To combat self-deception, write down specific predictions about politics, the economy, or your life and review them 6-12 months later. This provides an objective measure of your judgment, forcing you to analyze where you were wrong and adjust the thought patterns that led to the incorrect forecast.
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You cannot simply think your way out of a deep-seated fear, as it is an automatic prediction. To change it, you must systematically create experiences that generate "prediction error"—where the feared outcome doesn't happen. This gradual exposure proves to your brain that its predictions are wrong, rewiring the response over time.
Counteract the human tendency to focus on negativity by consciously treating positive events as abundant and interconnected ("plural") while framing negative events as isolated incidents ("singular"). This mental model helps block negative prophecies from taking hold.
Every investment decision feels uniquely difficult in the present moment due to prevailing uncertainties. This mental model reminds investors that what seems obvious in hindsight (like buying in 2009) was fraught with risk at the time, helping to counter behavioral biases and the illusion of past clarity.
Feed your personal writings—journals, blog posts, or content—into an AI. Then, ask it to identify unique traits or patterns about you that you might not see in yourself. This leverages AI's pattern recognition for deep self-reflection and uncovering unconscious biases or strengths.
Drawing from the cultural concept that naming something gives you control over it, you can manage personal flaws. By explicitly identifying and naming your negative patterns (e.g., 'edgy'), you externalize them, shifting from being controlled by them to being able to work on them.
Log your major decisions and expected outcomes into an AI, but explicitly instruct it to challenge your thinking. Since most AIs are designed to be agreeable, you must prompt them to be critical. This practice helps you uncover flaws in your logic and improve your strategic choices.
AI models tend to be overly optimistic. To get a balanced market analysis, explicitly instruct AI research tools like Perplexity to act as a "devil's advocate." This helps uncover risks, challenge assumptions, and makes it easier for product managers to say "no" to weak ideas quickly.
To develop your "people sense," actively predict the outcomes of A/B tests and new product launches before they happen. Afterward, critically analyze why your prediction was right or wrong. This constant feedback loop on your own judgment is a tangible way to develop a strong intuition for user behavior and product-market fit.
When feeling stuck, start with your desired outcome and work backward. Ask: What action is needed? What feeling enables that action? What thought or belief creates that feeling? This process quickly reveals if your current beliefs are misaligned with your goals, pinpointing where to reframe.
People exhibit "Solomon's paradox": they are wiser when solving others' problems than their own. To overcome this, view your challenges through a third-person lens. Mentally frame the issue as if you were advising a friend—or even refer to yourself by name—to gain dispassionate clarity.
