Infrastructure for scientific superintelligence

An AI research lab building the tools, evaluations and environments that enable models to accelerate discovery in the empirical sciences.

Capability breakthroughs in the empirical sciences will come from novel, scalable ways to generate data and verify outcomes.

About

Synthetic Sciences is an AI research lab building infrastructure for scientific superintelligence.

AI systems are already producing breakthroughs in mathematics, with new discoveries arriving in rapid succession. Mathematics has fast, trustworthy verification, and research there needs little more than a model and a proof checker. The empirical sciences have neither, which leaves two problems.

The first is intelligencein·tel·li·gence nounthe ability to acquire and apply knowledge and skills.. Experiments are slow, expensive and approximate, so models can’t brute-force the solution space. The best scientists make progress anyway. What sets them apart is experimental taste: knowing which experiments are worth running, anticipating what they will show and understanding what the results mean. We think taste can be formalized, measured and trained.

The second is infrastructurein·fra·struc·ture nounthe basic physical and organizational structures and facilities (e.g. buildings, roads, power supplies) needed for the operation of a society or enterprise.. A model with taste still needs compute, instruments, simulators, often physical facilities, and a harness to operate them. Almost none of it is built for autonomous agents.

Our goal is to advance both. We’re going after machine learning first, where experiments run on compute and the infrastructure already exists. In the physical sciences, experiments depend on instruments that agents can’t yet operate, so we’ll build the labs that let them. The first, Lab 0, is coming online soon.

In each domain, we build long-horizon evaluations of whether models can form hypotheses, run experiments, interpret failures and make progress under resource constraints, then work out how to train those capabilities at scale.

If you’d like your models to be better at research, write to founders@syntheticsciences.ai.

We’re a small, talent-dense team of International Olympiad medalists and AI researchers, backed by Y Combinator, Z Fellows, Charlie Songhurst, Walter Kortschak, Edward Lando, scout funds from Sequoia and a16z, and others. We’re hiring exceptional researchers and engineers: team@syntheticsciences.ai.