Huge amounts of cash are being poured into generative artificial intelligence, a technology from the San Francisco Bay Area that has taken the world by storm. But the relentless hype is making a bubble that experts say is bound to burst. So far, the price-performance ratio has been modest, while there are various serious problems.
It was lower than two years ago that San Francisco-based OpenAI released its generative AI bot ChatGPT, sparking an arms race amongst major tech firms, a flood of enterprise capital for AI startups, and a growing variety of firms trying to cut costs and increase productivity by integrating the technology into virtually every product and repair possible.
Investors have greater than 24 billion US dollars in generative AI, in response to consulting giant EY, and technology firms plan to spend $1 trillion Goldman Sachs predicts that the approaching years will see an enormous change in AI infrastructure. Many engineers see great potential within the technology, which uses patterns and relationships in data to generate text, images and sounds, but others see critical shortcomings in it.
“Everyone wants to make money in the AI race,” says San Jose-based Howard Young, who integrates AI software into computer systems at tech giant AAEON to enhance urban infrastructure, industrial processes, manufacturing and medicine. Young was on the Reuters Momentum AI conference in San Jose this month together with a whole lot of other engineers and executives. “I'm not seeing the real, organic revenue yet,” Young says. “Even with the best minds in Silicon Valley, it's going to take some time.”
People within the technology industry are “significantly overestimating” the present capabilities of generative AI, and it stays questionable to what extent it’s going to improve, says Jim Covello, chief equity analyst at Goldman Sachs.
“The technology is nowhere near where it needs to be to be useful,” Covello said in a generative AI newsletter from the bank in June. “If AI technology ends up having fewer use cases and lower adoption than is currently widely expected, it's hard to imagine that this won't be problematic for many companies investing in the technology today.”
David Cahn, a partner at Silicon Valley enterprise capital giant Sequoia, identified in a June blog post that there’s a “speculative frenzy” surrounding generative AI that’s resulting in an “illusion” emanating from Silicon Valley that “we can all get rich quick.”
Generative AI, known in tech circles as “GenAI,” is certainly a bubble that's about to burst, and damage is looming — but that's nothing recent for Silicon Valley, says Steve Blank, an associate professor of management science and engineering at Stanford University. Blank compared the technology to the bubbling 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 several iterations and restructuring to separate the wheat from the chaff,” Blank said.
For Bay Area startups, it's not removed from the reality once they say, “You won't get funding if you don't have AI in your title or your story,” Blank said. “That's crazy. That giant sucking sound you hear is all the lemmings spending money on the next big thing. One or two of them will strike gold. The rest will lose their shirts.”
The inevitable bursting of that bubble might be less damaging than the implosion of the dot-com bubble, “simply because many companies spending money today are better capitalized than the companies back then,” says Goldman Sachs' Covello.
The transformation within the business world that was touted with huge investments in generative AI has not happened. The expensive technology is essentially unable to unravel complex problems that may enable comprehensive automation of tasks and jobs.
Meanwhile, the technology continues to be suffering from problems which are either growing pains or fundamental flaws, depending on who's talking. The key developers of generative AI are battling in court against artists, photographers, authors, programmers, music labels and newspapers – including this one – for allegedly stealing copyrighted material by crawling the web to “train” AI models.
The training and deployment of generative AI has upended Google and Microsoft's progress toward meeting their climate and sustainability goals. Both firms reported dramatic increases of their electricity and water usage last yr from AI-related data processing and storage. Chatbots and generative search proceed to supply errors and falsehoods. Propagandists use the technology to spread disinformation, students use it to cheat, and evildoers use it for scams and bullying. State legislatures introduced nearly 200 bills to oversee and regulate AI last yr.
Nevertheless, those that imagine within the promise of generative AI are convinced that almost all challenges might be overcome through innovation, they usually point to essential early deployments and powerful potential applications.
“The industry is just emerging,” said Blank. “The earth is still molten. We are beginning to see the outlines of the continents.”
“Right now, it's just starting to rise,” Leong said. “When it can walk and run, I think we'll be overwhelmed by the impact.”
Shomit Ghose, a UC Berkeley lecturer and enterprise capitalist, identified that generative AI has been used to develop a drug currently being tested on humans that’s getting used to treat a lung disease that may result in cancer. The technology can be starting to hurry up weather forecasting. Ghose believes an excessive amount of goes into the AI technologies that underpin generators like ChatGPT and too little into other kinds of generative AI which are starting to revolutionize science.
Energy firms are already using this technology to Making power grids more efficientand integrate wind and solar energy with maximum impact, in response to the International Energy Agency.
Shobie Ramakrishnan, chief digital and technology officer at pharmaceutical giant GSK, told attendees on the Momentum AI conference that the corporate was capable of increase production of its shingles vaccine by a million doses by utilizing generative artificial intelligence to create “digital twins” that replicate factory operations using software.
“It’s a technology,” Ramakrishnan said, “that is both underrated and overrated.”
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