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So he founded a startup, Symbolica AI, to do just that. Elsewhere, a report co-authored by Stanford and Epoch AI, an independent AI research Institute, finds that the cost of training cutting-edge AI models has increased substantially over the past year and change. With costs poised to climb higher still — see OpenAI’s and Microsoft’s reported plans for a $100 billion AI data center — Morgan began investigating what he calls “structured” AI models. Symbolic AI solves tasks by defining symbol-manipulating rule sets dedicated to particular jobs, such as editing lines of text in word processor software. Symbolic AI needs well-defined knowledge to function, in other words — and defining that knowledge can be highly labor-intensive.