As the Middle East pushes forward with ambitious AI strategies, its focus has largely revolved around generative AI and large language models (LLMs). But to unlock the full economic potential of AI—particularly in sectors like finance, healthcare, sustainability, and manufacturing—the region will need to embrace a complementary and often more powerful tool: large quantitative models (LQMs).
According to McKinsey, generative AI could generate $21–$35 billion in annual value across the GCC, supporting a broader $150 billion opportunity from AI technologies by 2030. But LLMs, which are designed to work with and produce human language, aren’t suited to solving deeply quantitative, scientific, or data-heavy challenges. That’s where LQMs come in.
What Are LQMs—and Why Do They Matter?
Unlike LLMs, which are trained on vast quantities of text, LQMs are designed to process and simulate complex numerical data, often using scientific models and physical equations. In domains where precision, reliability, and modeling are paramount—like climate prediction, drug discovery, or energy systems—LQMs outperform LLMs by a wide margin.
For example, predicting the path of a hurricane or modeling a next-generation battery isn’t a language problem. It requires the ability to simulate physics, chemistry, or biological interactions. LQMs generate insights based on first principles and scientific models, rather than depending on pre-existing online data.
Real-World Impact in the Middle East
The UAE’s leadership in climate and sustainability, recently on display at COP28, is a perfect use case. LQMs could improve climate modeling, optimize renewable energy integration, and support net-zero policymaking.
In manufacturing and R&D, LQMs can accelerate innovation. Companies like Aramco have already begun integrating quantitative AI models to boost downstream product value.
In sectors like pharmaceuticals, LQMs can simulate molecular behavior at the electron level, reducing reliance on expensive, high-failure clinical trials. In material science, they can identify new combinations of compounds for batteries or construction materials—without waiting for physical prototypes.
Not a Replacement—But a Critical Complement
While LLMs excel at interaction, summarization, and idea generation, they are not precision tools. They are trained on what’s available online—which means they’re limited by existing knowledge. LQMs, on the other hand, can create new, trustworthy data by solving equations in new contexts, giving them near-limitless applications in data-rich domains.
Rather than replacing LLMs, LQMs complement them, especially when both language and numerical reasoning are required. For instance, a user interface powered by an LLM can be used to navigate insights generated by a backend LQM.
The Path Forward: Precision AI for the GCC
As the GCC economies diversify and invest in advanced technologies, LQMs offer a strategic advantage in areas where data, simulation, and optimization intersect. These include:
Drug discovery
Energy transition
Climate modeling
Financial forecasting
Industrial engineering
While LLMs help generate ideas and content, LQMs help solve problems with precision and depth.
GCC countries have made impressive strides in rolling out national AI strategies and embracing GenAI. But to sustain momentum and solve the region’s most complex challenges, decision-makers must expand their AI toolbox.
Investing in large quantitative models could prove just as transformative—if not more—than their language-based counterparts.
Source: Arabian Business
