When FORTRAN was introduced in 1957, it revolutionized scientific computing by enabling engineers and researchers to express complex mathematical operations in a compact, intuitive way using arrays—structured collections of data that simplify calculations on large datasets. This foundational concept laid the groundwork for modern tensors, which are now central to advanced AI systems and scientific simulations. Today, frameworks like NumPy and PyTorch leverage tensors to efficiently perform computations across massive datasets, underpinning everything from deep learning models to physical simulations. Now, researchers are pushing the boundaries of tensor programming beyond discrete data structures and into the continuous domain. Traditionally, tensors operate on grids of discrete values—think of pixels in an image or entries in a matrix. But in many real-world applications, such as fluid dynamics, quantum mechanics, or climate modeling, phenomena evolve continuously over space and time. Representing these systems with discrete grids can introduce approximation errors and limit precision. The new approach extends tensor algebra to continuous functions, allowing mathematical operations to be performed directly on smooth, infinite-dimensional objects. This shift enables more accurate modeling of physical systems and reduces the need for coarse discretization. By treating functions as continuous tensors, researchers can apply familiar tensor operations—like contractions, decompositions, and transformations—directly to continuous domains, preserving mathematical elegance and computational efficiency. This advancement opens new possibilities in AI-driven scientific discovery. For instance, neural operators—AI models that learn mappings between continuous functions—can now be built more naturally using continuous tensor frameworks. These models can simulate complex physical processes with fewer training examples and greater generalization, potentially accelerating breakthroughs in materials science, weather prediction, and autonomous systems. The work represents a significant evolution in how we think about computation, bridging the gap between symbolic mathematics and numerical algorithms. By bringing the power of tensor programming into the continuous world, researchers are not only enhancing the accuracy of simulations but also creating a unified language for expressing both discrete and continuous phenomena in a single computational framework.