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Worldwide IT spending is forecast to reach $6.369 trillion in 2026, up 14.2% from 2025, according to Gartner’s July 27 estimate. That is a forecast—not a confirmed total—and the increase is concentrated in AI-related data-center systems, cloud infrastructure and software rather than spread evenly across technology markets.
What the $6 trillion forecast means
Gartner’s latest estimate puts worldwide IT spending at $6.369 trillion in 2026, compared with $5.577 trillion in 2025. The implied increase is about $792 billion. Gartner’s forecast covers a broad basket of technology products and services, including data-center systems, devices, software, IT services, Infrastructure as a Service (IaaS) and communications services. It is not a measure of corporate software budgets alone, nor is it the same as semiconductor revenue, data-center construction or cloud spending.
The estimate is based on Gartner’s analysis of sales from more than 1,000 vendors, alongside primary and secondary research. It remains a market forecast, not an audited tally of spending that has already occurred. Gartner’s July 27, 2026 forecast is the latest estimate cited here.
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The widely reported $6 trillion milestone first appeared in Gartner’s October 2025 forecast. Subsequent estimates moved higher as the year progressed:
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| Forecast date | 2026 spending estimate | Forecast growth |
|---|---|---|
| October 22, 2025 | $6.08 trillion | 9.8% |
| February 3, 2026 | $6.15 trillion | 10.8% |
| April 22, 2026 | $6.317 trillion | 13.5% |
| July 27, 2026 | $6.369 trillion | 14.2% |
The first estimate is documented in Gartner’s October 2025 release; the later revisions are in its February and April updates. The revisions are a reminder that headlines about future spending depend on when the forecast was published.
Where Gartner expects the money to go
Data-center systems have the highest projected growth rate, while software remains one of the largest spending categories. The table shows Gartner’s 2025 and 2026 estimates and projected year-over-year growth:
| Category | 2025 | 2026 | 2026 growth |
|---|---|---|---|
| Data-center systems | $506 billion | $822 billion | 62.5% |
| Devices | $790 billion | $868 billion | 9.8% |
| Software | $1.271 trillion | $1.468 trillion | 15.5% |
| Services | $1.492 trillion | $1.570 trillion | 5.3% |
| IaaS | $222 billion | $287 billion | 29.3% |
| Communications services | $1.296 trillion | $1.354 trillion | 4.4% |
| Overall IT | $5.577 trillion | $6.369 trillion | 14.2% |
Source: Gartner, July 27, 2026. Gartner presents IaaS as a separate line alongside a broader services figure; do not add every displayed row to reconstruct the total without checking the category definitions.
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Data-center systems are growing fastest, but their projected $822 billion is still less than software, services or communications services in absolute terms. The software increase also matters: companies increasingly encounter AI through features added to existing enterprise applications, not only through standalone AI products. By contrast, services and communications are forecast to grow more slowly than the overall market.
AI is a major accelerator—but the figures overlap
Gartner separately forecasts worldwide AI spending of $2.596 trillion in 2026, up 47% year over year. Its AI-spending framework includes infrastructure, services, software, models, cybersecurity, data and development platforms. Infrastructure accounts for $1.432 trillion of that estimate. The other listed categories include AI services at $585.5 billion, software at $453.2 billion and models at $32.6 billion, with additional amounts for cybersecurity and development, data and machine-learning platforms.
Those AI figures are not an extra $2.596 trillion to add to the $6.369 trillion IT forecast. AI infrastructure, software and services are also counted within broader IT categories. The two forecasts classify related spending in different ways. Gartner’s May 19 AI forecast also says spending through 2026 is dominated by technology vendors and hyperscalers, while many enterprise AI efforts remain focused on tactical productivity and efficiency gains.
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Much of the infrastructure build-out supports a chain of requirements, not just model training. Training uses large amounts of computing power to build models; inference uses computing power each time a model responds to a user or application. Both depend on accelerators and servers, but also on networking to move data, memory and storage to feed workloads, and data-center capacity with adequate electricity and cooling. AI-enabled cloud platforms and software then make that capacity available to businesses.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAgentic workflows—systems that can carry out multiple steps and make repeated model calls—can increase demand for inference. But higher spending on capacity does not by itself show that the capacity is fully utilized or that customers are earning returns from it.
Who is paying, and why cloud matters
The market total does not mean every company is raising its IT budget by 14.2%. The spending reflects different buyers and channels: hyperscalers and technology vendors building infrastructure; enterprises buying cloud, software and services; consumers purchasing devices and connectivity; and public-sector organizations funding areas such as security and digital services. The forecast’s especially rapid data-center and IaaS growth points to the importance of capacity investment by cloud providers and other technology vendors, not just direct enterprise purchases.
Cloud is one route through which the build-out reaches customers. A cloud bill may include compute, storage, networking, managed databases, AI APIs, security, observability or software delivered as a service. Gartner’s IaaS estimate is one component of its wider IT-spending picture, not a synonym for all cloud revenue. Separately, IDC forecast that public-cloud spending would surpass $1 trillion in 2026, citing growth in platform-as-a-service and AI platforms. That is a distinct forecast with a different market definition; it should not be added to Gartner’s total.
For buyers, the shift can change the mix of costs: some spending may move from owned equipment and capital expenditure to cloud consumption and recurring software subscriptions, while other organizations invest directly in infrastructure. Either way, AI use can bring additional costs for data, security, governance, monitoring and human review.
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Gartner has warned that inflation, supply shortages, higher hardware and memory costs, changing priorities and limited enterprise headcount can strain budgets. Several other factors complicate the interpretation of a fast-growing market:
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- AI investment concentration: A substantial part of the acceleration is tied to AI infrastructure. If demand, monetization or deployment takes longer than expected, forecasts for servers, networking, memory and data-center systems could be revised.
- Power and physical capacity: Data centers need electricity, cooling, land, permits and grid connections. Demand for computing capacity can outpace the infrastructure needed to deliver it.
- Supply-chain inflation: Higher prices for accelerators, servers, memory and networking equipment can raise nominal spending without producing a proportional increase in available computing capacity.
- Uncertain enterprise returns: Gartner says many enterprises are pursuing incremental productivity projects and have difficulty demonstrating tangible outcomes. Spending growth is not proof that AI investments are profitable or transformative.
- Uneven market conditions: A record total can coexist with modest growth in communications and broad IT services, and with tighter budgets for buyers facing higher costs.
The available figures are Gartner’s forecasts, not a universal consensus from all market researchers. They also do not establish how much of the planned infrastructure will be completed, utilized or economically successful.
What technology buyers can take from the number
The forecast is useful as a signal that vendors are investing heavily and that AI, cloud capacity and software are reshaping budget priorities. It is not a reason on its own to expand a company’s technology budget. Before approving an AI or infrastructure project, decision-makers can ask:
- What outcome is the spending meant to produce? Set a measurable target such as lower processing cost, faster service, higher revenue, improved resilience or reduced risk.
- What workload is being funded? Training, inference, analytics and conventional computing have different capacity and cost profiles.
- How will usage be billed? Understand consumption charges, commitments, reservations, storage and data-transfer costs, and how expenses change if use grows.
- Is the supporting foundation ready? Check data quality, access controls, security, governance, network performance, latency and local power or capacity constraints.
- Can the organization control vendor dependence? Consider data export, portability, interoperability and the cost of changing providers.
- Have total operating costs been included? Budget for monitoring, security, compliance, model evaluation and human oversight as well as licenses or compute.
- How will benefits be verified? Define a baseline and review actual results. A new feature or larger cloud bill is not itself evidence of productivity or financial return.
For personal-finance readers, the headline is best understood as an economic indicator, not a forecast of an individual household’s technology bills. It does suggest that technology costs and investment are becoming more significant across the economy, but the effects vary by employer, industry and service provider.
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