The AI Race Just Shifted Gears - Lqms Are Now Driving Real-World Results

A little over two years since OpenAI kicked off the AI race, the launch of DeepSeek's R1 model added a new dimension to the conversation, bringing fresh attention to the role of smaller models, open-source innovation, and compute optimisation. Virtually overnight, the industry's focus shifted from sheer model size to efficiency, accessibility, and practical deployment. Now, with an increasing number of high-performance models available as open-source, traditional barriers to entry are eroding. This is forcing AI providers to rethink their value propositions, and set a new direction for those seeking an AI advantage.

Yes, LLMs remain a powerful tool, and the diversity of thought and approach afforded by the open-source community will only accelerate innovation. However, increasing accessibility to powerful models also signals the need for differentiation beyond raw model capability. This shift is already pushing the industry toward new frontiers, where AI innovation is no longer just about who can design and deploy AI solutions that solve complex business challenges with measurable results. Among the most promising of these are Large Quantitative Models (LQMs).

AI's exciting new avenue

The key difference between LLMs and LQMs lies in the data they are trained on and the problems they are designed to solve. LLMs are built on vast amounts of textual data, enabling them to understand and generate human language, making them ideal for tasks like answering questions, generating content, and facilitating natural interactions. LQMs, on the other hand, are trained on numerical data, leveraging machine learning to analyse complex datasets, identify patterns, and drive data-driven decision-making in fields like finance, healthcare, and scientific research.

For the UAE and its GCC neighbors, where economic vision projects emphasise homegrown innovation, LQMs present significant opportunities. In areas such as pharmaceutical discovery and petrochemical R&D, these models offer advanced analytical capabilities that can accelerate breakthroughs and enhance decision-making.

From near-horizon to here-now

LQMs are not just the future of AI - they are already delivering real-world impact for industry pioneers today. However, broader market adoption has been hindered by the complexity of implementation. Unlike LLMs, LQMs require a blend of deep domain expertise, sophisticated software engineering, and robust data management capabilities. This makes in-house development challenging unless organisations are prepared to make significant upfront investments. But securing such investment depends on a compelling business case, which requires leaders to identify high-value applications within their operations. Fortunately, decision-makers can draw inspiration from existing real-world deployments where LQMs have demonstrated clear advantages over conventional AI approaches.

Accelerated drug discovery

The UAE is forging a reputation as a medical tourism hub, having drawn widespread respect for its decisiveness during the COVID crisis by being among the first-to-market with life-saving treatments and running one of the world's most successful vaccination programs. The country is eager to bolster its drug-discovery credentials, and LQMs can play a crucial role by establishing links between the chemical structure of compounds and their biological activity, allowing researchers to optimise drug candidates more effectively. Unlike traditional AI models, LQMs excel at capturing intricate relationships within complex datasets, enabling more precise predictions and deeper insights - key advantages in pharmaceutical R&D, where accuracy and efficiency are paramount. They can model molecular interactions, predict protein folding, and accelerate hypothesis testing, significantly reducing the time and cost associated with bringing new therapies to market.

These same characteristics make LQMs valuable in other high-stakes, data-intensive fields, such as materials science, where they can identify novel compounds with desirable properties, or financial risk modeling, where they can uncover complex patterns in high-noise, low-signal economic data. As adoption grows, industries that rely on deep scientific or strategic reasoning will increasingly see LQMs drive breakthroughs.

Fuelling advancements in oil & gas

LQMs are also being used by the region's petrochemical sector as its players pursue growth within the confines of net-zero and sustainability commitments. Saudi Aramco is currently developing a differentiable computational fluid dynamics (CFD) solver for use in oil and gas processing facilities. LQMs can simulate how gases and liquids interact, allowing Aramco to optimise a critical business process while still reducing emissions and waste.

What makes LQMs particularly well suited to enhancing the petrochemical production chain is their ability to model complex chemical reactions and process optimisations with high fidelity, even when data is sparse or highly specialised. By analysing reaction kinetics, refining efficiencies, and material properties, LQMs help drive breakthroughs in catalyst design, fuel formulation, and carbon capture technologies.

Such advantages translate over to other industries that require precise modeling of intricate physical systems, such as advanced manufacturing, where they can optimise production workflows, or aerospace engineering, where they can enhance aerodynamics and materials performance.

Impetus for ideation

Effectively leveraging LQMs requires a clear understanding of their capabilities and the challenges they are best suited to address. Organisations should begin by identifying high-impact problems that rely on quantitative analysis. Industries such as biopharma, energy, and aerospace frequently require scientific precision - whether in predicting molecular behavior in drug discovery or simulating battery performance in energy storage.

Once a quantifiable problem has been defined, the next step is to evaluate the availability of high-fidelity data. LQMs can both perform simulations and utilise simulation-generated data, making them particularly effective in domains where experimental testing is costly or impractical. However, the quality and relevance of this data are critical - models must be trained on datasets that accurately reflect the systems they are designed to analyse. A robust data pipeline is essential to ensure consistency and reliability.

The ultimate measure of an LQM's effectiveness is its ability to generate actionable insights with measurable business impact. Some LQMs can predict key performance metrics - such as battery efficiency - at a fraction of the time required by conventional approaches, leading to accelerated R&D cycles. By enabling faster iteration and deeper optimisation, these models not only provide a competitive edge but also open the door to transformative breakthroughs that can reshape entire industries.

Opportunity abounds

PwC estimates that AI could generate $320 billion in economic value for the Middle East by 2030, but capturing this opportunity requires strategic investment in the right technologies. LQMs stand out as one of the most effective tools in the AI arsenal, offering a level of precision and adaptability that traditional models struggle to match. However, their impact hinges on business leaders recognising where they can drive the most value. The organisations that move swiftly to understand and deploy LQMs in the right areas will be the ones best positioned to capitalise on AI's economic promise.

The writer is Head of AI Strategy & Partnerships at SandboxAQ.

MENAFN24032025000049011007ID1109351207