AI is no longer experimental. It is becoming industrialized. This brief is for infra, data center, and AI platform teams who have to justify 2026 spend. As you’re planning for 2026, the shift from hype to operational scale is accelerating.
In 2026, three signals will set the context:
- Gartner projects global IT spend to pass $6.08 trillion in 2026 for the first time, making AI infrastructure and data center decisions board-level issues.
- Executives plan to increase AI investment across the next three years, which means 2026 capacity plans must defend ROI.
- Model and hardware efficiency keep compounding, which flips many “bigger is better” assumptions.
We track compute demand, inference latency, training cost and the infrastructure limits that shape them. The goal is smarter tradeoffs, faster deployment and stronger AI strategies in a market moving toward scale.
Although you will find trends across those metrics and the key constraints. Each role gets clear actions. We also point to internal resources to support aligned planning.
This is not a snapshot of hype. It is a clear view of how AI systems will be built and scaled in the year ahead. So, let’s dive in!
Data Center Power, Efficiency & Cooling
1. IEA projects data centers could consume more than 1,000 TWh of electricity in 2026. (IEA)
Image Source: IEA
2. Data-center electricity consumption is forecasted to double by 2030 (reaching about ~945 TWh), with AI being a central factor through 2026. (IEA)
3. Power demand for global data centers is set to grow 50% by 2027 and 165% by 2030 compared to 2023 levels. (Goldman Sachs)
Image Source: Goldman Sachs
4. IDTechEx forecasts liquid cooling components to grow at 13% CAGR from 2026 to 2036. (IDTechEx)
5. Omdia projects the data center cooling market to reach $16.8B by 2028, with liquid cooling as a leading technology. (Omdia)
Image Source: Omdia
6. Google reports a trailing twelve-month PUE of 1.09 across mature hyperscale data centers. (Google)
7. Deloitte predicts global data center electricity consumption could roughly double to 1,065 TWh by 2030. (Deloitte)
Cloud & Hyperscaler Growth/ Capex & DC Market
8. Citi projects AI-related capital expenditures by hyperscalers will reach $490 billion in 2026 and could surpass $2.8 trillion by 2029. (MSN)
9. The global AI data-center market is expected to grow from USD 236.44 billion in 2025 to 76 billion by 2030 at a CAGR of 31.6% during the forecast period. (MarketsAndMarkets)
10. Oracle expects over 40% cloud growth in FY2026 due to AI demand. (Oracle)
11. Dell anticipates a $20 billion AI-server run-rate in the next two years. (Dell)
12. Airtel and Google are developing India’s first mega AI hub and datacenter scheduled between 2026 and 2030. (Google) and (Airtel)
Compute Platforms & Performance (GPUs & Rack-scale)
13. Qualcomm plans to launch its AI200 and AI250 rack-scale data center inference performance for the AI era. (Qualcomm)
14. NVIDIA GB200 NVL72 links 72 Blackwell GPUs and 36 Grace CPUs as one NVLink domain for rack-scale acceleration. (NVIDIA)
15. NVIDIA claims up to ~30× faster real-time trillion-parameter inference with GB200 NVL72. (NVIDIA)
16. Google Cloud A4X VMs, powered by GB200 NVL72, target extra-large reasoning and long-context LLM jobs. (Google)
17. MLPerf Training v5.0 results indicate Blackwell clusters training at least ~2.6× faster than comparable Hopper clusters at scale. (NVIDIA)
18. Over 30 AI models have been trained at the scale of GPT-4. (EPOCH AI)
19. AI factory plans for 1–5 GW campuses imply feasible single-run training budgets of ~1e28–3e29 FLOPs before 2030. (EPOCH AI)
Semiconductors & Memory Supply Chain
20. Gartner predicts that the spending on AI-optimized servers (GPUs + other accelerators) will be $330 million, up from $268M in 2025. (Gartner)
21. Gartner predicts that the spending on AI processing semiconductors will be $268 million in 2026. (Gartner)
22. Samsung in talks to supply 6th-generation’s HBM4 memory production with Nvidia. (Samsung)
23. The wafer-fabrication equipment market will reach $122 billion in 2026, up 10% from (SEMI)
Image Source: SEMI
24. Spending on DRAM memory is expected to grow 12% in 2026, driven by capacity expansion and investment in HBMs. (SEMI)
25. NAND spending is expected to grow at 10% in 2026 as AI storage (SEMI)
Edge & Endpoint AI
26. Gartner analysts forecast AI-enabled PCs are forecasted to reach 143 million units and a 55% market share by 2026. (Gartner)
27. The Edge-AI hardware market is projected to grow from $26.1 billion in 2025 to $58.9 billion by 2030, at a 17.6% CAGR. (MarketsAndMarkets)
28. Worldwide edge computing spend is projected to reach ~$378 billion by 2028. (IDC)
Enterprise Adoption & Industry Verticals
29. McKinsey’s 2025 survey finds that 78% of organizations use AI in at least one function. (McKinsey)
30. The global AI market size is expected to show an annual growth rate (CAGR 2025-2031) of 36.89%, resulting in a market volume of US$1.68tn by 2031. (Statista)
31. Strategic technology trends in 2026 are intertwined, reflecting AI powered hyperconnectivity and the need for responsible innovation, operational excellence and digital trust. (Gartner)
32. AI in healthcare market was estimated at USD 26.69 billion in 2024 and is forecast to rise from USD 36.96 billion in 2025 to about USD 613.81 billion by 2034, a 36.83% CAGR for 2025 to 2034. (Precedence Research)
Image Source: Precedence Research
33. The AI-driven drug discovery market is expected to reach $133.9 billion by 2034, with 2026 marking a key commercialization inflection. (Towards Healthcare)
Image Source: Towards Healthcare
34. Generative AI in drug discovery is calculated at US$ 318.55 million in 2025 and is projected to reach around US$ 2847.43 million by 2034. (Towards Healthcare)
35. The global AI in manufacturing market is expected to grow from USD 7.60 billion in 2025 to USD 62.33 billion by 2032. (Fortune Business Insights)
36. Generative AI in manufacturing is expected to reach USD 630.72 million in 2025 and is anticipated to reach $13.9 million by 2034. (Precedence Research)
37. The AI in the retail market is projected to reach USD 40.74 billion by 2030, growing at a CAGR of 23.0% from 2025 to 2030. (Grand View Research)
38. The AI in supply-chain market is expected to grow to $50.01 billion by 2031 at a 22.9% CAGR from 2026 to 2032. (MarketsandMarkets)
39. Leaders report 77% ROI within 12 months on supply-chain AI and momentum is carrying into 2026 rollouts. (Forbes)
40. The global AI in telecom market is projected to reach USD 11.29 billion by 2030, growing at a CAGR of 28.2% from 2023 to 2030. (Grand View Research)
Image Source: Grand View Research
41. The automotive AI market is projected to reach USD 38.45 billion in 2030 from USD 18.83 billion in 2025, growing at a CAGR of 15.3% from 2025 to 2030. (MarketsandMarkets)
42. IDTechEx’s report, “Autonomous Driving Software and AI in Automotive 2026-2046,” spans private-car and robotaxi software up to SAE L4 and provides 20-year market forecasts. (IDTechEx)
43. The global AI in education market is projected to reach $32.3 billion by 2030 at a 31.2% CAGR. (Grand View Research)
44. According to Gartner, up to 40% of enterprise applications could feature integrated task-specific agents by 2026, up from less than 5% today. (Gartner)
45. 51% of European IT and cybersecurity professionals foresee AI-driven attacks and deepfakes causing sleepless nights in 2026. (ISACA)
46. The AI cybersecurity solutions market will reach USD 93.75 billion by 2030, growing at a CAGR of 24.4% from 2025 to 2030. (Grand View Research)
47. The AI in agriculture market is projected at $4.7 billion in 2028, with precision agriculture and vision analytics driving workloads. (MarketsandMarkets)
48. The AI in banking market is projected at $45.6 billion in 2026 to nearly $379.41 billion by 2034. (Precedence Research)
Image Source: Precedence Research
49. AI software spending is projected to grow at $270 million in 2026. (Gartner)
50. Website visitors from AI search are 4.4x more valuable than visitors from organic search. (Semrush)
Key Takeaway
You lead in 2026 by turning statistics into action. Use these AI/ML statistics to plan workloads, budgets and sitting against power, silicon and latency limits.
Emphasize efficiency, inference scale and practical AI infrastructure trends.
With clear ML workload stats and AI/ML workload trends in 2026, you can reduce risk and speed delivery now.
