A16z's foundational belief is that founders, not hired "professional CEOs," should lead their companies long-term. The firm is structured as a network of specialists to provide founders with the knowledge and connections they lack, enabling them to grow into the CEO role and succeed.

a16z's investment philosophy is to assess founders on how world-class they are at their core strengths. Horowitz warns it's a mistake to pass on a uniquely talented founder due to fixable weaknesses (e.g., no go-to-market plan) and an equal mistake to back a less talented founder just because they lack obvious flaws.

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.

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.

Benchmark's successful AI investments (e.g., Sierra, Langchain) weren't the result of a top-down thematic strategy. Instead, their founder-centric approach led them to back exceptional individuals, which organically resulted in a diverse portfolio across the AI stack before it was obvious.

Unlike prior tech waves where founders aimed to build companies, many top AI founders are singularly focused on achieving AGI. This unified "North Star" creates a unique tension between long-term research and near-term product goals, leading to unconventional founder and company dynamics.

Investing in the world's top AI research teams carries a unique risk profile. While the business outcome has high variance, the capital risk is asymmetric. The founders are so valuable that an acqui-hire is a highly probable outcome, creating a floor on the investment's value.

The investment thesis for Harmonic AI was twofold: backing Vlad Tenev, a proven founder who is still rapidly learning and improving, and supporting a differentiated strategy focused on reinforcement learning for mathematics, which sidestepped the costly race for general-purpose AI models.

Horowitz instructs his team to focus on how exceptionally good a founder is at their core competency. He warns against two common errors: passing on a world-class individual due to fixable weaknesses, and investing in a founder with no glaring flaws but no world-class strengths.