Large Quantitative Models (LQMs) are AI systems that use quantitative data and the fundamental equations of physics, chemistry, and mathematics to simulate complex real-world systems with high precision. These models help scientists and engineers understand and predict complex behaviors in fields like materials science, drug discovery, and financial modeling. By simulating how molecules interact, improving chemical processes, or recreating physical phenomena, LQMs speed up innovation and solve problems that would otherwise take a lot of time and money to figure out through experiments.
LQMs aren’t just here to stay, they’re set to radically revolutionize the way we drive innovation. While Large Language Models (LLMs) have amassed media attention in recent years, they lack the capabilities to precisely simulate the physical world, leading to interesting yet inaccurate and unreliable outputs in the global economy’s largest disciplines. As Anima Anandkumar, Professor at the California Institute of Technology and a prominent thought leader in the field, put it: “If you ask an LLM to design an aircraft wing, it may come up with something, maybe even a drawing, but how do we know if it is any good?”. That is why SandboxAQ is pioneering the development of LQMs: to bridge the gap between business process automation AI and value creation AI for the real world.
An LLM is trained on text and images. In a traditional sense, an LLM is fed millions of pieces of content, and trained from those patterns. When an LLM is prompted to answer a question, it calculates the probability of each word or pixel that could come next from the previous and chooses the most suitable result. An LQM, on the other hand, is trained by the first principles equations that govern the real world, from mathematics to chemistry and physics. When presented with a real-world scenario, it formulates precise outcomes based on simulations using data generated from these equations. As a result, LQMs can be used to predict much more than words and don’t fall into the common trap of hallucinations, which are outputs from models recognizing patterns that are non-existent. They can reckon chemical and biological reactions, high-dimensional financial outcomes, disease infection rates, and even material science patterns to assess whether the aircraft wing alloy being used will be able to withstand extreme climates.
In today’s business landscape, speed, IP differentiation, and cost-efficacy are as important as ever. Traditional product development and value creation methods often require hundreds and thousands of hours of trial and error, leading to waste in productivity and resources. On the other hand, LQMs can serve as a force multiplier for efficiency, providing enterprises with a competitive edge both in terms of speed and costs. Below are just a few examples out of many that highlight how LQMs can transform the new innovation world order.
The Time to take action is now. The rapid advancement of Large Quantitative Models (LQMs) is offering companies a significant competitive edge. By accelerating innovation, reducing costs, and enhancing accuracy across various industries, LQMs are reshaping complex problem-solving approaches. From aerospace engineering to pharmaceuticals, product design to financial forecasting, LQMs are generating tangible value within organizational processes and manufacturing chains.
Integrating LQMs today positions organizations to lead in the coming decade, while hesitation may result in falling behind. The capability to simulate entire ecosystems, optimize processes, and achieve breakthroughs faster than ever is no longer a distant future—it's happening now, independent of the need for quantum computers.
The question is no longer if LQMs will transform industries, but how soon organizations will leverage them to stay ahead. Taking the leap now ensures a competitive advantage in the evolving landscape.
View our interactive demo to explore a subset of SandboxAQ’s real-world applications and start your LQM journey today.
