Xiaomi doubles down on chips, AI, OS to boost self-reliance

Xiaomi emphasizes its focus on core technologies such as chips, AI, and operating systems, aiming to strengthen China’s self-reliance, according to founder Lei Jun.

Lei said private tech firms should increase investment and accelerate commercialization of innovations, citing Xiaomi’s over 100 billion yuan spent on R&D in the past five years. The company has developed self-made chips and is working toward integrating its chip, OS, and AI models into a single device this year.

Chinese automakers such as Xpeng and Li Auto are also stepping up AI and robotics development.

The moves reflect a broader push by China’s private tech sector to boost innovation and competitiveness.

🔗 Source: South China Morning Post

🧠 Food for thought

Implications, context, and why it matters.

Xiaomi’s tech ambition is backed by a specific chip and a clear budget

Lei Jun said Xiaomi has spent over 100 billion yuan on R&D in the past five years. He also said Xiaomi plans 200 billion yuan (US$28 billion) in R&D over the next five years 1.
Lei Jun’s mention of ‘self-made chips’ ties to work already underway. Xiaomi says it will bring its chip, operating system, and AI models together in one device this year.
The chip is the XRing O1, a 10-core processor built on an advanced 3-nanometre process. It packs 19 billion transistors, matching Apple’s A18 series in scale 2.
Xiaomi engineers fit the design into a 109mm� die. Wccftech called that the smallest die among current-generation 3nm chipsets and compared it with Apple’s A18 Pro at 110mm� 3.

The push for in-house tech is opening a profitable business model

Xiaomi links chips, AI, and operating systems to support its ‘Human-Vehicle-Home Full Ecosystem’ 1.
The effort now brings in money, beyond the R&D bill.
Xiaomi said its smart EV, AI, and other new initiatives made a profit for the first time in the third quarter. The group delivered a record 29 billion yuan (US$4.1 billion) in quarterly revenue, up 199% year on year 4.
The approach offers a playbook for hardware makers that want to move past thin device margins by building AI-driven ecosystems.