Building the Future of AI: Hammerspace's Approach to Data, GPUs, and Enterprise Growth

I had the chance to speak with Sam Newnam, Senior Director of AI Solutions at Hammerspace on The Ravit Show, about the challenges and opportunities enterprises face as they scale AI infrastructure.

We began with the name itself, Hammerspace. A concept borrowed from comic books, now representing a very real approach to how data can be accessed, unified, and delivered without limits.

From there, we explored the pressures shaping enterprise AI today. The rise of AI agents and enterprise-scale deployments is creating new challenges around data gravity and data friction.

Sam shared why this is no longer just about storage, but about what he calls “GPU gravity”, moving data efficiently to where the models and GPUs actually are.

Hammerspace’s view is clear: enterprises do not need to forklift data into proprietary silos or rewrite workflows just to enable AI. Instead, an open, Linux-native, standards-based approach allows data to remain where it lives, whether on-prem or across clouds, while streaming on demand to the infrastructure that needs it.

We closed with a look ahead. As AI workloads continue to grow, the ability to unify data seamlessly and avoid lock-in will become a critical competitive differentiator.

Conversations like this highlight a big shift: AI success is not just about the model. It is about how well enterprises prepare their data infrastructure to keep pace with it.

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