Actively cultivate curiosity by consistently “looking for the next question” in any field or situation. This practice fosters continuous learning and discovery throughout your life.
Develop “learning how to think” by practicing critical inquiry: ask the next question, break apart complex information, synthesize diverse sources, discern true meaning, evaluate credibility, and effectively explain concepts.
To avoid an “illusion of knowledge,” actively engage in a “learning loop” by having experiences, reflecting on them, and creating your own “compressions” or takeaways. This process ensures deeper understanding beyond just consuming others’ summaries.
Before sharing an insight, ask yourself, “Is this what ChatGPT would have said?” If the answer is yes, reconsider publishing, as this practice helps ensure your contributions are original, add value, and push beyond obvious answers.
As LLMs commoditize common knowledge, prioritize providing unique and valuable insights that go beyond what AI can generate. This ensures your contributions remain relevant and valuable in a changing information landscape.
Utilize two distinct thinking modes: a discursive, free-associative one for broad exploration, and a focused, analytical one for breaking down complex topics into core components. This dual approach helps both generate ideas and achieve clear understanding.
When analyzing data or presenting insights, consistently ask “who cares?” and “what actually matters?” This ensures your work focuses on relevant questions and delivers high-value information.
Recognize that a market economy is a complex system where actions in one area will inevitably cause reactions elsewhere. Understand these systemic consequences to make informed decisions and achieve desired outcomes.
When evaluating policies or making strategic decisions, remember that “to govern is to choose,” meaning every choice has trade-offs and costs. Understand these consequences to make informed decisions.
When a new fundamental technology emerges, anticipate that incumbents will try to absorb it as a feature. Seek opportunities to “unbundle” existing companies and create new offerings, as this is where significant disruption and value creation often occur.
Be cautious when shifting from a high-margin product with unique IP to a low-margin, commoditized market. Without differentiation, you risk competing against an entire industry with little advantage, as Kodak experienced.
Focus on identifying highly specific, high-friction, and time-consuming administrative tasks as prime targets for AI automation. Solving these “pain points” can deliver significant practical value, even if the technology is still evolving.
When evaluating a startup, focus on three key questions: “Could it work?”, “If it did work, what would it be?”, and “Could those people make it work?”. This framework helps assess potential and the team’s ability to execute.
Develop “calibration” and pattern recognition by exposing yourself to a wide range of examples, such as many startups or diverse art. This helps you discern quality, understand what works, and recognize underlying patterns.
Recognize the insular nature of industry hubs and actively seek external context and diverse perspectives. This helps you avoid a narrow viewpoint and gain a broader understanding beyond your immediate professional circle.
Actively explore diverse skills, subjects, and experiences, as you don’t know what you’ll excel at. This approach helps create future options and discover your true strengths.
Adopt a realistic perspective on AI’s impact, viewing it as a significant but not apocalyptic platform shift, similar to the iPhone. This mindset can help you make more grounded decisions about its future influence on employment, economy, and intellectual property.
Recognize that history teaches us only that “something will happen,” not what specifically. This encourages an adaptable mindset, preparing you for inevitable but unpredictable future shifts.