Running Bigger AI Models, More Efficiently | SambaNova

TL;DR AI has moved from experimentation into production, and that shift exposes three hard constraints: models keep getting bigger, cost climbs steeply at scale, and power becomes a physical ceiling. On a recent Fortune panel, SambaNova CEO Rodrigo Liang and Adaption Labs CEO Sara Hooker agreed the industry’s central problem is now efficiency, though they approach it from different angles: Liang from the infrastructure side, Hooker from the model-architecture side. Large models are not going away.