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.
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To win the best pre-seed deals, investors should engage high-potential talent during their 'founder curious' phase, long before a formal fundraise. The real competition is guiding them toward conviction on their own timeline, not battling other VCs for a term sheet later.
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.
Low-cost AI tools create a new paradigm for entrepreneurship. Instead of the traditional "supervised learning" model where VCs provide a playbook, we see a "reinforcement learning" approach. Countless solo founders act as "agents," rapidly testing ideas without capital, allowing the market to reward what works and disrupting the VC value proposition.
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.
Instead of walking into a pitch unprepared, Reid Hoffman advises founders to use large language models to pre-emptively critique their business idea. Prompting an AI to act as a skeptical VC helps founders anticipate tough questions and strengthen their narrative before meeting real investors.
Venture capital firms are leveraging AI tools like Google's NotebookLM to process deal flow. They ingest investment memos and legal documents to analyze them against their investment thesis and even simulate a preliminary legal review.
The venture capital return model has shifted so dramatically that even some multi-billion-dollar exits are insufficient. This forces VCs to screen for 'immortal' founders capable of building $10B+ companies from inception, making traditionally solid businesses run by 'mortal founders' increasingly uninvestable by top funds.
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.
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.
Small, dedicated venture funds compete against large, price-insensitive firms by sourcing founders *before* they become mainstream. They find an edge in niche, high-signal communities like the Thiel Fellowship interviewing committee or curated groups of technical talent. This allows them to identify and invest in elite founders at inception, avoiding bidding wars and market noise.
