Study shows that tacking the “AI” label on products may drive people away

Buzzword fatigue and marketing basics

• Many see “AI-powered” as empty hype akin to past labels like “.com,” “digital,” “smart,” or “blockchain.”
• Core advice: market the problem solved and benefits delivered, not the underlying tech; “customers buy holes, not drills.”
• Some argue big firms still A/B test “AI” messaging successfully; others say this optimizes short-term clicks, not long-term trust.

Investor-driven hype vs consumer demand

• Strong view that AI is marketed primarily to investors and executives, with consumer-facing AI used as proof for the stock market.
• Enterprises often demand “AI” and “edge AI” in pitches even when technically unnecessary, because execs and consultants expect those buzzwords.
• Several note this can backfire: AI chat UIs trigger legal/privacy nightmares and can delay enterprise deals.

Consumer perception: skepticism, fatigue, and avoidance

• Many commenters now treat “AI” on a product as a red flag: implies gimmicks, unreliability, data collection, subscriptions, and “crapware.”
• Examples: AI-branded mice, toothbrushes, washing machines, TVs, browsers, and every kind of mobile app. Often the “AI” is just a cloud chatbot or simple heuristics.
• Some actively avoid apps, ads, and products with AI branding and see it as a useful filter against hype-driven offerings.

Real utility vs gimmicks and harm

• A minority describe concrete value: e.g., better photo editing, on-device semantic photo search, tutor-like help when learning.
• Others emphasize limits: LLMs are good at rewriting, pattern recognition, and corpus search but weak at novel creation and can be dangerously wrong if copied blindly.
• Serious harms are cited, such as AI tools used in healthcare decisions with very high error rates, and widespread replacement of robust search with weak chatbots.

Historical pattern and bubble concerns

• Repeated comparisons to crypto, NFTs, metaverse, IoT, and earlier AI bubbles. Many expect the “AI” label to become toxic again and be quietly replaced by terms like “machine learning.”
• Some fear this hype cycle will trigger another AI winter and overshadow genuinely valuable machine learning work that could otherwise deliver steady, real-world improvements.

Societal and labor angles

• One thread connects anti-AI sentiment to earlier resistance to globalization and automation in deindustrialized regions, arguing “adapt or die.”
• Others counter that corporate greed, trade policy, and political capture—not worker skepticism—are the main drivers of economic hardship.