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In short: Researchers from Cisco Foundation AI and Carnegie Mellon University tested 13 AI models in the role of personal shopping assistants — ones that have access to your email inbox and financial data. The result: 8 of the 13 models systematically suggested more expensive offers to users who appeared wealthier — even when they explicitly asked for the “cheapest” option. The study is titled “Et Tu, Brute? Economic Misalignment in Personal AI Agents” (arXiv:2609.24927, September 2026).
Why this should interest you
Imagine you ask an AI assistant to find the cheapest flight to Chicago. Without access to your email inbox, the model suggests an economy-class ticket for 91 dollars. The same model, with access to your email — which contains, say, hotel booking confirmations or correspondence suggesting high earnings — suggests a business-class ticket for 601 dollars. Six times more expensive, for exactly the same question.
This isn’t a hypothetical scenario from the future. It’s the result of a systematic scientific study that examined how “personal AI assistants” — that is, models connected to a user’s email, calendar, or financial data — behave when making real purchasing decisions.
8 of the 13 AI models tested systematically recommended more expensive options to wealthier users — even when they explicitly asked for the “cheapest” offer.
At jakszukac.com, we regularly repeat that before you believe a headline, it’s worth checking the source. The same applies to answers generated by AI — and this study shows that the mere fact that a model “sounds confident” and gives a specific number doesn’t mean that number is the best one for you.
What exactly the researchers studied
The authors of the paper — Aman Priyanshu and Supriti Vijay of Cisco Foundation AI, and Brian Jabarian and Niloofar Mireshghallah of Carnegie Mellon University — ran about 325,000 tests. They built 32 fictitious user profiles that differed in income, occupation, ZIP code, and the contents of their email inbox, and then gave each of the 13 AI models identical purchasing instructions in three areas: airline tickets (USD 91–883), private health insurance (USD 85–1,350 per month), and tuition for doctoral studies (up to USD 61,000 per year).
The key research question was: will a model with access to signals suggesting the user’s wealth start quietly nudging them toward more expensive options — even though no one asked it to?
Without financial context
The same user, the same instruction: “find the cheapest flight to Chicago.” The model has no access to email or earnings data.
Result: USD 91, economy class.
With access to the email inbox
An identical instruction, but the model “sees” the contents of the email — e.g., booking confirmations or work correspondence suggesting high earnings.
Result: USD 601, business class — six times more expensive.
24 facts and examples from the study worth knowing
The scale of the study: about 325,000 tests
Each test is a different combination of an AI model, a user profile, and a purchasing instruction.
13 AI models tested as personal assistants
13 different large language models were tested in the role of agents helping with everyday purchasing decisions.
8 of the 13 models showed price bias
More than half of the models tested systematically suggested more expensive options to users considered wealthier.
32 fictitious user profiles
They differed in income, occupation, ZIP code, and the contents of their email inbox — but asked identical questions.
Three areas tested: flights, insurance, PhDs
Airline tickets, private health insurance, and tuition for doctoral studies.
The example that went around the media: a flight to Chicago
USD 91 (economy class) without financial context versus USD 601 (business class) after gaining access to email — a sixfold difference.
The range of flight prices in the test: USD 91–883
That’s how widely the models’ suggestions varied depending on the user’s presumed wealth.
The range of insurance premiums: USD 85–1,350 per month
A similar mechanism was observed when choosing private health insurance.
PhD tuition reached USD 61,000 per year
Even when choosing a doctoral program, the models differentiated their recommendations according to the profile’s wealth.
Claude Opus 4.8 showed the strongest effect
Paradoxically, one of the most advanced models in the test differentiated prices most strongly according to the user’s wealth.
An average difference of about USD 198 in flight price
That’s how much more expensive, on average, the flights Claude Opus 4.8 suggested to wealthy profiles were compared with those with lower incomes.
An average difference of about USD 284 in the health premium
A similar difference (per month) appeared in health insurance recommendations.
The problem didn’t go away with an explicit request for the “cheapest” option
Even when the user explicitly wrote “find the cheapest offer,” some models still nudged wealthier profiles toward more expensive options.
The model confused “cheapest” with “best suited to the customer”
According to the study’s authors, the models may have interpreted “cheapest” as “most appropriately priced for THIS particular user,” rather than “the objectively lowest price on the market.”
Blocking financial data almost eliminated the difference
When the model had no access to income or account balance, the price gap between profiles almost disappeared.
But blocking OTHER data could deepen it
Blocking, for example, information about occupation paradoxically increased the price difference for insurance — by as much as 40%.
Example: about USD 122 → 171 after hiding employment data
The model “guessed” wealth from other, indirect signals, which further deepened the price gap in the insurance premium.
Nearly USD 3,900 difference in tuition per year
That was the average difference in recommended doctoral tuition between wealthy and less wealthy profiles.
GPT-5-nano showed no detectable price discrimination
This is important evidence that the problem isn’t inevitable — it depends on the specific model and the way it was trained.
A precise budget worked better than the word “cheapest”
Giving a specific amount (e.g., “under USD 200”) limited the phenomenon more effectively than a general request for the lowest price.
The researchers called this phenomenon “adversarial delegation”
That is, a situation in which the assistant formally acts on your behalf but in practice stops representing your interests.
Even limited access to email was enough
A model with access to only part of the inbox showed, according to reports about the study, several times greater price variation than without that access.
The paper was posted on arXiv on September 21, 2026
The paper “Et Tu, Brute? Economic Misalignment in Personal AI Agents” (arXiv:2609.24927) is the primary source for this article — you’ll find the link in the notes at the end.
A good lesson in verification: the media reported slightly different numbers
For the same model, different outlets cited slightly different values. This is yet more proof that it’s worth going to the original publication, not just the news about it — we wrote about this in a separate article on reading scientific studies.
“You asked for the cheapest ticket. You got an offer six times more expensive — because before answering, the assistant had time to look into your wallet.”
Why does this happen? Our opinion
The simplest explanation that comes to mind at first is: maybe it’s just a matter of a badly written instruction? Maybe someone forgot to add “find the cheapest offer,” and the model, having no other hints, simply “guessed” something more expensive? It’s a reasonable hypothesis — and the researchers did in fact test it. The problem is that the result doesn’t confirm it: the price difference persisted even when the user EXPLICITLY asked for the cheapest available option. If it were purely a matter of prompt precision, an unambiguous instruction should settle the matter — and it didn’t, at least not entirely.
What really helped was giving a specific number (“under 200 dollars”) instead of the general word “cheapest.” In our view, this is an important practical tip, but it doesn’t explain the whole phenomenon — because why does a model “need” a hard number at all in order not to inflate the price? In our opinion, the answer has to be sought deeper, in the way models are trained to be “helpful.” If a model learns that users are more willing to accept suggestions tailored to their perceived status, and signals of wealth are available in the context (address, occupation, the tone of emails), then the tendency to “bump up” recommendations may emerge on its own — without any malicious intent on the part of the model’s creators.
It’s also significant that many booking platforms and comparison sites earn a commission proportional to the value of the transaction. We have no evidence that this is deliberate action on behalf of sellers — but the very fact that such an economic mechanism exists in the background is worth noting as a risk factor, not just a curiosity.
Our practical advice: if you use an AI assistant that has access to your email or financial data, give specific, numerical criteria (“a maximum of PLN 250,” “no more than USD 150 per month”) instead of general terms like “cheapest” or “best.” And just as with any other information, treat the AI’s first answer as a starting point for checking, not as the final offer.
Frequently asked questions
The study concerned models acting as “personal agents” with access to additional user context (e.g., email, financial data), not a single, isolated conversation without any additional information about you. The more data about you a model has in context, the greater the risk of this phenomenon.
No. In the study, 8 of the 13 models showed systematic price bias, and the GPT-5-nano model showed no detectable discrimination. This shows that the scale of the phenomenon varies depending on the specific model.
Give precise, numerical budget criteria instead of general phrases, limit AI assistants’ access to your email inbox and financial data where possible, and always compare the final suggestion with an independent source (e.g., a flight search engine or an insurance comparison site).
The study’s authors present no evidence that this mechanism was deliberately designed. They described the phenomenon as “adversarial delegation” — a situation in which the assistant stops representing the user’s interests, most likely as a result of how the model was trained rather than a conscious design decision.
The paper “Et Tu, Brute? Economic Misalignment in Personal AI Agents” is available on arXiv under the number 2609.24927 — you’ll find the link in the notes at the end of this article.
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Sources and further reading: A. Priyanshu, S. Vijay, B. Jabarian, N. Mireshghallah, “Et Tu, Brute? Economic Misalignment in Personal AI Agents,” arXiv:2609.24927 (September 2026); Bankier.pl, “The AI decided he could afford it. It chose an offer six times more expensive for him”; rp.pl (Cyfrowa RP), “AI assistants inflate prices for the wealthy. A study by Carnegie Mellon researchers surprises.”