In a typically candid assessment of the current artificial intelligence landscape, the outspoken CEO of $134 billion software analytics firm Databricks, Ali Ghodsi, issued a stark warning regarding the ballooning valuations of AI startups that lack fundamental business metrics. Speaking at Fortune Brainstorm AI in San Francisco, Ghodsi blasted the trend of investors pouring capital into unproven companies, stating, “Companies that are worth, you know, billions of dollars with zero revenue, that’s clearly a bubble, right, and it’s, like, insane.” Ghodsi clarified that he sees a “huge bubble in many, many portions of the market.”
The vibes in the Valley are bad, in the opinion of Ghodsi, who holds a PhD in computer science. He said that even the investors fueling this frenzy are aware of the unsustainable nature of the market. In private conversations, he claimed, venture capitalists express exhaustion with the hype cycle, telling him, “Maybe I should just go on a break for, like, six months and come back and it’ll be, like, really financially good for me.”
Ghodsi said he agreed with the critique of circular financing among many players in the AI space, artificially inflating the market. Rather than viewing the bubble as near its popping point, Ghodsi predicts the “circular aspect” of the situation will deteriorate before it corrects. “I think like 12 months from now, it’ll be much, much, much worse.” Current market wobbles are actually a healthy signal for CEOs to “take a step back,” he added.
This skeptical view of the current market hype explains Databricks’ reluctance to rush toward an initial public offering (IPO), despite Ghodsi admitting to “flirting” with the idea. He highlighted that staying private at this point offers a strategic buffer against market volatility. He drew a sharp contrast between Databricks and competitors who rushed to go public during the 2021 boom, only to face severe corrections.
“In 2021, most of my peers, CEOs, they were like we got to IPO,” but by 2022, Ghodsi added, they were suddenly in cost-cutting mode, whereas Databricks was able to hired thousands of people. He emphasized that if a bubble does burst, remaining private would allow the company to continue investing in long-term AI utility rather than reacting to short-term stock fluctuations.
While the venture market overheats, Ghodsi argued that the reality of enterprise AI adoption is being throttled by corporate inertia, rather than a lack of technology. He identified security concerns and data governance as the primary bottlenecks for large organizations.
