Nvidia CEO Jensen Huang: AI Brings New Security Challenges, Cybersecurity Will Become the Next Growth Market

As enterprises begin deploying AI models and AI agents on a large scale, security issues are gradually extending from traditional IT systems to models, code, data, and automated workflows.

AI itself can serve as both an attack tool and a defense mechanism, fostering an increasingly close relationship between cybersecurity and AI.

“What better way to create demand than to create a problem,” Jensen Huang stated. “Who doesn’t want their market to be hysterical about their product and line up around the corner for it? And so there are responsible ways of doing it and there’s less attractive ways of doing it.”

Over the past few years, Nvidia's primary growth driver has come from AI data center construction. As cloud computing giants continue to increase capital expenditures, demand for GPUs, networking equipment, and complete computing systems has grown rapidly, making Nvidia the most direct beneficiary of this wave of AI infrastructure investment.

While Nvidia's primary revenue still comes from AI chips and data center systems, the company is expanding its scope into models, software, and industry solutions. If cybersecurity becomes a new market for AI applications, security software companies, cloud platforms, and large enterprises will need to deploy more inference computing power, thereby expanding their use of Nvidia's hardware and software.

The company has already begun working with enterprises such as CrowdStrike (CRWD), Cisco (CSCO), and Palantir (PLTR) to advance AI security partnerships. Nvidia can provide GPUs, Nemotron open models, and related development tools, while partners are responsible for integrating these capabilities into threat detection, enterprise data analytics, and security operations platforms.

This division of labor is similar to Nvidia's strategy in other industries; the company does not directly replace cybersecurity vendors, but instead provides the computing platform required to train and run security models. As enterprises add automated vulnerability detection, identity verification, and network monitoring capabilities, the computing workload required for security systems is also likely to increase.

Meanwhile, Nvidia's investment and M&A activities in recent years have also attracted growing market attention.

Last week, the company announced plans to acquire AI startup Hugging Face for approximately $13 billion, further expanding its footprint across the artificial intelligence value chain. Over the past period, Nvidia has built a broader ecosystem network by investing in various types of AI companies, with its scope continuously expanding from chips and models to software and AI applications.

However, this strategy has also sparked a new controversy: when a chipmaker invests in AI companies that may subsequently purchase Nvidia's chips and computing services, does it create a form of "circular dealmaking" that inflates the market's assessment of AI demand?

Jensen Huang disagrees with this notion.

He stated that when Nvidia invests, it does not simply inject capital into ecosystem partners, but rather assesses a company's genuine demand and commercial potential based on its own industry intelligence. He believes that Nvidia can see what problems customers are solving, giving it an informational advantage that other investors lack when making investment decisions.

Huang emphasized that Nvidia needs to see clear market demand before actually deploying corporate capital. In other words, a prospective investment target must first prove it already has real customers, rather than relying solely on capital market expectations to pitch a growth story.

This content was translated using AI and reviewed for clarity. It is for informational purposes only.