Navigating the Risky Terrain of Growth-Stage AI Startup Investments

The allure of investing in AI startups is undeniable, but the landscape is becoming increasingly fraught with risk. Established giants like OpenAI, Microsoft, and Google are rapidly expanding their capabilities, potentially overshadowing smaller companies. Simultaneously, new startups are achieving growth-stage status at an unprecedented pace.

Defining what constitutes “growth stage” for AI startups is now more complex than ever.

During TechCrunch AI Sessions, CapitalG partner Jill Chase highlighted a trend of companies reaching tens of millions in annual recurring revenue (ARR) and valuations exceeding $1 billion within just a year. While these metrics suggest maturity, such companies often lack critical safety measures, robust hiring processes, and experienced executive leadership.

“On one hand, that’s really exciting. It represents this brand new trend of extremely fast growth, which is awesome,” Chase noted. “On the other hand, it’s a little bit scary because I’m gonna pay at an $X billion valuation for this company that didn’t exist 12 months ago, and things are changing so quickly.”

Chase further emphasized the uncertainty: “Who knows who is in a garage somewhere, maybe in this audience somewhere, starting a company that in 12 months will be a lot better than this one I’m investing in that’s at $50 million ARR today. So it’s made growth investing a little confusing.”

To effectively navigate this complex environment, Chase advises investors to thoroughly assess the category and the founder’s ability to adapt swiftly and anticipate future challenges.

She cited AI coding startup Cursor as an example of a company that successfully capitalized on the right use case for AI code generation, given the available technology at the time.

However, maintaining a competitive edge requires continuous innovation. As Chase pointed out, “There will be, by the end of this year, AI software engineers. In that scenario, what Cursor has today is going to be a little less relevant. It is incumbent on the Cursor team to see that future and to think, okay, how do I start building my product so that when those models come out and are much more powerful, the product surface represents those and I can very quickly plug those in and switch into that state of code generation?”