Reversible computing chips that perform AI inference at a fraction of the energy of conventional silicon.

The end of Dennard scaling means every order-of-magnitude improvement in compute now costs the same order-of-magnitude more energy. AI training and inference are approaching the physical energy limits of conventional transistor architectures. Vaire's reversible computing chips perform logic operations that recover most of the energy used — breaking the compute-energy tradeoff.

The energy cost of frontier AI has become the most discussed infrastructure constraint in technology. Vaire's physics-level solution arrives exactly as that constraint becomes acute enough to reshape the industry.