To improve hiring decisions, founders should proactively meet top performers in roles they anticipate needing in 2-3 quarters. This isn't for immediate hiring but to build a mental model of excellence for that specific function and stage, which sharpens intuition when you do start recruiting.

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.

Waiting for perfect data leads to paralysis. A core founder skill is making hard decisions with incomplete information. This 'founder gut' isn't innate; it's developed by studying the thought processes—not just the outcomes—of experienced entrepreneurs through masterminds, advisors, or podcasts.

Leaders often feel they must have all the answers, which stifles team contribution. A better approach is to hire domain experts smarter than you, actively listen to their ideas, and empower them. This creates a culture where everyone learns and the entire company's performance rises.

Founders can use AI pitch deck analyzers as a "sparring partner" to receive objective feedback and iteratively improve their narrative. This allows them to identify weaknesses and strengthen their pitch without burning valuable relationships with real VCs on a premature version.

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 can generate hundreds of statistically novel ideas in seconds, but they lack context and feasibility. The bottleneck isn't a lack of ideas, but a lack of *good* ideas. Humans excel at filtering this volume through the lens of experience and strategic value, steering raw output toward a genuinely useful solution.

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.

If a highly successful person repeatedly makes decisions that seem crazy but consistently work, don't dismiss them. Instead, assume their model of reality is superior to yours in a key way. Your goal should be to infer what knowledge they possess that you don't.