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

AI companies worldwide to raise more than $100 billion in venture capital dollars in 2024, Crunchbase dataAn increase of over 80% compared to 2023. This accounts for nearly a third of the total VC dollars invested in 2024. This is funneling to AI companies in abundance.

The AI ​​industry has grown so much in the past two years that it’s filled with overlapping companies, startups are still only using AI in marketing, but not in practice, and legitimate diamond-in-the-rough AI startups are being crushed. Investors have their work cut out for them when it comes to finding startups that have the potential to become category leaders. Where do they even begin?

TechCrunch recently Survey 20 VC For those who support startups building for enterprises what gives an AI startup a moat, or what makes it stand out from its peers. More than half of respondents said the thing that would give AI startups an edge is the quality or rarity of the data they own.

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 differentiated data, technical research innovation and a compelling user experience.

Jason Mendel, a venture investor at Battery Ventures, agrees that the technology divide is shrinking. “I look for companies that have deep data and workflow aggregates,” 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 system of engagement and intelligence that customers rely on every day.”

Having proprietary, or hard-to-get, data becomes increasingly important for companies that are building vertical solutions Scott Bychuk, a partner at Norwest Venture Partners, says companies that are able to bring their unique data in-house are the startups with the most long-term potential.

Andrew Ferguson, a vice president at Databricks Ventures, said that rich 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, its CEO stopA startup that uses computer vision to identify crop pests and diseases, told TechCrunch that he thinks one of the reasons Pharmata was able to gain traction is because its model is trained on both customer data and data from the company’s own research and development. Kendra Kogan added that the fact that the company does all of its data labeling in house helps make a difference in model accuracy.

Jonathan Lehr, a co-founder and general partner at Work-Bench, added that it’s not just the data that companies have but also how they’re able to clean it and put it to use. “As a pureplay seed fund, we are focusing most of our energy on vertical AI opportunities to address business-specific workflows that require deep domain expertise and where AI is primarily an enabler of previously inaccessible (or too expensive to acquire) data acquisition and cleansing. It took hundreds or thousands of people hours,” Lehr said.

Beyond just data, VCs say they look for AI teams led by strong talent, companies that have strong existing integrations with other technologies, and companies that have a deep understanding of customer workflows.