AI stocks fall: Global artificial intelligence shares slide sharply in wake of Anthropic exec’s slowdown call

Global AI-related stocks fell on Monday after Anthropic CEO Dario Amodei called for a slowdown of the development of AI capabilities, with other major tech figures backing the proposal.

Investors are concerned that an industry-wide slowdown in AI development could have ripple effects for companies across the sector and curb adoption.

In Asia, South Korean heavyweights SK Hynix and Samsung Electronics closed down more than 6 per cent and 4 per cent, respectively. Shares of SoftBank, one of the biggest investors in OpenAI, fell 10 per cent in Japan.

In Europe, semiconductor and other AI-related stocks also fell sharply in early trading. Chip equipment giant ASML fell more than 5 per cent, Nokia was down around 8 per cent, and Infineon dropped more than 7 per cent. Other companies with business ties to the buildout of data centres, such as Siemens Energy and Schneider Electric, were also lower.

Major stocks also fell in US premarket trading. Memory chipmaker Micron was down around 5 per cent, Intel dropped nearly 6 per cent, while Nvidia was nearly 3 per cent lower. Several other semiconductor names also fell. Hyperscalers such as Microsoft, Amazon and Alphabet were all slightly lower.

The sell-off comes amid the growing debate over the risks posed by rapidly improving AI model capabilities that hit a crescendo last week after Jacob Coxon, a researcher at Anthropic who also previously worked at OpenAI, said he resigned out of concern that Anthropic and OpenAI are “gambling with our lives.”

That led to Anthropic safety researcher Evan Hubinger responding, saying he believes that there is a greater than 10 per cent chance AI will “kill all humans” within the next decade.

The posts caused a firestorm on social media and led to a response from major AI leaders. Anthropic CEO Dario Amodei on Saturday penned an essay calling for a slowdown in the pace of development of AI capabilities.

“We must slow the pace at which we improve the capabilities of AI models,” Amodei said.

Tech leaders support a slowdown

Amodei’s essay sparked rare consensus among leaders from Anthropic’s rivals. OpenAI CEO Sam Altman on Saturday said he agrees with Amodei that AI companies need to “pace the frontier.”

SpaceX CEO Elon Musk, who has been sounding the alarm on the risks posed by AI for several years, posted on X: “Dario is right.”

There are concerns that any kind of slowdown in the pace of AI development could impact multiple areas, from the purchasing of chips to the purchasing of computing power — where hundreds of billions of dollars of capital expenditure is headed.

“The kind of equity market rally has been based on AI growth and productivity gains … so if we do see that start to derail, then it could have an impact on equity performance going forward,” Zoe Gillespie, a senior director at RBC Brewin Dolphin, told CNBC’s Squawk Box Europe on Monday.

“Certainly, a lot of what we are looking into with equity returns is baked into the future earnings growth of these companies, and if that comes under threat then we may see this destabilize.”

While Amodei called for a slowing of the pace of frontier AI capabilities, he stopped short of pushing for a complete halt. In fact, he said that “progress will still seem fast.”

In a post on X on Monday, Altman said “pacing” does “not mean ‘stopping’.”

“Progress has been rapid and will continue to be. But it should be slower than it otherwise could be; interventions like safety cases and monitoring have significant costs,” Altman said.

Ben Barringer, global head of technology research at Quilter Cheviot, told CNBC on Monday that “while things may slow somewhat, the pace of change is still going to be vast.”

“Even if training and rollout is slowed, inference is still the area that the industry is short in supply. Demand still far outstrips supply, so even if things are to slow a little, company revenues are unlikely to be impacted,” Barringer said.

Inference refers to the actual running of AI versus training, where huge amounts of data are used to improve the underlying models.