Vast piles of cash are pouring into generative artificial intelligence, the Bay Area-born technology that has taken the world by storm, but relentless hype is inflating a bubble experts say is sure to burst. So far, the bang for the buck has mostly been a whimper, while serious problems abound.
It’s been less than two years since San Francisco’s OpenAI released its ChatGPT generative AI bot, sparking a Big Tech arms race, a torrent of venture capital funding for AI startups, and a bustling bandwagon of companies seeking to cut costs and boost productivity by embedding the technology in virtually every product and service imaginable.
Investors have tossed more than $24 billion at generative AI, according to consulting giant EY, and tech companies plan to spend $1 trillion on AI infrastructure in coming years, the bank Goldman Sachs predicts. Many technologists see enormous promise in the technology that uses patterns and relationships in data to generate text, imagery and sounds, while others see critical shortcomings.
“Everybody wants to make money in the AI race,” said Howard Young of San Jose, who integrates AI software with computer systems at technology giant AAEON to improve urban infrastructure, industrial processes, manufacturing and medicine. Young joined hundreds of other tech workers and executives this month at the Reuters Momentum AI conference in San Jose. “The true, organic revenue, I don’t see it yet,” Young said. “It’s going to take some time even with the best minds in Silicon Valley.”
People in tech “substantially overestimate” generative AI’s current capabilities, and how much it will improve remains in question, said Goldman Sachs’ chief global equity researcher Jim Covello.
“The technology is nowhere near where it needs to be in order to be useful,” Covello said in a generative AI-focused newsletter from the bank in June. “If AI technology ends up having fewer use cases and lower adoption than consensus currently expects, it’s hard to imagine that won’t be problematic for many companies spending on the technology today.”
David Cahn, a partner at Silicon Valley venture capital titan Sequoia, used a June blog post to point to a “speculative frenzy” around generative AI leading to a “delusion” spreading from Silicon Valley “that we’re all going to get rich quick.”
Generative AI, nicknamed “genAI” in tech circles, is definitely building a bubble bound to pop, and hurt is on the horizon — but that’s nothing new for Silicon Valley, said Steve Blank, an adjunct professor of management science and engineering at Stanford University. Blank compared the technology to the bubbly birth of the world wide web, and the dot-com crash that followed.
“It wasn’t that we were wrong about the web, it just took multiple iterations and it took a shakeout to separate the wheat from the chaff,” Blank said.
For Bay Area startups, it’s not far from the truth to say, “You’re not getting funded unless you have AI in your title or story,” Blank said. “It’s insane. That giant sucking sound you hear is all the lemmings throwing money at what is the next big thing. One or two of them will strike gold. The rest of the folks are going to lose their shirt.”
The inevitable popping of this bubble may not be as damaging as the dot-com implosion “simply because many companies spending money today are better capitalized than the companies spending money back then,” said Goldman Sachs’ Covello.
The business transformation touted in support of huge spending on generative AI has not materialized, with the expensive technology largely unable to solve complex problems that would allow widespread automation of tasks and jobs.
Meanwhile, the technology remains plagued by problems that are, depending on who’s talking, either growing pains or fundamental flaws. Generative AI’s major developers are fighting in court against artists, photographers, authors, coders, music labels and newspapers — including this one — over alleged theft of copyrighted material by scraping the internet to “train” AI models.
Training and operating generative AI has upended Google and Microsoft’s progress toward their climate and sustainability goals, with both companies reporting dramatic increases in electricity and water use last year from AI-related data processing and storage. Chatbots and generative search continue to produce errors and falsehoods. Propagandists use the technology to spread disinformation, students use it to cheat and ill-doers use it for scams and harassment. State legislatures introduced nearly 200 bills last year to oversee and regulate AI.
Still, those who believe in the promise of generative AI see innovation overcoming most challenges, and they point to important early uses and powerful potential applications.
“The industry is barely forming,” Blank said. “The earth is still molten. We’re starting to see the outlines of continents.”
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Already, the technology can help companies quickly identify top-performing employees — and workers being marginalized — simply by feeding it internal emails and texts and prompting it to find out who goes to whom with important questions and problems and who provides answers and resolutions, said Kon Leong, CEO of Milpitas data-management company ZL Technologies. Generative AI, Leong said, is “extremely powerful” in making sense of chaos.
“It’s just getting up off its knees at the moment,” Leong said. “By the time it’s walking and running, I think we’ll be floored by its implications.”
UC Berkeley lecturer and venture capitalist Shomit Ghose noted that generative AI was used to develop a drug, now in human trials, to treat a lung disease that can lead to cancer. The technology is also starting to turbo-charge weather forecasting. Ghose believes too much investment is going into the AI technologies underlying generators like ChatGPT, and too little into other types of generative AI beginning to revolutionize science.
Energy companies are starting to use the technology to make power grids more efficient, and integrate wind and solar power to maximum effect, according to the International Energy Agency.
Shobie Ramakrishnan, chief digital and technology officer for drugs giant GSK, told Momentum AI conference attendees the company’s use of generative AI to create “digital twins” that replicate its factory operations via software upped production of its shingles vaccine by a million doses.
“It’s a technology,” Ramakrishnan said, “that’s being both underestimated and overhyped at the same time.”
