For the last two years, AI scaling has been framed first as a race for GPUs, then as a race for power, then as a race for inference capacity. Now another important constraint is now becoming visible: context. At first, context sounds like a software problem, it sounds like prompt
For the last two years, AI scaling has been framed as a race for chips, then a race for power. Both are still true, but there is another race emerging, one where the ability to service real world workloads will be key to success. The next phase of enterprise AI
For the last two years, AI infrastructure has been framed as a race for chips, but that framing is now too narrow. The harder problem is increasingly physical: energisation, land, cooling, electrical equipment, and the ability to turn compute demand into live operating capacity. The IEA now projects data centre
Based on past experience, I believe Edge AI will not scale evenly. It will scale first where a physical system becomes too time sensitive, too distributed, and too operationally critical to rely on distant inference alone. Energy is now moving into that category. For years, edge computing has been discussed
Whilst currently behind the US in terms of investment and scaling, Europe’s edge opportunity is real. But the first durable demand will appear in a handful of workloads where latency, sovereignty, resilience and regulation make local inference essential. For years, edge computing has been discussed as though adoption would
The next phase of AI scaling will be won by the companies that can secure land, energy, and electrical infrastructure fast enough to bring capacity online!! For the last few years, the AI conversation has been dominated by chips, models, and compute. All focused on bigger clusters, faster training, more
Every few months, the same question returns: is AI a bubble? I think that fundamentally wrong; markets can overshoot, weak companies can fail, hype can run ahead of reality. But none of that really determines whether the underlying technology is foundational. The question I care about is whether AI is
The AI race is still often described as a race for chips, but that framing is rapidly becoming outdated. The tighter constraint is increasingly electricity: generation, transmission, substations, transformers, cooling, and the long construction timeline needed to turn ordered hardware into working capacity. The International Energy Agency (IEA) estimates data
For years, the data centre industry grew steadily in the background: more cloud adoption, more video, more software, more storage. AI has changed that. Data centres are no longer just digital real estate; they are becoming a core part of national industrial strategy, energy planning, and competitive advantage. The International
The AI infrastructure market is moving out of the experimental phase and into industrial scale. For rapidly growing neocloud providers, that shift is redefining what “data-centre delivery” actually means. This is no longer about fitting incremental compute into traditional colocation footprints. It is about designing, building and operating 100MW+ campuses
For the last two years, “AI infrastructure” has been shorthand for GPUs. Whoever could secure GPUs could train bigger models, serve more tokens, and ship faster. That mental model is now incomplete. What we’re seeing in 2026 is a shift in the true critical path: compute procurement is becoming
AI is pushing data centres into a new class of electricity consumer: high-density, highly continuous, and increasingly time-sensitive. At the same time, renewables are scaling rapidly, but grids were not designed for variable generation at today’s volumes. The result is a paradox we see more and more often: * Clean