AI companies across the globe raised more than $100 billion in venture capital dollars in 2024, according to Crunchbase data, an increase of more than 80% compared to 2023. It encompasses nearly a third of the total VC dollars invested in 2024. That’s a lot of money funneling into a lot of AI companies.
The AI industry has swelled so much in the last two years that it has become filled with overlapping companies, startups still using AI just in marketing, but not in practice, and legit diamond-in-the-rough AI startups grinding away. Investors have their work cut out for them when it comes to finding the startups that have the potential to be category leaders. Where do they even begin?
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.
Valeria Kogan, the CEO of Fermata, a startup that uses computer vision to detect pests and diseases on crops, told TechCrunch that she thinks one of the reasons Fermata was able to gain traction is that its model is trained off of both customer data and 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 when it comes to the accuracy of the model, Kogan added.
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 are able to clean it up and put it to work. “As a pureplay seed fund, we’re focusing most of our energy in vertical AI opportunities tackling business-specific workflows that require deep domain expertise and where AI is mainly an enabler of acquiring previously inaccessible (or highly expensive to acquire) data and cleaning it in a way that would’ve taken hundreds or thousands of man hours,” Lehr said.
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.
