Gogia, however, sees the initial impact playing out differently, with retrieval and vector search systems likely to benefit first.
“Retrieval systems are modular,” Gogia said. “You can isolate them, tweak them, test them without breaking everything else. And they already depend on compression to function at scale. So any improvement here hits immediately. Storage footprint comes down. Index rebuilds get faster. Refresh cycles improve. That is operational value, not theoretical value.”
Gogia said Google’s announcement represents a solid piece of engineering that addresses a real problem and could deliver meaningful benefits in the right contexts. However, he added that it does not change the underlying constraints, noting that AI systems remain limited by infrastructure, power, cost, and the complexity of making all the components work together.
