Current AI models for science are narrow surrogates for specific tasks. The grand vision is to build a foundation model for physics that understands a wide range of coupled, multi-physics phenomena. This single model could be used for simulation, inverse design, and control across many scientific and engineering domains.

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.

Startups and major labs are focusing on "world models," which simulate physical reality, cause, and effect. This is seen as the necessary step beyond text-based LLMs to create agents that can truly understand and interact with the physical world, a key step towards AGI.

Unlike traditional neural networks which require fixed-resolution inputs (e.g., pixels), neural operators model data as continuous functions. This allows them to "zoom in" and make predictions at resolutions higher than the training data, a crucial capability for multi-scale physical phenomena like weather patterns.

Large Language Models are limited because they lack an understanding of the physical world. The next evolution is 'World Models'—AI trained on real-world sensory data to understand physics, space, and context. This is the foundational technology required to unlock physical AI like advanced robotics.

Most AI weather models project the Earth onto a flat rectangle, causing simulations to become unstable and "blow up" over long periods. By incorporating the planet's spherical geometry using Fourier Neural Operators, models like ForecastNet remain stable for long-term climate rollouts, effectively becoming climate models.

While dominant in 1D language tasks, the quadratic complexity of transformers makes them computationally infeasible for high-resolution 3D and 4D physical simulations. An industrial-scale grid can represent a context length in the "hundreds of billions to even a trillion," far beyond what any transformer can handle.

PINs, which solve PDEs from scratch using only physics constraints, often fail on complex, time-dependent problems due to difficult optimization landscapes. Neural operators overcome this by using a data-driven, supervised learning approach, learning from existing solutions before applying physics constraints, making them more robust.

While traditional hybrid models still require solving differential equations during use, Physics-Informed Neural Networks (PINNs) learn the final outcome directly. They are trained to obey physical laws but don't need the equations for inference. This provides a significant speed advantage for computationally expensive systems like chromatography, making them more suitable for real-time applications.