For all their pitches promising something new, AI startups share many of the same questions as startups of years past: How do they know they’ve achieved the holy grail of product-market fit?
Product-market fit has been studied extensively over the years; entire books have been written on how to master this art. But as with many other things, AI is disrupting established practices.
“Honestly, this just couldn’t be more different from all the playbooks we’ve all been taught in tech in the past,” Ann Bordetsky, a partner at New Enterprise Associates, told a standing-room-only crowd at TechCrunch Disrupt in San Francisco. “It’s a completely different ball game.”
At the top of the list is the pace of change in the world of AI. “The technology itself is not static,” she said.
Nonetheless, there are ways for founders and operators to assess product market fit.
One of the best things to watch out for, Murali Joshi, partner at Iconiq, told the audience, is “sustainability of spending.” AI is still early in the adoption curve at many companies, and much of their spending is focused on experimentation rather than integration.
“More and more, we’re seeing people move away from experimental AI budgets and toward the basics of CXO budgets,” Joshi said. “It’s incredibly important to dig into this to ensure that this is a tool, a solution, a platform that is here to stay, as opposed to something that they’re just testing and trying.”
Techcrunch event
San Francisco
|
October 13-15, 2026
Joshi also suggested startups consider classic metrics: daily, weekly, and monthly active users. “How often do your customers interact with the tool and product they are paying for? »
Bordetsky agrees, adding that qualitative data can help nuance some quantitative measures that might suggest, but not confirm, whether customers are likely to stick with a product.
“If you talk to customers or users, even in qualitative interviews, which we tend to do very early on, it comes across very clearly,” she said.
Interviewing people in the executive suite can also be helpful, Joshi said. “Where does this fit in the technology stack?” he suggests asking them. He said startups should think about how they can become “stickier as a product in terms of core workflow.”
Finally, it’s important for AI startups to view product-market fit as a continuum, Bordetsky said. Product-market fit isn’t limited to one point in time,” she said. “It’s about learning to think about how maybe you can start with a little bit of product-market fit in your space and then really build it up over time.”
