FirstPrinciples announced today a philanthropic grant from the John Templeton Foundation, marking early institutional support for the AI Physicist.

The team at FirstPrinciples has reached the AI Physicist’s first autonomy milestone ahead of schedule, assembling the early loops of a system that will guide its own scientific reasoning from research to hypothesis and beyond.

Large Language Models have changed how we think, work, and do science, but can they truly reason like scientists? At FirstPrinciples, we’re exploring the limits of AI generalization and the promise of specialization through the development of the AI Physicist.

With a deep passion for physics, Shiraz Minwalla investigates the complexities of black holes, grey galaxies, and beyond.

FirstPrinciples presented an early demo of its AI Physicist at the Open Science Fair, held this year at CERN. The event sparked conversations on trust, openness, and the role of AI in research, underscoring how collaboration will shape the future of discovery.

In a research world defined by scale and AI-driven discovery, small colleges are proving impact isn’t measured in dollars. At places like Wellesley and Bowdoin, close mentorship, collaborative problem-solving, and a readiness to experiment with new tools are shaping the next generation of scientists to navigate a rapidly evolving research landscape.

Chain-of-thought reasoning has become a rare interface between human and machine logic. But a new paper warns that the window may be closing.

Physicist Neil Johnson has mapped the exact moment AI can flip from accurate to false, and he says understanding the physics could be the key to safer systems.

More scientists are leaving academia, trading tenure-track hurdles for the speed and flexibility of industry. For physicist Elizabeth Frank, that shift meant moving from mapping Mercury to mining the Moon — swapping publication bottlenecks for the fast, interdisciplinary problem-solving of space startups, and using AI to revive data gathered half a century ago.