Global IT Spending Will Soar to $6.15 Trillion by 2026—Fueled by an AI Infrastructure Arms Race

If you thought AI spending might cool off in 2026, think again. Gartner now expects global IT spending to reach a jaw-dropping $6.15 trillion by 2026—up 10.8% year over year—with AI infrastructure taking the biggest bite of that growth. That’s an upward revision from the analyst firm’s October call, signaling that the AI acceleration is still very much on.

What’s behind the surge? Hyperscalers are racing to build the data center muscle needed for ever-larger models. Enterprises are embedding generative AI in everything from customer support to software engineering. And underneath it all, memory and networking markets are reshuffling to feed an AI-hungry supply chain—leaving some device categories waiting in line.

In this deep dive, we’ll unpack what Gartner’s latest forecast means for your budgets, roadmaps, and competitive posture—and how to place smart bets while the AI frenzy heats up.

Source: Computerworld coverage of Gartner’s forecast

The Headline: $6.15 Trillion by 2026—and a Faster Pace Than Expected

Gartner’s latest view puts total IT spend at $6.15 trillion in 2026, a 10.8% jump, with the revision up from October’s $6.08 trillion outlook. That’s notable given ongoing chatter about an “AI bubble.” Instead of cooling, AI-aligned spending appears to be compounding.

A few standouts from the outlook, as reported by Computerworld: – Data center systems are leading growth as hyperscalers and enterprises build out AI infrastructure. – GenAI model spending is projected to grow 80.8% in 2026, as AI becomes table stakes for enterprise software. – Overall software spend stays red-hot, up 14.7%, with total software still above $1.4 trillion (slightly below a prior 15.2% estimate). – Device spending cools to 6.1% growth, totaling $836 billion—dragged down by supply constraints and rising memory prices as chipmakers prioritize AI servers.

For a broader historical lens on IT spend trends, see Gartner’s spending insights and recent commentary on enterprise IT priorities.

Why AI Infrastructure Is the Growth Engine

The physics of AI at scale are unforgiving: larger models, bigger datasets, and tighter latency requirements demand unprecedented compute, memory bandwidth, storage throughput, and power. That translates into spending—fast.

The Hyperscaler Capex Wars: AWS, Azure, Google Cloud

The world’s largest cloud platforms are racing to expand AI-ready capacity: – AWS is scaling GPU fleets and its custom silicon portfolio (e.g., Inferentia and Trainium) to improve performance per dollar. – Microsoft Azure continues to ramp specialized AI infrastructure, with deep ecosystem integrations for enterprise workloads. – Google Cloud advances its TPU lineup for training and inference, while tuning its stack for large-scale data and model orchestration.

These investments aren’t just about raw compute—they’re about total throughput. That includes networking fabrics, disaggregated storage architectures, and optimized software stacks that extract more performance from hardware.

GPUs, Custom Silicon, Memory, and Networking: The Supply Chain Strain

Under the hood, the AI build-out relies on: – High-end GPUs and accelerators (e.g., NVIDIA’s data center lineup, AMD’s MI-series, custom chips from AWS, Google, and others). See NVIDIA’s data center platform overview for context: NVIDIA Data Center. – High Bandwidth Memory (HBM), which has become a gating factor for training and inference capacity. Leading suppliers like SK hynix and Micron are aggressively expanding output to meet AI demand. – Ultra-high-speed networking for cluster-scale training (think InfiniBand or advanced Ethernet fabrics), which can become the bottleneck if not scaled alongside compute. – Power and cooling systems capable of supporting multi-megawatt data halls and liquid-cooled racks.

When HBM and top-end GPUs are prioritized for AI servers, downstream markets feel it. That’s one reason device spending growth is slowing, as memory prices rise and supply is diverted to higher-margin AI components.

The New Constraints: Power, Real Estate, and Sustainability

Even with money in hand, hyperscalers and large enterprises face practical limits: – Power availability and grid interconnects are becoming critical path items in key markets. Read more from the Uptime Institute on power, resiliency, and capacity planning. – Data center real estate with appropriate fiber, cooling, and zoning is tighter than it’s been in years. – Sustainability targets are reshaping deployment choices, from siting near renewable energy sources to deploying heat reuse and liquid cooling for efficiency gains.

The result: the spending curve is as much about physical scale and energy economics as it is about chips and code.

Software Spending: GenAI Becomes a Feature, Not a Fad

Gartner’s call that GenAI model spending will soar 80.8% in 2026—and that GenAI’s share of the software market will climb by 1.8 percentage points—speaks to how quickly AI is being absorbed into everyday tools.

From Pilots to Pervasive Features

Vendors are baking AI into: – Customer service platforms for agent assist, summarization, and intent detection. – Developer tools for code generation, test creation, security scanning, and refactoring. – Productivity suites for meeting notes, drafting, translation, and knowledge retrieval. – Data platforms that stitch together governance, lineage, and vector search to operationalize AI.

We’re moving from “new AI product” to “AI-enhanced everything.” The bill shows up in platform SKUs, usage-based add-ons, and higher-tier licenses.

What That Means for Buyers

Expect bundling. Many vendors will incentivize AI adoption via suite pricing rather than standalone SKUs.
Monitor usage creep. AI features can drive higher consumption (tokens, compute minutes, storage), affecting bills in subtle ways.
Insist on measurable value. Tie AI add-ons to KPIs like handle time reduction, code cycle time improvement, or sales conversion lift.

If you’re optimizing software spend, frameworks like FinOps are expanding beyond cloud infrastructure to include AI consumption and model lifecycle costs.

Devices Cool as Memory Prices Rise

Gartner’s device spending estimate—$836 billion in 2026 with just 6.1% growth—reflects a market cooling for reasons that are more supply-side than demand-side.

AI Servers Are Gobbling Up Memory

With memory manufacturers prioritizing HBM and AI server components, traditional DRAM/NAND for PCs and mobile devices face tighter supply and higher prices. That means: – OEMs may manage tighter inventories or staggered refreshes. – Enterprises could see extended lead times or higher TCO for large-scale device refresh programs. – Premium segments (workstations, AI-enabled laptops) may get more love than budget lines, as vendors chase higher margins.

Practical Takeaways for IT Procurement

Stagger refresh cycles and prioritize mission-critical user groups.
Expand approved vendor lists to mitigate single-supplier risk.
Consider device-as-a-service models to smooth capex spikes, but read the fine print on refresh flexibility and parts availability.

Sector-by-Sector Outlook: Where the Money Flows

Data Center Systems: King of the Hill

The hottest budget lines: – Accelerated compute (GPUs/TPUs/custom ASICs) for training and inference. – HBM-rich modules and expanded memory channels. – High-throughput storage (NVMe, all-flash arrays optimized for AI pipelines). – High-performance networking with low-latency fabrics. – Power and cooling retrofits, especially liquid cooling for dense racks.

For enterprises not building mega-scale clusters, investments focus on inference serving, retrieval-augmented generation (RAG) architectures, and private model hosting with strong data controls.

Cloud Services: Consumption with Caveats

Cloud remains the fastest route to AI capacity, but: – Spot and reserved pricing for AI instances can be volatile compared to general-purpose compute. – Data egress, vector database usage, and orchestration services can become hidden cost drivers. – Hybrid approaches—cloud for training, on-prem or colo for steady-state inference—are gaining traction to balance cost, control, and latency.

Explore provider AI stacks: – AWS AI and ML Services – Microsoft Azure AI – Google Cloud AI

Security: The Parallel Growth Curve

As AI pervades the stack, security spend rises alongside it: – Model security and governance (prompt injection defenses, output filtering, data leakage prevention). – Supply chain security for AI dependencies and open-source models. – Identity, secrets management, and policy engines woven into AI agents and automations. – Data security at rest and in use, including confidential computing where appropriate.

Expect consolidation pressures as platforms add AI-native security controls and posture management.

Networking and Edge: Latency as a Feature

AI moves closer to where data is created: – Edge inference for vision, speech, and anomaly detection in retail, manufacturing, and logistics. – 5G/Private LTE backbones for low-latency sensor and camera workloads. – Content delivery and API caching strategies to keep AI apps snappy and affordable.

Budgeting Playbook for 2025–2026

If you’re tuning budgets for the next 18–24 months, consider this checklist:

Right-size your AI ambition:
Identify 3–5 use cases with provable ROI and production feasibility.
Build a pilot-to-production pipeline with bake-off criteria for models and vendors.
Model total cost of AI ownership (TCAI):
Include data prep/labeling, vectorization, orchestration, inference serving, monitoring, and human-in-the-loop review.
Budget for observability: latency, hallucination rates, safety metrics, and per-request cost.
Diversify infrastructure bets:
Mix cloud and on-prem/colo where it makes sense.
Evaluate multiple accelerator vendors and memory suppliers to hedge supply risk.
Negotiate software intelligently:
Press for AI bundles tied to outcomes, not just seats.
Ask for transparent metering for AI features to avoid runaway usage.
Shore up data foundations:
Invest in data quality, lineage, and access governance—AI performance is limited by your data hygiene.
Consider retrieval strategies (RAG) that keep proprietary data secure and reduce model size/cost.
Build guardrails:
Establish AI governance councils, policy frameworks, and risk registers.
Define red/amber/green use cases by compliance and safety impact.
Upskill your teams:
Train developers, analysts, and business users on prompt engineering, evaluation metrics, and safe-use patterns.
Stand up an internal AI enablement program with reusable components and templates.
Track sustainability:
Include energy costs and PUE targets in AI infrastructure decisions.
Favor regions and providers with credible renewable energy sourcing.

Avoiding the AI Hype Trap: Metrics That Matter

If it doesn’t move a KPI, it’s a science project. Anchor your AI investments to measurable outcomes:

For customer support:
Average handle time, deflection rate, CSAT changes, and first-contact resolution.
For software engineering:
Cycle time, escaped defects, security findings time-to-remediation, and developer satisfaction.
For sales and marketing:
Qualified pipeline growth, conversion rates, and content throughput.
For operations:
Forecast accuracy, downtime reduction, waste/scrap reduction, and SLA adherence.

Complement outcome metrics with AI-specific quality and safety measures: – Groundedness/hallucination rate – Toxicity and bias thresholds – Latency percentiles (P50/P95) – Cost per thousand tokens or per task

Winners, Risks, and What’s Next

Likely Winners

Accelerator vendors and custom silicon providers—anyone improving performance per watt and per dollar.
Memory suppliers pivoting to HBM and AI-optimized stacks.
Hyperscalers that can scale capacity and offer balanced cost-performance.
Software platforms that deeply integrate GenAI while maintaining governance and security.

Where to Be Cautious

Device-centric categories with tight margins and exposure to memory price swings.
“AI-washed” software SKUs that raise costs without clear productivity gains.
Training at unnecessary scale—consider smaller, domain-adapted models where viable.

Watch the Regulatory Horizon

Data privacy and IP usage rules for training corpora.
Sector-specific AI safety and audit requirements.
Model transparency and provenance mandates.

Staying compliant could become as material to TCO as your choice of GPU.

What This Means for SMBs

You don’t need a hyperscaler budget to benefit: – Start with copilots and AI assistants in your existing tools (email, documents, CRM, helpdesk). – Use domain-specific or smaller open models to control costs while improving accuracy. – Lean on managed services for vector databases, observability, and content moderation. – Budget for data cleanup—it’s unglamorous, but it moves the needle.

Link up with vendor programs and partner ecosystems designed for SMB adoption to avoid bespoke integrations you can’t maintain.

Key Signals to Track Through 2026

HBM supply and pricing trends, which directly impact AI server availability.
Lead times for top-tier accelerators and network gear.
Cloud AI instance pricing and discount structures.
Power availability and data center permitting activity in your target regions.
Vendor announcements on embedded AI features—and how they’re priced.
Regulatory updates on AI governance, safety, and data use.

Staying agile against these signals helps prevent lock-in and sticker shock.

The Bottom Line

The forecast is clear: global IT spending is on track to hit $6.15 trillion in 2026, and AI infrastructure is the engine pulling the train. While software keeps climbing on the back of embedded GenAI, devices take a breather as memory and supply chains reorient toward high-margin AI servers. For technology leaders, the winning strategy blends ambition with discipline—picking high-ROI use cases, instrumenting costs and quality, and designing for flexibility across clouds, chips, and models.

If you build for outcomes, not just optics, 2026 can be the year AI shifts from exciting to essential in your organization.

FAQs

Q1: Is this AI spending surge just a bubble that will deflate? – While hype exists, Gartner’s upward revision suggests real, sustained demand—especially in infrastructure. The drivers (compute, memory, power) are capital-intensive and grounded in operational needs. That said, expect consolidation and a shakeout of me-too tools.

Q2: Should we train our own large models or use managed services? – Unless you have unique IP, data scale, and research talent, most organizations get better ROI using managed foundation models or fine-tuning smaller, domain-specific models. Reserve full-scale training for differentiated use cases.

Q3: How can we control cloud AI costs? – Use right-sized instances, spot/reserved capacity where safe, cache results, use RAG to shrink token usage, and set quotas. Adopt FinOps practices and require transparent metering from vendors.

Q4: What’s the biggest hidden cost in AI projects? – Data work. Cleaning, labeling, governance, and integrating retrieval pipelines often dwarf model licensing or compute line items. Also budget for observability and human review.

Q5: Do on-prem AI deployments still make sense? – Yes—for steady-state inference, data sovereignty, latency-sensitive apps, or cost predictability. Many enterprises blend cloud (for bursty training) with on-prem/colo (for predictable inference workloads).

Q6: How will device market softness affect our end-user computing plans? – Expect potential cost pressure and lead times on memory-heavy SKUs. Stagger refreshes, expand vendor options, and consider device-as-a-service for flexibility.

Q7: What governance should we put in place before scaling AI? – Define approved use cases, data access controls, model evaluation gates, incident reporting, and vendor risk assessments. Establish a cross-functional AI governance council and align with compliance early.

Q8: Where can I follow reliable updates on IT spending and AI infrastructure? – Check Computerworld, Gartner newsroom, and major cloud providers’ AI infrastructure pages (AWS, Azure, Google Cloud).

Final takeaway: The AI infrastructure boom is not a side story—it’s the storyline for IT through 2026. Anchor your roadmap in measurable value, diversify your tech stack to stay resilient, and invest in the data and governance foundations that turn AI spend into real business impact.

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