Huawei expects 60 percent jump in AI chip revenue
Reuters reported on May 1 that Huawei expects AI chip revenue to grow by at least 60 percent this year.
The figures, attributed to the Financial Times and carried by Reuters, point to roughly $12 billion in 2026 revenue, up from about $7.5 billion in 2025. Reuters also wrote that it could not immediately verify the FT report independently. Treat this as a reported expectation, not audited Huawei results.
The new point is not that Chinese cloud providers are buying Huawei chips. That was already visible after the DeepSeek V4 launch. The new point is scale: if the figure is right, Huawei is no longer just a fallback under export controls, but an AI infrastructure supplier with real domestic volume.
The FT report also points to the Ascend 950PR as a key driver, with an upgraded Ascend 950DT planned later in 2026. That should be read as a signal of faster product cadence, not proof that Huawei has caught Nvidia on performance, software or ecosystem depth.
For executives, the implication is that AI infrastructure is becoming more geopolitically segmented. Model choice, cloud choice and accelerator choice are now tied together. A vendor training or serving models on one chip stack in China, another in the US and a third in Europe may face different costs, delays, controls and compatibility limits.
The CIO consequence is practical: do not design AI architecture as if Nvidia/CUDA, one hyperscaler or one model family will always be available on the same terms. Ask where models run, which accelerators are used, which export-control or sanctions exposure the supplier has, and how workloads can move if capacity, price or regulation changes.
This is also a board-level risk question. AI cost is now shaped by geopolitics, not just consumption. Companies with critical data, Asian supply-chain exposure or large cloud commitments should maintain a simple risk matrix: model dependency, chip dependency, cloud dependency and exit plan. It sounds boring. That is exactly why it works.
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