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Home AI Article

Nvidia Calculates a $50 Trillion Market Opportunity in Physical AI Systems
(Synopsis)

by ainewsarticles
June 11, 2025
in AI Article, Business, Business & Finance, Favorite, Science, Science & Technology
Reading Time: 1 min read
Nvidia Calculates a $50 Trillion Market Opportunity in Physical AI Systems
4
VIEWS

At the Nvidia GTC Paris event, CEO Jensen Huang highlighted the company’s physical AI systems, which are expected to revolutionize multiple industries, representing a massive market opportunity of $50 trillion in areas like manufacturing, transportation, and humanoid robotics.

Nvidia’s automotive division is on track to generate $5 billion in revenue this year, with fully self-driving vehicles showing significant potential. Presently, only 1% of cars have L2+ capabilities, suggesting substantial room for growth.

During the event, Nvidia shared that its Drive AV software is now functional, first utilized in the Mercedes Benz CLA sedan and applicable for a range of purposes from infotainment to AI-driven driving. The company is also making strides in industrial AI through partnerships with Siemens and Deutsche Telekom to launch an industrial AI cloud in Germany.

New AI tools and simulations introduced by Nvidia aim to boost autonomous driving and robotics, with the Halo safety system receiving approval from leading certification bodies. The company plans to soon release the Isaac Sim code as open-source software, facilitating further improvements in autonomous vehicle software performance.

European countries such as France and Italy are investing in AI infrastructure with collaboration from companies like Domyn and telecom providers, deploying Nvidia systems for local AI projects. Nvidia’s expanded partnership with Siemens focuses on enhancing industrial AI and digital transformation, addressing an anticipated workforce shortage of 50 million workers over the next five years, further emphasizing the need for investment in humanoid robotics.

 

 

The ainewsarticles.com article you just read is a brief synopsis; the original article can be found here: Read the Full Article…

 

 

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