AI startups will need ‘quality of revenue’ to raise in 2025, seed VCs warn

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Fundraising in 2025 will continue to be a “tale of two cities,” VC Renata Quintini, co-founder of early-stage VC Renegade Partners, said onstage at TechCrunch Disrupt last week.

“Some companies, really having the promise of going after big markets growing fast, [will be] receiving a lot of funding and momentum.” But on the other side, the “companies that need to build real businesses and efficient businesses” will struggle to raise cash, Quintini warned.

She’s referring to the tight fundraising market that startups have faced in the era of higher interest rates. In 2023, some 3,200 startups died after they easily raised money during the party times of 2021. In 2024, VCs stampeded toward funding AI companies, and many other early-stage startups struggled to raise and more deaths occurred. Fintech was particularly brutal in 2024.

As 2025 arrives, the best chance of getting venture funding will be to have solid business fundamentals. (Unless you’re, say, a world-renowned AI scientist.) That means selling a product or service at a profitable price point that serves a sizable customer base.

But wait! There will be more to it than just landing paying customers, VC Corinne Riley, partner at Greylock warned onstage.

“There is no set milestone” of sales or growth that will turn VCs’ heads next year, Riley says. “There is no key number that if you achieve it you’re going to raise a successful Series A. What we’re looking for is actually the quality of the ARR [annual recurring revenue], and not the quantity of ARR.”

In other words, are customers sticking around once they’re on board? Do they tend to increase their spending with the startup over time? The startup might actually have fewer customers than competitors, and less revenue, but if the customers it signs stay, investors will write checks.

“We’re looking for a quality customer base that you’re going to be able to repeat over and over and over again once you have more capital,” Riley says. 

“This is what VCs mean when they talk about ‘you need to build moat,’” VC Elizabeth Yin, co-founder of Hustle Fund, said onstage. That’s a way to describe how to lock in customers so they won’t choose to leave. “The more unique things that you can do that other people can’t do, the more that helps you.”

Riley offered as an example a Greylock portfolio company called Braintrust, which helps developers build and evaluate the performance of their AI apps. Greylock was convinced to lead its $5 million seed deal because the founder, Ankur Goyal, landed early customers who were “taste makers in the industry.” He had Zapier, Coda, Airtable, and Instacart as customers, “people that were at the forefront of building products with AI,” she described. 

Well-connected marquee customers, should they be happy with the offering, bring in other such customers, and so the cycle goes. Braintrust has gone on to land other known tech names like Stripe and Notion, it says. And in October, Andreessen Horowitz’s Martin Casado led a $36 million Series A, with Greylock participating.

Such “quality of customers” has always mattered to investors — that’s why naming customers is important. But in 2025, after the AI gold rush, that metric will become even more critical because much of the revenue that so many AI startups have reeled in will turn out to be one-time-only revenue.

In another panel at Disrupt, one on bouncing back after a down round, VC Elliott Robinson, a partner with Bessemer Venture Partners, described the situation. At the start of 2024, the boards of nearly every large company bit their nails over AI and allocated fat exploratory budgets to their CIOs to go buy products and explore. 

“Now we’ve kind of lived through 18 and 24 months of buying AI stuff, and where we’ve seen companies go from zero to four [million dollars in revenue] and four to 20 [million],” he said. “The question now is, what’s going to be renewed? Because the CIO budget is starting to dry up.”

CIOs will only continue to buy stuff that has measurably made a difference. So all that revenue, and all those customers for so many AI startups — and potentially those in AI-adjacent areas, like application monitoring — does not mean that a particular startup is a good bet for the future.

Or, as Quintini describes, “At the end of the day, what you’re trying to do is, one, build something that compounds. Then number two, you’re either going to be in a business that you run faster than the other people, or you do something that other people cannot replicate.”

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