Qualcomm is deliberately recasting itself as an AI infrastructure company, extending its engineering and platform work beyond mobile handsets into data centers, automotive systems and wearable devices. The company is emphasizing agentic AI as the connective thread across these markets.
Durga Malladi, Qualcomm's Executive Vice President and General Manager for Technology Planning, Edge Solutions and Data Center, said in an interview that the firm's move into bespoke silicon for servers and cloud environments builds on a long history of tailoring platforms for distinct performance, power and connectivity requirements.
"We have a long, successful track record of taking a platform and customizing it for the specific performance, power, and connectivity needs of a given market," Malladi said. "We've been designing custom silicon at scale for different customers, and we see great opportunity to apply this discipline to the data center."
Qualcomm disclosed a custom silicon partnership with a leading hyperscaler during its most recent earnings call and said initial shipments are expected later this year. The company declined to identify the partner but confirmed it expects the relationship to span multiple product generations and to include central processing units, AI accelerators and custom application-specific integrated circuits. Qualcomm's CEO Cristiano Amon has previously described that business approach as "bespoke."
More information on the engagement and Qualcomm's broader data center plans is scheduled to be presented at the company's Investor Day in New York on June 24.
Malladi said demand from hyperscalers extends across Qualcomm's data center product set, including memory architectures aimed at alleviating inference-related bandwidth constraints. He emphasized that the needs of these customers go beyond raw compute, encompassing energy efficiency and inference-optimized designs.
"I’m not able to share specific names, but I will say that the demand signal from these companies is clear," he said. "In this Agentic AI era, there’s a need for inference-optimized energy-efficient solutions, and that’s exactly what we’re focused on, as we deliver leading CPU, AI accelerators and custom silicon, building a broad, diverse customer base in this space."
Outside the data center, Qualcomm pointed to its automotive business as an example of diversification gaining traction. The company reported a record $1.3 billion in automotive revenue in the second fiscal quarter, an increase of 38% year-over-year.
Qualcomm said its fifth-generation Snapdragon Digital Chassis, which is scheduled to begin shipping later this fiscal year, will provide roughly 12 times the neural processing performance of the prior generation.
On agentic AI specifically, Malladi argued Qualcomm's architecture is well suited to the model because agent orchestration typically requires efficient, heterogeneous compute distributed across devices and cloud infrastructure. That distribution, he noted, aligns with Qualcomm's focus on integrated systems that balance CPU and AI accelerators and enable intelligence to flow between device, edge and data center environments.
"Agentic AI requires heterogeneous compute across CPU and AI accelerators, with intelligence flowing seamlessly from device to edge and data centers," Malladi said. "Our technology is purposely built for distributed, power-efficient, end-to-end systems that can scale across environments. Our industry partners see that the future of AI is distributed, and given the breadth of our portfolio, Qualcomm is uniquely positioned to deliver that architecture at scale."
Malladi also characterized AI as foundational rather than ancillary to digital experiences going forward, saying the company aims to "re-architect and re-define AI working across the entire computing continuum - from devices to datacenters."
What this means for markets and sectors
Qualcomm's stated pivot touches multiple parts of the technology ecosystem. Its data center initiatives intersect with cloud infrastructure and memory suppliers; its automotive growth impacts vehicle semiconductor and software stacks; and work on wearables and edge devices links to consumer electronics and IoT suppliers. The firm's emphasis on energy-efficient, inference-optimized hardware also speaks to enterprises balancing performance with power and cost constraints.