Engineer our environments to expand humanity across the universe.
To get there, artificial intelligence has to understand reality. It cannot learn what nobody has measured. Wallace measures the physical world, models it and puts the models to work.
Artificial intelligence has read the internet. It has never measured reality.
To use artificial intelligence to discover more about reality, we have to datamaxx the physical world.
The internet is learned
Language models trained on text. Text says little about how cells, molecules and materials actually behave.
Physical data was never collected
Biology, chemistry and materials are measured rarely, inconsistently or not at all. Failed experiments are thrown away.
Measurement just got cheap
Robolab automates the lab and pushes the cost toward 1 cent per measurement. Measuring everything becomes affordable.
Models change the economics of testing. Every result trains a model that sells predictions, so there is a reason to test, and to keep testing.
Measure the world finely enough to predict it, then engineer it.
Measure
Physical world data at fine granularity and high frequency, from molecules to ecosystems.
Predict
Models trained on that data forecast what will happen before it happens.
Engineer
The same models show how to use the resources we have far better, and how to change outcomes.
The ultimate objective: engineer our environments to expand humanity across the universe, through better control of physics, chemistry and biology.
Complex systems are where artificial intelligence is blind.
The body over time
Healthy people, measured repeatedly. Almost every record today is one snapshot.
Microbiomes
Gut, soil and water microbes shift daily. Almost nobody samples often enough to model them.
Food as eaten
Composition, contaminants and residues change by batch, farm and season.
Pathogens and resistance
New variants and antimicrobial resistance appear faster than public data updates.
Soils and crops
Nutrients, microbes and residues per field, per season.
Water and air
Contaminants, spores and allergens change hour to hour. Most testing is monthly.
Batteries and materials
Ageing data takes years, and every new chemistry resets it.
Reactions that fail
Chemistry models learn only from published successes. Failures are never shared.
Complex systems drift. A model trained once goes stale. A model fed every day stays the reference, and agents keep paying to query it.
One model of the physical world, applied to the systems that shape life.
Biological systems
Humans, animals, plants, crops and microbes. Predict bodies and metabolism, design better molecules.
Ecosystems
How species, soils and water interact. Predict change and engineer healthier landscapes.
Weather
The chemistry and physics of the atmosphere. Forecast further ahead and design interventions.
Resources
Soils, minerals, water and feedstocks. Predict yield and grade, recover more and waste less.
Materials and chemistry
Catalysts, batteries, alloys and polymers. Predict properties and invent new materials.
Built environment
Concrete, steel and structures over decades. Predict ageing and failure before it happens.
Oceans
Currents, chemistry and marine life. Predict fisheries, carbon uptake and coastal change.
Space and extreme environments
Radiation, vacuum, heat and pressure. Data almost nobody can generate.
We start with biology, because human health matters most and its data barely exists.
01 · Now
Biology
Bodies over time, food, microbiomes and pathogens.
02 · Next
Chemistry and materials
Batteries, reactions, construction and corrosion.
03
Resources
Soils, crops, water and recycled feedstocks.
04
Ecosystems
Species, air, water and oceans.
05
Weather
Atmospheric chemistry and physics.
06
Space
Extreme environments almost nobody can test.
The same platform, Robolab measuring and Realm modelling, then extends to every other system.