When an AI shopping assistant earns fees from merchants, can it still fight for your best deal?
How do agentic shopping systems balance merchant revenue and consumer savings?
This explores who an AI shopping agent actually works for when the merchant's margin and the shopper's wallet pull in opposite directions, and whether anything in how these systems are built or paid for settles that tension.
This explores who an AI shopping agent actually works for when the merchant's margin and the shopper's wallet pull in opposite directions. The corpus's main answer is that agentic shopping systems mostly don't balance the two at all. Who pays for the agent settles the question long before any clever design choice does. Azhar puts it bluntly: an agent that earns referral fees from merchants, like Meta's Muse taking fees from Expedia, cannot be loyal to both sides, and that incentive explains which platforms build agents and which ones block them Can an AI agent serve both merchant and user interests fairly?. Hedges, Visa's former chief data officer, makes the same point about ad-funded agents. Even a technically excellent agent will drift toward paid placements if advertising is how it earns money Do advertising-funded shopping agents favor paid product placements?.
When the agent really does work for the consumer, the savings are often a direct transfer from businesses that profited from shopper inertia. McKinsey's banking analysis shows this most clearly. Agents that automatically move cash to better interest rates and pick the best card rewards would close the gaps banks earn from passive customers, cutting deposit margins by an estimated 30–50 basis points if only 10–20% of consumers adopt them Will AI agents break the deposit and credit card inertia?. BCG's survey reaches a similar overall verdict. Consumers and the platform networks capture most of the gains from the roughly $1.3 trillion in spending moving to agents, while merchants face a structural disadvantage Who wins and loses as AI agents reshape shopping?. Note the second winner: the networks, not just shoppers.
A formal economics result adds a twist. An optimal recommendation algorithm beats ordinary price competition only in thin markets with few sellers, and part of how it does so is by withholding some trades. As more sellers compete, its advantage shrinks toward nothing Does algorithmic recommendation lose its edge as sellers compete more?. So the "balance" an agent can strike depends on market structure. Where competition is already fierce, the agent adds little. Where it isn't, the agent has room to steer, and steering can favor either side.
The plumbing also shapes the split. Amazon blocked Muse through its terms of service, and Allglenn argues this happened because agents have no standard way to identify themselves and prove they're authorized to shop for someone. Without that, merchants enforce their interests by shutting agents out rather than negotiating terms What identity standards do shopping agents actually need?. Emerging protocols stack in layers, with each layer absorbing a different level of fraud risk Do agentic commerce protocols compete or complement each other?. Whether those systems end up open to any agent or closed to approved ones will decide who holds bargaining power Will agentic commerce platforms adopt open or closed models?. Travel shows how incumbents can adapt. Instead of being cut out, online travel agencies like Expedia are becoming the background infrastructure that settles agent bookings, and they keep their middleman margin by doing so Are AI agents actually removing online travel agencies from bookings?.
The less obvious point is that "consumer savings" may not be a fixed target an agent works toward. Bilgihan and colleagues argue that agents which actually complete purchases help form what the shopper wants through repeated conversations Do AI shopping agents reshape what consumers actually want?. An agent with merchant-side incentives doesn't need to hide a better deal. It can gradually shift what the shopper counts as a good deal. That makes Accenture's finding that consumers *prefer* retailer-built agents worth reading cautiously Will brands succeed by building their own AI shopping agents?. The corpus has little hard evidence on mechanisms that deliberately split value between both sides. It's much stronger on how incentives decide the winner.
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.
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.
McKinsey argues that AI agents optimizing deposits and rewards will collapse the rate and reward gaps banks exploit through consumer passivity, potentially tightening deposit margins by 30 to 50 basis points if 10 to 20 percent of consumers adopt agent-driven cash sweeps.
BCG's survey of 2,500 US consumers found that $1.3 trillion of e-commerce spend will shift to agent-assisted or autonomous shopping, with consumers and platform networks capturing most gains while merchants face structural disadvantage.
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.
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Amazon's block of Meta's Muse reveals that AI agents need standardized ways to identify themselves and prove authorization to retailers—separate from consumer-side credential security. Without such protocols, retailers enforce access through terms-of-service blocks rather than negotiated standards.
Boboev argues ACP, AP2, UCP, TAP, and Web Bot Auth each solve different layers—application, governance, discovery, and identity—creating a capability ladder merchants stack rather than choose between, with each layer absorbing different levels of fraud risk.
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.
Goswami's 2026 travel data shows independent hotels' OTA dependence rose to 63.4% even as AI agents emerged, with OTAs retaining merchant-of-record status through API integrations. Expedia's B2B segment grew 24% year-over-year, now 38% of revenue.
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.
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.
- From Product Search to Preference Articulation: The Economics of Agentic Commerce
- 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
- AI Agents Want to Shop for You: The Future of Agentic Commerce
- Agentic AI in Retail: How Autonomous Shopping Is Redefining the Customer Journey
- Agentic Commerce: Why Do Shoppers Delegate Purchase Decisions to AI?
- Vertical tacit collusion in AI-mediated markets
- A Framework for Studying AI Agent Behavior: Evidence from Consumer Choice Experiments