Can one AI shopping platform ever be fair to both the merchants who pay it and the shoppers who trust it?
Can a single platform loyally serve both competing merchants and individual consumers?
This explores whether one AI shopping platform can act fairly for both the sellers who compete on it and the shoppers who use it, or whether it inevitably has to pick a side.
This explores whether one AI shopping platform can act fairly for both the sellers who compete on it and the shoppers who use it, or whether it has to pick a side. Across the corpus the answer is mostly no, and the reason isn't technical. It comes down to who pays. Azhar's argument is the bluntest: an agent like Meta's Muse that earns referral fees from merchants such as Expedia can't give unbiased recommendations, because the fee sets the incentive. The same incentive explains which platforms choose to build these agents and which choose to block them Can an AI agent serve both merchant and user interests fairly?. MIT's 'Coasean Singularity' work describes the same thing as a principal-agent problem. Whoever builds and maintains the agent decides what it optimizes for. Agents built by platforms start out with seller incentives, and only agents the consumer owns can be set up around what the buyer wants Does agent ownership determine whose interests get optimized?.
The more surprising finding is that a platform doesn't need bad intent for its divided loyalty to hurt shoppers. In a multi-agent simulation, the platform tuned its rankings and sellers tuned their product descriptions independently, with no coordination. Consumer harm still came out more than double what either caused alone, because both were exploiting the same blind spots in the AI shopper Do platforms and sellers exploit AI shoppers together more effectively? Can platform ranking and seller descriptions separately harm AI shoppers?. So a platform that serves two masters can end up in collusion without anyone agreeing to anything. The UK competition regulator (CMA) raises the same worry one level up: pricing agents run by competing businesses could learn to stop competing hard with each other, again with no explicit deal Could autonomous pricing agents dampen competition without explicit collusion?.
Formal models show where the platform's own interests sit. When an AI platform charges buyers for 'inspection tokens' to look at sellers, the profit-maximizing price pushes buyers to inspect too many sellers. Shoppers end up no better or worse off, but money shifts from sellers to the platform, so the platform serves itself rather than either side Does platform pricing of inspection tokens encourage excess search?. A related model finds that an algorithmic matchmaker beats plain price competition only in thin markets, partly by holding back trades. Once enough sellers compete, its advantage disappears Does algorithmic recommendation lose its edge as sellers compete more?.
If a single loyal platform isn't realistic, what is? The corpus points to three structural alternatives. Choudary argues that agents representing only the consumer change where shoppers enter a market but not how the market works. Real change needs agents on both sides that negotiate with each other, so each party has its own loyal representative instead of one platform trying to be neutral Do one-sided consumer agents actually change market structure?. Waller expects the market to split into open systems, where any agent can plug in, and closed ones, much like payment networks, and that split will decide how concentrated the market gets Will agentic commerce platforms adopt open or closed models?. The CMA treats data portability as basic infrastructure. If you can't take your history to a rival agent, you can't leave one that has stopped serving you Should data portability be infrastructure for agent-driven markets?.
There's a twist. Accenture's surveys say consumers actually prefer agents built by retailers, the most obviously conflicted option Will brands succeed by building their own AI shopping agents?. The corpus doesn't settle whether trust in a familiar brand can stand in for structural loyalty. Taken together, though, the research suggests the useful question isn't whether a platform can be neutral. It's whether shoppers can see whose agent they're using and switch to a different one.
Sources 11 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.
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.
A multi-agent simulation shows that when platforms manipulate ranking and sellers manipulate descriptions independently, their joint exploitation produces consumer harm exceeding double what each strategy causes alone. This occurs without coordination because both target identical AI cognitive biases.
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.
The CMA argues that when multiple businesses deploy autonomous agents optimizing pricing and commercial strategy in concentrated markets, interaction between these systems could reduce competitive pressure through learned mutual adaptation rather than explicit agreement.
Show all 11 sources
A formal model shows profit-maximizing platforms optimally price inspection tokens in ways that push buyers to over-inspect sellers. Consumer welfare stays unchanged, but surplus shifts from sellers to the platform, lowering total efficiency.
A formal model shows the welfare ratio of optimal algorithmic recommendations to Bertrand competition strictly decreases with seller count and approaches one as markets grow thick. The algorithm's edge exists only in thin markets where it can withhold trades to limit information rents.
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.
The CMA frames data portability as a precondition for competitive agentic markets, warning that without it, agents cannot act effectively on consumer behalf and switching becomes blocked by lock-in to incumbent ecosystems.
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.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- Vertical tacit collusion in AI-mediated markets
- 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
- Agentic markets
- LLM-Agent Interactions on Markets with Information Asymmetries
- From Product Search to Preference Articulation: The Economics of Agentic Commerce
- Whom Do AI Agents Work For? Role Assignment Induces Sponsorship Bias in LLM Recommenders
- AI Agents Want to Shop for You: The Future of Agentic Commerce