New podcast from @a16z Is Non-Consensus Investing Overrated? Non-consensus investing is dangerous, but not because consensus is always right. In a candid discussion, Martin Casado explains that the tweet was about the danger of ignoring consensus when funding decisions rely on follow-on capital and market funding. He notes his decade in venture has produced about 200 investments, and he argues that being blinkered to what the market already values can mislead excellent ideas. He likens this to academia, where a paper can fail if its reception isn’t considered. Leo Polovets agrees that consensus matters eventually, but adds that some of his best bets were non-consensus early on, later paying off as proof points emerged. The dialogue centers on how perception shapes funding and how markets price risk. Casado cautions against equating a tough fundraising round with market consensus, arguing that many celebrated rounds come from companies priced above market when viewed over their lifetimes. He cites lists of big winners and notes that anecdotes don’t prove non-consensus was the driver; market efficiency means good companies attract high prices, and a misread can hurt founders as much as investors. The conversation turns to the founder experience: being deemed non-consensus can complicate follow-on rounds, while frugality and disciplined spending can accompany cautious growth. They discuss whether non-consensus tactics reflect true opportunity or simply market timing, and how to measure these effects with data. The discussion then widens to current market waves such as AI and deep tech, and the tension between hype and fundamentals. They examine defense tech, robotics, and humanoids, noting that unit economics in new arenas can be uncertain. They contrast model-like AI companies with those lacking clear profitability paths, and ask how seed funding and multi-stage rounds interact: does non-consensus seed lead to large later rounds, or do multi-stage players dominate top opportunities? The participants suggest markets may become more efficient with more capital, but price discipline remains critical. They conclude with plans to quantify ideas through cohort analyses of pricing versus outcomes and a data-driven test of whether the market truly prices winners differently, promising a follow-up discussion. transcripted.ai/episode/isnonc…