Global AI spending to hit $2.59 trillion in 2026, says Gartner forecast

· Source: Dataconomy · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Fundamental Awareness, quick

Summary

Gartner's latest forecast projects global spending on artificial intelligence to reach \$2.59 trillion in 2026, a 47% increase from the prior year. This figure reflects an upward revision from earlier estimates, primarily driven by escalating demand for AI infrastructure and agentic tools. AI infrastructure, including servers and data center components, accounts for over half of total AI investment. AI services follow, with AI software growing approximately 60% year-over-year. Spending on agentic AI software is expected to surge 141% to nearly \$202 billion in 2026, surpassing chatbots by 2027. Major technology companies, including Amazon, Microsoft, Alphabet, and Meta, have collectively raised their 2026 capital expenditure projections by about \$100 billion to \$725 billion, partly due to rising component costs. This investment surge is anticipated to reduce their combined free cash flow to a decade low of approximately \$4 billion in the third quarter. The broader AI market is expected to reach \$3.3 trillion by 2027 and \$4.7 trillion by 2029, indicating a 33% compound annual growth rate.

Key takeaway

For investors evaluating technology market trends, recognize the significant and accelerating capital shift towards AI. Global AI spending is projected to hit \$2.59 trillion in 2026, with major tech companies increasing their capex by \$100 billion. This surge, particularly in infrastructure and agentic AI, indicates robust long-term growth to \$4.7 trillion by 2029, despite short-term free cash flow impacts. Adjust your portfolio and strategic planning to account for this sustained, high-growth sector.

Key insights

Global AI spending is rapidly accelerating, driven by infrastructure and agentic AI, despite short-term ROI concerns.

Principles

In practice

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Editorial summary, takeaway, and curation by AIssential. Original article published by Dataconomy.