AI labs are racing to build data centers as large as Manhattan, each costing billions of dollars and consuming as much energy as a small city. The effort is driven by a deep belief in “scaling” — the idea that adding more computing power to existing AI training methods will eventually yield superintelligent systems capable of performing all kinds of tasks.
But a growing chorus of AI researchers say the scaling of large language models may be reaching its limits, and that other breakthroughs may be needed to improve AI performance.
That’s the bet Sara Hooker, Cohere’s former VP of AI Research and a Google Brain alumna, is taking with her new startup, Adaption Labs. She co-founded the company with fellow Cohere and Google veteran Sudip Roy, and it’s built on the idea that scaling LLMs has become an inefficient way to squeeze more performance out of AI models. Hooker, who left Cohere in August, quietly announced the startup this month to start recruiting more broadly.
I'm starting a new project.
Working on what I consider to be the most important problem: build
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