If AI agents start doing our shopping for us, can brands still win customers — or just win over the bots?
Can brands compete effectively if agents become the primary consumer decision layer?
This explores whether brands can still win customers when AI agents, rather than people browsing, decide what gets bought, and what that competition would look like.
This explores whether brands can still compete once AI agents sit between them and the shopper, deciding what gets considered and bought. The corpus's short answer is yes, but the contest moves. Brands would no longer mainly compete for human attention. They would compete to be picked by an agent. One note describes this as an 'agent attention economy': services start optimizing to be chosen by agents instead of clicked by people, and ranking and ad-like systems grow up around that new audience Will agents compete for attention just like users do?. Persuading a person and being selected by software are different skills, and that is the main change.
Who owns the agent may matter more than how good the brand is. MIT researchers argue that whoever builds and maintains an agent sets its goals. Platform-built agents tend to serve sellers by default, while agents the consumer controls can be set up around the buyer's interests Does agent ownership determine whose interests get optimized?. Ad-funded shopping agents add a further risk: placement fees can tilt recommendations no matter how well the agent is engineered Do advertising-funded shopping agents favor paid product placements?. So for a brand, 'competing' might mean paying for placement in someone else's agent, or building its own. Accenture's survey suggests consumers say they prefer retailer-built agents. That is a stated preference from a survey, not proof that such agents work at scale Will brands succeed by building their own AI shopping agents?. Whether platforms end up open to many agents or closed around proprietary ones, the way payment networks split, will decide how many entry points a brand has Will agentic commerce platforms adopt open or closed models?.
Some of this cuts in brands' favor. Choudary argues that agents acting only for the buyer change where shoppers enter a market but not how the market works. Real structural change needs seller-side agents that negotiate with buyer-side agents Do one-sided consumer agents actually change market structure?. That suggests a brand's strongest move is to field its own agent, not just optimize for other people's. Microsoft researchers argue that cutting the friction of communication between consumers and businesses lowers switching costs and could redistribute market power Could agent-to-agent communication reshape market power?. Andrew Chen adds that agents lack network effects, because they can talk to each other through standard interfaces and swap the model underneath Do AI agents actually benefit from network effects?. If he's right, no single agent becomes an unavoidable gatekeeper, which is good news for brands that worry about one dominant platform.
The less obvious point is that cheap agent search doesn't automatically reward the best brand. A 2026 market-design paper argues that imperfect agent search, plus near-identical product descriptions that are easy to generate, plus a flood of low-quality sellers, can leave markets worse off than before Can cheap agent search actually harm shopping markets?. In that world, a brand's edge may come from being clearly different and easy for a machine to read, not from producing more content. A formal model finds that an algorithm's advantage over ordinary seller competition shrinks as markets fill with sellers Does algorithmic recommendation lose its edge as sellers compete more?. Agents may reshape niche markets more than crowded ones. On pricing, the UK competition regulator (the CMA) warns that brands' own pricing agents could learn to soften competition with each other without any explicit agreement Could autonomous pricing agents dampen competition without explicit collusion?.
The idea you might not expect: brand loyalty may stop forming only in the shopper's head. Bilgihan and colleagues argue that agents which actually complete purchases help build what consumers want through repeated back-and-forth, so preferences form inside the loop between person and agent Do AI shopping agents reshape what consumers actually want?. The brand's real battleground may be the agent's memory of the user. One caveat: most of this material is argument, models, or surveys. The corpus doesn't yet have strong evidence on how brands actually fare once agents do the choosing.
Sources 12 notes
Research shows that as users delegate goals to autonomous agents, services must compete for agent selection rather than clicks. This drives agent-optimized discovery mechanisms, ranking systems, and recommendation infrastructure mirroring human-facing ad ecosystems.
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.
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.
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.
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.
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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.
Microsoft researchers argue the deeper economic impact of agentic AI lies not in task automation but in reducing communication frictions between consumers and businesses. This shift could expand consumer options and reorganize markets, though the paper provides conceptual rather than empirical support for these claims.
Chen argues that because agents can communicate via standard APIs and swap underlying LLMs without losing continuity, they function as tools rather than networks. Unlike messaging platforms, agents face no pressure to converge on a single system.
A 2026 market design paper argues that imperfect agent search, easy-to-generate similar descriptions, and low-quality seller entry can compound to create worse market equilibria than before agentic shopping became cheap, even though no direct search fees apply.
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.
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.
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.
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
- Vertical tacit collusion in AI-mediated markets
- From Product Search to Preference Articulation: The Economics of Agentic Commerce
- Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets
- The Agentic Economy
- Agentic markets
- When Agents Shop for You: Role Coherence in AI-Mediated Markets
- AI Agents Want to Shop for You: The Future of Agentic Commerce