The CEO of Nvidia, Jensen Huang, Traced a clear line between his approach to artificial intelligence (AI) and that of Elon Musk de Tesla, according to a new biography entitled “The Thinking Machine: Jensen Huang, Nvidia, and the most coveted microchip in the world” by Stephen Witt. The book, released in the United States on April 8, details how Huang Prefer to focus on building practical solutions rather than daring futuristic ideas. When asked by Witt to describe the future he plans, Huang would have replied: “I have the impression of interviewer Elon at the moment, and not me.”
“I will simply build the equipment that these guys need and see where it is going,” he told Witt according to an enterprise initiate report.
Biography compares the contrasting styles of the two technological leaders. According to Witt, Elon Musk often starts with an ambitious final goal – like living on Mars – then works back to develop the necessary technology. On the other hand, Huang adopts a more anchored approach, building step by step depending on existing tools and real needs.
Witt also writes that Huang “hates science fiction”, a key difference compared to the often inspired projects of Musk. The book describes Huang as someone who avoids speculation and focuses on today’s problem solving with available technology.
Jensen Huang calls into question the hypotheses of AI after success deeply
By relaxing the recent skepticism surrounding the domination of Nvidia on the ia flea market, Huang recently discussed the affirmations that competitors like China Deepseek have reached AI capacities comparable to much less material. Unveiling Blackwell ultra GPU from Nvidia to the Nvidia GPU Technology Conference (GTC), he said that everyone was wrong with the computer power you need for AI.
“Almost the whole world was wrong,” said Huang, adding that “the quantity of calculation we need due to the agency AI, following the reasoning, is easily 100 times more than we thought that we needed this time last year”.
Huang then stressed that Nvidia is well positioned to respond to these changing requests. He highlighted the importance of speed and scale in AI inference, declaring: “If you take too long to answer a question, the customer will not come back. It’s like web search.”