AI companies around the world will raise more than $100 billion in venture capital dollars in 2024, according to Crunchbase data, an increase of more than 80% compared to 2023. That’s almost a third of the total VC dollars invested in 2024. That’s a lot. money funneling to many AI companies. The AI industry has grown so much in the last two years that it has been filled with overlapping companies, startups that still use AI only in marketing, but not in practice, and legitimate diamond AI startups. 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 start? TechCrunch recently surveyed 20 VCs who have backed startups building companies about what AI startups have to offer, or what makes them different compared to their peers. More than half of the respondents said that the thing that will give AI startups is the quality or scarcity of proprietary data. Paul Drews, managing partner at Salesforce Ventures, told TechCrunch that it’s very difficult for AI startups to have a foothold because the landscape is changing so quickly. He added that he is looking for startups that have a different combination of data, technical research innovation, and interesting user experience. Jason Mendel, a venture investor at Battery Ventures, agrees that the technology moat is shrinking. “I look for companies that have deep data and workflow moats,” Mendel told TechCrunch. “Access to unique proprietary data allows companies to deliver better products than their competitors, while sticky workflows or user experiences allow them to become the core systems of engagement and intelligence that customers rely on every day.” Having proprietary, or hard-to-get, data is becoming increasingly important for companies creating vertical solutions. Scott Beechuk, a partner in Norwest Venture Partners, said that the company is able to front on the unique data that startups with the most long-term potential. Andrew Ferguson, vice president at Databricks Ventures, said that having rich customer data, and data that creates feedback in AI systems, makes them more effective and can help startups. Valeria Kogan, CEO of Fermata, a startup that uses computer vision to detect pests and diseases in plants, told TechCrunch that she thinks one of the reasons Fermata is gaining traction is that its models are trained on data and customer data. from the company’s own research and development center. The fact that the company does all the data labeling in-house also helps make a difference when it comes to the accuracy of the model, Kogan added. Jonathan Lehr, co-founder and general partner at Work-Bench, added that it’s not just about the data the company has but also how it cleans it and makes it work. “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 for data acquisition and cleaning that was previously inaccessible (or too expensive to acquire ) in a way that would take hundreds or thousands of man hours,” Lehr said. Beyond just data, VCs say they’re looking for AI teams led by strong talent, that have strong integrations with other technologies, and companies that have a deep understanding of customer workflows.
