ArtVersion Principal and Creative Director Goran Paun recently contributed to the Senior Executive AI Think Tank panel examining the emerging hardware technologies that could shape the next generation of artificial intelligence.
Published by Senior Executive as “From AI Innovation to Adoption: The Hardware Technologies to Watch,” the panel brought together AI and technology leaders to explore developments ranging from silicon photonics and composable memory to edge computing, chiplets, and reconfigurable architectures. The discussion also examined what will determine whether these technologies move beyond experimentation toward meaningful commercial adoption.
Paun focused on a growing challenge for organizations investing in AI infrastructure: the pace of AI development is increasingly faster than traditional hardware cycles.
“AI workloads are evolving faster than traditional hardware cycles,” Paun said. “That makes architectures that can remain useful across changing models, inference patterns and deployment environments especially interesting.”
He pointed to reconfigurable systems, chiplets, and tighter memory integration as areas with particular potential because they can provide greater flexibility as technical requirements evolve.
For Paun, however, the larger question extends beyond raw computing performance. Organizations making significant infrastructure investments also need to consider how long those investments will remain useful as AI models, workloads, and deployment strategies continue to change.
“The commercial test will be longevity as much as raw performance,” he said.
That long-term perspective becomes increasingly important as models become smaller, more specialized, and more frequently deployed at the edge. Hardware architectures that require organizations to repeatedly rebuild their surrounding software and infrastructure could introduce significant cost and complexity.
“The hardware that wins may be the hardware that adapts without requiring the surrounding software, infrastructure and operating model to be rebuilt every time AI changes direction,” Paun said.
Across the Senior Executive AI Think Tank discussions, a common theme emerged: the future of AI computing will depend on more than benchmark performance. Adaptability, energy efficiency, software compatibility, cost, infrastructure longevity, and the ability to support changing workloads will increasingly shape which technologies achieve meaningful adoption.