18 Months In: The Angel Investing Questions I Thought I'd Have Figured Out By Now
What 730 startup conversations taught me about the limits of pattern recognition
Eighteen months into angel investing, I've personally spoken to 730+ startups and invested in 44 of them—a 6% hit rate. I'm past the total beginner stage but nowhere near experienced. What surprises me most is how much I still don't know, despite seeing hundreds of pitches.
The biggest reality check: I expected to spot obvious winners after 18 months of practice. Instead, picking great startups remains incredibly difficult. My decision framework has evolved multiple times, but I still haven't met a single company that screamed 'future unicorn.' The startups I do invest in have strong teams and promising markets, but none feel like sure bets. I regularly pass on impressive founders simply because I can't get conviction—even when everything looks good on paper.
The metrics puzzle: I diligently track founder updates, revenue growth, and hiring patterns across my portfolio, but I still can't tell which metrics actually predict long-term success. Four of my companies have crossed $1M in revenue, one more is approaching that milestone, and many have hit $100K+. Yet none of these milestones give me confidence about which will become true winners. Early revenue growth might mean product-market fit—or just good initial sales execution that won't scale.
The confidence paradox: My decision-making process has become much more systematic—I have clearer criteria, better due diligence checklists, and faster pattern recognition. But paradoxically, I'm less confident about predicting outcomes than when I started. The more startups I see, the more I realize how many variables affect success that have nothing to do with my evaluation framework. Better process, lower certainty—which probably makes me a better investor.
The token feedback trap: Tokens offer much faster liquidity than equity—typically 3 years versus 5-10+ years for traditional startups. But this apparent advantage creates a problem: token prices often disconnect from actual business performance, making the feedback loop nearly useless for learning. I can sell a token for 10x returns while the underlying project struggles, or watch great execution get crushed by crypto market cycles. This means most token investments become trading decisions rather than long-term bets on business fundamentals—exactly the opposite of what makes angel investing educational.
Eighteen months in, I'm simultaneously more skilled and more humble. I can evaluate startups faster and with better frameworks, but I'm less certain about predicting winners. This isn't a bug—it's a feature. Angel investing operates on decade-long feedback loops, which means embracing uncertainty isn't just healthy, it's essential. The founders who impress me most also live comfortably with ambiguity while executing with conviction. Maybe that's the real skill I'm developing: getting better at making decisions without needing to be right.