Warnings About an AI Bubble Are Growing. When Could It Burst?

As long as the AI goldrush, or arms race, or revolution -- whatever you'd like to call it -- has been surging, so too has speculation that the billions in investment are fueling a massive bubble on par with the dot-com bust. Those warnings are growing louder. On Tuesday, James Ferguson, founding partner of the MacroStrategy Partnership, a macroeconomic research firm based in the U.K., offered a grim assessment of where the biggest thing in tech is headed: "Anyone who's sort of a bit long in the tooth and has seen this sort of thing before is tempted to believe it'll end badly," he said on Bloomberg's Merryn Talks Money podcast. Sequoia Capital, bullish on AI since its breathless early days, is also sounding the alarm. Last month, one of the firm's partners, David Cahn, wrote that the industry needs to generate $600 billion in annual revenue to sustain itself. Last September, he estimated the number at $200 billion. (OpenAI, by far the biggest player in the sector, had annualized revenues of $3.4 billion, an Information report found in June.) Goldman Sachs has also cast doubts on generative AI: Recently, the investment bank published a report called "Gen AI: Too Much Spend, Too Little Benefit?" Ironically, the report followed the rollout of generative AI tools across the company's workforce. The stock price of chipmaker Nvidia has surged more than 200 percent in the past year, boosting the value of the company to over $3 trillion. Tech stocks also rallied in 2023, largely based on the AI hype wave. Total venture investment in AI startups neared $50 billion in 2023, even as broader investment slumped to its lowest level in five years, at $285 billion globally. As Greg Hill, managing partner at Parkway Ventures told Inc. earlier this year: "The majority of companies are incorporating AI into their pitch decks" in an effort to attract venture dollars, even if AI isn't their core product. If AI is in a bubble, then the obvious next questions are how the bubble will burst and how many casualties there will be. While definitive answers are in short supply, Gayle Jennings-O'Bryne, CEO and general partner at VC firm Wocstar, offers an assessment on how the AI market got here and the issues pointing to inevitable fallout. Observing the cash thrown at various AI startups, Jennings-O'Bryne believes that some venture capitalists don't have "a real appreciation of the capital intensive nature of the AI technology that is being built right now." Large Language Model development, which is made possible by energy-draining server farms, is massively expensive. So far, many of the startups dependent on the process are short of viable business models. "The mindset of VCs, versus the reality of what these business models and companies are going to look like, [is] just going to propel what we're calling a bubble," she explains to Inc. The financial disconnect was further put in concrete terms by Ferguson of the MacroStrategy Partnership. He notes that Nvidia can't sustain the entire industry's growth on its own, especially when generative AI is still prone to hallucination: "Forget Nvidia charging more and more and more for its chips, you also have to pay more and more and more to run those chips on your servers. And therefore, you end up with something that is very expensive and has yet to prove [itself] anywhere really, outside of some narrow applications." The AI space is saturated with many startups that don't actually build their own AI technologies. "People are jumping on the AI bandwagon thinking that money will come because they somehow have incorporated AI into their business model. But in reality, what they may have done is just put a bit of AI functionality as a wrapper to a more traditional business model," Jennings-O'Bryne argues. While there is funding for AI startups, many of the recently developed tools and chatbots seem redundant. "What's the last [AI] product that wasn't a Q&A, or a chatbot, or coding based?" Phil Calçado, founder of the NYC-based AI coding startup Outropy, recently asked Inc. As VCs race to fund AI startups, Jennings-O'Bryne argues that other companies with even more compelling technologies could be ignored, and therefore languish. "What's happening is that all the other non-AI companies... they're not the darling of the market right now. So they're not getting the attention or the capital to grow. But there's some really good technology and really good businesses being built," she explains. So when could the bubble burst and what might it look like? You can start with the death of many startups and investors losing out on their bets. Jennings-O'Bryne believes the picture will only become crystal clear in around four to five years. "Within two years, we'll start to see some pressure, because investors have gotten more comfortable with asking for profits and returns and revenue and seeing those traction metrics," she says. Eventually, investors will want to see sustainable business models that yield profits. Jennings-O'Bryne says, "I'm thinking that [the] bust, if you will, is probably going to be four or five years out."