The performance gains from Nvidia's Hopper to Blackwell GPUs come from increased size and power, not efficiency. This signals a potential scaling limit, creating an opportunity for radically new hardware primitives and neural network architectures beyond today's matrix-multiplication-centric models.

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.

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.

Breakthroughs like neural network "pruning" can reduce model size by 90% without losing accuracy, offering a 10x reduction in inference costs. This highlights that algorithmic innovation, not just acquiring more hardware, will be a key competitive vector in the AI race, enabling more output with less energy.

While most focus on building more power infrastructure to meet AI's energy needs, the truly disruptive innovation may come from creating chips and models that are massively more energy-efficient. This contrarian view suggests the real investment opportunity might be in demand-side technology, not just supply-side energy production.

Successful AI models will be small, specialized ones that run efficiently on consumer CPUs at the edge (laptops, phones). This leverages existing hardware (e.g., Apple's M-series chips) and avoids costly cloud GPUs, creating a strategic advantage for companies like Apple.

As AI demand outstrips Earth's power supply, the industry is pursuing two strategies. Elon Musk is escaping the constraint by moving data centers to space. Everyone else must innovate on compute efficiency through new chip designs and model architectures to achieve 70-100x gains per token.

The intense power demands of AI inference will push data centers to adopt the "heterogeneous compute" model from mobile phones. Instead of a single GPU architecture, data centers will use disaggregated, specialized chips for different tasks to maximize power efficiency, creating a post-GPU era.

Adding more FLOPS to current AI chips is useless due to thermal throttling. Etched realized the solution is lowering voltage, which quadratically reduces power consumption. Inspired by bitcoin miners, they created a new power delivery system enabling chips to run at under half the voltage of GPUs.