---
id: "claim-magic-marketing-backfire"
type: "claim"
source_timestamps: ["§ Be Transparent and Honest"]
tags: ["marketing-ethics", "consumer-trust"]
related: ["concept-ai-magic-effect", "action-transparent-tradeoffs"]
confidence: "high"
testable: true
speakers: ["Chiara Longoni", "Gil Appel", "Stephanie M. Tully"]
sources: ["adoption"]
sourceVaultSlug: "hbr-seg-adoption"
originDay: 9
articleStem: "hbr-edu-39-understanding-ai-not-embrace"
sourceUrl: "https://hbr.org/2025/07/why-understanding-ai-doesnt-necessarily-lead-people-to-embrace-it"
sourceTitle: "Why Understanding AI Doesn’t Necessarily Lead People to Embrace It"
---
# Marketing AI as magical without delivering real benefits destroys trust

**Claim (confidence: high, testable):** Leveraging the [[concept-ai-magic-effect]] can successfully fuel *initial* enthusiasm and adoption among low-literacy consumers — but the strategy **backfires** if the AI product fails to deliver tangible benefits. Users who feel they were sold an illusion without underlying utility feel disappointed or manipulated, causing a permanent loss of trust. This is the ethical guardrail behind [[action-transparent-tradeoffs]].

> **Validation (enrichment): Supported in principle.** There is no direct experimental test of "magic marketing backfires," but broader AI-trust and ethics research strongly implies it. CloudResearch's "AI Paradox" report shows widespread use coexisting with unease, with disappointment when AI is confidently wrong; PLOS One work shows trust is maintained by precision and transparency and eroded by opaque failures. Responsible-AI guidance (OECD, EU AI Act, NIST AI RMF) warns that **overhyping capabilities without transparency undermines public trust** — and AI-ethics critics note that leaning on awe/mystique can shade into *manipulative design* (dark-pattern territory), especially in high-stakes domains.