Fears that the AI industry is overhyped are reportedly helping drive down tech stocks.
That’s according to a Financial Times report Wednesday (Aug. 20), which pointed to downturns in the European and Asian markets following declines of big name tech companies such as Nvidia, Arm and Palantir.
Helping fuel this drop, the report added, is a new report from MIT which found that most organizations are getting “zero return” on their investments into the generative artificial intelligence (AI) space.
“The story is spooking people,” a trader close to a multibillion-dollar US tech fund told the FT.
The MIT report found that only 5% of “integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable [profit and loss] impact.”
Meanwhile, OpenAI CEO Sam Altman warned last week that an AI bubble could be forming.
“Are investors over excited? My opinion is yes,” Altman said in an interview reported by The Verge. “I do think some investors are likely to lose a lot of money, and I don’t want to minimize that, that sucks. There will be periods of irrational exuberance. But on the whole the value for society will be huge.”
The FT report also noted the rise of Chinese firm DeekSeek, which earlier this year released a high-performing AI modelthat called into question the level of spending by American artificial intelligence firms.
DeepSeek claimed that model only cost $5.6 million to train using about 2,000 of slower Nvidia chips. That figure is significantly lower than what it took to train frontier models from companies like OpenAI, Google and Anthropic.
The news erased $600 billion of market value from Nvidia in a single day. While tech stocks rebounded, the FT says the incident highlighted investor sensitivity to negative AI news.
In related news, PYMNTS last week explored the cost of owning AI models for enterprises. While the cost of the models has fallen since 2022, the overall cost of ownership “has been resistant to declines,” said Muath Juady, founder of SearchQ.AI.
“The real expenses lie in the hidden infrastructure, including data engineering teams, security compliance, constant model monitoring, and integration architects necessary to connect AI with existing systems,” added Juady.
For every dollar spent on AI models, companies are spending $5 to $10 to make the models “production-ready and enterprise-compliant,” Juady told PYMNTS. “The integration challenges tend to be more expensive than the technology itself and require substantial investment in change management and process redesign, which many organizations underestimate.”
Source: https://www.pymnts.com/
