Explore 14 articles covering in-depth insights and practical guidance on compute, chips & energy demand.
Global AI infrastructure spending is projected to exceed $300 billion in 2026, with energy costs representing 30–40% of data center operating expenses. This guide breaks down GPU cluster pricing, cooling system economics, and power purchase agreement structures, showing how efficiency gains can reduce total cost of ownership by 20–35%.
A concrete implementation with numbers, lessons learned, and what to copy/avoid. Focus on implementation trade-offs, stakeholder incentives, and the hidden bottlenecks.
The 5–8 KPIs that matter, benchmark ranges, and what the data suggests next. Focus on KPIs that matter, benchmark ranges, and what 'good' looks like in practice.
What's working, what isn't, and what's next, with the trade-offs made explicit. Focus on unit economics, adoption blockers, and what decision-makers should watch next.
A practical primer: key concepts, the decision checklist, and the core economics. Focus on unit economics, adoption blockers, and what decision-makers should watch next.
Custom AI ASICs like Google TPUs and AWS Trainium deliver 2–5× better performance per watt than general-purpose GPUs for inference workloads, but GPUs retain flexibility advantages for training. This guide compares total energy consumption, cooling requirements, and carbon footprint across leading chip architectures for sustainability-focused deployments.
A step-by-step rollout plan with milestones, owners, and metrics. Focus on KPIs that matter, benchmark ranges, and what 'good' looks like in practice.
Essential KPIs for Compute, chips & energy demand across sectors, with benchmark ranges from recent deployments and guidance on meaningful measurement versus vanity metrics.
A comprehensive state-of-play assessment for Compute, chips & energy demand, evaluating current successes, persistent challenges, and the most promising near-term developments.
A practitioner conversation: what surprised them, what failed, and what they'd do differently. Focus on implementation trade-offs, stakeholder incentives, and the hidden bottlenecks.
A rigorous look at the most persistent misconceptions about Compute, chips & energy demand, with evidence-based corrections and practical implications for decision-makers.
Signals to watch, value pools, and how the landscape may shift over the next 12–24 months. Focus on implementation trade-offs, stakeholder incentives, and the hidden bottlenecks.
A forward-looking assessment of Compute, chips & energy demand trends in 2026, identifying the signals that matter, emerging winners, and red flags that practitioners should monitor.
Myths vs. realities, backed by recent evidence and practitioner experience. Focus on KPIs that matter, benchmark ranges, and what 'good' looks like in practice.