Investor Ross Gerber highlighted Nvidia Corp. (NASDAQ: NVDA) CEO Jensen Huang’s stage presence at Salesforce Inc.’s (NYSE: CRM) Dreamforce 2026 as a core reason for his continued investment in the chipmaker.

Gerber, CEO of Gerber Kawasaki, shared a clip of Huang’s playful exchange with Salesforce CEO Marc Benioff on X on Wednesday. The interaction underscored Huang’s ability to command attention, a trait Gerber links to Nvidia’s market edge.

Leadership and Market Sentiment

Gerber stated, "This is why I own Nvidia," emphasizing the significance of Huang’s persona. In August, following Nvidia’s second-quarter results, Gerber suggested the stock could eventually reach $450. He previously described the company as undervalued after the earnings report.

Investor Trung Phan characterized the Dreamforce moment as a potential "Benioff frame-mog," noting how Huang used humor to address the height difference between himself and Benioff. Huang remarked that evolution proved "big is unnecessary," adding that he is more comfortable on airplanes than his taller counterpart.

AI Infrastructure and Revenue Growth

Nvidia’s performance continues to drive investor optimism. The company reported fiscal second-quarter revenue of $96.2 billion, up 106% year over year. Data Center revenue, the primary growth engine, rose 117% to $89 billion.

Looking ahead, Nvidia forecast third-quarter revenue of $108 billion, plus or minus 2%. Huang used the Dreamforce platform to assert that AI spending remains robust, describing the infrastructure spending flywheel as "really, really flying." He noted that companies are rapidly building Nvidia-powered "AI factories."

Strategic Partnerships and Product Launches

Salesforce unveiled Koa, its first CRM reasoning model for Agentforce, during the event. Built on Nvidia Nemotron 3 Super and running inside Salesforce infrastructure, Koa highlights the deepening integration between the two tech giants. Huang walked into the crowd alongside Benioff to discuss AI’s next phase, reinforcing Nvidia’s central role in the ecosystem.

What the Numbers Show

The divergence between total revenue growth and Data Center expansion reveals concentration risk and opportunity. With Data Center revenue accounting for approximately 93% of total revenue ($89 billion of $96.2 billion), Nvidia’s financial health is heavily dependent on enterprise AI infrastructure demand. The 11% gap between total revenue growth (106%) and Data Center growth (117%) indicates that non-data center segments are growing slower than the core business, further concentrating earnings power in the Data Center division.

Independent research firm SemiAnalysis argues that the computing power required for AI safety and cybersecurity may sustain demand for Nvidia Corp. (NASDAQ: NVDA) GPUs, even as frontier model training slows.

Analyst Max Kan stated on a Tuesday podcast that market participants may be overlooking two critical factors supporting chip demand. First, AI companies continue to seek more computing capacity than can physically come online. Second, the process of making models safer itself requires significant compute resources.

Safety Work Eats Compute

Kan emphasized that AI labs increasingly use models to monitor other systems, test safeguards, and check reinforcement-learning environments. He noted that investors often underestimate the compute required for safety, alignment, and monitoring.

Interpretability research, which aims to understand how models reach answers, was also cited as a compute-intensive workload. Anthropic CEO Dario Amodei has echoed this view, proposing that the industry pace frontier AI by devoting more resources to alignment, interpretability, testing, and operational safeguards rather than halting training entirely.

Cybersecurity Cuts Both Ways

SemiAnalysis suggested that advanced AI could reshape cybersecurity by forcing companies to fight AI with AI. The panel referenced Greg Brockman’s description of a continuous security system at OpenAI that uses the latest models to constantly find and patch vulnerabilities.

As attackers deploy autonomous agents at scale, defenders may need equally capable AI systems running continuously to detect and stop them. One speaker described this as potentially "the most impactful shift in cybersecurity," arguing that firms delivering security outcomes rather than just tools could capture enormous value.

The hosts warned that major cybersecurity firms could become prime targets for increasingly capable AI attacks. They named:

• Okta Inc. (NASDAQ: OKTA)
• CrowdStrike Holdings Inc. (NASDAQ: CRWD)
• Palo Alto Networks Inc. (NASDAQ: PANW)
• Zscaler Inc. (NASDAQ: ZS)

Near-Term Pause Still Bites

OpenAI paused frontier reinforcement-learning training following its Hugging Face incident. Its largest planned frontier run remains on hold while smaller training and evaluations continue.

President Donald Trump called fears of runaway AI a "hoax" on Monday, questioning why an industry would seek rules that could bankrupt it. Polymarket traders assign a 16% chance to a U.S. AI safety law passing before 2027, with about $123,000 traded on the contract. The contract covers measures including restrictions on AI training or use and requirements for human oversight.

For Nvidia, the central question is whether slower frontier training cuts more GPU demand than safety, monitoring, and cybersecurity add back. Shares recovered 0.6% on Tuesday.

What the Numbers Show

The divergence between OpenAI’s paused large-scale training and SemiAnalysis’ argument for sustained demand hinges on the volume of compute allocated to non-training tasks. While the source does not quantify the exact ratio of safety compute to training compute, it highlights that safety, alignment, and cybersecurity are becoming distinct, resource-heavy workloads. This suggests that total GPU utilization may remain high even if traditional model training cycles slow, shifting the demand driver from pure scale expansion to operational safety and defense.