LQMs vs. LLMs: when AI stops talking and starts calculating

The article discusses the emerging role of Large Quantitative Models (LQMs) as a new class of AI systems that differ fundamentally from Large Language Models (LLMs). Unlike LLMs, which are trained on internet text to generate language-based outputs, LQMs are purpose-built to work with numerical, scientific, and physical data, enabling them to simulate complex real-world systems in fields like chemistry, biology, and physics. Fernando Dominguez, Head of Strategic Partnerships at SandboxAQ—a company at the forefront of AI and quantum technology integration—explains that LQMs can generate novel data not available in existing datasets, such as simulating trillions of molecular interactions. This capability allows LQMs to accelerate drug discovery, financial modeling, and navigation, offering a more quantitative and practical approach to AI-driven innovation. A key example highlighted is SandboxAQ’s collaboration with UCSF’s Institute for Neurodegenerative Diseases, where LQMs enabled the simulation of over 5 million molecular compounds in