Intel Names Qualcomm’s Katouzian to Lead Client & Physical AI Group

Intel is expanding its focus beyond traditional personal computers with the appointment of Alex Katouzian as executive vice president and general manager of the Client Computing and Physical AI Group. Previously at Qualcomm Technologies, where he led mobile, compute, and extended reality divisions, Katouzian will now align Intel’s client computing business with emerging applications in robotics and autonomous machines. “AI is creating opportunities at the edge, driving a change in client computing and physical AI systems,” says Intel CEO Lip-Bu Tan. This move signals a strategic push to embed Intel’s technology not just in computers, but as a core component of increasingly intelligent physical devices, broadening the company’s target market and future growth areas.

Alex Katouzian Leads Client Computing & Physical AI Group

The move demonstrates Intel’s intent to compete with Qualcomm in areas where the latter has established a strong foothold, leveraging Katouzian’s expertise to accelerate its own development. Aligning Intel’s client computing business with “physical AI systems” represents a substantial expansion of the company’s target market, moving beyond providing processors for AI-enabled devices to becoming a core component within the devices themselves. Katouzian’s role will be crucial in scaling innovation and delivering new computing experiences, building on Intel’s existing strengths in AI PC development and edge inference. Katouzian expressed enthusiasm for the opportunity, stating that Intel is creating the foundation for AI-driven transformation, from leading in AI PCs to scaling AI inference at the edge and accelerating the future of physical AI systems. He will officially join Intel in May, reporting directly to Tan, and will be tasked with reimagining client computing to encompass this broader vision of interconnected, AI-powered devices. The company anticipates that Katouzian’s leadership will be instrumental in capitalizing on the growing demand for intelligent systems across a diverse range of applications.

Pushkar Ranade Appointed CTO, Advances Emerging Technologies

Pushkar Ranade’s permanent appointment as chief technology officer signals Intel’s intent to pursue technologies beyond conventional silicon, building on an interim role where he already coordinated technology with overall business strategy. Ranade will oversee the development of nascent fields like quantum computing and photonics, and continue as chief of staff to CEO Lip-Bu Tan, ensuring a cohesive link between research and commercial objectives. This dual role underscores the company’s belief that fundamental technological advancements must be directly integrated with its core business goals, rather than existing as isolated research projects. Intel’s focus extends to neuromorphic computing, which mimics the human brain, and the exploration of novel materials, suggesting a long-term commitment to altering computing architectures.

The broadening of Intel’s technological scope is evident in its prioritization of areas beyond traditional semiconductor manufacturing; Ranade’s mandate includes driving development in fields where the company currently lacks established market dominance. This strategic shift acknowledges the limitations of continued scaling based solely on Moore’s Law and positions Intel to capitalize on emerging paradigms. Ranade’s leadership is expected to accelerate progress in areas requiring substantial investment and long-term vision, such as building practical quantum computers or creating entirely new classes of computing devices. This emphasis on future technologies complements the appointment of Alex Katouzian, and together the two executives represent a concerted effort to redefine Intel’s position in the computing ecosystem.

AI is creating unprecedented opportunities at the edge, driving a sea change in client computing and physical AI systems.

Lip-Bu Tan, Intel CEO
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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