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Steven Latré

Vice President, AI, imec

Search every verified Steven Latré interview, podcast appearance, and on-the-record quote — each transcript cross-checked by AI and human review to confirm speaker identity. Steven Latré, Vice President of AI at imec, appeared on the Sorcery podcast in late September 2026 to discuss the future of AI hardware and the semiconductor industry. He stated that the industry is approaching the physical limits of transistor miniaturization, noting that a transistor is now about one nanometer deep, which he described as roughly 100,000 times smaller than a human hair. Latré said this means the field is "at the end of physics of what we can still do" and that hardware innovation will need to become more inventive to continue improving chip performance. Latré also discussed current bottlenecks in AI infrastructure, including memory chip shortages, power constraints, and cooling challenges. He argued that the recent trend of building increasingly larger AI models, from 175 billion to around 10 trillion parameters, is likely to stop, and he observed that some companies are already abandoning the "scaling hypothesis" in favor of different approaches to address memory limitations. He described imec as a "hidden gem" in semiconductors, focused on the early stages of chip development, and suggested that future software evolution will be more closely tied to hardware, potentially increasing the value derived from that integration.

Recent appearances

  • We’re Reaching the Physical Limits of Chips

    AI's next bottleneck? It might not be GPUs. Memory prices have risen 700% this year, while power, cooling and data transfer are all becoming major constraints on the AI infrastructure buildout. At the same time, the semiconductor industry is approaching the physical limits of simply making transistors smaller. I sat down with Steven Latré, VP of AI at imec and Thema Board Member, and Adam Chambers of Strike Capital, to talk about what comes after the GPU and where the next wave of value in AI infrastructure could be created. We get into › Why memory is already one of AI's biggest bottleneck…

    Watch on YouTube ↗

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