CEO of $134B software giant slams companies with billions in funding but no revenue: ‘It’s clearly a bubble, isn’t it… it’s like insane’
In a generally frank assessment of the current artificial intelligence landscape, Ali Ghodsi, CEO of the $134 billion software analytics company Databricks, issued a stark warning about the skyrocketing valuation of AI startups that lack fundamental business metrics. Speaking at Fortune Brainstorm AI in San Francisco, Ghodsi blasted the tendency of investors to pour capital into unproven companies, saying: “Companies that are, you know, billions of dollars with zero revenue, that’s clearly a bubble, right, and it’s, like, crazy.” Ghodsi said he sees a “huge bubble in many, many parts of the market.”
The atmosphere in the Valley is bad, in the opinion of Ghodsi, a doctor in computer science. He said 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 take a break for about six months and come back and it will be really good financially for me.” »
Ghodsi said he agreed with the criticism of circular financing made by many in the AI sector, artificially inflating the market. Rather than seeing the bubble as nearing its bursting point, Ghodsi predicts that the “circular aspect” of the situation will deteriorate before it corrects itself. “I think in 12 months it will be much, much, much worse.” The current market swings are actually a healthy signal for CEOs to “take a step back,” he added.
The IPO Question and Strategic Patience
This skeptical view of the current market hype explains Databricks’ reluctance to rush toward an initial public offering (IPO), even though Ghodsi admits to having “flirted” with the idea. He emphasized that remaining private at this stage provides a strategic buffer against market volatility. It drew a stark contrast between Databricks and its competitors who rushed to go public during the 2021 boom, only to face severe corrections.
“In 2021, most of my peers, the CEOs, were like we’ve made it to the IPO,” but in 2022, Ghodsi added, they were suddenly in cost-cutting mode, as Databricks was able to hire thousands of people. He pointed out that if a bubble burst, remaining private would allow the company to continue investing in AI utilities in the long term rather than reacting to short-term stock fluctuations.
Real obstacles versus market hype
As the venture capital market overheats, Ghodsi says the reality of enterprise AI adoption is being held back by corporate inertia, rather than a lack of technology. It identified data security and governance issues as key bottlenecks for large organizations.
Databricks, which, according to its name, has many customers who hire it to sort their data, has many customers aged 10 and above, and they are all very reluctant when it comes to cybersecurity.
“The big problem that’s holding you back” in this scenario, Ghodsi said, “is that you can’t do anything because you’re so afraid of getting hacked.”
He said “AI lawyers,” or attorneys specializing in the emerging area of AI law, are now slowing operations by scrutinizing model regulations and policies. Furthermore, he described the data architecture within most existing organizations as “an absolute mess” resulting from 40 years of accumulation of software from different vendors, leaving data siled and difficult to access – and a lot of work for Databricks to do.
Where the true value lies
Despite his warnings about the bubble, Ghodsi remained optimistic about the specific and very useful applications of AI, particularly “AI agents” and “vibrational coding.” He revealed a surprising statistic: “For the first time, we see that more than 80% of databases launched on Databricks are not launched by humans but by AI agents. »
He argued that the base layer of the model – the technology provided by companies like OpenAI and Google – is becoming a product with low margins due to hypercompetitiveness. Instead, the real revenue potential lies in the application layer where agents perform specific work, such as drug discovery in healthcare or automated research in finance.
Ghodsi advised business leaders to end internal politics that block such progress. Noting the “fight” among leaders fighting to become “the AI man,” he offered direct advice: “Choose one person for your business” to lead the strategy, rather than creating a “three-headed monkey” of conflicted leadership.
