To achieve 1000x efficiency, Unconventional AI is abandoning the digital abstraction (bits representing numbers) that has defined computing for 80 years. Instead, they are co-designing hardware and algorithms where the physics of the substrate itself defines the neural network, much like a biological brain.

The next wave of AI silicon may pivot from today's compute-heavy architectures to memory-centric ones optimized for inference. This fundamental shift would allow high-performance chips to be produced on older, more accessible 7-14nm manufacturing nodes, disrupting the current dependency on cutting-edge fabs.

Digital computing, the standard for 80 years, is too power-hungry for scalable AI. Unconventional AI's Naveen Rao is betting on analog computing, which uses physics to perform calculations, as a more energy-efficient substrate for the unique demands of intelligent, stochastic workloads.

A key trend in AI models is "dynamism"—the ability to vary computation and memory usage per token, as seen in Mixture-of-Experts (MoE) architectures. Current hardware, designed before this trend, is inefficient. New chips must be built to accelerate these dynamic computations.

We are building AI, a fundamentally stochastic and fuzzy system, on top of highly precise and deterministic digital computers. Unconventional AI founder Naveen Rao argues this is a profound mismatch. The goal is to build a new computing substrate—analog circuits—that is isomorphic to the nature of intelligence itself.

Martin Shkreli makes a case for photonic computing—using light instead of electrons—as the next major paradigm in AI hardware. He argues that because matrix multiplications (95% of a GPU's job) are a natural function of light interference, photonic chips could offer an "insane speedup" with O-of-one complexity, making them a potential successor to GPUs.

EnCharge AI's innovation was to reframe in-memory analog compute not as a scaled-up memory problem, but as a high-precision analog design problem. They borrowed techniques from medical and aerospace circuits to overcome noise and enable massive efficiency gains.

Each layer of abstraction in modern computers—from digital 1s and 0s up to neural networks—is inherently "lossy" and creates inefficiency. A more efficient approach bypasses these layers by directly connecting the physics of a semiconductor to the structure of a neural network, mimicking how brains compute without linear algebra.

Unlike CMOS chips that perform simple addition/multiplication, photonic chips execute complex functions like Fourier transforms natively. This dramatically reduces data fetched from memory, targeting the 95% of energy currently consumed by data movement, not computation.