According to Liquid AI's CEO, the primary application of architectural research has become enabling efficiency—reducing cost, latency, and memory without sacrificing quality. The next major breakthroughs in AI *capability* are more likely to stem from new learning algorithms and data paradigms rather than architecture alone.

Unlike competitors, MatX's ML team conducts fundamental research, training LLMs to validate novel hardware choices. This allows them to safely "cut corners" on industry standards, such as using less precise rounding methods. This deep co-design of model and hardware creates a uniquely efficient product.

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.

While speed benchmarks are flashy, a model's memory usage is the true determinant of its viability. In real-world applications, AI models must share limited resources with other processes, making a low memory footprint more critical than a marginal speed advantage for successful deployment.

A major breakthrough for Liquid AI was finding a closed-form solution for the differential equations governing their neural networks, a problem unsolved since 1907. This eliminated the need for slow, step-by-step numerical solvers, enabling a massive leap in scalability from hundreds to potentially billions of neurons.

Chinese AI models like Kimi achieve dramatic cost reductions through specific architectural choices, not just scale. Using a "mixture of experts" design, they only utilize a fraction of their total parameters for any given task, making them far more efficient to run than the "dense" models common in the West.

Breaking from transformer dominance, Shopify leverages Liquid AI's state-space-like models for high-value tasks. For search query understanding, they run a 300M parameter Liquid model with an impressive 30ms end-to-end latency, a feat difficult to achieve with traditional architectures.

A fundamental constraint today is that the model architecture used for training must be the same as the one used for inference. Future breakthroughs could come from lifting this constraint. This would allow for specialized models: one optimized for compute-intensive training and another for memory-intensive serving.

Liquid AI's automated architecture search found that the winning formula for efficient models on CPUs is a hybrid approach. It combines a few attention layers for generalization with a majority of layers made from a simple, double-gated 1D convolution. This balances power and efficiency for on-device AI.