Humanity can now see across astonishing scales. We can image stars in galaxies billions of light-years away and reconstruct proteins atom by atom. While we master the cosmic & the microscopic, we struggle with the planetary.

We understand remarkably little about the Earth’s natural systems: rivers traversing continents, geological structures hidden beneath our feet, trenches deep in the ocean, or even fungi weaving through soil. These natural systems support the agriculture that feeds us, the energy that powers our lives, the climate that sustains life, and the natural beauty that makes us marvel.

Our planet’s messy, complex natural systems cannot be learned from the internet alone. Understanding them requires intelligence grounded in physical observation. Nature is not merely a static dataset; it is perhaps the richest learning environment we have. We can now build models that remain in contact with the physical world: observing, forming hypotheses, testing them against reality, and recalibrating. Weather forecasting already shows what this can look like. Satellites, weather stations, radar, ocean buoys, aircraft and atmospheric sensors continuously generate new observations, which are assimilated into models that constantly improve against what happens next.

The same principle applies across other fields in the Earth Sciences. Just as computational biology is transforming drug discovery and computational chemistry is making breakthroughs in discovering new materials, we believe models capable of representing the state and dynamics of the Earth can transform how humanity discovers, develops and manages its natural resources.

Alt Earth is building AI for Earth Sciences. The next frontier for AI is intelligence capable of understanding the physical systems of our planet.

Here’s how:

• Bringing together interdisciplinary teams of Earth scientists, ML engineers, applied mathematicians & on-ground operators
• Building lab infrastructure at a generational scale
• Deploying sensors widely to generate high-quality labelled data
• Training models by deploying them in the physical world

We began with carbon removal. Enhanced Rock Weathering (ERW) required understanding how rock, soil, water and climate interact across real landscapes. So we built field operations in Darjeeling and laboratory infrastructure capable of measuring them. Thousands of samples gave us proprietary data on soil chemistry, mineral composition, weathering and carbon movement. We used those observations to build models, used the models to guide where and how we intervened, and constantly measured the landscape to trace the final outcomes. That’s how we have removed ~10,000 t of CO₂ from the atmosphere to date.

Our next frontier is the subsurface. It underpins the minerals and energy systems that sustain modern industry, provides reservoirs for carbon and waste storage, and hosts many of the geological processes behind natural hazards. Yet we still cannot reconstruct the hidden structures of the subsurface from sparse observations. This is an inverse problem at planetary scale. Solving it would open an entirely new frontier for humanity, allowing us to understand rock bodies, faults, mineral systems, fluids, heat, and the pathways through which they move.

Alt Earth combines machine learning with applied mathematics, geophysics, geochemistry, geology, remote sensing, laboratory analysis and field experimentation to build models constrained by physics and tested against observation. We aim to infer the subsurface as one coherent physical system: its lithology, structure, fluids, mineralisation and rock properties, together with the geological history and active processes that produced them. That’s how we usher in an age of abundance—changing how we discover minerals for the green transition, develop geothermal resources, store carbon and assess geological risk.

Our scientific mission is to develop Natural General Intelligence—AI that learns from the Earth itself.

When scientists & engineers, theoreticians & experimentalists work together, science moves forward. LIGO built on Einstein’s theory of general relativity and gave humanity new tools to observe the universe. Sonar, magnetometry & seismological innovation confirmed Wegener’s ideas, giving us plate tectonics, a new way to understand the planet. The 2026 Nobel Prize in Physics recognised IceCube, which turned a cubic kilometre of Antarctic ice into a telescope for neutrinos. By looking down, we have made new discoveries about our vast cosmos. The subsurface demands the same spirit: new theories, new instruments, new models and sustained experimentation brought together to make an inaccessible world observable.

This is a Journey to the Centre of the Earth.