In fast-moving sectors, the investable options can seem to improve every few days, creating a dilemma for VCs: invest now or wait for a better team? The solution is to assume dozens of teams are working on any rational idea and focus on choosing the best one you can find now, rather than waiting indefinitely.
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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.
In a gold rush like AI, the shared 'why now' forces many founders into a pure speed-based strategy. This is a dangerous game, as it often lacks long-term defensibility and requires an incredibly hard-charging approach that not all teams can sustain.
The pace of AI-driven innovation has accelerated so dramatically that marginal improvements are quickly rendered obsolete. Founders must pursue ideas that offer an order-of-magnitude change to their industry, as anything less will be overtaken by the next wave of technology.
Since startups lack infinite time and money, an investor's key diligence question is whether the team can learn and iterate fast enough to find a valuable solution before resources run out. This 'learning velocity' is more important than initial traction or a perfect starting plan.
The ideal period for venture investment—after a company is known but before its success becomes obvious—has compressed drastically. VCs are now forced to choose between investing in acute uncertainty or paying massive, near-public valuations.
The mantra 'ideas are cheap' fails in the current AI paradigm. With 'scaling' as the dominant execution strategy, the industry has more companies than novel ideas. This makes truly new concepts, not just execution, the scarcest resource and the primary bottleneck for breakthrough progress.
While moats like network effects and brand develop over time, the only sustainable advantage an early-stage startup has is its iteration speed. The ability to quickly cycle through ideas, build MVPs, and gather feedback is the fundamental driver of success before achieving scale.
Conventional venture capital wisdom of 'winner-take-all' may not apply to AI applications. The market is expanding so rapidly that it can sustain multiple, fast-growing, highly valuable companies, each capturing a significant niche. For VCs, this means huge returns don't necessarily require backing a monopoly.
True alpha in venture capital is found at the extremes. It's either in being a "market maker" at the earliest stages by shaping a raw idea, or by writing massive, late-stage checks where few can compete. The competitive, crowded middle-stages offer less opportunity for outsized returns.
