Manually analyzing 30 data points builds deep intuition and overcomes the tech industry's bias for big data. It's enough to distinguish a major signal (e.g., a 60% rate) from a minor one (10%) and inform immediate action without complex analysis.
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To differentiate hype from reality, seed investors should practice "vibe coding": daily, hands-on experimentation with new developer tools. This provides an intuitive understanding of current technological capabilities, leading to better investment decisions and inoculating them against unrealistic expectations.
Top product teams like those at OpenAI don't just monitor high-level KPIs. They maintain a fanatical obsession with understanding the 'why' behind every micro-trend. When a metric shifts even slightly, they dig relentlessly to uncover the underlying user behavior or market dynamic causing it.
The most effective users of AI tools don't treat them as black boxes. They succeed by using AI to go deeper, understand the process, question outputs, and iterate. In contrast, those who get stuck use AI to distance themselves from the work, avoiding the need to learn or challenge the results.
The stock market is a 'hyperobject'—a phenomenon too vast and complex to be fully understood through data alone. Top investors navigate it by blending analysis with deep intuition, honed by recognizing patterns from countless low-fidelity signals, similar to ancient Polynesian navigators.
Julie Zhu observes that many of the fastest-growing companies grow so quickly they don't have time to build robust data logging and observability. They succeed on "good instincts and good vibes," only investing heavily in data infrastructure after growth eventually stalls.
The impulse to make all historical data "AI-ready" is a trap that can take years and millions of dollars for little immediate return. A more effective approach is to identify key strategic business goals, determine the specific data needed, and focus data preparation efforts there to achieve faster impact and quick wins.
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.
AI analysis tools tend to focus on the general topic of an interview, often overlooking tangential, unexpected "spiky" details. These anomalies, which pique a human researcher's curiosity, are frequently the source of the most significant product opportunities and breakthroughs.
Instead of seeking a "magical system" for AI quality, the most effective starting point is a manual process called error analysis. This involves spending a few hours reading through ~100 random user interactions, taking simple notes on failures, and then categorizing those notes to identify the most common problems.
We tend to stop analyzing data once we find a conclusion that feels satisfying. This cognitive shortcut, termed "explanatory satisfaction," is often triggered by confirmation bias or a desire for a simple narrative, preventing us from reaching more accurate, nuanced insights.
