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.

Jensen Huang emphasizes that Moore's Law is dead as a primary performance driver. The 50x gain from Hopper to Blackwell came overwhelmingly from architecture and computer science breakthroughs, with raw transistor improvements providing only marginal benefit.

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.

As GPU data transfer speeds escalate, traditional electricity-based communication between nearby chips faces physical limitations. The industry is shifting to optics (light) for this "scale-up" networking. Nvidia is likely to acquire a company like IR Labs to secure this photonic interconnect technology, crucial for future chip architectures.

Investor Shaun Maguire posits that the hardware industry is moving beyond the silicon-centric scaling of Moore's Law. The next wave of innovation will branch into entirely new "tech trees" such as humanoid robotics, silicon photonics, and orbital data centers, creating decades of new progress and distinct from semiconductor advancements.

The plateauing performance-per-watt of GPUs suggests that simply scaling current matrix multiplication-heavy architectures is unsustainable. This hardware limitation may necessitate research into new computational primitives and neural network designs built for large-scale distributed systems, not single devices.

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.

With Moore's Law over, computing progress now depends on networking vast numbers of chips. Lightmatter's photonic interconnects overcome the distance limits of copper cables, allowing thousands of GPUs kilometers apart to function as a single, cohesive supercomputer. This creates a new scaling vector for AI performance.

Today's transformers are optimized for matrix multiplication (MatMul) on GPUs. However, as compute scales to distributed clusters, MatMul may not be the most efficient primitive. Future AI architectures could be drastically different, built on new primitives better suited for large-scale, distributed hardware.