Build the NextFoundation Model

Abel is building a world-dynamics foundation model that learns how systems change, what drives them, and what happens when we act.

The world is recorded. It is not yet modeled.

The next foundation model will not only understand what the world says. It will understand how the world changes.

Real systems are not documents. They are states in motion—

thousands of variables, coupled across time, responding to every intervention.

Abel combines foundation models with causal intelligence.

It helps uncover the drivers and relationships inside complex systems — providing structural evidence for how models understand what matters.

Our mission is to build the foundation model that makes the changing world understandable, computable, and ultimately actionable.

Most AI learns what humans already do. Abel is built for what humans structurally cannot.

This is the next foundation model we build.

One model. Many dynamic systems.

Every market, grid, production line, and cell is still modeled separately — a team rebuilds the whole stack for each one, then rebuilds it again when the regime shifts.

Abel is built to identify a system from its measurements and context instead.

Measurements and context from every source become one picture of where the system is now.

RSI Loop — Feedback loop

A model that learns when reality answers back.

Building toward it today.

Our current work spans models, systems and learning loops that move us toward this foundation model.

Andrey: very fast causal discovery

Find which parts of a system drive the others, and see what changes when one of them moves.

Explore Andrey

Simons: time series foundation models

Learn how systems evolve across time, scale and regime, then roll them forward into possible futures.

Coming soon

Help build the model that holds the world

We look for people who have gone unusually deep somewhere: a medal, a theorem, a benchmark, a system that survived production.

We work with domain partners in markets, energy, industry, biology and science — especially where systems are wide, decisions are consequential, and real outcomes can close the learning loop.