Cortical Labs' neuron-based systems require 5,000 times fewer steps than GPU systems to learn goal-seeking behaviors, a massive advantage for real-world robotics where time cannot be accelerated.

Liquid AI's early, highly effective non-linear models faced a major scaling bottleneck. Non-linear relationships are difficult to "tensorize"—convert from sequential to parallel computations—which is essential for GPU efficiency. This is why linear systems like state-space models scale more easily.

AI model capabilities follow a predictable, non-linear scaling law: increasing training compute by 10x roughly doubles a model's capabilities. This exponential relationship, rather than an incremental one, is what will drive underappreciated and disruptive advancements across many industries.

The progress in deep learning, from AlexNet's GPU leap to today's massive models, is best understood as a history of scaling compute. This scaling, resulting in a million-fold increase in power, enabled the transition from text to more data-intensive modalities like vision and spatial intelligence.

The progression from early neural networks to today's massive models is fundamentally driven by the exponential increase in available computational power, from the initial move to GPUs to today's million-fold increases in training capacity on a single model.

A key surprise in AI development was the non-linear impact of scale. Sebastian Thrun noted that while AI trained on millions of documents is 'fine,' training it on hundreds of billions creates an 'unbelievably smart' system, shocking even its creators and demonstrating data volume as a primary driver of breakthroughs.

Liquid AI uses an automated system to discover neural architectures, avoiding human bias. Crucially, it bypasses misleading proxy metrics like perplexity by putting the target hardware in the loop and evaluating models directly on the customer's downstream tasks, optimizing for latency, memory, and quality.

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.

Traditional science failed to create equations for complex biological systems because biology is too "bespoke." AI succeeds by discerning patterns from vast datasets, effectively serving as the "language" for modeling biology, much like mathematics is the language of physics.