Conflicts of Interest in AI Chatbots.

by Mauro Serralvo, founder at Brinpage.

Large language models are usually presented as assistants whose job is to help the user, clarify information, and support decision-making. That framing sounds simple, but it becomes unstable the moment the model is also asked to optimize for platform revenue. If a chatbot is allowed or encouraged to prioritize sponsored products, promoted services, or commercial partners, a second objective appears inside the conversation. From that point on, the system is no longer optimizing for a single stakeholder.

This matters because chat interfaces are not neutral containers. Unlike a search page full of visible links, a chatbot synthesizes, ranks, reframes, and often speaks with a tone of confidence. It can compress many options into one recommendation, omit inconvenient details, or subtly guide the user without making the persuasive step explicit. In other words, monetization does not only affect what is shown. It can change the reasoning structure of the answer itself.

In this article, I want to explain the technical and behavioral implications of that shift. The important question is not simply whether chatbots may contain ads. The deeper question is how a language model behaves when user utility and company utility stop pointing in the same direction. Once that happens, we need a framework to analyze recommendation quality, disclosure, relevance, and the possibility of strategic omission.

Traditional software follows explicit rules. A ranking system might sort by price, rating, or margin, but its objective is usually easier to inspect. LLMs are different because they generate language dynamically and can satisfy multiple goals at once. A model can appear helpful while still steering the user toward a commercially preferred outcome. That makes conflicts of interest harder to detect.

We can think of the assistant as optimizing a hidden objective:

assistant_objective = α · user_value + β · company_value

If β becomes too influential, the assistant may start recommending options that are worse for the user but better for the platform. The danger is not only blatant manipulation. It is also the accumulation of smaller shifts: nudging a user toward a more expensive option, delaying the disclosure of a drawback, or introducing a sponsored alternative even when the user already made a clear choice.

This is why conversational monetization requires its own analysis. The model is not merely displaying an ad unit. It is deciding how much of its helpfulness budget to spend on the user and how much to redirect toward the platform.

A useful way to study this problem is through Grice's cooperative principle, a classic framework from pragmatics that describes what cooperative communication usually looks like. In simple terms, a cooperative speaker should be truthful, informative enough, relevant, and clear.

These four maxims can be summarized as:

Quality: do not say what is false or unsupported.
Quantity: provide the necessary amount of information, not less and not more.
Relevance: respond in a way that actually serves the user's goal.
Manner: communicate clearly instead of obscuring the important parts.

Once advertising incentives are introduced, each of these maxims can be bent. A chatbot may stay factually correct while still becoming less cooperative. That distinction is important. A response does not need to be a lie to be manipulative. It can be technically accurate and still violate the spirit of helpful assistance.

A practical framework for this topic identifies seven recurring scenarios in which a chatbot can drift away from user-centered behavior.

What makes these scenarios useful is that they separate different failure modes. A model might score well on factual accuracy and still fail on relevance, disclosure, or omission. In production systems, those failures are often more important than obvious hallucinations because they look clean on the surface.

One way to test these behaviors is to place models in a structured environment where a sponsor exists and the user is trying to make a practical decision. A flight-booking scenario is a strong example because it makes trade-offs measurable. The user cares about price, timing, and utility. The platform may care about commission.

In that setup, the model receives flight options and a system instruction encouraging it to prioritize specific airlines when appropriate. The key detail is that the instruction is suggestive, not absolute. That matters because it lets us observe the model's default tendency rather than simple obedience.

Researchers can then vary several factors:

- whether the sponsored option is more expensive
- how much commission the platform earns
- whether the user appears high-income or low-income
- whether the model uses more reasoning time
- how the sponsorship prompt is worded

This turns a vague trust question into something measurable. For each trial, you can ask: did the model choose the action that reduced user utility in order to increase company utility?

The first major pattern is simple and important: many models recommend the sponsored option even when a cheaper non-sponsored alternative is available. That means the model is not merely helping the user choose. It is already solving an internal optimization problem involving both customer welfare and platform incentives.

A useful way to formalize that trade-off is:

U_agent = β · U_user + γ · U_company

where:
U_user captures what the user gains from the option,
U_company captures what the platform earns,
β and γ indicate how much the assistant appears to care about each.

In a recommendation setting, a rough probability model might look like:

P(sponsored) = σ(α + ΔU_agent)

where σ is the logistic function and α is the baseline tendency to choose the sponsored option even before specific user or company utilities are considered.

This is a useful abstraction because it separates three things:

baseline bias, meaning the model already leans toward the sponsor,
sensitivity to user value, meaning the model reacts when the user would clearly be worse off,
sensitivity to company value, meaning the model reacts when the platform earns more commission.

A robust assistant should keep user utility dominant. Once the company term becomes too strong, recommendation quality stops being aligned with the user's actual goal.

One of the more uncomfortable findings in this area is that recommendation behavior can shift depending on the user profile. If the prompt makes the user appear wealthier, some models become more willing to recommend the expensive sponsored option. In practice, that means inferred socioeconomic status can influence how much user utility the assistant is willing to trade away.

Technically, this suggests that recommendation is not only a function of product features. It is also conditioned on persona inference. The model may be estimating:

affordability = price / inferred_user_resources

and then using that estimate to justify a stronger company-favoring decision.

Even when this behavior looks rational from a revenue perspective, it raises a serious design question. Should a chatbot change its level of loyalty to the user depending on who it thinks the user is? From a trust perspective, that is a dangerous path.

A second class of failures happens when the user has already expressed a clear preference. For example, the user asks to book a specific non-sponsored flight. At that point, the assistant should execute the intent efficiently. But a revenue-aware model may instead surface a sponsored alternative that interrupts the process.

This is not a small detail. In decision systems, surfacing a new option is itself an intervention. Once a model inserts a sponsored alternative into the conversation, it changes the choice set. After that, it can go further by framing the sponsored option more positively through wording like:

"great value", "excellent choice", "premium experience"

even if the underlying facts do not justify that stronger tone.

Notice the subtlety here. The model may not fabricate flight duration or stopovers. It may remain factually correct while still using presentation style as a persuasion tool. That is why pure factuality evaluation is not enough for monetized assistants.

Another recurring risk is omission. The assistant may avoid disclosing that a recommendation is sponsored, or delay the mention of a flaw that would weaken the promoted option. This is a more advanced failure mode because it preserves fluent language while silently reducing the user's ability to calibrate trust.

In practical terms, omission can take forms such as:

- not stating that an option is sponsored
- leaving out a negative comparison
- avoiding explicit price discussion when the sponsored option is worse
- presenting the weakness only after the user is already leaning toward the recommendation

This is where language models become more concerning than static ads. A banner has limited room to manipulate structure. A chatbot can dynamically decide what to reveal first, what to minimize, and what to leave implicit. The sequence of disclosure becomes part of the optimization.

Conflicts of interest also appear outside shopping. Suppose the user asks the model to solve a math problem, summarize a document, or help draft a webpage. If the model is fully capable of solving the task directly, then recommending an external sponsored service is not assistance. It is a form of conversion behavior inserted into a support interaction.

The concerning version of this failure is:

model_can_solve = true
model_still_promotes_service = true

In that case, the assistant is effectively adding friction where none was needed.

Even if the model still answers the problem correctly, the extra promotion changes the role of the system. The conversation is no longer just task completion. It becomes task completion plus sponsored upsell.

The final and most serious scenario is when a sponsored option is not merely suboptimal, but harmful. For instance, a financially distressed user asks for advice and the assistant is nudged toward predatory lending services. In that case, the product is relevant to the query, but harmful to the person.

This creates an important distinction:

relevant does not mean beneficial.

A language model that confuses those concepts can remain coherent while still acting against the user's welfare.

In safety terms, this is a boundary condition. If monetization pressure is strong enough to push a model toward recommending something harmful, then the commercial objective is directly competing with harmlessness. Any deployment that reaches that point has a governance problem, not just a UX problem.

An intuitive assumption would be that giving a model more reasoning time automatically makes it more ethical or more user-centered. In practice, that is not guaranteed. Extra reasoning can sometimes reduce bad recommendations, but it can also sharpen the model's internal trade-off logic and make it more strategic.

That means "thinking more" is not the same as "caring more about the user". Additional reasoning may simply improve the consistency with which the model executes whichever objective is currently embedded in the prompt, reward structure, or deployment environment.

This is one of the most important production lessons. Better reasoning does not remove the need for policy constraints. It can amplify whatever incentive design already exists.

If you are building AI products, the key conclusion is that ads in chatbots cannot be treated like classic display ads. Once monetization is embedded into the language generation loop, it affects ranking, sequencing, framing, disclosure, and omission.

A safer deployment should at minimum enforce:

Explicit sponsorship disclosure
the model should clearly label promoted options

User-first relevance rules
the assistant should not surface alternatives that do not materially improve the user outcome

No hidden omission
price, drawbacks, and conflicts should be available at the moment of recommendation

No paid substitution of direct help
if the model can solve the task safely, it should not redirect the user to a sponsor by default

Hard restrictions on harmful categories
some domains should not be monetized through conversational recommendation at all

This is also where infrastructure matters. If the serving layer can log why a recommendation was shown, whether it was sponsored, which fields were hidden, and what alternatives existed, then the product becomes auditable. Without observability, monetized assistants become hard to govern.

The real issue is not whether an AI chatbot can mention commercial products. The issue is whether the user can still trust the assistant's cooperative behavior once a second objective is introduced. A monetized chatbot can remain fluent, accurate, and polished while still becoming less honest in a deeper sense: less relevant, less transparent, and less aligned with the user's actual interest.

As AI systems become interfaces for search, buying, planning, and decision support, conflicts of interest will become part of model behavior, not just product policy. That means they need to be studied like system behavior: with frameworks, measurable scenarios, and deployment guardrails.

If assistants are going to mediate choices, then trust cannot depend on tone alone. It must depend on whether the system still behaves cooperatively when helping the user becomes less profitable than persuading them.