- Paul Romer spots a serious mistake in the hype surrounding AI technology.
- The economist states that AI will face adverse conditions in the future, driven by the lack of enough data.
- Within two years, the community will realize the hype was a bubble, says Romer.
Paul Michael Romer, an American economist and Nobel laureate, recently shared insights on the potential risks of AI technology. During a discussion at the Asian Investment Conference in Hong Kong on Wednesday, Romer warned that the AI space will face adverse conditions in the future due to the potential lack of sufficient data.
Drawing a parallel between the previous crypto hype bubble and the prevailing hype surrounding AI, Romer argued that the community is committing a grave mistake. He cited,
“Right now, there’s way too much confidence about the future trajectory of AI. When people project this forward, I think they’re at risk of making a very serious mistake.”
Over the recent months, many experts have voiced concerns about artificial intelligence, highlighting different aspects. For instance, the use of AI by bad actors or hackers to create harmful deepfakes has raised alarm. In addition, industry experts have cautioned against the serious threat of AI tools displacing human employees.
In 2022, AI technology gained widespread popularity with the introduction of OpenAI’s ChatGPT. Industry leaders like Microsoft, Alibaba Group Holding Ltd., Nvidia, and others have heavily invested in computational power and cloud capabilities.
However, Romer warned that the current acceptance and utility of the technology would not endure. He stated that within two years, the community would recognize that AI was overhyped and that “it really was a bubble.” He added,
“We’ve benefited from scaling up compute and ingesting a whole lot of data. Scaling up compute is pretty easy. It’s just more machines, more chips. But what’s going to happen is we’re not going to have enough data.”
Moreover, Romer compared AI to autonomous vehicles. Though firms like Tesla have promised fully automated cars, reliability issues and edge-case scenarios of their automation systems have hindered the realization of the promise.
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