Investing in the Age of Generative AI - by Kevin Zhang

By now, it should be obvious to anyone working on or investing in ML/AI that we’re currently in an AI “summer” — with the frothiness of the funding markets seemingly eclipsing that of the Web3 euphoria back in 2021. But underneath this sheen of euphoria, we are already seeing some early cracks in the market, which might serve as leading indicators of depressed future returns, even as funds continue to aggressively compete for and deploy capital into AI startups. In this post, I explore the current AI investment landscape and present a framework that a typical early-stage fund (funds in the $100-500m scale) might want to adopt. I’ll cover the following: I first dive into WHY there’s such an intensity in the funding of AI startups, both in the context of the SaaS slowdown in the public markets and Sarah Tavel’s now ubiquitous “AI startups: Sell work, not software” (which has since then been parroted by numerous other VCs) I then highlight some of the weakness & wonkiness in the generativ

By now, it should be obvious to anyone working on or investing in ML/AI that we’re currently in an AI “summer” — with the frothiness of the funding markets seemingly eclipsing that of the Web3 euphoria back in 2021. But underneath this sheen of euphoria, we are already seeing some early cracks in the market, which might serve as leading indicators of depressed future returns, even as funds continue to aggressively compete for and deploy capital into AI startups. In this post, I explore the current AI investment landscape and present a framework that a typical early-stage fund (funds in the $10