As generative AI systems advance, so too does their appetite for energy. Training and running large language models consumes vast amounts of electricity. AI’s energy demand is projected to double in the next five years, gobbling up 3 percent of total global electricity consumption. But what if AI chips could function more like the human brain, processing complex tasks with minimal energy? A growing chorus of scientists and engineers believes that the key might lie in organoid intelligence.
AI enthusiasts were introduced to the concept of brain-inspired chips in July at the United Nations’ AI for Good Summit in Geneva. There, David Gracias, a professor of chemical and biomolecular engineering at Johns Hopkins University, gave a talk discussing the latest research he’s led on biochips and their applications to AI. Focused on nanotech, intelligent systems, and bioengineering, Gracias’s research team is among the first to build a functioning biochip that combines neural organoids with advanced hardware, enabling chips to run on and interact with living tissue.
Organoid intelligence is an emerging field that blends lab-grown neurons with machine learning to create a new form of computing. (The term ‘organoid intelligence’ was coined by Johns Hopkins researchers including Thomas Hartung.) The neurons, called organoids, are more specifically three-dimensional clusters of lab-grown brain cells that mimic neural structures and functions. Some researchers believe that so-called biochips—organoid systems that integrate living brain cells into hardware—have the potential to outstrip silicon-based processors like CPUs and GPUs in both efficiency and adaptability. If commercialized, experts say biochips could potentially reduce the staggering energy demands of today’s AI systems while enhancing their learning capabilities.
“This is an exploration of an alternate way to form computers,” Gracias says.
How Do Biochips Mimic the Brain?
Traditional chips have long been confined to two-dimensional layouts, which can limit how signals flow through the system. This paradigm is starting to shift, as chipmakers are now developing 3D chip architectures to increase their devices’ processing power.
Similarly, biochips are designed to emulate the brain’s own three-dimensional structure. The human brain can support neurons with up to 200,000 connections—levels of interconnectivity Gracias says flat silicon chips can’t achieve. This spatial complexity allows biochips to transmit signals across multiple axes, which could enable more efficient information processing.
Gracias’s team developed a 3D electroencephalogram (EEG) shell that wraps around an organoid, enabling richer stimulation and recording than conventional flat electrodes. This cap conforms to the organoid’s curved surface, creating a better interface for stimulating and recording electrical activity.
To train organoids, the team uses reinforcement learning. Electrical pulses are…
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The post “Biochips Mimic the Brain to Cut AI Energy Use” by Aaron Mok was published on 08/09/2025 by spectrum.ieee.org