Microsoft’s Maia AI chip delayed to 2026 amid design challenges

Microsoft’s next-generation Maia AI chip, code-named Braga, has been delayed by at least six months, pushing mass production from 2025 to 2026, according to The Information. The delay highlights the challenges tech giants face in developing custom AI processors to reduce their dependence on Nvidia’s expensive chips, while competitors like Google and Amazon continue to advance their own chip development programs.

What you should know: The Braga chip was intended for Microsoft’s data centers this year but faces significant performance and production hurdles.

• When it eventually launches, the chip is expected to fall “well short of the performance of Nvidia’s Blackwell chip that was released late last year.”
• Unanticipated design changes, staffing constraints, and high turnover are contributing to the delay.
• Microsoft first introduced the Maia chip in November 2023 but has struggled to scale production compared to its Big Tech peers.

Why this matters: Custom AI chip development has become critical for cloud providers seeking to reduce costs and improve performance while lessening reliance on Nvidia’s dominant position in the AI hardware market.

• Like Amazon and Google, Microsoft is investing heavily in developing processors tailored for artificial intelligence operations and general-purpose applications.
• The delay puts Microsoft further behind competitors who have successfully deployed their own custom silicon at scale.

Competitive landscape: Microsoft’s chip development lags behind other major cloud providers who have made significant progress with their custom processors.

• Google has found success with its Tensor Processing Units and unveiled its seventh-generation AI chip in April, designed to accelerate AI application performance.
• Amazon announced its next-generation Trainium3 AI chip in December, scheduled for release later this year.
• Both companies have been able to customize their chips for specific needs while achieving better performance and cost efficiency.

The big picture: The semiconductor race among cloud giants reflects the broader battle for AI infrastructure dominance, where controlling the underlying hardware can provide significant competitive advantages in performance, cost, and capability differentiation.