AI companies worldwide raised 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 dollars invested in 2024. That is a lot of money pouring into many AI companies.
The AI industry has swelled so much in the last couple of years that it’s filled with overlapping companies, startups that still only use AI in marketing but not in practice, and legitimate AI startups with diamonds in the rough. Investors have their work cut out for them to find the startups that have the potential to be category leaders. Where do they even start?
TechCrunch recently surveyed 20 VCs backing enterprise startups about what gives an AI startup a moat or what makes it different compared to its peers. More than half of respondents said that what will give AI startups an advantage is the quality or scarcity of their proprietary data.
Paul Drews, 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 compelling user experience.
Jason Mendel, venture capitalist at Battery Ventures, agreed that technology moats are shrinking. “I look for companies that have deep data and workflow moats,” Mendel told TechCrunch. “Access to unique, proprietary data allows companies to offer better products than their competitors, while a consistent workflow or user experience allows them to become the core systems of engagement and intelligence that customers rely on every day.”
Owning proprietary or hard-to-reach data is becoming increasingly important for companies building vertical solutions. Scott Beechuk, partner at Norwest Venture Partners, said companies that can integrate their unique data are the startups with the most long-term potential.
Andrew Ferguson, vice president of Databricks Ventures, said having rich customer data and data that creates a feedback loop in an AI system makes it more efficient and can help startups stand out as well.
Valeria Kogan, the CEO of Fermata, a startup that uses computer vision to detect pests and diseases in crops, told TechCrunch that she believes one of the reasons Fermata has been able to gain traction is that the model its trained in data and customer data. from the company’s own research and development center. The fact that the company does all of its data labeling in-house also helps make a difference in model accuracy, Kogan added.
Jonathan Lehr, co-founder and general partner of Work-Bench, added that it’s not just the data companies have but how they can clean it and put it to work. “As a pure capital, we focus most of our energy on vertical AI opportunities, addressing workflows that require deep domain expertise and where AI is primarily an enabler of previously inaccessible data acquisition and cleansing (or too expensive to acquire). in a way that would take hundreds or thousands of man-hours,” Lehr said.
Beyond data, the VCs said they are looking for AI teams led by strong talent, teams that already have strong integrations with other technologies and companies that have a deep understanding of customer workflows.
