VCs Say AI Companies Need Proprietary Data To Stand Out

TechCrunch's Rebecca Szkutak reports: TechCrunch recently surveyed 20 VCs who back startups building for enterprises about what gives an AI startup a moat, or what makes it different compared to its peers. More than half of the respondents said that the thing that will give AI startups an edge is the quality or rarity of their proprietary data. Paul Drews, a managing partner at Salesforce Ventures, told TechCrunch that it's really hard for AI startups to have a moat because the landscape is changing so quickly. He added that he looks for startups that have a combination of differentiated data, technical research innovation, and a compelling user experience.

Jason Mendel, a venture investor at Battery Ventures, agreed that technology moats are diminishing. "I'm looking for companies that have deep data and workflow moats," Mendel told TechCrunch. "Access to unique, proprietary data enables companies to deliver better products than their competitors, while a sticky workflow or user experience allows them to become the core systems of engagement and intelligence that customers rely on daily." Having proprietary, or hard-to-get, data becomes increasingly important for companies that are building vertical solutions.

Scott Beechuk, a partner at Norwest Venture Partners, said companies that are able to home in on their unique data are the startups with the most long-term potential. Andrew Ferguson, a vice president at Databricks Ventures, said that having rich customer data, and data that creates a feedback loop in an AI system, makes it more effective and can help startups stand out, too. [...] Beyond just data, VCs said they look for AI teams led by strong talent, ones that have existing strong integrations with other tech, and companies that have a deep understanding of customer workflows.

Read more of this story at Slashdot.