Hyundai's Atlas Vs Tesla's Optimus: Is AI Taking Over Automobile Manufacturing?

Amid rapid scale deployment of artificial intelligence in every sector, the automobile manufacturing industry has not left outside of this gambit of AI-driven scalability and innovation.

While S&P Global note that AI is soon establishing itself as the "backbone of design, manufacturing, and in‑vehicle performance" in the automobile industry, the spotlight is however placed on humanoid robots being deployed on assembly lines to slash labour costs.

This trend is visibly evident in the production sites of South-Korean automotive major Hyundai Motor, and Elon Musk led Tesla.

While the world’s attention has been fixated on Elon Musk’s long-promised Optimus robot and the high-stakes AI race between the US and China, Hyundai Motor has emerged as a leader in humanoid robots.

While Elon Musk in Tesla's latest earnings called for increasing manufacturing Optimus robots, which are deployed in automotive manufacturing units, Hyundai Motor has narrowed down it focus on humanoid robots.

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In a move to upscale its AI capabilities, the Seoul-headquartered company announced plans to infuse $6.3 billion to robot production site, AI data centre, and hydrogen plant.

The artificial intelligence data centre will be capable of processing 50,000 units graphic processing units, which will enhance robot learning, and autonomous driving.

The newly appointed José Muñoz led Hyundai Motor Company has inked an investment pact with seven government agencies after partnering with Nvidia to also built a physical AI cluster using Blackwell accelerators.

Future of AI In Automobile Manufacturing

According to an S&P Global report, "Over the next decade, AI will transition from pilot programs to integrated systems that power autonomous fleets, smart factories, and live-vehicle diagnostics. One of the key game changers is predictive thermal management in electric motors."

For instance, ZF’s TempAI solution uses "machine learning models that boost temperature management in electric powertrains, increasing forecast accuracy by over 15%, and unlocks approximately 6% more peak power due to the precision of TempAI’s temperature prediction."

Risks of AI In Automotive Industry

Implementing AI in automotive software development certainly comes with its challenges, particularly in data management and intellectual property ownership within a fragmented supply chain. Navigating access rights to existing code and artifacts can be tricky, and ensuring data privacy is crucial when handling sensitive information from connected vehicles.

Cybersecurity is also a growing concern. More connected vehicles and smarter factories create more entry points for threats. Companies that openly map threats against mitigation plans—and publish clear compliance frameworks—show they take security seriously.

It’s also important to navigate the complex decision-making structures within OEMs, as siloed organizations can slow down progress. By collaborating closely with OEMs, S&P Global Mobility can tailor AI solutions to meet a company’s specific needs.