AI doesn't store data like a traditional database; it learns patterns and relationships, effectively compressing vast amounts of repetitive information. This is why a model trained on the entire internet can fit on a USB stick—it captures the essence and variations of concepts, not every single instance.

A 10x increase in compute may only yield a one-tier improvement in model performance. This appears inefficient but can be the difference between a useless "6-year-old" intelligence and a highly valuable "16-year-old" intelligence, unlocking entirely new economic applications.

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.

AI's strength lies in solving "differentiable" problems where being "close enough" is acceptable, like generating an image. Classical code is better for non-differentiable tasks requiring exact precision, like arithmetic or hashing. This framework helps architects decide where to deploy AI versus traditional algorithms.

The history of AI, such as the 2012 AlexNet breakthrough, demonstrates that scaling compute and data on simpler, older algorithms often yields greater advances than designing intricate new ones. This "bitter lesson" suggests prioritizing scalability over algorithmic complexity for future progress.

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.

The "bitter lesson" in AI research posits that methods leveraging massive computation scale better and ultimately win out over approaches that rely on human-designed domain knowledge or clever shortcuts, favoring scale over ingenuity.

Model architecture decisions directly impact inference performance. AI company Zyphra pre-selects target hardware and then chooses model parameters—such as a hidden dimension with many powers of two—to align with how GPUs split up workloads, maximizing efficiency from day one.

Models like Stable Diffusion achieve massive compression ratios (e.g., 50,000-to-1) because they aren't just storing data; they are learning the underlying principles and concepts. The resulting model is a compact 'filter' of intelligence that can generate novel outputs based on these learned principles.