Jensen Huang Ai Quotes

Structural prediction and protein design, once considered impossible problems, are now solvable. Grigoryan explains that the complexity of a protein and its possible states surpasses the number of atoms in the universe. “Those numbers are extremely challenging for any computational tools to deal with,” he said. But he believes a skilled protein biophysicist can examine a particular molecular structure and deduce its potential functions, suggesting there may be learnable general principles in nature—exactly the sort of operation that a “universal prediction engine” such as AI should be able to figure out. Generate:Biomedicines has applied AI to examine and map molecules at the cell level, and Grigoryan sees the potential to extend the same technique to the entire human body. Simulating how the human body will react is orders of magnitude more complicated, but Grigoryan thinks it will be possible. “Once you see it working, it’s hard to imagine it doesn’t just continue,” he said, referring to the power of AI.

And that’s not even close to the most expensive Nvidia product. Nvidia’s latest server rack system as of this writing, the Blackwell GB200 series, was specifically designed to train “trillion-parameter” AI models. It comes with seventy-two GPUs and costs $2 million to $3 million—the most expensive Nvidia machine ever made. The company’s top-end-product pricing isn’t merely increasing; it is accelerating.

Current AI models can now understand requests via context and because they can grasp natural conversational language. It is a major breakthrough. “The core of generative AI is the ability for software to understand the meaning of data,” Jensen said.16 He believes that companies will “vectorize” their databases, indexing and capturing representations of information and connecting it to a large language model, enabling users to “talk to their data.

Under Jensen Huang, Nvidia didn't just ride the AI wave; they built the surfboard, the wave pool, and then taught everyone how to surf through strategic bets on GPUs and CUDA

Notably, all eight Google scientists who authored the seminal “Attention Is All You Need” paper on the Transformer deep-learning architecture—which proved foundational for advancements in modern AI large language models (LLMs), including the launch of ChatGPT—soon after left Google to pursue AI entrepreneurship elsewhere. “It’s just a side effect of being a big company,” said Llion Jones, one of the coauthors of the Transformer paper.4 “I think the bureaucracy [at Google] had built to the point where I just felt like I couldn’t get anything done,” he added, expressing frustration with his inability to access resources and data.

Jensen himself has called AI a “universal function approximator” that can predict the future with reasonable accuracy. This applies as much in “high-tech” fields such as computer vision, speech recognition, and recommendations systems as it does in “low-tech” tasks such as correcting grammar or analyzing financial data. He believes that eventually it will apply to “almost anything that has structure.