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Sterling Anderson

GM Chief Product Officer and former Tesla/Aurora executive, responsible for GM's product, software, and vehicle architecture strategy including EV and autonomy initiatives.

Top 3 podcasts with Sterling Anderson

Ranked by the Snipd community
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206 snips
Oct 22, 2025 • 1h 18min

Why GM will give you Gemini — but not CarPlay

Mary Barra, the Chair and CEO of General Motors, and Sterling Anderson, GM's Chief Product Officer, dive into the future of automotive innovation. They reveal GM's exciting partnership with Google to launch the Gemini AI assistant in 2028. The duo discusses the bold move to drop Apple CarPlay from EVs, prioritizing in-car technology for enhanced safety. Barra shares insights on market shifts, the impact of EV tax credits, and their strategy to balance electric and gas vehicle portfolios amidst changing consumer demands.
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Oct 26, 2025 • 33min

General Motors product chief Sterling Anderson on GM’s next steps, guided by technology

Sterling Anderson, GM's Executive Vice President and Chief Product Officer, is reshaping the future of mobility. He discusses GM's revolutionary vision of cars as intelligent robots, highlighting centralized computing for smarter, evolving vehicles. Anderson reveals plans for eyes-off highway driving and new battery technologies aimed at improving range and cost. He emphasizes the need for agility in adapting to regulatory changes and how GM's approach to AI companions will enhance user experience while ensuring safety in autonomous systems.
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Nov 5, 2024 • 43min

Insights from 15 years leading the self-driving vehicle industry, with Sterling Anderson

Sterling Anderson, a pioneer in self-driving vehicles with a PhD from MIT, shares his insights on the future of transportation. He discusses Aurora's goal to launch fully autonomous trucks between Dallas and Houston by 2025. Sterling highlights the economic benefits of self-driving trucks, addressing driver shortages and safety challenges. The conversation also touches on the ongoing debate over data quality versus quantity in AI training, and contrasts global regulatory approaches to self-driving technology in the US, Europe, and China.

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