Bret Taylor, who has consistently been at the center of major tech shifts, credits his timing to more than just luck. He actively fights his own biases by finding someone he trusts who disagrees with him on a new technology and asking them to convince him he's wrong.
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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.
A key trait of effective tech leaders is the humility to solicit and immediately act upon external feedback. Google executive Schindler asking for and implementing Jim Cramer's suggestions for an NFL product shows a focus on rapid delivery and a lack of 'not-invented-here' syndrome.
Instead of defaulting to skepticism and looking for reasons why something won't work, the most productive starting point is to imagine how big and impactful a new idea could become. After exploring the optimistic case, you can then systematically address and mitigate the risks.
Certain individuals have a proven, high success rate in their domain. Rather than relying solely on your own intuition or A/B testing, treat these people as APIs. Query them for feedback on your ideas to get a high-signal assessment of your blind spots and chances of success.
While domain experts are great at creating incremental improvements, true exponential disruption often comes from founders outside an industry. Their fresh perspective allows them to challenge core assumptions and apply learnings from other fields.
To assess a founder's learning rate and critical thinking, Khosla intentionally advocates for ideas he disagrees with. This tactic reveals if a founder will blindly accept advice or critically examine it, demonstrating their ability to filter input—a key trait he looks for.
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.
Palo Alto Networks pursued cloud cybersecurity when experts claimed no one would trust it. Founder Nir Zook saw this skepticism not as a warning, but as a sign of a wide-open market with a significant competitive moat if they could prove the doubters wrong.
When an idea is met with a "wall of skepticism" from investors, it can be a positive sign of a good, non-obvious market. If every VC immediately validates your idea, it's likely too obvious and crowded. Proving early skeptics wrong with traction is a powerful path to building a defensible business.
To get Google's TPU team to adopt their AI, the AlphaChip founders overcame deep skepticism through a relentless two-year process of weekly data reviews, proving their AI was superior on every single metric before engineers would risk their careers on the unconventional designs.
