Zoho founder Sridhar Vembu weighed in on the limitations of large language models (LLMs) in a post on X, arguing that true creativity lies outside the boundaries of training data—and that’s where LLMs often falter.

“True creative work is 'out of the training distribution' work,” Vembu wrote, contrasting how LLMs operate with game-playing AI engines like those used in chess and Go. He noted that these engines, often powered by Monte Carlo Tree Search (MCTS), are capable of delivering genuinely creative moves because their foundational mechanics differ from LLMs. Advertisement

“Chess or Go engines do come up with creative moves,” he said, implying that the structure of those systems enables exploration beyond rote learning. “The foundational approach they use, Monte Carlo Tree Search, is

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