AI is saving me money in a place I never expected.
Everyone talks about productivity: code shipped faster, tasks automated, hours saved. Mine showed up somewhere duller. The fine print.
The small wins came first
Readers send me tips on using ChatGPT or Claude to find discounts and cheaper fares, and it genuinely works, hundreds of dollars saved over the past year without much effort.
I built a flight-search agent with access to Google’s flight API, Amadeus, and a few other sources. It has found real discounts twice. On routes I already know well, it still doesn’t beat me, but the direction is obvious.
A friend used Claude after a Lufthansa strike wiped out a trip. The model read the airline’s own contract of carriage, then helped him build a formal claim against the airline and, separately, an Austrian hotel that wouldn’t budge on a refund. He got most of the money back. Thousands of dollars, from arguing with two companies at once, in a language that wasn’t his.
Those are nice wins. The bigger one showed up somewhere I didn’t expect to look.
Medical bills are a different kind of maze
American healthcare has a strange property: you almost never learn the price of anything before you receive it. Hospitals don’t publish rates. You get treated, and weeks later, bills start arriving from separate companies, each with its own logic.
Inside a single bill there can be dozens of procedure codes, drug codes, insurer rules, a deductible, an out-of-pocket cap, a filing deadline, and a separate appeal deadline running on its own clock. Checking whether a bill is even correct requires knowing medicine, insurance, billing, and contract law at the same time. Almost nobody does.
The advantage in that system has always sat with the hospital and the insurer. A hospital has a billing department. An insurer has actuaries and staff lawyers. A patient gets a 20-page PDF and a due date.
What changed when we handed it to Claude
My son and I ran a stack of real bills through Claude this summer. The first pass was slow. Claude broke each bill down by code, cross-checked procedures against what the insurance plan actually covers, flagged mismatches, and pulled the specific deadlines the hospital or insurer was legally bound to meet. Then it drafted the appeal letters, and where a signature and a stamp mattered more than an email, it sent them, printed, signed, and mailed through an API.
The second pass went faster. That part alone tells you something about where this is headed.
The results were concrete, not theoretical. On one bill, the hospital simply stopped responding, missed its own procedural deadline, and the claim was dropped. On another, the insurer admitted after resubmission that it should have covered a charge it originally denied, and paid it. Several thousand dollars stayed with us instead of moving to whoever wins by outlasting the other side’s paperwork.
The oldest advantage just got smaller
Medical billing, insurance contracts, the tax code, bank fee schedules, most consumer-facing legal text, these systems didn’t become complicated by accident. A corporation can hire a department to navigate its own rules. An individual almost never can.
That asymmetry is exactly what a model reading a thousand pages in minutes starts to erase. I see the same shape in the US tax code, dense enough that even a good CPA specializes in one corner of it, where missing a single provision can cost tens of thousands of dollars. The tool that reads the whole thing without getting tired is new. The rules it’s reading are not.
A new category of business is forming on top of this: refund services for canceled flights, generalized to medical bills, insurance denials, and tax filings. An agent that takes the documents, checks them, drafts the challenge, tracks every deadline, and escalates when it has to. A handful of $200-a-year subscriptions that actually defend your interests already look like real value, even before the tooling around context and evidence gets any better than the manual, trial-and-error process we ran this summer.
I don’t think this stays quiet for long. Complexity that size protected real revenue for a long time, and the industries built on it won’t watch a model read through it for free. My guess is new rules first, then restrictions, then eventually a test case somewhere designed to make an example of someone for using one of these tools against a system that was never supposed to be readable.
That’s usually how it goes when a machine starts winning arguments that used to require a department.