On 19 May 2026, from the I/O stage in Mountain View, Sundar Pichai, the CEO of Google, declared that the company he leads had entered its “agentic Gemini era.” The announcement was accompanied by a slate of products — Gemini Omni and Gemini 3.5, new agentic experiences in Search, an agent-first development platform called Antigravity, and Gemini Spark, a 24/7 AI agent that runs in the background on Google Cloud and continues working when the user closes their laptop. The Gemi

Of the four worlds the Decision-Maker occupies — wealth, art, philanthropy, lineage — the last is the most recorded, and the most full of deliberate gaps. Genealogy, the reading of the lineage, is the discipline in which what is missing matters most. Mass AI and the genealogical platforms handle the surface fluently. They assemble names, dates, places. They suggest records, propose matches, extend a tree backward with apparent confidence. For a documented family, they will pr

In the art world of the Decision-Maker, alongside the figure of the New Collector, another figure has always been present: the Inheritor Collector. This figure did not arrive at the collection. They were born into it. The works were on the walls before they could read the labels, and the names — the artists, the periods, the provenance — were part of the architecture of childhood. The Inheritor Collector is not dazzled by the collection. They have lived inside it. The definin

The Decision-Maker lives in four worlds — wealth, art, philanthropy, lineage. Art is the most visible of them — and a collection is never a list of works. It is a personal statement, the most visible thing about its owner, and the most easily misread. Mass AI handles the surface fluently. It identifies the artists, places them within movements, locates them in market trajectories, summarises critical reception. A Decision-Maker asking what a particular work is, or what a part

The Decision-Maker lives in four worlds — wealth, art, philanthropy, lineage. Wealth is the most documented, and the most easily misread. Mass AI handles the vocabulary fluently. Discretionary trusts, family limited partnerships, holding structures, jurisdictional layering, generation-skipping vehicles — the terms are explained with confidence, the mechanisms described with precision. A Decision-Maker asking what a particular instrument is, or how it generally functions, will

Mass AI can answer almost any specialist question. That is precisely what makes it so easy to mistake for a specialist. The Decision-Maker asks a particular question — about a collection, a structure, a lineage, a foundation — and a credible-sounding response arrives. The answer is fluent. It uses the right vocabulary. It cites the right references. It satisfies the form of the question. On the strength of that fluency, the instrument is taken to be specialist. It is not. It

The AI powered instrument that does not know who you are will always meet you at the level of the abstraction you provide. Most AI is built for a general user. The conversation begins with translation — the Decision-Maker reducing the complexity of what they actually face into terms the instrument can process. The stakes get simplified. The context gets summarised. The things that cannot quite be said directly get left out. The exchange happens, but it happens at a distance f

A general-purpose AI is trained on the world. A Bespoke Confidante is trained on a person. Mass AI is built by drawing on vast quantities of human knowledge — text, conversation, data — processed at scale by the companies that build it. What emerges is an extraordinarily capable instrument that knows about everyone and everything in aggregate. By construction, it knows nothing about any one person in particular. A Bespoke AI Confidante is built on the same kind of underlying