Worldwide spending on AI is forecast to reach $2.67 trillion in 2026, a 49.5% increase from 2025, according to new research from Gartner. The forecast points to massive investment in AI infrastructure and growing enterprise adoption of AI-powered software and services.
The largest share of spending is going toward the hardware and cloud infrastructure needed to support AI workloads. Gartner expects AI infrastructure spending to reach $1.48 trillion this year, accounting for approximately 56% of the worldwide total.
The research firm expects spending on generative AI models to reach $28.3 billion in 2026, up 117% from $13 billion last year. Despite this rapid growth, models account for only about 1% of total AI spending, compared with the 56% devoted to infrastructure.
The huge difference between spending on infrastructure and models illustrates a key point about the AI market: Building and operating AI systems requires far more investment than purchasing the models themselves.
“The buildout of AI data center capacity is the largest infrastructure project humanity has ever undertaken,” said Gartner analyst John-David Lovelock.
AI Infrastructure Spending Accelerates
Gartner has raised its AI spending forecast several times this year, mostly in response to continued demand for infrastructure. In January, the research firm projected worldwide AI spending of $2.53 trillion for 2026, including $1.37 trillion for infrastructure. Its latest estimate adds approximately $143 billion to the overall forecast, with about $118 billion of that increase attributed to infrastructure.
The spending is being driven primarily by hyperscalers and technology service providers purchasing AI-optimized servers, semiconductors, networking equipment and cloud capacity. Technology providers account for approximately 35% of worldwide AI spending, according to Gartner.
Demand remains strong despite higher memory prices and the vast costs of building data centers equipped to handle today’s robust AI workloads. Gartner forecasts spending on AI-optimized IaaS will reach $42.3 billion in 2026, up 96% from last year.
Of that total, $23.3 billion will support inference, compared with $19 billion for training. Inference is expected to account for 59% of AI-optimized IaaS spending by 2027.
This shift toward inference reflects the costs of using AI applications for everyday business use. Unlike model training, which occurs during development and later updates, inference requires computing resources each time an application processes a request.
Agentic AI adds further demand for computing capacity. Gartner forecasts spending on AI agents and assistants will reach $29.2 billion in 2026.
Enterprise AI Software and Services Expand
Beyond infrastructure, Gartner forecasts AI services spending of $576.5 billion in 2026. The research firm expects AI transformation initiatives and smaller projects that extend the capabilities of existing software to create a $1.2 trillion opportunity for AI services by 2030.
Spending on AI software is forecast to reach $461.6 billion in 2026, as vendors embed AI capabilities into their existing products.
Gartner also raised its forecast for AI application development platforms, projecting 39% growth in 2026, compared with its previous estimate of 28%.
