Veteran VC Glenn Solomon notes that major value creation occurs during paradigm shifts (client-server, internet, mobile). He initially believed the cloud was the ultimate shift but now sees AI as a significantly larger, more impactful opportunity for venture investment.
Notable Capital's Glenn Solomon argues that massive VC funds are mathematically challenged. Their size forces them to write large, late-stage checks at high valuations, making it difficult to achieve the ownership percentages needed for outsized returns enjoyed by early-stage investors.
Notable Capital maintains an 84% term sheet win rate. Managing Partner Glenn Solomon attributes this to their small, focused team 'swarming' opportunities—where every partner and platform team member actively engages in the process—a stark contrast to siloed mega-funds.
Using Airtable as an example, Glenn Solomon warns that startups raising excessive capital often feel pressured to spend it to justify high valuations, even with poor metrics. This behavior frequently leads to failure, squandering capital that could have been preserved.
Glenn Solomon cautions against the VC obsession with fast markups. A company can raise subsequent rounds at higher valuations, creating impressive paper returns, yet still fail to produce a successful exit that justifies those prices, as seen with Airtable's late-stage investors.
Glenn Solomon argues against the trend of asset aggregation, stating that fund size must be determined by the firm's investment strategy. A strategy of making concentrated, early-stage bets naturally dictates a smaller fund size, while letting AUM demand dictate size corrupts the model.
Even if top venture outcomes reach $20B, there are only about four such deals annually. Glenn Solomon argues a mega-fund would need to win all of them and own a significant stake just to break even, making the math for outsized returns nearly impossible.
Investing in Anthropic at a $61.5B valuation was a difficult decision. Notable Capital gained conviction after seeing that all three major hyperscalers had invested, signaling a nearly unlimited capital backstop for the company's expensive model training needs.