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.
Bordetsky emphasized that quantitative metrics need qualitative backing. Traditional SaaS companies could rely purely on usage data, but AI tools require deeper investigation into how customers actually feel about the technology. "If you talk to customers or users, even in qualitative interviews, which we do tend to do a lot early on, that comes through very clearly," she noted.
The most revealing conversations happen with executives, according to Joshi. "Where does this sit in the tech stack?" he suggests asking them. The answer reveals whether an AI tool has become central to operations or remains peripheral. Startups that can position themselves as integral to core workflows - rather than bolt-on experiments - have a much higher chance of surviving budget cuts and competitive threats.
This focus on "stickiness" reflects a broader truth about AI adoption: companies are drowning in AI tools. Every department wants to try the latest model, creating a chaotic landscape where dozens of AI experiments compete for attention and resources. The winners aren't necessarily the most technically advanced - they're the ones that embed themselves so deeply into daily operations that removing them would disrupt business.
Perhaps most importantly, both investors stressed that AI product-market fit isn't a destination - it's a journey. "Product-market fit is not sort of one point in time," Bordetsky explained. "It's learning to think about how you maybe start with a little bit of product market fit in your space, but then really strengthen that over time."
This continuum approach makes sense given AI's rapid evolution. A startup might achieve initial product-market fit with one capability, then need to rebuild that fit as underlying models improve and customer expectations shift. It's a fundamentally different challenge from traditional software, where achieving product-market fit often meant the hard work was done.
The implications extend beyond individual startups to the broader venture ecosystem. Investors are having to develop new evaluation frameworks, founders are questioning established growth strategies, and even experienced operators are admitting their playbooks need updating. As AI continues reshaping entire industries, the very definition of startup success is evolving in real time.
The AI revolution isn't just changing how products work - it's rewriting the fundamental rules of how startups succeed. As traditional product-market fit metrics prove inadequate for AI's dynamic landscape, investors and founders are pioneering new approaches focused on budget durability, workflow integration, and continuous adaptation. For AI startups, the message is clear: forget everything you thought you knew about finding your market. The playbook is being written in real time, and only those willing to embrace this uncertainty will thrive in the new paradigm.
