“We see companies that are growing insane, and they’re still about 10 people or two people,” she said.<\/p>\n
“They’ve got hundreds of millions in ARR and hundreds of millions of users, and you know what, they don’t actually have all of that infrastructure, that logging, to be able to truly do data analysis,” she added, speaking of annual recurring revenue.<\/p>\n
Traditionally, companies didn’t hit 100 million users overnight. Slower growth gave teams years to build out logging systems, hire a data team, and develop “observability” \u2014 the ability to understand what’s actually driving user behavior and revenue, Zhuo said.<\/p>\n
But Zhuo warned that the growth won’t last forever. When the curve flattens, these startups will be “scrambling” to answer basic questions like why users churn, which features people value, and what levers really drive the business, she said.<\/p>\n
“At that point, that’s usually when people start investing a ton in data,” she added. “Data helps you figure out what’s actually happening.”<\/p>\n
Zhuo also said it’s important to rethink how success is measured in the AI era, especially given the speed at which some companies are growing.<\/p>\n
Products built around chatbots or conversational interfaces need new analytical methods. Instead of counting clicks or page views, “we have to probably use an LLM or a machine learning model to bucket user intent,” she said.<\/p>\n
Zhuo did not respond to a request for comment from Business Insider.<\/p>\n
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The boom of AI companies<\/p>\n
The breakneck pace Zhuo described reflects a broader trend across the industry.<\/p>\n
AI startups have been raising record amounts of money and soaring in valuations, with more than $35 billion raised in 2024, Business Insider reported last year.<\/p>\n
A number of investors are concerned that the AI market might be overheating and that we’re at risk of reliving the dot-com bubble burst<\/a> in 2000. Some are wondering whether large language models<\/a> are actually powerful enough to develop the long-desired superintelligence; some fear tech companies’ massive expenditures<\/a> won’t pay off; and some are worried that less experienced investors <\/a>are getting caught up in the hype.<\/p>\n