Latest news with #LQMs
Tahawul Tech
05-05-2025
- Business
- Tahawul Tech
'We don't just want to create technology, we want to have a positive impact on the world.' – Stefan Leichenauer, SandboxAQ
CNME Editor Mark Forker sat down with Stefan Leichenauer, VP of Engineering at SandboxAQ, to find out why more and more industries are increasingly opting to adopt Large Quantitative Models (LQMs) to solve their complex challenges, as opposed to LLMs. Leichenauer also outlined that ultimately their mission is to not just create technology, but instead to have a positive impact on society. Stefan Leichenauer is a man on a mission. He is driven by the fact that he works for a company that is committed to making the world a better place. That company is SandboxAQ, a B2B company that delivers AI solutions that addresses some of the world's greatest challenges. SandboxAQ was born out of Alphabet Inc. as an independent capital-backed company in 2022. Over the last number of years, it has grown exponentially across multiple global markets, and has a major partnership here in the Middle East region with Aramco. Leichenauer spoke to CNME, about why the company wants to deliver technologies that have a positive impact on society, and the critical role played by LQMs in enabling the transformation pf industries such as the Oil & Gas sector. In a recent op-ed, the VP of Engineering at SandboxAQ made the case for enterprises to shift their focus away from LLMs and to look at the LQMs to foster real change across their organisation. According to Leichenauer, LLMs have limitations, and in order to solve the really complex challenges facing the world then businesses need to start looking at LQMs. 'Firstly, let me say that I think LLMs are fantastic, and we are not working to get rid of them. However, LLMs can't do everything by themselves, and I think that's the point that I am making, and I think more and more people are starting to realise that LLMs have their limitations. If you look at the latest LLMs that have been released over the last 3 years, then it seems like every release has a new set of capabilities that can do so much more, but we have sort of hit a ceiling of late. If you examine the latest releases of Llama 4 and GPT4.5 they are only incrementally better than what has come previously. So, I think there has been a realisation that LLMs as a capability are great, processing text and generating images then it is fantastic, but there is a whole set of capabilities that LLMs are just not going to get to by themselves,' said Leichenauer. The capabilities that LLMs are not going to be able to get to by themselves is associated with quantitative reasoning, and this is where LQMs come to the fore. 'LQMs is designed to model the physical with chemistry, physics, and medicine, and is essentially focused on doing things that has absolutely nothing to do with language-based content. You need other tools in the tool box, and that's where LQMs come in. LQMs are basically providing those other tools in toolbox and they compliment the capabilities provided by LLMs,' said Leichenauer. In his op-ed, Leichenauer also claimed that when precision is paramount then LQMs are indispensable, and said momentum was beginning to swing in favour of LQMs. 'We're now seeing more proof points that LQMs. I think in the past people would have deployed LLMs on to any given problem to see what works, and what doesn't, and I think everyone has been doing proof of concept trials with LLMs, but they've fallen short for a couple of reasons. As I stated earlier, in some areas they are fantastic, but in other areas they have fallen short. One of the reasons for this is the fact that LLMs are very non-transparent in terms of their reasoning. LLMs will give you an answer, but why is it true? And the LLM could be hallucinating, and we know that's been a big problem in some areas. Hallucinations are fine when it comes to generating an image, maybe it has the wrong number of fingers, but when it comes to creating a new molecule for Aramco, that is designed to making their processing plants more efficient, then you can't get that wrong because that's going to cost you a billion dollars. You need your answer to be correct, you need it to be grounded in real understanding of the problem, and LQMs can provide that verifiability and transparency,' said Leichenauer. As aforementioned above, SandboxAQ have enjoyed great success since spinning out of Alphabet Inc. in 2022, and are working with some of the biggest companies in the Middle East, including Aramco, who are the biggest integrated energy and chemicals company in the world. He spoke about their partnership, and again reiterated their mission which is to build purposeful technology designed to improve society. 'Our goal at Sandbox at the end of the day is not to create technology, of course we love to create technology, but we are doing it for a purpose. Ultimately, our goal is to have a positive impact on the world, and it just so happens that LQM technology is a great way to have a positive impact. The impact areas that we care about the most such as the medicine, pharmaceuticals, medical devices and GPS free navigation is something that we are very passionate about. These are all powered by LQMs. In terms of our collaboration with Aramco, the oil & gas industry is a really important industry in the world. However, we are all acutely aware that as we move forward, we need to be better about being environmentally friendly, and more efficient with our energy and more sustainable. We need to always be looking at better techniques, and Aramco is a real leader and pioneer when it comes to these sort of techniques,' said Leichenauer. He went into more detail in relation to how LQM technology is enabling Aramco to transform, and how the technology is helping the global energy incumbent to be more sustainable and efficient. 'Aramco is not an AI company, they are an oil & gas company, so we are here to help our partners like Aramco to advance their operations to be able to do things in a much better way. SandboxAQ provides software tools, AI models and the LQMs that really help them to transform the way they operate their business. What we're doing with Aramco specifically is partnering with them to look closer at the oil & gas processing facilities. Ultimately, a lot of what is happening there is you've essentially got liquids and gases flowing through pipes and going through various kinds of processes, refineries and machines. However, in order to make those processes more efficient, one way to make them more efficient is to model them computationally better,' said Leichenauer. Leichenauer conceded that these processes are complex, but insisted that in order to make them more efficient and sustainable then companies like Aramco had to implement LQM technologies. 'It's a complex physical process, and if you want to make your plants more efficient, and reduce emissions and waste then modelling that process computationally allows you to make tweaks and changes virtually to enable you to implement them in real-life. Modelling all of those processes computationally is something that our software is helping Aramco with,' said Leichenauer. Leichenauer is delighted at the progress SandboxAQ has made with Aramco since their collaboration started, and believes that by 2030, it will fundamentally be a completely different business. 'The part that Sandbox has control over, and the computational modelling that enables these kinds of changes, the good news is, well from our perspective anyway is relatively simply compared to actually implementing these things physically. We have been working with Aramco for several months now, and we've already achieved significant milestones with our modelling. The LQMs that can do that sort of modelling and give you the answers and the playbook that what you need to do to make the changes those exist, and in a matter of months we have made huge progress on that. If I had to speculate a little bit then I'd guess that in the next 5 years we'll see a lot more changes coming through and being implemented. It may take longer to become 100% sustainable and 100% green, but in the oil and gas industry and other industries we can affect real changes and see real progress in a sort of 5-year timeline. By 2030 or so, a lot of the work we are doing today will have real tangible impact by then,' said Leichenauer. Another industry that SandboxAQ is looking to transform in order to ensure they are having a meaningful impact on society is the healthcare industry. 'The healthcare sector is a major industry for us. It is a major source of grand challenges for the world, but we have seen a lot of progress in the last years in terms of how technology is being used to transform healthcare. When we are talking about real positive impact on the world then there's almost no better place to have that impact than in healthcare. Within healthcare, there is obviously the pharmaceutical industry, and there's always a lot to do in that space, and in terms of medical diagnostics that is a space that also can be transformed. The MRI machine is an amazing machine, it transformed medicine when it was invented several decades ago, but it is big, it is expensive, and it's clunky, and it takes a lot of expertise to use it. The next-generation of medical diagnostic devices can bring the kind of transformative impact of the MRI machine, but in a form factor that is more like an ultrasound machine, where it can something that can be much smaller and can be in every hospital emergency room. That kind of technology is coming, and some of that is what we are working on and using LQMs to enable,' said Leichenauer. Leichenauer outlined that SandboxAQ is working on a diagnostic designed to tackle the issue of heart disease. 'We're working on a device right now using LQMs that is specifically for diagnosing heart disease, and various kinds of heart disease in an emergency room setting in a way that you could actually apply it to every patient that walks in complaining of heart problems, or persistent heart pain. One of the first things that you do is take five minutes to give them a scan using the machine, and that really improves the care of the patient, and heart disease is one of the biggest killers in the world, so this is a truly transformative device. At the minute, we have a prototype device being tested in hospitals right now, and within a couple of years I'd expect this device to be used on a everyday basis in hospitals. Early indications of the prototype is that we are on the right track, and appear to be doing a good job. However, you have to prove you're doing a good job and pass regulations and so on before you can actually go to market with such a device, but the technology is there and we are actively working on it,' said Leichenauer.
TECHx
17-04-2025
- Business
- TECHx
SandboxAQ Advances AI Research with NVIDIA DGX Collaboration
SandboxAQ has announced a collaboration with NVIDIA to drive faster innovation across industries. The company is leveraging the power of NVIDIA DGX to build advanced AI models that support breakthroughs in biopharma, chemicals, materials, finance, cybersecurity, navigation, and medical imaging. As a member of the NVIDIA Inception program, SandboxAQ is developing a next-generation Large Quantitative Model (LQM) platform. Built on NVIDIA DGX, this platform is designed to solve complex scientific and business problems with more speed, scale, and precision. With this collaboration, SandboxAQ is improving customer outcomes across several areas. First, drug, chemical, and materials development is becoming faster. Using NVIDIA DGX, SandboxAQ can reduce discovery cycles from years to weeks. This is done through simulations that replace slow lab experiments, helping teams test and validate new ideas quickly. Second, the partnership enables the creation of high-quality scientific datasets. By combining chemical and biological simulations, SandboxAQ can detect interactions that were previously hard to identify. These datasets improve model accuracy and reduce false positives. Third, a new AI Chemist powered by LQMs and NVIDIA DGX is transforming how discoveries are made. This tool can explore millions of chemical pathways automatically, allowing researchers to find new molecules and optimize compounds more efficiently. The collaboration builds on earlier success. In 2024, the companies achieved an 80x acceleration in quantum chemistry calculations using CUDA-accelerated DMRG. In 2025, they published research showing orbital optimization on a system with 82 electrons and 82 orbitals—double the size of previous simulations. This joint effort has broad impact. In healthcare, SandboxAQ helps pharma companies speed up preclinical testing. In materials science, it supports the development of sustainable processes and energy storage. In cybersecurity, it enables better modeling and prediction. The core advantage lies in SandboxAQ's LQMs. These AI models reflect the laws of science and economics. They provide deterministic and reliable outputs, unlike general-purpose AI models. This makes them ideal for high-stakes environments. According to Jack Hidary, CEO of SandboxAQ, the collaboration gives customers a clear advantage. 'By building on NVIDIA DGX, we help our customers innovate faster and lead with confidence,' he said. Alexis Bjorlin, Vice President of NVIDIA DGX Cloud, added, 'SandboxAQ is setting new standards in AI-native science. With NVIDIA DGX, they can deliver performance at scale and solve real-world problems.' This partnership highlights how advanced AI and high-performance computing are reshaping R&D. With NVIDIA DGX, SandboxAQ is unlocking new levels of discovery and impact across industries.
Yahoo
08-04-2025
- Business
- Yahoo
Nvidia And Google Bet Big On Quantum AI With $150M Investment In SandboxAQ, Raising Series E Round To $450 Million
Quantum computing may still feel like science fiction to most, but for a handful of major players, the future is unfolding much faster than expected. Nvidia (NASDAQ:NVDA) and Alphabet (NASDAQ:GOOGL, GOOG)) are the latest to double down on that future, pouring a $150 million investment into SandboxAQ, a startup blending quantum tech with artificial intelligence. That latest funding boost brings the company's Series E total to $450 million and pushes its valuation to $5.75 billion. According to Reuters, SandboxAQ has now raised $950 million, with support from giants like T. Rowe Price Associates (NASDAQ:TROW) and Breyer Capital, further solidifying its rise. Don't Miss: Hasbro, MGM, and Skechers trust this AI marketing firm — .Nvidia and Google's interest in quantum AI is a big step forward for the startup and the industry as a whole. At Nvidia's GTC Conference, CEO Jensen Huang broke down the speed at which quantum is developing, saying it is faster than almost everyone in the industry expected. A lot of quantum research thus far has been confined to academic circles, but we're starting to see momentum build around real-world applications, especially ones requiring massive computing power. SandboxAQ CEO Jack Hidary recently discussed the company's trajectory with Reuters. 'We've proven ourselves from the first round in terms of delivering on our promises to a number of customers, and I think strategic investors were attracted to those breakthroughs,' he said. Trending: Spun out of Alphabet in 2022, SandboxAQ has focused on developing large quantitative models that consume vast datasets, perform complex mathematical tasks, and conduct complicated statistical analyses. These models are already available via Google Cloud, and the applications reach far out to real-world computing. From speeding up drug discovery to changing how financial institutions model risk, LQMs provide a real-world advantage in areas where speed and accuracy equal billions of dollars. The deal for SandboxAQ isn't Google's only play in this area. In December, the tech giant announced a new class of quantum processors that overcame an obstacle for the field that had been standing for many years. Only a few months earlier, Google had participated in QuEra Computing's $230 million round, indicating that the strategy is for the company to support in-house research and development. Nvidia, on the other hand, is going down a more corporeal path. Best known for its chips that ignited the generative AI boom, the company has begun to invest heavily in what it calls 'physical AI,' which links data to the real world. That encompasses the establishment of its own quantum research division, suggesting a far longer game at is based in Palo Alto, California, and employs around 200 people in various roles to accelerate quantum capabilities into the mainstream. With its newest cash infusion, the company intends to intensify research and development while growing partnerships in crucial domains including biopharma, chemicals, and energy. SandboxAQ distinguishes itself with the strategic partnerships it has secured. Furthermore, the company's LQMs are designed to scale, fine-tune, and adapt, which is particularly appealing in industries where innovation cycles are accelerating and regulatory environments demand precision. Now, with the transition from quantum computing theory to application in full swing, SandboxAQ is emerging as one of only a handful of companies that can command a leadership position in this space. Google's and Nvidia's backing only shows how serious that ambition is, and how soon we might see quantum AI reshape everything we know, from medicine to finance. Read Next:Deloitte's fastest-growing software company partners with Amazon, Walmart & Target – Up Next: Transform your trading with Benzinga Edge's one-of-a-kind market trade ideas and tools. Click now to access unique insights that can set you ahead in today's competitive market. Get the latest stock analysis from Benzinga? APPLE (AAPL): Free Stock Analysis Report TESLA (TSLA): Free Stock Analysis Report This article Nvidia And Google Bet Big On Quantum AI With $150M Investment In SandboxAQ, Raising Series E Round To $450 Million originally appeared on © 2025 Benzinga does not provide investment advice. All rights reserved.
Khaleej Times
24-03-2025
- Business
- Khaleej Times
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.
Tahawul Tech
17-02-2025
- Business
- Tahawul Tech
SandboxAQ expands alliance with Deloitte to offer AI simulation software solutions
SandboxAQ announced it has expanded its relationship with Deloitte to offer SandboxAQ's AI LQM simulation products and solutions along with Deloitte's services capabilities to organisations worldwide. SandboxAQ's AQBioSim and AQChemSim solutions leverage Large Quantitative Models (LQMs) to accelerate product development across a broad range of sectors, including biopharma. Deloitte will augment these offerings with their data and life sciences experience combined with their deep business and technology acumen. This collaboration will help transform nearly every facet of healthcare and life sciences. 'SandboxAQ is excited to expand its long-standing strategic alliance with Deloitte, whose relationships with some of the world's largest organisations will accelerate the adoption of our quantitative AI technologies', said Andrew McLaughlin, Chief Operating Officer of SandboxAQ. 'AI simulation with Large Quantitative Models represents the next evolution of AI and will have a transformative impact on how organisations create value for their customers in ways that Large Language Models cannot'. Using LQMs in collaboration with Deloitte's Atlas AI™ knowledge graph capabilities will allow SandboxAQ's scientists to automatically extract new clinical hypotheses from literature, highlighting only those most likely to be correct. With Atlas AI, Deloitte brings its engineering skills and industry experience together to accelerate the drug discovery process. Deloitte's Atlas AI team and SandboxAQ will also collaborate with pharmaceutical companies and other organisations on data evaluation, exploratory data analysis, and AI model testing and evaluation to enhance the speed and accuracy of the drug discovery processes. Along the way, SandboxAQ's AI-powered molecular simulations will generate vast amounts of highly accurate, physics-based data, delivering new insights for drug development, target ID, and treatment response. 'With this expansion of our alliance, we're able to combine our extensive experience in life sciences, data, and research with SandboxAQ's leadership in AI simulation and Large Quantitative Models', said Aditya Kudumala, principal, Deloitte Consulting LLP. 'Together, we aim to advance drug discovery and materials science for leading academic, commercial, and public sector entities'. Organisations interested in this expanded Deloitte and SandboxAQ Alliance can contact partners@ Image Credit: SandboxAQ
