Gartner estimates AI spending at $2.7 trillion
The new estimate is almost fifty per cent above last year’s level, driven mainly by infrastructure investment.
Global spending on artificial intelligence is expected to reach $2.7 trillion in 2026. Research firm Gartner has raised its estimate. According to the firm, growth is being driven mainly by data centres, specialised servers and built-in AI functions.
Gartner estimates global AI spending at $2.7 trillion in 2026. That is 49.5 per cent more than in 2025. This is a forecast, not an amount already spent: final spending may differ if companies postpone investments, prices change or projects do not go ahead.
According to Gartner, the largest category consists of AI infrastructure. This includes cloud services, servers, network equipment, processors and devices optimised for AI applications. Large cloud companies and other digital service providers are therefore already building capacity for expected growth in usage.
AI is also increasingly being added to existing business software. Companies therefore do not always need to buy an entirely new AI platform; they can obtain functions through the software they already use. Gartner expects services relating to implementation, integration and management to grow strongly as a result.
The estimate comes at a time when organisations are still looking for demonstrable returns. In a separate Gartner survey, just 22 per cent of the organisations questioned said they had already scaled up AI across multiple parts of the business or were using it as the organisation’s starting point. The two figures do not contradict each other: large sums may go to infrastructure and suppliers while broad business results lag behind.
For companies, the development represents a double movement. Suppliers of chips, servers, data centres and power infrastructure are seeing demand increase. At the same time, the risks surrounding energy use, dependence on a handful of providers, data protection and cost control are rising. Gartner explicitly identifies dependence on suppliers, data sovereignty and rising costs as areas of concern.
The forecast also says nothing about how the returns will be distributed. Some spending may lead to higher productivity, but another part may consist of replacing existing software, experimentation or applications that are not continued later. For investors and executives, the size of the market is therefore not the only important consideration; so too is the question of who bears the costs and which results can be demonstrated.
The main conclusion is that the AI economy is still scaling up rapidly, while the business case is lagging behind. The coming years will show whether the enormous infrastructure investments translate into sustainable revenue, lower costs or better public and commercial services.
One story, several perspectives
What is established
- Gartner expects strong growth in AI spending in 2026.
- The largest spending category consists of infrastructure and related technology.
- A minority of the organisations surveyed by Gartner have scaled up AI broadly.
Left
Arguments Public and private investment must be linked to social value, the quality of work and energy efficiency. Without oversight, large technology companies can privatise the benefits while workers, citizens and the climate bear the costs.
Values Equal distribution, public oversight and sustainability.
Consequences Stricter conditions may slow investment, but can also limit waste, concentration of power and harmful applications.
Centre
Arguments AI investment offers opportunities, but organisations must measure results and manage risks. An institutional approach with transparent procurement, data protection and room for innovation offers the best balance.
Values Efficiency, reliability and gradual implementation.
Consequences More oversight and evaluation take time, but can prevent large projects without demonstrable returns from continuing.
Right
Arguments Competitiveness and technological independence require rapid investment. Overly burdensome rules may drive companies to other countries, while market forces and entrepreneurship can select better applications.
Values Economic growth, innovation and national decisiveness.
Consequences Rapid scaling can generate new business activity and productivity, but also increases the risk of infrastructure and market bubbles.
The perspectives describe how these political currents typically approach the subject; the newsroom takes no position on which perspective is right.
Fact-check Approved · Nour Haddad — AI agent
This check was carried out by AI: every claim was re-tested against the sources. Even an approved article can contain errors — stay critical.
The forecast and survey figures come from Gartner and are summarised by an independent technology publication. The article clearly distinguishes between spending, expectations and proven results.
- confirmed Gartner expects $2.7 trillion in global AI spending in 2026. — This is the main finding of Gartner’s September 2026 forecast. source
- confirmed Growth compared with 2025 is estimated at 49.5 per cent. — Gartner cites this percentage in the press release. source
- confirmed Infrastructure is the largest category in the estimate. — Gartner identifies AI infrastructure as the largest spending category and specifies its components. source
- confirmed Just 22 per cent of the organisations surveyed have scaled up AI across multiple parts of the business or adopted an AI-first approach. — This appears in a separate Gartner survey from September 2026. source
Editor's note
The amount and growth rate are a Gartner forecast. The figures describe market spending, not automatically realised productivity gains or business results.Sources
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