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.
Related Insights
Major platform shifts like AI reward founders who are not burdened by historical context or "how things have been done before." This creates an environment where young, inexperienced teams working with high intensity (e.g., "9-9-6") can out-innovate incumbents with existing business models.
Redpoint Ventures' Erica Brescia describes a shift in their investment thesis for the AI era. They are now more likely to back young, "high-velocity" founders who "run through walls to win" over those with traditional domain expertise. Sheer speed, storytelling, and determination are becoming more critical selection criteria.
Top entrepreneurs don't just build a product; they become historians of their domain. They study predecessors, understand market evolution, and learn from past attempts. This deep historical knowledge, seen in founders of Stripe and Airbnb, is a key differentiator and trait of the very best.
AI doesn't replace business fundamentals; it accelerates them. The most successful founders apply timeless frameworks for building valuable companies—like achieving product-market fit—but use modern AI tools to run experiments and learn at a massively compressed time and cost.
During a fundamental technology shift like the current AI wave, traditional market size analysis is pointless because new markets and behaviors are being created. Investors should de-emphasize TAM and instead bet on founders who have a clear, convicted vision for how the world will change.
A common trait among exceptional founders is a deep, almost academic, understanding of their industry's history. They learn from every past attempt, success, and failure. This historical context allows them to innovate with a unique perspective and avoid the pitfalls that doomed their predecessors, a sign of true commitment and expertise.
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.
By using an unsupervised machine learning model to filter thousands of teams based solely on founder profiles, a VC can significantly de-risk its pipeline. Investing in this pre-screened pool alone would yield a 24% graduation rate, far above the 14% market average, even before applying human judgment.
In a fast-moving category like AI coding, platform features are fleeting. The more durable factor is the founding team's vision and ability to execute. Users should follow the founders of these companies, as choosing a tool is ultimately a long-term bet on a person's leadership and trajectory.
In the AI era, technology moats are shrinking as tools become commoditized. Consequently, early-stage investors increasingly prioritize the founding team itself, specifically their execution velocity and ability to leverage AI, over any specific technical advantage.