potentially expanding from 30GW to 120GW.
• META is listed as the top pick for AI internet, with a $775 price target contingent on the monetization of APIs, advertising, and subscriptions.

Morgan Stanley raised its capital expenditure estimates for major hyperscale cloud providers in a sell-side research report. It projects the total capital expenditure for the top five platforms to reach $1.2 trillion in 2027 and $1.4 trillion in 2028, while maintaining META as its top pick in AI internet with a $775 price target.

These figures are based on the report’s model estimates and do not equate to official company guidance. Public versions of Morgan Stanley’s materials have already mentioned that global AI-related infrastructure investments could approach $3 trillion by 2028, with data center capital expenditure around $2.9 trillion. The $1.4 trillion figure for the top five platforms stems more from the sell-side's granular estimates for these major cloud and internet platforms.

The most newsworthy change in this report is the continued upward revision of AI infrastructure spending. By 2028, the available computing capacity of major platforms in the model is close to 120GW, roughly four times the 30GW in 2025. The construction cost per GW has also been raised. Newer platforms like GB200, GB300, and Vera Rubin require more memory, power, rack infrastructure, and engineering input.

For investors, the question has shifted from "Will AI giants spend money?" to "How quickly will this spending translate into revenue?" META is positioned prominently because, while facing higher AI capital expenditure pressure, it also has more direct monetization channels like advertising, consumer applications, model APIs, and subscription tools.

$1.4 Trillion Spend Bets on 120GW of Computing Power

The report raises capital expenditure expectations for the five major hyperscale cloud providers for 2027 and 2028 by 9% and 10%, respectively, to $1.2 trillion and $1.4 trillion. This scope covers AI infrastructure spending by Amazon, Google, Microsoft, META, and related SPCX entities.

Capacity expansion is a primary driver of the upward spending revision. In this model, the available computing capacity of major platforms rises from approximately 30GW in 2025 to nearly 120GW by 2028. Amazon's total capacity is estimated to reach about 35GW by 2028. Google is expected to add the most capacity between 2027 and 2028. META's capacity is projected to increase from roughly 3.5GW at the end of 2025 to 14GW in 2027 and 21GW in 2028.

Capital expenditure forecasts for the five major hyperscale cloud providers. Total spending is projected at $1.4 trillion in 2028, with upward revisions of 9% and 10% for 2027 and 2028 compared to previous estimates.

Available computing capacity is projected to rise from approximately 30GW in 2025 to nearly 120GW by 2028. META's capacity increases to 21GW, while Amazon's total reaches roughly 35GW.

There are nuances in META's capital expenditure estimates that need consideration. In the report model, META's capital expenditure for 2027 and 2028 is raised to $225 billion and $250 billion, respectively. Some public secondary reports citing Morgan Stanley mention a total of roughly $380 billion for META from 2027 to 2028, which may involve different scopes like total capex, AI infrastructure, gross figures, or including off-balance-sheet financing.

These differences don't change the main narrative: AI data center spending continues to pressure free cash flow, depreciation, and short-term EPS, and it will also determine whether future revenues from cloud, advertising, search, APIs, and enterprise tools materialize. Those who can more effectively convert computing capacity into billable products will find it easier to justify today's capital expenditures.

Cost per GW is Rising, Memory and Power Raise the Bar

The upward spending revision is not solely due to "building more data centers," but also because "each GW is becoming more expensive."

In the report's bottom-up cost model, the construction cost per GW for GB200 is approximately $35 billion, a 16% increase from the prior assumption. GB300 costs around $39 billion (up 19%), Vera Rubin about $49 billion (up 20%), Google's TPU v7 roughly $27 billion, and Amazon's Trainium 3 about $21 billion.

Updated cost estimates for deploying GPU and ASIC-based GW-scale data centers. GB200 is approximately $35 billion, GB300 $39 billion, and Vera Rubin $49 billion.

Cost pressures mainly stem from two areas. The memory component in high-end AI systems is increasing, while external data center costs – including power, land, cooling, power distribution, and construction – are also rising. The report assumes these external costs increase from roughly $10 million/MW to between $11 million and $19 million/MW.

This is a key reason why the expenditure curve for AI giants is unlikely to decline in the short term. While improved chip supply can alleviate some pressure, power access, rack infrastructure, construction, skilled labor, and local permitting will continue to lengthen construction timelines. Some project timelines may extend to around three years. The larger the capital expenditure, the faster the revenue side needs to demonstrate returns.

META's Focus Shifts to AI Monetization

META is listed as a top pick, primarily because its potential AI revenue streams are more concentrated than those of most other internet companies.

The report breaks down META's potential upside into areas like Meta AI search, new cloud services, API revenue, subscription tools, and advertising upgrades. Cumulatively, these could contribute roughly $10 to META's 2028 EPS. In a base scenario, META's 2028 EPS is $33.41. If some of these options materialize, there is further upside potential for EPS.

The cumulative contribution of META's five types of AI upside options to 2028 EPS. Base EPS is $33.41, with a total upside of approximately $10.

This estimate may not perfectly align with figures mentioned in some public secondary reports, such as "four products or catalysts" or "EPS upside of $1 to $3 in 2028," and should be viewed as a scenario analysis within this specific report. The actual impact on financial statements depends on product adoption rates, monetization capability, and compute utilization.

APIs represent the most straightforward entry point. Meta announced the public preview of its Meta Model API on July 9th. According to third-party sources like the pricing tracker Artificial Analysis, the input and output prices for the Muse Spark 1.1 API are $1.25 and $4.25 per million tokens, respectively, which is lower than some leading competitors.

The report model further assumes that 100MW of GB300 capacity dedicated to APIs corresponds to roughly 53,300 GPUs and 75% utilization. This could generate approximately $8.59 billion in revenue, $640 million in incremental EBIT, and contribute roughly $1.91 to 2028 EPS. This estimate relies on high utilization and sustained demand; low pricing alone can help attract customers but cannot guarantee profitability.

Subscription tools are another potential entry point. The model assumes that 25% of META's 15 million advertisers pay roughly $200 per month for tools like business agents or coding assistants. This could contribute about $8 billion in revenue and roughly $2 to 2028 EPS. Whether advertisers are willing to pay consistently depends on whether these tools deliver higher conversion rates, lower production costs, or stronger automation capabilities.

Amazon and Google Beneficiaries, But Revenue Validation Needed

Amazon and Google are also significant entities in this round of capital expenditure increases, though they serve more as reference points in this main narrative.

For Amazon, the report raises its AWS revenue growth outlook, projecting 40% and 36% growth in 2027 and 2028, respectively. It also estimates that AWS's backlog increased by roughly $110 billion quarter-over-quarter in Q2 to approximately $475 billion. As Amazon has not yet released its official Q2 earnings report, this backlog figure should be considered a sell-side estimate. Official filings confirm that AWS sales grew 28% year-over-year in Q1 2026, OpenAI made an additional $100 billion multi-year commitment, and cash capital expenditure continued to rise.

Google's strength lies in its full-stack capability encompassing the Gemini model, TPUs, and its cloud business. The report model indicates that Google will add the most capacity among major platforms in 2027 and 2028. A near-term pressure is that computational resources may still constrain product scaling, especially when search, cloud services, and model APIs compete for computing power simultaneously.

These trends point to the same practical issue: AI spending has entered the trillion-dollar realm, and the market will increasingly ask directly, "How much revenue does each dollar of capital expenditure generate?" Cloud services, AI search, APIs, advertising tools, and enterprise subscriptions will all become channels for validating spending returns.

Massive Spending Must Navigate Power, Approvals, and Real Demand

This round of capital expenditure increases has clear boundaries.

The first constraint is supply. Chips, HBM memory, rack infrastructure, power access, and skilled labor all affect construction speed. From planning to operation, AI data centers must navigate local permitting, grid upgrades, and construction cycles, preventing a linear rollout as assumed in some models.

The second constraint is politics and regulation. The occupation of power, water resources, and land by large data centers can trigger local resistance. Energy policy and local approval rhythms could also shift around the 2026 US midterm elections and the November 2028 presidential election.

The third constraint is demand. META's API, subscription, and advertising upgrades remain upside scenarios; revenue realization requires paying customers and sustained usage. Lower prices compared to competitors help attract customers, but long-term profitability depends on usage volume, gross margins, and tool ROI.

The $1.4 trillion capital expenditure paints a picture of a high-cost growth trajectory. Giants are pre-emptively locking in AI compute power, and the market will continue to ask when this compute will translate into revenue and profit. META's $775 price target is built on the gradual realization of AI monetization; the hardest step remains turning the EPS upside in the model into reported cash flow.