Insider Brief
- SandboxAQ raised $95 million in an oversubscribed secondary offering to provide employee liquidity, bringing its total funding to nearly $1 billion since spinning out of Alphabet in 2022.
- The company is developing Large Quantitative Models (LQMs), AI systems based on physics, chemistry, biology, and mathematics to simulate real-world systems in industries like biopharma, defense, and energy.
- Investors including Rizvi Traverse, Forge Global, and the Ava Family Office led the new round.
SandboxAQ has secured an additional $95 million in funding through a secondary offering aimed at providing long-term liquidity for employees, the company announced this week. The raise, which was oversubscribed, follows a $452 million primary round closed in April and brings the company’s total funding to nearly $1 billion since it spun out from Alphabet in 2022.
The new round was led by investors including Rizvi Traverse, Forge Global, and the Ava Family Office. The earlier $452 million primary round attracted major backers such as Google, NVIDIA, and Ray Dalio. Combined, the capital provides SandboxAQ with substantial runway to pursue its vision of building AI tools that deliver precision over probability and simulation over speculation.
“We chose to take a leading position in the SandboxAQ secondary because we believe quantitative AI is redefining entire industries – and SandboxAQ is pioneering that frontier,” said Suhail Rizvi, Managing Director of Rizvi Traverse. “Their unique approach to applying AI in domains like cybersecurity, drug discovery, and quantum simulation is unlike anything else we’ve seen in the market. The combination of technical depth, commercial traction, and long-term defensibility make SandboxAQ an exceptional opportunity for us to double down.”
While much of the artificial intelligence world remains focused on language models that summarize text or draft emails, SandboxAQ is betting on something different. The company is building what it calls Large Quantitative Models (LQMs) — a class of AI systems grounded not in language but in physics, chemistry, biology, and mathematics. These models are designed to simulate the real-world behavior of complex systems with a level of precision traditional generative AI can’t offer.
In a blog post announcing the funding, the company emphasized that many of the most urgent and economically important challenges facing industry today — from biopharmaceutical research to aerospace optimization — are fundamentally quantitative.
“The vast majority of enterprise challenges are not linguistic – they’re quantitative – so relying on probabilistic, language-based AI models will only take them so far,” the company wrote. “Industries that underpin the global economy – biopharma, energy, aerospace, automotive, defense, finance and others – don’t need probability. They need precision.”
Large Quantitative Models (LQMs) are grounded in physics, chemistry, biology, and mathematics, and built to simulate how complex systems interact. They’re redefining what AI can do:
- In healthcare, our AQBioSim platform is collapsing drug discovery timelines from a decade or more to months, accelerating R&D for breakthrough treatments while significantly lowering costs
- In defense and automotive, our AQChemSim platform is designing stronger, lighter metal alloys to better protect occupants while reducing fuel consumption.
- In energy, AQChemSim is improving battery chemistries, materials, designs and testing to support the growing global demand for electrification. At the same time, we’re working with leading petroleum companies to bring cleaner, higher-value products to market.
- In aerospace, our AQNav system is pioneering GPS-independent magnetic navigation to keep planes and passengers safe in contested environments.
- In cybersecurity, AQtive Guard is helping some of the world’s largest financial institutions, telcos, and government agencies proactively defend against AI- and quantum-based threats.
The company’s core LQM approach aims to enable scientific computing at massive scale. These models can simulate the interaction of particles, molecules, and engineered systems, allowing industries such as defense, energy, and finance to make highly optimized decisions in areas where even minor errors can be costly or dangerous. Unlike generative AI, which is primarily focused on content generation, LQMs are intended to help researchers and engineers answer hard scientific questions and make better predictions grounded in physical laws.
As of this year, the company has grown to include more than 87 PhDs and 110 engineers — a team it describes as “small but mighty.” SandboxAQ is betting that its talent pool, paired with strong backing from tech and financial leaders, will allow it to commercialize a new category of enterprise AI.
Still, the company has not disclosed which products are currently generating revenue or how soon LQMs will be deployed at scale across major industries. While the long-term promise of quantitative AI is considerable, transforming high-stakes sectors like aerospace and biopharma will likely take time, coordination, and regulatory alignment.
Looking ahead, SandboxAQ says it will continue to scale its platform and expand partnerships with enterprises seeking to apply AI to physical systems rather than digital ones. In a market dominated by text-based tools, the company’s message is clear: true transformation comes from scientific precision, not syntactic prediction.