There's a strong reluctance in venture capital to fund companies that are number two or three in a category dominated by a "kingmaker"—a startup already backed by a top-tier firm. This creates a powerful, self-fulfilling fundraising moat for the perceived leader, making it unpopular to back competitors.

New VCs often rush to make deals to prove themselves, but this leads to a portfolio of mediocre companies. These investments consume a disproportionate amount of time and energy, leaving no bandwidth to pursue the truly exceptional, career-making opportunities that may appear later.

Investors who lose money in a sector develop an emotional aversion, causing them to irrationally pass on the next great company in that space. This 'learning from mistakes' becomes a liability, prioritizing avoiding small losses (commission) over capturing huge wins (omission).

In new, rapidly growing categories like AI, waiting for a perfectly differentiated company is a mistake. Differentiation is achieved over time through speed and execution. The right strategy is to bet early on strong teams in categories you have high conviction in, even if the initial competitive moat isn't obvious.

Firms like Sequoia investing in direct competitors (OpenAI and Anthropic) shows that late-stage venture has evolved. When taking small, non-board seat stakes for hundreds of millions, firms act like public market funds, buying a portfolio of category leaders without the information access that would create a true conflict.

AI companies raise subsequent rounds so quickly that little is de-risked between seed and Series B, yet valuations skyrocket. This dynamic forces large funds, which traditionally wait for traction, to compete at the earliest inception stage to secure a stake before prices become untenable for the risk involved.

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 flood of VC money in AI isn't just funding winners; it's creating highly-valued competitors that are too expensive for incumbents to acquire. This is preventing the natural market consolidation seen in past tech cycles, leading to a prolonged period of intense competition.

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.