What we look for in AI companies (and why they are different)

Part of the challenge of “AI” is we keep raising the bar on what it means for something to be a machine intelligence. Early machine learning models have been quite successful in terms of real world impact. Large scale applications of machine learning today include Google Search and ads targeting, Siri/Alexa, smart routing on mapping applications, s... See more

There are some industries primed for what we call AI leapfrogging. Leapfrogging happens when an industry or market (usually an outmoded industry, or an emerging market) skips a step along the technology transformation chain.

These industries have historically not been digitized and don’t have a dominant cloud incumbent. We see greenfield opportuniti... See more

When transactions can be brokered by AI, a company’s mission and culture—if genuine—become its final differentiators. As tasks lose their distinctiveness, only the deep resonance of an organization’s ethos can hold human attention.

A lot of the focus on AI use, especially in the corporate world, has been stuck in what I call the “automation mindset” - viewing AI primarily as a tool for speeding up existing workflows like email management and meeting transcription. This perspective made sense for earlier AI models, but it's like evaluating a smartphone solely on its ability to... See more

Interface > Data > Model: The “interface” and “data” layers will further distinguish market leaders while the “models” layer becomes increasingly commoditized and pushed to the edge. As a growing number of our everyday use-cases of powerful GenerativeAI models fall below the frontier of “the best models,” they will be enabled by cheaper commoditize... See more