The rapid evolution of artificial intelligence (AI) technology has recently sparked intense global discussion within the tech community regarding security controls and the pace of research and development. With the widespread deployment of large language models at the application level, balancing innovative development with potential cybersecurity threats has become a core focus for corporate leaders and policymakers.

NVIDIA CEO Jensen Huang, speaking at Goldman Sachs' annual global technology conference, offered a different interpretation of the recent industry panic surrounding model "jailbreaking" and hacker threats. Meanwhile, Meta CEO Mark Zuckerberg expressed his views on social media, opposing the idea of pausing R&D to address risks and instead advocating for the establishment of an independent external evaluation system.

According to reports from various technology media outlets, although Huang and Zuckerberg addressed different aspects, both remain skeptical about "slowing down AI R&D." Huang pointed out that the current industry panic over cybersecurity jailbreaks is largely driven by the commercial psychology of the cybersecurity industry, which creates market demand by sensationalizing threats. He believes that cybersecurity defense is essentially an arms race of computing power, and that AI Agents will become the most powerful defensive shield in the future.

Zuckerberg further emphasized that the key to AI security lies in "Model Alignment," which he views as a survival threshold for commercial competition. If a product is not secure enough, it will naturally be eliminated by market mechanisms; therefore, he argues for promoting an independent external evaluation ecosystem rather than resorting to passive braking measures.

Regarding information consistency, both tech giants agree that AI security should be addressed through technical means and market competition rather than relying solely on administrative intervention or stagnant R&D. However, their arguments differ in focus: Huang leans toward technical optimism, viewing "computing power as a defensive tool," while also highlighting the marketing tactics of the cybersecurity industry. Zuckerberg focuses on "corporate governance and external auditing," advocating for building trust through third-party assessments.

Furthermore, regarding whether AI will replace human labor, there are signs of market correction. For instance, it was recently reported that Apple, considering the warmth and professionalism of human customer service in handling complex issues, has indefinitely shelved its original layoff plan to replace 5,000 AppleCare customer service representatives with AI agents.

These industry dynamics have had a significant impact on global technology policy and the labor market. Particularly in Europe, as the EU continues to push the AI Act and strengthen regulation of digital services, how companies can utilize the AI defense technologies proposed by Huang or implement the external evaluation mechanisms advocated by Zuckerberg while remaining compliant will become a critical issue. The case of Apple shelving its AI customer service replacement plan also reflects that in the European market, which places high value on consumer rights and service quality, companies must weigh the balance between technical efficiency and human emotional value when implementing AI automation.

However, discussions regarding AI security still face limitations and uncertainties. While the "independent external evaluation" proposed by Zuckerberg has theoretical value for building trust, there is currently no global consensus on how to define "independence" or the uniformity of evaluation standards. Although Huang’s attribution of cybersecurity panic to industry psychology highlights certain commercial operations, over-interpreting his remarks could lead to underestimating the social security risks that technical vulnerabilities might pose in practical applications. Therefore, observers suggest that the statements of these industry leaders should be viewed as references for technological development paths rather than the sole solution for AI risk management.

Future observations will focus on three key points: First, whether companies can effectively defend against cybersecurity threats when promoting AI Agent applications, as Huang expects; second, whether tech giants like Meta can implement credible external evaluation mechanisms that gain regulatory approval; and finally, whether the adjustments made by large enterprises like Apple regarding AI and human resource allocation will trigger other tech companies to follow suit, thereby affecting the global tech industry's employment structure and service models.