Imagine an AI that doesn't just write reports but designs groundbreaking chemical processes. That's the promise of Large Knowledge Models (LKMs), the next evolution beyond tools like ChatGPT. While current Large Language Models (LLMs) excel at generating human-like text, they struggle with the complex reasoning and deep domain expertise required for scientific fields like chemical engineering. LKMs aim to bridge this gap by incorporating fundamental principles of physics, chemistry, and biology, along with expert knowledge. This shift moves beyond purely data-driven approaches to create AI that truly understands the intricacies of chemical processes. The challenge lies in integrating symbolic AI, the realm of logic and reasoning, with the statistical power of machine learning. Early AI systems like ELIZA and SHRDLU hinted at this potential, but lacked the data and computational power to realize it. The transformer architecture, the backbone of LLMs, opened new doors, but even these models fall short when faced with scientific problems requiring deep domain expertise. Chemical engineering, unlike fields like computer vision, relies heavily on first principles and often deals with limited data. LKMs offer a solution by leveraging existing knowledge to reduce the need for massive datasets. This approach is crucial for tasks like explaining complex mathematical models, generating insights from chemical equations, and designing safer, more efficient processes. The development of LKMs is still in its early stages, but the potential is immense. Imagine AI that can not only predict reaction outcomes but also explain the underlying mechanisms, design new catalysts, and even automate hazard analysis. This future requires a collaborative effort, bringing together domain experts, knowledge engineers, and AI researchers to build the ontologies and tools that will empower LKMs. The journey from LLMs to LKMs is a challenging one, but the destination is an AI revolution that will transform chemical engineering and beyond.