AI Infrastructure

Because energy access now determines AI capability. UK organisations pay industrial electricity prices roughly four times those of their US competitors (Social Market Foundation 2025), and Goldman Sachs’ “Powering the AI Era” (2025) concludes that power, not capital, is the most pressing bottleneck for AI progress. When investment banks dedicate serious research to power constraints, energy sovereignty has moved from policy concern to strategic imperative. I set out the UK position in the sovereignty piece and the institutional confirmation in the Goldman Sachs analysis.

The instinct is understandable and the evidence points the other way. Every prior era of computing converted efficiency gains into more demand, not less, and AI inference is behaving the same way: cheaper inference releases workloads that cost and latency had been holding back. The Headroom Argument sets out the three forces behind this, and why Boards that read efficiency news as a budget signal risk under-resourcing the opportunity their competitors fund.

Because operators building behind-the-meter generation are transitioning from energy consumers into grid actors, with surplus capacity to sell and growing influence over national grids. In a system the size of the UK’s, that carries both opportunity and concentration risk, including questions of foreign ownership of critical national infrastructure. A New Grid Actor examines the transition and the governance frameworks Boards should establish before it forces answers upon them.

No, and the data points the same way. S&P Global (2025) found that 42% of companies abandoned most of their AI initiatives, up from 17% the year before. On my reading, much of that is generative AI applied to problems better solved by traditional machine learning or deterministic automation. The first question is still whether the use case is a good fit for generative AI at all. Selecting your enterprise LLM and The Return of Traditional AI make the case for matching capability to problem, with hybrid architectures where each discipline does what it does best.

Model Context Protocol is the standard that connects AI agents to the proprietary data and systems where competitive advantage lives, and the major enterprise vendors have already converged on it. The decision is being made now, often by default, inside technology teams. What agents can see, and what they do with what they see, is a governance question with a technology dimension. MCP Explained covers the standard and the questions to ask; The Inference Migration covers what always-on agents change.

They stop, and nothing degrades first. On 12 June 2026 a national-security directive forced a provider to switch off two deployed frontier models for every customer, and the organisations cut off were never its target. The AI Sovereignty Trilemma model holds that frontier capability bought cheaply surrenders sovereign control, and that surrender has moved up the stack: sovereign hosting, sovereign data, and sovereign infrastructure do not save a capability governed elsewhere. Model availability belongs on the Board’s risk register with a named owner and a fallback that survives the specific event, jurisdictional as well as commercial. When a Frontier Model Vanishes sets out the questions.

For most organisations, no, and the reason is scale, not timidity. On my count in A New Grid Actor, hyperscaler nuclear commitments now exceed 8GW, which is operators becoming grid actors, not customers buying certainty. Goldman Sachs’ “Powering the AI Era” (2025) shows why the comparison fails: the binding constraint is power, not capital, and no financing structure manufactures it. The Board’s questions are different: where the organisation’s energy exposure sits, whether its providers have secured their capacity, and, if on-site generation is in play, where it crosses from backup to grid export and into a different regulatory regime. The Goldman Sachs analysis sets out the constraint.

As a capability signal first and a cost signal second. Capacity is being built for demand the providers can already see, so the first question is what becomes newly possible, then which dependencies deepen, then what governance needs to exist before anyone acts. New nuclear, SMRs above all, arrives in the mid-2030s, so the gap is being filled by gas and the emissions accountability travels with the contract. A provider building its own generation is becoming a grid actor, with concentration and ownership questions a grid the UK’s size feels sooner than America’s. The Headroom Argument and A New Grid Actor supply the reading.