Simulation Theory Expands into Biotech and Life Sciences
SAN DIEGO, CA - February 16, 2026
Simulation Theory (Sim Theory), a deep tech software company developing high-performance solutions for AI workloads, announced its expansion into the biotech and life sciences sector. The company will focus on driving down the heavy compute costs associated with workloads such as next-generation sequencing (NGS), large-scale analytics, and AI-driven discovery.
As genomic data volumes and model complexity continue to grow, the cost of running AI and data pipelines is increasingly constrained by limited CPU and GPU capacity. Sim Theory’s patent-pending Thunder SDK is designed to unlock more performance from existing compute infrastructure by scaling applications efficiently across CPU cores and keeping GPUs fully utilized, helping organizations process larger datasets faster and at lower cost.
To support its entry into biotech, Sim Theory is forming a dedicated advisory board composed of life sciences and biotech business leaders. This advisory board will help guide the company’s product strategy, partnerships, and go-to-market efforts in areas such as genomic analysis, precision medicine, and computational biology, ensuring that the Thunder SDK is aligned with the operational realities and regulatory needs of the sector.
“Biotech and life sciences organizations are under intense pressure to extract more insight from growing volumes of data without greatly outstripping their compute budgets,” said Randy Culley, CTO and Co-Founder of Simulation Theory. “By bringing Thunder SDK to this space and working closely with experienced industry advisors, we aim to make advanced AI and analytics workloads more economically and operationally sustainable.”
About Simulation Theory
Simulation Theory (SimTheory) is a deep tech software company developing high-performance solutions to help enterprises close the growing gap between demand for AI computing power and available CPU and GPU capacity. Its patent-pending Thunder SDK enables applications to scale horizontally across all available CPU cores with minimal overhead, dramatically increasing the amount of work that can be done on existing CPUs while keeping GPUs continuously supplied with data so they can perform more AI and machine learning work. By extracting more parallelism from AI/ML and other compute-intensive workloads, without requiring a full rewrite of existing code, the Thunder SDK helps organizations run faster, more efficient pipelines in cloud, data center, and edge environments.
