This is correct for out of the box AIs. This is why I built Roundtable, our domain aware persistence layer for AI context management. It's what powers Pendrago

This is correct for out of the box AIs. This is why I built Roundtable, our domain aware persistence layer for AI context management. It's what powers Pendragon. Our protections aren't just "trust us", we show you how your data is controlled architecturally and secure. You don't necessarily need to provide everything. Arthur (our AI) is smart enough to see exactly which information it needs to answer a given question. but, yes, the more information you provide the easier of a time the AI will have in answering your question. Arthur doesn't guess. if there is crucial information it needs he will ask for it. it doesn't have to be a Plaid hook up, a csv or even a simple user response is a start. On your third point — you'd need an outcomes dataset — that's true for traditional ML, but it's not how this works. The normative layer is finance itself (life-cycle theory, tax rules, amortization) implemented as deterministic calculators, with the LLM doing explanation and elicitation. The paper under discussion is sort of the proof: the models already give theory-aligned advice with zero outcome training. The gap it found is input quality and statelessness, not a missing training set.