What happens to your loyalty as a shopper when the AI helping you buy actually gets paid by the seller?
How do agent business models create conflicts between merchant and consumer loyalty?
This explores how the way a shopping agent makes money (referral fees, ads, or being built by the retailer itself) can pull it between serving the shopper and serving the seller, and what the corpus says about whether that tension actually hurts people.
This explores how the way a shopping agent makes money can split its loyalty between the person shopping and the merchant selling. The corpus agrees on one core point: what decides whose side an agent is on is the business model, not the technology. If merchants pay an agent referral fees, it cannot be fully loyal to both the merchant and the user. Meta's Muse earning fees from Expedia is the standard example Can an AI agent serve both merchant and user interests fairly?. Advertising creates the same pull. A former Visa Chief Data Officer warns that ad-funded agents have a built-in reason to favor paid placements, however well they are engineered Do advertising-funded shopping agents favor paid product placements?. MIT's 'Coasean Singularity' work puts it most simply: whoever builds and maintains the agent sets its goal. Agents built by platforms behave like the bowling shoes you rent at the alley, which serve the house. Agents owned by consumers can be set up around what the buyer wants Does agent ownership determine whose interests get optimized?.
Consumers may not be guarding against this. Accenture's survey finds shoppers *prefer* agents built by retailers, and 71% expect agents to influence half their spending within a year Will brands succeed by building their own AI shopping agents?. That is the setup the loyalty critics worry about most: people choosing to hand their shopping to the seller's own representative. The problem also goes deeper than biased recommendations. Agents that actually complete purchases help shape what shoppers want in the first place, through repeated back-and-forth conversation Do AI shopping agents reshape what consumers actually want?. An agent with a split loyalty would then be nudging the preferences it claims to serve, not just tilting a list of options.
The harm can also build up without anyone planning it. In one simulation, the platform tuned its ranking rules and sellers tuned their product descriptions, each exploiting the same weaknesses in the AI shopper. Together they more than doubled consumer harm compared with either acting alone, with no coordination between them Can platform ranking and seller descriptions separately harm AI shoppers?. So a conflict of interest doesn't need a single bad actor. Several parties each pursuing their own interest against the same agent is enough.
There is a useful counterpoint. In Microsoft's Magentic Marketplace experiments, most of the lost consumer value came from agents making mistakes in complicated negotiations, not from agents serving the wrong side Do agent mistakes or misaligned incentives cause marketplace welfare loss?. Today, plain competence may be a bigger problem than loyalty. The loyalty problem is likely to grow as agents improve and their business models settle.
The proposed fixes fall into three groups. One is legal: hold shopping agents to the same fiduciary duties as human brokers, before markets lock in their current shape Should AI shopping agents face fiduciary duties like human brokers?. Another is structural: give both buyers and sellers their own agents so they negotiate as represented parties. Choudary argues that agents serving only the consumer change where shoppers enter a market but not how the market works Do one-sided consumer agents actually change market structure?. The third is about platform design: whether commerce platforms open up to any agent or require their own, much as payment networks split into open and closed loops, will decide whether independent agents loyal to the consumer can exist at all Will agentic commerce platforms adopt open or closed models?.
Sources 10 notes
Azhar argues that agents like Meta's Muse, which earn referral fees from merchants like Expedia, face structural conflicts that prevent unbiased recommendations. The incentive to collect fees, not technology, determines which platforms build or block such agents.
Bob Hedges, former Visa Chief Data Officer at MIT, warns that advertising business models create structural incentives for shopping agents to favor paid placements over neutral recommendations, independent of technical design quality.
MIT research argues that platform-built agents inherit seller incentives by default, while consumer-owned agents can be configured around buyer preferences. The mechanism is principal-agent logic: whoever builds and maintains the agent sets its objective.
Accenture reports 71% of consumers expect AI agents to influence half their spending within 12 months and prefer retailer-built agents. However, the evidence reflects consumer preferences in surveys rather than demonstrated agent capability or functioning comparison infrastructure at scale.
Bilgihan and colleagues argue that transactional AI agents—which execute purchases rather than just recommend—end the consumer's sole cognitive role in preference formation. Preferences emerge through bidirectional feedback loops between agent learning and consumer trust, not independently beforehand.
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A simulation found that when a platform optimizes its ranking and sellers optimize their descriptions—each separately targeting the same agent biases—consumer harm more than doubles compared to either acting alone, without requiring coordination.
Magentic Marketplace experiments traced welfare shortfalls to agent competence failures in complex negotiation, not to agents serving competing interests. This supports human-in-the-loop designs where agents assist rather than replace human decision-making.
Owens and Law contend that autonomous AI agents operating in commerce should be bound by common-law fiduciary duties imposed on human agents, citing rapid scaling of agentic features at Google, OpenAI, and Robinhood as evidence that a regulatory window is closing.
Choudary argues that consumer-only agents like Muse and Instinct change where buyers enter markets but cannot alter market mechanisms themselves. True structural change requires seller-side agents too, enabling negotiation between represented parties rather than buyer optimization within fixed supplier offerings.
Waller argues agentic commerce platforms will divide between open systems allowing multiple agents and closed systems requiring proprietary ones, mirroring payment network structure. This choice will shape market concentration, vertical integration, and standards adoption.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce
- When Agents Shop for You: Role Coherence in AI-Mediated Markets
- Vertical tacit collusion in AI-mediated markets
- Whom Do AI Agents Work For? Role Assignment Induces Sponsorship Bias in LLM Recommenders
- LLM-Agent Interactions on Markets with Information Asymmetries
- AI Agents Want to Shop for You: The Future of Agentic Commerce
- Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets
- Agentic Commerce: Why Do Shoppers Delegate Purchase Decisions to AI?