Power demand: +24% a year
McKinsey's estimate turns energy into the key variable of AI cost. Cloud regions with spare power will win workloads — ask your provider where your AI runs.
Chips, data centres, energy and cloud capacity: the physical layer that decides who can train and serve AI, and at what cost.
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Amazon (~$220B) and Alphabet ($195–205B) raised 2026 capex again, and Nvidia guides to $108B next quarter. The constraint has moved to electricity: data-centre power demand grows 24% a year, utilities and nuclear are being financed for AI, and local opposition is now a planning risk. Expect AI capacity to stay tight — and priced accordingly — into 2027.
McKinsey's estimate turns energy into the key variable of AI cost. Cloud regions with spare power will win workloads — ask your provider where your AI runs.
The first US small modular reactor permit (TVA, 300 MW) and LS Power's record $6B fund show capital moving from chips to electrons.
Amazon's $1B for communities near its data centres signals that social licence is part of the build-out budget.
DeepSeek and Huawei open-sourced Ascend programming tools to reduce CUDA dependence — a parallel AI stack is forming.
Lock in AI capacity and prices for 12 months where you can, keep a second provider ready, and ask for the data-centre region in every AI contract (cost, latency and GDPR depend on it).
Trackers stay on screen until a newer, verified value replaces them.
Source: Hargreaves Lansdown · Data Center Knowledge · ConstructConnect · as of Oct 11, 2026
Company guidance for calendar 2026 (midpoint for ranges). Microsoft reports on a June fiscal year and is not comparable.
Source: Fortune (Amazon) · Nasdaq (Alphabet) · ValueAdd VC (Meta) · as of Oct 11, 2026
Signals scored 9+ or structural shifts stay here for 90 days.
Data-centre revenue rose 117% year on year and management called next year's outlook supply-constrained. AI infrastructure demand shows no sign of slowing, which keeps GPU prices — and AI costs — firm.
Higher memory costs pushed the figure up from about $200B; AWS revenue grew 37% to $42.2B. Cloud AI capacity stays tight into 2027.
The range was $180–190B before. Together with Amazon and Meta, Big Tech's AI build-out is running well above $500B this year.
Badges show how long each signal stays: this week, this month, key context or archive.
Operators are turning to on-site generation, while uncertainty beyond 2030 makes it hard to size new builds. Expect energy to show up in AI pricing.
Nuclear SMRs move from slides to permits — a long-term answer to AI's power hunger, with GE Vernova Hitachi's boiling-water design.
Power, not chips, is becoming the bottleneck. Infrastructure investors are positioning upstream of AI capacity.
Education, job training, water and energy conservation: social licence is now part of the cost of AI capacity.
Hyperscalers are co-locating with large power sources, turning former power-plant sites into AI campuses.
Labs are building their own infrastructure teams — and Europe is on Anthropic's capacity map.
A second 500 MW phase arrives a year early: former crypto miners keep pivoting into AI hosting.
The release includes compute and chip-to-chip communication libraries and Ascend support for TileLang. China's AI stack is closing ranks around domestic chips, which matters for anyone pricing Chinese open models.
Its first debt financing pays for hardware at Bruyères-le-Châtel, run by Eclairion. Mistral aims for 200 MW of capacity across Europe by the end of 2027.
Spain is preparing its bid for the European Commission's AI gigafactory call, with candidate sites such as Móra la Nova and San Fernando de Henares. Winning one would put frontier-scale compute in Spain.
The range was $180–190B before. Together with Amazon and Meta, Big Tech's AI build-out is running well above $500B this year.
Higher memory costs pushed the figure up from about $200B; AWS revenue grew 37% to $42.2B. Cloud AI capacity stays tight into 2027.
Hosted by NAISS with RISE, it is one of 31 EuroHPC AI Factory sites. Startups and SMEs can apply for subsidised compute — worth knowing if you build models in Europe.
OpenAI and Sur Energy announced up to $25B and 500 MW in October 2025; the project is still being defined and later reports cite lower figures. Treat headline AI-campus numbers as options, not capacity.
Compute and energy prices flow straight into the price you pay for AI. Capacity announcements tell you where costs, latency and data-residency options are heading.
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