Research

Most contrarian entrepreneurs – those who challenge conventional “best practices” – struggle to attract resources, yet a few achieve outsized successes once their ideas gain traction. One explanation is that contrarian ideas are simply riskier: they fail more often, but the few that overcome deeper technical hurdles generate exceptional breakthroughs. Another explanation emphasizes commercialization barriers: investors avoid unproven alternatives but rush in once it becomes clear that a contrarian approach outperforms the status quo. To provide evidence on these views, I examine hundreds of AI contests where either a contrarian or mainstream method is revealed to be best. Using a difference‐in‐differences design that compares the likelihood of founding a startup between winners and close runner‐ups, I find that contrarian victors benefit disproportionately from a public win – suggesting ex‐ante validation is crucial for commercialization. Importantly, even narrow victories yield large gains for contrarian contestants because contests do more than reveal intrinsic quality – they trigger herding among investors, who disproportionately bid for the newly validated approach. As such, skewed returns for contrarians, in part, reflect that investors support too few heterodox ideas and strongly reward successful contrarians. Finally, I show that mainstream researchers adopt these validated contrarian methods only after more decisive proof, allowing contrarian entrepreneurs to secure resources without facing immediate competition.

It is well known that information frictions can make ideas hard to sell. However, due to a lack of pricing data, virtually all studies have focused on how these frictions affect the likelihood of selling ideas rather than on the welfare losses from missed transactions. First, I present a theoretical framework to highlight the importance of this distinction. If the best ideas are the hardest to sell and the idea distribution is skewed, the proportion of welfare lost to frictions could far exceed the proportion of unsold ideas. Next, I demonstrate the practical significance of this distinction by examining online auctions for digital startups. I use the fact that informational frictions are less severe for startups whose revenues are verified by the marketplace. My findings show that revenue-verified startups are only 6.3% more likely to sell but fetch prices that are 2.7 times higher. Simulations reveal that moving from universal access to complete absence of revenue verification reduces gains from trade by 47%. The disproportionate losses are driven by the concentration of potential gains from trade among a few unsold ideas. These results suggest that focusing solely on the number of ideas sold underestimates the true impact of informational frictions.

Common wisdom suggests that non-disclosure agreements (NDAs) can help sellers safely disclose their ideas to potential buyers. However, buyers could potentially refuse to sign a binding agreement prior to disclosure, making them more likely to walk away from the transaction. Using data from an online marketplace for digital startups, I test the role of NDAs in facilitating idea transactions. I observe which sellers require potential buyers to sign a confidentiality agreement and the information disclosed by sellers after signing the NDA. I find that confidentiality agreements enable sellers to share more information: sellers who require NDAs from potential bidders are 24% more likely to share their profit and loss statements compared to those who do not. However, confidentiality agreements discourage bidder participation: requiring confidentiality agreements is associated with 29% fewer bids, making it 6% less likely that the venture will be sold. Overall, the results cast doubt on the effectiveness of NDAs in fostering idea markets.

This paper examines the relationship between disagreement surrounding a startup proposition and its future success. I find that the more venture competition judges disagree on the quality of a startup, the more likely the startup is to succeed, particularly when its proposition is unique. To explain this correlation, I build on the notion that (i) entrepreneurs pursue opportunities based on their subjective beliefs (ii) common opinion alone cannot be a source of competitive advantage. Therefore, value is disproportionately created and captured by founders with unconventional ideas that spark disagreement, and potential investors should harness disagreement as a predictor of success. I leverage data from 67 venture competitions to show that the empirical implications of this theoretical framework are supported by the data, whereas alternative explanations (e.g., that judges disagree more about risky ventures) are not. Additionally, I provide insights into what evaluators tend to disagree more often (e.g., former entrepreneurs) and which aspects of a startup (e.g., business model) are most polarizing. This work has broad implications for investors and institutions that strive to evaluate the potential of startup ideas.