This study compared human and AI-generated consumer values using the Means-End Chain (MEC) model to assess the potential of Large Language Models (LLMs) as stand-ins or addons for human respondents in consumer research. We conducted twenty in-depth laddering interviews with human participants and identical simulated interviews (synthetic interviews) with three distinct LLMs: OpenAI’s GPT-4o, Google’s Gemini Pro, and Mistral Large. Smartphones were chosen as the research objective. All interview outputs were accurately coded into Attribute, Consequence, and Value categories, then analyzed for dominant laddering chains and Hierarchical Value Maps (HVM). Significant divergences emerged between human and LLM-generated values. Human respondents primarily prioritized “Camera,” “Battery/energy/durability,” and “Design/look/feel/display” at the attribute level, linking them to “High-quality imaging & memory preservation” and “Security & Reliability” as dominant values. Their motivational chains were pragmatic and risk-reducing. In contrast, LLMs emphasized “Chip performance/speed” and “Connectivity/OS connection/functions,” consistently highlighting “Fast performance & time-saving convenience” as a key consequence. Critically, LLMs frequently surfaced “Independence/Self-Actualization” and “Professional Success” as higher-order values, creating broader, more layered ladders. Price and cost considerations were notably minimal for LLMs compared to human interviews. While “Efficiency & Convenience” was a shared core value, the elaboration of other values differed substantially. The observed discrepancies stem from fundamental differences in how experiences are represented. Humans draw on episodic memory, leading to more practical, risk-averse motivations, potentially exhibiting confirmation bias. LLMs, however, recombine textual patterns from vast training data, unconstrained by lived trade-offs, which allows them to surface a richer but potentially more diffuse motivational web. This suggests that while LLM-generated ladders are useful for brainstorming broad positioning themes, they may overemphasize aspirational narratives and reflect an “online-culture” bias. A hybrid research strategy, using LLMs for preliminary insights and human interviews for validation, is therefore recommended to verify real consumer value drivers.