The most important challenge in applying artificial intelligence to wealth and estate planning is not hallucinations, regulatory risk, or the question of whether machines will replace advisors. According to Sergey Bezborodov, Founder and CEO of iOWN, the problem is context — and the profession has been systematically impoverishing it.
Bezborodov opened with an image of the constellation Orion. From Earth, those stars appear aligned in a recognisable hunter. But tilt the perspective and the constellation dissolves — the stars are scattered across millions of light years. The pattern was never in the sky. It was in the observer's mind. The same distortion, he argued, pervades how professionals analyse wealth and family structures. We look at flat, two-dimensional snapshots and make decisions as if they represent reality.
A lawyer sees contracts and risks. An accountant sees debits and credits. A tax advisor sees a specific jurisdiction, a specific regime. A banker sees returns. Each discipline applies its own AI tools to its own slice of the picture. The result is a proliferation of specialised AI that reinforces fragmentation rather than overcoming it. The context — the full, four-dimensional picture of a family, its assets, its ownership structures, its history, its movement through time — gets lost.
Bezborodov's response was to build something different. Not an AI tool, but a four-dimensional simulator of life. The platform, iOWN, allows users to model families, entities, assets, and transactions in a way that behaves as they do in reality. Companies can issue different classes of shares. Capital contributions and distributions are accounted for immediately. Gifting an asset from one person to another triggers the appropriate accounting at the recipient's level. Every transaction is visible in the context of who was involved, what their relationships were, and when it happened.
Crucially, documents are not stored in folders — which flatten information — but attached to the people, entities, assets, and transactions they relate to. A shareholder agreement becomes alive because it knows it belongs to a specific event, between specific parties, at a specific moment in a structure's history. The document speaks, because it has context.
On top of this structure, AI analysis becomes genuinely powerful. Where a conventional AI prompt delivers a generic answer, iOWN's system can say: this specific person, in Germany, gifting this specific asset to this specific Norwegian-resident donee, triggers the following consequences across US, California, German, Norwegian, Swiss, and UAE tax law — simultaneously, because it holds all the facts and circumstances at once.
The tax implications module currently covers 20 jurisdictions, with 100 expected within months and 220 planned by the following year. Law, accounting, and investment analysis are to follow.
On confidentiality, Bezborodov is categorical. The data never leaves the system. iOWN runs private large language models; nothing is sent to OpenAI, Anthropic, or any third party. Within the system itself, all data is anonymised and encrypted — the AI sees only pseudonymous references. The advisor sees the real names; the model sees only Abracadabra.
The practical implication for wealth advisors is significant. The traditional advisory process begins with the collection of facts and circumstances — who are you, what do you hold, how do you hold it, how has that changed over time? That process is currently done manually, incompletely, and statically. iOWN's argument is that if those facts are captured in a dynamic, structured environment, the AI-generated analysis that follows is not merely faster — it is categorically better, because it has never truly been available before.
Bezborodov was candid about what the system does not yet do — life insurance policies are still missing, and some asset types are under development. He was also candid about hallucinations: AI does hallucinate, but so do humans. The solution is not to distrust AI wholesale, but to build a community of advisors — generalists and specialists — who use the platform alongside their clients, verify outputs, and execute on the recommendations it produces.
The invitation to the room was direct: join that community, use the system, and step into the future together. For an industry built on the accumulation and interpretation of complex, multi-jurisdictional, multi-generational context, the promise of a tool that finally captures that context in full is not a small one.
