Twenty-eight published definitions of AI, coded against eight themes, show the three sectors emphasise systematically different things: 75% of industry definitions invoke human comparison against 14% of policy ones, which foreground autonomy and data-dependence instead. Statistical clustering of 27 real systems built between 1956 and 2025 finds three groups — and the smallest, four high-autonomy agentic systems, is defined by acting in the world rather than by scale or recency.

Fourteen actors scored across ten dimensions of AI power. The United States leads at 88.1, China at 72.7, and nine further actors sit within nine points of each other. But the ranking hides the leverage: 74.5% of tracked global AI compute sits in the US, and 100% of extreme-ultraviolet lithography comes from one Dutch firm neither superpower owns. The paper argues the frontier leaderboard has stopped measuring power, and that chokepoints, verification and standards are where strategy belongs.

Four US hyperscalers will spend ~$700bn on AI infrastructure in 2026, against $1.25bn for India's entire national AI mission — a 560-fold gap. The IMF finds capital and wealth inequality "always increase" under AI, with high earners gaining seven times what low earners do. Yet the US, EU and India are producing three visibly different economies from the same models. The paper argues ownership, not capability, decides which economy emerges, and scores five candidate systems against eight criteria.

Orchestrating human judgement and artificial intelligence for sustainable competitive advantage in luxury hospitality. Prepared for boards, owners and senior operating executives. Its argument: AI creates the most value, and the least risk, when deployed first against the parts of luxury hospitality guests never see.

An evidence-based landscape of 22 real, named AI deployments across Four Seasons, Ritz-Carlton, Hilton, Marriott, Rosewood, Aman, Mandarin Oriental, Accor, IHG and Hyatt — each coded on two questions: how much can it decide on its own, and how much does the guest notice it? Those two axes sort the successes from the failures better than anything about the technology itself, and they produce a decision tool that fits on a napkin.

Sixteen categories of AI, hundreds of vendors, and no shared reference architecture. This paper builds the taxonomy, prices it, and sets out the five-stage maturity model this series treats as canonical. Three findings carry the argument: vendor list prices understate true first-year cost by 40 to 60 per cent; operators routinely place themselves one to two stages above where an independent assessment would; and the workforce, not the budget, is the binding constraint.