AI Infrastructure & Architecture Summit Content Hub

This exclusive spotlight from The Global State of Generative AI in Enterprise Industry Report 2026 reveals how leading enterprises are building scalable, sustainable, and secure AI systems. Explore why composable GPU workspaces are replacing traditional data centres, how photonic networking cuts AI data transfer times by 40%, and why edge computing is reducing latency by up to 70%. See how next-gen chips deliver 25x lower energy use and help tackle up to 5M metric tons of projected AI e-waste, alongside insights on the $26B surge in GenAI infrastructure investment in 2024. Released ahead of the AI Infrastructure & Architecture Summit (November 4–5, London), this feature equips you to design platforms ready for scale and innovation. Download your complimentary copy now >>

By 2026, forecasts suggest the cloud computing market will be worth $947 billion: by 2028, $1 trillion.1–2 Cloud computing is transforming how businesses operate – 96% now use the cloud and it’s not just storage that has companies flocking.1 While some 60% of all business data is now stored in the cloud, 48% of businesses plan to migrate at least half of their applications to the cloud and a further 20% all of their applications.1–2 And with a staggering 82% of companies either exploring or already using artificial intelligence, those applications are increasingly going to rely on AI.3 Indeed, by 2028 50% of cloud computing resources will be devoted to AI and machine learning.

So, with businesses staking their futures on the future of the cloud and AI, ahead of the AI Infrastructure & Architecture Summit, join us as we take a look at eight reasons why that future is made of custom silicon. 

Explore the transformative power of artificial intelligence (AI) in this insightful article that delves into both the potential and challenges facing its widespread adoption. AI promises revolutionary advancements across sectors, from healthcare to logistics, yet the high cost of AI infrastructure—driven by expenses in hardware, software, expertise, and energy—poses a significant barrier. Discover practical strategies to reduce these costs, including right-sizing hardware, utilizing cloud platforms, optimizing model efficiency, and adopting MLOps practices.

Beyond cost management, the article highlights the importance of sustainability in AI, advocating for energy-efficient hardware and renewable energy solutions to address environmental concerns. Collaborative development between academia, industry, and government is essential to accelerate AI innovation, broaden accessibility, and ensure responsible growth. Download this resource to understand how to make AI both affordable and sustainable, unlocking its potential to drive progress across industries.

Download your complimentary copy now >>