Dario Amodei, co-founder and CEO of Anthropic, during the company’s Builder Summit in Bengaluru, India, Feb. 16, 2026.
Samyukta Lakshmi | Bloomberg | Getty Images
Stocks tied to the AI boom slumped on Monday, but cybersecurity companies rallied, as concerns about a slowdown in artificial intelligence development rippled across Wall Street.
Over the weekend, Anthropic CEO Dario Amodei called for AI model creators to slow the pace of development after fears voiced by researchers went viral. OpenAI CEO Sam Altman and SpaceX’s Elon Musk both said they agree with Amodei.
Chipmakers and server vendors that have seen their fortunes soar in the AI buildout plummeted on fears of dampened growth and rising inventories. Micron, Intel, Marvell Technology and Applied Materials dropped more than 4% each, and Nvidia fell about 3%. South Korean memory maker SK Hynix declined 7% in U.S. trading
Hewlett Packard Enterprise slid about 8%, while Dell and Oracle lost roughly 4% each. Neocloud CoreWeave, which Bernstein analysts flagged as one of the most exposed AI names given its massive rural data center reach, dropped 5%.
Shares of Nvidia, ASML and SoftBank year-to-date.
The narrative is having the opposite effect for cybersecurity companies, which are tasked with protecting companies and government agencies from AI-related attacks. Calls for those vendors to ramp up their capabilities hit a fever pitch this summer after OpenAI revealed that its agents orchestrated an attack on Hugging Face.
Palo Alto Networks and CrowdStrike, the industry leaders, jumped 14% and 15%, respectively, on Monday. Okta, Zscaler, Qualys and Netskope also saw double-digit gains.
CrowdStrike and Palo Alto have played a major role in model development, participating in early testing for unreleased OpenAI and Anthropic models.
The AI slowdown chatter also helped alleviate some concerns that have rocked the software industry. Salesforce, Adobe and ServiceNow all gained.
The big market moves come during the intensifying debate over the risks posed by rapidly improving AI model capabilities. Worries about the power of AI 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.”
Anthropic safety researcher Evan Hubinger responded by saying he believes that there is a greater than 10% 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. Amodei on Saturday penned wrote in an essay, “We must slow the pace at which we improve the capabilities of AI models.”
Tech leaders support a slowdown
With an expected outlay of over $1 trillion a year on AI infrastructure, the downstream effects could be significant.
“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,” Barringer said. Inference refers to the actual running of AI versus training, where huge amounts of data are used to improve the underlying models.
“Demand still far outstrips supply, so even if things are to slow a little, company revenues are unlikely to be impacted,” Barringer added.
