Our Moonshot

We now have more scientific knowledge than any individual, or even teams, can meaningfully use. It is scattered across subdomains and specialties, and as it grows, the burden of learning and applying it increases. At the same time, the questions themselves are becoming more complex. Together, these factors are turning scientific progress into a problem of reasoning capacity.

Physics is the foundation of how we understand the world around us, and progress in the field has historically driven breakthroughs across energy, infrastructure, medicine, and technology.

It is also a highly complex domain, requiring deep specialization and integration across vast domains of knowledge. This makes it a natural place to explore new ways of doing science with a large potential for impact.

When we expand the ways in which discovery can happen:

• Hypotheses can be explored in new ways
• Hidden connections across enormous datasets can become visible
• Intuition is supported in complex spaces
• Testing becomes dramatically faster
• Dead ends may be abandoned sooner
• Ambitious questions become more tractable

We are at an inflection point. Scientific questions have grown vastly more complex and data-rich, while AI capabilities have advanced to the point where they can meaningfully participate in the scientific process.

However, a structural gap is emerging. Academic institutions are not structured to build frontier AI models at scale, while advances in AI are largely developed outside of systems designed specifically for scientific discovery.

FirstPrinciples exists to bridge this gap by building specialized AI systems for science, designed around real research workflows.