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I recently had an email exchange with an Oblivious Investor reader about the (new for 2026) deduction of $1,000 ($2,000 if married filing jointly) for charitable contributions, for people who don’t itemize. The question was whether the deduction reduces adjusted gross income (AGI) or not.
The short answer is that, no, it does not reduce adjusted gross income.
But what’s perhaps more interesting is the conversation the reader was having at the same time, with Gemini AI, about the same topic. The reader had asked whether the deduction is an “above the line” or “below the line” deduction.
And the AI gave an answer that was frankly nonsensical. That’s likely because there’s conflicting writing on the internet about this topic, in part because the terms “above the line” and “below the line” are no longer sufficient to describe the categories of deductions that we have.
We used to have only:
- Adjustments to gross income (i.e., things that reduce adjusted gross income), which were known as above-the-line deductions.
- Itemized deductions, which were known as below-the-line deductions.
These days, we have a third category of deductions, which do not reduce adjusted gross income but which are not itemized deductions. Included in this category are:
- The $1,000 ($2,000 if MFJ) deduction in question for charitable contributions.
- The deduction for qualified business income (i.e., pass-through income).
- The deduction for qualified tip income.
- The deduction for qualified overtime income.
- The deduction for vehicle loan interest.
- The enhanced senior deduction.
Some people are calling these deductions “above the line” because they aren’t itemized. And some people are calling them “below the line” because they do not reduce adjusted gross income. And so human readers reading these different articles — as well as AI chatbots being trained on these articles — get confused.
When Gemini was asked to look specifically at the law itself and help interpret that, Gemini answered that it’s an above the line deduction, reducing AGI. Here’s Gemini’s explanation:
The specific language in Section 70424 of the Act that executes this change is as follows:
“(b) DEDUCTION ALLOWED IN COMPUTING ADJUSTED GROSS INCOME.—”
“Section 62(a) of the Internal Revenue Code of 1986 is amended by inserting after paragraph (21) the following new paragraph:”
“(22) CHARITABLE CONTRIBUTIONS OF INDIVIDUALS WHO DO NOT ELECT TO ITEMIZE DEDUCTIONS.— In the case of an individual who does not elect to itemize deductions for the taxable year, the deduction allowed by section 170(p).”
Gemini’s reasoning makes perfect sense — or it would, if that wording actually existed in the legislation. But it doesn’t. The entire wording Gemini is quoting is completely hallucinated. It doesn’t appear in the actual text of the law at all.
It’s remarkable though that the wording Gemini is providing is exactly what the law would say, if it were trying to make the deduction an adjustment to income. The AI is being very clever here — it just fundamentally misunderstands the assignment. It found wording from other pieces of legislation, in which a new deduction was made an adjustment to income. And it modified the wording exactly as needed, to fit the new context.
It’s exactly the sort of thing you’d want if you were using it to help write software code: finding something that worked in a very similar situation, and then modifying it in just the right way to fit this new situation.
But that’s precisely the opposite of what we want in tax research. We don’t want it to create new wording. We need it to specifically look at the wording that is there, understand that wording, and explain it to us.
AI chatbots can be very helpful, for a range of different tasks. But they can still get things wrong — and in ways in which no human ever would. (A human may very well answer the same tax question incorrectly, but no human in their right mind would completely fabricate a bunch of legislative wording as part of their answer.)
If you want to use AIs for tax research, there are a few things that can help improve the results:
- In your permanent account settings, give it some information along the lines of “Correctness is always a critical concern. If you are ever unsure of anything, please let me know and/or ask questions. Please never give me an answer of which you are unsure.” (Note though that this can cut down on hallucinations but will not eliminate them, as the model can sometimes be confident but incorrect.)
- In the actual prompt you give, do not only ask it to base its answer on the law, first ask it to find the text of the law itself. Confirm that it has read the text. And then ask your question. For example, start your conversation with: “Please look on the law.cornell.edu website for the IRC section about health savings accounts. Please find and read that.” And then go from there, with whatever questions you have about HSAs.
- And for any points that are absolutely critical (i.e., points where it would be very problematic if the information you received was wrong), ask it specifically where it is finding the information in question. And then go confirm that for yourself. Acceptable sources include Internal Revenue Code sections, Treasury Regulations, Revenue Rulings, Revenue Procedures, and judicial decisions. IRS publications or articles anywhere (including IRS.gov) are not sufficient.
It’s critical to understand that AIs do not behave the same way that humans do. The situations in which AIs make mistakes, and the ways in which they make mistakes, are different than for humans. So if you’re going to rely on AI answers for critical topics, you have to be thorough/explicit about confirming the information on your own.
I often think of an AI chatbot as an employee that is: smart, extremely eager to please, hardworking, and literally an alien from another planet. Imagine that that’s who you have sitting on a stool next to you. It’s ready to work! But you can’t assume that it will understand you in the same way that a human would. They can not only get things wrong, they can sometimes fundamentally misunderstand the assignment in a way that no human would (as in the case above, in which it decided that making up tax law was what the user wanted).
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