SAN FRANCISCO — The ease of landing early customers in AI is deceiving founders into believing they have found product-market fit when they have only found isolated deals, according to go-to-market strategists at TechCrunch Disrupt.
Luke Arno, go-to-market lead at Lovable, said the critical test is whether a startup can replicate its sales motion. If each customer requires a different approach to solve a different problem, the company has no repeatable engine—only revenue noise.
"If I bring on 10 new customers in 10 different ways to solve 10 different problems, sure, I brought revenue in, but I don't have a way to scale go-to-market," Arno said. "If I bring three customers in with a similar motion to solve a similar problem, now I have high confidence that I can scale."
This distinction matters because the AI market rewards speed. Ben Broca, who runs the AI platform Polsia with minimal headcount, cited the ease of copying AI products as a reason product-market fit remains elusive for most startups. Founders must identify which use cases actually stick before building a sales organization around them.
Arno advised founders to spend the first 90 days after commercial launch on "learning density or pattern recognition for where your customers are" rather than rushing to hire sales teams. Scaling prematurely on the back of one-off deals burns capital without establishing a durable competitive position.
Lovable, valued at over $13 billion with ARR exceeding $600 million, is an outlier. Tomasz Tunguz of Theory Ventures acknowledged the company's growth has set unrealistic expectations, but noted the pressure on founders is real: seed rounds today can range from $5 million to $500 million, compared to a narrower band when he began investing in 2008.
Tunguz stressed that the best-performing companies move quickly without losing focus on customer retention. Building a community around the product ensures growth persists after initial momentum fades.
