Books as Ritual & Depth in the Age of AI

In an era dominated by AI, where instant answers are readily available, books remain valuable not just as knowledge sources but as ritualistic and ceremonial objects. Unlike AI-generated summaries (which risk reducing reading to “pseudo-reading” by skipping the cognitive work of following an author’s logic), books are crafted by professionals, offering depth and intentionality. They counteract the “homogenization” of ideas in the age of user-generated content (e.g., short videos), preserving elite, curated perspectives.

Data, Information, Knowledge, Wisdom, and Insight: A Hierarchy

• Data ≠ Information: Raw data must be refined into meaningful information.
• Information ≠ Knowledge: Information overload (often noisy) requires human curation to become actionable knowledge.
• Knowledge ≠ Wisdom: Wisdom involves critical thinking, reasoning, and reflection—skills honed through learning processes (e.g., studying “useless” subjects like calculus, which trains neural connections for complex problem-solving).
• Wisdom ≠ Insight: Insight emerges from integrating wisdom, enabling breakthroughs (e.g., Einstein’s “aha” moment with relativity).

AI’s Limitations & the Human Edge

• Learning as Training: Human learning (e.g., education) trains the brain to develop “intuitive circuits” for judgment and discernment. Without this, reliance on AI could lead to intellectual “enslavement.”
• AI’s Current State: Large language models (LLMs) excel at knowledge retrieval but lack deep reasoning, logic, or self-reflection. Specialized models (e.g., reasoning-focused or coding-focused) are emerging, suggesting no single LLM will dominate; multi-model collaboration will prevail.

Demand: Pain Points, Needs, and “Pseudo-Needs”

• Authentic demand solves critical problems (e.g., pain points or “must-have” needs like daily phone use).
• Pseudo-demand may attract niche users (e.g., older generations dismissing “secondary yuan” culture) due to generational gaps but rarely scales.
• AI’s role: It amplifies capabilities but doesn’t replace human curation or workflow design (e.g., “hand-crafted” workflows vs. AI-generated ones, which remain error-prone).

Product Strategy & Personal Reflection

• The speaker emphasizes prioritizing authentic demand over chasing trends. For example, their AI product development began with building an audience via(science popularization) before launching, aligning with the mantra: “If you think last year’s self was foolish, you’re progressing.”

In essence, the discussion underscores the irreplaceable value of human-centric processes (reading, learning, critical thinking) amid rapid AI advancement, while advocating for pragmatic, user-focused strategies in product development.