Many late-stage investors focus heavily on data and metrics, forgetting that the quality of the leadership team remains as critical as in the seed stage. A new CEO, for example, can completely pivot a large company and reignite growth, a factor that quantitative analysis often misses.

Many investors focus on the current size of a company's competitive advantage. A better indicator of future success is the direction of that moat—is it growing or shrinking? Focusing on the trajectory helps avoid value traps like Nokia in 2007, which had a wide but deteriorating moat.

While a strong business model is necessary, it doesn't generate outsized returns. The key to successful growth investing is identifying a Total Addressable Market (TAM) that consensus views as small but which you believe will be massive. This contrarian take on market size is where the real alpha is found.

Top growth investors deliberately allocate more of their diligence effort to understanding and underwriting massive upside scenarios (10x+ returns) rather than concentrating on mitigating potential downside. The power-law nature of venture returns makes this a rational focus for generating exceptional performance.

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.

Founders are consistently and universally wrong about their financial projections, particularly cash runway. AI tools can provide an objective, data-driven forecast based on trailing growth, correcting for inherent founder optimism and preventing critical miscalculations.

For a proven, hyper-growth AI company, traditional business risks (market, operational, tech) are minimal. The sole risk for a late-stage investor is overpaying for several years of future growth that may decelerate faster than anticipated.

Applying industry-average growth rates to an emerging category leader is a critical mistake. A business like Shopify, with a powerful flywheel and network effects, is a power law winner that defies regression to the mean of its stagnant competitors. Its performance is simply not comparable.

Financial models struggle to project sustained high growth rates (>30% YoY). Analysts naturally revert to the mean, causing them to undervalue companies that defy this and maintain high growth for years, creating an opportunity for investors who spot this persistence.