AI models in venture capital remain effective over time because the core psychological and experiential profiles of successful founders don't change much. While markets and technologies evolve rapidly, the underlying human traits that lead to success are consistent, making historical data a reliable training set for founder screening.

Sequoia quantifies its search for 'outlier founders' in statistical terms. An exceptional founder is three standard deviations above the mean in a key trait, but a true outlier is four. This statistical lens explains their high bar, reviewing around 1,000 companies for every single investment.

Veteran investor Jason Lemkin argues that the quality of a top founder can be identified without a live conversation, based on asynchronous interactions like cold emails. Having closed multiple billion-dollar exits from such inbounds, he suggests AI could replicate and scale this initial screening process effectively.

Precursor Ventures makes "directional people bets" by investing smaller checks ($150-250K) in top-tier founders to fund their search for a viable business concept. This strategy prioritizes founder quality over the initial idea, recognizing that great founders can pivot to find product-market fit.

A new benchmark from the University of Oxford, VC-Bench, found that AI models like DeepSeek Chat can predict founder success (defined as a >$500M exit or raise) with 80% accuracy based on anonymized profiles. This starkly contrasts with the 23% accuracy of human VCs, questioning the notion that venture investing is an inimitable human art.

Acknowledging venture capital's power-law returns makes winner-picking nearly impossible. Vested's quantitative model doesn't try. Instead, it identifies the top quintile of all startups to create a high-potential "pond." The strategy is then to achieve broad diversification within this pre-qualified group, ensuring they capture the eventual outliers.

Contrary to the popular debate, venture is primarily an access game, not a picking game. The core challenge is building a system to see a high volume of exceptional founders and then win the allocation. Once that is achieved, selecting which ones to back becomes straightforward.

Achieving a top-decile graduation rate requires stacking multiple, distinct filters. Start with an algorithmic screen on founders to beat the market. Add a filter for co-investing with top VCs to improve further. The final layer is your own qualitative judgment to reach the target performance.

AI-powered VC introduction platforms are not just connectors; they are stringent gatekeepers reflecting the high bar of the current market. By assigning a "grade" and only facilitating introductions for high-scoring decks, these systems programmatically enforce VC standards at scale.