Paul Schell
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Paul Schell In The News
Forbes (2024-08-27)
“Nvidia’s monopolistic grip on the AI data center will be hard to break,” said Paul Schell, an analyst at global technology intelligence firm ABI Research. “Partnerships with independent software vendors for the creation of fine-tuned enterprise-grade applications to run on their platform will go one step further in tempting potential customers from competitors like Nvidia.”
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Data Center Knowledge (2024-08-20)
Paul Schell, industry analyst at global technology intelligence firm ABI Research also sees the AMD acquisition as a play against Nvidia. In his view, AI chip vendors are playing catch-up to market leader Nvidia, whose dominance stems from individual chip performance to overall systems-level designs consisting of interconnected servers, racks, and clusters.
“The next generation of AI models at scale will require optimized solutions across individual silicon all the way up to the entire domain-specific data center, and ZT’s legacy in systems design includes large deployments for hyperscalers,” Schell told Data Center Knowledge.
“Along with the rest of AMD’s AI data center portfolio, this purchase provides a more holistic offering, which has the potential to boost their data center scale offering.”
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IoT NoW (2024-07-23)
“NPUs for TinyML applications in personal and work devices (PWDs) are already well established. However, they are still nascent outside of this device vertical, and major vendors ST Microelectronics, Infineon and NXP Semiconductors are only just introducing this type of ASIC to their embedded portfolios,” said Paul Schell, the industry analyst at ABI Research. “By screening PWDs, we provided greater insight into our modeling for IoT applications, which spans 15 verticals, including the most significant, namely smart home and manufacturing.”
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CNN (2024-05-28)
Samsung’s “circle to search” feature, which allows users to quickly search for information on a device’s screen with a finger gesture, has received a lot of attention and is featured in marketing campaigns. Multimodal features – which refers to an AI system that can interpret and generate different types of data, such as text and images at the same time — like analysis of video footage and in-call spam detection could also form part of the tools, according to Paul Schell, industry analyst at tech intelligence firm ABI Research.
“Something similar would likely be included in an Apple offering, given its relative simplicity and appeal that goes beyond simple image search,” Schell said. “But verbal interactions with a bot like Siri will be much more natural and fluent, and its capabilities will go far beyond the previous, narrow domains, like news and weather updates.”
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Tech News World (2024-03-19)
“Apple appears to be behind its competitors in addressing generative AI, and this is partly because the pace of innovation has been so high that the timings of its yearly developer conference in summer and product release in autumn have created a mismatch at the current pace in the AI race,” said Paul Schell, an industry analyst with global technology intelligence firm ABI Research.
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Information Week (2024-03-12)
“The novel thing about this innovation cycle is on-device, generative AI,” says Paul Schell, industry analyst with ABI Research. Though AI has been on-device for some time, he says, its earlier iterations dealt with simpler, lighter workloads for such tasks as image enhancement or gaming applications.
AI chipsets could make AI more attractive to industries that have been reluctant to adopt the technology, according to Reese Hayden, senior analyst with ABI Research.
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Wire19 (2024-02-22)
“Cloud deployment will act as a bottleneck for generative AI to scale due to concerns about data privacy, latency, and networking costs. Solving these challenges requires moving AI inferencing closer to the end-user – this is where on-device AI has a clear value proposition as it eliminates these risks and can more effectively scale productivity-enhancing AI applications,” says Paul Schell, Industry Analyst at ABI Research. “What’s new is the generative AI workloads running on heterogenous chipsets, which distribute workloads at the hardware level between CPU, GPU, and NPU. Qualcomm, MediaTek, and Google were the first movers in this space, as all three are producing chipsets running LLMs on-device. Intel and AMD lead in the PC space.”
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