Officials in the United States are mulling over tighter controls on an export regulation that was created to minimize the availability of artificial intelligence (AI) chips to China.
According to officials close to the source, the stricter regulations would include clamping down on the level of computing power chips can have that are able to be exported. The sources say an update to the rules could come by late July.
Such restrictions on the sale of powerful computing chips have caused alarm to some of the industry’s major players.
Colette Kress, the chief financial officer of Nvidia, one of the world’s leading chip makers, commented on June 28 at an investors conference that:
“..if implemented, [restrictions] would result in a permanent loss of opportunities for U.S. industry to compete and lead in one of the world’s largest markets…”
Kress said an implementation of such regulations would not “immediately impact” the company’s financial results. In late May, the AI chip boom caused Nvidia to momentarily hit $1 trillion in value.
Cointelegraph reached out to the U.S. Department of Commerce for further comment on its potential decision.
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Initially, the restrictions against AI chip sales in China were issued by the Biden administration in October 2022 with the intention to slow down the semiconductor industry.
The October ban cut off Chinese developers from access to some of the more advanced chips on the market, including Nvidia’s A100 chips and the latest version, the H100. These two chips are among the most sought-after for high-level AI development.
In May Nvidia reported its second-quarter revenue forecast to be 50% higher than market estimates, along with a 28% increase in company shares.
It was around the same time that the company released additional AI-powered tools including an AI supercomputer that it created to help aid developers to produce ChatGPT successors.
Meanwhile, in China, local developers have been figuring out ways to skirt the impact of U.S. sanctions. Companies have been reported to be studying new methods to develop AI chips through the use of weaker semiconductors and combinations of chips currently available to them.
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