Big Tech's AI Spending Surge: Citigroup Forecasts $2.8 Trillion by 2029

Reviewed byNidhi Govil

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Citigroup raises its forecast for AI-related infrastructure spending by tech giants to $2.8 trillion through 2029, citing aggressive investments by hyperscalers and growing enterprise demand. The AI boom continues to fuel massive capital outlays and data center expansion.

Citigroup's Revised AI Spending Forecast

Citigroup has significantly raised its forecast for AI-related infrastructure spending by tech giants, projecting it to surpass $2.8 trillion through 2029, up from its earlier estimate of $2.3 trillion

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. This revision is attributed to aggressive early investments by hyperscalers and growing enterprise appetite for AI technologies

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Source: PYMNTS

Source: PYMNTS

Hyperscalers' Capital Expenditure

The Wall Street brokerage anticipates AI capital expenditure across hyperscalers to reach $490 billion by the end of 2026, an increase from its previous estimate of $420 billion

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. Data center operators, including Microsoft, Amazon, and Alphabet, have already invested billions to address capacity constraints hampering their ability to meet surging AI demand

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Source: Economic Times

Source: Economic Times

AI Compute Demand and Power Capacity

Citi estimates that global AI compute demand will require 55 gigawatts of new power capacity by 2030, translating to $2.8 trillion in incremental spend, with $1.4 trillion in the U.S. alone

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. The costs are substantial, with approximately $50 billion required for every 1 GW of compute capacity

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Shift in Funding Strategies

Big tech firms are no longer relying solely on profits to fund AI infrastructure. The enormous costs have led companies to borrow to keep up with demand

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. This shift is already impacting their financials, with spending starting to eat into free cash flows

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Novel Financing Approaches

Companies are exploring innovative financing methods. For instance, Oracle recently sold $18 billion of bonds in the second-largest U.S. debt deal this year to increase its cloud capacity

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. OpenAI struck a deal with Nvidia to deploy 10 GW of Nvidia systems over five years in exchange for a $100 billion equity investment, potentially reducing hardware costs by 10-15% through leasing arrangements

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Enterprise Adoption and Market Validation

Citi analysts point to production deployments at companies such as Eli Lilly, Hitachi, and Wolters Kluwer as clear external validation of AI's value

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. The rate of AI's technological progress is rapidly expanding the scope of potential applications, with companies like OpenAI and Meta rolling out AI-driven services with clear monetization prospects

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Long-term Projections and Risks

Bank of America analysts forecast that annual AI investments will nearly triple between 2025 and 2030, potentially exceeding $1.2 trillion

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. However, this massive spending introduces new risks for enterprises buying AI services, with concerns about balance sheet durability and the terms of financing becoming increasingly important

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