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.

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

The bias against neural nets, Hinton felt, was “ideological,” a word he pronounced in the same venomous tone that Huang had used to say “political.” The ideology of the research community at the time was that it was not enough that AI be useful. Instead, AI should somehow “unlock” the secrets of intelligence and encode them in math. The standard, 1,100-page AI textbook of the time was a survey of probabilistic reasoning, decision trees, and support-vector machines. The neural nets got just ten pages, with a brief discussion of backgammon up front. When Hinton’s colleague designed a neural net that outperformed state-of-the-art software for recognizing pedestrians, he couldn’t even get his paper admitted to a conference. “The reaction was well, that doesn’t count, because it doesn’t explain how the computation is done—it’s just not telling us anything,” Hinton said.

Catanzaro was uncorked now—I sensed that he didn’t often get to share this perspective at his job. “It doesn’t need to inhabit this biosphere. In fact, it doesn’t need to be on the Earth, either, because the thing about artificial intelligence is that it travels at the speed of light. Humans, you know, we actually have to lug bodies around. Artificial intelligence can move along a radio signal as long as there’s an antenna on the other side.” Free of the limitations of biology, Catanzaro explained, AI would rapidly spread throughout the solar system and beyond. “Humans are naturally confrontational—like, we’re territorial animals, and it’s built into our limbic system to defend our turf,” he said. “AI, if it’s truly intelligent, the things that it’s interested in are so much bigger than the little thin crust of Earth that the humans live on. I don’t think that it’s going to be interested in taking that from us. Rather, I feel like AI is going to want to take care of us.” • • • Serving as a zoo animal for a space computer was an experience to be savored at leisure.

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.

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.

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.

Now I saw where the fear was coming from. The executives were more afraid of Jensen yelling at them than they were of wiping out the human race.

But the final word went to Morris Chang. He didn’t attribute Huang’s success to his work ethic, which, at TSMC, would have been considered slightly above average—nor did he find him especially adaptable. Chang was ninety-two years old when I spoke with him, wearing a purple corded sweater and sitting in front of a striking piece of abstract art, his face serene, his hair completely white. In seventy years of corporate life across two continents he had seen every manner of executive there was to see. To him, the explanation was simple, and there was no secret: “His intellect is just superior.

For Dally, it was Huang’s tireless work ethic that made Nvidia succeed. Even Dally, who left no spare second in his day, could not quite believe the superhuman efforts of his boss. “The rest of us are just here to reduce the bandwidth demands on Jensen,” Dally said. “I mean, when does he sleep?” Diercks agreed: “His hobbies are work, email, and work.

The truth, not widely understood until later, was that the deep-learning revolution was as much a revolution in hardware as software. It was the product of not one but two unpopular, cast-off, discredited, and cash-starved technologies whose ideal form could only be revealed in synthesis. Neural nets running on parallel computers: these tightly coupled technologies were the twin strands of DNA for a new and powerful organism, looking to consume all the data in the world.

Krizhevsky himself escaped the dragnet. Rarely saying a word to anyone, he was not the ideal collaborator, and he departed Google in 2017. His share of the auction money was just under $15 million, enough not to work anymore, especially given the asceticism of his lifestyle. In 2019 he granted a Japanese news crew a visit inside his Bay Area home. Krizhevsky lived like a Benedictine monk, in a spartan apartment above a Vietnamese restaurant. The walls inside were completely bare; the only items of furniture were a desk, a couch, a digital piano, and a television; the only sign of life in the place was his house cat. Krizhevsky, the Orville Wright of the neural net, told the news crew he had walked away from the technology. “Maybe it’s just my personality,” he said, “but when I spend a very long time specializing in something, after about ten years I start to lose interest.