VCs say AI companies need proprietary data to stand out from the pack

AI companies across the globe will raise more than $100 billion in venture capital dollars by 2024, according to Crunchbase dataAn increase of more than 80% compared to 2023. It includes almost a third of the total VC money invested in 2024. It is a lot of money funneling in a lot of AI companies.

The artificial intelligence industry has swelled so much in the last couple of years that it has become full of overlapping companies, startups that still only use AI in marketing but not in practice, and legitimate diamond AI startups in the roar that fades away. Investors have their work cut out for them when it comes to finding startups that have the potential to be category leaders. Where do they even begin?

TechCrunch recently interviewed 20 VC that supports startups by building for businesses on what gives an AI startup a moat, or what makes it different from its peers. More than half of the respondents said that the thing that will give AI startups an advantage is the quality or rarity of their private 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, agrees that technological moats are shrinking. “I look for companies that have deep data and workflow pits,” Mendel told TechCrunch. “Access to unique, proprietary data enables companies to deliver better products than their competitors, while a sticky workflow or user experience enables them to become the core systems of engagement and intelligence that customers they rely on it every day.”

Having proprietary, or hard-to-obtain data is becoming increasingly important for companies building vertical solutions. Scott Beechuk, a partner at Norwest Venture Partners, said that the companies that are able to house in their unique data are the startups with the most long-term potential.

Andrew Ferguson, vice president of Databricks Ventures, said that having a wealth of customer data, and data that creates a feedback loop in an AI system, makes it more effective and can also help startups stand out.

Valeria Kogan, CEO of Stopa startup that uses computer vision to detect pests and diseases on crops, told TechCrunch that it thinks one of the reasons Fermata has been able to gain traction is that its model is built off of data of customers and data from the company’s research and development. center The fact that the company does all of its data labeling in-house also helps make a difference when it comes to model accuracy, Kogan added.

Jonathan Lehr, co-founder and general partner of Work-Bench, added that it’s not just the data that companies have, but also how they can clean it and put it to work. “As a pure seed fund, we focus most of our energy on vertical AI opportunities that address business-specific workflows that require deep domain expertise and where AI is primarily a enabler to acquire previously inaccessible (or very expensive to acquire) data in a way that would have taken hundreds or thousands of man hours,” said Lehr.

In addition to data, VCs said they are looking for AI teams led by strong talent, those with strong existing integrations with other technologies, and companies that have a deep understanding of customer workflows.