SAN FRANCISCO — Artificial intelligence companies like OpenAI are seeking to overcome unexpected delays and challenges in the pursuit of ever-bigger large language models by developing training techniques that use more human-like ways for algorithms to "think".

A dozen AI scientists, researchers and investors told Reuters they believe that these techniques, which are behind OpenAI's recently released o1 model, could reshape the AI arms race, and have implications for the types of resources that AI companies have an insatiable demand for, from energy to types of chips.

OpenAI declined to comment for this story.

After the release of the viral ChatGPT chatbot two years ago, technology companies, whose valuations have benefited greatly from the AI boom, have publicly maintained that "scaling up" current models through adding more data and computing power will consistently lead to improved AI models.

But now, some of the most prominent AI scientists are speaking out on the limitations of this "bigger is better" philosophy.

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