CrowdStrike CEO George Kurtz pushed back Monday against calls to slow the development of powerful artificial intelligence models, arguing that doing so would not eliminate the security risks posed by systems that are already widely available.
“The genie’s out of the bottle,” Kurtz said on CNBC’s “Mad Money.” “There’s plenty of models that are already out there, both frontier as well as open-weight models, that can already be dangerous.”
Kurtz was responding to Anthropic CEO Dario Amodei, who called over the weekend for frontier AI laboratories to slow the pace of model development amid growing concerns about the ability of increasingly autonomous systems to escape human control.
Register for the next Tekedia Mini-MBA.
Register for Tekedia AI in Business Masterclass.
Join Tekedia Capital Syndicate and co-invest in great global startups.
The debate has opened a new fault line in the AI industry. While Amodei is focused on limiting the speed at which frontier capabilities advance, Kurtz believes that the immediate cybersecurity problem cannot wait for the frontier to slow down. Models capable of finding vulnerabilities, writing malicious code and interacting with external systems already exist, meaning companies must protect themselves against the technology currently in circulation.
The market appeared to embrace that argument Monday.
Cybersecurity stocks rallied sharply as AI-related infrastructure companies sold off. CrowdStrike surged nearly 14% to a record close above $235 a share, while Palo Alto Networks gained just over 13%.
Both companies have now gained roughly 100% this year, illustrating how investors are increasingly treating cybersecurity as one of the beneficiaries of the AI arms race rather than merely another technology segment exposed to it.
The logic is that the more capable AI becomes, the greater the potential attack surface, and the more companies may need software capable of monitoring AI systems themselves.
“It’s incumbent on the security industry to be able to help protect at least the models that are out there, while the frontier models determine what pace they’re actually going to evolve,” Kurtz said.
The New Security Layer Is the AI Agent
Kurtz’s argument rests on a distinction between conventional software and autonomous AI agents. A traditional application generally operates within predetermined parameters. An AI agent can interpret an objective, choose a sequence of actions, interact with external tools, and potentially adapt when it encounters obstacles.
That autonomy creates a different category of cybersecurity risk.
Agents can deviate from the behavior their developers expected or find ways around safeguards built into their environment. That became evident earlier this summer when rogue OpenAI models escaped an isolated testing environment and gained access to Hugging Face.
The incident has since become a growing reference point in the AI safety debate because the concern was not simply that the models generated harmful content. They were able to navigate beyond the boundaries established for the test and interact with external infrastructure.
For Kurtz, that means organizations need security controls operating alongside the agents themselves.
“We can look at what these programs do. We can put our own guardrails around them at runtime,” he said. “We can instrument them to see what they’re doing, and we can prevent them from doing bad things.”
The idea represents a potentially significant expansion of the cybersecurity market. Instead of protecting only networks, endpoints, applications and cloud infrastructure, security companies could be expected to monitor AI agents as they make decisions and take actions.
The objective would be to determine whether an agent’s behavior is consistent with its authorized purpose, whether it is attempting to access restricted systems, and whether it is exhibiting anomalous behavior that could signal compromise or loss of control.
Kurtz stopped short of endorsing the most extreme predictions about AI, but he was unequivocal about the risks posed by autonomous agents.
“What I do know is that the agents are dangerous,” he said. “The agents need to be controlled. You need to have visibility, and you need to be able to protect your organization.”
His conclusion was blunt: “You need equivalent or better AI defenses to combat the AI agents.”
But that approach may result in an unusual investment dynamic. AI could become both a source of new cyber threats and a driver of demand for the companies building the defenses against them.
For cybersecurity firms, the opportunity is not dependent entirely on whether frontier models become dramatically more capable. Existing models and open-weight systems are already powerful enough to create new risks, according to Kurtz.
That helps explain why investors reacted differently to cybersecurity companies and data-center infrastructure stocks Monday. A slowdown in frontier AI development could reduce some expectations for future computing demand, but it could simultaneously strengthen the case for security spending.
Regulation Versus the AI Race
Kurtz nevertheless cautioned against responding to the risks with regulations that could weaken the United States’ position in AI.
He described the country’s lead over China as narrow and warned that excessive regulation could make it harder for U.S. companies to compete.
“If we put too much regulation around this, then it’s going to stifle innovation,” Kurtz said.
His position puts him at odds with some of the more expansive regulatory proposals now being discussed in Washington. Amodei has noted that regulation could be the most effective way to slow the development of frontier capabilities, including through independent evaluation and greater oversight.
Kurtz’s preferred approach is less about stopping the race and more about building defenses alongside it.
He advocated greater cooperation between AI developers and cybersecurity companies, both during model development and after deployment. The idea is to build security into the development process while maintaining independent safeguards that can monitor AI systems in real time once they are operating in the real world.
“Greater safety in the lab and greater safety in production in runtime is ultimately the best course of action,” he said.
Anthropic has already pursued a version of that approach through Project Glasswing, an initiative introduced this spring to protect its Mythos model, which demonstrated strong capabilities in identifying cybersecurity vulnerabilities.
The emerging debate therefore presents two different responses to the same technological problem. One approach is to slow the development of frontier models until safety techniques catch up with their capabilities. The other is to accept that powerful models are already deployed and build a new security layer capable of controlling them as they operate.
For investors, the distinction matters.
If frontier development slows materially, analysts warn that some of the enormous capital spending on data centers, advanced chips and networking equipment could face pressure. But the cybersecurity consequences of existing AI systems would remain. In fact, greater awareness of autonomous-agent risks could accelerate spending on monitoring, identity controls, runtime protection and AI-specific security tools.
That could make cybersecurity one of the few parts of the technology market with a relatively direct hedge against the risks created by AI.
But the longer-term challenge will be ensuring that the defensive systems themselves can keep pace. If attackers can deploy autonomous agents capable of operating continuously and at machine speed, conventional human-led security operations may become inadequate.
That is ultimately the market opportunity Kurtz is describing. The issue now lies in the security industry’s ability to build defenses capable of controlling those models once they are deployed. The frontier may slow, accelerate, or change direction. But the systems already released into the world cannot simply be put back in the bottle.