Research thesis

The Standard Model of particle physics is the most powerful and successful scientific theory ever developed. Since its formulation was finalized in the 1970s, it has survived 50 years of experimental scrutiny from quadrillions of collisions at particle accelerators worldwide, showing unprecedented agreement with experimental data and making it one of the most widely tested scientific theories.

We have before us the most powerful equations humanity has ever invented, and yet we can do very little with them: anything more complex than a few elementary particles cannot be solved for with any of the tools physicists have invented so far.

If only we had a machine that could construct precise solutions to the equations of the Standard Model for systems of arbitrary complexity, we would need very little additional knowledge to solve all of condensed matter, organic chemistry, and molecular biology, unlocking tremendous technological progress as a result.

Lacking such a machine, scientists must resort to experiments in wet labs, which are extremely slow, expensive, and error-prone. But determining the behavior of a molecule should no longer be an issue bottlenecked by experiments: these do not intend to falsify the theory, but to measure what it should already be able to tell us.

This problem becomes even more acute for research on “physics beyond the Standard Model” (BSM). Since the discovery of the Higgs boson, the frontier of fundamental physics has ground to a halt. There are thousands of candidate BSM theories that have been proposed, and direct searches at particle colliders to test each of them require years of experimental work and cost billions of dollars.

Thus, our goal is simple: to create such a machine, capable of uncovering the complexity that emerges from the laws of physics.

Our plan to build it is to gradually unravel the operating principles of intelligence and deploy them into exascale computational substrates.

We’re assembling the world’s leading research program at the intersection of theoretical physics and artificial intelligence focused on tackling this challenge.

If this sounds like the place for you, join us. The time is now.

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