AI Startups’ Path to Success: Moats and Proprietary Data
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.
What Sets AI Startups Apart?
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.
Moats and 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.”
Data and Vertical Solutions
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.
Data Quality and Cleanliness
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.
Other Factors that Matter
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.
Conclusion
In conclusion, proprietary data and a strong moat are key factors that set AI startups apart. VCs are looking for companies that have a unique combination of differentiated data, technical research innovation, and a compelling user experience. Additionally, data quality and cleanliness, as well as a deep understanding of customer workflows, are also important factors that can give AI startups an edge.
FAQs
Q: What gives an AI startup a moat?
A: According to VCs, proprietary data and a strong moat are key factors that set AI startups apart. This can include differentiated data, technical research innovation, and a compelling user experience.
Q: What is the most important factor in AI startups?
A: More than half of the VCs surveyed said that the quality or rarity of proprietary data is the most important factor in AI startups.
Q: How do VCs evaluate AI startups?
A: VCs evaluate AI startups based on factors such as proprietary data, technical research innovation, user experience, team talent, integrations with other tech, and deep understanding of customer workflows.
