The tiny worm Caenorhabditis elegans has a brain just about the width of a human hair. Yet this animal’s itty-bitty organ coordinates and computes complex movements as the worm forages for food. “When I look at [C. elegans] and consider its brain, I’m really struck by the profound elegance and efficiency,” says Daniela Rus, a computer scientist at MIT. Rus is so enamored with the worm’s brain that she cofounded a company, Liquid AI, to build a new type of artificial intelligence inspired by it.

Rus is part of a wave of researchers who think that making traditional AI more brainlike could create leaner, nimbler and perhaps smarter technology. “To improve AI truly, we need to … incorporate insights from neuroscience,” says Kanaka Rajan, a computational neuro­scientist at Harvard University.

“It’s just bigger, bigger, bigger,” says Subutai Ahmad, chief technology officer of Numenta, a company looking to human brain networks for efficiency. Traditional AI models are “so brute force and inefficient.”

In January, the Trump administration announced Stargate, a plan to funnel $500 billion into new data centers to support energy-hungry AI models. But a model released by the Chinese company DeepSeek is bucking that trend, duplicating chatbots’ capabilities with less data and energy. Whether brute force or efficiency will win out is unclear.