The Lab Mistake That Might Revolutionize Computing
IEEE Spectrum AI Mario Lanza
Researchers discovered that a standard MOSFET transistor, when its bulk terminal is left floating rather than grounded, exhibits neuron-like behavior by producing sudden current spikes followed by relaxation, potentially enabling more energy-efficient AI hardware. The accidental discovery occurred in 2024 when a student forgot to connect the bulk terminal while measuring a memory circuit, revealing current spiking with nonlinear properties similar to biological neurons. If this single-device approach can be reliably manufactured and scaled, it could replace the dozens or hundreds of transistors currently needed to simulate each artificial neuron, reducing the enormous energy consumption of AI data centers.
Why it matters
Today, you probably asked a question of a large language model, or accepted a connection suggestion on LinkedIn, or watched a recommended video on YouTube, or took a different route to work based on a traffic prediction from Google Maps. In other words, you probably used artificial intelligence. But what you might not know is how much energy that interaction consumed or why. AI requires processing massive amounts of data, which is usually done in large data centers populated by thousands of GPUs capable of executing up to trillions of operations per second. But each of those GPUs achieves that by consuming as much as 1,000 watts apiece. For comparison, if you’ve got a newer smartphone, it probably uses less than 1 W. That kilowatt figure puts GPUs on the same level as vacuum cleaners, dishwashers, and stoves, but with the big difference that data-center processors are operating uninterrupted around the clock.Fundamentally, a lot of this inefficiency is because GPUs are trying to simula
