VCs say AI companies need more to get public

The global AI industry will raise more than $100 billion in 2024, according to Crunchbase dataan increase of 80% compared to 2023. It includes almost a third of all VC investments in 2024. That’s a lot of money that goes into many AI companies.

The AI ​​industry has grown so much in the past two years that it’s filled with startups still using AI in marketing, but not in practice, and legitimate diamond startups that are on the verge of extinction. Investors have a job to prepare them when it comes to finding startups that have the potential to become team leaders. Where do they start?

TechCrunch recently surveyed 20 VCs those who introduce enterprise architecture to what gives AI its starting point, 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 quantity of their data.

Paul Drews, managing partner at Salesforce Ventures, told TechCrunch that it’s very difficult for AI startups to have a moat because the landscape is changing so quickly. He said he looks for startups with diverse data, technical research capabilities, and user experience.

Jason Mendel, an entrepreneur at Battery Ventures, agreed that technology is slowing down. “I’m looking for companies that have deep data and workflows,” Mendel told TechCrunch. “Access to unique, proprietary data enables companies to deliver better products than their competitors, while dynamic or user-experience processes allow them to become key operational processes and insights that customers rely on every day.”

Having proprietary, or hard-to-find, data is critical for companies developing targeted solutions. Scott Beechuk, a partner at Norwest Venture Partners, said the companies most likely to have their own private data are the ones with the longest tenure.

Andrew Ferguson, vice president at Databricks Ventures, said that having rich customer data, and data that creates inferences in the AI ​​system, makes it more effective and can help startups stand out, too.

Valeria Kogan, CEO of Stopstartup that uses computer vision to identify pests and diseases on crops, told TechCrunch that they think one of the reasons Fermata was able to attract attention is that its model is trained from both customer data and data from the company’s research and development. in the middle. The fact that the company makes all of its labels in-house also helps make a difference when it comes to model accuracy, Kogan added.

Jonathan Lehr, co-founder and general partner at Work-Bench, added that it’s not about what companies have but how they can clean and install it. “As a seed fund of pureplay, we are focusing most of our energy on sustainable AI opportunities for business processes that require deep expertise and where AI helps to find unreachable (or very expensive to find) and clean it. in a way that would have taken hundreds or thousands of people hours,” said Lehr.

Beyond data alone, VCs said they are looking for AI teams led by strong talent, with powerful integrations with other technologies, and companies with a deep understanding of customer journeys.