Analysis & Commentary
Independent analysis on AI governance, economics, sustainability and resilience — from practitioners who have carried the weight of being wrong.
Boards across regulated industries are approving AI investments at a pace that would be unthinkable for almost any other capital allocation. Yet one simple question often goes unanswered: if this decision proves wrong in three years, who answers for it?
Read article→Global AI spending shows no sign of slowing. Institutions are approving budgets on a trajectory that would have looked reckless for almost any other category of capital allocation five years ago. Yet ask whether the investment actually pays back, and the answer gets murkier the harder you look.
Read article→The energy consumption of large AI models is not a footnote in a sustainability report. It is a strategic variable that affects infrastructure costs, regulatory exposure, and the credibility of net-zero commitments.
Read article→The geography of AI is not neutral. Where models are trained, where data is stored, and where compute infrastructure sits are decisions with regulatory, geopolitical and operational consequences that most boards have not fully mapped.
Read article→In late July 2026, Nvidia entered talks to guarantee up to $250 billion of financing so that OpenAI could lease a 10-gigawatt data centre being built on former nuclear-enrichment land in Piketon, Ohio. Nvidia's own stock fell almost 5 percent on the day the talks were reported. Investors were not reacting to bad news about demand for AI. They were reacting to a question that is getting harder to avoid.
Read article→The AI Leadership Review brings independent, senior scrutiny to a consequential AI decision, examining its assumptions, dependencies and implications across the six fault lines. It helps leadership determine whether to proceed, change course, delay or stop.