The AI industry spent years chasing bigger models. Now it’s chasing efficiency

After spending years racing to build ever larger AI models, researchers and infrastructure providers are increasingly focused on a new problem: how to make those systems affordable enough to deploy at scale. 

Sara Hooker, cofounder and CEO of startup AI lab Adaption, told the audience at Fortune Brainstorm Tech on Tuesday that most of today’s AI is what she called “monolithic”—or stuck in time. That is, once a model is trained, the model’s knowledge and capabilities are essentially fixed. If something changes in the world, or if the model learns something useful from users, that knowledge doesn’t automatically become part of the model.

“You need models that can evolve,” she explained, “otherwise you end up with massive inefficiencies.” 

Still, for now, scale does matter—and the biggest models are not going away anytime soon, said Rodrigo Liang, CEO of AI chip company SambaNova, though there will be “plenty of room for more efficient models to come in.” For the time being, he explained, customers are left to struggle with the cost of scaling models; with energy-hungry infrastructure; and with finding enough AI chops. 

But Hooker focused on what’s next, saying that we’re at an “inflection point with massive urgency to chang...