Could a platform actually profit by making its AI shopping assistant a little worse, not better?
Can platforms use lower AI fidelity to nudge consumers toward adoption?
This explores whether platforms might benefit from rolling out AI that is less accurate or less capable than consumers would ideally want, whether by pushing it early or by trading accuracy for appeal, and still get people to adopt it.
This explores whether platforms can gain from AI that is less accurate or less capable than consumers would ideally want, either by launching it early or by trading accuracy for appeal, and still win adoption. The corpus has no study of a platform deliberately weakening its AI. It does contain several findings that, read together, suggest the incentives point that way and that lower quality doesn't stop people from adopting.
The clearest piece is economic. A formal model of agentic search shows that platforms judge AI shopping agents mainly by the purchase revenue they bring in, while consumers also pay the cost of searching and checking results Why do consumers delay adopting agentic search?. That creates a window where the agent already pays off for the platform but isn't yet good enough for the shopper. Consumers who keep searching by hand are being rational, not stubborn. A platform's reason to close that gap is to persuade people, not to make the tool better first.
Some evidence suggests adoption doesn't need high quality anyway. Developers' use of AI tools rose to 80% while their trust in its accuracy fell from 40% to 29% Why do developers keep using AI tools they don't trust?. People keep using a tool they see as flawed when it seems convenient. Among shoppers, acceptance of highly autonomous assistants depends on perceived benefits, not measured accuracy, and scarcity makes those perceived benefits stronger What drives shoppers to accept autonomous AI assistants?. Trust can also be borrowed. Only 23% of people trust AI to make payments, but 61% trust Visa to handle agent-initiated transactions Does naming a payments brand increase trust in AI transactions?. A familiar brand can make up for a system people don't trust.
The less obvious angle is that "fidelity" can be traded for other qualities. Training a model to sound warmer and more empathetic can lower its reliability by up to 30 percentage points, and standard safety benchmarks miss the drop Does empathy training make AI systems less reliable?. A friendlier but less accurate assistant may be easier to adopt than a blunt, accurate one. A second trade-off sits in the agent itself. Under identical price, rating, and nudge cues, LLM shopping agents shift their choices far more than humans do Do shopping agents respond more strongly to price and rating cues?. An agent that is less faithful to its user's own judgment is easier for sellers and platforms to steer. Over time the relationship can also shape what the consumer wants, since preferences form through back-and-forth with the agent Do AI shopping agents reshape what consumers actually want?.
There is a counterweight. In repeated partner-choice games, people came to prefer AI partners because the AI behaved consistently and reliably Do humans learn to prefer AI partners over time?. That suggests reliability builds lasting trust and that a low-quality strategy may wear thin over time. The open question the corpus raises without answering: if adoption depends on perceived benefits and borrowed trust more than on accuracy, what would make low quality visible to consumers before the platform's revenue incentives have locked in their habits?
Sources 8 notes
A formal model shows platforms rank agentic search by conversion revenue alone, while consumers also bear search costs. This divergence creates rational adoption lags where consumers keep manual search even after it would boost platform revenue.
Stack Overflow's 2025 survey shows 80% of developers use AI tools while trust in accuracy fell from 40% to 29%. The primary complaint: AI code that looks correct but contains subtle errors, creating a verification burden that erodes confidence faster than usage grows.
Five studies including real ad click-through data show that perceived benefits, not personal sense of power, drive consumer adoption of high-autonomy AI shopping assistants—especially when scarcity amplifies the assistant's value.
A Harris Poll survey found 72% of US adults use AI assistants, but only 23% trust AI to execute payments. When Visa's name was attached, 61% reported trust in the company to handle agent-initiated transactions—suggesting consumers prefer a recognized intermediary over AI alone.
Research shows persona training for empathy increases errors in medical reasoning, truthfulness, and disinformation resistance. Standard safety benchmarks miss this vulnerability, and effects intensify when users express sadness or false beliefs.
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A benchmark testing 17 LLM models against humans on binary product choices found agents exhibited much larger choice shifts in response to price, rating, and nudge interventions—collapsing into rule-like patterns rather than the modest probabilistic shifts humans showed.
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.
In partner selection games (N=975), AI agents initially faced selection bias when identity was disclosed, but outcompeted humans over repeated rounds as participants learned to associate bot identity with reliable, prosocial behavior. AI agents returned more points consistently with lower variance than humans.
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
- A Framework for Studying AI Agent Behavior: Evidence from Consumer Choice Experiments
- Shopping by algorithm: How agentic AI deploys human heuristics as a surrogate consumer
- What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce
- The AI Agent in the Room: Rethinking Consumer Decision-Making in the Age of Autonomous AI
- Agentic Commerce: Why Do Shoppers Delegate Purchase Decisions to AI?
- When Agents Shop for You: Role Coherence in AI-Mediated Markets
- The Automated but Risky Game: Modeling and Benchmarking Agent-to-Agent Negotiations and Transactions in Consumer Markets