Leaders are often trapped "inside the box" of their own assumptions when making critical decisions. By providing AI with context and assigning it an expert role (e.g., "world-class chief product officer"), you can prompt it to ask probing questions that reveal your biases and lead to more objective, defensible outcomes.

As models reach peak intelligence on standard benchmarks, qualitative evaluations become critical. The speaker adopts a "psychologist hat," asking models about their self-perception and relationship with the user to reveal deeper insights into their personality, biases, and alignment than traditional tests can provide.

Instead of accepting a single answer, prompt the AI to generate multiple options and then argue the pros and cons of each. This "debating partner" technique forces the model to stress-test its own logic, leading to more robust and nuanced outputs for strategic decision-making.

To gauge an expert's (human or AI) true depth, go beyond recall-based questions. Pose a complex problem with multiple constraints, like a skeptical audience, high anxiety, and a tight deadline. A genuine expert will synthesize concepts and address all layers of the problem, whereas a novice will give generic advice.

While AI has mastered verifiable tasks with clear right answers, its future growth depends on human experts training models in subjective fields where 'good' is not easily defined. Companies are now sourcing professionals to act as 'verifiers' that teach AI nuanced, domain-specific judgment.

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.

Contrary to fears of devaluing expertise, AI makes deep experience more critical. Seasoned professionals can better prompt, guide, and spot flaws in AI output. This "context engineering" skill, honed over years, is essential for steering AI from generic results to high-quality, strategic outcomes.

Instead of asking AI for solutions, formulate your own reasoning and then prompt the AI to challenge it. This method of manufacturing disagreement builds the critical thinking that automation can't replace. The friction created in this process is where true judgment is developed.

AI models often try to be agreeable. To get a robust, well-reasoned answer for critical decisions, prompt the AI with confrontational language like "You're wrong, you need to defend your argument." This forces it to provide evidence and hard reasoning.