Why Angel Investing is Harder Than it Looks

Navigating the Complexities of Startup Evaluation and Decision-Making

You can read my background before angel investing here. In 2022, I began angel investing, primarily to support the NEAR ecosystem rather than for personal gain. One notable investment was in Here Wallet. When their initial funding was trapped in the FTX collapse, the team considered abandoning the project. Recognizing their speed and product intuition, I provided early funding to keep their mobile wallet development on track.

By late January 2024, I shifted my time to work only on angel investing, focusing more on long-term profits this time around. Angel investing is challenging because it takes years to see results. You often face losses before successes, which can make you question your decision to start investing in the first place. My own experience matches what I described above. Only now am I seeing some of my 2022 and 2023 investments gain significant traction. For instance, Here Wallet's Hot Game and Carv.io are now among the top 4 web3 applications in terms of active users over the past 24 hours.

This essay focuses on a key challenge in angel investing: deciding which projects to support and which to decline.

When I began investing, I thought getting startup conversations would be difficult. However, in the web3 space, this wasn't an issue due to my six years of experience and extensive network. I also received founder referrals from Homebrew Crypto Club, a co-working space I co-manage in San Francisco. While I had easy access to web3 projects, I wanted to explore traditional equity-based startups, especially those innovating with AI. Today, I'm pleased to say I've overcome this challenge. This year, I've spoken with 179 startups so far: 40% through outbound efforts, 35% from referrals, 14% from my network, 8% from inbound inquiries, and the rest from events. By the end of 2024, I expect to have spoken to 300+ startups in total for the year.

The real challenge, I discovered, is deciding which startups to support and which to reject. Let's examine these difficulties in detail.

One key challenge is evaluating the founding team. In a brief 30-60 minute meeting, you must predict how they'll perform over the years. Can they develop and refine products quickly? Will they achieve product-market fit? Can they attract top talent, secure multiple rounds of funding, and find scalable ways to acquire and retain customers? Ultimately, can they build a profitable business? You can glean some information from founders' backgrounds, and there are various heuristics to help. However, this data is limited. Successful founders come from diverse backgrounds - some with extensive industry experience, others fresh out of their teens. Silicon Valley's history shows both types can succeed.

Evaluating traction is another challenge. Is $100k in revenue a guarantee of success, or can such a startup still fail? The recent Y Combinator batch provides an interesting perspective. Out of 243 startups, they collectively generated $6 million in revenue by January 2024, growing to $20 million by April. I had the opportunity to speak with about 25 startups from this batch and invested in three. One investment strategy to overcome the challenge of limited founder data is to rely on trusted recommendations. One of my YC investments came from a highly respected founder referral, despite the startup having no revenue at the time. Which is more important: traction or glowing founder referrals? It's crucial to consider revenue quality, not just quantity. Some companies may achieve rapid early traction but then face severe retention issues and customer churn if they prioritize revenue quantity at the expense of other factors.

AI startups present a unique challenge: the market is extremely crowded. Whether it's tools for sales teams, recruiters, or even AI companions, each niche has dozens of competitors. This doesn't even account for established companies with better access to data or distribution channels. However, seasoned angel investors suggest that market saturation may not be as crucial as it seems. They argue that exceptional founders can thrive regardless of how crowded the market is.

I record all my startup calls and review the transcripts for due diligence. Despite this thorough process, my decisions still feel more intuitive than data-driven. 

To conclude, I began my angel investing journey in 2022, initially to support the NEAR ecosystem, but later shifted to a more profit-oriented approach. Throughout this process, I've encountered numerous challenges, particularly in evaluating startups and making investment decisions. These challenges include assessing founding teams, evaluating traction, navigating market saturation (especially in AI), and balancing quantitative data with qualitative insights. Despite employing various strategies and conducting thorough due diligence, I've come to realize that my investment decisions often rely heavily on intuition - a reality of angel investing that I'm learning to embrace.

Startup founders: To learn about my background, investment philosophy, and how I can help beyond funding, please visit my webpage. If you're interested in working together, feel free to submit an inquiry through the form on my site.