In a revealing interview with Wired, Nvidia’s CEO, Jensen Huang, professed his daily reliance on Perplexity AI and ChatGPT for insights, sidelining competitors like Bard and Gemini.
“I’ve been using Perplexity. I enjoy ChatGPT as well. I use both almost every day.”
Huang’s preference underscores Perplexity AI’s unique proposition and suggests these tools meet Nvidia’s high standards more effectively than others.
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The allure of AI tools for research
Huang’s daily engagement with AI chatbots stems from a keen interest in leveraging these tools for research, specifically focusing on computer-aided drug discovery. This suggests a blend of personal and professional motivations driving his exploration of AI capabilities.
“Research. For example, computer-aided drug discovery. Maybe you would like to know about the recent advancements in computer-aided drug discovery.”
Perplexity AI, in particular, captivates with its claim as the “world’s first conversational answer engine.” The platform’s ease of use, comprehensive ‘library’ feature, and current affairs ‘discover’ feed stand out, offering users a seamless way to deepen their understanding of various topics.
Perplexity’s straightforward UI and insightful approach to content curation are seen on shared screenshots, and this is possibly the reason why Nvidia’s CEO might prefer this platform. The app’s layout encourages effortless searching, making it an efficient instrument for those who want to broaden their knowledge or stay informed about the newest technological advances and beyond.
Nvidia’s investments and future visions
Nvidia’s contribution to the AI development field is also exhibited by its participation in a $73.6 million Series B funding round. This allocation in an undisclosed venture represents a comprehensive approach to supporting and integrating AI technologies with the top players in the field. Further, this investment is in sync with Huang’s personal AI chatbots that reinforce the dogfooding concept, which is where executives work with products they are investing in or developing.
Additionally, Huang shared insights into Nvidia’s vision for the future of data centers, describing the concept of an “AI factory” likened to a power generator. This ambitious project, which has been under development for several years, signals Nvidia’s focus on remolding data center operations and efficiency. Huang’s discussions with TSMC executives about advanced packaging and capacity planning further highlight Nvidia’s focus on staying at the forefront of AI and semiconductor technology.
“We’re building a new type of data center. We call it an AI factory. An AI factory is much more like a power generator. It’s quite unique. We’ve been building it over the last several years.
Challenging Moore’s law and looking ahead
As explained by Huang, the acquisition of Mellanox by Nvidia represents a move to bypass the limitations of Moore’s Law at the data center level. This indicates a forward-thinking approach to scaling computing power and efficiency beyond traditional semiconductor advancements. The conversation also touched upon the anticipation surrounding Nvidia’s Blackwell-generation GPUs. Huang, however, maintained discretion on specifics, reflecting the competitive and fast-evolving nature of the AI and semiconductor industries.
“We looked at the way Moore’s law was formulated, and we said, “Don’t be limited by that. Moore’s law is not a limiter to computing.” We have to leave Moore’s law behind so we can think about new ways of scaling.”
The interview raised a pivotal question: how will Nvidia’s investments and Huang’s personal use of AI tools like Perplexity AI and ChatGPT shape the future of AI research and development? This question invites users to consider the implications of top executives’ preferences and practices for the broader tech landscape.
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