What if the real bottleneck holding back RWA growth wasn't the technology itself, but rather the computational overhead? That's the exact problem some teams are tackling right now—specifically focusing on trimming down proving times and slashing memory requirements.

After digging into the technical details, it becomes clear that the push to make AI inference more cost-effective isn't just nice-to-have. It's foundational. When you reduce the computational burden, you unlock faster settlement times and lower operational costs for on-chain systems.

The whitepaper breakdown reveals a solid approach: optimize the proving mechanism, cut memory footprint, and suddenly you've got a more efficient pipeline. It's the kind of incremental but critical work that rarely gets headlines, yet it fundamentally changes what's economically viable in the RWA space.