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.
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Answering "Why are you different?" requires deep thinking that connects strategy to operations. Han Shin of iFly explains that true differentiation is a cohesive system—his fund's thesis-driven research, concentrated portfolio, and large check sizes are all interconnected, enabling the deep, hands-on support that defines his value proposition.
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.
DeepMind's founders knew their ambitious AGI mission wouldn't appeal to mainstream VCs. They specifically targeted Peter Thiel, believing they needed "someone crazy enough to fund an AGI company" who valued ambitious, contrarian ideas over a clear business plan, demonstrating the importance of strategic investor-founder fit.
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.
Sequoia's founder taught that the best investments are in individuals who are both exceptional and "not so easy to get along with." These founders challenge convention and refuse to accept the world as it is, a trait that makes them unconventional but also uniquely capable of building category-defining companies.
An AI-native VC firm operates like a product company, developing in-house intelligence platforms to amplify human judgment. This is a fundamental shift from simply using tools like Affinity or Harmonics, creating a defensible operational advantage in sourcing, screening, and winning deals.
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.
Rabois's investment formula requires a founder to be the absolute best he's ever met in at least one specific dimension—be it intelligence, tenacity, or strategy. He avoids investing in founders who are merely B+ across the board, betting instead on extreme, world-class exceptionalism.
While technical founders excel at finding an initial AI product wedge, domain-expert founders may be better positioned for long-term success. Their deep industry knowledge provides an intuitive roadmap for the company's "second act": expanding the product, aligning ecosystem incentives, and building defensibility beyond the initial tool.
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.
