The Tough Tech problem we are solving
AI is increasingly limited by power. The processors running today's AI waste roughly 100,000 times more energy than physics requires, because conventional transistors spend energy every time they switch and release it as heat. A single modern AI compute rack can draw as much power as a small commercial building, and as demand grows, energy and cooling costs threaten to cap how much compute can be built and deployed.
About our solution
Adiabatic Machines addresses this challenge by building superconducting AI processors based on adiabatic logic, which avoids the heat losses of conventional switching. Spun out of research at MIT and MIT Lincoln Laboratory, its chips compute near the fundamental physical limit for energy efficiency, delivering orders of magnitude better efficiency, cooling included. Built with standard lithography processes and designed for existing software toolchains, the chips are straightforward to manufacture and adopt, enabling more compute without more power.