The old playbooks for finding product-market fit are useless in AI, according to top-tier investors speaking at TechCrunch Disrupt. With AI's breakneck pace making traditional metrics obsolete, venture partners from NEA and Iconiq are teaching founders an entirely different approach to gauging traction in the rapidly evolving artificial intelligence landscape.
The venture capital world just threw out decades of startup wisdom. At TechCrunch Disrupt in San Francisco, two of Silicon Valley's most experienced investors told a packed room that everything they thought they knew about product-market fit is wrong when it comes to AI.
"Honestly, it just could not be more different from all the playbooks that we've all been taught in tech in the past," Ann Bordetsky, a partner at New Enterprise Associates, told the standing room-only crowd. "It's a completely different ball game."
The reason? AI technology itself refuses to sit still. Unlike traditional software where features stabilize and user patterns emerge, AI capabilities shift monthly - sometimes weekly. This creates a unique challenge for founders trying to gauge whether they've achieved that holy grail of startup success: customers who can't live without their product.
Murali Joshi, a partner at Iconiq, has identified what he calls "durability of spend" as the most reliable signal. Most companies are still treating AI like an experiment, allocating small budgets for testing rather than integration. But when those experimental dollars start migrating to core executive budgets, that's when investors pay attention.
"Increasingly, we're seeing people really shift away from just experimental AI budgets to core office of the CXO budgets," Joshi explained to the audience. "Digging into that is super critical to ensure that this is a tool, a solution, a platform that's here to stay, versus something that they're just testing and trying out."
The shift represents a fundamental change in how enterprises approach AI adoption. Early AI budgets came from innovation labs or IT departments with minimal oversight. Now, as AI tools prove their worth, purchasing decisions are moving to C-suite executives who control major budget lines - a sign that AI is becoming mission-critical rather than nice-to-have.
But budget migration alone isn't enough. Joshi also advocates for classic engagement metrics with an AI twist: daily, weekly, and monthly active users matter, but the context is different. "How frequently are your customers engaging with the tool and the product that they're paying for?" he asked. In AI, sporadic usage often signals evaluation mode, while consistent daily engagement suggests workflow integration.
