How are AI startups changing the venture capital industry in 2025?

AI startups are now leaping from "zero to $100 million in revenue in a single year," forcing venture capitalists to rethink their investment approach. Investors are now focusing on factors like data generation, competitive moat, founder accomplishments, and technical depth, rather than just rapid revenue growth.

What challenges are early-stage AI startups facing in securing follow-on funding?

Even AI startups that grow rapidly from inception to $5 million in revenue are struggling to secure additional funding, as Series A investors are now applying the same rigorous standards to seed-stage startups that they previously reserved for more mature companies.

What is the debate around marketing versus technology for AI startups?

While some investors, like Steve Jang of Kindred Ventures, argue that both strong technology and go-to-market strategies are necessary, others, like Jon McNeill of DVx Ventures, emphasize the growing importance of go-to-market capabilities in attracting investor attention.

Home / Business and Economy / AI Startups Rewrite Venture Capital Playbook in 2025

AI Startups Rewrite Venture Capital Playbook in 2025

14 Nov

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Summary

AI startups leaping from "zero to $100 million in revenue in a single year"
Investors focus on data generation, competitive moat, founder experience, and technical depth
Rapid growth no longer guarantees follow-on funding for early-stage AI companies

AI Startups Rewrite Venture Capital Playbook in 2025

As of November 14th, 2025, the venture capital (VC) landscape has undergone a significant shift when it comes to backing AI startups. According to Aileen Lee, founder and managing partner of Cowboy Ventures, the rules of investing have changed dramatically, with some AI companies now leaping from "zero to $100 million in revenue in a single year."

However, this rapid growth is no longer the sole focus for Series A investors. Lee explains that they are now applying an "algorithmic formula" that considers factors such as the startup's data generation, competitive moat, founder accomplishments, and technical depth of the product. "Depending on what your company is, the output of the algorithmic formula is going to be different," she says.