GM And SAIC Extend China Joint Venture Through 2047

by News Editor

August 5, 2026

General Motors and China’s SAIC Motor have agreed to extend their long-running joint venture for another 20 years, signalling a continued commitment to the world’s largest automotive market despite rising geopolitical tensions and growing competition from Chinese manufacturers.

Originally established in 1997, the 50-50 partnership was due to expire next year. Under the new agreement, the joint venture will continue operating until 2047.

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A Long-Term Bet On China

The renewed partnership comes as global automakers face mounting challenges in China, where domestic brands have rapidly gained market share and reshaped the competitive landscape.

GM said the joint venture will continue focusing on Buick and Cadillac sales in China while also expanding exports of vehicles manufactured in the country to markets including the Middle East, Africa, South America, Mexico and the Asia-Pacific region.

Navigating A Changing Market

China was GM’s largest market between 2010 and 2023, but the company’s performance has weakened in recent years as competition intensified.

Following a restructuring programme that resulted in $1.1 billion in special charges last year, GM returned to profitability in China during the first half of 2026, reporting $248 million in equity income.

Since its launch nearly three decades ago, the joint venture has produced and delivered more than 20 million vehicles.

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Mirendil Signs $100 Million Google Cloud Deal To Advance Self-Improving AI

by News Editor

August 6, 2026

AI startup Mirendil has signed a multi-year agreement worth more than $100 million with Google Cloud to secure computing infrastructure for its self-improving AI research.

The partnership reflects growing competition among AI companies to lock in access to high-performance computing, while cloud providers race to attract promising startups developing next-generation AI models.

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Backing The Next Stage Of AI Research

Mirendil plans to use Google’s Tensor Processing Units (TPUs), Nvidia GPUs and managed training infrastructure to develop AI systems capable of improving their own performance over time.

Known as recursive self-improvement, the concept focuses on building AI that can refine its knowledge and capabilities with minimal human intervention. The technology is attracting growing interest across the industry, with several startups and leading AI labs exploring similar approaches.

According to co-founder and Chief Executive Behnam Neyshabur, the long-term goal is to develop AI that can automate scientific research and accelerate discoveries in fields such as medicine, biology and materials science.

Compute Capacity Becomes A Strategic Asset

Training increasingly advanced AI models requires enormous computing resources, making long-term infrastructure agreements a critical competitive advantage.

Mirendil said Google’s combination of TPUs and GPUs allows workloads to be matched with the most suitable hardware, improving efficiency while reducing costs for customers.

For Google Cloud, the agreement strengthens its position in the race to provide infrastructure for frontier AI developers, while giving the company exposure to one of the industry’s emerging approaches to next-generation artificial intelligence.

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