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. For the most demanding workloads, they are unavoidable, so the real question is how to run them efficiently rather than whether to run them at all.
Inference in the agentic era behaves nothing like the single-model throughput problem of the past. In Hooker's words, “inference is a different beast.” Constant data movement, not raw compute, is the bottleneck.
