AI Revenue Reaches 38% of Lenovo Sales as Shares Jump

Lenovo’s AI story, as Winston Cheng described it, is deliberately broader than the company’s long-standing identity as a global PC maker. Cheng said Lenovo has “the most complete portfolio” of non-iOS devices at global scale, spanning PCs, tablets, and smartphones, and that this device base is the foundation for its AI hardware strategy.

The other half of the strategy is infrastructure. Cheng framed Lenovo’s “hybrid strategy” as stretching “from the pocket to the cloud”: devices on one end, and AI compute infrastructure on the other. In the latest quarter, he said, Lenovo’s infrastructure business gained from demand for compute used in both AI training and AI inferencing.

That framing matters because Ed Ludlow pressed Cheng on whether the market’s reaction reflected a new appreciation for Lenovo’s infrastructure business. Bloomberg’s Hong Kong chart showed Lenovo up 19.77% intraday at HK$15.75. Separately, Ludlow said the company’s U.S.-listed ADRs were also seeing a major move, describing it as their biggest jump since 2008.

Cheng did not attribute the share move to a single quarter. His answer was that Lenovo is “in it for the long haul,” and that AI spending is an opportunity “for decades to come.” He called the current period “the AI decade for Lenovo” and said the company is in the first year of that journey.

The strategic claim is not that Lenovo recently discovered AI demand. Cheng said Lenovo has been investing in the relevant capabilities for more than 10 years. He pointed to the company’s ability to produce globally at scale, both for devices and infrastructure, as a differentiator. He also cited Lenovo’s IBM heritage from the 2015 acquisition, saying it gave the company a foundation for serving today’s AI compute needs because of its high-performance-compute “genetics” and liquid-cooling technology.

Bloomberg’s earnings graphic showed revenue of $21.59 billion, up 27% year over year; R&D expenses of $747.9 million, up 16%; and gross margin of 16.4%, unchanged from a year earlier. Cheng said AI revenue was about 38% of Lenovo’s total revenue in the latest quarter, with AI PCs a large component of that figure.

The PC-side AI case turns on whether agents become a reason for enterprises and knowledge workers to buy higher-performance devices at scale. Ludlow connected the issue to the shift from training to inference and to agentic AI, asking for evidence that more common use of AI agents would drive new PC acquisition.

Winston Cheng said Lenovo is already seeing strong and increasing demand for AI PCs across its portfolio. His argument was that as AI training develops and AI capabilities improve, people will need to “consume and interact with the agents through their devices.” That, in his view, makes upgrades to higher-performance devices a necessary part of the AI adoption path.

This is the core of Lenovo’s PC-side AI case: agents do not eliminate the device; they increase the importance of the endpoint through which workers interact with AI systems. Cheng did not provide unit-growth figures. He pointed instead to demand signals in Lenovo’s portfolio and to market behavior around AI companies seeking device relationships.

He cited OpenAI and others forming partnerships with device makers, whether through staffing or strategic arrangements, as evidence that AI companies care about the device layer. Cheng said Lenovo is also seeing this directly in its own conversations with AI companies that want to work with Lenovo from a go-to-market perspective.

The most concrete CFO answer from Winston Cheng concerned component pressure. Asked what was happening with memory and how severe the bottleneck was, Cheng said AI infrastructure spending is driving shortages not only in memory, but also in CPUs and GPUs. He described the demand as a multi-year opportunity, while saying the industry still needs to catch up in supply chain capacity.

For now, Cheng said, the imbalance is pushing memory prices higher. Lenovo’s ability to handle that pressure depends on the business line. On the AI infrastructure side, he said the company can pass through the pricing pressure because demand is strong enough to support it. On the device side, the exposure is sharper, particularly at the lower end of the market.

Cheng pointed to first-quarter IDC numbers and said companies that could not obtain supply had seen their market shares decline. He did not give specific vendor names or share figures. The commercial distinction is that constrained components are not only a cost issue; they can determine who can ship enough product to defend share.

Ed Ludlow then pressed on the internal tradeoff: if both Lenovo’s infrastructure business and PC business need common components, and infrastructure has the strongest demand, does Lenovo have to choose where to prioritize? Cheng’s answer was that Lenovo’s breadth is part of its leverage with suppliers. Because it can provide demand forecasts across PCs, tablets, smartphones, and servers, he said suppliers view Lenovo as a strategic partner.

That supplier relationship, Cheng said, helps Lenovo avoid reducing the problem to a simple server-versus-PC allocation fight. He said Lenovo still needs to supply longtime customers of products such as ThinkPad, which he described as “so well loved in the market,” and must ensure enough supply goes to that demand as well.

The practical claim is that Lenovo’s scale across categories gives it forecasting credibility with suppliers, and that credibility matters when memory and other AI-linked components are tight. But the pass-through economics are uneven: infrastructure demand can absorb higher component costs more readily than devices, especially lower-end devices.