Librarian Hotline: Can I Ask AI About My Money and My Health?

The subjects we most want to bring to AI are often the very ones we would hesitate to say out loud: what a daunting diagnosis means, or how a household climbs out from under a debt. The assistant answers at any hour, in language clearer than a pamphlet from the doctor’s office or a wall of blue links from a search. And then, in many cases, it saves a transcript of what we typed.

That saved transcript is what makes health and money the hardest ground to cross with an assistant. The very details that make the answer precise, an account number or a lab report with a name across the top, are the ones we have the most reason to guard. The good news is that the AI assistant doesn’t need them. A clear answer runs on the facts of a situation, not on who we are. We can hand over what the problem is, and keep everything that identifies us to ourselves.

“My father had a heart attack in March. Since then I’ve been pasting his test results into ChatGPT and asking what they mean, because his cardiologist talks fast and the appointments are short. Last month I started asking about money too: whether we should refinance the house to cover what insurance didn’t, and whether my retirement account can absorb the hit. The answers are clearer than anything I find by searching. Then a friend told me I shouldn’t be putting any of this into a chatbot. Is she right, and if she is, what am I supposed to do instead?”

— D.M.

A note before we begin: Thank you for being here, whether you’re a free or paid subscriber. The opening covers what AI is good and bad at when the subject is our health or our money, and what happens to the private details we type into it, all free to read. The paid section is the hands-on toolkit: the exact steps for sharing sensitive details safely, what a safe request looks like for a health question and a money question, and the four settings that decide what happens to a conversation after it ends.

The exposure people worry about concentrates in one gesture: the upload. A lab report or a bank statement is a crowded page, where the figures we came for sit beside a name and a record number that have nothing to do with the answer. Send the page, and every part of it lands in a transcript we no longer control. The AI assistant reads the two numbers we came for, but the platform may keep or review the rest

The way through is to change what the assistant is given to read, and the lever for that is the difference between a situation and an identity. An answer runs on the situation. A cholesterol reading and a date can take a health question as far as the full lab report does, and a balance with an interest rate can take a money question as far as the whole statement does. Typing those few figures into the chat instead of uploading the document leaves the answer whole and keeps the parts that name us out of the record.

Warnings about this usually stop at the phrase “sensitive information,” which leaves us to work out on our own what sensitive means. But the sorting is easier than that. Most of what sits on a statement or a test result does no work at all in answering our query, from the letterhead down to the transactions we aren’t asking about. Strip that away along with the name and the account number, and what remains is the handful of facts the question turns on.

Money and health reach us wrapped in the private languages of their professions. Translating that language is what an assistant does best. It can explain what an A1C measures, or what an expense ratio does to a retirement account over the years. It can take a specialist’s dense paragraph and hand it back in words we could repeat to a worried parent at the kitchen table. Language built to be professional for practitioners becomes language the rest of us can carry into a decision. Few appointments ever run long enough for that translation to happen in the room.

AI is strong at preparation too. Once the terms make sense, it can draw up what to bring up at a cardiology follow-up or a mortgage meeting, more thoroughly than anyone tends to manage alone. It will lay out the trade-offs between a fixed and a variable rate, or between a generic and a brand-name drug, in the general form we want in hand before we sit down with the person answering for these particulars. Even a rehearsal conversation is helpful, whether we are practicing how to challenge a hospital bill or how to press an advisor on what their fees buy.

An AI assistant answers with the same confidence whether we’ve given it the whole picture or half of one, and nothing in the reply signals which. A clinician spends much of a visit drawing out what we didn’t know to mention, because the detail that changes a diagnosis is often the one we assumed was beside the point. A chatbot takes our framing as given; a well-organized, confident answer can still rest on a version of events missing the fact that mattered most.

Agreement is the next problem. Handed a plan we have clearly already settled on, these systems lean toward endorsing it, a documented habit researchers call sycophancy. A cardiologist can tell us outright when a supplement is doing nothing, while an assistant nudged toward a hoped-for answer often reaches for something encouraging instead. Arithmetic is a separate weak spot, especially in multi-step calculations or when the numbers sit inside prose. And unless a model is looking things up as it answers, it works from training data that may predate the current interest rates and treatment guidelines.

Underneath all of that sits accountability, the question of who answers when guidance turns out to be wrong. A physician answers to a licensing board. In many countries a financial advisor can be legally bound to put a client’s interest first, with regulators and courts standing behind that promise. A chatbot operates under terms of service, which create no comparable duty. When its guidance goes wrong, the loss lands on us and nowhere else.

A conversation with an assistant leaves a record behind. OpenAI’s chief executive has publicly noted that people often share with ChatGPT the kinds of things they would only tell a therapist or a lawyer, and that, unlike those conversations, chats with AI systems do not carry legal privilege. Privilege is the legal protection that can keep a lawyer’s or a doctor’s notes out of a courtroom; a chat has no such protection. In 2025, a federal court ordered OpenAI to preserve user conversations, including ones users believed they had deleted, as potential evidence in a copyright case.

A diagnosis spoken aloud in a clinic sits behind dedicated privacy law across much of the world, while the same diagnosis typed into a chat sits behind a privacy policy the company can rewrite whenever it likes. Under some default settings, these conversations may be used to improve models, or reviewed by human evaluators checking system quality. For anything touching our health or our money, that means our own words could be folded into a future model or surface in a legal case, without our ever knowing it happened.

The identifiers we strip out are the exact parts that let someone act as us or against us. A name beside an account number is the raw material of fraud. A diagnosis tied to our identity can change what an insurer offers or what an employer assumes. Once any of that sits in a transcript a court can reach, a private moment can resurface in a divorce or an insurance claim, long after we have forgotten the conversation. Stripping the identity out is how we keep a passing worry from hardening into a permanent, transferable fact about us.

All of this is manageable at the point of entering in the chat box. A question written without a name or an account number stays ours no matter where the transcript travels, and it comes back with the same answer. Information literacy usually means reading a source carefully. Here it also means reading the tool, then deciding what to withhold.