NVIDIA CEO Jensen Huang leads world's first $4 trillion company
NVIDIA CEO Jensen Huang and U.S. President Donald Trump shake hands at an 'Investing in America' event in Washington, D.C., U.S., April 30, 2025. Leah Millis, REUTERS
Nvidia founder and CEO Jensen Huang on Friday, July 11, 2025 in Nvidia offices in Washington, D.C. Jack Gruber, USA TODAY
A screen displays news on "Nvidia hits $4T market cap" on the floor at the New York Stock Exchange (NYSE) in New York City, U.S., July 9, 2025. Jeenah Moon, REUTERS
Jensen Huang, CEO of Nvidia, is seen on stage next to a small robot during the Viva Technology conference dedicated to innovation and startups at Porte de Versailles exhibition center in Paris, France, June 11, 2025. Gonzalo Fuentes, REUTERS
British Prime Minister Keir Starmer shakes hands with Jensen Huang, CEO of Nvidia, after a panel discussion at London Tech Week, in London, Britain, June 9, 2025. Carl Court, Via REUTERS
Jensen Huang, CEO of Nvidia, gestures as he speaks during the Viva Technology conference dedicated to innovation and startups at Porte de Versailles exhibition center in Paris, France, June 11, 2025. Gonzalo Fuentes, REUTERS
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DeepSeek: A Paradigm Shift, What It Means For Humanity
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This approach also increased costs tremendously as compute and data demands spurred bigger and better processors. In late 2023 and 2024 and even now, the construction of power hungry data centers were thought to be the only way to improve the performance of the models. Limiting access to computing and the latest chips was thought to restrain China as a source of these powerful models. With DeepSeek that paradigm was shifted. Companies like Nvidia whose stock was heavily affected by the announcement have since recovered and thrived. The lessons were lost on global markets. The worst may yet to come as the companies buoyed by the rising of AI and its use are brought down to earth by a combination of new methods and the lessening of compute needed to do training as well as inference. Sunk costs and the costs of switching with their own powerful economic adherents prevent a longer term view and lock the American AI in their paths. Success breeds complacency and adherence to the model that produced success. In AI, a rapidly developing field, getting stuck on algorithms, process and practice is deadly. DeepSeek showed that just piling on computing and data does not make for exponential progress. This is a lesson from many fields, that is often ignored with an overused but wrong dictum 'This time it is different.' Innovation follows familiar patterns; slowly then rapidly. Efficiency The costs of training and running DeepSeek are much lower than for other models. The ratio in a recent presentation showed $6M for DeepSeek/ versus $600M for Llama (the open source model from Meta). One hundredth the cost. The costs for other models, including ChatGPT, are even more. The cost savings are a result of implementing DeepSeek's own discoveries in reinforcement learning and training using distillation. Further, the model is very efficient in generating Chinese language. As of three months ago, a large number of Chinese companies had joined the AI revolution by subscribing to DeepSeek. As the national champion, the government industrial policy supports DeepSeek. RL as a training method was invented in the University of Amherst. The recipients of the 2024 ACM Turing award, Andrew Barto and Richard Sutton were the inventors of the classic reinforcement learning techniques. For LLMs and other large models, such an approach falls under supervised learning. The model is refined by feedback, classically from humans, called RLHF (Reinforcement Learning with Human Feedback). This is called supervised fine- tuning. Humans are the supervisors. The paper released by the creators of DeepSeek R1 goes into detail on the way that they modified RL. Anything that involves humans in the loop at scale requires a lot of money. Removing the human in the loop makes training cheaper. A version of the model is used to fine-tune the other. 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