About

We’re a fast-growing, remote-first team of builders, researchers, engineers, and thinkers working across Canada, the US, the UK, and expanding globally. What brings us together is a shared curiosity about how the universe works, and a belief that we can build systems that help us explore it more effectively.

We are building Theo as a genuine partner in scientific discovery. With scientific rigor as a core design principle, Theo is intended as a new mode of research for exploring reality’s deepest questions.

What Drives Us

Curiosity

We never stop asking questions.

Ambition

We pursue meaningful progress on hard problems.

Taking Risks

We explore ideas that don’t yet have clear paths.

Collaboration

Knowledge is built collectively, across people, disciplines, and time.

Results

We hold ourselves accountable to measurable progress.

Our Mission

Our mission is to understand the nature of reality by advancing AI-driven discovery in fundamental science.

Science is bottlenecked.

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.

Why physics?

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.

What changes when reasoning scales

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

Why now?

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.

Scientific Advisory Board

Victor Galitski

Joint Quantum Institute, University of Maryland

Yang-Hui He

London Institute for Mathematical Sciences, Royal Institution of Great Britain